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Wang S, Zhu Y, Jang JC, Jiang M, Yue D, Zhong L, Yuan Y, Zhang M, You Z. Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China. Sci Total Environ 2024; 926:171951. [PMID: 38537836 DOI: 10.1016/j.scitotenv.2024.171951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/04/2024] [Accepted: 03/23/2024] [Indexed: 04/17/2024]
Abstract
A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.
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Affiliation(s)
- Shaoyi Wang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
| | - Ji-Cheng Jang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Ming Jiang
- Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
| | - Dingli Yue
- Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
| | - Liuju Zhong
- Guangdong Polytechnic of Environmental Protection Engineering, Foshan 528216, China
| | - Yingzhi Yuan
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Mengmeng Zhang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Zhiqiang You
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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Dai H, Liao H, Wang Y, Qian J. Co-occurrence of ozone and PM 2.5 pollution in urban/non-urban areas in eastern China from 2013 to 2020: Roles of meteorology and anthropogenic emissions. Sci Total Environ 2024; 924:171687. [PMID: 38485008 DOI: 10.1016/j.scitotenv.2024.171687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/25/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
We applied a three-dimensional (3-D) global chemical transport model (GEOS-Chem) to evaluate the influences of meteorology and anthropogenic emissions on the co-occurrence of ozone (O3) and fine particulate matter (PM2.5) pollution day (O3-PM2.5PD) in urban and non-urban areas of the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during the warm season (April-October) from 2013 to 2020. The model captured the observed O3-PM2.5PD trends and spatial distributions well. From 2013 to 2020, with changes in both anthropogenic emissions and meteorology, the simulated values of O3-PM2.5PD in the urban (non-urban) areas of the BTH and YRD regions were 424.8 (330.1) and 309.3 (286.9) days, respectively, suggesting that pollution in non-urban areas also warrants attention. The trends in the simulated values of O3-PM2.5PD were -0.14 and -0.15 (+1.18 and +0.81) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Sensitivity simulations revealed that changes in anthropogenic emissions decreased the occurrence of O3-PM2.5PD, with trends of -0.99 and -1.23 (-1.47 and -1.92) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Conversely, meteorological conditions could exacerbate the frequency of O3-PM2.5PD, especially in the urban YRD areas, but less notably in the urban BTH areas, with trends of +2.11 and +0.30 days yr-1, respectively, owing to changes in meteorology only. The increases in T2m_max and T2m were the main meteorological factors affecting O3-PM2.5PD in most BTH and YRD areas. Furthermore, by conducting sensitivity experiments with different levels of pollutant precursor reductions in 2020, we found that volatile organic compound (VOC) reductions primarily benefited O3-PM2.5PD decreases in urban areas and that NOx reductions more notably influenced those in non-urban areas, especially in the YRD region. Simultaneously, reducing VOC and NOx emissions by 50 % resulted in considerable O3-PM2.5PD decreases (58.8-72.6 %) in the urban and non-urban areas of the BTH and YRD regions. The results of this study have important implications for the control of O3-PM2.5PD in the urban and non-urban areas of the BTH and YRD regions.
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Affiliation(s)
- Huibin Dai
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Ye Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jing Qian
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
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Zhang L, Wang L, Ji D, Xia Z, Nan P, Zhang J, Li K, Qi B, Du R, Sun Y, Wang Y, Hu B. Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China. Sci Total Environ 2024; 922:171295. [PMID: 38417501 DOI: 10.1016/j.scitotenv.2024.171295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/23/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.
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Affiliation(s)
- Lei Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Dan Ji
- Suichang Meteorological Bureau, Suichang 323000, China
| | - Zheng Xia
- Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Peifan Nan
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
| | - Jiaxin Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Ke Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Bing Qi
- Hangzhou Meteorological Bureau, Hangzhou 310051, China
| | - Rongguang Du
- Hangzhou Meteorological Bureau, Hangzhou 310051, China
| | - Yang Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Hu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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Beig G, Anand V, Korhale N, Sobhana SB, Harshitha KM, Kripalani RH. Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India. Sci Total Environ 2024; 920:170963. [PMID: 38367732 DOI: 10.1016/j.scitotenv.2024.170963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 01/31/2024] [Accepted: 02/12/2024] [Indexed: 02/19/2024]
Abstract
The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022-23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.
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Affiliation(s)
- Gufran Beig
- National Institute of Advanced Studies, Indian Institute of Science Campus, Bengaluru 560012, India.
| | - V Anand
- Indian Institute of Tropical Meteorology, Pune, Ministry of Earth Sciences (MoES), India
| | - N Korhale
- Indian Institute of Tropical Meteorology, Pune, Ministry of Earth Sciences (MoES), India
| | - S B Sobhana
- Ministry of Environment, Forest and Climate Change, New Delhi, India
| | - K M Harshitha
- National Institute of Advanced Studies, Indian Institute of Science Campus, Bengaluru 560012, India
| | - R H Kripalani
- Indian Institute of Tropical Meteorology, Pune, Ministry of Earth Sciences (MoES), India
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Li R, Zhao J, Feng K, Tian Y. Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China. Sci Total Environ 2024; 920:170777. [PMID: 38331278 DOI: 10.1016/j.scitotenv.2024.170777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015-2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.
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Affiliation(s)
- Rumei Li
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Jinghao Zhao
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Kun Feng
- Shanxi Low-carbon Environmental Protection Industry Group Co., Ltd., Taiyuan 030012, China; Shanxi Ecological Environment Monitoring Center, Taiyuan 030027, China
| | - Yajun Tian
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China.
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Kahn D. The chymistry of rainbows, winds, lightning, heat and cold in Paracelsus. Ann Sci 2024:1-15. [PMID: 38557277 DOI: 10.1080/00033790.2024.2333038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024]
Abstract
Meteorology is not one of the most discussed topics in Paracelsus studies, although it is closely linked to both Paracelsus' medicine and cosmology. Furthermore, it appears to be at the very core of Paracelsus' famous matter theory of three chymical principles, mercury, sulphur and salt, known as the tria prima. By discussing prominent examples of Paracelsus' explanations on how the tria prima operate within the stars, this article shows how the Swiss physician conceived meteorology within his own body of knowledge, obviously constructed in opposition to the Aristotelian-scholastic tradition, how he based it on a peculiar interpretation of the Biblical creation story, and made it the proper laboratory of his chymical matter theory, applying it first systematically to the field of natural philosophy, especially to celestial phenomena, even before using it for his medical theory in his later writings.
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Affiliation(s)
- Didier Kahn
- CELLF 16-18, CNRS - Sorbonne Université, Paris, France
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Nath SJ, Girach IA, Harithasree S, Bhuyan K, Ojha N, Kumar M. Urban ozone variability using automated machine learning: inference from different feature importance schemes. Environ Monit Assess 2024; 196:393. [PMID: 38520559 DOI: 10.1007/s10661-024-12549-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/16/2024] [Indexed: 03/25/2024]
Abstract
Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.
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Affiliation(s)
- Sankar Jyoti Nath
- Centre for Environment and Energy Development, Ranchi, 834001, India
| | - Imran A Girach
- Space Applications Centre, Indian Space Research Organisation, Ahmedabad, 380015, India.
| | - S Harithasree
- Physical Research Laboratory, Ahmedabad, 380009, India
- Indian Institute of Technology, Gandhinagar, 382055, Gujarat, India
| | - Kalyan Bhuyan
- Centre for Atmospheric Studies, Dibrugarh University, Dibrugarh, 786004, India
| | - Narendra Ojha
- Physical Research Laboratory, Ahmedabad, 380009, India.
| | - Manish Kumar
- Centre for Environment and Energy Development, Ranchi, 834001, India
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Zhao YC, Sun ZH, Xiao MX, Li JK, Liu HY, Cai HL, Cao W, Feng Y, Zhang BK, Yan M. Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces. Environ Res 2024; 245:117995. [PMID: 38145731 DOI: 10.1016/j.envres.2023.117995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/27/2023] [Accepted: 12/18/2023] [Indexed: 12/27/2023]
Abstract
BACKGROUND The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. METHODS We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis. RESULT Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences. CONCLUSION The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.
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Affiliation(s)
- Yi-Chang Zhao
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China
| | - Zhi-Hua Sun
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China; China Pharmaceutical University, Nanjing, Jiangsu, 210009, PR China
| | - Ming-Xuan Xiao
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China; China Pharmaceutical University, Nanjing, Jiangsu, 210009, PR China
| | - Jia-Kai Li
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China
| | - Huai-Yuan Liu
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China; China Pharmaceutical University, Nanjing, Jiangsu, 210009, PR China
| | - Hua-Lin Cai
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China
| | - Wei Cao
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; Department of Medical Laboratory, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China
| | - Yu Feng
- China Pharmaceutical University, Nanjing, Jiangsu, 210009, PR China
| | - Bi-Kui Zhang
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China.
| | - Miao Yan
- The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China; International Research Center for Precision Medicine, Transformative Technology and Software Services, Hunan, PR China.
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Chen Z, Dou S, Zhao C, Xiao L, Lu Z, Qiu Y. Machine learning-assisted assessment of key meteorological and crop factors affecting historical mulch pollution in China. J Hazard Mater 2024; 465:133281. [PMID: 38134688 DOI: 10.1016/j.jhazmat.2023.133281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
Degraded mulch pollution is of a great concern for agricultural soils. Although numerous studies have examined this issue from an environmental perspective, there is a lack of research focusing on crop-specific factors such as crop type. This study aimed to explore the correlation between meteorological and crop factors and mulch contamination. The first step was to estimate the amounts of mulch-derived microplastics (MPs) and phthalic acid esters (PAEs) during the rapid expansion period (1993-2012) of mulch usage in China. Subsequently, the Elastic Net (EN) and Random Forest (RF) models were employed to process a dataset that included meteorological, crop, and estimation data. At the national level, the RF model suggested that coldness in fall was crucial for MPs generation, while vegetables acted as a key factor for PAEs release. On a regional scale, the EN results showed that crops like vegetables, cotton, and peanuts remained significantly involved in PAEs contamination. As for MPs generation, coldness prevailed over all regions. Aridity became more critical for southern regions compared to northern regions due to solar radiation. Lastly, each region possessed specific crop types that could potentially influence its MPs contamination levels and provide guidance for developing sustainable ways to manage mulch contamination.
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Affiliation(s)
- Zheng Chen
- Department of Environmental Science, College of Environmental Science and Engineering, Tongji University, China
| | - Shuguang Dou
- Department of Computer Science, College of Electronic and Information Engineering, Tongji University, China
| | - Cairong Zhao
- Department of Computer Science, College of Electronic and Information Engineering, Tongji University, China
| | - Liwen Xiao
- Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland
| | - Zhibo Lu
- Department of Environmental Science, College of Environmental Science and Engineering, Tongji University, China
| | - Yuping Qiu
- Department of Environmental Science, College of Environmental Science and Engineering, Tongji University, China.
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Chen L, Zhang F, Ren J, Li Z, Xu W, Sun Y, Liu L, Wang X. Changes in wintertime visibility across China over 2013-2019 and the drivers: A comprehensive assessment using machine learning method. Sci Total Environ 2024; 912:169516. [PMID: 38135088 DOI: 10.1016/j.scitotenv.2023.169516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 12/24/2023]
Abstract
Effective emission reduction measures have largely lowered the particulate concentration in China, but low-visibility events still occur frequently, greatly affecting people's daily life, travel, and health. In the context of carbon neutrality strategy and climate change, the mechanisms governing visibility changes may be undergoing a transformation. To address this critical issue, we have undertaken a comprehensive assessment by employing a novel approach that combines site observations, model-derived datasets, and machine learning techniques. Our analysis of the dataset shows varying degrees of improvement in wintertime visibility in regions such as North China, South China, and the Fenwei Plain over 2013-2019, but an unexpected deterioration (approximately 1 km yr-1) in central and southern China (CSC). We further elucidate key roles of PM2.5 reduction in these regions with visibility improvement; whereas the unsatisfactory visibility trend in CSC was caused by combined effect of relative humidity (RH) increase (47 %), aerosol hygroscopicity (κ) enhancement (9 %), and boundary layer (BLH) reduction (8 %), which greatly overwhelms the effect of PM2.5 reduction recently. Moreover, the study reveals a growing influence of RH on the wintertime visibility, reaching 40 % ± 24 % to the total contribution in 2019, while that of PM2.5 declined to 18 % ± 19 % and is expected to further diminish with emission reduction. Note those often-neglected factors-temperature, wind speed, BLH, and κ, account for over 40 % of the total contribution. Though the importance of aerosol hygroscopic growth to visibility was found decreasing recently, it retains non-negligible impacts on driving inter-annual visibility trends. This study yields innovative insights for air pollution control, underscoring the imperative of region-specific strategies to mitigate low-visibility events.
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Affiliation(s)
- Lu Chen
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, China
| | - Fang Zhang
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), 518055 Shenzhen, China.
| | - Jingye Ren
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Zhigang Li
- Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Weiqi Xu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Lingling Liu
- College of Environment and Science, Beijing Normal University, Beijing 100875, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China.
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Behera RR, Satapathy DR, Majhi A. Human health risk assessment model associated with PM2.5 bound metals in paradip port township, India. Chemosphere 2024; 350:141111. [PMID: 38176588 DOI: 10.1016/j.chemosphere.2024.141111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/06/2024]
Abstract
This study aimed to assess the environmental risk and human health risks associated with PM2.5-bound metals in Paradip city between January 2019 and December 2021. The seasonal average concentrations of PM2.5 were measured 91.43 ± 70.18 μg m-3, 103.40 ± 60.80 μg m-3, 124.74 ± 62.37 μg m-3, and 159.37 ± 77.88 μg m-3 in pre-monsoon, monsoon, post-monsoon, and winter season respectively. The highest and lowest concentrations are estimated in the winter and pre-monsoon season. Paradip city experienced tropical weather conditions with a hot and humid climate. The wind pattern shows that the predominant wind direction was observed from the south-south-west (SSW) direction. The metals in PM2.5 were analysed using an atomic absorption spectrophotometer (AAS) by air-acetylene flame using a hollow cathode lamp. The average metal concentration decreased in the order of Fe > Al > Zn > Pb > Cr > Mn > Ni > Cu > Co > Cd > As. The value of the geo-accumulation index (Igeo) was evaluated >1 for Cd, Fe, and Zn elements. The health risk assessment (HRA) results showed that non-carcinogenic risk (NCR) was higher through the inhalation route followed by ingestion and dermal contact. The cumulative NCR, which is expressed in terms of the hazard index (HI), is greater than 1 for infant (2.78E+00), child (2.53E+00), and adult (1.04E+00) via inhalation pathway. The total carcinogenic risk (TCR) for infants, children, and adults was estimated at 1.45E-04, 7.24E-05 and 1.25E-05, respectively, which exceeded the acceptable limit of 1.00E-06. Our comprehensive research plays an important role in both policymakers and relevant stakeholders for the preparation of city action plans concerning ambient air pollution, which can improve the air quality in and around Paradip city, India.
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Affiliation(s)
- Rashmi Ranjan Behera
- CSIR-Institute of Minerals and Materials Technology (IMMT), Environment and Sustainability Department, Bhubaneswar, Odisha, 751013 , India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Deepty Ranjan Satapathy
- CSIR-Institute of Minerals and Materials Technology (IMMT), Environment and Sustainability Department, Bhubaneswar, Odisha, 751013 , India.
| | - Arakshita Majhi
- CSIR-Institute of Minerals and Materials Technology (IMMT), Environment and Sustainability Department, Bhubaneswar, Odisha, 751013 , India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
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12
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de Pablo MÁ. Timelapse images datasets (2017-2022) from Livingston and Deception Islands, Antarctica, to study snow cover and weather conditions at the PERMATHERMAL monitoring network. Data Brief 2024; 52:109970. [PMID: 38186740 PMCID: PMC10770705 DOI: 10.1016/j.dib.2023.109970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 12/10/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
In the Deception and Livingston Islands, South Shetland Islands, Antarctica, two sites belonging to the international Circumpolar Active Layer Monitoring (CALM) network were established in 2005 and 2009, respectively, as part of the PERMATHERMAL network. In 2017, part of the installed instrumentation was upgraded, incorporating new CC5MPX automatic photographic cameras from Campbell Scientific to acquire three daily photographs at 5Mpx in resolution, 2592 × 1984 pixels in size, and JPEG format. The photographs are taken during the central hours of the day (14, 15, and 16 h GMT) to ensure maximum brightness, even during the Antarctic winter. Powered by batteries and solar panels, two cameras were installed at each site with a panoramic view, devoid of vegetation except for small patches of moss. At each study point, the cameras are oriented in different directions, providing diverse angles of the study area, and allowing observation of various fields of view in the environment. The result is a dataset of images acquired in the 2017-2022 period, organized by site, year, and month, capturing environmental conditions and spatial distribution of the snow cover. This dataset, besides documenting the snow cover, allows assessment of meteorological conditions, prevailing wind directions in the area during snow events, even in more distant areas from the study point due to the configured field of view. In the case of the images from Livingston Island, it also enables the study of the Limnopolar Lake's fluctuating water level during spring and summer. For the images from Deception Island, it provides insight into the presence of pack ice in Foster Port, relevant for volcanic activity and the safety of numerous vessels entering the caldera of the active volcano.
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Affiliation(s)
- Miguel Ángel de Pablo
- Unidad de Geología; Departamento de Geología, Geografía y Medio Ambiente; Facultad de Ciencias, Universidad de Alcalá. 28805 Alcalá de Henares, Madrid, Spain
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13
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Kloos S, Bigalke C, Neumair M, Menzel A. Weather, weekday, and vacation effects on webcam recorded daily visitor numbers in the alpine winter season. Int J Biometeorol 2024; 68:305-316. [PMID: 38036707 PMCID: PMC10794479 DOI: 10.1007/s00484-023-02591-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior.
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Affiliation(s)
- Simon Kloos
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, 85354, Freising, Germany.
| | - Carina Bigalke
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, 85354, Freising, Germany
| | - Matthias Neumair
- TUM School of Life Sciences, Life Science Systems, Technical University of Munich, 85354, Freising, Germany
| | - Annette Menzel
- TUM School of Life Sciences, Ecoclimatology, Technical University of Munich, 85354, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany
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14
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Ni Y, Yang Y, Wang H, Li H, Li M, Wang P, Li K, Liao H. Contrasting changes in ozone during 2019-2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions. Sci Total Environ 2024; 908:168272. [PMID: 37924894 DOI: 10.1016/j.scitotenv.2023.168272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/09/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
Ozone pollution is one of the most severe air quality issues in China that poses a serious threat to human health and ecosystems. During 2019-2021, the maximum daily 8-h average ozone concentrations in eastern China (110-122.5°E, 26-42°N) and the rest of China (ROC) show different decreasing patterns, with ozone concentrations in eastern China decreasing by 14.9 μg/m3, which is much larger than 4.8 μg/m3 in ROC. Here, based on two independent methods, the atmospheric chemical transport model (GEOS-Chem) simulations and the machine learning (ML) model (LightGBM) predictions, the reasons for the differences in ozone changes between eastern China and ROC during the warm season (April to September) are investigated. According to the GEOS-Chem (LightGBM) results, changes in the meteorological conditions contributed to an ozone decrease by 7.3 (6.8) μg/m3 in eastern China due to decreased chemical production and an ozone decrease by 6.8 (7.0) μg/m3 in ROC attributed to the weakened horizontal and vertical advection. With the influence of meteorological factors excluded, the observations show that changes in anthropogenic emissions resulted in an ozone decrease by 7.6 (8.1) μg/m3 in eastern China and an ozone increase by 2.0 (2.2) μg/m3 in ROC, which is primarily induced by the changes in NOx emissions. The surface measurements and satellite retrievals also indicate that the reduction in NOx emissions in ROC is less efficient than that in the more developed eastern China, leading to contrasting changes in ozone concentrations between eastern China and ROC during 2019-2021. Our results highlight the critical need to reduce ozone precursor emissions in the rest regions of China apart from eastern China.
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Affiliation(s)
- Yiqian Ni
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Yang Yang
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
| | - Hailong Wang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Huimin Li
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Mengyun Li
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Pinya Wang
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Ke Li
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
| | - Hong Liao
- Joint International Research Laboratory of Climate and Environment Change (ILCEC), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
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15
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Ahmad N, Lin C, Lau AKH, Kim J, Li C, Qin K, Zhao C, Lin J, Fung JCH, Li Y. Effects of meteorological conditions on the mixing height of Nitrogen dioxide in China using new-generation geostationary satellite measurements and machine learning. Chemosphere 2024; 346:140615. [PMID: 37931712 DOI: 10.1016/j.chemosphere.2023.140615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/04/2023] [Accepted: 11/02/2023] [Indexed: 11/08/2023]
Abstract
Nitrogen dioxide (NO2) plays a critical role in terms of air quality, human health, ecosystems, and its impact on climate change. While the crucial roles of the vertical structure of NO2 have been acknowledged for some time, there is currently limited knowledge about this aspect in China. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary satellite instrument capable of measuring the hourly columnar amount of NO2. The study presented here introduces the use of mixing height for NO2 in the atmosphere. A thorough examination of spatiotemporal variations in the mixing height of NO2 was conducted using data from both the GEMS and ground-based air quality monitoring networks. A random forest model based on machine learning techniques was utilized to examine how meteorological parameters affect the mixing height of NO2. The results of our study reveal a notable seasonal fluctuation in the mixing height of NO2, with the highest values observed during the summer and the lowest values during the winter. Additionally, there was an increasing diurnal trend from early morning to mid-afternoon. Moreover, the study discovered elevated NO2 mixing heights in the dry regions of northern China. The results also indicated a positive correlation between the mixing height of NO2 and temperature and wind speed, while negative associations were found with relative humidity and air pressure. The machine learning model's predicted NO2 mixing heights were in good agreement with the measurement-based outcomes, as evidenced by a coefficient of determination (R2) value of 0.96 (0.84 for the 10-fold cross-validation). These findings emphasize the noteworthy influence of meteorological variables on the vertical distribution of NO2 in the atmosphere and enhance our comprehension of the three-dimensional variations in NO2.
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Affiliation(s)
- Naveed Ahmad
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Changqing Lin
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Jhoon Kim
- Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, Korea
| | - Chengcai Li
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Kai Qin
- School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
| | - Chunsheng Zhao
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Jintai Lin
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Jimmy C H Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Ying Li
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
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16
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Folyovich A, Mátis R, Biczó D, Pálosi M, Béres-Molnár AK, Al-Muhanna N, Jarecsny T, Dudás E, Jánoska D, Toldi G, Páldy A. High average daily temperature in summer and the incidence of thrombolytic treatment for acute ischemic stroke. Encephale 2023:S0013-7006(23)00202-6. [PMID: 38040506 DOI: 10.1016/j.encep.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/07/2023] [Accepted: 09/19/2023] [Indexed: 12/03/2023]
Abstract
INTRODUCTION Meteorological factors can increase stroke risk; however, their impact is not precisely understood. Heat waves during summer increase total mortality. Therefore, we hypothesized that the average daily temperature in summer may correlate with the incidence of thrombolytic treatment for acute ischemic stroke in Budapest and Pest County, Hungary. METHODS We analyzed the relationship between the average daily temperature in summer months and the daily number of thrombolytic treatments (TT) performed with the indication of acute ischemic stroke between 1st June and 31st August each year from 2007 to 2016. The analysis was also performed after the omission of the data of the last day of the months due to possible psychosocial impact reported in our previous study. Spearman's correlation was used for statistical analysis. RESULTS No significant correlation was found between the average summer daily temperature and the number of TT in the entire sample of the 10-year period. When omitting the data of the last day of each month, positive correlations were suspected in 2014 (r=0.225, P=0.034) and 2015 (r=0.276, P=0.009). CONCLUSION Our findings did not confirm an association between the average daily temperature in summer and the daily number of TT throughout the examined 10-year period. However, importantly, in 2014 and 2015, the years with the highest average daily temperatures in this period, a positive correlation was found. The level of correlation is modest, indicating that risk factors, both meteorological and non-meteorological, other than the average temperature, play equally important roles in determining the incidence of thrombolytic treatment for acute ischemic stroke on the population level.
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Affiliation(s)
- András Folyovich
- Department of Neurology and Stroke, Szent János Hospital, Budapest, Hungary
| | - Réka Mátis
- Faculty of Public Governance and International Studies, University of Public Service, Budapest, Hungary
| | | | - Mihály Pálosi
- National Institute of Health Insurance Fund Management, Budapest, Hungary
| | | | - Nadim Al-Muhanna
- Department of Neurology and Stroke, Szent János Hospital, Budapest, Hungary
| | - Tamás Jarecsny
- Department of Neurology and Stroke, Szent János Hospital, Budapest, Hungary
| | - Eszter Dudás
- Department of Neurology and Stroke, Szent János Hospital, Budapest, Hungary
| | - Dorottya Jánoska
- Department of Neurology and Stroke, Szent János Hospital, Budapest, Hungary
| | - Gergely Toldi
- Liggins Institute, University of Auckland, Auckland, New Zealand.
| | - Anna Páldy
- National Public Health Center, Budapest, Hungary
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17
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Liang Y, Ma J, Tang C, Ke N, Wang D. Hourly forecasting on PM 2.5 concentrations using a deep neural network with meteorology inputs. Environ Monit Assess 2023; 195:1510. [PMID: 37989923 DOI: 10.1007/s10661-023-12081-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/31/2023] [Indexed: 11/23/2023]
Abstract
The PM2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM2.5 concentration prediction. In this study, we proposed a PM2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter's spatiotemporal correlation by concatenating the dataset with time series. The predicted PM2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers.
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Affiliation(s)
- Yanjie Liang
- School of Energy and Power Engineering, Shandong University, Jinan, 250061, China
| | - Jun Ma
- College of Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA
| | - Chuanyang Tang
- College of Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA
| | - Nan Ke
- College of Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA
| | - Dong Wang
- School of Energy and Power Engineering, Shandong University, Jinan, 250061, China.
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18
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Ding J, Han S, Wang X, Yao Q. Impact of air pollution changes and meteorology on asthma outpatient visits in a megacity in North China Plain. Heliyon 2023; 9:e21803. [PMID: 38027642 PMCID: PMC10651508 DOI: 10.1016/j.heliyon.2023.e21803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/08/2023] [Accepted: 10/28/2023] [Indexed: 12/01/2023] Open
Abstract
The effects of air pollution and meteorology on asthma is less studied in North China Plain. In the last decade, air quality in this region is markedly mitigated. This study compared the short-term effects of air pollutants on daily asthma outpatient visits (AOV) within different sex and age groups from 2014 to 2016 and 2017-2019 in Tianjin, with the application of distributed lag nonlinear model. Moreover, relative humidity (RH) and temperature as well as the synergistic impact with air pollutants were assessed. Air pollutants-associated risk with linear (different reference values were used) and non-linear assumptions were compared. In 2014-2016, PM10 and PM2.5 exhibited a larger impact on AOV, with the corresponding cumulative excess risks (ER) for every 10 μg/m3 increase at 1.04 % (95%CI:0.67-1.40 %, similarly hereafter) and 0.79 % (0.35-1.23 %), as well as increased to 43 % (26-63 %) and 20 % (10-31 %) at severe pollution. In 2017-2019, NO2 and MDA8 O3 exhibited a larger impact on AOV, with a cumulative ER for every 10 μg/m3 increase at 1.0 (0.63-1.4 %) and 0.36 % (0.15-0.57 %), with corresponding values of 7.9 % (4.8-11 %) and 5.6 % (2.3-9.0 %), at severe pollution. SO2 associated risk was only significant from 2014 to 2016. Cold effect, including extremely low temperature exposure and sharp temperature drop could generate a pronounced increase in AOV at 9.6 % (3.8-16 %) and 24 % (9.1-41 %), respectively. Moderate low temperature combined with air pollutants can enhance AOV during winter. Higher temperature in spring and autumn could trigger asthma by increasing pollen levels. Low RH resulted in AOV increase by 4.6 % (2.4-6.9), while higher RH generated AOV increase by 3.4 % (1.6-5.3). Females, children, and older adults tended to have a higher risk for air pollution, non-optimum temperature, and RH. As air pollution-associated risks on AOV tends to be weaker due to air quality improvement in recent years, the impact of extreme meteorological condition amidst climate change on asthma visits warrants further attention.
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Affiliation(s)
- Jing Ding
- Tianjin Environmental Meteorological Center, Tianjin 300070, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300070, China
| | - Suqin Han
- Tianjin Environmental Meteorological Center, Tianjin 300070, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300070, China
| | - Xiaojia Wang
- Tianjin Environmental Meteorological Center, Tianjin 300070, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300070, China
| | - Qing Yao
- Tianjin Environmental Meteorological Center, Tianjin 300070, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300070, China
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Garsa K, Khan AA, Jindal P, Middey A, Luqman N, Mohanty H, Tiwari S. Assessment of meteorological parameters on air pollution variability over Delhi. Environ Monit Assess 2023; 195:1315. [PMID: 37831195 DOI: 10.1007/s10661-023-11922-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.
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Affiliation(s)
- Kalpana Garsa
- Amity Centre for Air Pollution Control (ACAPC) & Amity Centre for Ocean-Atmospheric Science and Technology (ACOAST), Amity University Haryana, Gurugram, 122413, India
| | - Abul Amir Khan
- Amity Centre for Air Pollution Control (ACAPC) & Amity Centre for Ocean-Atmospheric Science and Technology (ACOAST), Amity University Haryana, Gurugram, 122413, India.
| | - Prakhar Jindal
- Space System Engineering, Delft University of Technology, Kluyverweg 1, 2629, HS, Delft, The Netherlands
| | - Anirban Middey
- CSIR-National Environmental Engineering Research Institute (NEERI), Kolkata Zonal Centre, Kolkata, West Bengal, 700107, India
| | - Nadeem Luqman
- Amity Institute of Behavioural and Allied Sciences (AIBAS), Amity University Haryana, Gurugram, 122413, India
| | - Hitankshi Mohanty
- Amity Centre for Air Pollution Control (ACAPC) & Amity Centre for Ocean-Atmospheric Science and Technology (ACOAST), Amity University Haryana, Gurugram, 122413, India
| | - Shubhansh Tiwari
- Amity Centre for Air Pollution Control (ACAPC) & Amity Centre for Ocean-Atmospheric Science and Technology (ACOAST), Amity University Haryana, Gurugram, 122413, India
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20
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de Lima BD, de Cássia Marques Alves R, de Oliveira GG, Paim BL. The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data. Environ Monit Assess 2023; 195:1305. [PMID: 37828253 DOI: 10.1007/s10661-023-11911-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/27/2023] [Indexed: 10/14/2023]
Abstract
The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40 µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10 µg/m3 to 50 µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world.
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Affiliation(s)
- Bianca Dutra de Lima
- State Center for Research in Remote Sensing and Meteorology, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Rio Grande Do Sul, 90501970, Brazil.
| | - Rita de Cássia Marques Alves
- State Center for Research in Remote Sensing and Meteorology, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Rio Grande Do Sul, 90501970, Brazil
| | - Guilherme Garcia de Oliveira
- State Center for Research in Remote Sensing and Meteorology, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Rio Grande Do Sul, 90501970, Brazil
| | - Bruna Lüdtke Paim
- State Center for Research in Remote Sensing and Meteorology, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Rio Grande Do Sul, 90501970, Brazil
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21
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Li JX, Luan Q, Li B, Dharmage SC, Heinrich J, Bloom MS, Knibbs LD, Popovic I, Li L, Zhong X, Xu A, He C, Liu KK, Liu XX, Chen G, Xiang M, Yu Y, Guo Y, Dong GH, Zou X, Yang BY. Outdoor environmental exposome and the burden of tuberculosis: Findings from nearly two million adults in northwestern China. J Hazard Mater 2023; 459:132222. [PMID: 37557043 DOI: 10.1016/j.jhazmat.2023.132222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/19/2023] [Accepted: 08/02/2023] [Indexed: 08/11/2023]
Abstract
We simultaneously assessed the associations for a range of outdoor environmental exposures with prevalent tuberculosis (TB) cases in a population-based health program with 1940,622 participants ≥ 15 years of age. TB status was confirmed through bacteriological and clinical assessment. We measured 14 outdoor environmental exposures at residential addresses. An exposome-wide association study (ExWAS) approach was used to estimate cross-sectional associations between environmental exposures and prevalent TB, an adaptive elastic net model (AENET) was implemented to select important exposure(s), and the Extreme Gradient Boosting algorithm was subsequently applied to assess their relative importance. In ExWAS analysis, 12 exposures were significantly associated with prevalent TB. Eight of the exposures were selected as predictors by the AENET model: particulate matter ≤ 2.5 µm (odds ratio [OR]=1.01, p = 0.3295), nitrogen dioxide (OR=1.09, p < 0.0001), carbon monoxide (OR=1.19, p < 0.0001), and wind speed (OR=1.08, p < 0.0001) were positively associated with the odds of prevalent TB while sulfur dioxide (OR=0.95, p = 0.0017), altitude (OR=0.97, p < 0.0001), artificial light at night (OR=0.98, p = 0.0001), and proportion of forests, shrublands, and grasslands (OR=0.95, p < 0.0001) were negatively associated with the odds of prevalent TB. Air pollutants had higher relative importance than meteorological and geographical factors, and the outdoor environment collectively explained 11% of TB prevalence.
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Affiliation(s)
- Jia-Xin Li
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Qiyun Luan
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashgar City 844000, China
| | - Beibei Li
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashgar City 844000, China
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Joachim Heinrich
- Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Comprehensive Pneumology Center (CPC) Munich, Member DZL, Germany; Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, Ludwig Maximilian University of Munich, Member DZL, Germany; German Center for Lung Research, Ziemssenstraße 1, 80336 Munich, Germany
| | - Michael S Bloom
- Department of Global and Community Health, George Mason University, Fairfax, VA 22030, USA
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, NSW 2006, Australia
| | - Igor Popovic
- UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia; Faculty of Medicine, School of Public Health, University of Queensland, Herston, 4006, Australia, School of Veterinary Science, University of Queensland, Gatton 4343, Australia
| | - Li Li
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashgar City 844000, China
| | - Xuemei Zhong
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashgar City 844000, China
| | - Aimin Xu
- Department of Laboratory Medicine, The First People's Hospital of Kashgar, Kashgar 844000, China
| | - Chuanjiang He
- Department of Laboratory Medicine, The First People's Hospital of Kashgar, Kashgar 844000, China; Department of Laboratory Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Kang-Kang Liu
- Department of Research Center for Medicine, the Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Xiao-Xuan Liu
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Gongbo Chen
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Mingdeng Xiang
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510080, China
| | - Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Guang-Hui Dong
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Xiaoguang Zou
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashgar City 844000, China.
| | - Bo-Yi Yang
- Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China.
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Tumusiime AG, Eyobu OS, Mugume I, Oyana TJ. A weather features dataset for prediction of short-term rainfall quantities in Uganda. Data Brief 2023; 50:109613. [PMID: 37808539 PMCID: PMC10551829 DOI: 10.1016/j.dib.2023.109613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/11/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Weather data is of great importance to the development of weather prediction models. However, the availability and quality of this data remains a significant challenge for most researchers around the world. In Uganda, obtaining observational weather data is very challenging due to the sparse distribution of weather stations and inconsistent data records. This has created critical gaps in data availability to run and develop efficient weather prediction models. To bridge this gap, we obtained country-specific time series hourly observational weather data. The data period is from 2020 to 2022 of 11 weather stations distributed in the four regions of Uganda. The data was accessed from the Ogimet data repository using the "climate" R-package. The automated procedures in the R-programming language environment allowed us to download user-defined data at a time resolution from an hourly to an annual basis. However, the raw data acquired cannot be used to learn rainfall patterns because it includes duplicates and non-uniform data. Therefore, this article presents a prepared and cleaned dataset that can be used for the prediction of short-term rainfall quantities in Uganda.
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Affiliation(s)
| | - Odongo Steven Eyobu
- College of Computing and IS, Makerere University, P.O Box, 7062, Kampala, Uganda
| | - Isaac Mugume
- College of Agricultural and Environmental Sciences, Makerere University, P.O Box, 7062, Kampala, Uganda
| | - Tonny J. Oyana
- College of Computing and IS, Makerere University, P.O Box, 7062, Kampala, Uganda
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23
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Kaur P, Dhar P, Bansal O, Singh D, Guha A. Temporal variability, meteorological influences, and long-range transport of atmospheric aerosols over two contrasting environments Agartala and Patiala in India. Environ Sci Pollut Res Int 2023; 30:102687-102707. [PMID: 37668783 DOI: 10.1007/s11356-023-29580-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 08/25/2023] [Indexed: 09/06/2023]
Abstract
The present study focused on the temporal variability, meteorological influences, potential sources, and long-range transport of atmospheric aerosols over two contrasting environments during 2011-2013. We have chosen Agartala (AGR) city in Northeast India as one of our sites representing the rural-continental environment and Patiala (PTA) as an urban site in Northwest India. The seasonal averaged equivalent black carbon (eBC) concentration in AGR ranges from 1.55 to 38.11 µg/m3 with an average value of 9.87 ± 8.17 µg/m3, whereas, at an urban location, PTA value ranges from 1.30 to 15.57 µg/m3 with an average value of 7.83 ± 3.51 µg/m3. The annual average eBC concentration over AGR was observed to be ~ 3 times higher than PTA. Two diurnal peaks (morning and evening) in eBC have been observed at both sites but were observed to be more prominent at AGR than at PTA. Spectral aerosol optical depth (AOD) has been observed to be in the range from 0.33 ± 0.09 (post-monsoon) to 0.85 ± 0.22 (winter) at AGR and 0.47 ± 0.04 (pre-monsoon) to 0.74 ± 0.09 (post-monsoon) at PTA. The concentration of eBC and its diurnal and seasonal variation indicates the primary sources of eBC as local sources, synoptic meteorology, planetary boundary layer (PBL) dynamics, and distant transportation of aerosols. The wintertime higher values of eBC at AGR than at PTA are linked with the transportation of eBC from the Indo-Gangetic Plain (IGP). Furthermore, it is evident that eBC aerosols are transported from local and regional sources, which is supported by concentration-weighted trajectory (CWT) analysis results.
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Affiliation(s)
- Parminder Kaur
- Department of Physics, Tripura University, West Tripura, Agartala, 799022, Tripura, India
| | - Pranab Dhar
- Department of Physics, Tripura University, West Tripura, Agartala, 799022, Tripura, India
| | - Onam Bansal
- Department of Civil Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh, India
| | - Darshan Singh
- Department of Physics, Punjabi University, Patiala, Punjab, India
| | - Anirban Guha
- Department of Physics, Tripura University, West Tripura, Agartala, 799022, Tripura, India.
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24
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Mihăilă D, Lazurca LG, Bistricean IP, Horodnic VD, Mihăilă EV, Emandi EM, Prisacariu A, Nistor A, Nistor B, Roșu C. Air quality changes in NE Romania during the first Covid 19 pandemic wave. Heliyon 2023; 9:e18918. [PMID: 37636459 PMCID: PMC10447937 DOI: 10.1016/j.heliyon.2023.e18918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/29/2023] Open
Abstract
This study analyzes for the first time uniformly and causally the level of pollution and air quality for the NE-Romania Region, one of the poorest region in the European Union. Knowing the level of pollution and air quality in this region, which can be taken as a benchmark due to its positional and economic-geographical attributes, responds to current scientific and practical needs. The study uses an hourly database (for five pollutants and five climate elements), from 2009 to 2020, from 19 air quality monitoring stations in northeastern Romania. Pollutant levels were statistically and graphically/cartographically modeled for the entire 2009-2020 interval on the distributive-spatial and regime, temporal component. Inter-station differences and similarities were analyzed causally. Taking advantage of the emergency measures between March 16 and May 14, 2020, we observed the impact of the event on the regional air quality in northeastern Romania. During the emergency period, the metropolitan area of Suceava (with over 100,000 inhabitants) was quarantined, which allowed us to analyze the impact of the quarantine period on the local air quality. We found that, in this region, air quality falls into class I (for NO2, SO2 and CO), II for O3 and III for PM10. During the lockdown periods NO2 and SO2 decreased for the entire region by 8.6 and 14.3%, respectively, and in Suceava by 13.9 and 40.1%, respectively. The causes of the reduction were anthropogenic in nature.
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Affiliation(s)
- Dumitru Mihăilă
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Liliana Gina Lazurca
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Ionel-Petruț Bistricean
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Vasilică-Dănuț Horodnic
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | | | - Elena-Maria Emandi
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Alin Prisacariu
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Alina Nistor
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | | | - Constantin Roșu
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
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Chen X, Dong J, Huang L, Chen L, Li Z, You N, Singha M, Tao F. Characterizing the 2020 summer floods in South China and effects on croplands. iScience 2023; 26:107096. [PMID: 37408686 PMCID: PMC10319219 DOI: 10.1016/j.isci.2023.107096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/20/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023] Open
Abstract
Floods occur more frequently in the context of climate change; however, flood monitoring capacity has not been well established. Here, we used a synergic mapping framework to characterize summer floods in the middle and lower reaches of the Yangtze River Plain and the effects on croplands in 2020, from both flood extent and intensity perspectives. We found that the total flood extent was 4936 km2 from July to August, and for flood intensity, 1658, 1382, and 1896 km2 of areas experienced triple, double, and single floods. A total of 2282 km2 croplands (46% of the flooded area) were inundated mainly from Poyang and Dongting Lake Basins, containing a high ratio of moderate damage croplands (47%). The newly increased flooding extent in 2020 was 29% larger than the maximum ever-flooded extent in 2015-2019. This study is expected to provide a reference for rapid regional flood disaster assessment and serving mitigation.
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Affiliation(s)
- Xi Chen
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jinwei Dong
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Lin Huang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Lajiao Chen
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Nanshan You
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Mrinal Singha
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Fulu Tao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- Natural Resources Institute Finland (Luke), 00790 Helsinki, Finland
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Lara R, Megido L, Suárez-Peña B, Negral L, Fernández-Nava Y, Rodríguez-Iglesias J, Marañón E, Castrillón L. Impact of COVID-19 restrictions on hourly levels of PM10, PM2.5 and black carbon at an industrial suburban site in northern Spain. Atmos Environ (1994) 2023; 304:119781. [PMID: 37090909 PMCID: PMC10089665 DOI: 10.1016/j.atmosenv.2023.119781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/02/2023] [Accepted: 04/11/2023] [Indexed: 05/03/2023]
Abstract
Due to the COVID-19 pandemic, lockdown restrictions were established around the world. Many studies have assessed whether these restrictions affected atmospheric pollution. Comparison between them is difficult as the periods of time considered are generally not the same and thus, different conclusions may be reached. Besides, most of them consider mean daily pollutant concentration, despite differences being observed according to the time of day. In this study, the hourly levels of PM10, PM2.5 and black carbon (BC) in an industrial suburban area in the north of Spain were analysed from May 2019 to June 2020 and compared with those from the literature, using the same period in each case. In general, the highest concentrations were reached when the wind direction came from the southwest (where a steelworks, a coal-fired power plant and other industries are located) and during the night-time, both before and during the lockdown. The highest concentrations of PM10, PM2.5 and BC were observed from December to February (on average: 45, 17 and 1.3 μg m-3, respectively). The decrease/increase in those pollutants levels during the lockdown were found to be highly dependent on the period considered. Indeed, PM10 can be found to decrease by up to 39% or increase by 12%; PM2.5 can decrease by 21% or increase by up to 36%; and BC, although it generally decreases (by up to 42%), can increase by 7.4%.
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Affiliation(s)
- Rosa Lara
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Laura Megido
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Beatriz Suárez-Peña
- Department of Materials Science and Metallurgical Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Luis Negral
- Department of Chemical and Environmental Engineering, Technical University of Cartagena, C.P 30202, Cartagena, Spain
| | - Yolanda Fernández-Nava
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Jesús Rodríguez-Iglesias
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Elena Marañón
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Leonor Castrillón
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
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27
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Vianna LFN, de Souza RV, Schramm MA, Alves TP. Using climate reanalysis and remote sensing-derived data to create the basis for predicting the occurrence of algal blooms, harmful algal blooms and toxic events in Santa Catarina, Brazil. Sci Total Environ 2023; 880:163086. [PMID: 36996989 DOI: 10.1016/j.scitotenv.2023.163086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 05/27/2023]
Abstract
This study aimed to form a basis for future predictive modeling efforts in support of the harmful algal blooms (HAB) surveillance program currently in force in the Brazilian State of Santa Catarina (SC). Data from monitoring toxin-producing algae were merged with both meteorological and oceanographic data and analyzed. Data from four sources were used in this study: climate reanalysis (air temperature, pressure, cloud cover, precipitation, radiation, U and V winds); remote sensing (chlorophyll concentration and sea surface temperature); Oceanic Niño Index; and HAB monitoring data (phytoplankton counts and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline). This study analyzed the period from 2007-01-01 to 2019-12-31 (7035 records in the HAB database) and used descriptive, bivariate and multivariate analyses to draw correlations among environmental parameters and the occurrence of algal blooms (AB), HAB and toxic events. Dinophysis spp. AB were the most registered type of event and tended to occur during the late autumn and winter months. These events were associated with high atmospheric pressure, predominance of westerly and southerly winds, low solar radiation and low sea and air temperature. An inverted pattern was observed for Pseudo-nitzschia spp. AB, which were mostly registered during the summer and early autumn months. These results give evidence that the patterns of occurrence of highly prevalent toxin-producing microalgae reported worldwide, such as the Dinophysis AB during the summer, differ along the coast of SC. Our findings also show that meteorological data, such as wind direction and speed, atmospheric pressure, solar radiation and air temperature, might all be key predictive modeling input parameters, whereas remote sensing estimates of chlorophyll, which are currently used as a proxy for the occurrence of AB, seem to be a poor predictor of HAB in this geographic area.
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Affiliation(s)
- Luiz F N Vianna
- Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina (Epagri), Rodovia Admar Gonzaga, 1.347, Itacorubi, Florianópolis, SC 88034-901, Brazil.
| | - Robson V de Souza
- Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina (Epagri), Rodovia Admar Gonzaga, 1.347, Itacorubi, Florianópolis, SC 88034-901, Brazil
| | - Mathias A Schramm
- Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina, Campus Itajaí, Av. Vereador Abrahão João Francisco, n° 3899, Ressacada, Itajaí, SC 88307-303, Brazil
| | - Thiago P Alves
- Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina, Campus Itajaí, Av. Vereador Abrahão João Francisco, n° 3899, Ressacada, Itajaí, SC 88307-303, Brazil
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Kalbande R, Kumar B, Maji S, Yadav R, Atey K, Rathore DS, Beig G. Machine learning based quantification of VOC contribution in surface ozone prediction. Chemosphere 2023; 326:138474. [PMID: 36958496 DOI: 10.1016/j.chemosphere.2023.138474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/14/2023] [Accepted: 03/20/2023] [Indexed: 06/18/2023]
Abstract
The prediction of surface ozone is essential attributing to its impact on human and environmental health. Volatile organic compounds (VOCs) are crucial in driving ozone concentration; particularly in urban areas where VOC limited regimes are prominent. The limited measurements of VOCs, however, hinder assessing the VOC-ozone relationship. This work applies machine learning (ML) algorithms for temporal forecasting of surface ozone over a metropolitan city in India. The availability of continuous VOCs measurement data along with meteorology and other pollutants during 2014-2016 makes it possible to deduce the influence of various input parameters on surface ozone prediction. After evaluating the best ML model for ozone prediction, simulations were carried out using varied input combinations. The combination with isoprene, meteorology, NOx, and CO (Isop + MNC) was the best with RMSE 4.41 ppbv and MAPE 6.77%. A season-wise comparison of simulations having all data, only meteorological data and Isop + MNC as input showed that Isop + MNC simulation gives the best results during the summer season (RMSE: 5.86 ppbv, MAPE: 7.05%). This shows the increased ability of the model to capture ozone peaks (high ozone during summer) relatively better when isoprene data is used. The overall results highlight that using all available data doesn't necessarily give best prediction results; also critical thinking is essential when evaluating the model results.
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Affiliation(s)
- Ritesh Kalbande
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Mohanlal Sukhadia University, Udaipur, India.
| | - Bipin Kumar
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - Sujit Maji
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
| | - Ravi Yadav
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; NUS Environmental Research Insitute, National University of Singapore, Singapore.
| | - Kaustubh Atey
- Indian Institute of Science Education and Research, Pune, India.
| | | | - Gufran Beig
- National Institute of Advanced Studies, Indian Institute of Science Campus, Bangalore, India.
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Liu X, Gao H, Zhang X, Zhang Y, Yan J, Niu J, Chen F. Driving Forces of Meteorology and Emission Changes on Surface Ozone in the Huaihe River Basin, China. Water Air Soil Pollut 2023; 234:355. [PMID: 37275321 PMCID: PMC10219803 DOI: 10.1007/s11270-023-06345-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 05/04/2023] [Indexed: 06/07/2023]
Abstract
Surface ozone (O3) pollution in China has become a serious environmental problem in recent years. In the present study, we targeted the HRB, a large region located in China's north-south border zone, to assess the driving forces of meteorology and emission changes on surface ozone. A Kolmogorov-Zurbenko (KZ) filter method was performed on the maximum daily average 8-h (MDA8) concentrations of ozone in the HRB during 2015-2020 to decompose the original time series. The findings demonstrated that the short-term (O3ST), seasonal (O3SN), and long-term components (O3LT) of MDA8 O3 variations accounted for 34.2%, 56.1%, and 2.9% of the total variance, respectively. O3SN has the greatest influence on the daily variation in MDA8 O3, followed by O3ST. In coastal cities, the influence of O3ST was enhanced. The influence of O3SN was stronger in the northwestern HRB. Air temperature is the prevailing variable that influences the photochemical formation of ozone. A clear phase lag (7-34 days) of the baseline component between MDA8 O3 and the atmospheric temperature was found in the HRB. Using multiple linear regression, the effect of temperature on ozone was removed. We estimated that the increase in ozone concentration in the HRB was mainly caused by the emission changes (79.4%), and the meteorological conditions made a small contribution (20.6%). This study suggests that reductions in volatile organic compounds (VOCs) will play an important role in further ozone pollution reduction in the HRB. Supplementary Information The online version contains supplementary material available at 10.1007/s11270-023-06345-1.
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Affiliation(s)
- Xiaoyong Liu
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Hui Gao
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Xiangmin Zhang
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Yidan Zhang
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
| | - Junhui Yan
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Jiqiang Niu
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
| | - Feiyan Chen
- School of Geographic Sciences, Xinyang Normal University, Xinyang, 464000 China
- Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, 464000 China
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Malik A, Aggarwal SG, Kunwar B, Deshmukh D, Shukla K, Agarwal R, Singh K, Soni D, Sinha P, Ohata S, Mori T, Koike M, Kawamura K, Kondo Y. Physical and chemical properties of PM 1 in Delhi: A comparison between clean and polluted days. Sci Total Environ 2023:164266. [PMID: 37225098 DOI: 10.1016/j.scitotenv.2023.164266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 05/26/2023]
Abstract
Considering the significance of PM1 aerosol in assessing health impacts of air pollution, an extensive analysis of PM1 samples collected at an urban site in Delhi is presented in this study. Overall, PM1 contributed to about 50 % of PM2.5 mass which is alarming especially in Delhi where particle mass loadings are usually higher than prescribed limits. Major portion of PM1 consisted of organic matter (OM) that formed nearly 47 % of PM1 mass. Elemental Carbon (EC) contributed to about 13 % of PM1 mass, whereas SO42- (16 %), NH4+ (10 %), NO3- (4 %) and Cl- (3 %) were the major inorganic ions present. Sampling was performed in two distinctive campaign periods (in terms of meteorological conditions and heating (fire activities), during the year 2019, each spanning two-week time, i.e. (i) September 3rd -16th (clean days), and (ii) November 22nd -December 5th (polluted days). The 24-h averaged mean concentrations of PM2.5 and BC during clean days (polluted days) were 70.6 ± 26.9 and 3.9 ± 1.0 μg m-3 (196 ± 104 and 7.6 ± 4.1 μg m-3) respectively, which were systematically lower (higher) than that of the annual mean (taken from studies conducted at same site in 2019) of 142 and 5.7 μg m-3, respectively. Changes in characteristic ratios (i.e., organic carbon (OC)/elemental Carbon (EC) and K+/EC) show an increase in biomass emissions during polluted days. Increase in biomass emission can be attributed to increase in heating practices (burning of biofuels such as wood logs, straw, and cow-dung cake) in- and around- Delhi because of fall in temperature in winter during second campaign. Furthermore, a significant increase in NO3- fraction in PM1 is observed during second campaign which shows fog processing of NOX due to conducive meteorological conditions in winters. Also, comparatively stronger correlation of NO3- with K+ during second campaign (r = 0.98 as compared to r = 0.5 during first campaign) suggests the increased heating practices to be a contributing factor for increased fraction of NO3- in PM1. We observed that during polluted days, meteorological parameters such as dispersion rate also played a major role in intensifying the impact of increased local emissions due to heating activities. Apart from this, change in the direction of regional emission transport to study site and the topology of Delhi are the possible reasons for the elevated pollution level, especially PM1 in onset of winter in Delhi. This study also suggests that black carbon measurement techniques used in current study (optical absorbance with heated inlet and evolved carbon techniques) can be used as reference techniques to determine the site-specific calibration constant of optical photometers for urban aerosol.
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Affiliation(s)
- Arpit Malik
- CSIR-National Physical Laboratory, New Delhi 110012, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Shankar G Aggarwal
- CSIR-National Physical Laboratory, New Delhi 110012, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
| | - Bhagawati Kunwar
- Chubu Institute of Advanced Studies, Chubu University, Kasugai 487-850, Japan
| | - Dhananjay Deshmukh
- Chubu Institute of Advanced Studies, Chubu University, Kasugai 487-850, Japan
| | - Kritika Shukla
- CSIR-National Physical Laboratory, New Delhi 110012, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Rishu Agarwal
- CSIR-National Physical Laboratory, New Delhi 110012, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Khem Singh
- CSIR-National Physical Laboratory, New Delhi 110012, India
| | - Daya Soni
- CSIR-National Physical Laboratory, New Delhi 110012, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Punaram Sinha
- Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Thiruvananthapuram 695547, India
| | - Sho Ohata
- Institute for Space-Earth Environmental Research, Nagoya University, Nagoya 464-0805, Japan
| | - Tatsuhiro Mori
- Department of Applied Chemistry, Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan
| | - Makoto Koike
- Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
| | - Kimitaka Kawamura
- Chubu Institute of Advanced Studies, Chubu University, Kasugai 487-850, Japan
| | - Yutaka Kondo
- National Institute of Polar Research, Tokyo 190-8518, Japan
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Ma X, Yin Z, Cao B, Wang H. Meteorological influences on co-occurrence of O 3 and PM 2.5 pollution and implication for emission reductions in Beijing-Tianjin-Hebei. Sci China Earth Sci 2023; 66:1-10. [PMID: 37359777 PMCID: PMC10205161 DOI: 10.1007/s11430-022-1070-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 01/17/2023] [Accepted: 01/30/2023] [Indexed: 06/28/2023]
Abstract
Co-occurrence of surface ozone (O3) and fine particulate matter (PM2.5) pollution (CP) was frequently observed in Beijing-Tianjin-Hebei (BTH). More than 50% of CP days occurred during April-May in BTH, and the CP days reached up to 11 in two months of 2018. The PM2.5 or O3 concentration associated with CP was lower than but close to that in O3 and PM2.5 pollution, indicating compound harms during CP days with double-high concentrations of PM2.5 and O3. CP days were significantly facilitated by joint effects of the Rossby wave train that consisted of two centers associated with the Scandinavia pattern and one center over North China as well as a hot, wet, and stagnant environmental condition in BTH. After 2018, the number of CP days decreased sharply while the meteorological conditions did not change significantly. Therefore, changes in meteorological conditions did not really contribute to the decline of CP days in 2019 and 2020. This implies that the reduction of PM2.5 emission has resulted in a reduction of CP days (about 11 days in 2019 and 2020). The differences in atmospheric conditions revealed here were helpful to forecast the types of air pollution on a daily to weekly time scale. The reduction in PM2.5 emission was the main driving factor behind the absence of CP days in 2020, but the control of surface O3 must be stricter and deeper. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11430-022-1070-y.
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Affiliation(s)
- Xiaoqing Ma
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044 China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044 China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080 China
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Bufan Cao
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044 China
| | - Huijun Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044 China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080 China
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
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Ou S, Wei W, Cheng S, Cai B. Exploring drivers of the aggravated surface O 3 over North China Plain in summer of 2015-2019: Aerosols, precursors, and meteorology. J Environ Sci (China) 2023; 127:453-464. [PMID: 36522077 DOI: 10.1016/j.jes.2022.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 06/17/2023]
Abstract
Continuous aggravated surface O3 over North China Plain (NCP) has attracted widely public concern. Herein, we evaluated the effects of changes in aerosols, precursor emissions, and meteorology on O3 in summer (June) of 2015-2019 over NCP via 8 scenarios with WRF-Chem model. The simulated mean MDA8 O3 in urban areas of 13 major cities in NCP increased by 17.1%∼34.8%, which matched well with the observations (10.8%∼33.1%). Meanwhile, the model could faithfully reproduce the changes in aerosol loads, precursors, and meteorological conditions. A relatively-even O3 increase (+1.2%∼+3.9% for 24-h O3 and +1.0%∼+3.8% for MDA8 O3) was induced by PM2.5 dropping, which was consistent with the geographic distribution of regional PM2.5 reduction. Meanwhile, the NO2 reduction coupled with a near-constant VOCs led to the elevated VOCs/NOx ratios, and then caused O3 rising in the areas under VOCs-limited regimes. Therein, the pronounced increases occurred in Handan, Xingtai, Shijiazhuang, Tangshan, and Langfang (+10.7%∼+13.6% for 24-h O3 and +10.2%∼+12.2% for MDA8 O3); while the increases in other cities were 5.7%∼10.5% for 24-h O3 and 4.9%∼9.2% for MDA8 O3. Besides, the meteorological fluctuations brought about the more noticeable O3 increases in northern parts (+12.5%∼+13.5% for 24-h O3 and +11.2%∼+12.4% for MDA8 O3) than those in southern and central parts (+3.2%∼+9.3% for 24-h O3 and +3.7%∼+8.8% for MDA8 O3). The sum of the impacts of the three drivers reached 16.7%∼21.9%, which were comparable to the changes of the observed O3. Therefore, exploring reasonable emissions-reduction strategies is essential for the ozone pollution mitigation over this region.
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Affiliation(s)
- Shengju Ou
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Wei Wei
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
| | - Bin Cai
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
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Sharma K, Kumar P, Sharma J, Thapa SD, Gupta A, Rajak R, Baruah B, Prakash A, Ranjan RK. Characterization of Polycyclic Aromatic Hydrocarbons (PAHs) associated with fine aerosols in ambient atmosphere of high-altitude urban environment in Sikkim Himalaya. Sci Total Environ 2023; 870:161987. [PMID: 36740072 DOI: 10.1016/j.scitotenv.2023.161987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/10/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Polycyclic Aromatic Hydrocarbons (PAHs) compounds are ubiquitous in ambient air due to their persistence, carcinogenicity, and mutagenicity. Gangtok being one of the cleanest cities in India located in Eastern Himalayan region, witnesses high developmental activities with enhanced urbanization affecting the ambient air quality. The present study aims to measure PM2.5 and PAHs in the ambient atmosphere of the Sikkim Himalaya to understand the influence of natural and anthropogenic activities on aerosol loading and their chemical characteristics. The PM2.5 samples were collected and analysed for the duration from Jan 2020 to Feb 2021.The seasonal mean concentrations of PM2.5 and PAHs were observed to be high during autumn and low during summer season. Overall, the annual mean concentration of PM2.5 was found higher than the prescribed limit of World Health Organization and National Ambient Air Quality Standards. The concentration of the 16 individual PAHs were found to be highest during autumn season (55.26 ± 37.15 ng/m3). Among the different PAHs, the annual mean concentration of fluorene (3.29 ± 4.07 ng/m3) and naphthalene (1.15 ± 3.76 ng/m3) were found to be the highest and lowest, respectively. The Molecular Diagnostic Ratio (MDR) test reveals higher contribution from heavy traffic activities throughout the winter and autumn seasons. The other possible sources identified over the region are fossil fuel combustion, and biomass burning. The multivariate statistical analysis (Multifactor Principal Component Analysis) also indicates a strong association between PM2.5 /PAHs and meteorological variables across the region in different seasons. The precipitation and wind pattern during the study period suggests that major contribution of the PM2.5 and PAHs were from local sources, with minimal contribution from long-range transport. The findings are important for comprehending the trends of PAH accumulation over a high-altitude urban area, and for developing sustainable air quality control methods in the Himalayan region.
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Affiliation(s)
- Khushboo Sharma
- Department of Geology, Sikkim University, Gangtok, Sikkim 737102, India
| | - Pramod Kumar
- Department of Geology, Sikkim University, Gangtok, Sikkim 737102, India
| | - Jayant Sharma
- Department of Geology, Sikkim University, Gangtok, Sikkim 737102, India
| | - Satkar Deep Thapa
- Department of Geology, Sikkim University, Gangtok, Sikkim 737102, India
| | - Aparna Gupta
- Department of Geology, Sikkim University, Gangtok, Sikkim 737102, India
| | - Rajeev Rajak
- Department of Geology, Sikkim University, Gangtok, Sikkim 737102, India
| | | | - Amit Prakash
- Department of Environmental Science, Tezpur University, Tezpur, Assam 784028, India
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Cha Y, Song CK, Jeon KH, Yi SM. Factors affecting recent PM 2.5 concentrations in China and South Korea from 2016 to 2020. Sci Total Environ 2023; 881:163524. [PMID: 37075994 DOI: 10.1016/j.scitotenv.2023.163524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/11/2023] [Accepted: 04/11/2023] [Indexed: 05/03/2023]
Abstract
This study used observational data and a chemical transport model to investigate the contributions of several factors to the recent change in air quality in China and South Korea from 2016 to 2020. We focused on observational data analysis, which could reflect the annual trend of emission reduction and adjust existing emission amounts to apply it into a chemical transport model. The observation data showed that the particulate matter (PM2.5) concentrations during winter 2020 decreased by -23.4 % (-14.68 μg/m3) and - 19.5 % (-5.73 μg/m3) in China and South Korea respectively, compared with that during winter 2016. Meteorological changes, the existing national plan for a long-term emission reduction target, and unexpected events (i.e., Coronavirus disease 2019 (COVID-19) in China and South Korea and the newly introduced special winter countermeasures in South Korea from 2020) are considered major factors that may affect the recent change in air quality. The impact of different meteorological conditions on PM2.5 concentrations was assessed by conducting model simulations by fixing the emission amounts; the results indicated changes of +7.6 % (+4.77 μg/m3) and + 9.7 % (+2.87 μg/m3) in China and South Korea, respectively, during winter 2020 compared to that during winter 2016. Due to the existing and pre-defined long-term emission control policies implemented in both countries, PM2.5 concentration significantly decreased from winter 2016-2020 in China (-26.0 %; -16.32 μg/m3) and South Korea (-9.1 %; -2.69 μg/m3). The unexpected COVID-19 outbreak caused the PM2.5 concentrations in China to decrease during winter 2020 by another -5.0 % (-3.13 μg/m3). In South Korea, the winter season special reduction policy, which was introduced and implemented in winter 2020, and the COVID-19 pandemic may have contributed to -19.5 % (-5.92 μg/m3) decrease in PM2.5 concentrations.
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Affiliation(s)
- Yesol Cha
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Chang-Keun Song
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea; Graduate School of Carbon Neutrality, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
| | - Kwon-Ho Jeon
- Department of Climate and Air Quality Research, National Institute of Environmental Research (NIER), Incheon, Republic of Korea
| | - Seung-Muk Yi
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea; Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
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Liu M, Huang Y, Hu J, He J, Xiao X. Algal community structure prediction by machine learning. Environ Sci Ecotechnol 2023; 14:100233. [PMID: 36793396 PMCID: PMC9923192 DOI: 10.1016/j.ese.2022.100233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
The algal community structure is vital for aquatic management. However, the complicated environmental and biological processes make modeling challenging. To cope with this difficulty, we investigated using random forests (RF) to predict phytoplankton community shifting based on multi-source environmental factors (including physicochemical, hydrological, and meteorological variables). The RF models robustly predicted the algal communities composed by 13 major classes (Bray-Curtis dissimilarity = 9.2 ± 7.0%, validation NRMSE mostly <10%), with accurate simulations to the total biomass (validation R2 > 0.74) in Norway's largest lake, Lake Mjosa. The importance analysis showed that the hydro-meteorological variables (Standardized MSE and Node Purity mostly >0.5) were the most influential factors in regulating the phytoplankton. Furthermore, an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models. The interpretation results disclosed that the environmental drivers (i.e., temperature, lake inflow, and nutrients) can jointly pose strong influence on the algal community shifts. This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.
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Affiliation(s)
- Muyuan Liu
- Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China
| | - Yuzhou Huang
- Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China
| | - Jing Hu
- Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China
| | - Junyu He
- Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China
- Ocean Academy, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China
| | - Xi Xiao
- Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China
- Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, Ministry of Natural Resources, Shanghai, 201206, China
- Donghai Laboratory, Zhoushan, Zhejiang, 316021, China
- Key Laboratory of Watershed Non-point Source Pollution Control and Water Eco-security of Ministry of Water Resources, College of Environmental and Resources Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
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Fu D, Shi X, Zuo J, Yabo SD, Li J, Li B, Li H, Lu L, Tang B, Qi H, Ma J. Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China? Environ Res 2023; 221:115282. [PMID: 36639012 PMCID: PMC9830900 DOI: 10.1016/j.envres.2023.115282] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/25/2022] [Accepted: 01/10/2023] [Indexed: 05/05/2023]
Abstract
To inhibit the COVID-19 (Coronavirus disease 2019) outbreak, unprecedented nationwide lockdowns were implemented in China in early 2020, resulting in a marked reduction of anthropogenic emissions. However, reasons for the insignificant improvement in air quality in megacities of northeast China, including Shenyang, Changchun, Jilin, Harbin, and Daqing, were scarcely reported. We assessed the influences of meteorological conditions and changes in emissions on air quality in the five megacities during the COVID-19 lockdown (February 2020) using the WRF-CMAQ model. Modeling results indicated that meteorology contributed a 14.7% increment in Air Quality Index (AQI) averaged over the five megacities, thus, the local unfavorable meteorology was one of the causes to yield little improved air quality. In terms of emission changes, the increase in residential emissions (+15%) accompanied by declining industry emissions (-15%) and transportation (-90%) emissions resulted in a slight AQI decrease of 3.1%, demonstrating the decrease in emissions associated with the lockdown were largely offset by the increment in residential emissions. Also, residential emissions contributed 42.3% to PM2.5 concentration on average based on the Integrated Source Apportionment tool. These results demonstrated the key role residential emissions played in determining air quality. The findings of this study provide a scenario that helps make appropriate emission mitigation measures for improving air quality in this part of China.
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Affiliation(s)
- Donglei Fu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China
| | - Xiaofei Shi
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; CASIC Intelligence Industry Development Co., Ltd, 50 Yongding Road, Beijing, 100089, China
| | - Jinxiang Zuo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China
| | - Stephen Dauda Yabo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China
| | - Jixiang Li
- College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China
| | - Bo Li
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China
| | - Haizhi Li
- Heilongjiang Provincial Ecological and Environmental Monitoring Center, 2 Weixing Road, Harbin, Heilongjiang, 150000, China
| | - Lu Lu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China
| | - Bo Tang
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China
| | - Hong Qi
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China; Heilongjiang Provincial Key Laboratory of Polar Environment and Ecosystem, 73 Huanghe Road, Harbin, Heilongjiang, 150000, China.
| | - Jianmin Ma
- College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China.
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Sharma A, Khare P, Singh N, Tiwari S, Chate DM, Kumar R. Anthropogenic aerosols in precipitation over the Indo-Gangetic basin. Environ Geochem Health 2023; 45:961-980. [PMID: 35391708 DOI: 10.1007/s10653-022-01236-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
This study investigated the concentration of heavy metals in rainwater (RW) at a semi-arid region of the Indo-Gangetic basin to understand the influence of local, regional, or long-range transport of air pollutants during the monsoon and non-monsoonal rain. The concentration of heavy metals in RW was determined using Atomic Absorption Spectrophotometer with Graphite Furnace, the scavenging ratio was estimated, and source interpretation was carried out using Principle Component Analysis (PCA) and HYSPLIT model. Ca was the highest contributor in RW followed by Na, Fe, Mg, and Al whereas Ba, Cr, Cu, Mn, Ni, Pb, and Zn were found in trace quantity. During the non-monsoon period, the crustal component (Ca) was the highest; however, during the monsoon, sea salt components (Na and Fe) were found higher. The scavenging ratio for metals was estimated and was found many times higher than those reported over European sites. The moderate concentration of heavy metal in RW was found with higher wind from South (S), South-West (SW), and North-West (NW) directions. Air mass back trajectory shows a significant contribution of metals from the Arabian Sea (South-Westerly wind) during active monsoon, whereas, in the non-monsoon season, the air masses mainly originated from the north-west indicating a contribution from wind-blown dust. The correlation analysis has shown the positive correlations between Ca and Mg, Mg and Na, Na and Cu, Al and Zn, Zn and Ba, Ba and Cr, and Cr and Zn. Principal Component Analysis (PCA) indicated loading of Ca, Na, Mg, Cu, Mn, and Ni in the first factor suggesting their crustal origin, whereas the second factor showed high loading of Al, Ba, Zn, Cr, and Ni indicating vehicular exhaust and industrial emission as their major sources, and loading for Ba and Mg in the third factor indicates the mixed contribution from both natural and anthropogenic sources in rainwater during the monsoon and non-monsoon periods. The data of this study can be used in the air pollution transport model. This study will help in source interpretation over the Indo-Gangetic basin and will help in planning for National Clean Air Program (NCAP) and deriving critical load.
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Affiliation(s)
| | - Puja Khare
- Department of Agronomy and Soil Science, CIMAP, Lucknow, India
| | - Nahar Singh
- Department of Chemistry, National Physical Laboratory, New Delhi, India
| | - Suresh Tiwari
- Indian Institute of Tropical Meteorology, New Delhi, India
| | - D M Chate
- Indian Institute of Tropical Meteorology, Pune, India
| | - Ranjit Kumar
- Department of Chemistry, Faculty of Science, Dayalbagh Educational Institute (Deemed University), Dayalbagh, Agra, India.
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Xu T, Zhang C, Liu C, Hu Q. Variability of PM 2.5 and O 3 concentrations and their driving forces over Chinese megacities during 2018-2020. J Environ Sci (China) 2023; 124:1-10. [PMID: 36182119 DOI: 10.1016/j.jes.2021.10.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/19/2021] [Accepted: 10/11/2021] [Indexed: 06/16/2023]
Abstract
Recently, air pollution especially fine particulate matters (PM2.5) and ozone (O3) has become a severe issue in China. In this study, we first characterized the temporal trends of PM2.5 and O3 for Beijing, Guangzhou, Shanghai, and Wuhan respectively during 2018-2020. The annual mean PM2.5 has decreased by 7.82%-33.92%, while O3 concentration showed insignificant variations by -6.77%-4.65% during 2018-2020. The generalized additive models (GAMs) were implemented to quantify the contribution of individual meteorological factors and their gas precursors on PM2.5 and O3. On a short-term perspective, GAMs modeling shows that the daily variability of PM2.5 concentration is largely related to the variation of precursor gases (R = 0.67-0.90), while meteorological conditions mainly affect the daily variability of O3 concentration (R = 0.65-0.80) during 2018-2020. The impact of COVID-19 lockdown on PM2.5 and O3 concentrations were also quantified by using GAMs. During the 2020 lockdown, PM2.5 decreased significantly for these megacities, yet the ozone concentration showed an increasing trend compared to 2019. The GAMs analysis indicated that the contribution of precursor gases to PM2.5 and O3 changes is 3-8 times higher than that of meteorological factors. In general, GAMs modeling on air quality is helpful to the understanding and control of PM2.5 and O3 pollution in China.
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Affiliation(s)
- Tianyi Xu
- School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China; Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Chengxin Zhang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China.
| | - Cheng Liu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China.
| | - Qihou Hu
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
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Pan W, Gong S, Lu K, Zhang L, Xie S, Liu Y, Ke H, Zhang X, Zhang Y. Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 3. Mechanism assessment of O 3 trends by a model. Sci Total Environ 2023; 857:159592. [PMID: 36272478 DOI: 10.1016/j.scitotenv.2022.159592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/14/2022] [Accepted: 10/16/2022] [Indexed: 06/16/2023]
Abstract
A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM2.5 and O3 in China. Following accompanying papers on synoptic circulation impact and key weather elements and emission contributions (Gong et al., 2022a; Gong et al., 2022b), an emission-driven Observation-based Box Model (e-OBM) was developed to study the impact mechanisms on O3 trend and quantitatively assess the effects of variation in the emissions control over 2013-2020 for Beijing, Chengdu, Guangzhou and Shanghai. Compared with the original OBM, the e-OBM not only improves the performance to simulate the hourly O3 peak concentration in daytime, but also reasonably reproduces the maximum daily 8-hour average (MDA8) O3 concentrations in the four cities. Based upon the sensitivity experiments, it is found that the meteorology is the dominant driver for the MDA8 O3 trend, contributing from about 32 % to 139 % to the variations. From the mechanistic point of view, the variations of meteorology lead to the enhancement of atmospheric oxidation capacity and the acceleration of O3 production. Further evaluation to the emission changes in four cities shows that the O3-precursors relationships of the four cities have been changed from the VOC-limited regime in 2013 to the transition regime or near-transition regime in 2020. Though the NOx/VOCs ratios have been obviously decreased, the emission reductions up to 2020 were still not enough to mitigate O3 pollution in these cities. It is emphasized in this study that the strengthened control measures with maintaining a certain ratio of NOx and VOCs should be implemented to further curb the increasing trend of O3 in urban areas.
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Affiliation(s)
- Weijun Pan
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Sunling Gong
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and Technology, 999078, Macao.
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Lei Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Shaodong Xie
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuhan Liu
- Department of Nuclear Safety, China Institute of Atomic Energy, Beijing 102413, China
| | - Huabing Ke
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xiaoling Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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Järvi L, Kurppa M, Kuuluvainen H, Rönkkö T, Karttunen S, Balling A, Timonen H, Niemi JV, Pirjola L. Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone. Sci Total Environ 2023; 856:158974. [PMID: 36174693 DOI: 10.1016/j.scitotenv.2022.158974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Urban air pollutant concentrations are highly variable both in space and time. In order to understand these variabilities high-resolution measurements of air pollutants are needed. Here we present results of a mobile laboratory and a drone measurements made within a street-canyon network in Helsinki, Finland, in summer and winter 2017. The mobile laboratory measured the total number concentration (N) and lung-deposited surface area (LDSA) of aerosol particles, and the concentrations of black carbon, nitric oxide (NOx) and ozone (O3). The drone measured the vertical profile of LDSA. The main aims were to examine the spatial variability of air pollutants in a wide street canyon and its immediate surroundings, and find the controlling environmental variables for the observed variability's. The highest concentrations with the most temporal variability were measured at the main street canyon when the mobile laboratory was moving with the traffic fleet for all air pollutants except O3. The street canyon concentration levels were more affected by traffic rates whereas on surrounding areas, meteorological conditions dominated. Both the mean flow and turbulence were important, the latter particularly for smaller aerosol particles through LDSA and N. The formation of concentration hotspots in the street network were mostly controlled by mechanical processes but in winter thermal processes became also important for aerosol particles. LDSA showed large variability in the profile shape, and surface and background concentrations. The expected exponential decay functions worked better in well-mixed conditions in summer compared to winter. We derived equation for the vertical decay which was mostly controlled by the air temperature. Mean wind dominated the profile shape over both thermal and mechanical turbulence. This study is among the first experimental studies to demonstrate the importance of high-resolution measurements in understanding urban pollutant variability in detail.
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Affiliation(s)
- Leena Järvi
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, Helsinki 00014, Finland; Helsinki Institute of Sustainability Science, University of Helsinki, P.O. Box 4, Helsinki 00014, Finland.
| | - Mona Kurppa
- Kjeller Vindteknikk, Tekniikantie 14, Espoo 02150, Finland
| | - Heino Kuuluvainen
- Aerosol Physics Laboratory, Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 692, Tampere 33014, Finland
| | - Topi Rönkkö
- Aerosol Physics Laboratory, Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 692, Tampere 33014, Finland
| | - Sasu Karttunen
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, Helsinki 00014, Finland
| | - Anna Balling
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, Helsinki 00014, Finland
| | - Hilkka Timonen
- Atmospheric Composition Research, Finnish Meteorological Institute, P.O. Box 503, Helsinki 00101, Finland
| | - Jarkko V Niemi
- Helsinki Region Environmental Services Authority, Ilmalantori 1, Helsinki 00240, Finland
| | - Liisa Pirjola
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, Helsinki 00014, Finland; Department of Automotive and Mechanical Engineering, Metropolia Applied University, P.O. Box 4071, Vantaa 01600, Finland
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Shao M, Xu X, Lu Y, Dai Q. Spatio-temporally differentiated impacts of temperature inversion on surface PM 2.5 in eastern China. Sci Total Environ 2023; 855:158785. [PMID: 36116664 DOI: 10.1016/j.scitotenv.2022.158785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/31/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM2.5. The peak time of PM2.5 was found around 18:00-22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM2.5 pollution, more attention needs to be paid to the nocturnal PM2.5 not only at surface level but also at higher levels in the presence of TIs.
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Affiliation(s)
- Min Shao
- School of Environment, Nanjing Normal University, Nanjing 210046, China
| | - Xiaoying Xu
- School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210031, China
| | - Yutong Lu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210046, China
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China.
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Lee HJ, Bell ML, Koutrakis P. Drought and ozone air quality in California: Identifying susceptible regions in the preparedness of future drought. Environ Res 2023; 216:114461. [PMID: 36181900 DOI: 10.1016/j.envres.2022.114461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 09/22/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
California experienced extreme and prolonged drought conditions during the early 2010s. To date, little is known regarding the influence of drought on air quality. Our study quantified site-specific associations between drought (defined by the Standardized Precipitation-Evapotranspiration Index; SPEI) and daily maximum 8-h ozone (O3) concentrations for California, USA, and then pooled these associations for the years 2009-2015. Overall, ambient O3 concentration was higher during droughts by 1.18 ppb (95% confidence interval (CI) = 1.00-1.36). The sensitivity of O3 to drought was greater during the warm season than during the cool season (1.73 ppb versus 0.79 ppb higher O3 during droughts) with substantial regional variation. In a pooled analysis with meteorological parameters as potential effect modifiers, the spatial heterogeneity of drought-O3 associations was explained strongly by average relative humidity for each season (71.9% (warm season) and 73.4% (cool season) of the drought-O3 associations explained), followed by the drought-related changes in relative humidity (47.6% (warm season)) and temperature (53.6% (cool season)). The pooled regression further identified regions susceptible for drought-related O3 increases as those with relatively low average relative humidity (10-25th percentiles or 44.3-47.3%) and larger drought-related decrease in relative humidity and increase in temperature. As the drought events are projected to occur with increased frequency and intensity in the era of climate change, the excess health burdens from O3 exposures attributed to the projected drought events need to be taken into account when allocating air quality and health resources. The impacts of O3 on health during droughts would confound the health burdens from the drought itself.
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Affiliation(s)
- Hyung Joo Lee
- Division of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, South Korea.
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, 06511, USA
| | - Petros Koutrakis
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
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Asif M, Mahajan P. Impact of COVID-19 lockdown and meteorology on the air quality of Srinagar city: A temperate climatic region in Kashmir Himalayas. Hyg Environ Health Adv 2022; 4:100025. [PMID: 37520075 PMCID: PMC9474402 DOI: 10.1016/j.heha.2022.100025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/08/2022] [Indexed: 06/17/2023]
Abstract
The deadly transmission of the coronavirus forced all countries to implement lockdowns to restrict the transmission of this highly infectious disease. As a result of these lockdowns and restrictions, many urban centers have seen a positive impact on air quality with a significant reduction in air pollution. Therefore, in this study, the impact of COVID-19 lockdown vis-a-vis meteorological parameters on the ambient air quality of Srinagar city was examined. In this regard, we have evaluated the temporal variation of six different key air pollutants (PM10, PM2.5, SO2, NO2, O3, and NH3) along with meteorological parameters (relative humidity, rainfall, temperature, wind speed, and wind direction). The duration of the study was divided into three periods: Before Lockdown(BLD), Lockdown (LD), and Partial Lockdown(PLD). Daily average data for all the parameters was accessed from one of the real-time continuous monitoring stations of the central pollution control board (CPCB) at Rajbagh Srinagar. Some air pollutants have decreased, according to the results, while others have increased. The air quality index (AQI) decreases overall by 6.15 percent compared to before lockdown, and it never exceeds the "moderate" category. The AQI was in the following order for both lockdown and pre-lockdown periods: satisfactory > moderate > good. However, for partial lockdown, it was moderate > satisfactory > good. It was observed that the maximum decrease was seen in the concentration of NO2, NH3 with 75.11% and 69.18%. A modest decrease was observed in PM10 at 3.8%. While SO2 and O3 had an upward trend of 85.82% and 48.74%, The NO2 to SO2 ratio reveals that the emissions of NO2 have substantially decreased due to the complete restriction of transport systems. From principal component analysis for all three study periods, PM10 and PM2.5 were combined into a single component, inferring their shared behavior and source of origin. SO2 and O3 demonstrated identical behavior during the lockdown and partial lockdown periods of study. According to the findings of the study, it is beneficial for the government, environmentalists, and policymakers to impose rigorous lockdown measures, particularly during extreme air pollution events, in order to reduce the damage caused by automotive and industrial emissions.
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Affiliation(s)
- Mohammad Asif
- Department of Botanical & Environmental Sciences, Guru Nanak Dev University, Amritsar, Punjab 143005, India
| | - Pranav Mahajan
- Punjab School of Economics Guru Nanak Dev University, Amritsar, Punjab 143005, India
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Kruezi E, Habek D, Luetić A, Marton I, Prka M, Srnec L, Plačko-Vršnak D, Košec V, Kuna K. IS THERE A RELATIONSHIP BETWEEN COMPLICATIONS OF EARLY PREGNANCY AND BIOMETEOROLOGICAL FORECAST? Acta Clin Croat 2022; 61:629-635. [PMID: 37868180 PMCID: PMC10588383 DOI: 10.20471/acc.2022.61.04.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 04/30/2021] [Indexed: 10/24/2023] Open
Abstract
The aim of our study was to connect the possible complications of early pregnancy (miscarriage and symptomatic ectopic pregnancy) up to the 12th week of gestation with biometeorological conditions while assuming a greater number of incidents with an unfavorable biometeorological forecast. We performed a retrospective observational study using medical data of a single medical center of Department of Gynecology and Obstetrics, Sveti Duh University Hospital and meteorological data from the Croatian Meteorological and Hydrometeorological Service in Zagreb. We tracked the number of visits to the gynecology and obstetrics emergency unit on a daily basis during 2017. Days with five or more visits were selected and underwent further analysis, during which the number of miscarriages and symptomatic ectopic pregnancies was noted. The information from the biometeorological forecast was then extracted and added to the database. Our results did not show a statistically significant difference between the groups determined by biometeorological forecast in the number of spontaneous abortions or ectopic pregnancy. Also, statistically significant results did not follow the expected trend of the increasing number of complications related to worse biometeorological forecast, or vice versa, a decreased number of complications with better forecast. Our single-center retrospective analysis of emergency unit visits related to weather conditions did not show a connection between the complications of early pregnancy and biometeorological conditions. However, different results could emerge in future studies. Considering the large and high-quality database collected for this study, efforts in researching the connection between other gynecologic pathologies and weather conditions will be feasible.
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Affiliation(s)
- Egon Kruezi
- Department of Gynecology and Obstetrics, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
| | - Dubravko Habek
- Department of Gynecology and Obstetrics, Sveti Duh University Hospital, Catholic University of Croatia, Zagreb, Croatia
| | - Ana Luetić
- Department of Gynecology and Obstetrics, Sveti Duh University Hospital, Catholic University of Croatia, Zagreb, Croatia
| | - Ingrid Marton
- Department of Gynecology and Obstetrics, Sveti Duh University Hospital, Catholic University of Croatia, Zagreb, Croatia
| | - Matija Prka
- Department of Gynecology and Obstetrics, Sveti Duh University Hospital, Catholic University of Croatia, Zagreb, Croatia
| | - Lidija Srnec
- Croatian Meteorological and Hydrometeorological Service, Zagreb, Croatia
| | | | - Vesna Košec
- Department of Gynecology and Obstetrics, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
| | - Krunoslav Kuna
- Department of Gynecology and Obstetrics, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
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45
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Hodgson JR, Chapman L, Pope FD. Amateur runners more influenced than elite runners by temperature and air pollution during the UK's Great North Run half marathon. Sci Total Environ 2022; 842:156825. [PMID: 35752238 DOI: 10.1016/j.scitotenv.2022.156825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/06/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
The short- and long-term impacts of air pollution on human health are well documented and include cardiovascular, neurological, immune system and developmental damage. Additionally, the irritant qualities of air pollutants can cause respiratory and cardiovascular distress. This can be heightened during exercise and especially so for those with respiratory conditions such as asthma. Meteorological conditions have also been shown to adversely impact athletic performance; but research has mostly examined the impact of pollution and meteorology on marathon times or running under laboratory settings. This study focuses on the half marathon distance (13.1 miles/21.1 km) and utilises the Great North Run held in Newcastle-upon-Tyne, England, between 2006 and 2019. Local meteorological (temperature, relative humidity, heat index and wind speed) and air quality (ozone, nitrogen dioxide and PM2.5) data is used in conjunction with finishing times of the quickest and slowest amateur participants, along with the elite field, to determine the extent to which each group is influenced in real-world conditions. Results show that increased temperatures, heat index and ozone concentrations are significantly detrimental to amateur half marathon performances. The elite field meanwhile is influenced by higher ozone concentrations. It is thought that the increased exposure time to the environmental conditions contributes to this greater decrease in performance for the slowest participants. For elite athletes that are performing closer to their maximal capacity (VO2 max), the higher ozone concentrations likely results in respiratory irritation and decreased performance. Nitrogen dioxide and PM2.5 pollution showed no significant relationship with finishing times. These results provide additional insight into the environmental effects on exercise, which is particularly important under the increasing effects climate change and regional air pollution. This study can be used to inform event organisation and start times for both mass participation and major elite events with the aim to reduce heat- and pollution-related incidents.
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Affiliation(s)
- James R Hodgson
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Lee Chapman
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Francis D Pope
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom.
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Liu W, Mao Y, Hu T, Shi M, Zhang J, Zhang Y, Kong S, Qi S, Xing X. Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain. Environ Res 2022; 213:113719. [PMID: 35753370 PMCID: PMC9225942 DOI: 10.1016/j.envres.2022.113719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Stringent pollution control measures are generally applied to improve air quality, especially in the Spring Festival in China. Meanwhile, human activities are reduced significantly due to nationwide lockdown measures to curtail the COVID-19 spreading in 2020. Herein, to better understand the influence of control measures and meteorology on air pollution, this study compared the variation of pollution source and their health risk during the 2019 and 2020 Spring Festival in Linfen, China. Results revealed that the average concentration of PM2.5 in 2020 decreased by 39.0% when compared to the 2019 Spring Festival. Organic carbon (OC) and SO42- were the primary contributor to PM2.5 with the value of 19.5% (21.1%) and 23.5% (25.5%) in 2019 (2020) Spring Festival, respectively. Based on the positive matrix factorization (PMF) model, six pollution sources of PM2.5 were indicated. Vehicle emissions (VE) had the maximum reduction in pollution source concentration (28.39 μg· m-3), followed by dust fall (DF) (11.47 μg· m-3), firework burning (FB) (10.39 μg· m-3), coal combustion (CC) (8.54 μg· m-3), and secondary inorganic aerosol (SIA) (3.95 μg· m-3). However, the apportionment concentration of biomass burning (BB) increased by 78.7%, indicating a significant increase in biomass combustion under control measures. PAHs-lifetime lung cancer risk (ILCR) of VE, CC, FB, BB, and DF, decreased by 44.6%, 43.2%, 34.1%, 21.3%, and 2.0%, respectively. Additionally, the average contribution of meteorological conditions on PM2.5 in 2020 increased by 20.21% compared to 2019 Spring Festival, demonstrating that meteorological conditions played a crucial role in located air pollution. This study revealed that the existing control measures in Linfen were efficient to reduce air pollution and health risk, whereas more BB emissions were worthy of further attention. Furthermore, the result was conducive to developing more effective control measures and putting more attention into unfavorable meteorological conditions in Linfen.
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Affiliation(s)
- Weijie Liu
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, China
| | - Yao Mao
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China
| | - Tianpeng Hu
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China
| | - Mingming Shi
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China
| | - Jiaquan Zhang
- Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, China
| | - Yuan Zhang
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China
| | - Shaofei Kong
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China
| | - Shihua Qi
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China
| | - Xinli Xing
- School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, China.
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47
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Orak NH. Effect of ambient air pollution and meteorological factors on the potential transmission of COVID-19 in Turkey. Environ Res 2022; 212:113646. [PMID: 35688216 PMCID: PMC9172252 DOI: 10.1016/j.envres.2022.113646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 05/22/2023]
Abstract
There is a need to improve the understanding of air quality parameters and meteorological conditions on the transmission of SARS-CoV-2 in different regions of the world. In this preliminary study, we explore the relationship between short-term air quality (nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM2.5, PM10)) exposure, temperature, humidity, and wind speed on SARS-CoV-2 transmission in 41 cities of Turkey with reported weekly cases from February 8 to April 2, 2021. Both linear and non-linear relationships were explored. The nonlinear association between weekly confirmed cases and short-term exposure to predictor factors was investigated using a generalized additive model (GAM). The preliminary results indicate that there was a significant association between humidity and weekly confirmed COVID-19 cases. The cooler temperatures had a positive correlation with the occurrence of new confirmed cases. The low PM2.5 concentrations had a negative correlation with the number of new cases, while reducing SO2 concentrations may help decrease the number of new cases. This is the first study investigating the relationship between measured air pollutants, meteorological factors, and the number of weekly confirmed COVID-19 cases across Turkey. There are several limitations of the presented study, however, the preliminary results show that there is a need to understand the impacts of regional air quality parameters and meteorological factors on the transmission of the virus.
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Affiliation(s)
- Nur H Orak
- Marmara University, Department of Environmental Engineering, Istanbul, Turkey.
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Tian J, Hopke PK, Cai T, Fan Z, Yu Y, Zhao K, Zhang Y. Evaluation of impact of "2+26″ regional strategies on air quality improvement of different functional districts in Beijing based on a long-term field campaign. Environ Res 2022; 212:113452. [PMID: 35597294 DOI: 10.1016/j.envres.2022.113452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 04/30/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
Consecutive measurements of ambient fine particulate matter (PM2.5) from February 2016 to April 2018 have been performed at four representative sites of Beijing to evaluate the impact of "2 + 26" regional strategies implemented in 2017 for air quality improvement in non-heating period (2017NH) and heating period (2017H). The decrease of PM2.5 were significant both in 2017NH (20.2% on average) and 2017H (43.7% on average) compared to 2016NH and 2016H, respectively. Eight sources were resolved at each site from the PMF source apportionment including secondary nitrate, traffic, coal combustion, soil dust, road dust, sulfate, biomass/waste burning and industrial process. The results show that the reductions of industrial process, soil dust, and coal combustion were most effective among all sources at each site after the regional strategies implementation with the large reductions in potential source areas. The decrease of coal combustion in 2017NH were larger than 2017H at all sites while that of soil dust and industrial sources were the opposite. Insignificant reduction of coal combustion contribution at the suburban site in the heating period indicated that rural residential coal burning need further control. The industrial source control in the suburbs were least effective compared with other districts. Traffic was the largest contributer at each site and control of traffic emissions were more effective in 2017H than 2017NH. The local nature and increase of biomass/waste burning contributions emphasized the effect of fireworks and bio-fuel use in rural areas and incinerator emissions in urban districts. Secondary nitrate and sulfate were mainly impacted by the regional transport from southern adjacent areas and favorable meteorological conditions played an important part in the PM2.5 abatements of 2017H. Secondary nitrate became a more major role in the air pollution process because of the larger decrease of sulfate. Finally suggestions for future control are made in this study.
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Affiliation(s)
- Jingyu Tian
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Philip K Hopke
- Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, 13699, USA; Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Tianqi Cai
- Institute of Electronic System Engineering, Beijing, 100854, China
| | - Zhongjie Fan
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing, China
| | - Yue Yu
- Sino-Japan Friendship Centre for Environmental Protection, Beijing, 100029, China
| | - Kaining Zhao
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanxun Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, 361021, China.
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Zio S, Lamien B, Tiemounou S, Adaman Y, Tougri I, Beidari M, Boris OWYS. Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters. Infect Dis Model 2022; 7:448-462. [PMID: 35845472 PMCID: PMC9270784 DOI: 10.1016/j.idm.2022.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/14/2022] [Accepted: 06/24/2022] [Indexed: 12/23/2022] Open
Abstract
The novel coronavirus has affected all regions of the world, but each country has experienced different rates of infection. In West Africa, in particular, infection rates remain low as compared to other parts of the world. This heterogeneity in the spread of COVID-19 raises a lot of questions that are still unanswered. However, some studies point out that people's mobility, size of gatherings, rate of testing, and weather have a great impact on the COVID-19 spread. In this work, we first evaluate the correlation between meteorological parameters and COVID-19 cases using Spearman's rank correlation. Secondly, multi-output Gaussian processes (MOGP) are used to predict the daily confirmed COVID-19 cases by exploring its relationships with meteorological parameters. The number of daily reported COVID-19 cases, as well as, weather variables collected from March 9, 2020, to October 18, 2021, were used in the analysis. The weather variables considered in the analysis are the mean temperature, relative humidity, wind direction, insolation, precipitation, and wind speed. The predicting model was constructed exploiting the correlation between the data of the daily confirmed COVID-19 cases and data of the weather variables. The results show that a significant correlation between the daily confirmed COVID-19 cases was found with humidity, wind direction, wind speed, and insolation. These parameters are used to construct the predictive model using the Multi-Output Gaussian process (MOGP). Different combinations of the data of meteorological parameters together with the data of daily reported COVID-19 cases were used to derive different models. We found that the best predictor is obtained using the combination of Humidity and insolation. This model is then used to predict the daily confirmed COVID-19 cases knowing the humidity and Insolation.
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Affiliation(s)
- Souleymane Zio
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Bernard Lamien
- Institut du Génie Industriel et Textile, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Sibiri Tiemounou
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Yoda Adaman
- Agence Nationale de la Météorologie du Burkina Faso, ANAM, Burkina Faso
| | - Inoussa Tougri
- Institut du Génie Industriel et Textile, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Mohamed Beidari
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Ouedraogo W Y S Boris
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
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Lara R, Megido L, Negral L, Suárez-Peña B, Castrillón L. Impact of COVID-19 restrictions on the dry deposition fraction of settleable particulate matter at three industrial urban/suburban locations in northern Spain. Atmos Environ (1994) 2022; 284:119216. [PMID: 36373064 PMCID: PMC9637955 DOI: 10.1016/j.atmosenv.2022.119216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/28/2022] [Accepted: 05/29/2022] [Indexed: 05/09/2023]
Abstract
Ninety 24-h samples of the dry deposition fraction of settleable particulate matter (DSPM) were collected at one suburban industrial site ('EMA') and two urban industrial sites ('Lauredal' and 'Laboratory') in the western area of Gijón (North of Spain) from December 2019 to June 2020. The levels registered point to an environmental issue that should receive close attention from environmental authorities. Before lockdown restrictions due to COVID-19 were established, all samples collected at the EMA site exceeded 300 mg·m-2·d-1 (the Spanish limit value until 2002). Large amounts of DSPM were also registered at the Lauredal and Laboratory sites, maximum levels reaching 1039.2 and 672.7 mg·m-2·d-1, respectively. Seven metals were analysed in DSPM samples: Al, Ca, Fe, K, Mg, Mn and Na. Fe reached the highest values: 2473.4, 463.4 and 293.3 mg·m-2·d-1 (EMA, Lauredal and Laboratory sites, respectively). This study quantifies the reductions in the DSPM levels registered (on average, 97.2, 73.5 and 90.5% at the EMA, Lauredal and Laboratory sites, respectively) during the lockdown, which involved the restriction of population mobility and industrial activity. The influence of wind speed and its direction were also assessed to better understand the role of these restrictions in the observed reductions. The concentrations of all the metals in the DSPM were reduced by more than 75%, on average, except for K at the Laboratory and Lauredal sites. These decreases were much higher than those found by other authors for smaller fractions of the atmospheric particulate matter (PM10, PM2.5). The findings of the present study highlight the importance of DSPM in highly industrialized urban/suburban locations and indicate the direction that legal measures might take, given the influence of anthropogenic emissions.
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Affiliation(s)
- Rosa Lara
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Laura Megido
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Luis Negral
- Department of Chemical and Environmental Engineering, Technical University of Cartagena, Cartagena, Spain
| | - Beatriz Suárez-Peña
- Department of Materials Science and Metallurgical Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Leonor Castrillón
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
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