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Li X, Abdullah LC, Sobri S, Syazarudin Md Said M, Aslina Hussain S, Poh Aun T, Hu J. Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal. J Air Waste Manag Assoc 2023; 73:649-678. [PMID: 37449903 DOI: 10.1080/10962247.2023.2232744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/31/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023]
Abstract
Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.
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Affiliation(s)
- Xiaoju Li
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
- Department of Resource and Environment, Xichang University, Xichang City, Sichuan Province, China
| | - Luqman Chuah Abdullah
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Shafreeza Sobri
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Mohamad Syazarudin Md Said
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Siti Aslina Hussain
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia
| | - Tan Poh Aun
- SOx NOx Asia Sdn Bhd, Subang Jaya, Selangor, Malaysia
| | - Jinzhao Hu
- Department of Resource and Environment, Xichang University, Xichang City, Sichuan Province, China
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Xie F, Guo L, Wang Z, Tian Y, Yue C, Zhou X, Wang W, Xin J, Lü C. Geochemical characteristics and socioeconomic associations of carbonaceous aerosols in coal-fueled cities with significant seasonal pollution pattern. Environ Int 2023; 179:108179. [PMID: 37666041 DOI: 10.1016/j.envint.2023.108179] [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: 06/07/2023] [Revised: 08/26/2023] [Accepted: 08/31/2023] [Indexed: 09/06/2023]
Abstract
Carbonaceous aerosols, comprising organic carbon (OC) and elemental carbon (EC), are critical component of fine particulate matter (PM2.5), with diverse impacts on air quality and human health. This study investigated the concentrations and seasonal patterns of carbonaceous species in PM2.5 during both the heating season (January 2021) and non-heating season (July 2021) in three coal-fueled cities in northern China, as well as the differences in carbonaceous aerosols and their associations with socioeconomic parameters in cities situated on either side of the "Hu Line" in China. The results showed that, owing to intensified coal combustion and unfavorable meteorological conditions, levels of OC, EC, and OC/EC ratios were higher in winter compared to summer. Moreover, the presence of dust (DU) and light pollution (LP) days resulted in elevated OC levels but decreased EC levels. The Char-EC/Soot-EC ratios were highest during LP, followed by CL and DU. A source apportionment analysis demonstrated that coal burning, vehicle exhaust, road dust, and biomass burning were the primary contributors to carbonaceous aerosols, as confirmed by diagnostic ratios, Char-EC/Soot-EC ratios, and PCA analysis. Furthermore, our study found that carbonaceous aerosols concentrations and source apportionment primarily varied with diurnal and seasonal trends and different pollution types. Additionally, at the national scale, population density and urban green space exhibited a positive correlation with OC/EC ratios (p < 0.05), while energy consumption per unit of GDP showed a negative correlation (p < 0.05). The observation that OC/EC ratios were lower in coal-fueled cities than in economy-based cities suggests a more severe pollution scenario. These findings highlight the importance of comprehending of the seasonal variation and chemical characteristics of carbonaceous aerosol for understanding air pollution sources and characteristics, which is essential for both air quality management and human health.
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Affiliation(s)
- Fei Xie
- School of Ecology and Environment, Inner Mongolia University, 010021 Hohhot, China; Inner Mongolia Environmental Monitoring Center, 010011 Hohhot, China
| | - Linhao Guo
- School of Ecology and Environment, Inner Mongolia University, 010021 Hohhot, China
| | - Zichun Wang
- School of Ecology and Environment, Inner Mongolia University, 010021 Hohhot, China; School of Environmental Science and Technology, Dalian University of Technology, 116024 Dalian, China
| | - Yongli Tian
- Inner Mongolia Environmental Monitoring Center, 010011 Hohhot, China
| | - Caiying Yue
- Inner Mongolia Environmental Monitoring Center, 010011 Hohhot, China
| | - Xingjun Zhou
- Inner Mongolia Environmental Monitoring Center, 010011 Hohhot, China
| | - Wei Wang
- School of Ecology and Environment, Inner Mongolia University, 010021 Hohhot, China; Institute of Environmental Geology, Inner Mongolia University, 010021 Hohhot, China
| | - Jinyuan Xin
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Changwei Lü
- School of Ecology and Environment, Inner Mongolia University, 010021 Hohhot, China; Institute of Environmental Geology, Inner Mongolia University, 010021 Hohhot, China.
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Anand A, Garg VK, Agrawal A, Mangla S, Pathak A. Distribution and concentration pathway of particulate pollution during pandemic-induced lockdown in metropolitan cities in India. Int J Environ Sci Technol (Tehran) 2023:1-14. [PMID: 37360554 PMCID: PMC10258753 DOI: 10.1007/s13762-023-05025-1] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 01/23/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023]
Abstract
To characterize the pollutant dispersal across major metropolitan cities in India, daily particulate matter (PM10 and PM2.5) data for the study areas were collected from the National Air Quality Monitoring stations database provided by the Central Pollution Control Board (CPCB) of India. The data were analysed for three temporal ranges, i.e. before the pandemic-induced lockdown, during the lockdown, and after the upliftment of lockdown restrictions. For the purpose, the time scale ranged from 1st April to 31st May for the years 2019 (pre), 2020, and 2021 (post). Statistical distributions (lognormal, Weibull, and Gamma), aerosol optical thickness, and back trajectories were assessed for all three time periods. Most cities followed the lognormal distribution for PM2.5 during the lockdown period except Mumbai and Hyderabad. For PM10, all the regions followed the lognormal distribution. Delhi and Kolkata observed a maximum decline in particulate pollution of 41% and 52% for PM2.5 and 49% and 53% for PM10, respectively. Air mass back trajectory suggests local transmission of air mass during the lockdown period, and an undeniable decline in aerosol optical thickness was observed from the MODIS sensor. It can be concluded that statistical distribution analysis coupled with pollution models can be a counterpart in studying the dispersal and developing pollution abatement policies for specific sites. Moreover, incorporating remote sensing in pollution study can enhance the knowledge about the origin and movement of air parcels and can be helpful in taking decisions beforehand.
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Affiliation(s)
- A. Anand
- Department of Environmental Sciences and Technology, Central University of Punjab, Ghudda, Bathinda, Punjab India
| | - V. K. Garg
- Department of Environmental Sciences and Technology, Central University of Punjab, Ghudda, Bathinda, Punjab India
| | - A. Agrawal
- Department of Mathematics and Statistics, Central University of Punjab, Ghudda, Bathinda, Punjab India
| | - S. Mangla
- International Institute for Population Sciences, Mumbai, India
| | - A. Pathak
- Department of Statistics, Ramjas College, University of Delhi, Delhi, India
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Yang J, Ji Q, Pu H, Dong X, Yang Q. How does COVID-19 lockdown affect air quality: Evidence from Lanzhou, a large city in Northwest China. Urban Clim 2023; 49:101533. [PMID: 37122825 PMCID: PMC10121109 DOI: 10.1016/j.uclim.2023.101533] [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: 11/09/2022] [Revised: 03/04/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's "morning peak" of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.
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Affiliation(s)
- Jianping Yang
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Qin Ji
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hongzheng Pu
- School of Management, Chongqing University of Technology, Chongqing 400054, China
| | - Xinyang Dong
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China
| | - Qin Yang
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
- University of Chinese Academy of Sciences, Beijing, China
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Luo Y, Xu L, Li Z, Zhou X, Zhang X, Wang F, Peng J, Cao C, Chen Z, Yu H. Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources. Environ Sci Pollut Res Int 2023; 30:55092-55111. [PMID: 36884176 PMCID: PMC9994416 DOI: 10.1007/s11356-023-25757-4] [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] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO2, NO2, CO, O3, PM2.5, and PM10 were 8.61-13.76 μg m-3, 26.53-36.06 μg m-3, 0.79-1.31 mg m-3, 82.24-87.62 μg m-3, 37.98-51.10 μg m-3, and 84.15-97.47 μg m-3. The concentrations of air pollutants (except O3) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM10 in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.
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Affiliation(s)
- Yutian Luo
- College of Sciences, Shihezi University, Xinjiang, 832003 China
| | - Liping Xu
- College of Sciences, Shihezi University, Xinjiang, 832003 China
| | - Zhongqin Li
- College of Sciences, Shihezi University, Xinjiang, 832003 China
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000 China
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730070 China
| | - Xi Zhou
- Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000 China
| | - Xin Zhang
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000 China
| | - Fanglong Wang
- State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Tianshan Glaciological Station, Chinese Academy of Sciences, Lanzhou, 730000 China
| | - Jiajia Peng
- College of Sciences, Shihezi University, Xinjiang, 832003 China
| | - Cui Cao
- College of Sciences, Shihezi University, Xinjiang, 832003 China
| | - Zhi Chen
- College of Sciences, Shihezi University, Xinjiang, 832003 China
| | - Heng Yu
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730070 China
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Yu X, Shan B, Chen Y, Zhang Q, Ren Q, Lv Y. Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants. Environ Monit Assess 2023; 195:290. [PMID: 36629982 DOI: 10.1007/s10661-023-10917-3] [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: 07/17/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.
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Affiliation(s)
- Xinwei Yu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China, 250101
| | - Baoyan Shan
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China, 250101.
| | - Yanqiu Chen
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China, 250101
| | - Qiao Zhang
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China, 250101
| | - Qixin Ren
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China, 250101
| | - Yongqiang Lv
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China, 250101
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