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Mai Z, Shen H, Zhang A, Sun HZ, Zheng L, Guo J, Liu C, Chen Y, Wang C, Ye J, Zhu L, Fu TM, Yang X, Tao S. Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:15691-15701. [PMID: 38485962 DOI: 10.1021/acs.est.3c07907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.
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Affiliation(s)
- Zelin Mai
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Aoxing Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haitong Zhe Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Lianming Zheng
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jianfeng Guo
- Shenzhen Ecology and Environment Monitoring Centre of Guangdong Province, Shenzhen 518049, China
| | - Chanfang Liu
- Shenzhen Ecology and Environment Monitoring Centre of Guangdong Province, Shenzhen 518049, China
| | - Yilin Chen
- School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Chen Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jianhuai Ye
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shu Tao
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Institute of Carbon Neutrality, Peking University, Beijing 100871, China
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Zhang C, Xie Y, Shao M, Wang Q. Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172544. [PMID: 38643875 DOI: 10.1016/j.scitotenv.2024.172544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/30/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Ground-level ozone (O3) has been an emerging concern in China. Due to its complicated formation mechanisms, understanding the effects of influencing factors is critical for making effective efforts on the pollution control. This study aims to present and demonstrate the practicality of a data-driven technique that applies a machine learning (ML) model coupled with the SHapley Additive exPlanations (SHAP) approach in O3 simulation and sensitivity analysis. Based on hourly measured concentrations of O3 and its major precursors, as well as meteorological factors in a northern area of Nanjing, China, a Light Gradient Boosting Machine (LightGBM) model was established to simulate O3 concentrations in different seasons, and the SHAP approach was applied to conduct in-depth analysis on the impacts of influencing factors on O3 formation. The results indicated a reliable performance of the ML model in simulating O3 concentrations, with the coefficient of determination (R2) between the measured and simulated larger than 0.80, and the impacts of influencing factors were reasonably evaluated by the SHAP approach on both seasonal and diurnal time scales. It was found that although volatile organic compounds (VOCs) and nitrogen oxides (NOx), as well as temperature and relative humidity, were generally the main influencing factors, their sensitivities to O3 formation varied significantly in different seasons and with time of the day. This study suggests that the data-driven ML model is a practicable technique and may act as an alternative way to perform mechanism analysis to some extent, and has immense potential to be applied in both problem research and decision-making for air pollution control.
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Affiliation(s)
- Chenwu Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Yumin Xie
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Min Shao
- School of Environment, Nanjing Normal University, Nanjing 210046, China
| | - Qin'geng Wang
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
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3
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Dong Z, Jiang Y, Wang S, Xing J, Ding D, Zheng H, Wang H, Huang C, Yin D, Song Q, Zhao B, Hao J. Spatially and Temporally Differentiated NO x and VOCs Emission Abatement Could Effectively Gain O 3-Related Health Benefits. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9570-9581. [PMID: 38781138 DOI: 10.1021/acs.est.4c01345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The increasing level of O3 pollution in China significantly exacerbates the long-term O3 health damage, and an optimized health-oriented strategy for NOx and VOCs emission abatement is needed. Here, we developed an integrated evaluation and optimization system for the O3 control strategy by merging a response surface model for the O3-related mortality and an optimization module. Applying this system to the Yangtze River Delta (YRD), we evaluated driving factors for mortality changes from 2013 to 2017, quantified spatial and temporal O3-related mortality responses to precursor emission abatement, and optimized a health-oriented control strategy. Results indicate that insufficient NOx emission abatement combined with deficient VOCs control from 2013 to 2017 aggravated O3-related mortality, particularly during spring and autumn. Northern YRD should promote VOCs control due to higher VOC-limited characteristics, whereas fastening NOx emission abatement is more favorable in southern YRD. Moreover, promotion of NOx mitigation in late spring and summer and facilitating VOCs control in spring and autumn could further reduce O3-related mortality by nearly 10% compared to the control strategy without seasonal differences. These findings highlight that a spatially and temporally differentiated NOx and VOCs emission control strategy could gain more O3-related health benefits, offering valuable insights to regions with severe ozone pollution all over the world.
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Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qian Song
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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4
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Fang H, Wang W, Wang R, Xu H, Zhang Y, Wu T, Zhou R, Zhang J, Ruan Z, Li F, Wang X. Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123769. [PMID: 38499173 DOI: 10.1016/j.envpol.2024.123769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/05/2024] [Accepted: 03/09/2024] [Indexed: 03/20/2024]
Abstract
In response to regional ozone (O3) pollution, Chinese government has implemented air pollution control measures in recent years. Here, a case study was performed at an O3-polluted city, Wuhu, in Yangtze River Delta region of China to investigate O3 variation trend and the relationship to its precursors after implementation of Clean Air Action Plan Phase II, which aims to reduce O3 pollution. The results showed that peak O3 concentration was effectively reduced since Clean Air Action Plan Phase II. Due to significant NOx reduction, O3 formation tended to shift from volatile organic compound (VOC)-limited regimes to NOx-limited regimes during 2018-2022. VOC/NOx ratios measured in 2022 revealed that peak O3 concentration tended to respond positively to NOx. Apart from high-O3 period, Wuhu was still in a VOC-limited regime. The relationship of maximum daily 8-h ozone average and NO2 followed a lognormal distribution with an inflection point at 20 μg m-3 of NO2, suggesting that Wuhu should conduct joint control of VOC and NOx with a focus on VOC reduction before the inflection point. Alkenes and aromatics were suggested to be preferentially controlled due to their higher ozone formation potentials. Using random forest meteorological normalization method, meteorology had a positive effect on O3 concentration in 2018, 2019 and 2022, but a negative effect in 2020 and 2021. The meteorology could explain 44.0 ± 19.1% of the O3 variation during 2018-2022. High temperature favors O3 production and O3 pollution occurred more easily when temperature was over 25 °C, while high relative humidity inhibits O3 generation and no O3 pollution was found at relative humidity above 70%. This study unveils some new insights into the trend of urban O3 pollution in Yangtze River Delta region since Clean Air Action Plan Phase II and the findings provide important references for formulating control strategies against O3 pollution.
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Affiliation(s)
- Hua Fang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China; Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt, Wuhu, 241000, China.
| | - Wenjing Wang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Ran Wang
- Wuhu Institute of Ecological Environmental Sciences, Wuhu, 241000, China
| | - Hongling Xu
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Ying Zhang
- Wuhu Ecological and Environmental Monitoring Center of Anhui Province, Wuhu, 241005, China
| | - Ting Wu
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China; Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt, Wuhu, 241000, China.
| | - Ruicheng Zhou
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Jianxi Zhang
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Zhirong Ruan
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Feng Li
- School of Ecology and Environment, Anhui Normal University, Wuhu, 241000, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China
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5
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Li P, Chen C, Liu D, Lian J, Li W, Fan C, Yan L, Gao Y, Wang M, Liu H, Pan X, Mao J. Characteristics and source apportionment of ambient volatile organic compounds and ozone generation sensitivity in urban Jiaozuo, China. J Environ Sci (China) 2024; 138:607-625. [PMID: 38135424 DOI: 10.1016/j.jes.2023.04.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 12/24/2023]
Abstract
In recent years, many cities have taken measures to reduce volatile organic compounds (VOCs), an important precursor of ozone (O3), to alleviate O3 pollution in China. 116 VOC species were measured by online and offline methods in the urban area of Jiaozuo from May to October in 2021 to analyze the compositional characteristics. VOC sources were analyzed by a positive matrix factorization (PMF) model, and the sensitivity of ozone generation was determined by ozone isopleth plotting research (OZIPR) simulation. The results showed that the average volume concentration of total VOCs was 30.54 ppbv and showed a bimodal feature due to the rush-hour traffic in the morning and at nightfall. The most dominant VOC groups were oxygenated VOCs (OVOCs, 29.3%) and alkanes (26.7%), and the most abundant VOC species were acetone and acetylene. However, based on the maximum incremental reactivity (MIR) method, the major VOC groups in terms of ozone formation potential (OFP) contribution were OVOCs (68.09 µg/m3, 31.5%), aromatics (62.90 µg/m3, 29.1%) and alkene/alkynes (54.90 µg/m3, 25.4%). This indicates that the control of OVOCs, aromatics and alkene/alkynes should take priority. Five sources of VOCs were quantified by PMF, including fixed sources of fossil fuel combustion (27.8%), industrial processes (25.9%), vehicle exhaust (19.7%), natural and secondary formation (13.9%) and solvent usage (12.7%). The empirical kinetic modeling approach (EKMA) curve obtained by OZIPR on O3 exceedance days indicated that the O3 sensitivity varied in different months. The results provide theoretical support for O3 pollution prevention and control in Jiaozuo.
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Affiliation(s)
- Pengzhao Li
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Chun Chen
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China; Henan Key Laboratory of Environmental Monitoring Technology, Henan Ecological Environment Monitoring and Safety Center, Zhengzhou 450046, China
| | - Dan Liu
- Henan Key Laboratory of Environmental Monitoring Technology, Henan Ecological Environment Monitoring and Safety Center, Zhengzhou 450046, China
| | - Jie Lian
- Jiaozuo Ecological Environment Monitoring Center of Henan Province, Jiaozuo 454003, China
| | - Wei Li
- Jiaozuo Ecological Environment Monitoring Center of Henan Province, Jiaozuo 454003, China
| | - Chuanyi Fan
- Jiaozuo Ecological Environment Monitoring Center of Henan Province, Jiaozuo 454003, China
| | - Liangyu Yan
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yue Gao
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Miao Wang
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Hang Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Xiaole Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Jing Mao
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China.
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Tao C, Zhang Q, Huo S, Ren Y, Han S, Wang Q, Wang W. PM 2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170009. [PMID: 38220017 DOI: 10.1016/j.scitotenv.2024.170009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/04/2024] [Accepted: 01/06/2024] [Indexed: 01/16/2024]
Abstract
Numerous studies have linked ozone (O3) production to its precursors and fine particulate matter (PM2.5), while the complex interaction effects of PM2.5 and volatile organic compounds (VOCs) on O3 remain poorly understood. A systematic approach based on an interpretable machine learning (ML) model was utilized to evaluate the primary driving factors that impact O3 and to elucidate how changes in PM2.5, VOCs from different sources, NOx, and meteorological conditions either promote or inhibit O3 formation through their individual and synergistic effects in a tropical coastal city, Haikou, from 2019 to 2020. The results suggest that under low PM2.5 levels, alongside the linear O3-PM2.5 relationship observed, O3 formation is suppressed by PM2.5 with higher proportions of traffic-derived aerosol. Vehicle VOC emissions contributed maximally to O3 formation at midday, despite the lowest concentration. VOCs from fossil fuel combustion and industry emissions, which have opposing effects on O3, act as inhibitors and promoters by inducing diverse photochemical regimes. As PM2.5 pollution escalates, the impact of these VOCs reverses, becoming more pronounced in shaping O3 variation. Sensitivity analysis reveals that the O3 formation regime is VOC-limited, and effective regional O3 mitigation requires prioritizing substantial VOC reductions to offset enhanced VOC sensitivity induced by the co-reduction in PM2.5, with a focus on industrial and vehicular emissions, and subsequently, fossil fuel combustion once PM2.5 is effectively controlled. This study underscores the potential of the SHAP-based ML approach to decode the intricate O3-NOx-VOCs-PM2.5 interplay, considering both meteorological and atmospheric compositional variations.
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Affiliation(s)
- Chenliang Tao
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
| | - Qingzhu Zhang
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China.
| | - Sisi Huo
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
| | - Yuchao Ren
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
| | - Shuyan Han
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
| | - Qiao Wang
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
| | - Wenxing Wang
- Big Data Research Center for Ecology and Environment, Environmental Research Institute, Shandong University, Qingdao 266237, PR China
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7
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Zheng L, Adalibieke W, Zhou F, He P, Chen Y, Guo P, He J, Zhang Y, Xu P, Wang C, Ye J, Zhu L, Shen G, Fu TM, Yang X, Zhao S, Hakami A, Russell AG, Tao S, Meng J, Shen H. Health burden from food systems is highly unequal across income groups. NATURE FOOD 2024; 5:251-261. [PMID: 38486126 DOI: 10.1038/s43016-024-00946-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/21/2024] [Indexed: 03/27/2024]
Abstract
Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China's food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.
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Affiliation(s)
- Lianming Zheng
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Wulahati Adalibieke
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Feng Zhou
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China.
- College of Geography and Remote Sensing, Hohai University, Nanjing, China.
| | - Pan He
- School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK.
| | - Yilin Chen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
- School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen, China
| | - Peng Guo
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Jinling He
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Yuanzheng Zhang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Peng Xu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China
| | - Chen Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Jianhuai Ye
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Guofeng Shen
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Shunliu Zhao
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Amir Hakami
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Tao
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London, UK.
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China.
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8
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Chen SP, Liu WT, Cheng FY, Wang CH, Huang SM, Wang JL. Ozone containment through selective mitigation measures on precursors of volatile organic compounds. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:167953. [PMID: 37865244 DOI: 10.1016/j.scitotenv.2023.167953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/27/2023] [Accepted: 10/18/2023] [Indexed: 10/23/2023]
Abstract
Abatement of volatile organic compounds (VOCs) ozone reduction is usually carried out by reducing the total amount of VOCs without considering reactivity between different species. This study incorporates the concept of maximum incremental reactivity (MIR) and speciation profiles into the industrial emission inventory of Taiwan to target organic species from industrial sources with the greatest ozone formation potentials (OFPs). These high OFP sources/species are then mitigated to assess the O3 reduction amount (ΔO3) with Community Multiscale Air Quality (CMAQ) modeling under VOC-limited conditions. The objective is to minimize the number of target sources/species and their tonnage while achieving maximum O3 reduction. This approach is referred to as the Selective Precursor Mitigation (SPM). A case study of a high ozone episode (September 4-10, 2020) was chosen for illustration, during which a relatively stagnant atmospheric condition with minimal transboundary ozone occurred. A series of scenarios to target the highest OFP chemicals/industries for mitigation are compared for the achievable max. ΔO3, areas affected (area coverage), and reduction efficiency. For instance, by reducing the ten leading industry classes with the island's highest OFPs (OFPind), up to 19 % of max. 1-h ΔO3 can be expected. If, however, the same tonnage of VOCs as that of OFPind is distributed to all industries without considering the reactivity, called the overall mitigation (OM), comparable results to those of OFPind were found, but the number of sources needed to be managed with OM would increase by nearly three times (29,662 for OM vs. 11,981 for OFPind). Further reducing the management scale by only zooming in the ten highest OFP chemicals within the ten leading OFP industries (OFPsp) would result in relatively limited area coverage. Still, major ozone hot spots could be alleviated. Although the domain is set on the island of Taiwan, the SPM approach is universally applicable to other regions worldwide to gain the maximum ozone reduction effect at a minimized societal cost.
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Affiliation(s)
- Sheng-Po Chen
- Center for Environmental Monitoring and Technology, National Central University, Taoyuan, Taiwan.
| | - Wen-Tzu Liu
- Department of Chemistry, Chung Yuan Christian University, Taoyuan, Taiwan
| | - Fang-Yi Cheng
- Department of Atmospheric Sciences, National Central University, Taoyuan, Taiwan
| | - Chieh-Heng Wang
- Center for Environmental Studies, National Central University, Taoyuan, Taiwan
| | - Shih-Ming Huang
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan
| | - Jia-Lin Wang
- Center for Environmental Monitoring and Technology, National Central University, Taoyuan, Taiwan; Department of Chemistry, National Central University, Taoyuan, Taiwan.
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9
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Wang R, Wang L, Sun J, Zhang L, Li Y, Li K, Liu B, Zhang J, Wang Y. Maximizing ozone control by spatial sensitivity-oriented mitigation strategy in the Pearl River Delta Region, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:166987. [PMID: 37717781 DOI: 10.1016/j.scitotenv.2023.166987] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/06/2023] [Accepted: 09/09/2023] [Indexed: 09/19/2023]
Abstract
The Pearl River Delta (PRD) has long been plagued by severe O3 pollution, particularly during the autumn. A regional O3 pollution episode influenced by the Western Pacific Subtropical High in September 2021 was characterized by near-surface O3 escalation due to strong photochemical reactions within the planetary boundary layer. This event was targeted to develop effective control strategies through investigation of precursor control type and scope based on the high-order decoupled direct method (HDDM) and integrated source apportionment method (ISAM) of CMAQ. Generally, the majority of areas (67.0 %) were under NOx-limited regime, which should strengthen afternoon NOx control inferred by positive convex O3 responses. However, high emission and heavily polluted areas located in central PRD were under VOC-limited regime (11.6 %) or mixed regime (15.0 %). The remaining areas (6.4 %) were under NOx-titration or insensitive conditions. Regarding source apportionment, Guangdong province contributed 32.3 %-58.4 % to MDA8 O3 of PRD, especially higher proportion (>50 %) to central areas. Overall, local-focused NOx/VOC emission reductions had limited effects on O3 mitigation for receptor cities compared to regional-cooperative regulation. When region-wide VOC emission reduction was implemented, MDA8 O3 in VOC-limited grids exhibited the largest declines (2.3 %-4.1 %, 3.9- 7.0 μg·m-3). However, unified NOx control contributed to increasing MDA8 O3 in VOC-limited grids (most stations located for air quality evaluation) whereas decreased MDA8 O3 by 2.1 %- 5.7 %, 3.0- 8.2 μg·m-3 in large-scale NOx-limited grids. The sensitivity-oriented regional control avoided O3 rebound and achieved the greatest decline of 3.4 %- 5.0 %, 5.7- 8.4 μg·m-3 in VOC-limited grids; additionally, time-refined dynamic aggressive NOx control decreased peak O3 by an extra 1.2- 6 μg·m-3, both of which facilitate the regulation for the forecasting O3 episodes. These findings suggest that in heavily polluted environments, the enhancement of O3 regulation benefits requires meticulous, coordinated, and dynamic NOx and VOC controls spanning the entire region based on high-resolution analysis of heterogeneous O3-NOx-VOC sensitivity. Furthermore, emission reduction gains should be more reasonably reflected through increasing in-situ observations covering multi-sensitivity regions.
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Affiliation(s)
- Runyu Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Jiaren Sun
- Guangdong Province Engineering Laboratory for Air Pollution Control, Guangdong Provincial Key Laboratory of Water and Air Pollution Control, South China Institute of Environmental Sciences, MEE, Guangzhou 510655, China.
| | - Lei Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanyuan Li
- Xinjiang Weather Modification Office, Urumqi 830002, China; Xinjiang Weather Modification Engineering Technology Research Center, Urumqi 830002, China
| | - Ke Li
- 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 210044, China
| | - Boya Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxin Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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10
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He J, Shen H, Lei T, Chen Y, Meng J, Sun H, Li M, Wang C, Ye J, Zhu L, Zhou Z, Shen G, Guan D, Fu TM, Yang X, Tao S. Investigation of Plant-Level Volatile Organic Compound Emissions from Chemical Industry Highlights the Importance of Differentiated Control in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21295-21305. [PMID: 38064660 DOI: 10.1021/acs.est.3c08570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
The chemical industry is a significant source of nonmethane volatile organic compounds (NMVOCs), pivotal precursors to ambient ozone (O3), and secondary organic aerosol (SOA). Despite their importance, precise estimation of these emissions remains challenging, impeding the implementation of NMVOC controls. Here, we present the first comprehensive plant-level assessment of NMVOC emissions from the chemical industry in China, encompassing 3461 plants, 127 products, and 50 NMVOC compounds from 2010 to 2019. Our findings revealed that the chemical industry in China emitted a total of 3105 (interquartile range: 1179-8113) Gg of NMVOCs in 2019, with a few specific products accounting for the majority of the emissions. Generally, plants engaged in chemical fibers production or situated in eastern China pose a greater risk to public health due to their higher formation potentials of O3 and SOA or their proximity to residential areas or both. We demonstrated that targeting these high-risk plants for emission reduction could enhance health benefits by 7-37% per unit of emission reduction on average compared to the current situation. Consequently, this study provides essential insights for developing effective plant-specific NMVOC control strategies within China's chemical industry.
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Affiliation(s)
- Jinling He
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huizhong Shen
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Tianyang Lei
- Department of Earth System Sciences, Tsinghua University, Beijing 100080, China
| | - Yilin Chen
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, U.K
| | - Haitong Sun
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1 EW, U.K
- Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Chen Wang
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jianhuai Ye
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Lei Zhu
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhihua Zhou
- Shenzhen Ecological and Environmental Monitoring Center of Guangdong Province, Shenzhen 518055, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Dabo Guan
- Department of Earth System Sciences, Tsinghua University, Beijing 100080, China
| | - Tzung-May Fu
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xin Yang
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shu Tao
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Institute of Carbon Neutrality, Peking University, Beijing 100871, China
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11
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Sun HZ, Zhao J, Liu X, Qiu M, Shen H, Guillas S, Giorio C, Staniaszek Z, Yu P, Wan MW, Chim MM, van Daalen KR, Li Y, Liu Z, Xia M, Ke S, Zhao H, Wang H, He K, Liu H, Guo Y, Archibald AT. Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality. Innovation (N Y) 2023; 4:100517. [PMID: 37822762 PMCID: PMC10562756 DOI: 10.1016/j.xinn.2023.100517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/17/2023] [Indexed: 10/13/2023] Open
Abstract
Ever-increasing ambient ozone (O3) pollution in China has been exacerbating cardiopulmonary premature deaths. However, the urban-rural exposure inequity has seldom been explored. Here, we assess population-scale O3 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence. We find Chinese population have been suffering from climbing O3 exposure by 4.3 ± 2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities. Rural residents are broadly exposed to 9.8 ± 4.1 ppb higher ambient O3 than the adjacent urban citizens, and thus urbanization-oriented migration compromises the exposure-associated mortality on total population. Cardiopulmonary excess premature deaths attributable to long-term O3 exposure, 373,500 (95% uncertainty interval [UI]: 240,600-510,900) in 2019, is underestimated in previous studies due to ignorance of cardiovascular causes. Future O3 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.
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Affiliation(s)
- Haitong Zhe Sun
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Junchao Zhao
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xiang Liu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Minghao Qiu
- Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Serge Guillas
- Department of Statistical Science, University College London, London WC1E 6BT, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Chiara Giorio
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Zosia Staniaszek
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Michelle W.L. Wan
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Man Mei Chim
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Kim Robin van Daalen
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge CB2 0BD, UK
- Barcelona Supercomputing Center, Department of Earth Sciences, 08034 Barcelona, Spain
| | - Yilin Li
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Zhenze Liu
- School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Mingtao Xia
- Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Shengxian Ke
- State Key Laboratory of New Ceramics and Fine Processing, Key Laboratory of Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Haifan Zhao
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
| | - Haikun Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Kebin He
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Huan Liu
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Alexander T. Archibald
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- National Centre for Atmospheric Science, Cambridge CB2 1EW, UK
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12
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Wen Y, Liu M, Zhang S, Wu X, Wu Y, Hao J. Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:14299-14309. [PMID: 37706680 DOI: 10.1021/acs.est.3c04909] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Vehicle emissions in China have been decoupled from rapid motorization owing to comprehensive control strategies. China's increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle emission measurements were collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon (THC) and nitrogen oxide (NOX) in January and July could be up to 1.7 times in northern China. Thus, the emission ratios of THC and NOX can vary dramatically among various seasons and provinces, which have not been considered well by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O3) and fine particulate matter (PM2.5) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM2.5 emissions decreased by 69, 51, and 61%, respectively, during 2010-2019. However, the controls of NOX and ammonia (NH3) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NOX, PM2.5, and NH3 emissions were anticipated to reduce by 35, 36, 35, 45, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56, 58, 74, 53, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors to alleviate haze and O3 problems.
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Affiliation(s)
- Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Min Liu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China
| | - Xiaomeng Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China
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13
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Wang Q, Sheng D, Wu C, Zhao J, Li F, Yao S, Ou X, Li W, Chen J. Exploring ozone formation rules and concentration response to the change of precursors based on artificial neural network simulation in a typical industrial park. Heliyon 2023; 9:e20125. [PMID: 37810165 PMCID: PMC10559865 DOI: 10.1016/j.heliyon.2023.e20125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/12/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
Industrial parks have more complex O3 formation mechanisms due to a higher concentration and more dense emission of precursors. This study establishes an artificial neural network (ANN) model with good performance by expanding the moment and concentration changes of pollutants into general variables of meteorological factors and concentrations of pollutants. Finally, the O3 formation rules and concentration response to the changes of volatile organic compounds (VOCs) and nitrogen oxides (NOx) was explored. The results showed that the studied area belonged to the NOx-sensitive regime and the sensitivity was strongly affected by relative humidity (RH) and pressure (P). The concentration of O3 tends to decrease with a higher P, lower temperature (Temp), and medium to low RH when nitric oxide (NO) is added. Conversely, at medium P, high Temp, and high RH, the addition of nitrogen dioxide (NO2) leads to a larger decrease capacity in O3 concentration. More importantly, there is a local reachable maximum incremental reactivity (MIRL) at each certain VOCs concentration level which linearly increased with VOCs. The general maximum incremental reactivity (MIR) may lead to a significant overestimation of the attainable O3 concentration in NOx-sensitive regimes. The results can significantly support the local management strategies for O3 and the precursors control.
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Affiliation(s)
- Qiaoli Wang
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Dongping Sheng
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Chengzhi Wu
- Trinity Consultants, Inc. (China Office), Hangzhou, 310012, China
| | - Jingkai Zhao
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Feili Li
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Shengdong Yao
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Xiaojie Ou
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
| | - Wei Li
- Key Laboratory of Biomass Chemical Engineering of the Ministry of Education, Institute of Industrial Ecology and Environment, College of Chemical and Biological Engineering, Zhejiang University (Zijingang Campus), Hangzhou, 310030, China
| | - Jianmeng Chen
- College of Environment, Zhejiang University of Technology, Hangzhou, 310032, China
- Zhejiang University of Science & Technology, Hangzhou, 310023, China
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14
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Su F, Xu Q, Yin S, Wang K, Liu G, Wang P, Kang M, Zhang R, Ying Q. Contributions of local emissions and regional background to summertime ozone in central China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 338:117778. [PMID: 37019021 DOI: 10.1016/j.jenvman.2023.117778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/14/2023] [Accepted: 03/19/2023] [Indexed: 06/19/2023]
Abstract
Source contributions and regional transport of maximum daily average 8-h (MDA8) O3 during a high O3 month (June 2019) in Henan province in central China are explored using a source-oriented Community Multiscale Air Quality (CMAQ) model. The monthly average MDA8 O3 exceeds ∼70 ppb in more than half of the areas and shows a clear spatial gradient, with lower O3 concentrations in the southwest and higher in the northeast. Significant contributions of anthropogenic emissions to monthly average MDA8 O3 concentrations of more than 20 ppb are predicted in the provincial capital Zhengzhou, mostly due to emissions from the transportation sector (∼50%) and in the areas in the north and northeast regions where industrial and power generation-related emissions are high. Biogenic emissions in the region only contribute to approximately 1-3 ppb of monthly average MDA8 O3. In industrial areas north of the province, their contributions reach 5-7 ppb. Two CMAQ-based O3-NOx-VOCs sensitivity assessments (the local O3 sensitivity ratios based on the direct decoupled method and the production ratio of H2O2 to HNO3) and the satellite HCHO to NO2 column density ratio consistently show that most of the areas in Henan are in NOx-limited regime. In contrast, the high O3 concentration areas in the north and at the city centers are in the VOC-limited or transition regimes. The results from this study suggest that although reducing NOx emissions to reduce O3 pollution in the region is desired in most areas, VOC reductions must be applied to urban and industrial regions. Source apportionment simulations with and without Henan anthropogenic emissions show that the benefit of local anthropogenic NOx reduction might be lower than expected from the source apportionment results because the contributions of Henan background O3 increase in response to the reduced local anthropogenic emissions due to less NO titration. Thus, collaborative O3 controls in neighboring provinces are needed to reduce O3 pollution problems in Henan effectively.
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Affiliation(s)
- Fangcheng Su
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; Institute of Environmental Sciences, Zhengzhou University, Zhengzhou, 450001, China
| | - Qixiang Xu
- Institute of Environmental Sciences, Zhengzhou University, Zhengzhou, 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China
| | - Shasha Yin
- Institute of Environmental Sciences, Zhengzhou University, Zhengzhou, 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China
| | - Ke Wang
- Institute of Environmental Sciences, Zhengzhou University, Zhengzhou, 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China
| | - Guangjin Liu
- College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China; Institute of Environmental Sciences, Zhengzhou University, Zhengzhou, 450001, China
| | - Peng Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, 200438, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, 200438, China
| | - Mingjie Kang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China
| | - Ruiqin Zhang
- Institute of Environmental Sciences, Zhengzhou University, Zhengzhou, 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou, 450001, China.
| | - Qi Ying
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843-3136, USA.
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15
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Yuan Y, Wang K, Sun HZ, Zhan Y, Yang Z, Hu K, Zhang Y. Excess mortality associated with high ozone exposure: A national cohort study in China. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2023; 15:100241. [PMID: 36761466 PMCID: PMC9905662 DOI: 10.1016/j.ese.2023.100241] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 05/24/2023]
Abstract
Emerging epidemiological studies suggest that long-term ozone (O3) exposure may increase the risk of mortality, while pre-existing evidence is mixed and has been generated predominantly in North America and Europe. In this study, we investigated the impact of long-term O3 exposure on all-cause mortality in a national cohort in China. A dynamic cohort of 20882 participants aged ≥40 years was recruited between 2011 and 2018 from four waves of the China Health and Retirement Longitudinal Study. A Cox proportional hazard regression model with time-varying exposures on an annual scale was used to estimate the mortality risk associated with warm-season (April-September) O3 exposure. The annual average level of participant exposure to warm-season O3 concentrations was 100 μg m-3 (range: 61-142 μg m-3). An increase of 10 μg m-3 in O3 was associated with a hazard ratio (HR) of 1.18 (95% confidence interval [CI]: 1.13-1.23) for all-cause mortality. Compared with the first exposure quartile of O3, HRs of mortality associated with the second, third, and highest exposure quartiles were 1.09 (95% CI: 0.95-1.25), 1.02 (95% CI: 0.88-1.19), and 1.56 (95% CI: 1.34-1.82), respectively. A J-shaped concentration-response association was observed, revealing a non-significant increase in risk below a concentration of approximately 110 μg m-3. Low-temperature-exposure residents had a higher risk of mortality associated with long-term O3 exposure. This study expands current epidemiological evidence from China and reveals that high-concentration O3 exposure curtails the long-term survival of middle-aged and older adults.
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Affiliation(s)
- Yang Yuan
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Kai Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Haitong Zhe Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
- Department of Earth Sciences, University of Cambridge, Cambridge, CB2 3EQ, UK
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Zhiming Yang
- School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, 310058, China
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
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16
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Zhan J, Ma W, Song B, Wang Z, Bao X, Xie HB, Chu B, He H, Jiang T, Liu Y. The contribution of industrial emissions to ozone pollution: identified using ozone formation path tracing approach. NPJ CLIMATE AND ATMOSPHERIC SCIENCE 2023; 6:37. [PMID: 37214635 PMCID: PMC10186276 DOI: 10.1038/s41612-023-00366-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 05/03/2023] [Indexed: 05/24/2023]
Abstract
Wintertime meteorological conditions are usually unfavorable for ozone (O3) formation due to weak solar irradiation and low temperature. Here, we observed a prominent wintertime O3 pollution event in Shijiazhuang (SJZ) during the Chinese New Year (CNY) in 2021. Meteorological results found that the sudden change in the air pressure field, leading to the wind changing from northwest before CNY to southwest during CNY, promotes the accumulation of air pollutants from southwest neighbor areas of SJZ and greatly inhibits the diffusion and dilution of local pollutants. The photochemical regime of O3 formation is limited by volatile organic compounds (VOCs), suggesting that VOCs play an important role in O3 formation. With the developed O3 formation path tracing (OFPT) approach for O3 source apportionment, it has been found that highly reactive species, such as ethene, propene, toluene, and xylene, are key contributors to O3 production, resulting in the mean O3 production rate (PO3) during CNY being 3.7 times higher than that before and after CNY. Industrial combustion has been identified as the largest source of the PO3 (2.6 ± 2.2 ppbv h-1), with the biggest increment (4.8 times) during CNY compared to the periods before and after CNY. Strict control measures in the industry should be implemented for O3 pollution control in SJZ. Our results also demonstrate that the OFPT approach, which accounts for the dynamic variations of atmospheric composition and meteorological conditions, is effective for O3 source apportionment and can also well capture the O3 production capacity of different sources compared with the maximum incremental reactivity (MIR) method.
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Affiliation(s)
- Junlei Zhan
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Wei Ma
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Boying Song
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Zongcheng Wang
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029 China
| | - Xiaolei Bao
- Hebei Chemical & Pharmaceutical College, Shijiazhuang, 050026 China
- Hebei Provincial Academy of Environmental Sciences, Shijiazhuang, 050037 China
- Bayin Guoleng Vocational and Technical College, Korla, 841002 China
| | - Hong-Bin Xie
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024 China
| | - Biwu Chu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China
| | - Hong He
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085 China
| | - Tao Jiang
- Hebei Provincial Meteorological Technical Equipment Center, Shijiazhuang, 050021 China
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029 China
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024 China
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17
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Li C, Li F, Cheng Q, Guo Y, Zhang Z, Liu X, Qu Y, An J, Liu Y, Zhang S. Divergent summertime surface O 3 pollution formation mechanisms in two typical Chinese cities in the Beijing-Tianjin-Hebei region and Fenwei Plain. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 870:161868. [PMID: 36731547 DOI: 10.1016/j.scitotenv.2023.161868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Recently, severe summertime ozone (O3) pollution has swept across most areas of China, especially the Beijing-Tianjin-Hebei (BTH) region and Fenwei Plain. By focusing on Beijing and Yuncheng, which are two typical cities in the BTH region and the Fenwei Plain, we intended to reveal the neglected fact that they had disparate emission features and atmospheric movements but suffered from similar high-O3 pollution levels. Field observations indicated that Yuncheng had lower volatile organic compound (VOC) and NOx concentrations but higher background O3 levels. The model simulation verified that both photochemical reactions and net O3 generation were stronger in Beijing. Ultimately, faster net O3 generation rates (8.4 ppbv/h) plus lower background O3 values in Beijing and lower net O3 generation rates (6.2 ppbv/h) plus higher background O3 values in Yuncheng caused both regions to reach similar O3 peak values in July 2020. However, different O3 control measures were appropriate for the two cities according to the different simulated O3-VOCs-NOx responses. Additionally, as surface O3 levels are greatly affected by the ongoing O3 production/depletion process that occurs in three dimensions, exploring the effects of spatially distributed O3 on surface O3 should be high on the agenda in the future.
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Affiliation(s)
- Chenlu Li
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Feng Li
- Jining Ecological Environment Monitoring Center, Jining 272000, China
| | - Qiang Cheng
- Dongchangfu Branch of Liaocheng Ecological Environment Bureau, Liaocheng 252000, China
| | - Yitian Guo
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Ziyin Zhang
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Xingang Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China.
| | - Yu Qu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Junling An
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yafei Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Siqing Zhang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
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18
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Wang Z, Li X, Ma J, He H. Eco-friendly in-situ synthesis of monolithic NiFe layered double hydroxide for catalytic decomposition of ozone. CATAL COMMUN 2023. [DOI: 10.1016/j.catcom.2023.106635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
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19
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Wang L, Zhao Y, Shi J, Ma J, Liu X, Han D, Gao H, Huang T. Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 318:120798. [PMID: 36464118 DOI: 10.1016/j.envpol.2022.120798] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/24/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 μg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.
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Affiliation(s)
- Li Wang
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, 730000, China
| | - Yuan Zhao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Jinsen Shi
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, 730000, China
| | - Jianmin Ma
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xiaoyue Liu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Dongliang Han
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, 730000, China
| | - Hong Gao
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Tao Huang
- Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
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20
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Liu P, Xue C, Ye C, Liu C, Zhang C, Wang J, Zhang Y, Liu J, Mu Y. The Lack of HONO Measurement May Affect the Accurate Diagnosis of Ozone Production Sensitivity. ACS ENVIRONMENTAL AU 2023; 3:18-23. [PMID: 37101842 PMCID: PMC10125324 DOI: 10.1021/acsenvironau.2c00048] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 04/28/2023]
Abstract
Recently, deteriorating ozone (O3) pollution in China brought the precise diagnosis of O3 sensitive chemistry to the forefront. As a dominant precursor of OH radicals, atmospheric nitrous acid (HONO) plays an important role in O3 production. However, its measurement unavailability in many regions especially for second- and third-tier cities may lead to the misjudgment of the O3 sensitivity regime derived from observation-based models. Here, we systematically assess the potential impact of HONO on diagnosing the sensitivity of O3 production using a 0-dimension box model based on a comprehensive summer urban field campaign. The results indicated that the default mode (only the NO + OH reaction is included) in the model could underestimate ∼87% of observed HONO levels, leading to an obvious decrease (∼19%) of net O3 production in the morning, which was in line with the previous studies. The unconstrained HONO in the model was found to significantly push O3 production toward the VOC-sensitive regime. Additionally, it is unrealistic to change NO x but constrain HONO in the model due to the dependence of HONO formation on NO x . Assuming that HONO varied proportionally with NO x , a stronger NO x -sensitive condition could be achieved. Therefore, effective reduction of NO x should be given more attention together with VOC emission control for O3 mitigation.
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Affiliation(s)
- Pengfei Liu
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
| | - Chaoyang Xue
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
| | - Can Ye
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
| | - Chengtang Liu
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
| | - Chenglong Zhang
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
- University
of Chinese Academy of Sciences; Beijing100049, China
| | - Jinhe Wang
- Resources
and Environment Innovation Research Institute, School of Municipal
and Environmental Engineering, Shandong
Jianzhu University, Ji’nan250101, China
| | - Yuanyuan Zhang
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
- University
of Chinese Academy of Sciences; Beijing100049, China
| | - Junfeng Liu
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
- University
of Chinese Academy of Sciences; Beijing100049, China
| | - Yujing Mu
- Research
Center for Eco-Environmental Sciences, Chinese Academy of Sciences; Beijing100085, China
- University
of Chinese Academy of Sciences; Beijing100049, China
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21
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Sun HZ, Yu P, Lan C, Wan MW, Hickman S, Murulitharan J, Shen H, Yuan L, Guo Y, Archibald AT. Cohort-based long-term ozone exposure-associated mortality risks with adjusted metrics: A systematic review and meta-analysis. Innovation (N Y) 2022; 3:100246. [PMID: 35519514 PMCID: PMC9065904 DOI: 10.1016/j.xinn.2022.100246] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/16/2022] [Indexed: 11/30/2022] Open
Abstract
Long-term ozone (O3) exposure may lead to non-communicable diseases and increase mortality risk. However, cohort-based studies are relatively rare, and inconsistent exposure metrics impair the credibility of epidemiological evidence synthetization. To provide more accurate meta-estimations, this study updates existing systematic reviews by including recent studies and summarizing the quantitative associations between O3 exposure and cause-specific mortality risks, based on unified exposure metrics. Cross-metric conversion factors were estimated linearly by decadal observations during 1990-2019. The Hunter-Schmidt random-effects estimator was applied to pool the relative risks. A total of 25 studies involving 226,453,067 participants (14 unique cohorts covering 99,855,611 participants) were included in the systematic review. After linearly unifying the inconsistent O3 exposure metrics , the pooled relative risks associated with every 10 nmol mol-1 (ppbV) incremental O3 exposure, by mean of the warm-season daily maximum 8-h average metric, were as follows: 1.014 with 95% confidence interval (CI) ranging 1.009-1.019 for all-cause mortality; 1.025 (95% CI: 1.010-1.040) for respiratory mortality; 1.056 (95% CI: 1.029-1.084) for COPD mortality; 1.019 (95% CI: 1.004-1.035) for cardiovascular mortality; and 1.074 (95% CI: 1.054-1.093) for congestive heart failure mortality. Insignificant mortality risk associations were found for ischemic heart disease, cerebrovascular diseases, and lung cancer. Adjustment for exposure metrics laid a solid foundation for multi-study meta-analysis, and widening coverage of surface O3 observations is expected to strengthen the cross-metric conversion in the future. Ever-growing numbers of epidemiological studies supported the evidence for considerable cardiopulmonary hazards and all-cause mortality risks from long-term O3 exposure. However, evidence of long-term O3 exposure-associated health effects was still scarce, so more relevant studies are needed to cover more populations with regional diversity.
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Affiliation(s)
- Haitong Zhe Sun
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Earth Sciences, University of Cambridge, Cambridge CB2 3EQ, UK
| | - Pei Yu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Changxin Lan
- Institute of Reproductive and Child Health, Key Laboratory of Reproductive Health, National Health Commission of the People’s Republic of China, Beijing 100191, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Michelle W.L. Wan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Sebastian Hickman
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Jayaprakash Murulitharan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Huizhong Shen
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Le Yuan
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Yuming Guo
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Alexander T. Archibald
- Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- National Centre for Atmospheric Science, Cambridge CB2 1EW, UK
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22
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The Independent Impacts of PM2.5 Dropping on the Physical and Chemical Properties of Atmosphere over North China Plain in Summer during 2015–2019. SUSTAINABILITY 2022. [DOI: 10.3390/su14073930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Great changes occurred in the physical and chemical properties of the atmosphere in the North China Plain (NCP) in summer caused by PM2.5 dropping from 58 μg/m3 in 2015 to 36.0 μg/m3 in 2019. In this study, we first applied the WRF-Chem model to quantify the impact of PM2.5 reduction on shortwave radiation reaching the ground (SWDOWN), planetary boundary layer height (PBLH), and the surface concentration of air pollutants (represented by CO). Simulation results obtained an increase of 15.0% in daytime SWDOWN and 9.9% in daytime PBLH, and a decrease of −5.0% in daytime CO concentration. These changes were induced by the varied PM2.5 levels. Moreover, the variation in SWDOWN further led to a rise in the NO2 photolysis rate (JNO2) over this region, by 1.82 × 10−4~1.91 × 10−4 s−1 per year. Afterwards, we employed MCM chemical box model to explore how the JNO2 increase and the precursor decrease (CO, VOCs, and NOx) influenced O3 and HOx radicals. The results revealed that the photolysis rate (J) increase would individually cause a change on daytime surface O3, OH, and HO2 radicals by +9.0%, +18.9%, and +23.7%; the corresponding change induced by the precursor decrease was −2.5%, +1.9%, and −2.3%. At the same time, the integrated impacts of the change in J and precursors cause an increase of +6.3%, +21.1%, and +20.9% for daytime surface O3, OH, and HO2. Generally, the atmospheric oxidation capacity significantly enhanced during summer in NCP due to the PM2.5 dropping in recent years. This research can help understand atmosphere changes caused by PM2.5 reduction comprehensively.
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23
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Trends and Variability of Ozone Pollution over the Mountain-Basin Areas in Sichuan Province during 2013–2020: Synoptic Impacts and Formation Regimes. ATMOSPHERE 2021. [DOI: 10.3390/atmos12121557] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sichuan Province, the most industrialized and populated region in southwestern China, has been experiencing severe ozone pollution in the boreal warm season (April–September). With a surface ozone monitoring network and reanalysis dataset, we find that nearly all cities in Sichuan Province showed positive increasing trends in the warm-season ozone levels. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.0 ppb (4.8%) year−1 as a whole, with slightly larger trends in some sites such as a site in Zigong (5.2 ppb year−1). Seasonally, the monthly ozone level in Sichuan peaks from May to August (varies with year). The predominant warm-season synoptic patterns were objectively identified based on concurrent hourly meteorological fields from ERA5. High-pressure systems promote ozone production and result in high ozone concentrations, due to strong solar radiation as well as hot and dry atmospheric conditions. The increased occurrence of high-pressure patterns probably drives the ozone increase in Sichuan. When ozone pollution is relatively weak (with MDA8 ozone around 170 μg m−3), the air quality standard could be achieved in the short term by a 25% reduction of NOx and VOCs emissions. Strengthened emission control is needed when ozone pollution is more severe. Our study provides implications for effective emission control of ozone pollution in Sichuan.
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