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Tong Y, Yan Y, Lin J, Kong S, Tong Z, Zhu Y, Yan Y, Sun Z. Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950-2014. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 357:124397. [PMID: 38906406 DOI: 10.1016/j.envpol.2024.124397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024]
Abstract
Due to a lack of long-term observations in China, reports on historical ozone concentration are severely limited. In this study, by combining observation, reanalysis and model simulation data, XGBoost machine learning algorithm is used to correct the surface ozone concentration from CMIP6 climate model, and the long-term and large-scale surface ozone concentration of China during 1950-2014 is obtained. The long-term evolutions and trends of ozone and meteorological effects on interannual ozone variations are further analyzed. The results reveal that CMIP6 historical simulations have a large underestimation in ozone concentrations and their trends. The XGB-derived ozone are closer to observations, with R2 value of 0.66 and 0.74 for daily and monthly retrievals, respectively. Both the concentrations and exceedances of ozone in most parts of China have shown increasing trends from 1950 to 2014. The daily mean ozone concentration without climate change effects is estimated to be 117 ppb in the year 1950 averaged over China. It indicates that the increase in anthropogenic emissions of China has a significant contribution to ozone enhancement between 1950 and 2014. The higher ozone growth rates of XGB retrievals than those from the model indicate a regional surface ozone penalty due to the warming climate. The relatively significant increment in ozone are estimated in the Central and Western China. Seasonally, the ozone enhancement is largest in spring, indicating a shift in seasonal variation of ozone. Given the uncertainty in simulating historical ozone by climate model, we show that machine learning approaches can provide improved assessment of evolution in surface ozone, along with valuable information to guide future model development and formulate future ozone pollution prevention and control policies.
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Affiliation(s)
- Yuanxi Tong
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China
| | - Yingying Yan
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China.
| | - Jintai Lin
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Shaofei Kong
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan, 430074, China
| | - Zhixuan Tong
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China
| | - Yifei Zhu
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China
| | - Yukun Yan
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China
| | - Zhan Sun
- Department of Atmospheric Science, School of Environmental Sciences, China University of Geosciences, Wuhan, 430074, China
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Zheng Y, Cao W, Zhao H, Chen C, Lei Y, Feng Y, Qi Z, Wang Y, Wang X, Xue W, Yan G. Identifying Key Sources for Air Pollution and CO 2 Emission Co-control in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:15381-15394. [PMID: 39136294 DOI: 10.1021/acs.est.4c03299] [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: 08/20/2024]
Abstract
China is confronting the dual challenges of air pollution and climate change, mandating the co-control of air pollutants and CO2 emissions from their shared sources. Here we identify key sources for co-control that prioritize the mitigation of PM2.5-related health burdens, given the homogeneous impacts of CO2 emissions from various sources. By applying an integrated analysis framework that consists of a detailed emission inventory, a chemical transport model, a multisource fused dataset, and epidemiological concentration-response functions, we systematically evaluate the contribution of emissions from 390 sources (30 provinces and 13 socioeconomic sectors) to PM2.5-related health impacts and CO2 emissions, as well as the marginal health benefits of CO2 abatement across China. The estimated source-specific contributions exhibit substantial disparities, with the marginal benefits varying by 3 orders of magnitude. The rural residential, transportation, metal, and power and heating sectors emerge as pivotal sources for co-control, with regard to their relatively large marginal benefits or the sectoral total benefits. In addition, populous and heavily industrialized provinces such as Shandong and Henan are identified as the key regions for co-control. Our study highlights the significance of incorporating health benefits into formulating air pollution and carbon co-control strategies for improving the overall social welfare.
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Affiliation(s)
- Yixuan Zheng
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Wenxin Cao
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Hongyan Zhao
- Center for Atmospheric Environmental Studies, School of Environment, Beijing Normal University, Beijing 100875, China
| | - Chuchu Chen
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
- Center of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Yu Lei
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Yueyi Feng
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Zhulin Qi
- College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yihao Wang
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Xianen Wang
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Wenbo Xue
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
- Center of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Gang Yan
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing 100041, China
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Tan Z, Feng M, Liu H, Luo Y, Li W, Song D, Tan Q, Ma X, Lu K, Zhang Y. Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8815-8824. [PMID: 38733566 DOI: 10.1021/acs.est.3c08761] [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: 05/13/2024]
Abstract
This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.
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Affiliation(s)
- Zhaofeng Tan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Miao Feng
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Hefan Liu
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Yina Luo
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Wei Li
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Danlin Song
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Qinwen Tan
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Xuefei Ma
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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Dai H, Liao H, Wang Y, Qian J. Co-occurrence of ozone and PM 2.5 pollution in urban/non-urban areas in eastern China from 2013 to 2020: Roles of meteorology and anthropogenic emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171687. [PMID: 38485008 DOI: 10.1016/j.scitotenv.2024.171687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/25/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
We applied a three-dimensional (3-D) global chemical transport model (GEOS-Chem) to evaluate the influences of meteorology and anthropogenic emissions on the co-occurrence of ozone (O3) and fine particulate matter (PM2.5) pollution day (O3-PM2.5PD) in urban and non-urban areas of the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during the warm season (April-October) from 2013 to 2020. The model captured the observed O3-PM2.5PD trends and spatial distributions well. From 2013 to 2020, with changes in both anthropogenic emissions and meteorology, the simulated values of O3-PM2.5PD in the urban (non-urban) areas of the BTH and YRD regions were 424.8 (330.1) and 309.3 (286.9) days, respectively, suggesting that pollution in non-urban areas also warrants attention. The trends in the simulated values of O3-PM2.5PD were -0.14 and -0.15 (+1.18 and +0.81) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Sensitivity simulations revealed that changes in anthropogenic emissions decreased the occurrence of O3-PM2.5PD, with trends of -0.99 and -1.23 (-1.47 and -1.92) days yr-1 in the BTH (YRD) urban and non-urban areas, respectively. Conversely, meteorological conditions could exacerbate the frequency of O3-PM2.5PD, especially in the urban YRD areas, but less notably in the urban BTH areas, with trends of +2.11 and +0.30 days yr-1, respectively, owing to changes in meteorology only. The increases in T2m_max and T2m were the main meteorological factors affecting O3-PM2.5PD in most BTH and YRD areas. Furthermore, by conducting sensitivity experiments with different levels of pollutant precursor reductions in 2020, we found that volatile organic compound (VOC) reductions primarily benefited O3-PM2.5PD decreases in urban areas and that NOx reductions more notably influenced those in non-urban areas, especially in the YRD region. Simultaneously, reducing VOC and NOx emissions by 50 % resulted in considerable O3-PM2.5PD decreases (58.8-72.6 %) in the urban and non-urban areas of the BTH and YRD regions. The results of this study have important implications for the control of O3-PM2.5PD in the urban and non-urban areas of the BTH and YRD regions.
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Affiliation(s)
- Huibin Dai
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Ye Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jing Qian
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
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5
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Kusumaningtyas SDA, Tonokura K, Muharsyah R, Gunawan D, Sopaheluwakan A, Iriana W, Lestari P, Permadi DA, Rahmawati R, Samputra NAR. Comprehensive analysis of long-term trends, meteorological influences, and ozone formation sensitivity in the Jakarta Greater Area. Sci Rep 2024; 14:9605. [PMID: 38671080 PMCID: PMC11053138 DOI: 10.1038/s41598-024-60374-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010-2019) and rural Bogor sites (2017-2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia' s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration.
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Affiliation(s)
- Sheila Dewi Ayu Kusumaningtyas
- Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG), Jl. Angkasa I, No.2, Kemayoran, Jakarta, 10720, Indonesia.
- Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan.
| | - Kenichi Tonokura
- Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8563, Japan.
| | - Robi Muharsyah
- Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG), Jl. Angkasa I, No.2, Kemayoran, Jakarta, 10720, Indonesia
| | - Dodo Gunawan
- School of Meteorology, Climatology, and Geophysics (STMKG), Agency for Meteorology, Climatology, and Geophysics of Republic of Indonesia (BMKG), Pondok Betung, Tangerang Selatan, Indonesia
| | - Ardhasena Sopaheluwakan
- Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG), Jl. Angkasa I, No.2, Kemayoran, Jakarta, 10720, Indonesia
| | - Windy Iriana
- Department of Environmental Engineering, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology (ITB), Jl. Ganesa No. 10, Bandung, 40132, Indonesia
- Center for Environmental Studies, Bandung Institute of Technology (ITB), Jl. Sangkuriang No.42 A, Bandung, 40135, Indonesia
| | - Puji Lestari
- Department of Environmental Engineering, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology (ITB), Jl. Ganesa No. 10, Bandung, 40132, Indonesia
| | - Didin Agustian Permadi
- Department of Environmental Engineering, Faculty of Civil Engineering and Planning, National Institute of Technology (ITENAS), Jl. PKH. Mustopha No.23, Bandung, 40124, Indonesia
| | - R Rahmawati
- Jakarta Provincial Environmental Agency, Jl. Mandala V No.67, RT.1/RW.2, Cililitan, Jakarta, 13640, Indonesia
| | - Nofi Azzah Rawaani Samputra
- Jakarta Provincial Environmental Agency, Jl. Mandala V No.67, RT.1/RW.2, Cililitan, Jakarta, 13640, Indonesia
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Zhang L, Wang L, Ji D, Xia Z, Nan P, Zhang J, Li K, Qi B, Du R, Sun Y, Wang Y, Hu B. Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171295. [PMID: 38417501 DOI: 10.1016/j.scitotenv.2024.171295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/23/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.
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Affiliation(s)
- Lei Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Dan Ji
- Suichang Meteorological Bureau, Suichang 323000, China
| | - Zheng Xia
- Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Peifan Nan
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
| | - Jiaxin Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Ke Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Bing Qi
- Hangzhou Meteorological Bureau, Hangzhou 310051, China
| | - Rongguang Du
- Hangzhou Meteorological Bureau, Hangzhou 310051, China
| | - Yang Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Hu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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7
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Pyae TS, Kallawicha K. First temporal distribution model of ambient air pollutants (PM 2.5, PM 10, and O 3) in Yangon City, Myanmar during 2019-2021. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123718. [PMID: 38447651 DOI: 10.1016/j.envpol.2024.123718] [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: 12/06/2023] [Revised: 02/15/2024] [Accepted: 03/03/2024] [Indexed: 03/08/2024]
Abstract
Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2-98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.
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Affiliation(s)
- Tin Saw Pyae
- International Program of Hazardous Substances and Environmental Management, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Kraiwuth Kallawicha
- College of Public Health Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.
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8
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Liu R, Shao M, Wang Q. Multi-timescale variation characteristics of PM 2.5 in different regions of China during 2014-2022. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:171008. [PMID: 38369160 DOI: 10.1016/j.scitotenv.2024.171008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024]
Abstract
Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.
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Affiliation(s)
- Rui Liu
- 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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9
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Yan D, Jin Z, Zhou Y, Li M, Zhang Z, Wang T, Zhuang B, Li S, Xie M. Anthropogenically and meteorologically modulated summertime ozone trends and their health implications since China's clean air actions. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 343:123234. [PMID: 38154777 DOI: 10.1016/j.envpol.2023.123234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/21/2023] [Accepted: 12/24/2023] [Indexed: 12/30/2023]
Abstract
Elevated ozone (O3) has emerged as the major air quality concern since China's clean air actions, offsetting the health benefits gained from improved air quality. Given the shifted ozone chemical regimes and recently boosted extreme weather in China, it's essential to rethink the O3 trends since 2013 for evaluations of air pollution mitigation policy. Here, we examine the anthropogenically and meteorologically modulated summertime O3 trends across China at different stages of the clean air actions using multi-source observations combined with multi-model calculations. Ozone increases steadily in China between 2013-2022, with a fast increase rate of 4.4 μg m-3 yr-1 in Phase I and a much smaller 0.6 μg m-3 yr-1 in Phase II of Action Plan. Results highlight that the deteriorative O3 pollution in Phase I and early Phase II is dominated by the nonlinear O3-emission response. Persistent decline in O3 precursors has shifted its chemical regime in urban areas and began to show a positive influence on ozone mitigation in recent years. Meteorological influence on O3 variations is minor until 2019 (∼10%), but it greatly accelerates or relieves the O3 pollution after then, showing comparable contribution to emissions. Epidemiological model predicts totally 0.8-3.0 thousand yr-1 more deaths across China with altered anthropogenic emissions since clean air actions, and additional health burdens by -1.5-0.3 thousand yr-1 from perturbated meteorology. This study calls for stringent emission control and climate adaptation strategies to attain the ozone pollution mitigation in China.
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Affiliation(s)
- Dan Yan
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Zhipeng Jin
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Yiting Zhou
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Mengmeng Li
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China; Frontiers Science Center for Critical Earth Material Cycling, Nanjing, 210023, China.
| | - Zihan Zhang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Bingliang Zhuang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Shu Li
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Min Xie
- School of Environment, Nanjing Normal University, Nanjing, 210023, China
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10
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Zheng H, Kong S, Seo J, Yan Y, Cheng Y, Yao L, Wang Y, Zhao T, Harrison RM. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022. ENVIRONMENT INTERNATIONAL 2024; 183:108361. [PMID: 38091821 DOI: 10.1016/j.envint.2023.108361] [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: 07/03/2023] [Revised: 11/02/2023] [Accepted: 11/29/2023] [Indexed: 01/25/2024]
Abstract
Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.
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Affiliation(s)
- Huang Zheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China.
| | - Jihoon Seo
- Climate and Environmental Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Yingying Yan
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Yi Cheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Liquan Yao
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Yanxin Wang
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China
| | - Roy M Harrison
- School of Geography, Earth and Environment Sciences, University of Birmingham, Birmingham B15 2TT, UK; Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, PO Box 80203, Jeddah, Saudi Arabia.
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11
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Bhattarai H, Tai APK, Val Martin M, Yung DHY. Impacts of changes in climate, land use, and emissions on global ozone air quality by mid-21st century following selected Shared Socioeconomic Pathways. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167759. [PMID: 37832689 DOI: 10.1016/j.scitotenv.2023.167759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 09/12/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Surface ozone (O3) is a major air pollutant and greenhouse gas with significant risks to human health, vegetation, and climate. Uncertainties around the impacts of various critical factors on O3 is crucial to understand. We used the Community Earth System Model to investigate the impacts of land use and land cover change (LULCC), climate, and emissions on global O3 air quality under selected Shared Socioeconomic Pathways (SSPs). Our findings show that increasing forest cover by 20 % under SSP1 in East China, Europe, and the eastern US leads to higher isoprene emissions leading 2-5 ppb increase in summer O3 levels. Climate-induced meteorological changes, like rising temperatures, further enhance BVOC emissions and increase O3 levels by 10-20 ppb in urban areas with high NOx levels. However, higher BVOC emissions can reduce O3 levels by 5-10 ppb in remote environments. Future NOx emissions control reduces O3 levels by 5-20 ppb in the US and Europe in all SSPs, but reductions in NOx and changes in oxidant titration increase O3 in southeast China in SSP5. Increased NOx emissions in southern Africa and India significantly elevate O3 levels up to 15 ppb under different SSPs. Climate change is equally important as emissions changes, sometimes countering the benefits of emissions control. The combined effects of emissions, climate, and land cover result in worse O3 air quality in northern India (+40 %) and East China (+20 %) under SSP3 due to anthropogenic NOx and climate-induced BVOC emissions. Over the northern hemisphere, surface O3 decreases due to reduced NOx emissions, although climate and land use changes can increase O3 levels regionally. By 2050, O3 levels in most Asian regions exceed the World Health Organization safety limit for over 150 days per year. Our study emphasizes the need to consider complex interactions for effective air pollution control and management in the future.
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Affiliation(s)
- Hemraj Bhattarai
- Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Amos P K Tai
- Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China; State Key Laboratory of Agrobiotechnology and Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China.
| | - Maria Val Martin
- Leverhulme Centre for Climate Change Mitigation, School of Biosciences, University of Sheffield, Sheffield, UK.
| | - David H Y Yung
- Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
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12
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Fu X, Chen D, Wang X, Li Y, Lang J, Zhou Y, Guo X. The impacts of ship emissions on ozone in eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166252. [PMID: 37574059 DOI: 10.1016/j.scitotenv.2023.166252] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/10/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023]
Abstract
Tropospheric ozone (O3), which is one of the main pollutants impeding air quality compliance, has received considerable attention in China. As maritime transportation continues to expand, the effect of ship emissions on air quality is becoming increasingly important. In this study, the Weather Research and Forecast model (WRF), the Community Multiscale Air Quality model (CMAQ), and the integrated process rate (IPR) module provided in the CMAQ are applied to evaluate the impacts of ship emissions on O3 concentration at a national scale in China, including the spatiotemporal characteristics and influencing pathways. Ship emissions can increase or decrease O3 concentrations, with varying effects in different seasons and regions. In the winter, spring, and fall, ship emissions were predicted to decrease O3 concentrations in most areas, whereas in the summer, they increase the O3 concentration, even in regions far away from the coastline, thus adversely affecting the Yangtze River Delta (YRD) and Pearl River Delta (YRD). Additionally, owing to differences in the emissions of volatile organic compounds and nitrogen oxides, the northern and southern regions of the YRD respond differently to ship emissions. Additionally, the influence of ship emissions on the diurnal variation of O3 in the summer was investigated, where significant differences were indicated between cities. The IPR was used to investigate the individual processes contributing to changes in the O3 concentration caused by ship emissions. The transport process appears to be the primary contributor to O3 production, whereas chemistry and dry deposition played key roles in O3 loss. This study provides an in-depth insight into the impacts of ship emissions on O3 in China, which can facilitate the formulation of corresponding environmental policies.
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Affiliation(s)
- Xinyi Fu
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
| | - Dongsheng Chen
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
| | - Xiaotong Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
| | - Yue Li
- Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Jianlei Lang
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
| | - Ying Zhou
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
| | - Xiurui Guo
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
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13
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Zhang L, Wang L, Liu B, Tang G, Liu B, Li X, Sun Y, Li M, Chen X, Wang Y, Hu B. Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166182. [PMID: 37562614 DOI: 10.1016/j.scitotenv.2023.166182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023]
Abstract
Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.
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Affiliation(s)
- Lei Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China.
| | - Boya Liu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Guiqian Tang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Baoxian Liu
- Beijing Key Laboratory of Airborne Particulate Matter Monitoring Technology, Beijing Municipal Ecological Environmental Monitoring Center, Beijing 100048, China
| | - Xue Li
- Beijing Municipal Ecology and Environment Bureau, Beijing 100048, China
| | - Yang Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Mingge Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute Chinese Academy of Sciences, Beijing 100101, China
| | - Xianyan Chen
- National Climate Center, China Meteorological Administration, Beijing 100081, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Hu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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14
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Bui LT, Lai HTN, Nguyen PH. Benefits of Short-term Premature Mortality Reduction Attributed to PM 2.5 Pollution: A Case Study in Long an Province, Vietnam. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2023; 85:245-262. [PMID: 37468649 DOI: 10.1007/s00244-023-01012-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 06/28/2023] [Indexed: 07/21/2023]
Abstract
PM2.5 pollution exposure is the leading cause of disease burden globally, especially in low- and middle-income countries, including Vietnam. Therefore, economic damage in this context must be quantified. Long An province in the Southern Key Economic (SKE) region was selected as a research area. This study aimed to evaluate PM2.5-related human health effects causing early deaths attributable to respiratory, cardiovascular, and circulatory diseases in all ages and genders. Health end-points and health impact estimation, economic loss model, groups of PM2.5 concentration data, data of exposed population, data of baseline premature mortality rate, and data of health impact functions were used. Hourly PM2.5 concentration data sets were generated specifically using the coupled Weather Research and Forecasting Model (WRF)/Community Multiscale Air Quality Modelling System (CMAQ) models. Daily PM2.5 pollution levels considered mainly in the dry season (from January to April 2018) resulted in 12.9 (95% CI - 0.6; 18.7) all-cause premature deaths per 100,000 population, of which 7.8 (95% CI 1.1; 7.1), 1.5 (95% CI - 0.2; 3.1), and 3.6 (95% CI - 1.5; 8.5) were due to respiratory diseases (RDs; 60.54%), cardiovascular diseases (CVDs; 11.81%), and circulatory system diseases (CSDs; 27.65%) per 100,000 population, respectively. The total economic losses due to acute PM2.5 exposure-related premature mortality cases reached 62.0 (95% CI - 2.7; 89.6) billion VND, equivalent to 8.3 (95% CI - 0.4; 12.0) million USD. The study outcomes contributed remarkably to the generation and development of data sources for effectively managing ambient air quality in Long An.
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Affiliation(s)
- Long Ta Bui
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
| | - Han Thi Ngoc Lai
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
| | - Phong Hoang Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
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15
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Bui LT, Nguyen NHT, Nguyen PH. Chronic and acute health effects of PM 2.5 exposure and the basis of pollution control targets. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:79937-79959. [PMID: 37291347 DOI: 10.1007/s11356-023-27936-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
Ho Chi Minh City (HCMC) is changing and expanding quickly, leading to environmental consequences that seriously threaten human health. PM2.5 pollution is one of the main causes of premature death. In this context, studies have evaluated strategies to control and reduce air pollution; such pollution-control measures need to be economically justified. The objective of this study was to assess the socio-economic damage caused by exposure to the current pollution scenario, taking 2019 as the base year. A methodology for calculating and evaluating the economic and environmental benefits of air pollution reduction was implemented. This study aimed to simultaneously evaluate the impacts of both short-term (acute) and long-term (chronic) PM2.5 pollution exposure on human health, providing a comprehensive overview of economic losses attributable to such pollution. Spatial partitioning (inner-city and suburban) on health risks of PM2.5 and detailed construction of health impact maps by age group and sex on a spatial resolution grid (3.0 km × 3.0 km) was performed. The calculation results show that the economic loss from premature deaths due to short-term exposure (approximately 38.86 trillion VND) is higher than that from long-term exposure (approximately 14.89 trillion VND). As the government of HCMC has been developing control and mitigation solutions for the Air Quality Action Plan towards short- and medium-term goals in 2030, focusing mainly on PM2.5, the results of this study will help policymakers develop a roadmap to reduce the impact of PM2.5 during 2025-2030.
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Affiliation(s)
- Long Ta Bui
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
| | - Nhi Hoang Tuyet Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
| | - Phong Hoang Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
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16
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Lu Z, Guan Y, Shao C, Niu R. Assessing the health impacts of PM 2.5 and ozone pollution and their comprehensive correlation in Chinese cities based on extended correlation coefficient. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 262:115125. [PMID: 37331289 DOI: 10.1016/j.ecoenv.2023.115125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/20/2023]
Abstract
The coordinated control of PM2.5 and ozone pollution is becoming more and more important in the current and next stage of Chinese environmental pollution control. Existing studies are unable to provide sufficient quantitative assessments of the correlation of PM2.5 and ozone pollution to support the coordinated control of the two air pollutants. This study develops a systematic method to comprehensively assess the correlation between PM2.5 and ozone pollution, including the evaluation of the impact of two air pollutants on human health and the extended correlation coefficient (ECC) for assessing the bivariate correlation index of PM2.5-ozone pollution in Chinese cities. According to the latest studies on epidemiology conducted in China, we take cardiovascular and cerebrovascular diseases and respiratory diseases as the ozone pollution's health burden when evaluating the health impact of ozone pollution. The results show that the health impact of PM2.5 in China decreases by 25.9 % from 2015 to 2021, while the health impact of ozone increases by 11.8 %. The ECC of 335 cities in China shows an increasing-decreasing trend but has generally increased from 2015 to 2021. The study provides important support for an in-depth understanding of the correlation and development trend of Chinese PM2.5 and ozone pollution by classifying the comprehensive PM2.5-ozone correlation performances of Chinese cities into four types. China or other countries will get better environmental benefits by implementing different coordinated management approaches for different correlative types of regions based on the assessment method in this study.
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Affiliation(s)
- Zhirui Lu
- College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Yang Guan
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100041, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Chaofeng Shao
- College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
| | - Ren Niu
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100041, China.
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17
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Xiang S, Guo X, Kou W, Zeng X, Yan F, Liu G, Zhu Y, Xie Y, Lin X, Han W, Gao Y. Substantial short- and long-term health effect due to PM 2.5 and the constituents even under future emission reductions in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 874:162433. [PMID: 36841405 DOI: 10.1016/j.scitotenv.2023.162433] [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: 01/09/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Heavy pollution events of fine particulate matter (PM2.5) frequently occur in China, seriously affecting the human health. However, how meteorological factors and anthropogenic emissions affect PM2.5 and the major constituents, as well as the subsequent health effect, remains unclear. Here, based on regional climate and air quality models Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ), the PM2.5 and major constituents in China at present and mid-century under the carbon neutral scenario Shared Socioeconomic Pathways (SSP)1-2.6 are simulated. Due to anthropogenic emission reduction, concentrations of PM2.5 and the constituents decrease substantially in SSP1-2.6. The long-term exposure premature deaths at present are 2.23 million per year in mainland China, which is projected to increase by 76 % under SSP1-2.6 despite emission reduction, primarily attributable to aging which strikingly offsets the effect of air quality improvement. The number of annual premature deaths resulting from short-term exposure is 228,104 in mainland China at present, which is projected to decrease in the future. Using North China Plain as an example, we identify that among the major constituents of PM2.5, organic carbon leads to the most short-term exposure deaths considering the largest exposure-response coefficient. Regarding the abnormally meteorological conditions, we find, relative to low relative humidity (RH) and non-stagnation, the compound events, defined as concurrence of high RH and atmospheric stagnation, exhibit an amplified role inducing larger premature deaths compared to the additive effect of the individual event of high RH and atmospheric stagnation. This nonlinear effect occurs at both present and future, but diminished in future due to emission reductions. Our study highlights the importance of considering both the long- and short-term premature deaths associated with PM2.5 and the constituents, as well as the critical effect of extreme weather events.
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Affiliation(s)
- Shengnan Xiang
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Xiuwen Guo
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Wenbin Kou
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Xinran Zeng
- Zhejiang Institute of Meteorological Sciences, Hangzhou 310008, China
| | - Feifan Yan
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Guangliang Liu
- Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China
| | - Yuanyuan Zhu
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing 100191, China
| | - Xiaopei Lin
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China
| | - Wei Han
- Department of Pulmonary and Critical Care Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao 266100, China
| | - Yang Gao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, and Laoshan Laboratory, Qingdao 266100, China.
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18
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Jiang Y, Ding D, Dong Z, Liu S, Chang X, Zheng H, Xing J, Wang S. Extreme Emission Reduction Requirements for China to Achieve World Health Organization Global Air Quality Guidelines. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4424-4433. [PMID: 36898019 DOI: 10.1021/acs.est.2c09164] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A big gap exists between current air quality in China and the World Health Organization (WHO) global air quality guidelines (AQG) released in 2021. Previous studies on air pollution control have focused on emission reduction demand in China but ignored the influence of transboundary pollution, which has been proven to have a significant impact on air quality in China. Here, we develop an emission-concentration response surface model coupled with transboundary pollution to quantify the emission reduction demand for China to achieve WHO AQG. China cannot achieve WHO AQG by its own emission reduction for high transboundary pollution of both PM2.5 and O3. Reducing transboundary pollution will loosen the reduction demand for NH3 and VOCs emissions in China. However, to meet 10 μg·m-3 for PM2.5 and 60 μg·m-3 for peak season O3, China still needs to reduce its emissions of SO2, NOx, NH3, VOCs, and primary PM2.5 by more than 95, 95, 76, 62, and 96% respectively, on the basis of 2015. We highlight that both extreme emission reduction in China and great efforts in addressing transboundary air pollution are crucial to reach WHO AQG.
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Affiliation(s)
- 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
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - 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 Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuchang Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- Institute for Atmospheric and Climate Science, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- Transport Planning and Research Institute, Ministry of Transport, Laboratory of Transport Pollution Control and Monitoring Technology, Beijing 100028, China
| | - 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
| | - 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
| | - 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
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19
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Chen L, Liao H, Zhu J, Li K, Bai Y, Yue X, Yang Y, Hu J, Zhang M. Increases in ozone-related mortality in China over 2013-2030 attributed to historical ozone deterioration and future population aging. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159972. [PMID: 36356763 DOI: 10.1016/j.scitotenv.2022.159972] [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: 07/31/2022] [Revised: 10/18/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
We systematically examine historical and future changes in premature respiratory mortalities attributable to ozone (O3) exposure (O3-mortality) in China and identify the leading cause of respective change for the first time. The historical assessment for 2013-2019 is based on gridded O3 concentrations generated by a multi-source-data-fusion algorithm; the future prediction for 2019-2030 uses gridded O3 concentrations projected by four Coupled Model Intercomparison Project Phase 6 (CMIP6) models under three Shared Socioeconomic Pathways (SSP) scenarios. During 2013-2019, national annual O3-mortality is 176.3 thousand (95%CI: 123.5-224.0 thousand) averaged over 2013-2019 with an increasing trend of 14.1 thousand yr-1 (95%CI: 10.2-17.4 thousand yr-1); sensitivity experiments show that the O3-mortality varies at a rate of +12.7 (95%CI: 9.2-15.6), +5.8 (95%CI: 4.0-7.4), +1.0 (95%CI: 0.7-1.2), -5.4 (95%CI: -6.9 to -3.7) thousand yr-1, owing to changes in O3 concentration, population age structure, population size, mortality rate for respiratory disease, respectively. The deterioration of O3 air quality, shown as significant increase in O3 concentration, is identified as the primary factor which contributes 90.1 % of 2013-2019 O3-mortality rise. Compared with O3-mortality estimated in this study, the widely-used O3-mortality assessment method based on urban-site-dominant O3 measurements generates close national O3-mortality but overestimates (underestimates) provincial O3-mortality in coastal (central) provinces. From 2019 to 2030, national O3-mortality is projected to increase by 50.4-103.7 thousand under different SSP scenarios. The change in age structure (i.e. population aging) alone will result in significant O3-mortality rises of 137.9-160.5 thousand. Compared with 2013-2019 rapid O3 increase (+2.5 μg m-3 yr-1 at national level), O3 concentrations are projected to increase at a lower rate (+0.4 μg m-3 yr-1 in SSP5-8.5) or even decrease (-0.7 μg m-3 yr-1 in SSP1-2.6) from 2019 to 2030. Therefore, population aging, in place of O3 air quality deterioration, will become the leading cause of future O3-mortality rises during the coming decade.
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Affiliation(s)
- Lei Chen
- 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; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Jia Zhu
- 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
| | - 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
| | - Yang Bai
- 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
| | - Xu Yue
- 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
| | - Yang Yang
- 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
| | - Jianlin Hu
- 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
| | - Meigen Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
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20
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Zhang L, He J, Gong S, Guo X, Zhao T, Che H, Wang H, Zhou C, Mo J, Gui K, Zheng Y, Li L, Zhong J, Zhang X. Modeling study on the roles of the deposition and transport of PM 2.5 in air quality changes over central-eastern China. J Environ Sci (China) 2023; 123:535-544. [PMID: 36522012 DOI: 10.1016/j.jes.2022.10.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 10/20/2022] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
The role of PM2.5 (particles with aerodynamic diameters ≤ 2.5 µm) deposition in air quality changes over China remains unclear. By using the three-year (2013, 2015, and 2017) simulation results of the WRF/CUACE v1.0 model from a previous work (Zhang et al., 2021), a non-linear relationship between the deposition of PM2.5 and anthropogenic emissions over central-eastern China in cold seasons as well as in different life stages of haze events was unraveled. PM2.5 deposition is spatially distributed differently from PM2.5 concentrations and anthropogenic emissions over China. The North China Plain (NCP) is typically characterized by higher anthropogenic emissions compared to southern China, such as the middle-low reaches of Yangtze River (MLYR), which includes parts of the Yangtze River Delta and the Midwest. However, PM2.5 deposition in the NCP is significantly lower than that in the MLYR region, suggesting that in addition to meteorology and emissions, lower deposition is another important factor in the increase in haze levels. Regional transport of pollution in central-eastern China acts as a moderator of pollution levels in different regions, for example by bringing pollution from the NCP to the MLYR region in cold seasons. It was found that in typical haze events the deposition flux of PM2.5 during the removal stages is substantially higher than that in accumulation stages, with most of the PM2.5 being transported southward and deposited to the MLYR and Sichuan Basin region, corresponding to a latitude range of about 24°N-31°N.
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Affiliation(s)
- Lei Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Jianjun He
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Sunling Gong
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Xiaomei Guo
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China; Weather Modification Office of Sichuan Province, Chengdu 610072, China
| | - Tianliang Zhao
- Climate and Weather Disasters Collaborative Innovation Center, Nanjing University of Information Science &Technology, Nanjing 210044, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Chunhong Zhou
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Jingyue Mo
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Ke Gui
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yu Zheng
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Lei Li
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Junting Zhong
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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21
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Li N, Zhang H, Zhu S, Liao H, Hu J, Tang K, Feng W, Zhang R, Shi C, Xu H, Chen L, Li J. Secondary PM 2.5 dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan. ENVIRONMENT INTERNATIONAL 2023; 171:107725. [PMID: 36599225 DOI: 10.1016/j.envint.2022.107725] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/30/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
The Clean Air Plan has been active in China since 2013 to mitigate severe PM2.5 pollution. In this study, we applied the air quality model WRF-Chem to simulate PM2.5 in the Yangtze River Delta (YRD) region of China in 2017, with the aim of assessing the air quality improvement and its associated health burden in the final year of the Clean Air Plan. To better describe the fate of various PM2.5 compositions, we updated the chemical mechanisms in the model beforehand, including heterogeneous sulfate reactions, aqueous secondary organic aerosol (SOA) uptake, and volatility basis set (VBS) based SOA production. Both the observation and simulation results agreed that the stringent clear air action effectively reduced the PM2.5 pollution levels by ∼ 30 %. The primary PM2.5 (-6 ∼ - 16 % yr-1) showed a more significant decreasing trend than the secondary PM2.5 (-2 ∼ - 8 % yr-1), which was mainly caused by the directivity of the clear air actions and the worsening ozone pollution in the recent years. The inconsistent decreasing trends of PM2.5 components subsequently led to an increasing proportion of secondary PM2.5. Nitrate particles, higher in the central and western YRD region, have replaced sulfate and have become the largest component of secondary inorganic aerosols year-round, except in summer, when strong ammonium nitrate evaporation occurs. In addition, SOA remains an important component (21 ∼ 22 %) especially in summer, most of which is produced from the oxidation and ageing of semi/intermediate volatile organic compounds (S/IVOC). Furthermore, we quantified the associated health impacts and found that the Clean Air Plan has largely reduced premature mortality due to PM2.5 exposure in the YRD region from 399.1 thousand to 295.7 thousand. Our study highlights the benefits of the Clean Air Plan and suggests that subsequent PM2.5 improvement should be geared more towards controlling secondary pollutants.
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Affiliation(s)
- Nan 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 & Technology, Nanjing, 210044, China
| | - Haoran Zhang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Shuhan Zhu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Keqin Tang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Weihang Feng
- Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, Helsinki, 00014, Finland
| | - Ruhan Zhang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
| | - Chong Shi
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
| | - Hongmei Xu
- Department of Environmental Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Lei Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Jiandong 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 & Technology, Nanjing, 210044, China
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22
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Tan Z, Lu K, Ma X, Chen S, He L, Huang X, Li X, Lin X, Tang M, Yu D, Wahner A, Zhang Y. Multiple Impacts of Aerosols on O 3 Production Are Largely Compensated: A Case Study Shenzhen, China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:17569-17580. [PMID: 36473087 DOI: 10.1021/acs.est.2c06217] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Tropospheric ozone (O3) is a harmful gas compound to humans and vegetation, and it also serves as a climate change forcer. O3 is formed in the reactions of nitrogen oxides and volatile organic compounds (VOCs) with light. In this study, an O3 pollution episode encountered in Shenzhen, South China in 2018 was investigated to illustrate the influence of aerosols on local O3 production. We used a box model with comprehensive heterogeneous mechanisms and empirical prediction of photolysis rates to reproduce the O3 episode. Results demonstrate that the aerosol light extinction and NO2 heterogeneous reactions showed comparable influence but opposite signs on the O3 production. Hence, the influence of aerosols from different processes is largely counteracted. Sensitivity tests suggest that O3 production increases with further reduction in aerosols in this study, while the continued NOx reduction finally shifts O3 production to an NOx-limited regime with respect to traditional O3-NOx-VOC sensitivity. Our results shed light on the role of NOx reduction on O3 production and highlight further mitigation in NOx not only limiting the production of O3 but also helping to ease particulate nitrate, as a path for cocontrol of O3 and fine particle pollution.
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Affiliation(s)
- Zhaofeng Tan
- Institute of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871Beijing, China
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
| | - Xuefei Ma
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871Beijing, China
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
| | - Shiyi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871Beijing, China
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
| | - Lingyan He
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055Shenzhen, China
| | - Xiaofeng Huang
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055Shenzhen, China
| | - Xin Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871Beijing, China
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
| | - Xiaoyu Lin
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055Shenzhen, China
| | - Mengxue Tang
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, 518055Shenzhen, China
| | - Dan Yu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871Beijing, China
| | - Andreas Wahner
- Institute of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, 100871Beijing, China
- International Joint Laboratory for Regional Pollution Control, 52428Jülich, Germany
- International Joint Laboratory for Regional Pollution Control, 100871Beijing, China
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23
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Gao P, Deng F, Chen WS, Zhong YJ, Cai XL, Ma WM, Hu J, Feng SR. Health Risk Assessment of Inhalation Exposure to Airborne Particle-Bound Nitrated Polycyclic Aromatic Hydrocarbons in Urban and Suburban Areas of South China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15536. [PMID: 36497610 PMCID: PMC9739065 DOI: 10.3390/ijerph192315536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/13/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
Airborne particulates (PM2.5 and TSP) were collected from outdoor and indoor areas at urban (Haizhu District) and suburban (Huadu District) sites from 2019 to 2020 in Guangzhou. Three nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) in the airborne particulates were identified by a gas chromatograph equipped with a triple-quadrupole mass spectrometer. In the Haizhu District and Huadu District, the nitro-PAH concentrations in PM2.5 and TSP did not show a significant decrease from winter to summer. From 2019 to 2020, the difference in the average concentration of nitro-PAHs in PM2.5 and TSP in Guangzhou was relatively low and had no statistical significance. The diagnostic ratios of 2-nitrofluorene (2-NF)/1-nitropyrene (1-NP) in TSP are less than five, while for 2-NF/1-NP in outdoor PM2.5 in the summer of 2019 and 2020 are more than five, which indicates that nitro-PAHs in the atmospheric PM2.5 in Guangzhou during summer mainly originated from the secondary formation of atmospheric photochemical reactions between parent PAHs and oxidants (·OH, NO3, and O3). 9-Nitroanthracene (9-NT) made the most significant contribution to the total nitro-PAH concentration. The incremental lifetime cancer risks (ILCRs) of nitro-PAHs in PM2.5 and TSP by inhalation exposure indicated low potential health risks in the urban-suburban of Guangzhou.
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Affiliation(s)
- Peng Gao
- Institute of Architecture and Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
| | - Feng Deng
- Institute of Architecture and Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
| | - Wei-Shan Chen
- Institute of Architecture and Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
| | - Yi-Jia Zhong
- Institute of Architecture and Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
| | - Xiao-Lu Cai
- Institute of Architecture and Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
| | - Wen-Min Ma
- Tianjin Key Laboratory of Water Resources and Environment, School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Jian Hu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Shu-Ran Feng
- School of Business, Hong Kong Baptist University, Hongkong 999077, China
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24
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Zhang J, Liu P, Song H, Miao C, Yang J, Zhang L, Dong J, Liu Y, Zhang Y, Li B. Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15060. [PMID: 36429779 PMCID: PMC9690158 DOI: 10.3390/ijerph192215060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.
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Affiliation(s)
- Jiejun Zhang
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475004, China
| | - Pengfei Liu
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475004, China
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
- Institute of Urban Big Data, Henan University, Kaifeng 475004, China
| | - Hongquan Song
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
- Institute of Urban Big Data, Henan University, Kaifeng 475004, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, 475004, China
| | - Changhong Miao
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475004, China
| | - Jie Yang
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475004, China
| | - Longlong Zhang
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Junwu Dong
- College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
| | - Yi Liu
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng 475004, China
| | - Yunlong Zhang
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Bingchen Li
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
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25
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Zhang L, Wu P, Niu M, Zheng Y, Wang J, Dong G, Zhang Z, Xie Z, Du M, Jiang H, Liu H, Cao L, Pang L, Lv C, Lei Y, Cai B, Zhu Y. A systematic assessment of city-level climate change mitigation and air quality improvement in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 839:156274. [PMID: 35644391 DOI: 10.1016/j.scitotenv.2022.156274] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
China is facing dual challenges of air pollution and climate change. By using city-level data, we comprehensively assessed air quality and CO2 emission changes from 2015 to 2019 for 335 Chinese cities. We selected important regions for air pollution control and categorized all cities into different classes according to their development levels. Our novel approach revealed new insights on different patterns of changes of PM2.5, O3, and CO2 by region and city class. We found that PM2.5 concentrations decreased remarkably due to mandatory city-level reduction targets, especially in the Beijing-Tianjin-Hebei (-27%) region. Nonetheless, O3 concentrations and CO2 emissions increased in 91% and 69% of Chinese cities, respectively. Observed CO2 emission reductions in more developed cities were mainly due to prominent energy intensity reduction and energy structure improvement. Our study indicates a lack of synergy in air pollution control and CO2 mitigation under current policies in China. To address both challenges holistically, we suggest setting mandatory city-level CO2 emission reduction targets and reinforcing clean energy and energy efficiency measures.
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Affiliation(s)
- Li Zhang
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China; Institute of Environment and Sustainability, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Pengcheng Wu
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Muchuan Niu
- Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Junxia Wang
- State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China
| | - Guangxia Dong
- State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China
| | - Zhe Zhang
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Zixuan Xie
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Mengbing Du
- Department of Public Policy, City University of Hong Kong, Kowloon Tong, Hong Kong 999077, China
| | - Hanying Jiang
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Hui Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Libin Cao
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Lingyun Pang
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Chen Lv
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Yu Lei
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China; Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China.
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing 100012, China.
| | - Yifang Zhu
- Institute of Environment and Sustainability, University of California Los Angeles, Los Angeles, CA 90095, United States; Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, United States.
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Bi Z, Ye Z, He C, Li Y. Analysis of the meteorological factors affecting the short-term increase in O 3 concentrations in nine global cities during COVID-19. ATMOSPHERIC POLLUTION RESEARCH 2022; 13:101523. [PMID: 35996529 PMCID: PMC9385202 DOI: 10.1016/j.apr.2022.101523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 07/25/2022] [Accepted: 08/05/2022] [Indexed: 05/15/2023]
Abstract
Surface ozone (O3) is a major air pollutant around the world. This study investigated O3 concentrations in nine cities during the Coronavirus disease 2019 (COVID-19) lockdown phases. A statistical model, named Generalized Additive Model (GAM), was also developed to assess different meteorological factors, estimate daily O3 release during COVID-19 lockdown and determine the relationship between the two. We found that: (1) Daily O3 significantly increased in all selected cities during the COVID-19 lockdown, presenting relative increases from -5.7% (in São Paulo) to 58.9% (in Guangzhou), with respect to the average value for the same period in the previous five years. (2) In the GAM model, the adjusted coefficient of determination (R2) ranged from 0.48 (Sao Paulo) to 0.84 (Rome), and it captured 51-85% of daily O3 variations. (3) Analyzing the expected O3 concentrations during the lockdown, using GAM fed by meteorological data, showed that O3 anomalies were dominantly controlled by meteorology. (4) The relevance of different meteorological variables depended on the cities. The positive O3 anomalies in Beijing, Wuhan, Guangzhou, and Delhi were mostly associated with low relative humidity and elevated maximum temperature. Low wind speed, elevated maximum temperature, and low relative humidity were the leading meteorological factors for O3 anomalies in London, Paris, and Rome. The two other cities had different leading factor combinations.
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Affiliation(s)
- Zhongsong Bi
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
- School of Architecture and Civil Engineering, Huangshan University, Huangshan, 245041, China
| | - Zhixiang Ye
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
| | - Yunzhang Li
- College of Architecture and Environment, Sichuan University, Chengdu, 610065, China
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27
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Qian J, Liao H, Yang Y, Li K, Chen L, Zhu J. Meteorological influences on daily variation and trend of summertime surface ozone over years of 2015-2020: Quantification for cities in the Yangtze River Delta. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155107. [PMID: 35398137 DOI: 10.1016/j.scitotenv.2022.155107] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/14/2022] [Accepted: 04/03/2022] [Indexed: 06/14/2023]
Abstract
We quantify the meteorological influences on daily variations and trends of maximum daily 8-h average ozone (MDA8 O3) concentrations by using multiple linear regression (MLR) and Lindeman, Merenda, and Gold (LMG) approaches. Different from previous region-based studies, we pay special attention to meteorological influences at city scale. Over 2015-2019, daily changes in key meteorological parameters could explain 47%-74% of the observed daily variations in summertime MDA8 O3 concentrations in Yangtze River Delta (YRD) and four cities (Shanghai, Nanjing, Hangzhou, and Hefei), with RH being the top driver. Over years of 2015-2020, daily concentrations of MDA8 O3 obtained from MLR equations (MDA8O3_MLR) of the local cities always had better performance than those of YRD. Compared with the observed daily MDA8 O3 in June-July-August (JJA) over the studied period, daily MDA8O3_MLR of the local cities (of YRD) had correlation coefficients of 0.73 (0.63), 0.75 (0.74), 0.79 (0.78), and 0.76 (0.73) in Shanghai, Nanjing, Hangzhou, and Hefei, respectively, and the MDA8O3_MLR of the local cities (of YRD) captured 54% (17%), 63% (51%), 52% (27%) of the observed O3-polluted days (days with MDA8 O3 concentration exceeding 160 μg m-3) in Shanghai, Nanjing, and Hangzhou, respectively. The meteorologically driven trends (Trend_Met) in MDA8 O3 were calculated using the established MLR equations. Over 2015-2019, the observed trends (Trend_Obs) and Trend_Met in MDA8 O3 were mostly positive in YRD, Nanjing, Hangzhou, and Hefei. In Shanghai, Trend_Obs, Trend_Met, and anthropogenically driven trend (estimated as Trend_Obs minus Trend_Met) of MDA8 O3 in JJA over 2015-2019 were -1.3, +1.0, and -2.3 μg m-3 y-1, respectively, indicating that the emission control measures alleviated O3 pollution in this city. Our results suggest that it is necessary to establish MLR equations at city scale to account for the role of meteorology in the actions of O3 pollution control.
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Affiliation(s)
- Jing Qian
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China.
| | - Yang Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
| | - Ke Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
| | - Lei Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
| | - Jia Zhu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmental Cleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China
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28
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Guo Y, Li K, Zhao B, Shen J, Bloss WJ, Azzi M, Zhang Y. Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020. CHEMOSPHERE 2022; 300:134608. [PMID: 35430204 DOI: 10.1016/j.chemosphere.2022.134608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/12/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O3 showed significant declines from 2013 to 2020, with PM2.5 annual decline rates of 6-9% in most cities. In contrast, O3 concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O3 to NO2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.
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Affiliation(s)
- Yong Guo
- Department of Building Science, Tsinghua University, Beijing, China; Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Beijing, China
| | - Kangwei Li
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, F-69626, Villeurbanne, France.
| | - Bin Zhao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Jiandong Shen
- Hangzhou Environmental Monitoring Center Station, Hangzhou, 310007, China
| | - William J Bloss
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Merched Azzi
- New South Wales Department of Planning, Industry and Environment, PO Box 29, Lidcombe, NSW, 1825, Australia
| | - Yinping Zhang
- Department of Building Science, Tsinghua University, Beijing, China; Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Beijing, China
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29
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Guan Y, Xiao Y, Chu C, Zhang N, Yu L. Trends and characteristics of ozone and nitrogen dioxide related health impacts in Chinese cities. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 241:113808. [PMID: 35759982 DOI: 10.1016/j.ecoenv.2022.113808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/02/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
Ambient ozone pollution has been becoming severe and attributed to considerable health impacts in China. Nitrogen dioxide (NO2) is involved in atmospheric ozone production while also affecting public health directly. Joint control ozone and NO2 pollution would be of significance. This study quantitatively assessed the health impact attributed to ambient ozone and NO2 pollution in 338 Chinese cities from 2015 to 2020. The results reveal the generally opposite trends of ozone- and NO2-related health impacts in China. From 2015-2020, respiratory and chronic obstructive pulmonary disease (COPD) health impacts attributed to ozone in 338 cities increased by 65.30% and 63.98%. The NO2-attributed health impacts decreased by 24.80% and 24.62%. In 2020, the ozone- and NO2-related respiratory health impacts were 3.96 million DALYs (disability-adjusted life years) and 1.47 million DALYs. High health impacts are concentrated in big cities and city clusters. In 2020, the sum of ozone- and NO2-related respiratory health impacts in the top 20 cities was 0.98 million DALYs and 0.44 million DALYs, accounting for 24.70% and 30.24% of the 338 cities. The population attribution fraction analysis identified the increasing distributional consistency of ozone and NO2-related health impacts, emphasizing the necessity and possible efficiency of ozone-NO2 joint control. Emission source analysis based on gridded data provided a reference for understanding health impacts and developing targeted strategies.
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Affiliation(s)
- Yang Guan
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Yang Xiao
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China; The Center for Beautiful China, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Chengjun Chu
- Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
| | - Lei Yu
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China.
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30
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Gong S, Zhang L, Liu C, Lu S, Pan W, Zhang Y. Multi-scale analysis of the impacts of meteorology and emissions on PM 2.5 and O 3 trends at various regions in China from 2013 to 2020 2. Key weather elements and emissions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 824:153847. [PMID: 35189213 DOI: 10.1016/j.scitotenv.2022.153847] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/26/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM2.5 and O3 in China. Following an accompanying paper on synoptic circulation impact (Gong et al., 2022), using a multi-linear regression model, the trends of key meteorological elements at local scale, i.e., temperature, relative humidity, solar radiation, PBL height, precipitation and wind speed, are analyzed and correlated with the trends of PM2.5 and O3 levels to identify significantly influencing factors in seven Chinese cities. Furthermore, with additional emission surrogates introduced in the regression model, the impacts on the trends by meteorology and emission were separated and quantified. Results show that the increasing trends of O3 at most Chinese cities were largely attributed to the trends of meteorological elements of temperature and solar radiation, while the trends of PM2.5 are mostly contributed by the emission reduction measures of PM2.5 and its precursors. The meteorology alone can explain approximately 57-80% of the O3 variations and only 20-33% of the PM2.5 variations. With the addition of emission surrogates, this explanation percentage is increased to about 57-82% for O3 but significantly enhanced to 71-83% for PM2.5.
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Affiliation(s)
- Sunling Gong
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Lei Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Cheng Liu
- University of Science and Technology of China, Hefei, China
| | - Shuhua Lu
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Weijun Pan
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yuanhang Zhang
- College of Environmental Sciences and Engineering, Peking University, China
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31
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Xiao Q, Geng G, Xue T, Liu S, Cai C, He K, Zhang Q. Tracking PM 2.5 and O 3 Pollution and the Related Health Burden in China 2013-2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:6922-6932. [PMID: 34941243 DOI: 10.1021/acs.est.1c04548] [Citation(s) in RCA: 82] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 and O3 exposures and quantified the long- and short-term exposure related premature deaths during 2013-2020 with respect to the two-stage clean air actions (2013-2017 and 2018-2020). We find a 48% decrease in national PM2.5 exposure during 2013-2020, although the decrease rate has slowed after 2017. At the same time, O3 pollution worsened, with the average April-September O3 exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM2.5 concentration. It is also noticed that with smaller PM2.5 reduction, the avoided long-term exposure associated deaths in 2017-2020 (13%) was greater than that in 2013-2017 (9%), because the exposure-response curve is nonlinear. As a result of the efforts in reducing PM2.5-polluted days with the daily average PM2.5 higher than 75 μg/m3 and the considerable increase in O3-polluted days with the daily maximum 8 h average O3 higher than 160 μg/m3, deaths attributable to the short-term O3 exposure were greater than those due to PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.
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Affiliation(s)
- Qingyang Xiao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100080, China
| | - Shigan Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
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32
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Wang X, Gu W, Wang F, Liu L, Wang Y, Han X, Xie Z. A potential controlling approach on surface ozone pollution based upon power big data. SN APPLIED SCIENCES 2022; 4:164. [PMID: 35574248 PMCID: PMC9086420 DOI: 10.1007/s42452-022-05045-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 11/26/2022] Open
Abstract
AbstractSurface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO2, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R2 value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO2 and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO2, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways.
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Affiliation(s)
- Xin Wang
- State Grid Anhui Electric Power Research Institute, Hefei, 230026 Anhui China
| | - Weihua Gu
- Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026 Anhui China
| | - Feng Wang
- State Grid Anhui Electric Power CO. LTD, Hefei, 230026 Anhui China
| | - Li Liu
- State Grid Anhui Electric Power CO. LTD, Hefei, 230026 Anhui China
| | - Yu Wang
- State Grid Anhui Electric Power Research Institute, Hefei, 230026 Anhui China
| | - Xuemin Han
- State Grid Anhui Electric Power Research Institute, Hefei, 230026 Anhui China
| | - Zhouqing Xie
- Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026 Anhui China
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33
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Deng C, Tian S, Li Z, Li K. Spatiotemporal characteristics of PM 2.5 and ozone concentrations in Chinese urban clusters. CHEMOSPHERE 2022; 295:133813. [PMID: 35114261 DOI: 10.1016/j.chemosphere.2022.133813] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
Despite China's public commitment to emphasise air pollution investigation and control, trends in PM2.5 and ozone concentrations in Chinese urban clusters remain unclear. This study quantifies the spatiotemporal variations in PM2.5 and surface ozone at the scale of Chinese urban clusters by using a long-term integrated dataset from 2015 to 2020. Nonlinear Granger causality testing was used to explore the spatial association patterns of PM2.5 and ozone pollution in five megacity cluster regions. The results show a significant downward trend in annual mean PM2.5 concentrations from 2015 to 2020, with a decline rate of 2.8 μg m-3 yr-1. By contrast, surface ozone concentrations increased at a rate of 2.1 μg m-3 yr-1 over the 6 years. The annual mean PM2.5 concentrations in urban clusters show significant spatial clustering characteristics, mainly in Beijing-Tianjin-Hebei (BTH), Fenwei Plain (FWP), Northern slope of Tianshan Mountains urban cluster (NSTM), Sichuan Basin urban cluster (SCB), and Yangtze River Delta (YRD). Surface ozone shows severe summertime pollution and distributional variability, with increased ozone pollution in major urban clusters. The highest increases were observed in BTH, Yangtze River midstream urban cluster (YRMR), YRD, and Pearl River Delta (PRD). Nonlinear Granger causality tests showed that PM2.5 was a nonlinear Granger cause of ozone, further supporting the literature's findings that PM2.5 reduction promoted photochemical reaction rates and stimulated ozone production. The nonlinear test statistic passed the significance test in magnitude and statistical significance. FWP was an exception, with no significant long-term nonlinear causal link between PM2.5 and ozone. This study highlights the challenges of compounded air pollution caused primarily by ozone and secondary PM2.5. These results have implications for the design of synergistic pollution abatement policies for coupled urban clusters.
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Affiliation(s)
- Chuxiong Deng
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.
| | - Si Tian
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.
| | - Zhongwu Li
- School of Geographic Sciences, Hunan Normal University, Changsha, Hunan, 410081, PR China.
| | - Ke Li
- Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of China), Key Laboratory of Applied Statistics and Data Science, School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, PR China.
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34
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Dong C, Li J, Qi Y. Decomposing PM 2.5 air pollution rebounds in Northern China before COVID-19. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:28688-28699. [PMID: 34988793 PMCID: PMC8731191 DOI: 10.1007/s11356-021-17889-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 11/27/2021] [Indexed: 05/30/2023]
Abstract
China's efforts to curb air pollution have drastically reduced its concentrations of fine particulate matter (PM2.5) from 2013 to 2018 nationwide. However, few studies examined the most recent changes in PM2.5 concentrations and questioned if the previous PM2.5 declining trend was sustained or not. This study took a deep dive into the PM2.5 trend for 136 northern cities of China from 2015 to early 2020 before the coronavirus disease 2019 (the COVID-19 hereafter) crisis, using ground-based PM2.5 data notably adjusted for a key measurement method change. We find that mean PM2.5 concentrations in northern China increased by 5.16 µg/m3 in 2019, offsetting 80% of the large reduction achieved in 2018. The rebound was more significant during the heating seasons (HS; Nov to next Mar) over the 2 years: 10.49 µg/m3 from the 2017 HS to the 2019 HS. A multiple linear regression analysis further revealed that anthropogenic factors contributed to around 50% of the PM2.5 rebound in northern cities of China. Such a significant role of anthropogenic factors in driving the rebound was tightly linked to deep cuts in PM2.5 concentrations in the previous year, systemic adjustment of policy targets and mitigation measures by the government, and the rising marginal cost of these measures. These findings suggest the need to chart a more sustainable path for future PM2.5 emission reductions, with an emphasis on key regions during key pollution periods.
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Affiliation(s)
- Changgui Dong
- School of Public Administration and Policy, Renmin University of China, Beijing, 100872, China
- National Academy of Development and Strategy, Renmin University of China, Beijing, 100872, China
| | - Jiaying Li
- School of Public Administration and Policy, Renmin University of China, Beijing, 100872, China.
| | - Ye Qi
- Thrust of Innovation, Policy and Entrepreneurship and Institute for Public Policy, The Hong Kong University of Science and Technology, Hong Kong, China.
- School of Public Policy and Management, Tsinghua University, Beijing, 100084, China.
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35
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An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073597] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, have achieved good performance in recent years. However, these methods require sufficient data to train the model. The performance of these methods is limited for the sites where the data is lacking, such as the newly constructed monitoring sites. To deal with this problem, an improved deep learning model based on the hybrid transfer learning strategy is proposed for predicting PM2.5 concentration in this paper. In the proposed model, the maximum mean discrepancy (MMD) is used to select which station in the source domain is most suitable for migration to the target domain. An improved dual-stage two-phase (DSTP) model is used to extract the spatial–temporal features of the source domain and the target domain. Then the domain adversarial neural network (DANN) is used to find the domain invariant features between the source and target domains by domain adaptation. Thus, the model trained by source domain site data can be used to assist the prediction of the target site without degradation of the prediction performance due to domain drift. At last, some experiments are conducted. The experimental results show that the proposed model can effectively improve the accuracy of the PM2.5 prediction at the sites lacking data, and the proposed model outperforms most of the latest models.
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Ou S, Wei W, Cai B, Yao S, Wang K, Cheng S. Exploring the causes for co-pollution of O 3 and PM 2.5 in summer over North China. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:289. [PMID: 35314915 DOI: 10.1007/s10661-022-09951-4] [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: 09/18/2021] [Accepted: 03/12/2022] [Indexed: 06/14/2023]
Abstract
Co-pollution of surface O3 and PM2.5 has become the most predominant type of air pollutions in Beijing-Tianjin-Hebei region in the hot season since 2017, particularly in May-July. Analysis based on observational data showed that co-pollution was always accompanied by high temperature, moderate relative humidity, extremely high SO2, and higher NO2. We also found that the meteorology and precursor dependence of O3 was similar between co-pollution and O3- single pollution. While PM2.5 in co-pollution was more related to temperature, relative humidity, and precursors, that in PM2.5-singe pollution were more related to small winds. These results indicate that co-pollution seemed to be more affected by atmospheric chemistry. According to the PM2.5 components, secondary inorganic aerosols (SIA) composed 44.3-48.7% of PM2.5 in co-pollution, while those accounting for 42.1-46.5% and 41.2-44.3%, respectively, in O3- and PM2.5-single pollution, which further confirmed the relatively stronger atmospheric chemistry processes in co-pollution. And the high proportion of SIA in co-pollution was mainly attributed to SO42-, which was observed to rapidly boom in non-refractory submicron aerosol (NR-PM1) on the condition of high level of O3 at daytime. Additionally, we further explored the interactions of O3 and PM2.5 in co-pollution. It was found that most (~61.9%) co-pollution episodes were initiated by high O3 at daytime; while for other episodes, high PM2.5 firstly occurred under the more stable meteorological conditions, and then accumulation of precursors further induced high O3. A higher SIA concentration was observed in O3-initiated co-pollution, indicating that the atmospheric oxidation in co-pollution caused by chemical processes was stronger than that by physical processes, which was further approved by the higher values of SOR and NOR in O3-initiated co-pollution. This observational study revealed that controlling O3 and precursor SO2 is the key to abating co-pollution in the hot season.
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Affiliation(s)
- Shengju Ou
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, 100124, Beijing, China
| | - Wei Wei
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, 100124, Beijing, China.
| | - Bin Cai
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, 100124, Beijing, China
| | - Shiyin Yao
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, 100124, Beijing, China
| | - Kai Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, 100124, Beijing, China
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, 100124, Beijing, China
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Jiang Y, Xin J, Wang Z, Tian Y, Tang G, Ren Y, Wu L, Pan X, Wang Y, Jia D, Ma Y, Wang L. The dynamic multi-box algorithm of atmospheric environmental capacity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:150951. [PMID: 34656590 DOI: 10.1016/j.scitotenv.2021.150951] [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: 09/19/2021] [Revised: 09/30/2021] [Accepted: 10/09/2021] [Indexed: 06/13/2023]
Abstract
It is very important for air pollution prevention and control to accurately quantify atmospheric environment capacity (AEC) in the planetary boundary layer (PBL). This study developed a high temporal-resolution dynamic multi-box algorithm to estimate PM2.5 AEC with a PBL ceilometer and Doppler wind profile lidar in Beijing City. Compared with the traditional A-value method, two primary improvements are introducing the time coefficient and vertical multi-box assumption into the original box model. The algorithm can accurately calculate the PM2.5 AEC under different circulation patterns and predict the short-time dynamic change of AEC. The results show that the time coefficient effectively reduced the estimation errors when the initial PM2.5 concentration, horizontal wind speed and PBL heights change greatly with time, such situation is consistent with most circulation patterns. And the improvement of multi-box model is much more remarkable when the PM2.5 concentration and horizontal wind change greatly in the vertical direction, such as A, NE and W type circulations. The ideal AEC under polluted circulation patterns won't increase infinitely with wind speed and PBL height, generally less than 30 t/h. The horizontal advection has a much greater effect on expanding the capacity of PM2.5 than the vertical diffusion under clean circulation patterns, and the maximum value of ideal AEC can reach 50 t/h. The positive residual AEC under clean circulations indicates surplus capacity for PM2.5 because of vigorous turbulences, while weak diffusion and ventilation conditions under polluted circulations lead to negative residual AEC and insufficient capacity of atmosphere.
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Affiliation(s)
- Yunyan Jiang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinyuan Xin
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongli Tian
- Inner Mongolia Autonomous Region environmental monitoring central station, Huhhot 010090, China
| | - Guiqian Tang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yuanzhe Ren
- Inner Mongolia Autonomous Region environmental monitoring central station, Huhhot 010090, China
| | - Lin Wu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaole Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ying Wang
- The College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Danjie Jia
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongjing Ma
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, 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
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38
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Qiu M, Zigler C, Selin NE. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions. ATMOSPHERIC CHEMISTRY AND PHYSICS 2022; 22:10551-10566. [PMID: 36845997 DOI: 10.5281/zenodo.6857259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.
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Affiliation(s)
- Minghao Qiu
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Corwin Zigler
- Department of Statistics and Data Science, University of Texas at Austin, Texas, USA
| | - Noelle E Selin
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Qiu M, Zigler C, Selin NE. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions. ATMOSPHERIC CHEMISTRY AND PHYSICS 2022; 22:10551-10566. [PMID: 36845997 PMCID: PMC9957566 DOI: 10.5194/acp-22-10551-2022] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.
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Affiliation(s)
- Minghao Qiu
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Corwin Zigler
- Department of Statistics and Data Science, University of Texas at Austin, Texas, USA
| | - Noelle E. Selin
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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40
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Li Y, Li J, Zhao Y, Lei M, Zhao Y, Jian B, Zhang M, Huang J. Long-term variation of boundary layer height and possible contribution factors: A global analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148950. [PMID: 34271389 DOI: 10.1016/j.scitotenv.2021.148950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/15/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Boundary layer height (BLH) plays an important role in regulating global weather/climate, as well as the dispersion and transportation of pollutants. Until now, however, the attribution and contributions of different controlling factors to BLH long-term variability and trends have not been quantified on a global scale. The long-term radiosonde dataset was used in this study to retrieve global BLH climatology; seasonal, diurnal, long-term variation and trends were analyzed over a 39-year period (1980-2018). Statistical results show that the global distribution of the BLH and its trend have apparent day-night differences. BLH during daytime is deeper during clear-sky conditions compared to cloudy sky conditions, indicating a significant effect of clouds; BLH during nighttime is deeper under cloudy conditions. BLH was also found to vary over different land types; dry and hot soil exhibits a deeper BLH than those of wet and cool soil. The long-term variation and trend of BLH are highly influenced by near-surface meteorological parameters. In particular, based on multiple linear stepwise regression models and the contribution calculation method, this investigation initiatively quantifies the influences of meteorological parameters on global BLH long-term variation and trend. Our results emphasized that a 10 m wind speed (WS) and low tropospheric stability (LTS) have significant contributions to long-term BLH variation; WS and LTS anomalies alternately dominated the contribution of the diurnal cycle of the BLH anomaly. Annual BLH recorded an average increasing trend (38.9-42.1 m/decade), and LTS is more dominant than WS from a contribution perspective, especially for increased BLH anomaly. Contributions from near-surface temperature (T) and relative humidity (RH) also play important roles. However, a decreasing WS trend dominated the decreased trends of BLH anomaly, accounting for nearly 40% of the total contribution.
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Affiliation(s)
- Yarong Li
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Jiming Li
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China.
| | - Yuxin Zhao
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Miao Lei
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Yang Zhao
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Bida Jian
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
| | - Min Zhang
- Inner Mongolia Institute of Meteorological Sciences, Hohhot, Inner Mongolia, China
| | - Jianping Huang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
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Lin C, Lau AKH, Fung JCH, Song Y, Li Y, Tao M, Lu X, Ma J, Lao XQ. Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148575. [PMID: 34175602 DOI: 10.1016/j.scitotenv.2021.148575] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/27/2021] [Accepted: 06/16/2021] [Indexed: 06/13/2023]
Abstract
Previous studies on long-term ozone (O3) variations in China have reported inconsistent conclusions on the role of meteorological factors in controlling said variations. In this study, we used an observation-based decomposition model to conduct an up-to-date investigation of the effects of meteorological factors on the variations in nitrogen dioxide (NO2) and O3 concentrations in China in the summer from 2013 to 2020. The variations in NO2 and O3 concentrations after removing the major meteorological effects were then analyzed to improve our understanding of O3 formation regimes. Ground measurements show that both NO2 and O3 concentrations decreased in eastern, central, and southeastern China (e.g., NO2 and O3 concentrations in Wuhan reduced by 4.3 and 6.2 ppb, respectively), which was not anticipated. Analyses of meteorological effects showed that reduced wind strength, decreased temperature, and increased relative humidity significantly reduced O3 concentrations in eastern and central China (e.g., by 10.5 ppb in Wuhan). After removing the major meteorological effects, the O3 trends were reversed in eastern and central China (e.g., increased by 4.9 ppb in Wuhan). The contrasting trends in NO2 and O3 concentrations suggest that their O3 formations were sensitive to volatile organic compounds (VOC-limited regime). In southeastern China, both NO2 and O3 concentrations decreased, implying that the O3 formation regimes changed to mixed sensitive or nitrogen oxide-limited (NOx-limited) regimes. The meteorological effects varied by region and may play a dominant role in controlling the long-term O3 variation. Our results indicate that the attribution of O3 variation to emission control without accounting for meteorological effects can be misleading.
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Affiliation(s)
- Changqing Lin
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Alexis K H Lau
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Jimmy C H Fung
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yushan Song
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China; Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Ying Li
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Minghui Tao
- Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
| | - Xingcheng Lu
- Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Jun Ma
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China
| | - Xiang Qian Lao
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
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42
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Guan Y, Xiao Y, Wang Y, Zhang N, Chu C. Assessing the health impacts attributable to PM 2.5 and ozone pollution in 338 Chinese cities from 2015 to 2020. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 287:117623. [PMID: 34171728 DOI: 10.1016/j.envpol.2021.117623] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/13/2023]
Abstract
China has effectively reduced the fine particulate (PM2.5) pollution from 2015 to 2020. Ozone pollution and related health impacts have become severe contemporaneously. The coordinated control of PM2.5 and ozone is becoming a new issue for China's air pollution control. This study quantitatively assessed the health impacts attributed to PM2.5 and ozone pollution in 338 Chinese cities from 2015 to 2020 and estimated the possible health benefits from achieving dual concentration targets during 2021-2025. Results show PM2.5 caused a total health impact of 2.45 × 107 disability-adjusted life years (DALYs) in 2020. All-cause and respiratory ozone-related health impact in 2020 was 1.04 × 107 DALYs and 1.56 × 106 DALYs. Between 2015 and 2020, the PM2.5-related health impacts decreased by 14.97%, while those ozone-related increased by 94.61% and 96.54% for all-cause and respiratory. Cities in the North China Plain have suffered higher health impacts attributable to PM2.5 and ozone pollution, indicating that the two-pollutant coordinated control is primarily needed. By achieving aggressive concentration target (decreasing 10%) between 2020 and 2025, China will reduce the PM2.5-related health impacts in 338 cities by 1.56 × 106 DALYs (improving 6.37%). By achieving general target (decreasing 10% or within the Interim target-1 of World Health Organization), the PM2.5-related health benefit will be 7.98 × 105 DALYs (improving 3.25%). The deteriorating ozone health risks will also be improved. Controlling air pollution in large cities and regional center cities can achieve remarkable health benefits. Due to the inter-region, inter-province, and inter-city difference of health impacts, targeted and differentiated pollution prevention and control need to be implemented.
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Affiliation(s)
- Yang Guan
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Yang Xiao
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Yameng Wang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing, 100012, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
| | - Chengjun Chu
- Center of Environmental Status and Plan Assessment, Chinese Academy of Environmental Planning, Beijing, 100012, China
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Wang C, Wang Y, Shi Z, Sun J, Gong K, Li J, Qin M, Wei J, Li T, Kan H, Hu J. Effects of using different exposure data to estimate changes in premature mortality attributable to PM 2.5 and O 3 in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 285:117242. [PMID: 33957508 DOI: 10.1016/j.envpol.2021.117242] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/20/2021] [Accepted: 04/23/2021] [Indexed: 06/12/2023]
Abstract
The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM2.5) and ozone (O3) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM2.5 and O3 in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O3-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM2.5-related mortality, and up to 30% for the O3-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 103 people/km2, mostly located in Central China, for both PM2.5 and O3. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.
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Affiliation(s)
- Chunlu Wang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Yiyi Wang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Zhihao Shi
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Jinjin Sun
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Kangjia Gong
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Jingyi Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Momei Qin
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA
| | - Tiantian Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
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Duan W, Wang X, Cheng S, Wang R, Zhu J. Influencing factors of PM 2.5 and O 3 from 2016 to 2020 based on DLNM and WRF-CMAQ. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 285:117512. [PMID: 34090076 DOI: 10.1016/j.envpol.2021.117512] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/19/2021] [Accepted: 05/31/2021] [Indexed: 06/12/2023]
Abstract
In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM2.5 and O3 and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM2.5 and O3 changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO2, NO2, and O3, among which NO2 tended to be the dominant influencing factor. O3 pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM2.5 and low concentration of NO2, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM2.5 and O3, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM2.5 pollution but aggravated O3 pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM2.5 and O3. This research may help understand the influencing factors of PM2.5 and O3 and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.
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Affiliation(s)
- Wenjiao Duan
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Xiaoqi Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China.
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Ruipeng Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Jiaxian Zhu
- Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China
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45
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Hu X, Sun H, Luo X, Ni S, Yan Y. Health and economic impacts from PM 2.5 pollution transfer attributed to domestic trade in China: a provincial-level analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:49559-49573. [PMID: 33934261 DOI: 10.1007/s11356-021-13954-y] [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: 11/24/2020] [Accepted: 04/12/2021] [Indexed: 06/12/2023]
Abstract
In recent years, severe air pollution has had a serious impact on the health and economy of residents and has attracted great attention. Due to the spatial separation between consumption and production, the transfer of PM2.5 pollution and its health and economic effects caused by interprovincial trade have not been taken seriously. In this study, economic, atmospheric, and epidemiological models were combined to assess air pollution transfer and its health and economic impacts that are attributed to provincial trade in China. The analyses were performed under the PM2.5 transfer scenario in which economically developed areas in eastern China transferred many health and economic impacts to inland areas through interprovincial trade in 2012. As a result of interprovincial trade, 1980 (95% CI 0, 4114) extra deaths and 208,000 (95% CI 74.5, 395.6) additional illnesses accrued, but 0.184 (95% CI 0.017, 0.372) billion USD of residents' economic loss was avoided in China. The results illustrate the serious impact of domestic trade on regional health and economics. It is necessary to comprehensively consider supply chains in designing policies to mitigate the negative health and economic impacts of air pollution across China.
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Affiliation(s)
- Xueyuan Hu
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, People's Republic of China.
| | - Han Sun
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, People's Republic of China
- Research Center of Resource and Environmental Economics, China University of Geosciences, Wuhan, 430074, People's Republic of China
| | - Xi Luo
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, People's Republic of China
| | - Shan Ni
- School of Economics and Management, China University of Geosciences, Wuhan, 430074, People's Republic of China
| | - Yingying Yan
- School of Environmental Studies, China University of Geosciences, Wuhan, 430074, People's Republic of China
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Sulaymon ID, Zhang Y, Hopke PK, Hu J, Zhang Y, Li L, Mei X, Gong K, Shi Z, Zhao B, Zhao F. Persistent high PM 2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China. ENVIRONMENTAL RESEARCH 2021; 198:111186. [PMID: 33930403 PMCID: PMC9750169 DOI: 10.1016/j.envres.2021.111186] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 05/21/2023]
Abstract
Lockdown measures to curtail the COVID-19 pandemic in China halted most non-essential activities on January 23, 2020. Despite significant reductions in anthropogenic emissions, the Beijing-Tianjin-Hebei (BTH) region still experienced high air pollution concentrations. Employing two emissions reduction scenarios, the Community Multiscale Air Quality (CMAQ) model was used to investigate the PM2.5 concentrations change in this region. The model using the scenario (C3) with greater traffic reductions performed better compared to the observed PM2.5. Compared with the no reductions base-case (scenario C1), PM2.5 reductions with scenario C3 were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. During high-pollution days in scenario C3, the percentage reductions in PM2.5 concentrations in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 6.10, 3.61, and 3.67%, respectively. However, significant increases caused by unfavorable meteorological conditions counteracted the emissions reduction effects resulting in high air pollution in BTH region during the lockdown period. This study shows that effective air pollution control strategies incorporating these results are urgently required in BTH to avoid severe pollution.
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Affiliation(s)
- Ishaq Dimeji Sulaymon
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanxun Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China; CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, 361021, China.
| | - Philip K Hopke
- Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, 13699, USA; Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Yang Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lin Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xiaodong Mei
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kangjia Gong
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Zhihao Shi
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Bin Zhao
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, USA
| | - Fangxin Zhao
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
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Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model. REMOTE SENSING 2021. [DOI: 10.3390/rs13132525] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM2.5, PM10, O3, and CO validation dataset were 9.027 μg/m3, 20.312 μg/m3, 10.436 μg/m3, and 0.097 mg/m3, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM2.5, PM10, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM2.5, PM10, O3, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O3 emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas.
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Fu S, Guo M, Fan L, Deng Q, Han D, Wei Y, Luo J, Qin G, Cheng J. Ozone pollution mitigation in guangxi (south China) driven by meteorology and anthropogenic emissions during the COVID-19 lockdown. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 272:115927. [PMID: 33143981 PMCID: PMC7588315 DOI: 10.1016/j.envpol.2020.115927] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/23/2020] [Accepted: 10/23/2020] [Indexed: 05/20/2023]
Abstract
With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O3) pollution was generally amplified in China. However, the O3 levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016-2019, while O3 gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O3 concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O3 pollution mitigation during the lockdown. A 7.84 μg/m3 drop in O3 concentration was driven by meteorology, with other decreases (4.11 μg/m3) explained by reduced anthropogenic emissions of O3 precursors. Taken together, the lower NO2/SO2 ratios (1.25-2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O3 concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24-58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O3 reduction in the Guangxi region of South China.
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Affiliation(s)
- Shuang Fu
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Meixiu Guo
- Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China
| | - Linping Fan
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qiyin Deng
- College of Environment, Hohai University, Nanjing, Jiangsu, 210098, China
| | - Deming Han
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Ye Wei
- Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China
| | - Jinmin Luo
- Beihai Ecology and Environment Agency, Beihai, Guangxi, 536000, China
| | - Guimei Qin
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinping Cheng
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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49
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Zhu J, Chen L, Liao H, Yang H, Yang Y, Yue X. Enhanced PM 2.5 Decreases and O 3 Increases in China During COVID-19 Lockdown by Aerosol-Radiation Feedback. GEOPHYSICAL RESEARCH LETTERS 2021; 48:e2020GL090260. [PMID: 33612877 PMCID: PMC7883051 DOI: 10.1029/2020gl090260] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 05/20/2023]
Abstract
We apply an online-coupled meteorology-chemistry model (WRF-Chem) embedded with an improved process analysis to examine aerosol-radiation feedback (ARF) impacts on effectiveness of emission control due to Coronavirus Disease 2019 (COVID-19) lockdown over North China Plain. Emission reduction alone induces PM2.5 decrease by 16.3 μg m-3 and O3 increase by 10.2 ppbv during COVID-19 lockdown. The ARF enhances PM2.5 decrease by 2.7 μg m-3 (16.6%) and O3 increase by 0.8 ppbv (7.8%). The ARF-induced enhancement of PM2.5 decline is mostly attributed to aerosol chemistry process, while enhancement of O3 rise is ascribed to physical advection and vertical mixing processes. A set of sensitivity experiments with emission reductions in different degrees indicate that the ARF-induced enhancements of PM2.5 declines (O3 rises) follow a robust linear relationship with the emission-reduction-induced PM2.5 decreases. The fitted relationship has an important implication for assessing the effectiveness of emission abatement at any extent.
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Affiliation(s)
- Jia Zhu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science & TechnologyNanjingChina
| | - Lei Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science & TechnologyNanjingChina
- Key Laboratory of Meteorological DisasterMinistry of EducationJoint International Research Laboratory of Climate and Environment ChangeCollaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science & TechnologyNanjingChina
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science & TechnologyNanjingChina
| | - Hao Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science & TechnologyNanjingChina
| | - Yang Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science & TechnologyNanjingChina
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science & TechnologyNanjingChina
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50
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Sbai SE, Mejjad N, Norelyaqine A, Bentayeb F. Air quality change during the COVID-19 pandemic lockdown over the Auvergne-Rhône-Alpes region, France. AIR QUALITY, ATMOSPHERE, & HEALTH 2021; 14:617-628. [PMID: 33488840 PMCID: PMC7813977 DOI: 10.1007/s11869-020-00965-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 11/04/2020] [Indexed: 05/18/2023]
Abstract
UNLABELLED Under the rapid spread of coronavirus diseases (COVID-19) worldwide, a complete lockdown was imposed in France from March 17th to May 11th, 2020 to limit the virus spread. This lockdown affected significantly the atmospheric pollution levels due to the restrictions of human activities. In the present study, we investigate the evolution of air quality in the Auvergne-Rhône-Alpes region, focusing on nine atmospheric pollutants (NO2, NO, PM10, PM2.5, O3, VOC, CO, SO2, and isoprene). In Lyon, center of the region, the results indicated that NO2, NO, and CO levels were reduced by 67%, 78%, and 62%, respectively, resulting in a decrease in road traffic by 80%. However, O3, PM10, and PM2.5 were increased by 105%, 23%, and 53%, respectively, during the lockdown. The increase in ozone is explained by the dropping in NO and other gases linked to human activity, which consume ozone. Thus, the increase of solar radiation, sunshine, temperature, and humidity promoted the O3 formation during the lockdown. Besides, rising temperature enhances the BVOC emissions such as isoprene. In addition, volatile organic component (VOC) and SO2 remain almost stable and oxidation of these species leads to the formation of ozone and organic aerosol, which also explains the increase in PM during the lockdown. This study shows the contribution of atmospheric photochemistry to air pollution. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11869-020-00965-w.
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Affiliation(s)
- Salah Eddine Sbai
- Department of physics, Laboratoires de physique des hauts Energies Modélisation et Simulation, Mohammed V University in Rabat, Rabat, Morocco
| | - Nezha Mejjad
- Department of Geology, Faculty of Sciences, Ben M’Sik Hassan II University, Casablanca, Morocco
| | | | - Farida Bentayeb
- Department of physics, Laboratoires de physique des hauts Energies Modélisation et Simulation, Mohammed V University in Rabat, Rabat, Morocco
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