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Li L, Zhang D, Hu W, Yang Y, Zhang S, Yuan R, Lv P, Zhang W, Zhang Y, Zhang Y. Improving VOC control strategies in industrial parks based on emission behavior, environmental effects, and health risks: A case study through atmospheric measurement and emission inventory. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161235. [PMID: 36586688 DOI: 10.1016/j.scitotenv.2022.161235] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Industrial parks have a very important impact on regional economic development, but the extremely complex and relatively concentrated volatile organic compound (VOC) emissions from industrial parks also result in it being difficult to control VOCs. In this study, we took a large integrated industrial park in the upper reaches of the Yangtze River as an example, conducted a 1-year monitoring campaign of ambient air VOCs, and established a speciated VOC emission inventory based on the measured chemical profiles of the key industries. The comprehensive control index (CCI) of 125 VOCs was evaluated using the entropy weighting method based on comprehensive consideration of three aspects, namely, emission behavior, environmental effects, and health risks of VOCs, to identify priority VOC species and their key sources for VOC control in industrial parks. The total estimated VOC emissions in the industrial park in 2019 were 6446.96 t. Steel production, sewage treatment, natural gas chemical industry, pharmaceuticals, and industrial boilers were the main sources of VOC emissions. In terms of VOC components, halocarbons, aromatics, and OVOCs were the largest groups of VOCs emitted from the industrial park, accounting for 73.75 % of the total VOC emissions. Using the entropy weighting method, we evaluated the index weights of five parameters: emissions, ozone formation potential, secondary organic aerosol formation potential, hazard quotient, and lifetime cancer risk. Based on the CCI, five control levels for VOC species were further established. The VOC species in Level I and Level II, which contain species such as naphthalene, 2-chlorotoluene, benzene, acrolein, and chloroform, should be considered as extremely important priority control species. These results serve as a reference for the development of precise control strategies for VOCs in industrial parks.
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Affiliation(s)
- Ling Li
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China
| | - Dan Zhang
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China; School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China.
| | - Wei Hu
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China
| | - Yi Yang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Sidi Zhang
- Guangdong Sino-co-flourish Environmental Protection Technology Co, Ltd, Guangdong 510200·China
| | - Rui Yuan
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China
| | - Pingjiang Lv
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China
| | - Weidong Zhang
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China
| | - Yong Zhang
- Key Laboratory for Urban Atmospheric Environment Integrated Observation & Pollution Prevention and Control of Chongqing, Chongqing Research Academy of Eco-Environmental Sciences, Chongqing 401147, China; Southwest Branch of Chinese Research Academy of Environmental Sciences, Chongqing 401147, China
| | - Yunhuai Zhang
- School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China
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Han L, Qin T, Sun Z, Ren H, Zhao N, An X, Wang Z. Influence of Urbanization on the Spatial Distribution of Associations Between Air Pollution and Mortality in Beijing, China. GEOHEALTH 2023; 7:e2022GH000749. [PMID: 36925585 PMCID: PMC10013134 DOI: 10.1029/2022gh000749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/06/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
This study investigated the influence of urbanization on the intra-city spatial distribution of associations between air pollution and mortality in Beijing, China. First, we utilized the generalized additive model to establish the exposure-response associations of PM2.5, O3, with nonaccidental and cardiorespiratory mortality between urban and suburban areas. Second, we assessed district-specific air pollution-related mortality and analyzed how these associations were affected by the degree of urbanization. Finally, we analyzed the changes in air pollution-related mortality before and after the enforcement of the Air Pollution Prevention and Control Action Plan (referred to as the Action Plan). The effect estimates of PM2.5 for nonaccidental mortality were 0.20% (95% CI: 0.12-0.28) in urban areas and 0.46% (95% CI: 0.35-0.58) in suburban areas per 10 μg/m3 increase in PM2.5 concentrations. The corresponding estimates of O3 were 0.13% (95% CI: -0.04-0.29) in urban areas and 0.34% (95% CI: 0.12-0.56) in suburban areas per 10 μg/m3 increase in O3 concentrations; however, the difference between the estimates of O3 in urban and suburban areas was not statistically significant. The district-specific results suggested that the estimated risks increased along with urban vulnerability levels for the effects of PM2.5. Implementing the Action Plan reduced the mortality risks of PM2.5, but the risks of O3 increased in some districts. However, the difference in the estimates between the pre- and post-emission reductions was not statistically significant. Our study indicated that populations living in less urbanized areas are more vulnerable to the adverse effects of air pollution in Beijing, particularly for PM2.5.
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Affiliation(s)
- Ling Han
- State Key Laboratory for Infectious Disease Prevention and ControlNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Tian Qin
- State Key Laboratory for Infectious Disease Prevention and ControlNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Zhaobin Sun
- Institute of Urban MeteorologyChina Meteorological AdministrationBeijingChina
- Joint International Research Laboratory of Atmospheric and Earth System SciencesSchool of Atmospheric SciencesNanjing UniversityNanjingChina
- China Meteorological Administration Urban Meteorology Key LaboratoryBeijingChina
| | - Hongyu Ren
- State Key Laboratory for Infectious Disease Prevention and ControlNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Na Zhao
- State Key Laboratory for Infectious Disease Prevention and ControlNational Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina
| | - Xingqin An
- Institute of Atmospheric CompositionChinese Academy of Meteorological SciencesBeijingChina
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMAChinese Academy of Meteorological SciencesBeijingChina
| | - Zhanshan Wang
- State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingChina
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Li X, Li B, Guo L, Feng R, Fang X. Research progresses on VOCs emission investigations via surface and satellite observations in China. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2022; 24:1968-1981. [PMID: 36000414 DOI: 10.1039/d2em00175f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Volatile organic compounds (VOCs) are important precursors of severe pollution of ozone (O3) and secondary organic aerosols in China. Fully understanding the VOCs emission is crucial for making regulations to improve air quality. This study reviews the published studies on atmospheric VOCs concentration observations in China and observation-based estimation of China's VOCs emission strengths and emission source structures. The results reveal that direct sampling and stainless-steel-tank sampling are the most commonly used methods for online and offline observations in China, respectively. The GC-MS/FID is the most commonly used VOCs measuring instrument in China (in 60.8% of the studies we summarized). Numerous studies conducted observation campaigns in urban areas (76.2%) than in suburban (17.1%), rural (18.1%), and background areas (14.3%) in China. Moreover, observation sites are largely set in eastern China (83.8%). Though there are published studies reporting observation-based China's VOCs emission investigation, these kinds of studies are still limited, and gaps are found between the results of top-down investigation and bottom-up inventories of VOCs emissions in China. In order to enhance the observation-based VOCs emission investigations in China, this study suggests future improvements including: (1) development of VOCs detection techniques, (2) strengthening of atmospheric VOCs observations, (3) improvement of the accuracy of observation-based VOCs emission estimations, and (4) facilitation of better VOCs emission inventories in China.
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Affiliation(s)
- Xinhe Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Bowei Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Liya Guo
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Rui Feng
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
| | - Xuekun Fang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Spatial modeling of ambient concentrations of volatile organic compounds in Montreal, Canada. Environ Epidemiol 2022; 6:e226. [PMID: 36249265 PMCID: PMC9555929 DOI: 10.1097/ee9.0000000000000226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 08/03/2022] [Indexed: 11/06/2022] Open
Abstract
Volatile organic compounds (VOCs) are components of the complex mixture of air pollutants within cities and can cause various adverse health effects. Therefore, it is necessary to understand their spatial distribution for exposure assessment in epidemiological studies.
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Sun M, Zhou Y, Wang Y, Zheng X, Cui J, Zhang D, Zhang J, Zhang R. Seasonal discrepancies in peroxyacetyl nitrate (PAN) and its correlation with ozone and PM 2.5: Effects of regional transport from circumjacent industrial cities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 785:147303. [PMID: 33933769 DOI: 10.1016/j.scitotenv.2021.147303] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/16/2021] [Accepted: 04/18/2021] [Indexed: 06/12/2023]
Abstract
Peroxyacetyl nitrate (PAN) is the most important reservoir of nitrogen oxides, with effects on atmospheric oxidation capacity and regional nitrogen distribution. The first yearlong observational study of PAN was conducted from September 2018 to August 2019 at a suburban site and an urban site in Zhengzhou, Henan Province, central China. Compared with studies over the past two decades, summer PAN pollution at the suburban site and winter PAN pollution at both sites were more significant, with annual average concentrations of 1.96 ± 1.44 and 2.01 ± 1.59 ppbv, respectively. Seasonal PAN discrepancies between the urban and suburban areas were analyzed in detail. Active PAN formation, regional transport, photochemical precursors, and PAN lifetime played key roles during seasons with elevated PAN (winter and spring). According to the results of cluster analysis and potential source contribution function analysis, during the cold months, short-distance air mass transport from the east, south, and southeast of Henan Province and southern Hebei Province increased PAN pollution in urban Zhengzhou. PAN source areas were located in circumjacent industrial cities surrounding Zhengzhou except in the northeastern direction. Based on the relationships between pollutant concentrations, wind speed, and wind direction, a strong positive correlation between PAN and PM2.5 (and O3) existed in winter due to their joint transport. A slow-moving, low-height air mass passed through surrounding industrial cities before reaching the study area, carrying both pollutants and leading to strong consistency between PAN and O3 levels. The long-term PAN characteristics described in this study will help clarify the causes of regional air pollution in inland city agglomerations. Moreover, the PAN correlations and joint transport of PAN and PM2.5 (or O3) support the use of PAN as an indicator of air pollution introduced from surrounding industrial areas.
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Affiliation(s)
- Mei Sun
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Ying Zhou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Yifei Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xiaochen Zheng
- Institute of Environmental Engineering (IfU), ETH Zürich, 8093 Zürich, Switzerland
| | - Jia'nan Cui
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Dong Zhang
- Research Institute of Environmental Science, College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Jianbo Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| | - Ruiqin Zhang
- Research Institute of Environmental Science, College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001, China
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