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Lu X, Zhang D, Wang L, Wang S, Zhang X, Liu Y, Chen K, Song X, Yin S, Zhang R, Wang S, Yuan M. Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China. J Environ Sci (China) 2025; 149:406-418. [PMID: 39181653 DOI: 10.1016/j.jes.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 08/27/2024]
Abstract
Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.
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Affiliation(s)
- Xuan Lu
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Dong Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Lanxin Wang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Shefang Wang
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Xinran Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Yali Liu
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Keying Chen
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xinshuai Song
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Shasha Yin
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China.
| | - Ruiqin Zhang
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Shanshan Wang
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Minghao Yuan
- Zhengzhou Ecological Environment Monitoring Center of Henan Province, Zhengzhou 450000, China
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Niu Y, Yan Y, Dong J, Yue K, Duan X, Hu D, Li J, Peng L. Evidence for sustainably reducing secondary pollutants in a typical industrial city in China: Co-benefit from controlling sources with high reduction potential beyond industrial process. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135556. [PMID: 39173373 DOI: 10.1016/j.jhazmat.2024.135556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 08/15/2024] [Accepted: 08/15/2024] [Indexed: 08/24/2024]
Abstract
Under China's strict industrial control measures, the reduction of secondary pollutants (O3 and secondary organic aerosols [SOA]) and precursors (volatile organic compounds [VOCs] and NOx) caused by industrial processes has encountered bottlenecks. In this study, the net O3 formation rate (Net [O3]) in summer and the self-reaction rate between peroxy radicals (Self-Rnxs) in winter are used to characterize the formation potentials of O3 and SOA, respectively. Assuming that the precursor reduction ratio based on emission inventories is approximately equal to that based on observed concentrations, this study combines emission inventory and observation-based model (OBM) methods to indicate the potential source of secondary pollutants reduction. The findings show that strict control measures implemented by local governments, particularly those targeting industrial processes and fossil fuel combustion, are effective in reducing VOCs and NOx emissions during summer, and the two sources result in 3.8 % and 5.3 % decrease in the Net (O3), respectively. Similarly, control measures focusing on industrial processes help to significantly reduce VOCs emissions during winter, resulting in an 8.0 % decrease in Self-Rnxs. However, current measures for industrial processes are stringent and have little potential for further reduction. Therefore, additional sources with higher reduction potentials beyond industrial processes should be subject to stringent controls in industrial cities. Given the limited emission reduction potential associated with industrial processes, this study provides perspectives for sustained reduction of secondary pollutants in industrial cities.
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Affiliation(s)
- Yueyuan Niu
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Yulong Yan
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China.
| | - Jiaqi Dong
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
| | - Ke Yue
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
| | - Xiaolin Duan
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Dongmei Hu
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Junjie Li
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
| | - Lin Peng
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China.
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3
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Zhang M, Chen C, Sun Y, Wang Y, Du P, Ma R, Li T. Association between Ambient Volatile Organic Compounds Exposome and Emergency Hospital Admissions for Cardiovascular Disease. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5695-5704. [PMID: 38502526 DOI: 10.1021/acs.est.3c08937] [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: 03/21/2024]
Abstract
The limited research on volatile organic compounds (VOCs) has not taken into account the interactions between constituents. We used the weighted quantile sum (WQS) model and generalized linear model (GLM) to quantify the joint effects of ambient VOCs exposome and identify the substances that play key roles. For a 0 day lag, a quartile increase of WQS index for n-alkanes, iso/anti-alkanes, aromatic, halogenated aromatic hydrocarbons, halogenated saturated chain hydrocarbons, and halogenated unsaturated chain hydrocarbons were associated with 1.09% (95% CI: 0.13, 2.06%), 0.98% (95% CI: 0.22, 1.74%), 0.92% (95% CI: 0.14, 1.69%), 1.03% (95% CI: 0.14, 1.93%), 1.69% (95% CI: 0.48, 2.91%), and 1.85% (95% CI: 0.93, 2.79%) increase in cardiovascular disease (CVD) emergency hospital admissions, respectively. Independent effects of key substances on CVD-related emergency hospital admissions were also reported. In particular, an interquartile range increase in 1,1,1-trichloroethane, methylene chloride, styrene, and methylcyclohexane is associated with a greater risk of CVD-associated emergency hospital admissions [3.30% (95% CI: 1.93, 4.69%), 3.84% (95% CI: 1.21, 6.53%), 5.62% (95% CI: 1.35, 10.06%), 8.68% (95% CI: 3.74, 13.86%), respectively]. We found that even if ambient VOCs are present at a considerably low concentration, they can cause cardiovascular damage. This should prompt governments to establish and improve concentration standards for VOCs and their sources. At the same time, policies should be introduced to limit VOCs emission to protect public health.
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Affiliation(s)
- Mengxue Zhang
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yue Sun
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yanwen Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Runmei Ma
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Tiantian Li
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Wen M, Deng W, Huang J, Zhang S, Lin Q, Wang C, Ma S, Wang W, Zhang X, Li G, An T. Atmospheric VOCs in an industrial coking facility and the surrounding area: Characteristics, spatial distribution and source apportionment. J Environ Sci (China) 2024; 138:660-670. [PMID: 38135429 DOI: 10.1016/j.jes.2023.04.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/23/2023] [Accepted: 04/23/2023] [Indexed: 12/24/2023]
Abstract
Industrial coking facilities are an important emission source for volatile organic compounds (VOCs). This study analyzed the atmospheric VOC characteristics within an industrial coking facility and its surrounding environment. Average concentrations of total VOCs (TVOCs) in the surrounding residential activity areas (R1 and R2), the coking facility (CF) and the control area (CA) were determined to be 138.5, 47.8, 550.0, and 15.0 µg/m3, respectively. The cold drum process and coking and quenching areas within the coking facility were identified as the main polluting processes. The spatial variation in VOCs composition was analyzed, showing that VOCs in the coking facility and surrounding areas were mainly dominated by aromatic compounds such as BTX (benzene, toluene, and xylenes) and naphthalene, with concentrations being negatively correlated with the distance from the coking facility (p < 0.01). The sources of VOCs in different functional areas across the monitoring area were analyzed, finding that coking emissions accounted for 73.5%, 33.3% and 27.7% of TVOCs in CF, R1 and R2, respectively. These results demonstrated that coking emissions had a significant impact on VOC concentrations in the areas surrounding coking facility. This study evaluates the spatial variation in exposure to VOCs, providing important information for the influence of VOCs concentration posed by coking facility to surrounding residents and the development of strategies for VOC abatement.
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Affiliation(s)
- Meicheng Wen
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Weiqiang Deng
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Jin Huang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Shu Zhang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Qinhao Lin
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Chao Wang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Shengtao Ma
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Wanjun Wang
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Xin Zhang
- Institute of Environmental Science, Shanxi University, Taiyuan 030006, China
| | - Guiying Li
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Taicheng An
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution control, Guangdong University of Technology, Guangzhou 510006, China; Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment, Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
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Li M, Wan Y, Qian X, Wang A, Mahai G, He Z, Li Y, Xu S, Xia W. Urinary metabolites of multiple volatile organic compounds among pregnant women across pregnancy: Variability, exposure characteristics, and associations with selected oxidative stress biomarkers. ENVIRONMENT INTERNATIONAL 2023; 173:107816. [PMID: 36805810 DOI: 10.1016/j.envint.2023.107816] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
Volatile organic compounds (VOCs) are a group of pollutants pervasive in daily life with identified adverse health effects. However, no study has investigated the variability in VOC metabolites during pregnancy and their relationships with oxidative stress biomarkers in pregnant women. In the present study, the variability of 21 selected VOC metabolites was examined and their relationships with three selected oxidative stress biomarkers measured in spot urine samples at three trimesters of 1094 pregnant women were analyzed. Nineteen VOC metabolites were ubiquitous in the urine samples with detection rates ranging from 75.9% to 100%. Monohydroxybutenyl mercapturic acid (MHBMA) and s-phenyl mercapturic acid (PMA) had detection rates lower than 1.00%. Intraclass correlation coefficients (ICCs) of the detected analytes at three trimesters ranged 0.07-0.24, and the concentrations were highest in the first trimester. Higher concentrations of some VOC metabolites were related with participant characteristics including higher pre-pregnancy body mass index (BMI), lower education level, unemployment during pregnancy, multiparity, and sampling season of summer or winter. In repeated cross-sectional analyses, interquartile range (IQR) increases in the 19 detected VOC metabolites were positively related with 8-hydroxy-2'-deoxyguanosine (8-OHdG), 8-hydroxyguanosine (8-OHG), and 4-hydroxy nonenal mercapturic acid (HNEMA) with the estimates ranging from 9.00% to 204%. The mixture effect of the VOC metabolites on the oxidative stress biomarkers was further assessed using weighted quantile sum regression (WQS) models and the results showed that the WQS index of VOC metabolite mixture was significantly associated with 8-OHdG (β: 0.37, 0,32, and 0.39 at the 1st, 2nd, and 3rd trimester, respectively), 8-OHG (0.38, 0.32, and 0.39) and HNEMA (1.21, 1.08, and 1.10). Glycidamide mercapturic acid (GAMA), and trans,trans-muconic acid (MU) were the strongest contributors of the mixture effect on 8-OHdG, 8-OHG, and HNEMA, respectively. Overall, urinary concentrations of the VOC metabolites during pregnancy were strongly associated with the oxidative stress biomarkers.
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Affiliation(s)
- Mei Li
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
| | - Yanjian Wan
- Institute of Environmental Health, Wuhan Centers for Disease Control & Prevention, Wuhan, Hubei 430024, PR China.
| | - Xi Qian
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
| | - Aizhen Wang
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
| | - Gaga Mahai
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
| | - Zhenyu He
- Institute of Environmental Health, Wuhan Centers for Disease Control & Prevention, Wuhan, Hubei 430024, PR China.
| | - Yuanyuan Li
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
| | - Shunqing Xu
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
| | - Wei Xia
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
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Chen S, Wei W, Chen K, Wang X, Han L, Cheng S. Diagnosis of photochemical O 3 production of urban plumes in summer via developing the real-field IRs of VOCs: A case study in Beijing of China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 318:120836. [PMID: 36528196 DOI: 10.1016/j.envpol.2022.120836] [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: 09/22/2022] [Revised: 11/28/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
This study mainly developed an estimate method for photochemical ozone (O3) production from urban plumes in hot season, through simulating O3 evolution from precursors locally emitted and determining the real-field O3 increment reactivity (IR) of volatile organic compounds (VOCs) based on the box chemical model. Our simulation on June-2019 indicated that Beijing local emissions produced O3 at the rate of 0.7-9.2 ppb/h and led to an O3 increase of 48.9 ppb during 05:00-18:00, accounting for 68.3% of the observed O3 increase. The maximum level and production rate of simulated O3 showed a linear response to VOCs, therefore we can use VOCs levels in urban plumes to quantify O3 formation in summer. The IR (g O3 formed per g VOCs) was calculated on the actual precursor and meteorology condition of this megacity, 0.12-4.90 g/g for individual VOCs and 1.49 g/g for comprehensive TVOCs. The weighted average of individual IRs agreed well with that of TVOCs, but these IRs were 34.5% of MIR values that were widely used in references. It's noteworthy that these IRs had greater sensitivity to precursor levels, and broadly remained stable under the fixed VOCs:NOx. Considering the synchronous reductions of precursors in Beijing, we applied these IRs to quantify chemical O3 evolution from Beijing local emissions in summer of recent years, declining from 63.5 ppb in 2016 to 44.0 ppb in 2020 for June. The contributions of the diagnosed chemical O3 to Beijing O3 better matched with the atmospheric transport paths on daily basis, higher than 100% when the transport paths starting from the clean neighbor cities, but lower to 45%-66% when the transport paths originating from the highly-polluted neighbor cities. This consistence indicated the reliability of our IR calculation method for quickly estimating chemical O3 production of urban plumes in summer.
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Affiliation(s)
- Saisai Chen
- Department of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Wei Wei
- Department of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing, 100124, China.
| | - Kang Chen
- Department of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Xiaoqi Wang
- Department of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing, 100124, China
| | - Lihui Han
- Department of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing, 100124, China
| | - Shuiyuan Cheng
- Department of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing, 100124, China
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7
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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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Shi Y, Liu C, Zhang B, Simayi M, Xi Z, Ren J, Xie S. Accurate identification of key VOCs sources contributing to O 3 formation along the Liaodong Bay based on emission inventories and ambient observations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 844:156998. [PMID: 35787908 DOI: 10.1016/j.scitotenv.2022.156998] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 06/15/2023]
Abstract
In order to achieve the precise control of the volatile organic compounds (VOCs) species with high ozone (O3) formation contribution from key sources in Panjin and Yingkou, two coastal industrial cities with severe O3 pollution along the Liaodong Bay, northeast China, the ambient concentrations of 99 VOCs species were measured online at urban-petrochemical (XLT), suburban-industrial (PP), and rural (XRD) sites in July 2019, contemporary monthly anthropogenic VOCs emission inventories were developed. The source contribution of ambient VOCs resolved by positive matrix factorization (PMF) model was comparable with emission inventories, and the location of VOCs sources were speculated by potential source contribution function (PSCF). 17.5 Gg anthropogenic VOCs was emitted in Panjin and Yingkou in July 2019 with potential to form 54.7 Gg-O3 estimated by emission inventories. The average VOC mixing ratios of 47.1, 26.7, and 16.5 ppbv was observed at XLT, PP, and XRD sites, respectively. Petroleum industry (22 %), organic chemical industry (21 %), and mobile vehicle emission (19 %) were identified to be the main sources contributing to O3 formation at XLT site by PMF, while it is organic chemical industry (33 %) and solvent utilization (28 %) contributed the most at PP site. Taking the subdivided source contributions of emission inventories and source locations speculated by PSCF into full consideration, organic raw chemicals manufacturing, structural steel coating, petroleum refining process, petroleum products storage and transport, off-shore vessels, and passenger cars were identified as the key anthropogenic sources. High O3-formation contribution sources, organic chemical industry and solvent utilization were located in the industrial parks at the junction of the two cities and the southeast of Panjin, and petroleum industry distributed in the whole Panjin and offshore areas. These results identify the key VOCs species and sources and speculate the potential geographical location of sources for precisely controlling ground-level O3 along the Liaodong Bay.
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Affiliation(s)
- Yuqi Shi
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China
| | - Chang Liu
- Liaoning Ecological and Environmental Service Center, Shenyang, Liaoning 110161, PR China
| | - Baosheng Zhang
- Department of Ecology and Environment of Liaoning Province, Shenyang, Liaoning 110161, PR China
| | - Maimaiti Simayi
- College of Resources and Environments, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, PR China
| | - Ziyan Xi
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China
| | - Jie Ren
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China
| | - Shaodong Xie
- College of Environmental Sciences and Engineering, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, PR China.
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Niu Y, Yan Y, Chai J, Zhang X, Xu Y, Duan X, Wu J, Peng L. Effects of regional transport from different potential pollution areas on volatile organic compounds (VOCs) in Northern Beijing during non-heating and heating periods. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 836:155465. [PMID: 35500706 DOI: 10.1016/j.scitotenv.2022.155465] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/11/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
Despite the adoption of air quality control measures, the influence of regional transport on volatile organic compounds (VOCs) pollution has gradually increased in Beijing. In this study, the whole observation period (September 24 to December 12, 2020) was divided into heating period and non-heating period to explore the impact of changing VOCs sources in different observation periods, and also classified into "Type-N" and "Type-S" periods both in non-heating period and heating period to explore the impact of regional transport from the northern and southern regions of sampling site, respectively. The average VOCs concentrations in northern Beijing during observation period were 22.6 ± 12.6 ppbv, which showed a decrease trend in recent years compared with other studies. And higher VOCs concentrations were observed in Type-S than in Type-N period. The positive matrix factorization results showed that vehicular exhaust dominated VOCs (26.1%-33.7%), but coal combustion could not be ignored in heating period, when it was twice that in non-heating period. In particular, coal combustion dominated VOCs in southern trajectories (30.9%) in heating period. The analysis of concentration weighted trajectory showed that coal combustion was affected by regional transport from the southeast regions of Beijing, while vehicular exhaust was affected by urban and the southeast regions of Beijing. Regarding human health risks, the carcinogenic risks of benzene and ethylbenzene exceeded the acceptable cancer risk value (1 × 10-6), and were higher in Type-S than in Type-N period. The results indicated that regional transport from urban areas and the areas south of Beijing had a significant impact on VOCs in northern Beijing. Thus, targeted control measures for different potential pollution regions are important for controlling VOCs pollution in Beijing.
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Affiliation(s)
- Yueyuan Niu
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Yulong Yan
- Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China.
| | - Jianwei Chai
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Xiangyu Zhang
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Yang Xu
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Xiaolin Duan
- MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Jing Wu
- Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
| | - Lin Peng
- Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing 100044, China; School of Environment, Beijing Jiaotong University, Beijing 100044, China
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Spatiotemporal Evolution and Prediction of AOT in Coal Resource Cities: A Case Study of Shanxi Province, China. SUSTAINABILITY 2022. [DOI: 10.3390/su14052498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
As aerosols in the air have a great influence on the health of residents of coal resource-based cities, these municipalities are confronting the dilemma of air pollution that is caused by the increase of suspended particles in the atmosphere and their development process. Aerosol optical thickness could be used to explore the aerosol temporal and spatial variations and to develop accurate prediction models, which is of great significance to the control of air pollution in coal resource-based cities. This paper explored the temporal spatial variation characteristics of aerosols in coal resource-based regions. A total of 11 typical coal-resource prefecture-level cities in the Shanxi Province were studied and inverted the aerosol optical thickness (AOT) among these cities based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and analyzed the significant factors affecting AOT. Through inputting significant correlation factors as the input variables of NARX (nonlinear auto regressive models with exogenous inputs) neural network, the monthly average AOTs in the Shanxi Province were predicted between 2011 and 2019. The results showed that, in terms of time series, AOT increased from January to July and decreased from July to December, the maximum AOT was 0.66 in summer and the minimum was 0.2 in autumn, and it was related to the local monsoon, temperature, and humidity. While as far as the space alignment is concerned, the figure for AOT in Shanxi Province varied significantly. High AOT was mainly concentrated in the centre and south and low AOT was focused on the northwestern part. Among the positively correlated factors, the correlation coefficient of population density and temperature exceeded 0.8, which was highly positive, and among the negatively correlated factors, the correlation coefficient of NDVI exceeded -0.8, which was highly negative. After improving the model by adding the important factors that were mentioned before, the error between the predicted mean value and the actual mean value was no more than 0.06. Considering this charge, the NARX neural network with multiple inputs can contribute to better prediction results.
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