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Zhang S, Fu M, Zhang H, Yin H, Ding Y. Emission control status and future perspectives of diesel trucks in China. J Environ Sci (China) 2025; 148:702-713. [PMID: 39095202 DOI: 10.1016/j.jes.2023.06.010] [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: 03/06/2023] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 08/04/2024]
Abstract
Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.
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Affiliation(s)
- Shihai Zhang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Mingliang Fu
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Hefeng Zhang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Hang Yin
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yan Ding
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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2
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Ye Y, Tao Q, Wei H. Public health impacts of air pollution from the spatiotemporal heterogeneity perspective: 31 provinces and municipalities in China from 2013 to 2020. Front Public Health 2024; 12:1422505. [PMID: 39157526 PMCID: PMC11327077 DOI: 10.3389/fpubh.2024.1422505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/24/2024] [Indexed: 08/20/2024] Open
Abstract
Air pollution has long been a significant environmental health issue. Previous studies have employed diverse methodologies to investigate the impacts of air pollution on public health, yet few have thoroughly examined its spatiotemporal heterogeneity. Based on this, this study investigated the spatiotemporal heterogeneity of the impacts of air pollution on public health in 31 provinces in China from 2013 to 2020 based on the theoretical framework of multifactorial health decision-making and combined with the spatial durbin model and the geographically and temporally weighted regression model. The findings indicate that: (1) Air pollution and public health as measured by the incidence of respiratory diseases (IRD) in China exhibit significant spatial positive correlation and local spatial aggregation. (2) Air pollution demonstrates noteworthy spatial spillover effects. After controlling for economic development and living environment factors, including disposable income, population density, and urbanization rate, the direct and indirect spatial impacts of air pollution on IRD are measured at 3.552 and 2.848, correspondingly. (3) China's IRD is primarily influenced by various factors such as air pollution, economic development, living conditions, and healthcare, and the degree of its influence demonstrates an uneven spatiotemporal distribution trend. The findings of this study hold considerable practical significance for mitigating air pollution and safeguarding public health.
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Affiliation(s)
- Yizhong Ye
- School of Hospital Economics and Management, Anhui University of Chinese Medicine, Hefei, China
- Key Laboratory of Data Science and Innovative Development of Chinese Medicine in Anhui Province Philosophy and Social, Hefei, China
| | - Qunshan Tao
- School of Hospital Economics and Management, Anhui University of Chinese Medicine, Hefei, China
- Key Laboratory of Data Science and Innovative Development of Chinese Medicine in Anhui Province Philosophy and Social, Hefei, China
| | - Hua Wei
- School of Hospital Economics and Management, Anhui University of Chinese Medicine, Hefei, China
- Key Laboratory of Data Science and Innovative Development of Chinese Medicine in Anhui Province Philosophy and Social, Hefei, China
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Cheng B, Ma Y, Qin P, Wang W, Zhao Y, Liu Z, Zhang Y, Wei L. Characterization of air pollution and associated health risks in Gansu Province, China from 2015 to 2022. Sci Rep 2024; 14:14751. [PMID: 38926518 PMCID: PMC11208435 DOI: 10.1038/s41598-024-65584-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk-based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level.
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Affiliation(s)
- Bowen Cheng
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Yuxia Ma
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China.
| | - Pengpeng Qin
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Wanci Wang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Yuhan Zhao
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Zongrui Liu
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Yifan Zhang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Linbo Wei
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China.
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Ou C, Li F, Zhang J, Jiang P, Li W, Kong S, Guo J, Fan W, Zhao J. Multi-scenario PM2.5 distribution and dynamic exposure assessment of university community residents: Development and application of intelligent health risk management system integrated low-cost sensors. ENVIRONMENT INTERNATIONAL 2024; 185:108539. [PMID: 38460243 DOI: 10.1016/j.envint.2024.108539] [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: 11/11/2023] [Revised: 02/01/2024] [Accepted: 02/26/2024] [Indexed: 03/11/2024]
Abstract
Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community "QC", as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) "Air health management (AHM)" could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) < public scenario (PS) < outdoor scenario (OS) < catering scenario (CS). Except for CS, the outdoor PM2.5 in other scenarios was higher than indoor by 5-10 μg/m3. The multi-scenario population weighted annual average exposure concentration was 37.1 μg/m3, which was 78 % of the annual average outdoor concentration. The exposure concentration of 5 groups: cooks > outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.
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Affiliation(s)
- Changhong Ou
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Fei Li
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China.
| | - Jingdong Zhang
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China.
| | - Pei Jiang
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Wei Li
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Shaojie Kong
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Jinyuan Guo
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Wenbo Fan
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
| | - Junrui Zhao
- Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
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Zhao H, Wang Y, Zhang Z. Increased ground-level O 3 during the COVID-19 pandemic in China aggravates human health risks but has little effect on winter wheat yield. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 338:122713. [PMID: 37813142 DOI: 10.1016/j.envpol.2023.122713] [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: 07/19/2023] [Revised: 10/01/2023] [Accepted: 10/07/2023] [Indexed: 10/11/2023]
Abstract
In January 2020, the novel coronavirus (COVID-19) outbreak emerged in China, prompting the enforcement of stringent lockdown measures nationwide to contain its spread. Multiple studies have demonstrated that these measures successfully reduced the levels of air pollutants except for ozone (O3). However, the potential risks of nationwide O3 changes during this period remain uncertain. To address this gap, we evaluated the ecological and health effects of O3 using hourly O3 data from 1 January to 17 June in both 2020 and 2019. Our results indicated that all health and ecological indicators, except SUM06 (sum of all hourly O3 over 60 ppb), during the COVID-19 pandemic in 2020 increased most obviously in Stages 2 and 3 with the strictest control measures, compared to the same period in 2019. The national premature deaths due to short-term O3 exposure during Stages 2-3 in 2020 totaled 146,558 (95% CI: 79,386-213,730) for all non-accidental causes and 82,408 (95% CI: 30,522-134,295) for cardiovascular diseases, increasing by 18.78% and 18.76% in 2019, respectively. The most significant increase in health risks occurred in Hubei, followed by Jiangxi, Zhejiang, Hunan, and Shaanxi. In addition, the estimated national winter wheat production losses (WWPL) attributable to O3 amounted to 50.6 and 51.1 million metric tons for 2019 and 2020, respectively. Among the major winter wheat-producing provinces, Anhui and Jiangsu experienced a larger increase in WWPL, while Shandong and Hebei suffered a greater decrease in 2020 compared to 2019, resulting in little overall change in WWPL between the two years. These findings provided direct evidence of the harmful effects of O3 during the COVID-19 pandemic and serve as a valuable reference for future air pollution control.
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Affiliation(s)
- Hui Zhao
- School of Resources and Environmental Engineering, Jiangsu University of Technology, Changzhou, 213001, China; Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science & Technology, Nanjing, 210044, China.
| | - Yiyi Wang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science & Technology, Nanjing, 210044, China; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Zhen Zhang
- Shaanxi Meteorological Service Center of Agricultural Remote Sensing and Economic Crops, Xi'an, 710014, China
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Li X, Abdullah LC, Sobri S, Md Said MS, Hussain SA, Aun TP, Hu J. Long-Term Air Pollution Characteristics and Multi-scale Meteorological Factor Variability Analysis of Mega-mountain Cities in the Chengdu-Chongqing Economic Circle. WATER, AIR, AND SOIL POLLUTION 2023; 234:328. [PMID: 37200574 PMCID: PMC10175934 DOI: 10.1007/s11270-023-06279-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/29/2023] [Indexed: 05/20/2023]
Abstract
Currently, air quality has become central to global environmental policymaking. As a typical mountain megacity in the Cheng-Yu region, the air pollution in Chongqing is unique and sensitive. This study aims to comprehensively investigate the long-term annual, seasonal, and monthly variation characteristics of six major pollutants and seven meteorological parameters. The emission distribution of major pollutants is also discussed. The relationship between pollutants and the multi-scale meteorological conditions was explored. The results indicate that particulate matter (PM), SO2 and NO2 showed a "U-shaped" variation, while O3 showed an "inverted U-shaped" seasonal variation. Industrial emissions accounted for 81.84%, 58% and 80.10% of the total SO2, NOx and dust pollution emissions, respectively. The correlation between PM2.5 and PM10 was strong (R = 0.98). In addition, PM only showed a significant negative correlation with O3. On the contrary, PM showed a significant positive correlation with other gaseous pollutants (SO2, NO2, CO). O3 is only negatively correlated with relative humidity and atmospheric pressure. These findings provide an accurate and effective countermeasure for the coordinated management of air pollution in Cheng-Yu region and the formulation of the regional carbon peaking roadmap. Furthermore, it can improve the prediction accuracy of air pollution under multi-scale meteorological factors, promote effective emission reduction paths and policies in the region, and provide references for related epidemiological research. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11270-023-06279-8.
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Affiliation(s)
- Xiaoju Li
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
- Xichang University, No. 1 Xuefu Road, Anning Town, Xichang City, 615000 Sichuan Province China
| | - Luqman Chuah Abdullah
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Shafreeza Sobri
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Mohamad Syazarudin Md Said
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Siti Aslina Hussain
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor Malaysia
| | - Tan Poh Aun
- SOx NOx Asia Sdn Bhd, UEP Subang Jaya, 47620 Selangor Darul Ehsan Malaysia
| | - Jinzhao Hu
- Xichang University, No. 1 Xuefu Road, Anning Town, Xichang City, 615000 Sichuan Province China
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Wang J, Li J, Li X, Fang C. Characteristics of Air Pollutants Emission and Its Impacts on Public Health of Chengdu, Western China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192416852. [PMID: 36554731 PMCID: PMC9779229 DOI: 10.3390/ijerph192416852] [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: 10/13/2022] [Revised: 12/01/2022] [Accepted: 12/13/2022] [Indexed: 05/06/2023]
Abstract
Pollution caused by PM2.5 and O3 are common environmental problems which can easily affect human health. Chengdu is a major central city in Western China, and there is little research on the regional emissions and health effects of air pollution in Chengdu. According to the Multi-resolution Emissions Inventory of the Chinese Model, 2017 (MEIC v1.3), this study compiled the air pollutant emission inventory of Chengdu. The results show that the pollutant emission of Chengdu is generally higher in winter than in summer. The southeast area of Chengdu is the key area where emissions of residential and industrial sectors are dominant. Through air quality simulation with a Weather Research and Forecasting model, coupled with the Community Multiscale Air Quality (WRF-CMAQ), the health effects of PM2.5 and O3 in winter and summer in Chengdu of 2017 were investigated. The primary pollutant in winter is PM2.5 and O3 in summer. PM2.5 pollution accounted for 351 deaths in January and July 2017, and O3 pollution accounted for 328 deaths in the same period. There were 276 deaths in rural areas and 413 in urban areas. In January and July 2017, the health economic loss caused by PM2.5 accounted for 0.0974% of the gross regional product (GDP) of Chengdu in 2017, and the health economic loss caused by O3 accounted for 0.0910%.
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Affiliation(s)
- Ju Wang
- College of New Energy and Environment, Jilin University, Changchun 130012, China
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130012, China
- Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130012, China
- Correspondence: ; Tel.: +86-131-0431-7228
| | - Juan Li
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Xinlong Li
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Chunsheng Fang
- College of New Energy and Environment, Jilin University, Changchun 130012, China
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130012, China
- Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130012, China
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Guo B, Wu H, Pei L, Zhu X, Zhang D, Wang Y, Luo P. Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign. ENVIRONMENT INTERNATIONAL 2022; 170:107606. [PMID: 36335896 DOI: 10.1016/j.envint.2022.107606] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 μg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 μg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 μg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 μg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, Shaanxi 710068, China; School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710043, China.
| | - Xiaowei Zhu
- Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA.
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Pingping Luo
- School of Water and Environment, Chang'an University, Xi'an, Shaanxi 710054, China.
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9
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Transition metal disulfide (MoTe2, MoSe2 and MoS2) were modified to improve NO2 gas sensitivity sensing. J IND ENG CHEM 2022. [DOI: 10.1016/j.jiec.2022.11.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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10
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Tu P, Tian Y, Hong Y, Yang L, Huang J, Zhang H, Mei X, Zhuang Y, Zou X, He C. Exposure and Inequality of PM 2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912137. [PMID: 36231437 PMCID: PMC9564533 DOI: 10.3390/ijerph191912137] [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: 08/12/2022] [Revised: 09/18/2022] [Accepted: 09/21/2022] [Indexed: 05/02/2023]
Abstract
Fine particulate matter (PM2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m-3 per year from 2000 to 2016. The proportion of the population exposed to high PM2.5 (above 35 μg m-3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM2.5.
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Affiliation(s)
- Peiyue Tu
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Ya Tian
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Yujia Hong
- Wuhan Britain-China School, Wuhan 430034, China
| | - Lu Yang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
| | - Jiayi Huang
- Woodsworth College, University of Toronto, Toronto, ON M5S1A9, Canada
| | - Haoran Zhang
- Department of Geography, University of Washington, Seattle, WA 98195, USA
- Correspondence: (H.Z.); (C.H.); Tel.: +86-15727359013 (C.H.); Fax: +86-2769111990 (C.H.)
| | - Xin Mei
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
| | - Yanhua Zhuang
- Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
| | - Xin Zou
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
- Correspondence: (H.Z.); (C.H.); Tel.: +86-15727359013 (C.H.); Fax: +86-2769111990 (C.H.)
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