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Wu H, Guo B, Guo T, Pei L, Jing P, Wang Y, Ma X, Bai H, Wang Z, Xie T, Chen M. A study on identifying synergistic prevention and control regions for PM 2.5 and O 3 and exploring their spatiotemporal dynamic in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:122880. [PMID: 37944886 DOI: 10.1016/j.envpol.2023.122880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/18/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023]
Abstract
Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.
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Affiliation(s)
- Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China; Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Xi'an, Shaanxi, 710043, China
| | - Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China.
| | - Tengyue Guo
- Department of Geological Engineering, Qinghai University, Xining, Qinghai, 810016, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, Shaanxi, 710068, China
| | - Peiqing Jing
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430072, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi, 710119, China
| | - Xuying Ma
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Haorui Bai
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Zheng Wang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Tingting Xie
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
| | - Miaoyi Chen
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi, 710054, China
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Lv L, Wei P, Hu J, Chu Y, Liu X. High-spatiotemporal-resolution mapping of PM 2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator. ENVIRONMENT INTERNATIONAL 2024; 183:108421. [PMID: 38194757 DOI: 10.1016/j.envint.2024.108421] [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: 10/31/2023] [Revised: 12/28/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024]
Abstract
Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87-0.92 and error indices decreasing by 50-67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.
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Affiliation(s)
- Lingling Lv
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China; School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Peng Wei
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China
| | - Jingnan Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China.
| | - Yangxi Chu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China
| | - Xiao Liu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China
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Xia Y, Zhang Y, Ji Q, Cheng X, Wang X, Sabel CE, He H. Sediment core records and impact factors of polycyclic aromatic hydrocarbons in Chinese lakes. ENVIRONMENTAL RESEARCH 2023; 235:116690. [PMID: 37474088 DOI: 10.1016/j.envres.2023.116690] [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: 03/27/2023] [Revised: 07/14/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Lake sediment is a natural sink for polycyclic aromatic hydrocarbons (PAHs). PAH sedimentation characteristics and their impact factors of Chinese lakes have mainly been qualitative assessed. However, quantitative impacts of PAH sedimentation from different factors have not been well analyzed. To fill this gap, we screened PAH sedimentation records from the literature, for 51 lakes in China and other regions of the world, to identify historical concentration variation and the impact factors of PAHs in different regions, in lake sediment. The results show that PAH concentrations in the sediment core in the selected Chinese lakes (478 ± 812 ng/g dry weight (dw)) were significantly lower than those in North America (5518 ± 6572 ng/g dw) and Europe (3817 ± 4033 ng/g dw). From 1900 to 2015, most of the lakes in China showed an increasing trend of PAH sedimentation concentrations, with the lakes in Southeastern China showed a decreasing trend of PAH concentration in the period of 2001-2015, which was later than the peak times shown in Western countries (1941-1970). The 2-3-ring PAHs were the main components in the sediment core of Chinese lakes, but the proportion to the total PAHs decreased from 72% in 1900-1940 to 55% in 2001-2015. Generalized additive modeling (GAM) was adopted to simulate the associations between PAH sedimentation records and the impact factors. There are large regional variations of economic and industrial development in China. The impact factors of PAH accumulation in the lake sediments differ in different regions. However, population and the consumption of coal, pesticides, and fertilizer were identified to be the most important impact factors influencing PAH sedimentation. The Chinese government needs to strengthen control measures on pollutant discharge to reduce the anthropogenic impact of PAH sedimentation in lakes.
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Affiliation(s)
- Yubao Xia
- School of Environment, Nanjing Normal University, Nanjing, Jiangsu, 210023, PR China
| | - Yanxia Zhang
- School of Environment, Nanjing Normal University, Nanjing, Jiangsu, 210023, PR China; Aarhus Institute of Advanced Studies, Aarhus University, 8000, Aarhus, Denmark; BERTHA - Big Data Centre for Environment and Health, Department of Public Health, Aarhus University, 8000, Aarhus, Denmark.
| | - Qingsong Ji
- School of Environment, Nanjing Normal University, Nanjing, Jiangsu, 210023, PR China
| | - Xinying Cheng
- School of Environment, Nanjing Normal University, Nanjing, Jiangsu, 210023, PR China
| | - Xinkai Wang
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, PR China
| | - Clive E Sabel
- BERTHA - Big Data Centre for Environment and Health, Department of Public Health, Aarhus University, 8000, Aarhus, Denmark; Department of Public Health, Aarhus University, 8000, Aarhus, Denmark
| | - Huan He
- School of Environment, Nanjing Normal University, Nanjing, Jiangsu, 210023, PR China; College of Ecological and Resource Engineering, Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, Wuyi University, Wuyishan, Fujian, 354300, PR China.
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Zhang X, Gou P, Chen W, Li G, Huang Y, Zhou T, Liu Y, Nie W. Spatiotemporal distribution characteristics of ecosystem health and the synergetic impact of its driving factors in the Yangtze River Delta, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:85184-85197. [PMID: 37380860 DOI: 10.1007/s11356-023-28412-0] [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: 02/08/2023] [Accepted: 06/17/2023] [Indexed: 06/30/2023]
Abstract
In recent years, . the rapid development of the Yangtze River Delta in China has led to increasingly serious regional eco-environmental problems. Therefore, it is of great significance for the construction of ecological civilization to study the ecosystem health in the Yangtze River Delta. In this paper, the assessment framework of "Vigor-Organization-Resilience" was used to assess the ecosystem health index (EHI) of the Yangtze River Delta from 2000 to 2020, and then the spatial autocorrelation method was used to analyze the agglomeration of EHI in 314 counties in this region. Finally, the eXtreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanation (SHAP) model were combined to explore the synergistic impact of EHI driving factors. The results show that (1) from 2000 to 2020, the EHI in the Yangtze River Delta is at the level of ordinary health, and gradually decreased; (2) the EHI has significant spatial positive correlation and aggregation; (3) the driving factors in descending order of importance are urbanization level (UL), precipitation (PRE), PM2.5 (PM), normalized difference vegetation index (NDVI), and temperature (TEMP); and (4) the relationship between UL and EHI is logarithmic; PRE and EHI are quartic polynomial; PM, NDVI, TEMP, and EHI are quadratic polynomial. The results of this paper are of great significance to the management and restoration of the ecosystem in this region.
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Affiliation(s)
- Xuepeng Zhang
- Nanhu Laboratory, Research Center of Big Data Technology, Jiaxing, 314000, China.
| | - Peng Gou
- Nanhu Laboratory, Research Center of Big Data Technology, Jiaxing, 314000, China
| | - Wei Chen
- College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, 100083, China
| | - Guangchao Li
- College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, 100083, China
| | - Yingshuang Huang
- Nanhu Laboratory, Research Center of Big Data Technology, Jiaxing, 314000, China
| | - Tianyu Zhou
- Nanhu Laboratory, Research Center of Big Data Technology, Jiaxing, 314000, China
| | - Yang Liu
- Nanhu Laboratory, Research Center of Big Data Technology, Jiaxing, 314000, China
| | - Wei Nie
- Nanhu Laboratory, Research Center of Big Data Technology, Jiaxing, 314000, China
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Gong J, Ding L, Lu Y, Qiong Zhang, Yun Li, Beidi Diao. Scientometric and multidimensional contents analysis of PM 2.5 concentration prediction. Heliyon 2023; 9:e14526. [PMID: 36950620 PMCID: PMC10025157 DOI: 10.1016/j.heliyon.2023.e14526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023] Open
Abstract
The foundation for the environmental department to take suitable measures and make a significant contribution towards improving air quality is the precise and dependable prediction of PM2.5 concentration. It is essential to review the development process and hotspots of PM2.5 concentration prediction studies over the past 20 years (2000-2021) comprehensively and quantitatively. This study used detailed bibliometric methods and CiteSpace software to visually analyze the PM2.5 pollution level. The outcomes found that the prediction research phases of PM2.5 can be broadly divided into three phases and enter the rapid growth phase after 2017. Five categories of keywords are clustered, and the forecasting data and forecasting methods are typical cluster representatives. Then, the construction and processing of PM2.5 concentration prediction datasets, the prediction methods and technical processes, and the determination of the prediction spatial-temporal scales are the main content of the analysis. In the future, it is necessary to concentrate on multi-source data fusion for PM2.5 concentration prediction at multiple spatial-temporal scales and focus on technology integration and innovative applications in forecasting models, especially the optimal use of deep machine learning methods to improve prediction accuracy and practical application conversion.
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Affiliation(s)
- Jintao Gong
- The Library, Ningbo Polytechnic, Ningbo 315800, China
| | - Lei Ding
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
- Corresponding author. Industrial Economic Research Center Around Hangzhou Bay, Ningbo Polytechnic; 1069 Xinda Road, 315800, Ningbo, China. ;
| | - Yingyu Lu
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
| | - Qiong Zhang
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
| | - Yun Li
- Research Center of Industrial Economy Around Hangzhou Bay, Ningbo Polytechnic, Ningbo 315800, China
| | - Beidi Diao
- School of Economics and Management, China University of Mining and Technology, No.1 Daxue Road, 221116, Xuzhou, China
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