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Wang H, Zhang M, Niu J, Zheng X. Spatiotemporal characteristic analysis of PM 2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province. Front Public Health 2023; 11:1295468. [PMID: 38115845 PMCID: PMC10728471 DOI: 10.3389/fpubh.2023.1295468] [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: 09/16/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.
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Affiliation(s)
- Hua Wang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Mingcheng Zhang
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Jiqiang Niu
- Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, China
| | - Xiaoyun Zheng
- Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, China
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Wang C, Guo M, Jin J, Yang Y, Ren Y, Wang Y, Cao J. Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China. PLANTS (BASEL, SWITZERLAND) 2022; 11:2847. [PMID: 36365300 PMCID: PMC9655052 DOI: 10.3390/plants11212847] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/09/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health.
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Affiliation(s)
- Chengkang Wang
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Mengyue Guo
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Jun Jin
- Research Institute of Architecture, Southeast University, Nanjing 210096, China
| | - Yifan Yang
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Yujie Ren
- Graduate School of Human-Environment Studies, Kyushu University, Fukuoka 819-0395, Japan
| | - Yang Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Jiajie Cao
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
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Sun Q, Cao B, Jiang Y, Zhuang J, Zhang C, Jiang B. Association between ambient particulate matter (PM 2.5/PM 10) and first incident ST-elevation myocardial infarction in Suzhou, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:62690-62697. [PMID: 35404033 DOI: 10.1007/s11356-022-20150-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Interests in evaluation of the effect of air pollution and weather conditions on cardiovascular disease have increased. However, the relationship between short-term particulate matter (PM) exposure and first incident ST-elevation myocardial infarction (STEMI) remains unclear. Medical records were collected from December 2013 to December 2016. A total of 1354 patients with first incident STEMI were included. The daily average of air pollution and weather conditions were calculated. In this case-crossover study, conditional logistic regression was performed to assess the association between daily concentrations of PM and first incident STEMI. The daily average of PM2.5 and PM10 were 58.9 μg/m3 and 80.2 μg/m3, respectively. In this case-crossover study, single-pollutant models showed that each 10 μg/m3 increase in PM2.5 was associated with a percent change of 3.36, 95% confidence interval (CI): (1.01-5.77), or in PM10 percent change of 2.1%, 95%CI: (0.2-4.04) for patients with first incident STEMI. The association remained stable after adjusting for ozone (O3). The results from subgroup analysis showed the association slightly enhanced in women, elder patients, patients with history of diabetes, patients without history of smoking, and cold seasons. The p values were not significant between these strata, which may be due to small sample size. This investigation showed that short-term PM exposure associated with first incident STEMI in Suzhou. Given the effect of PM on the first incident STEMI, strategies to decrease PM should be considered.
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Affiliation(s)
- Qian Sun
- Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital 4 of Nantong University, the First Hospital of Yancheng, Yancheng City, Jiangsu Province, China
| | - Bangming Cao
- Department of Cardiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai City, Shandong Province, China
| | - Yufeng Jiang
- Department of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou City, Jiangsu Province, China
| | - Jin Zhuang
- Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital 4 of Nantong University, the First Hospital of Yancheng, Yancheng City, Jiangsu Province, China
| | - Chi Zhang
- Department of Cardiology, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China.
| | - Bin Jiang
- Department of Cardiology, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
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The Correlation Analysis between Air Quality and Construction Sites: Evaluation in the Urban Environment during the COVID-19 Pandemic. SUSTAINABILITY 2022. [DOI: 10.3390/su14127075] [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
This research studies the data on air quality and construction activities from 29 January 2020 to 30 April 2020. The analysis focuses on three sample districts of Hangzhou’s Xiacheng, Gongshu, and Xiaoshan districts. The samples, respectively, represent low-level, mid-level, and high-level districts in the scale of construction projects. The correlative relationships are investigated, respectively, in the periods of ‘pandemic lockdown (29 January 2020–20 February 2020)’ and ‘after pandemic lockdown (21 February 2020–30 April 2020)’. The correlative equations are obtained. Based on the guideline values of air parameters provided by the Chinese criteria and standards, the recommended maximum scales of construction projects are defined. The numbers of construction sites are 16, 118, and 311 for the Xiacheng, Gongshu, and Xiaoshan districts during the imposed lockdown period, respectively, and 19, 88, 234, respectively, after the lockdown period. Because the construction site is only one influential factor on the air quality, and the database is not large enough, there are some limitations in the mathematical model and the management plan. Possible problem solving techniques and future studies are introduced at the end of the research study.
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Zhu L, Zhang Y, Wu Z, Zhang C. Spatio-Temporal Characteristics of SO 2 across Weifang from 2008 to 2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182212206. [PMID: 34831963 PMCID: PMC8624775 DOI: 10.3390/ijerph182212206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/14/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022]
Abstract
China has achieved good results in SO2 pollution control, but SO2 pollution still exists in some areas. Analyzing the spatio-temporal distribution of SO2 is critical for regional SO2 pollution prevention and control. Compared with existing air pollution studies that paid more attention to PM2.5, NO2 and O3, and focused on the macro scale, this study took the small-scale Weifang city as the research area, analyzed the temporal and spatial changes in SO2, discussed the migration trajectory of SO2 pollution and explored the impact of wind on SO2 pollution. The results show that the average annual concentration of SO2 in Weifang has exhibited a downward trend in the past 13 years, showing the basic characteristics of “highest in winter, lowest in summer and slightly higher in spring and autumn”, “highest on Sunday, lowest on Thursday and gradually decreasing from Monday to Thursday” and “highest at 9 a.m., lowest at 4 p.m. and gradually increasing from midnight to 9 a.m.”. SO2 concentration showed obvious spatial heterogeneity: higher in the north and lower in the south. In addition, Shouguang, Changyi and Gaomi were seriously polluted. The SO2 pollution shifted from south to northeast. The clean wind direction (southeast wind and northeast wind) of Weifang city accounted for about 41%, and the pollution wind direction (northwest wind and west wind) accounted for about 7%. Drawing from the multi-scale analysis, vegetation, precipitation, temperature, transport situation and human activity were the most relevant factors. Limited to data collection, more quantitative research is needed to gain insight into the influence mechanism in the future.
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The Driving Influence of Multi-Dimensional Urbanization on PM 2.5 Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000-2018. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179389. [PMID: 34501979 PMCID: PMC8430555 DOI: 10.3390/ijerph18179389] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/31/2021] [Accepted: 09/03/2021] [Indexed: 12/22/2022]
Abstract
Africa’s PM2.5 pollution has become a security hazard, but the understanding of the varying effects of urbanization on driven mechanisms of PM2.5 concentrations under the rapid urbanization remains largely insufficient. Compared with the direct impact, the spillover effect of urbanization on PM2.5 concentrations in adjacent regions was underestimated. Urbanization is highly multi-dimensional phenomenon and previous studies have rarely distinguished the different driving influence and interactions of multi-dimensional urbanization on PM2.5 concentrations in Africa. This study combined grid and administrative units to explore the spatio-temporal change, spatial dependence patterns, and evolution trend of PM2.5 concentrations and multi-dimensional urbanization in Africa. The differential influence and interaction effects of multi-dimensional urbanization on PM2.5 concentrations under Africa’s rapid urbanization was further analyzed. The results show that the positive spatial dependence of PM2.5 concentrations gradually increased over the study period 2000–2018. The areas with PM2.5 concentrations exceeding 35 μg/m3 increased by 2.2%, and 36.78% of the African continent had an increasing trend in Theil–Sen index. Urbanization was found to be the main driving factor causing PM2.5 concentrations changes, and economic urbanization had a stronger influence on air quality than land urbanization or population urbanization. Compared with the direct effect, the spillover effect of urbanization on PM2.5 concentrations in two adjacent regions was stronger, particularly in terms of economic urbanization. The spatial distribution of PM2.5 concentrations resulted from the interaction of multi-dimensional urbanization. The interaction of urbanization of any two different dimensions exhibited a nonlinear enhancement effect on PM2.5 concentrations. Given the differential impact of multi-dimensional urbanization on PM2.5 concentrations inside and outside the region, this research provides support for the cross-regional joint control strategies of air pollution in Africa. The findings also indicate that PM2.5 pollution control should not only focus on urban economic development strategies but should be an optimized integration of multiple mitigation strategies, such as improving residents’ lifestyles, optimizing land spatial structure, and upgrading the industrial structure.
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Study on Coupled Relationship between Urban Air Quality and Land Use in Lanzhou, China. SUSTAINABILITY 2021. [DOI: 10.3390/su13147724] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The intensification of global urbanization has exacerbated the negative impact of atmospheric environmental factors in urban areas, thus threatening the sustainability of future urban development. In order to ensure the sustainability of urban atmospheric environments, exploring the changing laws of urban air quality, identifying highly polluted areas in cities, and studying the relationship between air quality and land use have become issues of great concern. Based on AQI data from 340 air quality monitoring stations and urban land use data, this paper uses inverse distance weight (IDW), Getis-Ord Gi*, and a negative binomial regression model to discuss the spatiotemporal variation of air quality in the main urban area of Lanzhou and its relationship with urban land use. The results show that urban air quality has characteristics of temporal and spatial differentiation and spatially has characteristics of agglomeration of cold and hot spots. There is a close relationship between urban land use and air quality. Industrial activities, traffic pollution, and urban construction activities are the most important factors affecting urban air quality. Green spaces can reduce urban pollution. The impact of land use on air quality has a seasonal effect.
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Xie Q, Sun Q. Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031132. [PMID: 33525318 PMCID: PMC7908386 DOI: 10.3390/ijerph18031132] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 11/18/2022]
Abstract
Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.
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Affiliation(s)
- Qijiao Xie
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China;
- Key Laboratory of Regional Development and Environmental Response (Hubei Province), Wuhan 430062, China
- Correspondence: ; Tel.: +86-027-8866-1699
| | - Qi Sun
- Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China;
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