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Fu L, Wang Q, Li J, Jin H, Zhen Z, Wei Q. Spatiotemporal Heterogeneity and the Key Influencing Factors of PM 2.5 and PM 10 in Heilongjiang, China from 2014 to 2018. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191811627. [PMID: 36141911 PMCID: PMC9517409 DOI: 10.3390/ijerph191811627] [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: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 05/06/2023]
Abstract
Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014-2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.
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Affiliation(s)
- Longhui Fu
- School of Forestry, Northeast Forestry University, Harbin 150040, China
- Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
| | - Qibang Wang
- School of Forestry, Northeast Forestry University, Harbin 150040, China
| | - Jianhui Li
- School of Forestry, Northeast Forestry University, Harbin 150040, China
| | - Huiran Jin
- School of Applied Engineering and Technology, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Zhen Zhen
- School of Forestry, Northeast Forestry University, Harbin 150040, China
- Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
- Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
- Correspondence: (Z.Z.); (Q.W.)
| | - Qingbin Wei
- Key Laboratory of Forest Plant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, China
- School of Geographical Sciences, Harbin Normal University, Harbin 150025, China
- Correspondence: (Z.Z.); (Q.W.)
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Liu J, Ho HC. A Framework for Characterizing the Multilateral and Directional Interaction Relationships Between PM Pollution at City Scale: A Case Study of 29 Cities in East China, South Korea and Japan. Front Public Health 2022; 10:875924. [PMID: 35651854 PMCID: PMC9149247 DOI: 10.3389/fpubh.2022.875924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Transboundary particulate matter (PM) pollution has become an increasingly significant public health issue around the world due to its impacts on human health. However, transboundary PM pollution is difficult to address because it usually travels across multiple urban jurisdictional boundaries with varying transportation directions at different times, therefore posing a challenge for urban managers to figure out who is potentially polluting whose air and how PM pollution in adjacent cities interact with each other. This study proposes a statistical analysis framework for characterizing directional interaction relationships between PM pollution in cities. Compared with chemical transport models (CTMs) and chemical composition analysis method, the proposed framework requires less data and less time, and is easy to implement and able to reveal directional interaction relationships between PM pollution in multiple cities in a quick and computationally inexpensive way. In order to demonstrate the application of the framework, this study applied the framework to analyze the interaction relationships between PM2.5 pollution in 29 cities in East China, South Korea and Japan using one year of hourly PM2.5 measurement data in 2018. The results show that the framework is able to reveal the significant multilateral and directional interaction relationships between PM2.5 pollution in the 29 cities in Northeast Asia. The analysis results of the case study show that the PM2.5 pollution in China, South Korea and Japan are linked with each other, and the interaction relationships are mutual. This study further evaluated the framework's validity by comparing the analysis results against the wind vector data, the back trajectory data, as well as the results extracted from existing literature that adopted CTMs to study the interaction relationships between PM pollution in Northeast Asia. The comparisons show that the analysis results produced by the framework are consistent with the wind vector data, the back trajectory data as well as the results using CTMs. The proposed framework provides an alternative for exploring transportation pathways and patterns of transboundary PM pollution between cities when CTMs and chemical composition analysis would be too demanding or impossible to implement.
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Affiliation(s)
- Jianzheng Liu
- School of Public Affairs, Xiamen University, Xiamen, China
| | - Hung Chak Ho
- Department of Anaesthesiology, LKS Faculty of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.,Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
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Wang S, Gao J, Guo L, Nie X, Xiao X. Meteorological Influences on Spatiotemporal Variation of PM 2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:1607. [PMID: 35162629 PMCID: PMC8834796 DOI: 10.3390/ijerph19031607] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 11/20/2022]
Abstract
Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing-Tianjin-Hebei region during 2014-2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m-3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.
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Affiliation(s)
- Suxian Wang
- College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China;
| | - Jiangbo Gao
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Rd., Beijing 100101, China;
| | - Linghui Guo
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China;
| | - Xiaojun Nie
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China;
| | - Xiangming Xiao
- Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA;
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van de Beek E, Kerckhoffs J, Hoek G, Sterk G, Meliefste K, Gehring U, Vermeulen R. Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1067-1075. [PMID: 33378199 DOI: 10.1021/acs.est.0c0680610.1021/acs.est.0c06806.s001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm3. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.
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Affiliation(s)
- Esther van de Beek
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Jules Kerckhoffs
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Gerard Hoek
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Geert Sterk
- Department of Physical Geography, Utrecht University, 3508 TC Utrecht, The Netherlands
| | - Kees Meliefste
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Ulrike Gehring
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center, University of Utrecht, 3584 CK Utrecht, The Netherlands
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van de Beek E, Kerckhoffs J, Hoek G, Sterk G, Meliefste K, Gehring U, Vermeulen R. Spatial and Spatiotemporal Variability of Regional Background Ultrafine Particle Concentrations in the Netherlands. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1067-1075. [PMID: 33378199 PMCID: PMC7818655 DOI: 10.1021/acs.est.0c06806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Studies of the health effects of ultrafine particles (UFPs) in large nationwide cohorts are currently hampered by a lack of knowledge about spatial and spatiotemporal variations in regional background UFPs. We measured the UFP (10-300 nm) at 20 regional background locations (3 × 2 weeks) across the Netherlands and a reference site continuously over a total period of 14 months in 2016-2017. We compared the overall averages for each site and used kriging to create a regional background spatial map of the Netherlands. Spatiotemporal variability was analyzed by correlating time-series of 2 and 24 h average concentrations. The overall average measured UFP concentrations at the 20 locations ranged from 3814 to 7070 particles/cm3. We found the spatial correlation in the UFP concentrations up to 180 km and clear differences between the north and the more populated southern parts of the country. The average temporal correlation between 2 and 24 h average UFP concentrations was 0.50 (IQR: 0.36-0.61) and 0.58 (IQR: 0.44-0.75), respectively. Temporal correlation declined weakly with a distance between sites, from 0.58 for sites within 80 km of each other to 0.47 for sites farther away. The substantial spatial variation in the regional background UFP concentrations suggests that regional variation may contribute importantly to exposure contrast in nationwide health studies of UFP.
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Affiliation(s)
- Esther van de Beek
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Jules Kerckhoffs
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
- E-mail:
| | - Gerard Hoek
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Geert Sterk
- Department
of Physical Geography, Utrecht University, 3508 TC Utrecht, The Netherlands
| | - Kees Meliefste
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Ulrike Gehring
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
| | - Roel Vermeulen
- Institute
for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
- Julius
Center for Health
Sciences and Primary Care, University Medical
Center, University of Utrecht, 3584 CK Utrecht, The Netherlands
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Vlachogiannis DM, Xu Y, Jin L, González MC. Correlation networks of air particulate matter ( PM 2.5 ): a comparative study. APPLIED NETWORK SCIENCE 2021; 6:32. [PMID: 33907706 PMCID: PMC8062950 DOI: 10.1007/s41109-021-00373-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/08/2021] [Indexed: 05/05/2023]
Abstract
Over the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations' time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014-2020). We learn that the use of hourly PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks' complexity through node subsampling. The end result separates the temporal series of PM 2.5 in set of regions that are similarly affected through the year.
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Affiliation(s)
- Dimitrios M. Vlachogiannis
- Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 USA
- Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA 94720 USA
| | - Yanyan Xu
- Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 USA
- Department of City and Regional Planning, University of California at Berkeley, Berkeley, CA 94720 USA
| | - Ling Jin
- Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 USA
| | - Marta C. González
- Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 USA
- Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, CA 94720 USA
- Department of City and Regional Planning, University of California at Berkeley, Berkeley, CA 94720 USA
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Web-Based Visualization of Scientific Research Findings: National-Scale Distribution of Air Pollution in South Korea. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17072230. [PMID: 32224988 PMCID: PMC7177515 DOI: 10.3390/ijerph17072230] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 03/17/2020] [Accepted: 03/23/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND As scientific findings of air pollution and subsequent health effects have been accumulating, public interest has also been growing. Accordingly, web visualization is suggested as an effective tool to facilitate public understanding in scientific evidence and to promote communication between the public and academia. We aimed to introduce an example of easy and effective web-based visualization of research findings, relying on predicted concentrations of particulate matter ≤ 10 µg/m3 (PM10) and nitrogen dioxide (NO2) obtained from our previous study in South Korea and Tableau software. Our visualization focuses on nationwide spatial patterns and temporal trends over 14 years, which would not have been accessible without our research results. METHODS Using predicted annual average concentrations of PM10 and NO2 across approximately 250 districts and maps of administrative divisions in South Korea during 2001-2014, we demonstrate data preprocessing and design procedures in the Tableau dashboard, comprising maps, time-series plots, and bar charts. RESULTS Our visualization allows one to identify high concentration areas, a long-term temporal trend, and the contrast between two pollutants. The application of easy tools for user-interactive options in Tableau suggests possible easy access to the scientific knowledge of non-experts. CONCLUSION Our example contributes to future studies that develop the visualization of research findings in further intuitive designs.
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Wei Q, Zhang L, Duan W, Zhen Z. Global and Geographically and Temporally Weighted Regression Models for Modeling PM 2.5 in Heilongjiang, China from 2015 to 2018. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16245107. [PMID: 31847317 PMCID: PMC6950195 DOI: 10.3390/ijerph16245107] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 01/10/2023]
Abstract
Objective: This study investigated the relationships between PM2.5 and 5 criteria air pollutants (SO2, NO2, PM10, CO, and O3) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R2 and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R2 (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial-temporal heterogeneity and possible solutions for modeling the relationships between PM2.5 and 5 criteria air pollutants for Heilongjiang province, China.
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Affiliation(s)
- Qingbin Wei
- School of Forestry, Northeast Forestry University, Harbin 150040, China; (Q.W.); (W.D.)
| | - Lianjun Zhang
- Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA;
| | - Wenbiao Duan
- School of Forestry, Northeast Forestry University, Harbin 150040, China; (Q.W.); (W.D.)
| | - Zhen Zhen
- School of Forestry, Northeast Forestry University, Harbin 150040, China; (Q.W.); (W.D.)
- Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China
- Correspondence: ; Tel.: +86-187-4568-7693
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Huang T, Yu Y, Wei Y, Wang H, Huang W, Chen X. Spatial-seasonal characteristics and critical impact factors of PM2.5 concentration in the Beijing-Tianjin-Hebei urban agglomeration. PLoS One 2018; 13:e0201364. [PMID: 30235240 PMCID: PMC6147404 DOI: 10.1371/journal.pone.0201364] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 07/13/2018] [Indexed: 11/19/2022] Open
Abstract
As China's political and economic centre, the Beijing-Tianjin-Hebei (BTH) urban agglomeration experiences serious environmental challenges on particulate matter (PM) concentration, which results in fundamental or irreparable damages in various socioeconomic aspects. This study investigates the seasonal and spatial distribution characteristics of PM2.5 concentration in the BTH urban agglomeration and their critical impact factors. Spatial interpolation are used to analyse the real-time monitoring of PM2.5 data in BTH from December 2013 to May 2017, and partial least squares regression is applied to investigate the latest data of potential polluting variables in 2015. Several important findings are obtained: (1) Notable differences exist amongst PM2.5 concentrations in different seasons; January (133.10 mg/m3) and December (120.19 mg/m3) are the most polluted months, whereas July (38.76 mg/m3) and August (41.31 mg/m3) are the least polluted months. PM2.5 concentration shows a periodic U-shaped variation pattern with high pollution levels in autumn and winter and low levels in spring and summer. (2) In terms of spatial distribution characteristics, the most highly polluted areas are located south and east of the BTH urban agglomeration, and PM2.5 concentration is significantly low in the north. (3) Empirical results demonstrate that the deterioration of PM2.5 concentration in 2015 is closely related to a set of critical impact factors, including population density, urbanisation rate, road freight volume, secondary industry gross domestic product, overall energy consumption and industrial pollutants, such as steel production and volume of sulphur dioxide emission, which are ranked in terms of their contributing powers. The findings provide a basis for the causes and conditions of PM2.5 pollution in the BTH regions. Viable policy recommendations are provided for effective air pollution treatment.
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Affiliation(s)
- Tianhang Huang
- School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China
| | - Yunjiang Yu
- International Business School, Shanghai Lixin University of Accounting and Finance, Shanghai, China
- * E-mail: (YY); (YW)
| | - Yigang Wei
- School of Economics and Management, Beihang University, Beijing, China
- Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
- * E-mail: (YY); (YW)
| | - Huiwen Wang
- School of Economics and Management, Beihang University, Beijing, China
- Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China
| | - Wenyang Huang
- School of Economics and Management, Beihang University, Beijing, China
| | - Xuchang Chen
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
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