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Banerjee B, Kundu S, Kanchan R, Mohanta A. Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-33608-z. [PMID: 38761262 DOI: 10.1007/s11356-024-33608-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/04/2024] [Indexed: 05/20/2024]
Abstract
Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.
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Affiliation(s)
- Biplab Banerjee
- Department of Geography, Faculty of Science, The MS University Baroda, Vadodara, India, 390002.
| | - Sudipta Kundu
- Department of Geography, Faculty of Science, CSJM University of Kanpur, Kanpur, India
| | - Rolee Kanchan
- Department of Geography, Faculty of Science, The MS University Baroda, Vadodara, India, 390002
| | - Agradeep Mohanta
- Department of Botany, Faculty of Science, The MS University Baroda, Vadodara, 390002, India
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deSouza PN, Chaudhary E, Dey S, Ko S, Németh J, Guttikunda S, Chowdhury S, Kinney P, Subramanian SV, Bell ML, Kim R. An environmental justice analysis of air pollution in India. Sci Rep 2023; 13:16690. [PMID: 37794063 PMCID: PMC10551031 DOI: 10.1038/s41598-023-43628-3] [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: 03/22/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023] Open
Abstract
Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters.
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Affiliation(s)
- Priyanka N deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA.
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India.
| | - Ekta Chaudhary
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India
| | - Sagnik Dey
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, New Delhi, India
- Centre of Excellence for Research on Clean Air, IIT Delhi, New Delhi, India
- School of Public Policy, IIT Delhi, New Delhi, India
| | - Soohyeon Ko
- Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea
| | - Jeremy Németh
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, CO, USA
| | - Sarath Guttikunda
- Transportation Research and Injury Prevention (TRIP) Centre, Indian Institute of Technology, New Delhi, 110016, India
- Urban Emissions, New Delhi, 110019, India
| | | | - Patrick Kinney
- School of Public Health, Boston University, Boston, MA, USA
| | - S V Subramanian
- Harvard Center for Population and Development Studies, Bow Street, Cambridge, MA, 02138, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | - Rockli Kim
- Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul, South Korea.
- Division of Health Policy and Management, College of Health Science, Korea University, Seoul, South Korea.
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Tomar G, Nagpure AS, Jain Y, Kumar V. High-Resolution PM 2.5 Emissions and Associated Health Impact Inequalities in an Indian District. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:2310-2321. [PMID: 36730212 DOI: 10.1021/acs.est.2c05636] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Health and livelihood impacts from ambient air pollution among populations in developing countries are disproportional. These disparities are often overlooked due to a lack of information on microlevel emission data, especially in smaller cities and rural areas. The current work in an Indian district, Saharanpur, proposes the use of novel data sets to estimate microlevel emissions from air-polluting infrastructure sectors in urban and rural areas for use in pollutant transport models. Health impacts estimated based on the surface PM2.5 concentration suggest that the rate of premature deaths is 158 (95% CI: 122-163) and 143 (95% CI: 65-151) deaths per 100 000 people in urban and rural areas, respectively. Sixty-eight percent of the 6372 (95% CI: 3321-6987) annual premature deaths occurs in rural areas. Depicting higher contribution-exposure disparities among socioeconomic groups, the study observed that compared to their contribution to air pollution, low socioeconomic status (SES) groups in the region experience 6,7, 7, and 26% more premature deaths from PM2.5 exposure for industries, household cooking fuel burning, open waste burning, and transportation, respectively. The majority of disability-adjusted life years (DALYs) in the study domain are observed in economically weaker worker categories. Reduced income due to the loss of these life years will significantly impact these groups due to their dependence on daily wages for basic life necessities. Microlevel pollution mitigation policies with a focus on these inequalities are critical for promoting environmental equity and justice.
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Affiliation(s)
- Gaurav Tomar
- Centre for Rural Development & Technology, Indian Institute of Technology Delhi, New Delhi110016, India
- World Resources Institute, New Delhi110016, India
| | | | - Yash Jain
- Centre for Rural Development & Technology, Indian Institute of Technology Delhi, New Delhi110016, India
| | - Vivek Kumar
- Centre for Rural Development & Technology, Indian Institute of Technology Delhi, New Delhi110016, India
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S SK, Bagepally BS, Rakesh B. Air pollution attributed disease burden and economic growth in India: Estimating trends and inequality between states. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2022; 7:100069. [PMID: 37383938 PMCID: PMC10305879 DOI: 10.1016/j.lansea.2022.100069] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Background Air pollution is one of the major contributors to the disease burden in India after malnutrition. We examined the relation, and state-wise disparities in air pollution attributed to disease burden (APADB) concerning gross state domestic product (GSDP) and growth in motor vehicles in India. Methods We retrieved disability-adjusted life year (DALY) estimates for India due to air pollution from the Global Burden of Disease Studies, injuries, and Risk Factors Study (GBD). We examined the association between APADB with GSDP and the growth in the number of registered motor vehicles in India during the 2011 to 2019 period. Concentration indices and Lorenz curves were used to explore the variation in APADB across individual states. Findings Except for a few states, APADB is inversely proportional to GSDP. Growth in motor vehicles was also negatively correlated with the APADB in n=19 states. The concentration index explained a 47% inequality in APADB between individual states and exhibited a decline (45%) during 2019 compared to 2011. The unevenness in APADB among Indian states is evident from the analysis as the states occupying the 6th or 7th decile and above in terms of GDP, urbanization and population contribute more than 60 per cent of the total APADB. Interpretation The APADB is inversely correlated with GSDP for most of the states, and the negative correlations were conspicuous when APADB per 100,000 population was analysed. The concentration index and Lorenz revealed the presence of APADB inequality between states in terms of GSDP, population, urbanisation, and total factories. Funding Not applicable.
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Affiliation(s)
- Sajith Kumar S
- Health Technology Assessment Resource Centre, ICMR-National Institute of Epidemiology, Chennai, India
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5
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Luo Z, Shen G, Men Y, Zhang W, Meng W, Zhu W, Meng J, Liu X, Cheng Q, Jiang K, Yun X, Cheng H, Xue T, Shen H, Tao S. Reduced inequality in ambient and household PM 2.5 exposure in China. ENVIRONMENT INTERNATIONAL 2022; 170:107599. [PMID: 36323065 DOI: 10.1016/j.envint.2022.107599] [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: 06/02/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
The society has high concerns on the inequality that people are disproportionately exposed to ambient air pollution, but with more time spent indoors, the disparity in the total exposure considering both indoor and outdoor exposure has not been explored; and with the socioeconomical development and efforts in fighting against air pollution, it is unknown how the exposure inequality changed over time. Based on the city-level panel data, this study revealed the Concentration Index (C) in ambient PM2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM2.5; however, the total PM2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM2.5 exposure caused 1.13 (0.63-1.73) million premature deaths in 2019, with only 14 % were high-income population. The toughest-ever air pollution countermeasures have reduced ambient PM2.5 exposures effectively that, however, benefited the rich population more than the others. The transition to clean household energy sources significantly affected on indoor air quality improvements, as well as alleviation of ambient air pollution, resulting in notable reductions of the total PM2.5 exposure and especially benefiting the low-income groups. The negative C values decreased from 2000 to 2019, indicating a significantly reducing trend in the total PM2.5 exposure inequality over time.
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Affiliation(s)
- Zhihan Luo
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
| | - Yatai Men
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenxiao Zhang
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenjun Meng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Wenyuan Zhu
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, United Kingdom
| | - Xinlei Liu
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Qin Cheng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ke Jiang
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Xiao Yun
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Hefa Cheng
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Tao Xue
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Huizhong Shen
- College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
| | - Shu Tao
- College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; College of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China
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Sorensen C, Lehmann E, Holder C, Hu J, Krishnan A, Münzel T, Mb R, Rn S. Reducing the health impacts of ambient air pollution. BMJ 2022; 379:e069487. [PMID: 36223913 DOI: 10.1136/bmj-2021-069487] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- C Sorensen
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
- Department of Emergency Medicine, Columbia Irving Medical Center, New York, NY, USA
| | - E Lehmann
- Harvard Global Health Institute, Cambridge, MA, USA
| | - C Holder
- Department of Humanities, Health and Society, Florida International University Herbert Wertheim College of Medicine, Miami, Florida, USA
| | - J Hu
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Zhangjiang Institute, Fudan University, Shanghai, China
| | - A Krishnan
- Centre for Community Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - T Münzel
- Department of Cardiology, University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Rice Mb
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Salas Rn
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Harvard Global Health Institute, Cambridge, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
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7
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Sheoran K, Siwal SS, Kapoor D, Singh N, Saini AK, Alsanie WF, Thakur VK. Air Pollutants Removal Using Biofiltration Technique: A Challenge at the Frontiers of Sustainable Environment. ACS ENGINEERING AU 2022; 2:378-396. [PMID: 36281334 PMCID: PMC9585892 DOI: 10.1021/acsengineeringau.2c00020] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
![]()
Air pollution is
a central problem faced by industries during the
production process. The control of this pollution is essential for
the environment and living organisms as it creates harmful effects.
Biofiltration is a current pollution management strategy that concerns
removing odor, volatile organic compounds (VOCs), and other pollutants
from the air. Recently, this approach has earned vogue globally due
to its low-cost and straightforward technique, effortless function,
high reduction efficacy, less energy necessity, and residual consequences
not needing additional remedy. There is a critical requirement to
consider sustainable machinery to decrease the pollutants arising
within air and water sources. For managing these different kinds of
pollutant reductions, biofiltration techniques have been utilized.
The contaminants are adsorbed upon the medium exterior and are metabolized
to benign outcomes through immobilized microbes. Biofiltration-based
designs have appeared advantageous in terminating dangerous pollutants
from wastewater or contaminated air in recent years. Biofiltration
uses the possibilities of microbial approaches (bacteria and fungi)
to lessen the broad range of compounds and VOCs. In this review, we
have discussed a general introduction based on biofiltration and the
classification of air pollutants based on different sources. The history
of biofiltration and other mechanisms used in biofiltration techniques
have been discussed. Further, the crucial factors of biofilters that
affect the performance of biofiltration techniques have been discussed
in detail. Finally, we concluded the topic with current challenges
and future prospects.
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Affiliation(s)
- Karamveer Sheoran
- Department of Chemistry, M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana 133207, India
| | - Samarjeet Singh Siwal
- Department of Chemistry, M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana 133207, India
| | - Deepanshi Kapoor
- Department of Chemistry, M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana 133207, India
| | - Nirankar Singh
- Department of Chemistry, M. M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana 133207, India
| | - Adesh K. Saini
- Department of Biotechnology, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana 133207, India
| | - Walaa Fahad Alsanie
- Department of Clinical Laboratories Sciences, The Faculty of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Vijay Kumar Thakur
- Biorefining and Advanced Materials Research Center, Scotland’s Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh EH9 3JG, United Kingdom
- School of Engineering, University of Petroleum & Energy Studies (UPES), Dehradun 248007, Uttarakhand, India
- Centre for Research & Development, Chandigarh University, Mohali 140413, Punjab, India
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8
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Boso À, Martínez A, Somos M, Álvarez B, Avedaño C, Hofflinger Á. No Country for Old Men. Assessing Socio-Spatial Relationships Between Air Quality Perceptions and Exposures in Southern Chile. APPLIED SPATIAL ANALYSIS AND POLICY 2022; 15:1219-1236. [PMID: 35607513 PMCID: PMC9117586 DOI: 10.1007/s12061-022-09446-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 03/29/2022] [Indexed: 06/15/2023]
Abstract
An extensive body of research has been noted that many socially deprived communities tend to live in areas characterized by higher levels of outdoor air pollution. Whilst there is an expanding literature documenting this disproportionate distribution, most previous studies have taken place in the Global North, have focused with industrial or vehicle air pollution sources and have tend to ignore the complex interactions between exposures, public perceptions and social factors. In this paper, we investigate the social vulnerability to and risk perceptions of air pollution sourced from domestic heating in two Chilean cities with particularly high levels of PM2.5 during winter months. To this end, we integrate primary survey data, with geographically detailed estimates of air pollution exposures and area-level characteristics obtained from the Chilean Census. We first examine the spatial distribution of PM2.5 exposures and air quality perceptions, and subsequently explore relationships between socio-demographic characteristics, air pollution exposure, and health concerns. Our results revel evident spatial patterns of dispersion, with some neighborhoods being more polluted than others. Age and percentage of roofs in poor condition in the participant's census tract are the best predictors of PM2.5 exposure. We find no correlation between perceived and real levels of contamination. Our multivariate analysis indicates that personal perceptions of air quality are significantly associated with age, gender, family structure, and heating behaviors. Such detailed depictions provide insights into potential meaningful strategies to improve air quality and highlight the need to incorporate measures to better protect older adults.
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Affiliation(s)
- Àlex Boso
- Deparment of Social Science, Faculty of Social Science, Education and Humanities & Butamallín Research Centre for Global Change, Universidad de La Frontera, Avenida Francisco Salazar 01145, Temuco, Chile
- Department of Environment, Socio-Technical Research, CIEMAT, Avenida Complutense 40, 28040 Madrid, Spain
| | - Aner Martínez
- Department of Civil Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Avenida Francisco Salazar 01145, 4780000 Temuco, Chile
| | - Marcelo Somos
- Butamallín Research Centre for Global Change & Department of Forest Sciences, Faculty of Agriculture and Forest Sciences, Universidad de La Frontera, Avenida Francisco Salazar 01145, 4780000 Temuco, Chile
| | - Boris Álvarez
- Núcleo en Ciencias Sociales Y Humanidades & Butamallín Research Centre for Global Change, Universidad de La Frontera, Avenida Francisco Salazar 01145, Temuco, Chile
| | - Constanza Avedaño
- Department of Civil Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Avenida Francisco Salazar 01145, Temuco, Chile
| | - Álvaro Hofflinger
- Núcleo en Ciencias Sociales Y Humanidades & Butamallín Research Centre for Global Change, Universidad de La Frontera, Avenida Francisco Salazar 01145, Temuco, Chile
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Gardner-Frolick R, Boyd D, Giang A. Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:2843-2860. [PMID: 35133145 DOI: 10.1021/acs.est.1c01739] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.
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Affiliation(s)
- Rivkah Gardner-Frolick
- Department of Mechanical Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada
| | - David Boyd
- Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver V6T 1Z4, Canada
| | - Amanda Giang
- Department of Mechanical Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada
- Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver V6T 1Z4, Canada
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Liang L, Wang Z. Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18116177. [PMID: 34200515 PMCID: PMC8201052 DOI: 10.3390/ijerph18116177] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/24/2021] [Accepted: 05/29/2021] [Indexed: 01/13/2023]
Abstract
This paper systematically summarizes the hierarchical cross-regional multi-directional linkage in terms of air pollution control models implemented in the Beijing-Tianjin-Hebei urban agglomeration, including the hierarchical linkage structure of national-urban agglomeration-city, the cross-regional linkage governance of multiple provinces and municipalities, the multi-directional linkage mechanism mainly involving industry access, energy structure, green transportation, cross-regional assistance, monitoring and warning, consultation, and accountability. The concentration data of six air pollutants were used to analyze spatiotemporal characteristics. The concentrations of SO2, NO2, PM10, PM2.5, CO decreased, and the concentration of O3 increased from 2014 to 2017; the air pollution control has achieved good effect. The concentration of O3 was the highest in summer and lowest in winter, while those of other pollutants were the highest in winter and lowest in summer. The high pollution ranges of O3 diffused from south to north, and those of other pollutants decreased significantly from north to south. Finally, we suggest strengthening the traceability and process research of heavy pollution, increasing the traceability and process research of O3 pollution, promoting the joint legislation of different regions in urban agglomeration, create innovative pollution discharge supervision mechanisms, in order to provide significant reference for the joint prevention and control of air pollution in urban agglomerations.
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Affiliation(s)
- Longwu Liang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
- Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenbo Wang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
- Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
- College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence:
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11
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Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18094800. [PMID: 33946259 PMCID: PMC8124940 DOI: 10.3390/ijerph18094800] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/25/2021] [Accepted: 04/27/2021] [Indexed: 11/16/2022]
Abstract
Climate change and rapid urbanization currently pose major challenges for equitable development in megacities of the Global South, such as Delhi, India. This study considers how urban social inequities are distributed in terms of burdens and benefits by quantifying exposure through an urban heat risk index (UHRI), and proximity to greenspace through the normalized difference vegetation index (NDVI), at the ward level in Delhi. Landsat derived remote sensing imagery for May and September 2011 is used in a sensitivity analysis of varying seasonal exposure. Multivariable models based on generalized estimating equations (GEEs) reveal significant statistical associations (p < 0.05) between UHRI/NDVI and several indicators of social vulnerability. For example, the proportions of children (β = 0.922, p = 0.024) and agricultural workers (β = 0.394, p = 0.016) are positively associated with the May UHRI, while the proportions of households with assets (β = -1.978, p = 0.017) and households with electricity (β = -0.605, p = 0.010) are negatively associated with the May UHRI. In contrast, the proportions of children (β = 0.001, p = 0.633) and agricultural workers (β = 0.002, p = 0.356) are not significantly associated with the May NDVI, while the proportions of households with assets (β = 0.013, p = 0.010) and those with electricity (β = 0.008, p = 0.006) are positively associated with the May NDVI. Our findings emphasize the need for future research and policies to consider how socially vulnerable groups are inequitably exposed to the impact of climate change-related urban heat without the mitigating effects of greenspace.
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