Air Quality and Environmental Injustice in India: Connecting Particulate Pollution to Social Disadvantages

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling

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.

An environmental justice analysis of air pollution in India

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.

High-Resolution PM2.5 Emissions and Associated Health Impact Inequalities in an Indian District

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 PM_2.5 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 PM_2.5 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.

Air pollution attributed disease burden and economic growth in India: Estimating trends and inequality between states

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 6^th or 7^th 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.

Reduced inequality in ambient and household PM2.5 exposure in China

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 PM_2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM_2.5; however, the total PM_2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM_2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 exposure inequality over time.

Air Pollutants Removal Using Biofiltration Technique: A Challenge at the Frontiers of Sustainable Environment

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.

No Country for Old Men. Assessing Socio-Spatial Relationships Between Air Quality Perceptions and Exposures in Southern Chile

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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework

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.

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

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, PM_2.5, 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.

Social Inequities in Urban Heat and Greenspace: Analyzing Climate Justice in Delhi, India

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.