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Urban air pollution is a complex problem, which requires a multi-pronged approach to understand its dynamics. In the current study, various aspects of air pollution over Bengaluru city were studied utilizing simultaneous reference-grade measurements (during the period July 2019 to June 2020) of fine particulate matter mass concentration (PM 2.5 ), aerosol black carbon mass concentrations (BC), and surface ozone (O 3 ) concentrations. The study period mean PM 2.5 , BC, and O 3 were observed to be 26.8 ± 11.5 µg m -3 , 5.6 ± 2.8 µg m -3 , and 25.5 ± 12.4 ppb, respectively. Statistical methods such as principal component analysis, moving average subtraction method, conditional bivariate probability function, and concentration weighted trajectory analysis were performed to understand the dynamics of air pollution over Bengaluru and its long-range transportation pathways. Some of the major findings from the statistical analyses include (i) contrasting association in BC versus O 3 and PM 2.5 versus O 3 ; (ii) around one-fourth of the observed receptor site BC was contributed by local sources/emissions; and (iii) the source locations potentially contributing to BC and PM 2.5 were spatially different. In Bengaluru, long-term exposure to PM 2.5 resulted in around 3413, 3393, 1016, and 147 attributable deaths for the health endpoints chronic obstructive pulmonary disorder, ischemic heart disease, stroke, and lung cancer, respectively. Long-term exposure to O 3 resulted in around 155 attributable deaths for respiratory diseases, as estimated by the AirQ + model. Finally, the limitations of the study in terms of data availability and analysis have been detailed., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Data regarding which microenvironments drive exposure to air pollution in low and middle income countries are scarce. Our objective was to identify sources of time-resolved personal PM 2.5 exposure in peri-urban India using wearable camera-derived microenvironmental information. We conducted a panel study with up to 6 repeated non-consecutive 24 h measurements on 45 participants (186 participant-days). Camera images were manually annotated to derive visual concepts indicative of microenvironments and activities. Men had slightly higher daily mean PM 2.5 exposure (43 μg/m 3 ) compared to women (39 μg/m 3 ). Cameras helped identify that men also had higher exposures when near a biomass cooking unit (mean (sd) μg/m 3 : 119 (383) for men vs 83 (196) for women) and presence in the kitchen (133 (311) for men vs 48 (94) for women). Visual concepts associated in regression analysis with higher 5-minute PM 2.5 for both sexes included: smoking (+93% (95% confidence interval: 63%, 129%) in men, +29% (95% CI: 2%, 63%) in women), biomass cooking unit (+57% (95% CI: 28%, 93%) in men, +69% (95% CI: 48%, 93%) in women), visible flame or smoke (+90% (95% CI: 48%, 144%) in men, +39% (95% CI: 6%, 83%) in women), and presence in the kitchen (+49% (95% CI: 27%, 75%) in men, +14% (95% CI: 7%, 20%) in women). Our results indicate wearable cameras can provide objective, high time-resolution microenvironmental data useful for identifying peak exposures and providing insights not evident using standard self-reported time-activity., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM 2.5 ) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM 2.5 concentrations during June 2015-May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM 2.5 concentration was ∼30 μg m -3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m -3 ) during 2-5% of observation days. Average concentrations were ∼25 μg m -3 higher during winter than during monsoon and ∼8 μg m -3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM 2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM 2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, wearable cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of wearable camera data in environmental exposure assessment., (Copyright © 2017 Elsevier GmbH. All rights reserved.)

• Significant reduction in BC is observed over Bengaluru during the COVID-19 lockdown. • Reduction in daily PM 2.5 is not statistically significant in all the study sites. • Percentage reduction in PM 2.5 varied spatially within the city. • Reduction in total BC is dictated by the decrease in BC from fossil fuel burning. Leveraging the COVID-19 India-wide lockdown situation, the present study attempts to quantify the reduction in the ambient fine particulate matter concentrations during the lockdown (compared with that of the pre-lockdown period), owing to the highly reduced specific anthropogenic activities and thereby pollutant emissions. The study was conducted over Bengaluru (India), using PM 2.5 (mass concentration of particulate matter having size less than or equal to 2.5 µm) and Black Carbon mass concentration (BC) data. Open-access datasets from pollution control board (PCB) were also utilised to understand the spatial variability and region-specific reduction in PM 2.5 across the city. The highest percentage reduction was observed in BC ff (black carbon attributable to fossil fuel combustion), followed by total BC and PM 2.5. No decrease in BC bb (black carbon attributable to wood/biomass burning) was observed, suggesting unaltered wood-based cooking activities and biomass-burning (local/regional) throughout the study period. Results support the general understanding of multi-source (natural and anthropogenic) nature of PM 2.5 in contrast to limited-source (combustion based) nature of BC. The diurnal amplitudes in BC and BC ff were reduced, while they remained almost the same for PM 2.5 and BC bb. Analysis of PCB data reveal the highest reduction in PM 2.5 in an industrial cluster area. The current lockdown situation acted as a natural model to understand the role of a few major anthropogenic activities (viz., traffic, construction, industries related to non-essential goods, etc.) in enhancing the background fine particulate matter levels. Contemporary studies reporting reduction in surface fine particulate matter and satellite retrieved columnar Aerosol Optical Depth (AOD) during COVID-19 lockdown period are discussed. [ABSTRACT FROM AUTHOR]

High-resolution air pollution maps can help researchers and governments better characterize and mitigate pollution levels in a given region. Land Use Regression (LUR) modeling is a statistical approach capable of predicting pollution levels at a high spatial resolution. In this study, we used pollution data (for the calendar year 2019) from a dense (compared to other Indian regions) regulatory monitoring network in Delhi, India, to develop simple linear and interpretable LUR models for various criteria pollutants. The observed annual mean PM 2.5 and PM 10 over Delhi were found to be ∼110 μg m−3 and 220 μg m−3, respectively. The PM concentration levels were 2.5–4 times higher than the prescribed national ambient standards, while the gaseous criteria pollutants were found to be within the standards (over most of the study area). The performance of the developed LUR models ranged from poor to moderate levels (adjusted-R2 values of the models were between 0.14 and 0.63). Land use and road-network related variables were found to be the most common predictors of the observed pollution levels. Moderately performing models (11 out of the developed 20) were then used to predict pollution levels at 50 m spatial intervals and to identify the most polluted districts. The advantages and limitations of using the existing regulatory network data for LUR development, and the other probable potential reasons responsible for the underperformance of the developed models are extensively discussed. To our knowledge, this is one of the few studies carried over Indian region to develop LUR models utilizing regulatory monitoring network data. • Use of regulatory data for building Land Use Regression (LUR) models for Delhi, India. • The LUR models explained variance ranged between 14% and 63% across various pollutants. • Highly polluted districts identified based on the ultra-high resolution spatial mapping. [ABSTRACT FROM AUTHOR]

Investigations on the characteristics of air pollution in African regions are fewer compared to other parts of the world. This study presents the temporal characteristics of PM 2.5 and PM 10 (mass concentration of particulate matter with aerodynamic size less than or equal to 2.5 and 10 µm, respectively) and their ratio (PM 2.5 /PM 10) over Durban, South Africa for the period 2019–2020. The study further explores the satellite aerosol optical depth (AOD) and particulate matter (PM) relationship using an advanced statistical model. The PM data used in this study is obtained from ground-based monitoring stations being maintained by the South African Air Quality Informative System (SAAQIS) network. Within Durban, PM 2.5 and PM 10 showed an increasing trend toward the south. The annual mean PM 2.5 (PM 10) ranged between 15 and 24 μg m−3 (37 and 54 μg m−3) across the four study sites in Durban. Winter is observed to be the most polluted season among all, with daily mean PM levels exceeding the World Health Organization (WHO) standards. PM 2.5 , PM 10 , and the PM 2.5 /PM 10 ratios exhibited pronounced diurnal variations. Concentration Weighted Trajectory (CWT) analysis identified the potential non-local source regions and long-range transportation pathways of PM. Investigation into the impact of COVID-19 lockdown on PM revealed a maximum of 15% reduction compared to a business-as-usual (BAU) year. The observed reduction in PM during lockdown is compared and contrasted with that observed in other cities worldwide. The statistical model's tenfold cross-validation (CV) coefficient of determination (R2) ranged between 0.6 and 0.8. The model predictions can be highly useful in the spatial mapping of PM over the measurements-deficit African regions. [ABSTRACT FROM AUTHOR]

• PM 2.5 /PM 10 ratio characteristics over India using multi-site and multi-year data. • Suppressed PM 2.5 /PM 10 ratio variations compared to that observed in PM concentrations. • MERRA-2 derived PM 2.5 /PM 10 ratio found to be overestimating during colder months. • Weak relationship between the PM 2.5 /PM 10 ratio and meteorological parameters. The PM 2.5 /PM 10 ratio (PM 2.5 and PM 10 are defined as mass concentration of particles having aerodynamic diameter less than 2.5 and 10 µm respectively) is one of the important parameters in understanding the severity of the fine mode surface particulate matter pollution. The present study characterises PM 2.5 /PM 10 ratio estimates from eight Indian urban sites with varying levels of urbanization. Five years (2015–2019) of collocated PM 2.5 , PM 10 , and meteorological (ambient temperature, relative humidity (RH), and wind speed) measurements are used to understand the spatial and temporal variability in the PM 2.5 /PM 10 ratio at different scales and to investigate its relationship with meteorological parameters. Over the study sites, the seasonal mean PM 2.5 /PM 10 ratio varied between 0.31 ± 0.08 (mean ± standard deviation) and 0.65 ± 0.13. Seasonally, the highest PM 2.5 /PM 10 ratio was observed during winter and post-monsoon seasons. Sites in the Indo-Gangetic Plain (IGP) exhibited higher PM levels (PM 2.5 and PM 10) and higher PM 2.5 /PM 10 ratios than the corresponding values recorded at other sites. The seasonal mean PM 2.5 /PM 10 ratio estimated (over the study sites) using MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) ranged between 0.25 ± 0.08 and 0.77 ± 0.16, and exhibited consistent overestimation (when compared to values derived from measurements) during winter and pre-monsoon seasons. Grossly, the PM 2.5 /PM 10 ratio exhibited a weak association with meteorological parameters. Interestingly, despite variations in geography, population, anthropogenic activities and PM concentrations across seasons and sites, the PM 2.5 /PM 10 ratio showed low variability. [ABSTRACT FROM AUTHOR]

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM 2.5 ) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) μg/m 3 for PM 2.5 and 2.7 (0.5) μg/m 3 for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM 2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM 2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies. [ABSTRACT FROM AUTHOR]