Emission sources influencing high particulate air pollution levels and related mortality in India have been studied earlier on country‐wide and sub‐national scales. Here, we use novel data sets of emissions (for 2019) and observations created under the Carbonaceous Aerosol Emissions, Source Apportionment, and Climate Impacts network in India (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1) in WRF‐Chem simulations to evaluate drivers of high PM2.5 levels during episodes and in airsheds with different pollution levels. We identify airsheds in “extreme” (110–140 μg/m3), “severe” (80–110 μg/m3) and “significant” (40–80 μg/m3) exceedance of the Indian annual ambient air quality standard (National Ambient Air Quality Standards [NAAQS]) of 40 μg/m3 for PM2.5. We find that primary organic matter and anthropogenic mineral matter (largely coal fly‐ash) drive high PM2.5 levels, both annually and during high PM2.5 episodes. PM2.5 episodes are driven by organic aerosol in north India (Mohali) in wintertime but are additionally influenced by mineral matter and secondary inorganics in central (Bhopal), south India (Mysuru) and eastern India (Shyamnagar). Across airsheds in exceedance of the NAAQS and during high PM2.5 episodes, primary PM2.5 emissions arise largely from the residential sector (50%–75%). Formal sector emissions (industry, thermal power and transport; 40%–55%) drive airshed and episode scale PM2.5 exceedance in northern and eastern India. Agricultural residue burning emissions predominate (50%–75%) on episode scales, both in northern and central India, but not on annual scales. Interestingly, residential sector emissions strongly influence (60%–90%) airsheds in compliance with the NAAQS (annual mean PM2.5 < 40 μg/m3), implying the need for modern residential energy transitions for the reduction of ambient air pollution across India.

Low cost particulate matter sensors are receiving significant attention as they can be used in large number for spatial and temporal measurement of PM mass and number concentration. However, the data reliability is questionable as these sensors are affected by numerous parameters such as temperature, relative humidity, and hygroscopicity of particles. To ensure accurate and reliable measurement of particulate matter concentrations, performance evaluation and calibration of LCS co-located with reference instrument at site is essential. In this study, the performance of the low-cost sensor (APT Maxima) was evaluated with the reference instrument SASS (Speciation Air Sampling System) sampler for developing suitable calibration factor that include impact of the meteorological parameter such as relative humidity and temperature, and hygroscopicity. In this study, we have demonstrated a systematic physics-based method for calibration of LCS based on κ-Köhler theory and Mie theory. For comparison with statistical models, linear regression and machine learning algorithm were also applied. In physics-based model, calculated total light scattered intensity shows good linearity with reference PM2.5 measurements. Physics based model performed better for both the sites as compared to MLR, kNN, RF, and GB ML algorithms with R2, RMSE, and MAE values of 0.72,18.21, and 13.36 for Bhopal site, and 0.91, 7.84, and 5.76 for Kashmir site, respectively. Study indicates that physics-based approach for LCS calibration is suitable and can be transferable to different sites.

COALESCE, a collaborative network studying the impacts of carbonaceous aerosols, measured PM2.5 mass and its constituents at several regionally representative sites in India. This study reconciles the reconstructed mass (RCM) from PM2.5 chemical constituents with gravimetric PM(2.5 )mass measured during 2019 at three COALESCE sites (Bhopal, Mesra, and Mysuru). To evaluate the extent of mass closure, the spatiotemporal variability of organic matter/organic carbon (OM/OC), filter sampling artifacts of OC, nitrate, and aerosol liquid water (ALW) were assessed. The annual loss of total particulate OC from Teflon filters varied across Bhopal (0.46-1.35 mu g m(-3)), Mesra (0.30-0.92 mu g m(-3)), and Mysuru (0.16-0.68 mu g m(-3)). Likewise, the range of nitrate volatilization was 0.7-2.7 mu g m(-3) at Bhopal, 0.5-2 mu g m(-3) at Mesra, and 1.3-2.2 mu g m(-3)at Mysuru. Also, the range of ALW mass was 2.6-6.8 mu g m(-3) at Bhopal, 3.2-9.5 mu g m(-3) at Mesra, and 0.8-3.8 mu g m(-3) at Mysuru. Seasonality was observed in the value of OM/OC with an increase during the pre-monsoon season at all sites viz., Bhopal (2.20), Mesra (1.61), and Mysuru (2.02) compared to their annual averages, suggesting enhanced contribution of secondary organics to OC. The method developed for estimating RCM in this study resulted in better mass closure compared to using conventional RCM algorithms. Overall, this study provides a robust data validation strategy and India-specific species coefficients for use in the RCM algorithm. The outcomes of this study can guide regional aerosol sampling, chemical analyses, and model-measurement reconciliation efforts.

&lt;p&gt;Fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) is one of the major atmospheric components that is responsible for poor air quality and adverse health and climate effects. An identification of both primary emission sources as well as secondary formation mechanisms of PM&lt;sub&gt;2.5&lt;/sub&gt; is important to develop effective and efficient strategies to control and mitigate these adverse effects.&lt;/p&gt; &lt;p&gt;The COVID-19 pandemic had a significant impact on air quality across the globe through reduction in source emissions. This study examines the impact of the lockdown measures on PM&lt;sub&gt;2.5&lt;/sub&gt; and its chemical composition in Bhopal, India by comparisons with pre-lockdown period. Positive Matrix Factorization (PMF) (Paatero and Tapper, 1994) is the most widely used approach for factor analysis-based source apportionment studies. In this study the EPA PMF program version 5.0, was used to solve the PMF model. A comprehensive suite of instruments was used to measure the 24-hour integrated PM&lt;sub&gt;2.5&lt;/sub&gt; mass and its chemical composition collected onto various filter substrates every other day for two years (2019-2020) at Bhopal. This period coincides with the pre-lockdown, lockdown, and post-lockdown phases in India. The mass concentrations during the study period ranged between 54.7 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; during pre-lockdown and 45.1 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; in lockdown phase. PMF5 was applied to a dataset of organic and elemental carbon fractions (OC1, OC2, OC3, OC4, OP, EC1, EC2, EC3), nine major water-soluble inorganic components namely F&lt;sup&gt;-&lt;/sup&gt;,Cl&lt;sup&gt;-&lt;/sup&gt;, NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;- &lt;/sup&gt;,SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;-2&lt;/sup&gt;,Na&lt;sup&gt;+&lt;/sup&gt;, NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;,Mg&lt;sup&gt;+2&lt;/sup&gt;, K&lt;sup&gt;+&lt;/sup&gt;, Ca&lt;sup&gt;+2&lt;/sup&gt;, and elements (Al, Mg, Ca, Si, P, K, V, Ti, Co, Ni, Cu, As, Cr, Cd, Fe, Ni, Zn, Se, Sb, Ba, Pb) were used in the analysis.&lt;/p&gt; &lt;p&gt;Overall, the combined datasets (2019-2020) approach helped in better model resolution as several zeroes were present in both the loading and score matrices compared to a model run with 2019 data alone. An 8-factor solution was resolved with factors identified as coal and gasoline combustion, biomass burning, secondary sulfate, secondary nitrate, re-suspended crustal dust, diesel emissions, brick kiln emissions and mixed industrial emissions. Further, assessment of the pre-COVID and lockdown scenarios revealed a decreased in the mass contribution of diesel emissions (21.3%), mixed industrial emissions (13.7%), secondary sulfate (10.6%) and secondary nitrate (4.7%) during the lockdown phase compared to the pre-lockdown period at the study site. However, there was no decrease in the biomass burning source contribution due to no curbs on agricultural activities during the lockdown period in India. Overall, this study provides key insight into the source composition and contribution variations due to the reduction of specific anthropogenic source emissions due to COVID-19 lockdowns. Further, it is an added impetus for policymakers to implement targeted strategies and regulations, to reduce local and regional air pollution.&lt;/p&gt;

BackgroundParticulate matter (PM2.5 and PM10) linked heavy metals exposures have been associated with an increased risk of cardiovascular and respiratory diseases. Biomonitoring of human hair and nail can be instrumental in quantifying the toxicological effects of such heavy metals, owing to non-invasive nature. The present study aims to examine heavy metals in human hair and nails in adult men and male children and to investigate the factors affecting their increased levels.MethodHair and nails samples (N=40) were collected from consenting men (25-70 years) and male children (5-14 years) from four different neighborhoods in urban Mumbai (Borivali, Powai, Chembur, and Mulund). Using Inductively Coupled Plasma-Mass Spectrometry (ICPMS), heavy metals such as Lead, Cadmium, Arsenic, Calcium, Zinc, Nickel, Manganese and Iron were quantified after acid-assisted microwave digestion. A structured questionnaire was used to collect information on demography, commute behavior, occupational hours and type of work, medical history, and dietary intake.Results Out of the trace metals studied, Pb emerged as the dominant metal, followed by Mn, As, Cd and Ni. The mean levels of Pb were found to be highest in Powai (0.02769±0.000997 ug/g of hair). Across all the sites, near-road heavy metal concentrations were significantly higher than those away from the road [Pb (36.23% higher), Cd (10.41% higher), and Ni (6.54% higher)]. Our results suggest that ambient air pollution and vehicular sources are responsible for heavy metals exposure in urban areas. ConclusionThis novel study can bridge the gap between knowledge of metal toxicity and its quantifiable exposures in the human population as a result of particulate matter pollution in urban areas. It will not only help in understanding the extent of heavy metal exposures but will also help to expand the current repository of information on the heavy metal fraction of particulate matter and its stark detrimental effects on human health.