Characterization of microplastics in outdoor and indoor air in Ranchi, Jharkhand, India: First insights from the region.

The study focused on detecting and characterizing microplastics in outdoor and indoor air in Ranchi, Jharkhand, India during post-monsoon (2022) and winter (2023). Stereo microscopic analysis showed that plastic fibres had a dominant presence, fragments were less abundant, whereas fewer films could be detected in indoor and outdoor air. The atmospheric deposition of microplastics outdoors observed 465 ± 27 particles/m2/day in PM10 and 12104 ± 665 and 13833 ± 1152 particles/m2/day in PM2.5 in quartz and PTFE, respectively during the post-monsoon months. During winter, microplastic deposition rates in PM10 samples were found to be 689 ± 52 particles/m2/day and 19789 ± 2957 and 30087 ± 13402 in quartz and PTFE particles/m2/day respectively in PM2.5. The mean deposition rate in indoor environment during post-monsoon was 8.3 × 104 and 1.03 × 105 particles/m2/day in winter. During the post-monsoon period in PM10, there were fibres from 7.7 to 40 µm and fragments from 2.3 µm to 8.6 µm. Indoor atmospheric microplastics, fibres ranged from 1.2 to 47 µm and fragments from 0.9 to 16 µm present respectively during the post-monsoon season. Fibres and fragment sizes witnessed during winter were 3.6-6.9 µm and 2.3-34 µm, respectively. Indoor air films measured in the range of 4.1-9.6 µm. Fourier transform infrared analysis showed that outdoor air contained polyethylene, polypropylene, Polystyrene, whereas indoor air had polyvinyl chloride. Polyethylene mainly was present in outdoor air, with lesser polypropylene and polystyrene than indoors, where polyvinyl chloride and polyethylene were in dominant proportions. Elemental mapping of outdoor and indoor air samples showed the presence of elements on the microplastics. The HYSPLIT models suggest that the particles predominantly were coming from North-West during the post-monsoon season. Principal component analysis indicated wind speed and direction influencing the abundance of microplastics. Microplastics concentration showed strong seasonal influence and potential to act as reservoir of contaminants.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India.

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 µg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 µg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 µg m-3), which showed better output due to the frequent observation of PM2.5 >100 µg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 µg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Characterization of fine particulate matter water-soluble inorganic ions and estimation of aerosol acidity at three COALESCE network sites - Mysuru, Bhopal, and Mesra - in India.

The study was carried out to understand the chemical, spatiotemporal characteristics of water-soluble inorganic ions (WSIIs), their association with PM2.5 mass, and aerosol acidity in three COALESCE (carbonaceous aerosol emissions, source apportionment, and climate impacts) network sites of India (Mesra - Eastern India, Bhopal - Central India and Mysuru - Southern India). Alternate-day 24-h integrated bulk PM2.5 samples were collected during 2019 along with on-site meteorological parameters. Annual average PM2.5 concentrations were 67 ± 46 µg m-3, 54 ± 47 µg m-3, and 30 ± 24 µg m-3 at Mesra, Bhopal, and Mysuru, respectively. PM2.5 concentrations exceeded the annual mean (40 µg m-3) recommended by the National Ambient Air Quality Standards (NAAQS) at Mesra and Bhopal. WSIIs existed in PM2.5 mass at Mesra (50.5%), Bhopal (39.6%), and Mysuru (29.2%). SO42-, NO3-, and NH4+ (SNA) were major secondary inorganic ions in total WSIIs, with an annual average of 88.4% in Mesra and 82.0% in Bhopal 78.4% in Mysuru. Low NO3-/SO42- ratios annually at Mesra (0.41), Bhopal (0.44), and Mysuru (0.24) indicated that stationary sources dominated vehicular emissions (1.0). Aerosol acidity varied from region to region and season to season depending on the presence of NH4+, the dominant counter-ion to neutralize anions. Aerosols were near-neutral or alkaline at all three sites, except during the pre-monsoon season in Mysuru. An assessment of neutralization pathways for major anions [SO42- + NO3-] suggests that they mainly existed as sulfate and nitrate salts such as ammonium sulfate ((NH4)2SO4) and ammonium bisulfate (NH4HSO4) in conjunction with ammonium nitrate (NH4NO3).

Reassessing the availability of crop residue as a bioenergy resource in India: A field-survey based study.

Second-generation bioenergy, a carbon neutral or negative renewable resource, is crucial to achieving India's net-zero emission targets. Crop residues are being targeted as a bioenergy resource as they are otherwise burned on-field, leading to significant pollutant emissions. But estimating their bioenergy potential is problematic because of broad assumptions about their surplus fractions. Here, we use comprehensive surveys and multivariate regression models to estimate the bioenergy potential of surplus crop residues in India. These are with high sub-national and crop disaggregation that can facilitate the development of efficient supply chain mechanisms for its widespread usage. The estimated potential for 2019 of 1313 PJ can increase the present bioenergy installed capacity by 82% but is likely insufficient alone to meet India's bioenergy targets. The shortage of crop residue for bioenergy, combined with the sustainability concerns raised by previous studies, imply a need to reassess the strategy for the use of this resource.

Pollution characteristics and ecological risks of trace elements in PM2.5 over three COALESCE network sites - Bhopal, Mesra, and Mysuru, India.

Time-synchronized, 24 h integrated PM2.5 trace element (TE) measurements made as a part of the COALESCE project (Venkataraman et al., 2020) at Bhopal, Mesra, and Mysore during all of 2019 were analyzed in this study. The concentrations of 15 key elements ranged between 0.05 ng m-3 and 50 µg m-3 across the study sites. Pronounced seasonal variation of elements from multiple source classes showed that the crustal origin elements (Al, Si, Fe, Ti, and Ca) peaked during the pre-monsoon season, while the anthropogenic activities driven element (P, S, K, V, Mn, Cu, Zn, and Pb) concentrations increased during the winter and post-monsoon seasons. Spearman correlation coupled with hierarchical clustering separated the matrix of elements into three common clusters at all sites, corresponding to crustal sources, combustion and biomass burning emissions, and industrial/non-exhaust vehicular emissions, respectively. Furthermore, episodes of metal pollution throughout the year were examined using characteristic radar charts of TEs to identify the association between TE sources and poor air quality. For example, maximum metal pollution in Bhopal occurred during the post-monsoon season, attributable to biomass burning, dust storms, industrial and non-exhaust vehicular emissions. Finally, an ecological risk assessment revealed that the risk index was higher than the threshold value of 600 for all heavy metals at all sites. Pb, Cu, and Zn were the top contributors to 'extremely high risk' amongst all heavy metals. Overall, the results show that although TE concentrations at all three locations were much lower than in other urban locations in India, the risk from heavy metals to the ecosystem (and likely to human health) cannot be ignored. The findings warrant a full source apportionment of fine PM to better identify TE-rich source contributions and future studies to examine the atmospheric processing and eco-system uptake of TEs.