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Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM 10 : 318.65 ± 45.80 µg/m 3 ) and 2023 (PM 10 : 383.70 ± 61.49 µg/m 3 ), compared to 2008 (PM 10 : 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM 10 and urban indices (+ 0.63) and a negative correlation between PM 10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study attempts to examine the morphological, elemental and physical characteristics of PM 10 over the Indian Himalayan Region (IHR) using FTIR and scanning electron microscopy-energy dispersive X-ray (SEM-EDX) analysis. The study aimed at source identification of PM 10 by exploring the inorganic ions, organic functional groups, morphology and elemental characteristics. The pollution load of PM 10 was estimated as 63 ± 22 μg m -3 ; 53 ± 16 μg m -3 ; 67 ± 26 μg m -3 and 55 ± 11 μg m -3 over Mohal-Kullu, Almora, Nainital and Darjeeling, respectively. ATR-FTIR spectrum analysis revealed the existence of inorganic ions (SiO 4 4- , TiO 2 , SO 4 2- , SO 3 - , NO 3 - , NO 2 - , CO 3 2- , HCO 3 - , NH 4 + ) and organic functional groups (C-C, C-H, C=C, C≡C, C=O, N-H, C≡N, C=N, O-H, cyclic rings, aromatic compounds and some heterogeneous groups) in PM 10 which may arise from geogenic, biogenic and anthropogenic sources. The morphological and elemental characterization was performed by SEM-EDX, inferring for geogenic origin (Al, Na, K, Ca, Mg and Fe) due to the presence of different morphologies (irregular, spherical, cluster, sheet-like solid deposition and columnar). In contrast, particles having biogenic and anthropogenic origins (K, S and Ba) have primarily spherical with few irregular particles at all the study sites. Also, the statistical analysis ANOVA depicts that among all the detected elements, Na, Al, Si, S and K are site-specific in nature as their mean of aw% significantly varied for all the sites. The trajectory analysis revealed that the Uttarakhand, Jammu and Kashmir, the Thar Desert, Himachal Pradesh, Pakistan, Afghanistan, Nepal, Sikkim, the Indo-Gangetic Plain (IGP) and the Bay of Bengal (BoB) contribute to the increased loading of atmospheric pollutants in various locations within the IHR., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Air pollution is one of the main environmental issues in densely populated urban areas like Delhi. Predictions of the PM2.5 concentration must be accurate for pollution reduction strategies and policy actions to succeed. This research article presents a novel approach for forecasting PM2.5 pollution in Delhi by combining a pre-trained CNN model with a transformer-based model called CATALYST (Convolutional and Transformer model for Air Quality Forecasting). This proposed strategy uses a mixture of the two models. To derive attributes of the PM2.5 timeline of data, a pre-existing CNN model is utilized to transform the data into visual representations, which are analyzed subsequently. The CATALYST model is trained to predict future PM2.5 pollution levels using a sliding window training approach on extracted features. The model is utilized for analyzing temporal dependencies in PM2.5 time-series data. This model incorporates the advancements in the transformer-based architecture initially designed for natural language processing applications. CATALYST combines positional encoding with the Transformer architecture to capture intricate patterns and variations resulting from diverse meteorological, geographical, and anthropogenic factors. In addition, an innovative approach is suggested for building input-output couples, intending to address the problem of missing or partial data in environmental time-series datasets while ensuring that all training data blocks are comprehensive. On a PM2.5 dataset, we analyze the proposed CATALYST model and compare its performance with other standard time-series forecasting approaches, such as ARIMA and LSTM. The outcomes of the experiments demonstrate that the suggested model works better than conventional methods and is a potential strategy for accurately forecasting PM2.5 pollution. The applicability of CATALYST to real-world scenarios can be tested by running more experiments on real-world datasets. This can help develop efficient pollution mitigation measures, impacting public health and environmental sustainability., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Recent advancements in environmental monitoring and analysis have created public and institutional awareness on the social and health impacts of air pollution at public places of tourists' attraction. Monuments stand as the celebrated remnants of bygone representations in the social and cultural tradition of any civilised state. India, being one of the oldest and live civilisations, owns numerous places of historical evidences in the form of both constructed museums and living monuments such as temples and palaces. Continuous exposure to the emission of gaseous and particulate pollutants has made remarkable evidences of damage to the artefacts and monumental structures located in major cities of the world. The aim of this study is to present an overview of the scientific attempts pertaining to the evaluation of impacts of air pollution and other meteorological changes on the historical monuments in India in the context of the global scenario. It is observed that seasonal fluctuations in the outdoor climate and increased human activities in the vicinity of the museums have plausible impacts on the immediate changes in the indoor air quality. The variations in the outdoor air quality are greatly affected by the traffic emissions and industrial emissions, while the indoor air quality is mostly affected by the improper ventilation and lack of proper control measures. The study provides strategies for developing air quality standards for museum environment and proposes a few technical and administrative solutions to improve the air quality for indoor museums as well as outdoor monuments., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The driving pattern of three-wheeled auto-rickshaws is governed by commuter's demands on certain fixed roads that offer flexible mobility solutions in a mid-size city. This flexibility creates unique driving patterns, frequent stop-and-go conditions, frequent acceleration, braking, and excessive idling, which affect emission rates. Existing emission testing regulation based on the driving cycle does not represent real-world conditions. In this paper, the real-world driving cycle of three-wheeled auto-rickshaw has been developed to provide realistic CO, HC, and NO X pollutants and see the effect of introducing modal shift of electric auto-rickshaw to reduce emission for India. Two policy scenarios were evaluated (1) with a 5% modal shift to electric auto-rickshaw, and (2) without modal shift. The results indicate that with a 5% shift to electric auto-rickshaw, by 2030, emissions will decrease by 6.30% compared to the baseline scenario. Further, by 2030, the projected CO emission would be 1,696,670 ton/year, and HC and NO X emissions would be 2,067,371 ton/year. Results can be useful for policy interventions towards cleaner fuel and the aggressive adoption for reducing pollution from auto-rickshaw., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Stubble burning is an emerging environmental issue in Northern India, which has severe implications for the air quality of the region. Although stubble burning occurs twice during a year, first during April-May and again in October-November due to paddy burning, the effects are severe during October-November months. This is exacerbated by the role of meteorological parameters and presence of inversion conditions in the atmosphere. The deterioration in the atmospheric quality can be attributed to the emissions from stubble burning which can be perceived from the changes observed in land use land cover (LULC) pattern, fire events, and sources of aerosol and gaseous pollutants. In addition, wind speed and wind direction also play a role in changing the concentration of pollutants and particulate matter over a specified area. The present study has been carried out for the states of Punjab, Haryana, Delhi, and western Uttar Pradesh to study the influence of stubble burning on the aerosol load of this region of Indo-Gangetic Plains (IGP). In this study, the aerosol level, smoke plume characteristics, long-range transport of pollutants, and affected areas during October-November from year 2016 to 2020 were examined over the Indo-Gangetic Plains (Northern India) region by the satellite observations. By MODIS-FIRMS (Moderate Resolution Imaging Spectroradiometer-Fire Information for Resource Management System) observations, it was revealed that there was an increase in stubble burning events with the highest number of events being observed during the year 2016 and then a decrease in the number of events in subsequent years from 2017 to 2020. MODIS observations revealed a strong AOD gradient from west to east. The prevailing north-westerly winds assist the spread of smoke plumes over Northern India during the peak burning season of October to November. The findings of this study might be used to expand on the atmospheric processes that occur over northern India during the post-monsoon season. The pollutant, smoke plume features, and impacted regions of biomass-burning aerosols in this region are critical for weather and climate research, especially given the rising trend in agricultural burning over the previous two decades., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This research paper examines the exposure to particulate matter (PM) and its deposition on the human respiratory tract (HRT) in 12 critical urban zones - institutional zone, commercial zone, construction zone, hospital zone, landfill zone, industrial zone, residential zone, high-traffic zone, main roads, medium-traffic zone secondary roads, low-traffic zone, coastal zone, and environmentally sensitive zone. This study measured the size-segregated PM concentrations using a Grimm aerosol spectrometer. The multiple-path particle dosimetry model assesses particles' total and regional deposition mass rates for different urban zones. A stochastic model of the 60th percentile is used to illustrate the deposition of PM in the human lung. The deposition rate of PM in the HRT is examined for the different urban zones from different emission sources. The analysis shows that the PM concentration in zone V (dumpsite zone) is at an elevated level (i.e., PM 10  = 570.4 µg/m 3 , PM 2.5  = 128.3 µg/m 3 , and PM 1  = 28.1 µg/m 3 ) and lowest at zone XII (eco-sensitive zone) (i.e., PM 10  = 25.1 µg/m 3 , PM 2.5  = 1 6.9 µg/m 3 , and PM 1  = 14.8 µg/m 3 ). Further, dumpsite, commercial, and eco-sensitive zones are identified to be critical zones that influence higher deposition in the tracheobronchial and pulmonary regions. The investigation concludes that local turbulence and emission source significantly impacts air quality and deposition of PM at HRT. In addition, as the PM diameter decreases, the acidity of PM increases, and it can penetrate deep into the lower airways. Since this can have profound consequences, it is imperative to better understand the deposition of PM across various urban zones., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The mass concentrations of PM 2.5 were measured at a tropical megacity, Bengaluru, India, for the year 2015. The mean mass concentrations showed large fluctuations on day to day basis with values less than the Indian National Ambient Air Quality Standard (INAAQS) of 60 µg m -3 . The observed annual mean mass concentration of 28 ± 11 µg m -3 is also within the INAAQS value of 40 µg m -3 . The diurnal trend of PM 2.5 concentration showed bimodal distribution, with the primary peak in the morning and the secondary one during the late evening hours. The timing of the peaks matched with rush traffic hours. Strong seasonality is observed in the diurnal concentration of PM 2.5 with the highest value during winter (50 ± 22 µg m -3 ) and the lowest of (11 ± 5 µg m -3 ) in the monsoon. The weekend PM 2.5 mass concentrations were less than those on the weekdays up to a maximum of 100%. The decrease in PM 2.5 mass concentration was also observed on the day of the strike when many busses were off the road. Vehicular traffic is suggested as one of the primary contributors of PM 2.5 in this region. The health risk assessment in this study, points to ischemic heart disease as the primary cause of PM 2.5 -induced death., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. Air quality predictions have evolved to be more helpful for management. Recently, better performance and ability have developed due to the involvement of machine learning (ML) and artificial intelligence (AI) in forecasting air quality in urban cities in India. This paper focuses on air pollution as a significant ecological problem that directly impacts human health and the distribution of an environmental system in urban areas. Hence, we have developed advanced models for daily AQI forecasting to understand the air effluence level in the upcoming days. In this research, six data-driven models have been developed and implemented for daily AQI forecasting in the study area; it is crucial for understanding the future air pollution levels to plan and control air pollution in the entire city. The developed model is applied to air quality datasets. A comparison of the performance of ML models tested here indicates that the XGBoost algorithm achieves the highest coefficient of determination (R 2 ) and root-mean-square deviation (RMSE) value of 0.99 and lower values value of 4.65 than other models in the testing phase. The results of the artificial neural network (ANN) algorithm are slightly lower than the extreme gradient boosting (XGBoost model); the ANN model results are as R 2 , mean squared error (MSE), and RMSE values of 0.99, 13.99, and 198.88, respectively. All the models were subjected to a ten-fold cross-validation model. However, the RF cross-validation model outperforms other models; the RF model result shows the R 2 , RMSE, and MSE values of 0.99, 3.64, and 4.12, respectively. This study also employed two interpretable models, namely feature importance analysis and Shapley additive explanation (SHAP), to evaluate both the global and local methods in a manner that is independent of specific ML models. The feature importance shows that particle matter (PM) 2.5, PM10, carbon monoxide (CO), and nitrogen oxides (NO x ) were the most influential variables. The results determined that such novel DL and ML models may improve the accuracy of AQI forecasts and understanding of air pollution, particularly in metropolitan cities., Competing Interests: Declarations Consent for publication All authors approved the final manuscript. Disclaimer All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The global apprehension regarding the ubiquitous presence of microplastics (MPs) and their associated health risks underscore a significant challenge. However, our understanding on the occurrence and characteristics of this emerging class of pollutants in the different environmental compartments remains limited. For instance, despite housing approximately 20-25% of the global population, the evidence of the atmospheric MPs in Indian Subcontinent is exceedingly rare. Hence, we for the first-time present data on the depositional flux, chemical composition, morphological features of the atmospheric MPs collected from the foothills of Indian Himalayas. The total number of MPs for the collected samples ranged from 65 to 752 particles, with an average of 317 ± 171 particles count. The average flux of atmospheric MPs was 2256 ± 1221 particles/m 2 /day and varied significantly from 462 particles/m 2 /day to 5346 particles/m 2 /day. The highest deposition (5346 particles/m 2 /day) of atmospheric MPs was recorded during the 3rd week of sampling, coinciding with the Diwali festival. Based on the visual characteristics, we determined that the size of MPs ranged from 67 to 2320 μm, with a predominant presence of smaller particles (<1200 μm), primarily composed of fragments and films/sheets. Raman spectroscopy indicated that the analyzed MPs were mainly composed of 4 different polymer types, including PE (46.8 ± 7.2 %), PP (20.9 ± 7.4 %), PS (15.6 ± 3.8 %) and PET (16.7 ± 9.9 %). We further highlighted the extent to which climate variables control the deposition of atmospheric MPs in this urban conglomerate located in the foothills of Himalayas. Our Lagrangian parcel tracking approach showed that the greater frequencies are of local origin and clustered near to the studied region. We also speculate that atmospheric microplastics can be transported along the westerly winds. Though we did not observe any significant relation (p > 0.05) between meteorological parameters and the quantity of atmospheric MPs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October-November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32-49% to surface CO concentration while crop residue burning contributes 27-44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53-77%) of which 10-28% is from the domestic sector and 14-55% is from the transport sector. Agricultural waste burning contributes about 15-30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56-76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

High concentrations of ambient NO 2 causes serious air pollution and could also pose great threats to human health. However, the long-term trends (20-year) and potential health effects of ambient NO 2 exposure globally still shows high uncertainties. In this work, the field measurements, satellite dataset, GEOS-Chem output, and multiple geographical covariates were incorporated into the multi-stage model to investigate the global evolutions of ambient NO 2 during 2000-2019. The results indicated that the cross-validation (CV) R 2 values of ambient NO 2 based on multi-stage model displayed satisfied performance (R 2  = 0.78), which was superior to the individual model. Besides, the out-of-bag R 2 was 0.75, which suggested the multi-stage model showed the better transferability. At the spatial scale, the NO 2 concentrations followed the order of China (16.9 ± 9.0 μg/m 3 ) > India (15.5 ± 5.6 μg/m 3 ) > United States (10.7 ± 5.6 μg/m 3 ) > Europe (7.7 ± 4.5 μg/m 3 ), which was in consistent with the anthropogenic NO x emission. At the temporal scale, the ambient NO 2 levels in China experienced persistent increases (0.29 μg/m 3 /year) during 2000-2013, whereas they showed slight decreases (-0.23 μg/m 3 /year) during 2013-2019. The ambient NO 2 levels in the United States experienced continuous decreases during 2000-2019 (-0.20 μg/m 3 /year), while both of India and Europe remained relatively stable. Long-term NO 2 exposure inevitably increased premature mortalities. The global premature all-cause mortalities associated with the excessive NO 2 exposure increased from 288,169 (95% CI: 43,650, 527,971) to 461,301 (95% CI: 69,973, 843,996) in the past 20 years. This study would provide sufficient policy support for future ambient NO 2 mitigation., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rui Li reports financial support was provided by East China Normal University. Employment reports a relationship with East China Normal University that includes: employment. Rui Li has patent pending to Yes. We declare all of the authors do not have interest conflicts. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Ultrafine particles (UFP) associated with air quality and health impacts are a major concern in growing urban regions. Concentrations of UFP (particles of size between 10 and 100 nm) and accumulation mode (N acc ) (particles of size >100 and up to 1000 nm), are analyzed over a highly polluted megacity, Delhi, in conjunction with vehicular flow density, during peak (morning, and evening) and non-peak hours. UFP contributes ≥60% to total particle concentration during autumn and monsoon. UFP concentrations are about 50,000 particles per cm 3 in winter which reduces to about 25,000 particles during monsoon. N acc are about 20,000 (winter) and 10,000 (monsoon) particles per cm 3 . UFP concentration and N acc during peak hours are at least twice higher than those obtained in non-peak hours, confirming the dominant influence of emissions from vehicular exhaust in the study region. Seasonal analysis of UFP size distribution reveals that direct emissions dominate the particle concentrations during winter and autumn, whereas new particle formation mechanism contributes the highest in spring and summer. Assessment of inhalable particle number concentration and particle deposition in the human respiratory tract using Multiple Path Particle Dosimetry (MPPD) model, performed for the first time, shows that the order in which these particles deposit in the human respiratory tract is alveoli > bronchiole > bronchus. The deposition ranges between 10 and 18 million nanoparticles during different hours of the day, whereas the estimated inhalable particle concentration (IPN) varies between 0.5 and 1 billion. Results on the IPN during activities classified from light (walking), medium, heavy, very heavy to severe (long-distance running) provide insights into health effects on vulnerable populations. These quantitative results obtained over a megacity on hourly and seasonal variations of nanoparticles along with IPN and deposition rates for different activities are important, and are invaluable inputs for developing mitigation policies aimed to improve air quality and public health, both of which are major concerns in South Asia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The cities of North India, such as Delhi, face a significant public health threat from severe air pollution. Between October 2021 and January 2022, 79 % of Delhi's daily average PM 2.5 (Particulate matter with an aerodynamic diameter ≤ 2.5 μm) values exceeded 100 μg/m 3 (the permissible level being 60 μg/m 3 as per Indian standards). In response to this acute exposure, using Respiratory Face Masks (RFMs) is a cost-effective solution to reduce immediate health risks while policymakers develop long-term emission control plans. Our research focuses on the health and economic benefits of using RFMs to prevent acute exposure to PM 2.5 pollution in Delhi for different age groups. Our findings indicate that, among the fifty chosen RFMs, M50 has greatest potential to prevent short-term excess mortality (908 in age ranges 5-44), followed by M49 (745) and M48 (568). These RFMs resulted in estimated economic benefits of 500.6 (46 %), 411.1 (37 %), and 313.4 (29 %) million Indian Rupee (INR), respectively during October-January 2021-22. By wearing RFMs such as M50, M49, and M48 during episodes of bad air quality, it is estimated that 13 % of short-term excess mortality and associated costs could be saved if at least 30 % of Delhi residents followed an alert issued by an operational Air Quality Early Warning System (AQEWS) developed by the Ministry of Earth Sciences. Our research suggests that RFMs can notably decrease health and economic burdens amid peak PM 2.5 pollution in post-monsoon and winter seasons until long-term emission reduction strategies are adopted. It is suggested that an advisory may be crafted in collaboration with statutory bodies and should be disseminated to assist the vulnerable population in using RFMs during winter. The analysis presented in this research is purely science based and outcomes of study are in no way to be construed as endorsement of product., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors also declare that The Ministry of Earth Science (MoES) of the Government of India, and the Indian Institute of Tropical Meteorology (IITM) may not fully endorse the findings from this study, and authors don't have any commercial interests with manufacturers of masks presented in this study., (Copyright © 2024. Published by Elsevier B.V.)

This research paper aims to provide a comparative trend analysis of CO 2 emissions from the two largest emitters, India and China. The analysis focuses on the main sources of CO 2 emissions-coal, oil, cement, and gas and their annual data and global share percentages from 1960 to 2019. The study uses non-parametric trend analysis methods, which do not rely on assumptions of normality, outliers, or data length. Pettitt's test, a well-established non-parametric method, was used to detect sudden shifts in the data. The Mann-Kendall (MK) test and Sen's slope estimator were then applied to identify the presence or absence of monotonic linear trends and assess the magnitude of the slopes. In addition, the innovative trend analysis (ITA) method was used, which is particularly effective in detecting and visualizing monotonic, non-monotonic, and sub-trends. The ITA method has the advantage of presenting these trends in a graphical format. According to the results of Pettitt's test, an abrupt change was detected in India in 1989 for all sources of CO 2 emissions. In China, however, an abrupt change was detected in 1989 for coal and gas-related sources, while other sources showed a change point in 1990. The results of the MK test and the ITA method showed that all sources show only monotonic increasing trends. Based on the results of Sen's slope estimator, the average rate of change of CO 2 emissions is significantly higher in China than in India in all categories after the detection of the abrupt change point. Policymakers should promote the adoption of renewable energy sources, such as solar, wind, hydro, and geothermal, and also implement strategies to control deforestation to counteract the abnormal increase in CO 2 emissions. Finally, this research lays a solid foundation for future studies on CO 2 emissions., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Over a span of 34 years (1987-2019), an in-depth analysis of PM 10 , SO 2 , and NO 2 trends across India was conducted using data from the National Ambient Air Quality Monitoring Programme's manual monitoring stations in 336 cities. The study encompassed six geographical regions over three time blocks, revealing a correlation between the expansion of monitoring networks and the nation's economic growth. Regions like the densely populated Indo-Gangetic Plains (IGP) and Central India consistently hosted more monitoring stations, while the Himalayan and Northeast regions saw substantial increases from initial scarcity. SO 2 concentrations showed a declining trend, while NO 2 levels remained relatively stable with intermittent fluctuations. Conversely, national average PM 10 concentrations exhibited an upward trajectory, notably spiking by 128 % between 2006 and 2009 due to economic activities, construction, network expansion, the 2009 drought, and heightened coal consumption. Spatially, pollutant concentrations across three blocks demonstrated improved SO 2 levels, several cities exceeding NO 2 standards, and persistently high PM 10 levels in the IGP. PM 10 levels in block 3 were lower than in block 2, reflecting effective policy interventions. State rankings, however, did not consistently reflect pollutant trends across blocks. Regionally, the IGP consistently had the highest PM 10 concentrations, while the Northeast recorded the lowest. Population-weighted exposure levels indicated an overall increase in public exposure to PM 10 . Analysis of major city per region aligned with national trends, as evidenced by Delhi (IGP), Guwahati (Northeast), Vadodara (Northwest), and Bhopal (Central) showing increased PM 10 concentrations since 2006, followed by intermittent declines. In contrast, Shimla (Himalayan) and Chennai (Southern) exhibited distinct patterns. Major industrial cities such as Parwanoo, Bongaigaon, Angul and Talcher, and Visakhapatnam mirrored national trends, with PM 10 levels rising since 2009, highlighting the significant impact of industrial activities on air quality. This research underscores the need for targeted, effective mitigation strategies based on spatial and temporal pollutant trends., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO 2 ), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO 2 ) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O 3 ), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NO X ). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO 2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The elemental accumulation has emerged as a major environmental concern due to various anthropogenic sources such as vehicles, road dust, and industrial activities, contributing to the agglutination of elements to airborne Suspended Particulate Matter (SPM). SPM-bound elements accumulate on plant surfaces impact air quality and human health due to their noxiousness. Therefore, plants' ability to capture and mitigate air pollutants plays a crucial role in urban areas. This study aimed to investigate the levels and distribution of twenty-six elements, comprised of heavy metals (Cd, Pb, Cr, Cu Zn, Co, Ni, Fe, Mn, Ag, Mo, V, Ga, and Bi), light metals (B, As, Te, and Se), and metalloids (Al, Li, Sr, K, Mg, Na, Ca, and Ba) accumulated on the surface and inside the leaves of dominant plant species during the pre-and post-monsoon at six categorized (commercial, traffic-prone, residential, educational, greenbelt and industrial areas) locations in Delhi, India. In addition, the Metal Accumulation Index (MAI) was determined, and the statistical analysis was conducted using two-way ANOVA, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA). In the pre-and post-monsoon, two-way ANOVA revealed significant differences (P < 0.05) in metal concentrations. During the pre-monsoon plants exhibited the highest metal accumulation (∼21%) at the Anand Vihar (commercial) in Delhi, with the maximum average concentrations of Cr (118.25 mg/kg), Cu (204.38 mg/kg), Zn (293.27 mg/kg), and Fe (2721.17 mg/kg). Ficus benghalensis L exhibited the maximum 213.73 MAI at the Anand Vihar in the pre-monsoon. Ni and Cr indicated the highest correlation (P < 0.05, r = 0.82) in the PCA test. HCA test revealed similarity (∼87.7%) at ITO (traffic-prone) and Okhla Phase-2 (industrial) in F. religiosa regarding metal concentration patterns. Findings highlighted seasonal elemental pollutants uptake dynamics of plant species and explored species-specific metal accumulation, revealing potential implications of metal-tolerant plants for urban greenbelt., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The present work deals with the seasonal variations in the contribution of sources to PM 2.5 and PM 10 in Delhi, India. Samples of PM 2.5 and PM 10 were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM 2.5 and PM 10 were 131 ± 79 μg m -3 and 238 ± 106 μg m -3 , respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM 2.5 and PM 10 were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM 2.5 and PM 10 as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM 2.5 and PM 10 . All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

This study, conducted in Agra, India, examined the mass concentrations, chemical compositions, and seasonal variations of submicron particles (PM 1 ). The concentrations of metals, water-soluble inorganic ions (WSIs) including anions (F - , Cl - , NO₃ - , SO₄ 2- ) and cations (Ca 2+ , K + , Mg 2+ , NH₄ + , Na + ), organic carbon (OC) and elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) in PM 1 extract were determined using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES), Ion Chromatography, Thermogravimetric Analysis and Gas Chromatography-Mass Spectrometry (GC-MS) respectively. For morphological observation of PM 1 particles, Field Emission Scanning Electron Microscopy-Energy Dispersive X-ray spectrometry (FESEM-EDS) was used. The annual average concentration of PM 1 was 82.9 ± 33.4 μg/m 3 , which exceeds the World Health Organisation's (WHO) safe limit for PM 2.5 of 5 μg/m 3 by a factor of 17. The PM 1 mass composition included metals (31 %), WSIs (28 %), OC and EC (9.8 %), and PAHs (0.4 %). Winter recorded the highest PM 1 concentration (96.1 ± 25.8 μg/m 3 ), followed by post-monsoon, summer, and monsoon seasons. The average concentration of PAHs was 364.6 ± 226.6 ng/m 3 . Positive Matrix Factorization (PMF) identified traffic, emissions from biomass/coal and wood combustion, industrial/stationary sources, and secondary aerosols as potential contributors. The Ames test revealed the presence of frameshift mutations and base pair substitutions, especially in winter and post-monsoon. Additionally, PM 1 exhibited cytotoxic effects on V-79 cells, with heightened toxicity during winter and prolonged exposure in other seasons. This study underscores the urgent need to address local emission sources and establish regulatory standards for PM 1 in urban areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Vehicular emissions are considered one of the major anthropogenic sources of greenhouse gases and poor air quality in metropolitan cities. This study aims to see the correlation of CO 2 , CH 4 , and CO through monitoring over a period from December 2020 to October 2021 covering three seasons' winter, summer, and monsoon at two different traffic locations of Delhi having different traffic volumes, road patterns, and traffic management. The annual average morning concentration of CO 2 , CH 4 and CO was found (533 ± 105), (7.3 ± 3.1), (10.7 ± 3.0) ppm at Najafgarh and (480 ± 70), (5.2 ± 1.8), (7.8 ± 2.8) ppm at Rajendra Place, respectively. A relationship between concentration of all three gases and meteorological parameters such as temperature, humidity, wind speed and wind direction has also been investigated using Pearson correlation coefficient and pollution rose diagram. A comparable pattern in concentration was observed for all three gases in spatial (location) and temporal (diurnal) distribution. The concentration trend of CO 2 in different seasons is winter > summer > monsoon, while in the case of CH 4 winter = summer > monsoon but not any seasonal trend was noted in CO case. It is observed that CO 2 has a good relation with CO (a tracer for vehicular emission) in terms of diurnal variation, whereas, CH 4 does not represent a relation with CO and CO 2 diurnally, suggesting that vehicles are the source of CO 2 but not much contributing to other greenhouse gases like CH 4., Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Secondary aerosols constitute a significant fraction of atmospheric aerosols, yet our understanding of their formation mechanism and fate is very limited. In this work, the secondary organic aerosol (SOA) formation and aging of ambient air of Delhi are studied using a potential aerosol mass (PAM) reactor, an oxidation flow reactor (OFR), coupled with aerosol chemical speciation monitor (ACSM), proton transfer reaction time of flight mass spectrometer (PTR-ToF-MS), and scanning mobility particle sizer with counter (SMPS + C). The setup mimics atmospheric aging of up to several days with the generation of OH radicals. Variations in primary volatile organic compounds (VOCs) and oxygenated volatile organic compounds (OVOCs) as a function of photochemical age were investigated. Primary VOCs such as benzene, toluene, xylene, trimethyl benzene, etc. decrease and OVOCs like formic acid, formaldehyde, acetone, ethanol, etc. increase substantially upon oxidation in OFR. The highest organic aerosol (OA) enhancement was observed for the 4.2 equivalent photochemical days of aging i.e., 1.84 times the ambient concentration, and net OA loss was observed at very high OH exposure, typically after 8.4 days of photochemical aging due to heterogeneous oxidation followed by fragmentation/evaporation. In ambient air, OA enhancement is highest during nighttime due to the high concentrations of precursor VOCs in the atmosphere. SMPS + C results demonstrated substantial new particle formation upon aging and decrement in preexisting aerosol mass. This is the first experimental study conducting an in-situ evaluation of potential SOA mass generated from the ambient aerosols in India., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R 2  = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Organophosphate esters (OPEs) are a class of emerging and ubiquitous contaminants that are attracting increasing attention, and their large-scale use as flame retardants and plasticizers has led to their pervasive presence in the environment, although their broader impacts remain unknown. In this study, 11 OPEs were measured in the atmosphere of Southeast Asia and Southwest China during 2016. The ∑ 11 OPEs were higher in this region (78.0-1670 pg/m 3 , mean 458 pg/m 3 ) than in many remote areas, lower than in developed regions, and comparable to levels in many developing country cities. Generally, the ∑ 11 OPEs were higher in urban (105-1670 pg/m 3 , mean 538 pg/m 3 ) than in suburban (78.0-1350 pg/m 3 , mean 388 pg/m 3 ). Seasonal variations of OPEs in the air were more pronounced in Cambodia and Laos, especially for Triphenyl Phosphate (TPHP). Seasonal variations of ∑ 11 OPEs in most regions correspond to changes in temperature and rainfall. Biomass burning may be also a factor in facilitating OPE emissions from biomass materials or soil into the atmosphere of Southeast Asia. The random forest analysis showed that among these, rainfall had the greatest effect on the seasonal variation of atmospheric OPE concentrations, followed by biomass burning and temperature. The inter-regional variation of ∑ 11 OPEs in Southeast Asia was related to population and economic development in each region. Airflow trajectories indicated that the OPEs in this region were mainly from local sources. The health risk assessment revealed that the inhalation exposure risks of OPEs to the residents in the study areas were very low during the sampling period, but may be increasing., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Intensive crop residue burning (CRB) in northern India triggers severe air pollution episodes over the Indo-Gangetic Plain (IGP) each year during October and November. We have quantified the contribution of hotspot districts (HSDs) and total CRB to poor air quality over the IGP. Initially, we investigated the spatiotemporal distribution of CRB fire within the domain and pinpointed five HSD in each Punjab and Haryana. Furthermore, we have simulated air quality and quantified the impact of CRB using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), incorporating recent anthropogenic emissions (EDGAR v5) and biomass burning emissions (FINN v2.4) inventories, along with MOZART-MOSAIC chemistry. The key finding is that HSDs contributed ∼80% and ∼50% of the total fire counts in Haryana and Punjab, respectively. The model effectively captured observed PM₂.₅ concentrations, with a normalized mean bias (NMB) below 0.2 and R-squared (R 2 ) exceeding 0.65 at the majority of validation sites. However, some discrepancies were observed at a few sites in Delhi, Punjab, Haryana, and West Bengal. The National Capital Region experienced the highest PM₂.₅ concentrations, followed by Punjab, Haryana, Uttar Pradesh, Bihar, and West Bengal. Moreover, HSDs were responsible for about 70% of the total increase in CRB-induced PM₂.₅ in the western, central, and eastern cities, and around 50% in the northern cities. By eliminating CRB emissions across the domain, we could potentially save approximately 18,000 lives annually. Policymakers, scientists, and institutions can leverage the framework to address air pollution at national and global scales by targeting source-specific hotspots. This approach, coupled with appropriate technological and financial solutions, can contribute to achieving climate change and sustainable development goals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017-2019) in India (10-20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05-0.1, 0.05, 0.05 and 0.05-0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1-20%) and maize (1-10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM 2.5 ) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM 2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM 2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Continuous Emission Monitoring Systems (CEMS) are devices used to measure and report real-time emission of air pollutants. Although CEMS have been extensively deployed in developed countries to ensure compliance with emission standards and enhance their environmental performance, their adoption in India is still in its early stages. The present study aims to evaluate the effectiveness of CEMS in India, identify obstacles in terms of policy, regulation, technology and finance that impede their adoption and suggest mechanisms and incentives to facilitate their expansion. The findings indicate that CEMS offer benefits for air pollution control in India by improving monitoring accuracy, transparency, accountability and enforcement. The study also highlights institutional challenges faced by CEMS, including the absence of a certification system, lack of quality assurance measures, issues with data validation and challenges in its calibration as well as integration concerns with existing regulatory framework. To address these challenges effectively it is recommended that India must develop a policy framework for CEMS along with regulations. Essential steps such as establishing a certification and accreditation system should be taken while enhancing stakeholders' capacity and awareness., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ashish Tiwari reports a relationship with Uttar Pradesh Climate Change Authority Directorate of Environment Vineet Khand-6, Gomti Nagar, Lucknow, Uttar Pradesh 226,010 that includes: employment. NA If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Atmospheric pollution is a serious problem in many countries, including India, and it is generally considered as an urban issue. To fill the knowledge gap about particulate pollution and its adverse health effects in rural India for well-informed region-specific policy interventions, we present new insights on the rural pollution of India in terms of PM 2.5 . Here, we analyse PM 2.5 pollution and its associated health burden in rural India using satellite and reanalyses data for the period 2000-2019. We observe a gradual and consistent rise of atmospheric pollution in rural areas of India. The highest PM 2.5 levels are observed in Indo-Gangetic Plain (IGP) during winter and post-monsoon seasons (107.0 ± 17.0 and 91.0 ± 21.7 μg/m 3 , respectively). A dipole reversal in seasonal trends between winter and post-monsoon seasons is found for black carbon (BC) and organic carbon (OC) in the rural IGP. The rural North West India (NWI) experiences elevated PM 2.5 concentrations due to dust storms, while the rural hilly region (HR) in the Himalaya remains the least polluted region in India. The highest PM 2.5 associated cardiopulmonary mortality in 2019 is observed in the rural IGP districts (1000-5100), whereas the highest mortality due to lung cancer at district level accounts for 10-60 deaths. The highest mortality attributed to PM 2.5 is observed in districts of Uttar Pradesh, Bihar, West Bengal, Punjab, Haryana and Rajasthan. The priority-wise segregation of states as per World Health Organisation (WHO) Interim targets (ITs), as assessed in this study, might be helpful in implementation and development of policies in phases. We, therefore, present the first detailed study on the PM 2.5 pollution in rural India, and provide valuable insights on its distribution, variability, sources and associated mortality, and emphasize the need for addressing this issue to protect public health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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/m 2 /day in PM 10 and 12104 ± 665 and 13833 ± 1152 particles/m 2 /day in PM 2.5 in quartz and PTFE, respectively during the post-monsoon months. During winter, microplastic deposition rates in PM 10 samples were found to be 689 ± 52 particles/m 2 /day and 19789 ± 2957 and 30087 ± 13402 in quartz and PTFE particles/m 2 /day respectively in PM 2.5 . The mean deposition rate in indoor environment during post-monsoon was 8.3 × 10 4 and 1.03 × 10 5 particles/m 2 /day in winter. During the post-monsoon period in PM 10 , 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study aims to investigate the characteristics of carbonaceous aerosols and estimate emission factor (EF) based on roadway tunnel measurements, from two distinct vehicular fleets: an all light-duty vehicle (LDV) fleet, and a mixed fleet of 80% LDV and 20% heavy-duty vehicle (HDV). Carbonaceous content (organic carbon: OC and elemental carbon: EC) in total fine particles (PM 2.5 ) accounted for 41% ± 6.8% in LDV fleet and 48% ± 7.2% in mixed fleet. While higher volatile OC dominated in the LDV fleet emissions, in mixed fleet, lower volatile OC and EC emissions dominated due to the presence of higher HDV and super-emitter (SE) fractions which led to significantly higher optically active absorbing aerosols. Reconstructed HDV fleet EF was higher than LDV fleet by 36 times (PM 2.5 ), 19 times (OC) and 51 times (EC). Our findings emphasize the significance of implementing vehicle inspection and maintenance programs, coupled with decarbonization of HDVs to mitigate on-road vehicular emissions in India., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Chandra Venkataraman reports financial support was provided by the Department of Science and Technology, Government of India. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study reports size-resolved dithiothreitol (DTT)-based oxidative potential (OP: total and water-soluble) in rural kitchens using liquefied petroleum gas (LPG), firewood (FW), and mixed biomass (MB) fuels in northeastern (NE) India. In comparison to LPG, volume-normalized total OP (OP total(v) DTT ) was enhanced by a factor of ∼5 in biomass-using kitchens (74 ± 35 to 78 ± 42 nmol min -1 m -3 ); however, mass-normalized total OP (OP total(m) DTT ) was similar between LPG and FW users and higher by a factor of 2 in MB-using kitchens. The water-insoluble OP (OP wi(v, m) DTT ) fraction in OP total(v, m) DTT was greater than 50% across kitchens. Size distributions across kitchens and OP DTT categories ranged from unimodal to trimodal. OP ws(v) DTT was driven by metals as well as organics across size fractions while OP wi(v) DTT was majorly constrained by metals with an increasing importance of organics in fine particles of biomass-using kitchens. Multiple linear regression analysis revealed that Cu and Ba explained 71% of the OP total(v) DTT variability in LPG-using kitchens, while water-soluble organic carbon (WSOC) and Ba were responsible for 44% variability in FW-using kitchens. Finally, the high internal dose of OP total(v) DTT (28-31 nmol min -1 m -3 ) in biomass-using kitchens established the severity of oxidative stress on the exposed population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Ground-level ozone (O 3 ) concentration is rising in Asia, which accommodates the world's top-two wheat producers (China and India). Because wheat is among the species of high O 3 sensitivity, yield loss due to rising O 3 in Asia is a major threat to global wheat supply. We estimated the relationships between O 3 dose on AOT40 (accumulated daytime O 3 concentrations above 40 ppb for 90 days) and relative wheat yield for four wheat producing regions: China, India, Europe and North America using results of O 3 elevation experiments conducted therein. When compared on the same AOT40, the estimated yield loss was greatest in China followed by India, Europe, and North America in this order. In China, Europe and North America, the yield loss was primarily due to the reduction of single grain weight, whereas in India reduction of the number of grains contributed more to the yield loss than single grain weight. The greater response of the number of grains to O 3 in India can be explained by the earlier start of O 3 elevation, but the seasonal change in O 3 concentrations cannot explain the lower yield loss in North America than China and India. Referring to the past reports of lower yield sensitivity to O 3 in older cultivars, we compared the year of release of cultivars between the regions. In North America, they used cultivars released in 1980s or earlier, whereas in China they used cultivars released in 2000s. In Europe and India, most cultivars were released between those in North America and China. The difference in cultivars could therefore be a cause the differential yield response among the regions. We argue that the O 3 -induced yield loss should be quantified using the dose-response relationships for each region accounting for the effects of seasonal change in O 3 concentrations, cultivars and climate on the yield response., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Exposure to a total of 51 targeted and non-targeted polycyclic aromatic hydrocarbons (PAHs) and their oxygenated and alkylated derivatives associated with size-segregated aerosol was investigated in rural kitchens using liquefied petroleum gas (LPG), mixed biomass (MB) and firewood (FW) fuels in northeastern India. The averaged PM 10 -associated parent-, alkylated-, and oxygenated-PAHs concentrations increased notably from LPG (257, 54, and 116 ng m -3 ) to MB (838, 119, and 272 ng m -3 ) to FW-using kitchens (2762, 225, and 554 ng m -3 ), respectively. PAHs were preferentially associated with the PM 1 fraction with contributions increasing from 80 % in LPG to 86 % in MB and 90 % in FW-using kitchens, which in turn was dominated by <0.25 μm particles (54-75 % of the total). A clear profile of enrichment of low-molecular weight PAHs in cleaner fuels (LPG) and a contrasting enrichment of high-molecular weight PAHs in biomass-based fuels was noted. The averaged internal dose of Benzo[a]pyrene equivalent was the lowest in the case of LPG (19 ng m -3 ), followed by MB (161 ng m -3 ) and the highest in FW users (782 ng m -3 ). Estimation of incremental lifetime cancer risk (ILCR) from PAH exposure revealed extremely high cancer risk in biomass users (factors of 8-40) compared to LPG. The potential years of life lost (PYLL) and PYLL rate averaged across kitchen categories was higher for lung cancer (PYLL: 10.55 ± 1.04 years; PYLL rate: 204 ± 426) compared to upper respiratory tract cancer (PYLL: 10.02 ± 0.05 years; PYLL rate: 4 ± 7), and the PYLL rates for biomass users were higher by factors of 9-56 as compared to LPG users. These findings stress the need for accelerated governmental intervention to ensure a quick transition from traditional biomass-based fuels to cleaner alternatives for the rural population of northeastern India., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM 2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R 2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R 2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Paddy-residue burning is associated with poor air quality in north-west India during October-November every year. However, till date a quantitative study of its contribution to ambient volatile organic compounds (VOCs) using highly time-resolved measurements within the region has been lacking. Several VOCs like benzene are carcinogenic and also fuel formation of secondary pollutants such as secondary organic aerosol (SOA) and ozone. Here, we undertake quantitative source-apportionment using a PMF source-receptor model on a high-quality in-situ measured dataset of 54 VOCs in Punjab, India, and validate the model results using source profiles. The contribution of the seven most dominant sources to the total VOC mass concentrations were: daytime photochemistry and biogenic VOCs (BVOCs) (26%), followed by solid-fuel usage and waste-disposal (18%), traffic (two-wheeler 14% and four-wheeler 10%), photochemically aged biomass burning (17%), industries and solvent usage (9%), and fresh paddy residue burning (6%). Ozone production potential was dominated by solid fuel usage and waste disposal (25%), followed by traffic (two-wheeler 11% and four-wheeler 12%), BVOCs and photooxidation products (21%), photochemically aged biomass burning (16%), industries & solvent usage (9%) and fresh paddy residue burning (6%). SOA production was dominated by traffic (two-wheeler 26% and four-wheeler 28%) followed by solid fuel usage and waste disposal (22%), photochemically aged biomass burning emissions (15%) with minor contribution from industries & solvents (6%), fresh paddy residue burning (2%) and photochemistry and biogenic VOCs (1%). Comparisons with global emission inventories REASv3.2.1 and EDGARv4.3.2, showed both overestimate the industry and solvent source. Further, EDGARv4.3.2 underestimated the traffic source whereas paddy residue burning emissions are absent in REASv3.2.1. Although the overall mass contribution of paddy-residue burning emissions isn't high, our results show that health-relevant compounds emitted directly and formed photochemically from biomass burning sources active at this time are majorly responsible for the unhealthy air., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Benzene, toluene, ethylbenzene, and xylene (BTEX) pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects, carcinogenic properties, and adverse effects on air quality. BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities, necessitating the assessment of atmospheric concentrations and their spatial variation. This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India. The study investigates the correlation of BTEX with other criteria pollutants and meteorological parameters, aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions. Additionally, the paper categorizes various regions in India according to the Air Quality Index (AQI) based on BTEX pollution levels. The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central, eastern, and western regions. In contrast, the southern zone experiences the least pollution with BTEX. Seasonal analysis indicates that winter and post-monsoon periods, characterized by lower temperatures, are associated with higher BTEX levels due to the accumulation of localized emissions. When comparing the different zones in India, high traffic emissions and localized activities, such as solvent use and solvent evaporation, are found to be the primary sources of BTEX. The findings of the current study aid in source characterization and identification, and better understanding of the region's air quality problems, which helps in the development of focused BTEX pollution reduction and control strategies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India., (© 2023. The Author(s).)

Objective: We investigated associations between source-specific fine particulate matter (PM 2.5 ) exposure and hemoglobin levels among children in low- and middle-income countries (LMICs)., Method: 36,675 children aged < 5 years were collected in 11 LMICs during 2017. We associated child hemoglobin with 20 source-specific PM 2.5 , and calculated changes in hemoglobin that could be attributed to different PM 2.5 -mixture scenarios, established using real-world data from 88 Asian and African LMICs (AA-LMICs)., Results: Multiple-source analysis revealed PM 2.5 produced by solvents (change in hemoglobin for 1-μg/m 3 increment in PM 2.5 : -10.34 g/L, 95% CI -14.88 to -5.91), industrial coal combustion (-0.51 g/L, 95% CI -9.25 to -0.08), road transportation (-0.50 g/L, 95% CI -6.96 to -0.29), or waste handling and disposal (-0.34 g/L, 95% CI -4.38 to -0.23) was significantly associated with a decrease in hemoglobin level. Decreases in hemoglobin attributable to the PM 2.5 mixtures were co-determined by the concentrations and their source profiles. The largest PM 2.5 -related change in hemoglobin was -10.25 g/L (95% CI -15.54 to -5.27) for a mean exposure of 61.01 μg/m 3 in India., Conclusion: Association between PM 2.5 and a decrease in hemoglobin was affected by variations in PM 2.5 source profiles. Source-oriented interventions are warranted to protect children in LMICs from air pollution., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Considering the significance of PM 1 aerosol in assessing health impacts of air pollution, an extensive analysis of PM 1 samples collected at an urban site in Delhi is presented in this study. Overall, PM 1 contributed to about 50 % of PM 2.5 mass which is alarming especially in Delhi where particle mass loadings are usually higher than prescribed limits. Major portion of PM 1 consisted of organic matter (OM) that formed nearly 47 % of PM 1 mass. Elemental carbon (EC) contributed to about 13 % of PM 1 mass, whereas SO 4 2- (16 %), NH 4 + (10 %), NO 3 - (4 %) and Cl - (3 %) were the major inorganic ions present. Sampling was performed in two distinctive campaign periods (in terms of meteorological conditions and heating (fire) activities), during the year 2019, each spanning two-week time, i.e. (i) September 3 rd -16 th (clean days), and (ii) November 22 nd -December 5 th (polluted days). Additionally, PM 2.5 and black carbon (BC) were measured simultaneously for subsequent analysis. The 24-h averaged mean concentrations of PM 2.5 and BC during clean days (polluted days) were 70.6 ± 26.9 and 3.9 ± 1.0 μg m -3 (196 ± 104 and 7.6 ± 4.1 μg m -3 ), respectively, which were systematically lower (higher) than that of the annual mean (taken from studies conducted at same site in 2019) of 142 and 5.7 μg m -3 , respectively. Changes in characteristic ratios (i.e., organic carbon (OC)/elemental carbon (EC) and K + /EC) of chemical species detected in PM 1 show an increase in biomass emissions during polluted days. Increase in biomass emission can be attributed to increase in heating practices (burning of biofuels such as wood logs, straw, and cow-dung cake) in- and around- Delhi because of fall in temperature during second campaign. Furthermore, a significant increase in NO 3 - fraction of PM 1 is observed during second campaign which shows fog processing of NO X due to conducive meteorological conditions in winters. Also, comparatively stronger correlation of NO 3 - with K + during second campaign (r = 0.98 as compared to r = 0.5 during first campaign) suggests the increased heating practices to be a contributing factor for increased fraction of NO 3 - in PM 1 . We observed that during polluted days, meteorological parameters such as dispersion rate also played a major role in intensifying the impact of increased local emissions due to heating activities. Apart from this, change in the direction of regional emission transport to study site and the topology of Delhi are the possible reasons for the elevated pollution level, especially PM 1 during winter in Delhi. This study also suggests that black carbon measurement techniques used in current study (optical absorbance with heated inlet and evolved carbon techniques) can be used as reference techniques to determine the site-specific calibration constant of optical photometers for urban aerosol., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This study evaluates the non-cancer risks (NCR) and cancer risks (CR) in outdoor working personnel in Dhanbad city, exposed to volatile organic compounds (VOCs) and particulate matter (PM) present in the ambient air. Dhanbad is known for its coal mines and is one of the most polluted cities in India and the world. Sampling was conducted in different functional zones; namely, traffic intersections, industrial, and institutional areas, to estimate the concentration of different PM-bound heavy metals and VOCs in the ambient air using Inductively coupled plasma-optical emission spectrometry (ICP-OES) and gas chromatography (GC) respectively. Our results show that the concentration levels (of VOCs and PM) and health risks were maximum at the traffic intersection area, followed by the industrial and institutional areas. The major contribution to CR came from chloroform, naphthalene, and PM-bound chromium, while the contribution to NCR mainly came from naphthalene, trichloroethylene, xylenes, and PM-bound chromium, nickel, and cadmium. It was observed that CR and NCR from VOCs are quite comparable (average CR voc 8.92E-05 and NCR voc 6.82) to that from the PM-bound heavy metals (average CR PM 9.93E-05 and NCR PM 3.52). According to the sensitivity analysis performed using Monte-Carlo Simulation, it was also found that the output risk is most significantly influenced by the pollutant concentration followed by exposure duration and exposure time. The study reveals that Dhanbad city is not only a critically polluted area but also a highly hazardous and cancer-prone area due to the pollution arising from incessant coal mining activities and heavy vehicular movement. Given the paucity of data on exposure to VOCs in ambient air and their risk assessment pertaining to coal mining cities of India, our study provides useful information and insight for the regulatory and enforcing authorities to devise appropriate strategies for air pollution and health risk management in such cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 Elsevier B.V. All rights reserved.)

Thermal elemental carbon (EC), optical black carbon (BC), organic carbon (OC), mineral dust (MD), and 7-wavelength optical attenuation of 24-hour ambient PM 2.5 samples were measured/estimated at a regionally representative site (Bhopal, central India) during a business-as-usual year (2019) and the COVID-19 lockdowns year (2020). This dataset was used to estimate the influence of emissions source reductions on the optical properties of light-absorbing aerosols. During the lockdown period, the concentration of EC, OC, BC 880 nm , and PM 2.5 increased by 70 % ± 25 %, 74 % ± 20 %, 91 % ± 6 %, and 34 % ± 24 %, respectively, while MD concentration decreased by 32 % ± 30 %, compared to the same time period in 2019. Also, during the lockdown period, the estimated absorption coefficient (b abs ) and mass absorption cross-section (MAC) values of Brown Carbon (BrC) at 405 nm were higher (42 % ± 20 % and 16 % ± 7 %, respectively), while these quantities for MD, i.e., b abs-MD and MAC MD values were lower (19 % ± 9 % and 16 % ± 10 %), compared to the corresponding period during 2019. Also, b abs-BC-808 (115 % ± 6 %) and MAC BC-808 (69 % ± 45 %) values increased during the lockdown period compared with the corresponding period during 2019. It is hypothesized that although anthropogenic emissions (chiefly industrial and vehicular) reduced drastically during the lockdown period compared to the business-as-usual period, an increase in the values of optical properties (b abs and MAC) and concentrations of BC and BrC, were likely due to the increased local and regional biomass burning emissions during this period. This hypothesis is supported by the CBPF (Conditional Bivariate Probability Function) and PSCF (Potential Source Contribution Function) analyses for BC and BrC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The wide spread of the coronavirus disease (COVID-19) has significantly influenced human activities around the world, providing a unique opportunity to investigate the response of air pollution to anthropogenic emission reduction. Compared with numerous studies on conventional air pollutants, atmospheric ammonia (NH 3 ) that has matched sources from both anthropogenic and natural emissions has been rarely investigated. Here we assess impacts of the COVID-19 lockdown on ambient NH 3 variation across India, one of the most severe NH 3 pollution region in the world. The role of meteorology in shaping emission contribution to NH 3 pollution and respective contribution of each emission source to ambient NH 3 before and after the COVID-19 outbreak were investigated using the XGBoost algorithm coupled with WRF-Chem model. Results showed that ambient NH 3 concentrations in the seven major cities (Hyderabad, Bengaluru, Chennai, Delhi, Lucknow, Kolkata and Mumbai) decreased by 2.1-53.8 % whereas in Ahmedabad increased by 20.3 % during the COVID-19 lockdown period. Obvious decrease in NH 3 in Indo-Gangetic Plain (-17.1 %) was mainly driven by favorable meteorology, whereas the slight decline in NH 3 in South India was mainly driven by epidemic-related emission control (-8.56 %). Source appointment results showed that the contribution of industrial emission (Ind) to ambient NH 3 in most megacities showed a decreasing trend (between 11 % and 26 %) during the lockdown period. However, the reduction effect was mostly compensated by increasing contributions (15-25 %) of residential emission (Res) or agricultural soil emission (Ags). Particularly, in Ahmedabad and Lucknow ambient NH 3 increased by 20.3 % and 12 % during the lockdown period, the reduction effect of Ind on ambient NH 3 (-23 % and -11 %, respectively) was absolutely compensated by enhanced contribution of Res (24 %) and Ags (12 %), respectively. Our results highlight the importance of eliminating residential and agricultural NH 3 emissions especially in North India., Competing Interests: Declaration of competing interest The contact author has declared that she has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

The rapid economic development in environmentally sensitive zone of Himalayas resulted in the increased generation of tourism waste. However, the accounting methodology for accumulation of tourism waste in the hilly terrain was found to be missing. Accordingly, the socio-economic factors influencing the tourism waste generation were identified, and their correlation analysis was performed. The tourism waste generated within and outside an urban local body was quantified over a period of 12 years (2008-2019) using a novel methodology, considering the socioeconomic factors, such as economic significance, geographical terrain, location of tourist destinations and tourism-related activities. The spatial dependency of tourism waste accumulation in the Himalayan state of Himachal Pradesh, India was analyzed using the geographically weighted regression. Furthermore, the air pollutants' emission (PM 2.5 , PM 10 , CO, SO 2 and NO x ) from the open burning of neglected tourism waste were also quantified and compared with the existing literature., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Mass-size distribution of respirable aerosol and 13 associated trace elements (TEs) were investigated in rural kitchens using liquefied petroleum gas (LPG), firewood and mixed biomass fuels across three northeastern Indian states. The averaged PM 10 (particulate matter with aerodynamic diameter ≤ 10 μm) and ΣTE concentrations were 403 and 30 μg m -3 for LPG, 2429 and 55 μg m -3 for firewood, and 1024 and 44 μg m -3 for mixed biomass-using kitchens. Mass-size distributions were tri-modal with peaks in the ultrafine (0.05-0.08 μm), accumulation (0.20-1.05 μm), and coarse (3.20-4.57 μm) modes. Respiratory deposition, estimated using the multiple path particle dosimetry model, ranged from 21 % to 58 % of the total concentration across fuel types and population age categories. Head, followed by pulmonary and tracheobronchial, was the most vulnerable deposition region, and children were the most susceptible age group. Inhalation risk assessment of TEs revealed significant non-carcinogenic as well as carcinogenic risk, especially for biomass fuel users. The potential years of life lost (PYLL) was the highest for chronic obstructive pulmonary disease (COPD: 15.9 ± 3.8 years) followed by lung cancer (10.3 ± 0.3 years) and pneumonia (10.1 ± 0.1 years), while the PYLL rate was also highest for COPD, with Cr(VI) being the major contributor. Overall, these findings reveal the significant health burden faced by the northeastern Indian population from indoor cooking using solid biomass fuels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This study reports the chemical characterization of the carbonaceous component of PM 2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) collected over a year-long campaign from a regional site in Shyamnagar, West Bengal, in the Indo-Gangetic Plains (IGP), India. The carbonaceous fractions (elemental and organic carbon), mass concentrations, and stable carbon isotopic composition (δ 13 C value) of aerosols were measured and utilized to characterize the sources and understand the atmospheric processing of aerosols. Cluster analysis, Potential Source Contribution Function (PSCF) modeling, and fire count data were analyzed to decipher the pattern of air masses, source contributions, and extent of burning activities. The PM 2.5 mass concentrations were significantly higher during winter (168.3 ± 56.3 μg m -3 ) and post-monsoon (109.8 ± 59.1 μg m -3 ) compared to the monsoon (29.8 ± 10.7 μg m -3 ) and pre-monsoon (55.1 ± 23.0 μg m -3 ). Organic carbon (OC), elemental carbon (EC), and total carbon (TC) concentrations were also several factors higher during winter and post-monsoon compared to monsoon and pre-monsoon. The winter and post-monsoon experienced the impact of air masses from upwind IGP. On the other hand, long-range transported air masses from the South-West direction dominated during monsoon and pre-monsoon, which are also relatively cleaner periods. The average δ 13 C during post-monsoon and winter was ∼1‰ higher compared to monsoon and pre-monsoon. The vehicular exhaust and biomass/biofuel burning contributed dominantly in winter and post-monsoon. In comparison, lower δ 13 C in pre-monsoon and monsoon might be attributed to the dominance of biomass/biofuel combustion. Photochemical-induced aging of the anthropogenic aerosols resulted in a higher δ 13 C of TC in winter and post-monsoon, whereas the mixing of different local sources in pre-monsoon and monsoon resulted in lower δ 13 C values. These findings benefit policymakers in strategizing proper and effective management of biomass/biofuel burning in the IGP to minimize air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Tritium ( 3 H) is one of the important long-lived radioisotopes in the gaseous effluent released from Pressurised Heavy Water Reactors (PHWR). For the first time, the studies on diurnal variation of atmospheric tritium concentration was carried out using an in-house developed automatic air moisture collection sampler at Kakrapar Gujarat site, India, where PHWRs are operational. Correlation of diurnal variation of atmospheric tritium concentration with meteorological parameters such as absolute humidity, ambient temperature and wind speed is studied and presented in this paper. Positive and significant correlation of atmospheric 3 H concentration was observed with respect to the absolute humidity. Negative correlation was observed in ambient temperature and wind speed. The diurnal maximum and minimum of 3 H concentration was found during 21.00-23.00 h (night) and 13.00-16.00 h (day), respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Time-synchronized, 24 h integrated PM 2.5 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)