Search

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India. [ABSTRACT FROM AUTHOR]

During 2019 to 2022, a comprehensive study was conducted across 71 stations spanning over 15 states in India to investigate air quality variations during the pre-Diwali, Diwali, and post-Diwali periods. The investigation, centred on the extensive use of firecrackers during festive times, revealed a short-term deterioration in air quality with long-lasting impacts. PM2.5 and PM10 levels were found to significantly increase during Diwali and the post-Diwali period compared to the pre-Diwali period. The levels of PM2.5 and PM10 experience respective increases of 44.66% and 11.03%, 29.12% and 05.41%, 52.63% and 23.38%, and 39.65% and 17.61% during Diwali period from 2019 to 2022, where 68.84% and 34.32%, 44.42% and 29.42%, and 48.70% and 25.22% in 2019, 2021 and 2022 in post-Diwali period compared to the pre-Diwali. The Mann–Kendall and Sen's slope analyses reveal statistically significant positive trends (increasing) in both PM10 and PM2.5 levels during the Diwali festival period. Notably, the study introduced the Health Hazard-based Air Quality Index (HAQI), which demonstrated higher values than the National Ambient Air Quality Index (NAAQI). Unlike NAAQI, which considers individual pollutant sub-index values, and considered highest one of sub-index values in AQI analysis. HAQI provides a more comprehensive assessment of health risks associated with exposure to multiple pollutants effects. In 2022, AQI and HAQI values during pre-Diwali were 114 and 146.18, during Diwali were 173 and 332.18, and post-Diwali were 191 and 414.14, respectively. These findings underscore the elevated risk during Diwali and the post-Diwali period due to heightened pollution levels, emphasizing the need for public awareness and potential mitigation strategies. The study calls attention to the importance of considering cumulative health impacts and suggests the adoption of HAQI for more informative air quality assessments. [ABSTRACT FROM AUTHOR]

In the last few years, urban trees have emerged as an effective nature‐based solution to mitigate increasing air pollutant levels due to urbanization and industrialization. This study aims to assess the synergistic effect of urban trees on improving air quality by combining real‐time PM2.5 monitoring with the i‐Tree Eco model. The monitoring was conducted during rush hours with high traffic volume and during non‐rush hours, in both the tree alley and a non‐tree road section within the industrial areas of the north‐west region of the National Capital Territory of Delhi, India. The i‐Tree Eco model was run using the diameter at breast height values of tree species present in the study area, and the PM2.5 reduction ability of the trees was quantified. The results from both approaches indicated that urban trees can significantly reduce the traffic‐fed PM2.5 concentrations. Therefore, it is suggested that tree plantations be integrated into air pollution management strategies in urbanized regions with high traffic volumes. Although this study explores the initial link between trees and air quality in Delhi, further research incorporating local wind speed and direction measurements would provide a more comprehensive understanding of how trees influence air quality in any highly polluted urban setting. [ABSTRACT FROM AUTHOR]

Winter fog and severe aerosol loading in the boundary layer over northern India, particularly in the Indo-Gangetic Plain (IGP), disrupt the daily lives of millions of people in the region. To better understand the role of aerosol–radiation (AR) feedback on the occurrence, spatial extent, and persistence of winter fog, as well as the associated aqueous chemistry in fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). While WRF-Chem was able to represent the fog formation for the 23–24 December 2017 fog event over the central IGP in comparison to station and satellite observations, the model underestimated PM 2.5 concentrations compared to the Central Pollution Control Board (CPCB) of India monitoring network. While evaluating aerosol composition for fog events in the IGP, we found that the WRF-Chem aerosol composition was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with secondary aerosols, particularly the chloride aerosol fraction, being strongly underpredicted (∼ 66.6 %). Missing emission sources (e.g., industry and residential burning of cow dung and trash) and aerosol and chemistry processes need to be investigated to improve model–observation agreement. By investigating a fog event on 23–24 December 2017 over the central IGP, we found that the aerosol–radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM 2.5 concentrations and relative humidity (RH) within the boundary layer. Factors affecting the feedback include loss of aerosols through deposition of cloud droplets and internal mixing of absorbing and scattering aerosols. Aqueous-phase chemistry increases the PM 2.5 concentrations, which subsequently affect the aerosol–radiation feedback by both increased mass concentrations and aerosol sizes. With aerosol–radiation interaction and aqueous-phase chemistry, fog formation began 1–2 h earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. The increase in RH in both experiments was found to be important for fog formation as it promoted the growth of aerosol size through water uptake, increasing the fog water content over the IGP. The results from this study suggest that the aerosol–radiation feedback and secondary aerosol formation play an important role in the air quality and the intensity and lifetime of fog over the IGP, yet other feedbacks, such as aerosol–cloud interactions, need to be quantified. [ABSTRACT FROM AUTHOR]

India, one of the most dynamic ancient civilizations, possesses a multitude of historical artifacts, with 37 of its notable architectural structures recognized as UNESCO World Heritage Sites. Yet, the ever-changing climate, especially air pollution, expedites the natural deterioration of historic sites and diminishes their aesthetic appeal, causing socio-economic damage. With this in mind, the current study aims to offer a logical scientific foundation for the implications of air pollution, seasonal shifts, and COVID-19 on 14 significant historical places in India during the year 2019-20. Delhi, among the cities most severely affected by atmospheric pollution, recorded an alarming air quality index (AQI) of 102–141, which can intensify the risk of cultural sites to corrode and deteriorate. Analysis reveals that the winter season had elevated levels of NO2 and particle pollution (PM2.5, PM10), whereas summer had the higher levels of O3. Throughout the 5-month lockdown period, ozone levels exhibited an elevation, contrasting with the reduction observed in other air parameters. Notably, there was a substantial 70% decrease in particulate matter concentration, which had previously remained stable over the course of the year. Variations in geographic locales and anthropogenic influences contribute significantly to the dose-response statistics, revealing an unprecedented elevation in corrosion risks to historical limestone and sandstone structures across target sites. Moreover, the research addresses available Governmental initiatives, and effective strategies designed to safeguard heritage sites against the corrosion and material degradation, offering a comprehensive exploration of protective measures. Thereby, the current research is centred on establishing a foundational understanding of the impact of air pollution on cultural heritage, utilizing a comparison to the year with the lowest air pollution levels, which can aid policymakers in enhancing risk management and implementing a robust national mandate for the preservation of cultural heritage sites against corrosion. [ABSTRACT FROM AUTHOR]

This study assessed the air quality status in different functional zones of Dhanbad—a coal-mining and industrial hub, based on the measurement of aromatic and halogenated volatile organic compounds (VOCs) using gas chromatography. The study encompasses source apportionment of VOCs and their chemical reactivity in terms of OH radical loss rate (LOH), ozone-forming potential (OFP), and their secondary organic aerosol forming potential (SOAp). Furthermore, prioritization of VOCs based on a fuzzy-analytical hierarchical process (F-AHP) has also been done. The results found xylene species to have the highest concentration in all three seasons across traffic-intersection and industrial zones and toluene at the institutional zone. The study identified four sources using positive matrix factorization (PMF) model, viz., mixed traffic exhaust (35%), coal combustion sources (30%), industrial (26%), and solvent usage (9%). LOH and SOAp were ~ 16 times more at the industrial and traffic-intersection zone than the institutional zone. The aromatic species contributed 97% to the OFP, and many species exhibited less contribution to the mixing ratio of VOCs but displayed a high contribution to LOH, OFP, and SOAp, suggesting the need to prefer reactivity-based strategies in addition to concentration-based strategies in the future for their regulation. The F-AHP-based priority component analysis identified 16 species out of 29 in the priority watch list (nine in tier-1, four in tier-2, and three in tier-3). The paucity of data and lack of ambient air quality standards on VOCs (except benzene) make it difficult to determine which aspect should be dealt with first and which species require more attention. Therefore, the F-AHP method used in this study could help identify the influencing parameters to be considered while devising efficient VOC management policies. [ABSTRACT FROM AUTHOR]

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (≤ 15 µg m−3) and O3 (≤ 100 µg m−3) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m−3) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 µg m−3) and O3 (> 100 µg m−3) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5‐O3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution. [ABSTRACT FROM AUTHOR]

The significance of air quality monitoring for analyzing impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments, ranging from expensive reference stations that provide accurate data to low-cost sensors that provide less accurate air quality measurements. In this research, we use a combination of sensors and monitors, which we call hybrid instruments, and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM 2.5 concentration). Note that there can be several constraints such as those on budget, the minimum number of reference stations to be placed, or the set of important regions where at least one sensor should be placed. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and that works on the principles of evolution. The second one is a greedy algorithm that selects the locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM 2.5 emissions, or available budget. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

The study investigates the effect of emissions of particulate matter arising out of different construction activities from five construction sites in Kanpur city, India. Limited research studies on exposure assessments related to particulate matter (PM) emissions from construction activities in the Indian context contribute to the uniqueness of this study. It was observed that for all study areas the concentrations of particulate matter (PM2.5 and PM10) exceeded the National Ambient Air Quality Standard (NAAQS) by a factor of 18–20. The highest concentration of PM10 (1308; 1267 µg/m3) and PM2.5 (1056; 1123 µg/m3) was associated with construction activity related to cutting and laying of tiles observed over the two monitoring campaigns conducted during November–December 2019 and January–February 2020. The human health risk exposure assessment assessed on relative risk (RR) determined on WHO methodology showed that the highest RR values of 2.669 and 4.13 were obtained for emissions of PM10 and PM2.5, respectively, from laying and cutting of tiles. The attributable factor (AF) from both PM10 (AF = 0.6253) and PM2.5 (AF = 0.757) also showed higher possibility of respiratory diseases for workers associated with laying and cutting of tiles. Health impact assessment conducted using the International Council for Radiological Protection (ICRP) model also confirmed that maximum and minimum deposition rates of particulates occurred for construction activities associated with cutting and laying of tiles and painting and washing, respectively. This suggests potential occupational health hazards for workers involved in construction activities being exposed to high levels of PM concentrations. [ABSTRACT FROM AUTHOR]

Over the past few decades, rapid industrialization and urbanization have severely deteriorated urban air quality. Forecasting the value of air pollutants and predicting the Air Quality Index (AQI) are crucial in improving the control measures for tackling air pollution. The current research proposes meteorological AQI and pollutants concentration-based AQI predictor. It consists of the pollutant predictor module and Air Quality Index predictor module. In this work, (a) ARIMA, ANN, RFR, XGBoost, LSTM, VAR, and ARMA are used to forecast the concentration of pollutants, (b) support vector regressor (SVR) is then used to build an AQI prediction model using forecasted air pollution concentration (in part 'a') and meteorological factors such as wind speed, temperature, and humidity, and (c) the performance of SVR with different kernel functions has been investigated. Also, the impact of considering past AQI values for predicting future AQI values has been analyzed. The dataset used in this work has been acquired from the official website of the Central Pollution Control Board, Ministry of Environment, Forest and Climate Change, Government of India. The pollutant prediction models were evaluated using MAE and RMSE as the performance parameters. It has been observed that ARIMA and ANN can be used for efficient and accurate forecasting of the concentration of air pollutants. Further, it has been found that the proposed SVR model works effectively using linear kernel function (with one lookback value), with MAE being 29 and RMSE being 44.5. This proposed predictor may help people to plan their daily activities. [ABSTRACT FROM AUTHOR]

Imphal Valley, situated in Manipur, India, stands as an intermontane valley of great ecological significance, notably hosting Loktak Lake. This research delves into the Land Use Land Cover Change (LULCC) within the Imphal Valley from 2016 to 2021 and assesses their impact on air quality across distinct land cover types. We used Sentinel‐1 & 2 data, ALOS PALSAR Digital Elevation Model, and applied Random Forest (RF), a machine learning algorithm for effective land use and land cover (LULC) mapping. Additionally, Sentinel‐5P data was utilized to monitor air quality parameters (CO, HCHO, NO2, SO2, Aerosol Index) spanning 2019 to 2021. The overall accuracies for the LULC maps employing a k (k = 3) fold approach for accuracy assessment varied between 88% and 92%, with corresponding Kappa coefficient ranging from 0.85 to 0.90. Noteworthy trends emerged from our analysis, revealing an increase in settlements and horticulture farms and a decline in forested areas and phumdis (floating biomass). Our findings highlight a mean concentration of CO ranging between 0.045 mol/m2 and 0.055 mol/m2 in different land cover types during February and March (2019–2021). Furthermore, we observed maximum mean HCHO, NO2, SO2, and aerosol index concentrations in March. Pollution levels surged during forest fires and shifting agriculture seasons, while aerosol levels declined during the lockdown period. This integrated approach emphasized on the comprehensive analysis of the dynamic interplay between LULCC and air quality in the Imphal Valley. This intricate relationship between LULC changes and air quality dynamics in the Imphal Valley, contribute to our understanding of the environmental dynamics in this ecologically vital region. Plain Language Summary: This research article titled "ML based Hybrid Synthetic Aperture Radar (SAR) and Optical LULC Mapping and change analysis with variations in air quality of the Imphal Valley, North‐East India" investigates LULC changes in the Imphal Valley from 2016 to 2021 and their impact on air quality (2019–2021). It employs SAR images, Sentinel 2 satellite images, and topographic features to map LULC using a RF Machine Learning Classifier. The study reveals changes in land cover types and their influence on pollutant concentrations, utilizing Sentinel 5P data for air quality monitoring. The integration of Sentinel data and topographic features enhances the accuracy of the analysis. Key Points: We mapped land use and land cover from 2016 to 2021 using Sentinel 1 and Sentinel 2 data sets with the Random Forest ML Algorithm of Imphal Valley located in Manipur, India and analyzed air quality parametersThere was a significant increase in settlement and a substantial decrease in Phumdis, Vegetation, and PaddyThe air quality was found to be adversely affected from January to April (2019, 2020, and 2021) owing to forest fires caused possibly due to shifting cultivation practices [ABSTRACT FROM AUTHOR]

The spread of the Coronavirus disease 2019 (COVID-19) has impacted human life severely since November 2019. The urban centers in the world, especially, were highly affected by the diseases. Several socioeconomic and environmental factors probably enhanced the spread of the pandemic and consequent mortality. Many studies examining environmental factors, such as air quality, in urban centers indicate the roles of those factors in the spread of diseases and consequent mortality. However, other socioeconomic factors that directly or indirectly elevate the mass death of people are seldom studied. The present study explores the socioeconomic factors and air quality influencing COVID-19 deaths in urban India. We randomly selected 19 Indian cities and collected each city's socioeconomic and air quality data from reliable and open sources. The data were analyzed using multivariate data analysis techniques using R statistics. The results showed significant positive relationships, population, and total area of the urban centers. [ABSTRACT FROM AUTHOR]

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHPEntropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman's rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to survive, mankind needs air. The quality of life depends on the purity of the air we breathe in. Hazardous pollutants are stirred up in our environment by various activities everyday. In a developing country like India, which has huge population, protecting public health is important. In general, air quality is measured using the air quality index, which records the pollutants level in the air. These recordings are to be mentioned across various places, to know about the air quality level. The advancement of the recent artificial intelligence can be replaced by the human efforts and to automate the system in flowless manner. Hence, an effort has been taken in this paper, by proposing a framework of predicting the air quality seasonally using regularized combinational LSTM (REG-CLSTM). For an efficient air quality level prediction with an improved error rate, time, and accuracy, the study has implemented Reg-CLSTM with a large amount of real-time dataset. To improvise the comprehensiveness and the potentiality of the proposed model, the significant feature is extracted by the rough set-wrapper method. The significant challenge is to provide a seasonal limit range for each considered pollutant in place of a generalized range partition. Aiming at this problem, the proposed model is able to identify the highest concerning pollutants occurrence in individual seasons. Through this study, pyramid learning-based hybridized deep learning framework is developed which can play a crucial role in warning the policy maker to reduce the activities that instigate air pollution. [ABSTRACT FROM AUTHOR]

During the pre- and post-monsoon season, the eastern and western coasts are highly vulnerable to cyclones. The tropical cyclone "Tauktae" formed in the Arabian Sea on 14 May 2021 and moved along the west coast of India, and landfall occurred on 17 May 2021. During the cyclone, the maximum wind speed was 220 km/h with a pressure of 935 mb affecting meteorological, atmospheric parameters, and weather conditions of the northern and central parts of India causing devastating damage. Analysis of satellite, Argo, and ground data show pronounced changes in the oceanic, atmospheric, and meteorological parameters associated during the formation and landfall of the cyclone. During cyclone generation (before landfall), the air temperature (AT) was maximum (30.51 °C), and winds (220 km/h) were strong with negative omega values (0.3). The relative humidity (RH) and rainfall (RF) were observed to be higher at the location of the cyclone formation in the ocean and over the landfall location, with an average value of 81.28% and 21.45 mm/day, respectively. The concentration of total column ozone (TCO), CO volume mixing ratio (COVMR), H2O mass mixing ratio (H2O MMR), aerosol parameters (AOD, AE) and air quality parameter (PM) was increased over land and along the cyclone track, leading to a deterioration in the air quality. The strong wind mixes the air mass from the surroundings to the local anthropogenic emissions, and causing strong mixing of the aerosols. The detailed results show a pronounced change in the ocean, land, meteorological, and atmospheric parameters showing a strong land–ocean-atmosphere coupling associated with the cyclone. [ABSTRACT FROM AUTHOR]

The frequency, intensity, and duration of extreme heatwaves are projected to increase in the global context of climate change. However, evidence of how anthropogenic emissions respond to heatwaves and further impact air quality remains elusive. Here, we use satellite remote sensing measurements alongside chemical transport model simulations to reveal abrupt variations in primary and secondary air pollutants introduced by extreme heatwaves. We highlight evidence from China and India, where satellite sulfur dioxide (SO2) and nitrogen dioxide (NO2) columns over thermal power plants enhance consistently responding to heatwaves. We attribute such spiked emissions to soaring electricity use and demonstrate that bottom‐up inventories underestimate the emissions from the power sector by 34.9% for the selected case. Elevated emissions facilitate fine particulate matter (PM2.5) and ozone (O3) formation over thermal power plants in an inhomogeneous manner, due to the combined effect of atmospheric oxidizing capacity, thermal decomposition of peroxyacetyl nitrate, planetary boundary layer rise, and air stagnation. Our results underscore the emerging challenge of pollution control attributable to the increasing climate penalty and the necessity of targeted control strategies and alternative energy sources during heatwaves. Plain Language Summary: Heatwaves are associated with high ambient temperatures and adverse health outcomes, but how anthropogenic emissions of atmospheric pollutants respond to heatwaves remains less explored. Here, we provide evidence from space for the first time that sulfur dioxide (SO2) and nitrogen dioxide (NO2) over thermal power plants enhance consistently responding to heatwaves. We attribute such spiked emissions to soaring electricity use, a feedback previously overlooked by state‐of‐the‐art emission inventories. The enhanced emissions increase levels of fine particulate matter (PM2.5) and surface ozone (O3) in an inhomogeneous manner during heatwaves due to the combined effect of chemistry and meteorology. Key Points: We detect enhanced SO2 and NO2 columns over power plants during heatwavesWe provide evidence of increasing electricity use and emissions in the power sectorElevated precursor emissions facilitate PM2.5 and O3 formation in an inhomogeneous manner under the combined effect of chemistry and meteorology [ABSTRACT FROM AUTHOR]

The study attempts to examine the impact of firework activities during Diwali Festival on ambient air quality of Jodhpur city. Air quality parameters particulate matter of diameter 10 μm (PM10), particulate matter of diameter 2.5 μm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2) and heavy metals in PM2.5 like Pb, Ni, Ba, Al, As and Sr are monitored at two locations, for 15 days, starting from 7 days before the festival of Diwali, on the day of the festival (Diwali) and 7 days after Diwali. On the occasion of Diwali, it was discovered that the 24-h average levels of various pollutants were significantly elevated compared to regular days preceding the festival. Specifically, at the HBO site, the concentrations were notably increased, with sulfur dioxide (SO2) reaching 5.62 times higher, nitrogen dioxide (NO2) at 3 times higher, particulate matter of diameter 10 μm (PM10) at 2.35 times higher, and particulate matter of diameter 2.5 μm (PM2.5) at 1.01 times higher than the usual levels before Diwali. Similarly, at the PTMM site, there were substantial elevations in pollutant concentrations during Diwali compared to pre-festival days, with SO2 registering 2.53 times higher, NO2 at 2.37 times higher, PM2.5 at 1.9 times higher, and PM10 at 1.57 times higher levels than normal. Concentration of Al, Ba, Sr and Pb at HBO site and Al at PTMM site was highest on Diwali day. Air quality index which was in good category on normal days before Diwali, fell into poor category starting from the day before Diwali and remain in poor category on normal days after Diwali. The result indicates the worsening of ambient air quality during Diwali which can adversely impact the human health in terms of various respiratory complications. [ABSTRACT FROM AUTHOR]

Air pollution exerts a profound impact on both public health and the natural environment. In India, festivals like Diwali also contaminate the air by releasing pollutants into the atmosphere. It is essential to identify the most polluted region by estimating these pollutants. Since air quality assessment involves multiple air pollutants, there may be inherent uncertainty associated with data. This study employs a fuzzy Multi Attribute Decision Making (MADM) framework fuzzy Analytical Hierarchy Process-Entropy-fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-Entropy-FVIKOR) to model the impact of air pollution as a decision-making problem to address the uncertainty and assess the air quality during the Diwali festival from 2019 to 2021 in Tamil Nadu, India. An integrated weighting approach is utilised to determine the weights of the air pollutants using a fuzzy Analytical Hierarchy Process and Entropy methods. Mainly, the fuzzy VIKOR approach is employed to rank the polluted regions. The validation of the proposed model is established through a comparative analysis using Spearman's rank correlation with two other existing fuzzy MADM methods. Furthermore, a sensitivity analysis is conducted to evaluate the influence of priority weights and the interdependence of pollutants in determining regional rankings. The results conclude that a strong positive correlation is attained between the proposed and existing methods and the highest levels of air pollution during the festival period are observed in Gandhi Nagar (2019), Rayapuram (2020), T. Nagar, Sowcarpet and Triplicane (2021) in their respective years. These findings substantiate the consistency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Declining air quality is highly ignored and very common form of degradation of the environment in nations that are both developed and developing. There are several contaminants in the air that have been identified in various studies on air pollution. The crucial parameters of all air pollutants are gaseous and particle pollution. The present study was undertaken to estimate the quality of ambient air in Chandrapur district, Maharashtra state of India. In this paper, an effort has been made to study the standing and trend of Sulphur dioxide (SO2), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Ammonia (NH3), Respirable Suspended Particulate Matter i.e.; PM10, PM2.5, toxic pollutants i.e.; lead, arsenic, nickel, benzo [a] pyarene and benzene and hydrocarbons. The results clearly show that, all parameters of ambient air quality monitoring values were some shown slightly below permissible limit. The ambient air quality monitoring data show that the Chandrapur district has substantial air pollution concerns in terms of SO2, NOx, PM, and other air pollutants. However, after comparing these values with NAAQS levels, the yearly averages of these air pollutants have been found to be below than the NAAQS levels. However, without effective mitigation measures, the concentrations of these pollutants will rise at quite alarming rate. [ABSTRACT FROM AUTHOR]

Globally, there are significant worries about the rise in air pollution (AP) from substances that are harmful to human health, different living forms, and unfavorable environmental imbalances. To overcome the problem, AI-based prediction model is the need of the hour. Therefore, an attempt was made to develop a novel AP prediction system based on Xavier Reptile Switan-h-based Long-Short Term Memory (XRSTH-LSTM), which undergoes fine-tuning at various steps such as pre-processing, attribute extraction, and air-quality index prediction, in order to reduce computational cost and also to increase accuracy as well as precision. The dataset used to train the proposed methodology is Air Quality Data in India (2015–2020), taken from publically available sources Kaggle. The dataset includes information on the AQI and air quality at different stations in numerous Indian cities at hourly and daily intervals. The accuracy has been calculated using MSE, MAPE, RMSE, precision, recall, and F-measure. The robustness of the proposed model is tested using parameters such as negative predicted value and Mathew correlation coefficient. The proposed model is found to efficiently process air quality with an improved accuracy of 98.52% and precision of 99.79%, which is 0.74% higher than the existing state-of-the-art model. The testing findings showed that the proposed approach worked better than the current models and offered a higher rate of accuracy in predicting air pollution. [ABSTRACT FROM AUTHOR]

Lucknow is one of the most polluted metro-city in India with increasing vehicular density and fuel consumption in the last three decades. The study was conducted during years 2019–2021 for measurement of fine particulate matter (PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2), respirable particulate matter (PM10), and noise levels at nine selected sites; 4 residential, 4 commercial, and 1 industrial, encompassing prior-to-lockdown, during-lockdown, and after-lockdown periods. Values of PM10 for prior-to-lockdown, during-lockdown, and after-lockdown period ranged from 133.2 to 197.4, 77.0 to 135.0, and 91.4 to 148.0 µg/m3, respectively while values of PM2.5 were 66.5 to 93.6, 41.9 to 67.5 and 49.5 to 98.6 µg/m3, respectively. Corresponding values of SO2 ranged from 8.7 to 12.8, 5.5 to 7.6, and 11.4 to 17.6 µg/m3, respectively while values of NO2 were 24.6 to 57.0, 20.5 to 32.8, and 26.1 to 43.8 µg/m3, respectively. Order of the trace metals associated with PM2.5 is Co < Cd < As < Cr < Ni < Cu < Pb < Mn < K < Zn, Co < Cd < As < Cr < Cu < Ni < Pb < Mn < Zn < K and Cd < Co < As < Cr < Cu < Ni < Pb < Mn < K < Zn in the same periods. Statistical data evidenced that the air quality of the city witnessed drastic improvement during the COVID-19 pandemic. WHO AIRQ + was utilized to calculate attributable health risk and post-neonatal disease burden; showing 1447 ± 768 estimated number of cases attributable to ambient PM10 per lakh of population. Regulatory authorities need to establish new benchmarks for the prevention and management of public health risks for urban resilience and environmental management for episodic events in the near future. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM) in the atmosphere has become a significant air contaminant with substantial health consequences. Although airborne remote sensing and ground sensor monitoring can offer air quality datasets containing PM2.5, there are limitations to effectively analysing large long‐term datasets. The research aims to evaluate air quality over the Himalayan region using the multifractal approach for the PM2.5 data time series. Fractal dimension (FD), Hurtz exponent (H), and predictability (PI) are estimated using the rescaled range. PM2.5 is found to have high concentration and frequency throughout the day. The same is found in the night hours during peak tourism months. The hourly PM2.5 time series datasets have shown multifractality. The primary reason for this is emissions produced by vehicles and anthropocentric activities in the region. The H is used to assess the dynamic features of the PM2.5 time series in terms of persistence and self‐correlation. In the context of climate change studies, it is crucial to monitor the spatial distribution and dynamic behaviour of PM2.5 in the Himalayan foothills. This study aims to provide prediction analyses and air quality index (AQI) estimates and demonstrate how PM2.5 concentrations alter the sensitive environment throughout the micro to macro scale. This will help us to build a long‐term strategy for reducing the harmful effect of increasing pollution levels on the ecosystem and human health. [ABSTRACT FROM AUTHOR]

Air Pollution is constantly causing a severe effect on the environment and public health. Prediction of air quality is widespread and has become a challenging issue owing to the enormous environmental data with time-space nonlinearity and multi-dimensional feature interaction. There is a need to bring out the spatial and temporal factors that are influencing the prediction. The present study concentrates on the correlation prediction of spatial and temporal relations. A Deep learning technique has been proposed for forecasting the accurate prediction. The proposed Bi_ST model is evaluated for 17 cities in India and China. The predicted results are evaluated with the performance metrics of RMSE, MAE, and MAPE. Experimental results demonstrate that our method Bi_ST accredits more accurate forecasts than all baseline RNN and LSTM models by reducing the error rate. The accuracy of the model obtained is 94%. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern in India, and its adverse health effects are well documented. Climate change is likely to exacerbate this problem by altering weather patterns and increasing the frequency and severity of extreme events. This paper examines the potential impact of climate change on ambient air pollution in India and its implications for policy design. Our analysis reveals that pollution in India is highly sensitive to variation in weather, particularly in the densely populated Indus-Gangetic Plain. Using our estimated relationship between weather and pollution, we predict that changing weather patterns will increase average PM2.5 concentrations by 3.1 µg/m3, leading to a loss of 364 million years of life expectancy. To address this challenge, we propose an emissions fee calibrated to be highest in regions most vulnerable to persistently high levels of pollution and most sensitive to future deterioration in air quality due to climate change. [ABSTRACT FROM AUTHOR]

The winter period is most ideal for studying near-surface aerosols in the Indo-Gangetic plains (IGP) of India, since this period is inundated with significantly higher concentrations of aerosols across the unique geographical domain because of shallow atmospheric boundary layer. This study focuses on analysing the concentration of the biotic component of aerosols (bioaerosols) in a central location of the IGP and estimating their dominance in ambient particulate matter (PM) from 2021 to 2023. Observations showed that bioaerosol concentrations also increased significantly with the increasing concentrations of PM2.5 and PM10, suggesting that bioaerosols are a dominant component of the total aerosol load in the atmosphere. The total microbe's concentration (collectively fungi and bacteria) was found to be 94 to 226 cfu m−3 in PM2.5 and 167 to 375 cfu m−3 in PM10 where bacteria contributed 81.12 and 79.99%, respectively. The contribution of fungal spores in PM2.5 and PM10 remained as 18.88 and 20.01%, respectively, in the total microbes in the respective particulate matter. In the bioaerosols, fungi, namely Aspergillus, Cladosporium, and Penicillium, were dominant, and bacteria, namely E. coli, Mammaliicoccus and Enterobacter, were prevalent in both the PM size regimes. The most prominent microbial presence was observed when the temperature ranged between 16 and 20°C and relative humidity between 80 and 85%. The outcomes of the present study will be useful for further research on the health effect of the bioaerosols in the IGP. [ABSTRACT FROM AUTHOR]

Elevated emission of particulate matter (both PM2.5 and PM10) is not limited to urban areas. It's the major pollutant that drives the air quality across the Indian sub-continent as well as across the globe with adverse health impacts. Moreover, India is home to many polluted cities in recent years that are among the list of most polluting cities in the globe. Therefore, the identification of sources of particulate matter and their quantification along with spatial variability has become of paramount importance from the modelling point of view. The present work is an attempt to develop a high-resolution (~10kmx~10km) national inventory of particulate (both PM2.5 and PM10) pollutants for India for the base year 2020 using IPCC methodology. The study quantifies the emission load from all possible sources in the county using the best possible resolution activity data and bottom-up approach. The estimated annual emission for PM2.5 and PM10, are calculated to be 15.8 Tg/yr, and 8.3 Tg/yr respectively. The developed emission dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.7885103 (Sahu et al., 2023). Transport-driven windblown road dust remains the dominating source of PM10 emission, while transport and industry are the most important sources of PM2.5. The unattended anthropogenic source - municipal solid waste burning is found to be emerging as a new threat followed by crop residue burning. The developed new surface dataset has formulated a few recommendations of possible mitigation strategies for India and would be a critical tool for modelling studies. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic situation has highly restricted the human movements including public transport, commercial and industrial activities in India since the end of March 2020. With the necessity to demonstrate the effectiveness of local emissions, the study aims in analysing the relative variation of ambient air quality before and during COVID-19 with the aid of in situ measurement and Sentinel 5P space technologies. ESA Sentinel 5P Level 2 data \sets including Carbon monoxide (CO), nitrogen dioxide (NO2), Sulphur dioxide (SO2), and Ozone (O3)were pre-processed in SNAP and QGIS platform for the extraction of air quality parameters. Results concluded that the concentration of air quality parameters CO, NO2, SO2, and O3reduced 50.52%, 85.81%, 71.61% and 313.4%, respectively, between March and June 2020. Accuracy of air quality parameters CO, NO2, SO2, and O3obtained from Sentinel 5P datasets are found to be 89.5%, 83.54%, 86% and 94%, respectively, when compared with the in situ measurements. As a favourable outcome of the COVID 19 lockdown, better air quality is recorded over the vulnerable locations due to the absence of significant activities and with the improvement in the air quality the study illustrates the importance of temporary pollution source control measures in a suitable time interval. Integration of in situ measurements and satellite datasets will remove the conventional barriers and provide spatially distributed observations that can assist the decision-makers in formulating the best air pollution control strategy. [ABSTRACT FROM AUTHOR]

The significance of air quality monitoring for analyzing the impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments ranging from expensive reference stations that provide accurate data to low-cost sensors that provide reasonable air quality measurements. In this research, we use a combination of sensors and monitors, which we call as hybrid instruments and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM2.5 concentration). Note that there can be several constraints such as those on budget, minimum number of reference stations to be placed, set of important regions where at least one sensor should be placed and so on. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and works on the principles of evolution. The second one is a greedy algorithm that selects locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM2.5 concentration, budget available, etc. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb. [ABSTRACT FROM AUTHOR]

Fourteen Indian cities, including urban and rural locations, were chosen for the present study across India, with unhealthy air quality based on National Air Quality Index (NAQI > 100). However, it was found that NAQI values over the locations are driven by the undifferentiated mass concentration of particulate matter (PM, both PM 10  and PM 2.5 ) than other criteria pollutants. The PM 2.5  and PM 10  concentration during the winter violated the National Ambient Air Quality Standards (NAAQS) of India by two to five times at six urban locations, with the mean daily PM 2.5  concentration averaged over the month; the the largest being at Patna (353 µg m − 3 ) during the winter and lowest at Bengaluru (27 µg m − 3 ) during the summer. The analysis of chemical species, in general, indicated NO 2  (SO 2 , CO) as having a 25% to 70% (16% to 50%, 16% to 85%) increase in concentration from the summer to winter, which is adequately reflected in higher fuzzy scores during the winter. Thus, to provide a realistic approach to air quality management, the present study focuses on identifying priority-based locations requiring immediate mitigation measures by developing a Prioritized Clean Air Assessment Tool (PCAT). The tool utilizes a fuzzy-based algorithm to incorporate the cumulative effect of all six criteria pollutants, taking into consideration the severity of their expected health implications. Using PCAT, Delhi and Varanasi cities are identified for prioritized mitigation considering the NAAQS of India, unlike all cities (except Bengaluru) during the winter and nine out of fourteen cities during the summer, considering the NAQI. Using more stringent WHO guideline values in PCAT, six cities out of fourteen were identified requiring immediate mitigation during the winter and summer months; locations such as Solapur and Patna are identified to need season-specific mitigation measures during the summer and winter, respectively. The tool is simplistic, user-friendly, and quickly evaluates multiple locations simultaneously to provide priority sites. [ABSTRACT FROM AUTHOR]

• The spatio-temporal variations of BTEX across India were studied. • AQI based on BTEX pollution levels were used to categorise different zones in India. • The northern zone shows high pollution due to BTEX, and south zone shows the least. • Traffic emissions, solvent use and evaporation are the major sources of BTEX in India. 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. [ABSTRACT FROM AUTHOR]

Winter fog and severe aerosol loading in the boundary layer over north India, especially in the Indo-Gangetic Plain (IGP), cause disruption in the daily lives of millions of people in the region. To understand better the role of atmospheric aerosols on the occurrence, spatial extent, and persistence of winter fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). Results from WRF-Chem represented relative humidity (RH) and fog formation in agreement with observations when using the ERA-Interim reanalysis data as meteorological initial/boundary conditions and soil nudging were applied. WRF-Chem successfully simulates the spatial distribution and magnitude of PM2.5 when evaluated with observations from the Central Pollution Control Board of India (CPCB) monitoring network. However, the aerosol composition predicted by WRF-Chem was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with chloride aerosol fraction being strongly underpredicted (~66.6 %). By investigating a fog event on December 23–24, 2017 over central IGP, we found that the aerosol-radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM2.5 concentrations and RH within the boundary layer. The increase in RH is found to be important for fog formation and it promoted the growth of aerosol size and increased aerosol activation in the polluted environment over IGP. Loss of aerosols through deposition of cloud droplets is found to be a significant aerosol loss process during fog. The internal mixing of absorbing aerosols and hygroscopic growth reduces the single scattering albedo impacting aerosol-radiation feedbacks. Aqueous-phase chemistry increases the PM2.5 concentrations during fog events which subsequently participates in aerosol-radiation feedback. With aerosol-radiation interaction and aqueous phase chemistry, fog formation began 1–2 hours earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. These processes were also found to increase RH, stabilize the boundary layer, increase PM2.5 promoting aerosol activation, and thus increasing the fog water content over IGP. This suggests that the aerosol-radiation feedback and secondary aerosols play an important role in the air quality and in the intensity and lifetime of fog over IGP. [ABSTRACT FROM AUTHOR]

Over the past decades, air pollution has turned out to be a major cause of environmental degradation and health effects, particularly in developing countries like India. Various measures are taken by scholars and governments to control or mitigate air pollution. The air quality prediction model triggers an alarm when the quality of air changes to hazardous or when the pollutant concentration surpasses the defined limit. Accurate air quality assessment becomes an indispensable step in many urban and industrial areas to monitor and preserve the quality of air. To accomplish this goal, this paper proposes a novel Attention Convolutional Bidirectional Gated Recurrent Unit based Dynamic Arithmetic Optimization (ACBiGRU-DAO) approach. The Attention Convolutional Bidirectional Gated Recurrent Unit (ACBiGRU) model is determined in which the fine-tuning parameters are used to enhance the proposed method by Dynamic Arithmetic Optimization (DAO) algorithm. The air quality data of India was acquired from the Kaggle website. From the dataset, the most-influencing features such as Air Quality Index (AQI), particulate matter namely PM2.5 and PM10, carbon monoxide (CO) concentration, nitrogen dioxide (NO2) concentration, sulfur dioxide (SO2) concentration, and ozone (O3) concentration are taken as input data. Initially, they are preprocessed through two different pipelines namely imputation of missing values and data transformation. Finally, the proposed ACBiGRU-DAO approach predicts air quality and classifies based on their severities into six AQI stages. The efficiency of the proposed ACBiGRU-DAO approach is examined using diverse evaluation indicators namely Accuracy, Maximum Prediction Error (MPE), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC). The simulation result inherits that the proposed ACBiGRU-DAO approach achieves a greater percentage of accuracy of about 95.34% than other compared methods. [ABSTRACT FROM AUTHOR]

An effective micro-level air quality management plan requires high-resolution monitoring of pollutants. India has already developed a vast network of air quality monitoring stations, both manual and real time, located primarily in urban areas, including megacities. The air quality monitoring network consists of conventional manual stations and real time Continuous Ambient Air Quality Monitoring Stations (CAAQMS) which comprise state-of-the-art analysers and instruments. India is currently in the early stages of developing and adopting economical portable sensor (EPS) in air quality monitoring systems. Protocols need to be established for field calibration and testing. The present research work is an attempt to develop a performance-based assessment framework for the selection of EPS for air quality monitoring. The two-stage selection protocol includes a review of the factory calibration data and a comparison of EPS data with a reference monitor, i.e. a portable calibrated monitor and a CAAQMS. Methods deployed include calculation of central tendency, dispersion around a central value, calculation of statistical parameters for data comparison, and plotting pollution rose and diurnal profile (peak and non-peak pollution measurement). Four commercially available EPS were tested blind, out of which, data from EPS 2 (S2) and EPS 3 (S3) were closer to reference stations at both locations. The selection was made by evaluating monitoring results, physical features, measurement range, and frequency along with examining capital cost. This proposed approach can be used to increase the usability of EPS in the development of micro-level air quality management strategies, other than regulatory compliance. For regulatory compliance, additional research is needed, including field calibration and evaluating EPS performance through additional variables. This proposed framework may be used as starting point, for such experiments, in order to develop confidence in the use of EPS. [ABSTRACT FROM AUTHOR]

Smart energy management, along with clean technologies and advanced pollution control measures has proven to be an effective strategy for reducing air pollution in both developed and transitioning economies and holds significant potential to reduce emissions from industries and power plants—two of the major sources of air pollution in many of India's urban areas. This manuscript presents the results from a project and provides a critical proof of concept to show that developing country cities can achieve significant, measurable reductions in air pollution, Volatile Organic Compound emissions and greenhouse gas emissions through investing in smart energy management and advance control technologies. Achieving this goal, however, requires addressing two key challenges: (1) lack of a demonstrated link between smart energy management and air quality improvements in India; and (2) the need to scale up development, financing, and implementation of smart energy solutions, including energy efficiency and clean technology measures that can help address air pollution in industrialized urban areas, critically polluted areas by offsetting fossil fuel use for diesel‐ and coal‐fired electricity generation. [ABSTRACT FROM AUTHOR]

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution. [ABSTRACT FROM AUTHOR]

The current study was conducted around the province of Haldwani City, Uttarakhand, India, to understand the seasonal variation of ambient air pollutants (PM2.5, PM10, SO2, and NO2) and their impact on four tree species, i.e., neem (Azadirachta indica), mountain cedar (Toona ciliate), bottlebrush (Callistemon citrinus), and guava (Psidium guajava) during 2020–2021. Multiple linear regression (MLR)-based prediction analysis showed that the selected air quality variables (PM2.5, PM10, SO2, and NO2) had a significant impact on the biochemical responses of selected tree spp. including, pH, ascorbic acid (AA), total chlorophyll content (T. Chl.), relative water content (RWC), and dust deposition potential. In this, the coefficient of variance (R2) of the developed models was in the range of 0.70–0.98. The ambient air pollutants showed significant seasonal variations as depicted by using the air pollution tolerance index (APTI) and anticipated performance index (API). The tree species from polluted sites observed more pollution tolerance than the tree species from the control site. Regression analysis showed a significant positive association between the biochemical characteristics and APTI, with the highest influence by AA (R2 = 0.961) followed by T. Chl., RWC, and pH. The APTI and API score was observed as maximum for A. indica and minimum for C. citrinus. The impact of air pollutants on the morphology of foliar surface was investigated by the scanning electron microscopy (SEM) and recorded various dust deposition patterns, stomatal blockages, and damage of guard cells in the trees growing along the polluted site (S2). The present study can assist environmental managers to examine the pollution-induced variables and develop an effective green belt for combating air pollution in polluted areas. [ABSTRACT FROM AUTHOR]

Extreme smog in India widely impacts air quality in late autumn and winter months. While the links between emissions, air quality and health impacts are well‐recognized, the association of smog and its intensification with climatic trends in the lower troposphere, where aerosol pollution and its radiative effects manifest, are not understood well. Here we use long‐term satellite data to show a significant increase in aerosol exceedances over northern India, resulting in sustained atmospheric warming and surface cooling trends over the last two decades. We find several lines of evidence suggesting these aerosol radiative effects have induced a multidecadal (1980–2019) strengthening of lower tropospheric stability and increase in relative humidity, leading to over fivefold increase in poor visibility days. Given this crucial aerosol‐radiation‐meteorological feedback driving the smog intensification, results from this study would help inform mitigation strategies supporting stronger region‐wide measures, which are critical for solving the smog challenge in India. Plain Language Summary: Severe air pollution in India and its impacts on air quality and public health are worsening. Extreme smog episodes are frequently observed in northern India associated with the highest aerosol concentrations and hazardous visibility conditions. It is well‐known that anthropogenic emissions directly affect pollution, but it remains unclear from an observational perspective how the stability of the lower troposphere, where aerosol pollution builds up, impacts the long‐term evolution of smog. Using a multidecadal analysis of satellite, ground and reanalysis data sets, here we show sustained intensification of extreme smog associated with the strengthening of lower tropospheric stability, potentially amplified by aerosol‐induced atmospheric warming. Solving the smog crisis in India is increasingly critical given the strongly linked aerosol‐radiation‐meteorological interactions. Key Point: Past 40‐year observations reveal aerosol‐induced radiation‐meteorological feedbacks have intensified extreme smog in India [ABSTRACT FROM AUTHOR]

The COVID-19 first case was reported in India on January 30, 2020, and in Chhattisgarh on March 19, 2020, and since then a sharp surge has been noticed. The government of India imposed a nationwide lockdown on March 25, 2020, a complete suspension of human activities, viz., industry, construction, transport, academic institutions, etc., which resulted in an improvement in air quality (a decrease in PM2.5 and PM10, alteration in NO2 and an increase in O3). But the rate of cases of COVID-19 has increased sharply, and eventually, under economic pressure, the lockdown was withdrawn on June 1, 2020, which further accelerated the exponential growth of COVID-19 cases. We noticed that in the Chhattisgarh State of India, the alteration in the air quality index during the lockdown period is continuing even after the restoration of anthropogenic activities. Among PM2.5, PM10, NO2, and O3, the behavior of NO2 was found to be different than others; it was found to increase during the lockdown period but further decreased with the resumption of anthropogenic activities. We conclude that the air quality index has an insignificant impact on COVID-19 infection. [ABSTRACT FROM AUTHOR]

The deteriorating air quality has become a matter of concern throughout the world with significant seasonal variations on a regional and transboundary scale. The selection of control strategies is comparatively more challenging on a regional or transboundary scale owing to the lack of accurate assessment of the contribution of various pollution sources. The present paper attempts to review the application of regional air quality models for accurate prediction. The main focus of the review is to understand the operational challenges of carrying out WRF-Chem model simulation considering technical expertise, time and computational requirements. WRF-Chem is a regional-scale chemistry transport model, which has been used by researchers in the past to assess transboundary and regional ambient air quality and its impacting sources. The model generally takes input from global databases which are mostly dynamic variables and need to be updated with primary survey data. The high-resolution local information, including Land-Use-Land-Cover, local anthropogenic emissions and biomass emissions, is an important database for the model and must be updated at regular intervals. This regional scale chemistry transport model is computationally expensive and requires specialised knowledge, thus, challenging to be used by various air quality managing agencies operating at state and city levels. The review also discusses agencies involved in regional scale modelling in different countries with their roles and responsibilities. Considering this complexity, it is recommended to phase-wise develop a centre of excellence for regional air quality modelling in the country. [ABSTRACT FROM AUTHOR]

Hair salons use many hair products that have toxic chemicals in them. These toxic chemicals include volatile organic compounds, formaldehyde, and particulate matter. Daily exposure to these pollutants causes severe health issues in the long run. This study aims to find the concentration of the air pollutants such as PM1, PM2.5, PM10, TVOC, CO2, and formaldehyde in four hair salons located in Coimbatore, Tamil Nadu, India. In this paper, we propose an IoT-based air quality monitoring system with integrated sensors to monitor the concentration of air pollutants remotely via ThingSpeak data analytics cloud platform in hair salons. The maximum 15 min average concentration values of PM1, PM2.5, and PM10 were 128, 154, and 169 µg/m3 respectively. The TVOC levels exhibited a rapid increase of about 80–90% during facials and hair gel application and a peak value of about 5248.25 ppb was measured at salon 2. Also, weekend and weekday comparison is done. It was found that the weekend concentrations of the measured pollutants are comparatively higher than weekday concentrations. After analyzing the pollutant concentration, the effects of primary health parameters such as blood pressure and pulse rate of the hairdressers are measured. One-third of the hairdressers displayed high blood pressure values with a maximum of 161/104 which falls under stage 2 hypertension. Also, secondary parameters such as temperature, humidity, ventilation type, and number of customers are also measured. From the overall analysis, it is suggested that adequate ventilation and regulated product usage are said to reduce the effects of indoor air pollution. [ABSTRACT FROM AUTHOR]

Background: Poor air quality has been linked to cognitive deficits in children, but this relationship has not been examined in the first year of life when brain growth is at its peak. Methods: We measured in-home air quality focusing on particulate matter with diameter of <2.5 µm (PM2.5) and infants' cognition longitudinally in a sample of families from rural India. Results: Air quality was poorer in homes that used solid cooking materials. Infants from homes with poorer air quality showed lower visual working memory scores at 6 and 9 months of age and slower visual processing speed from 6 to 21 months when controlling for family socio-economic status. Conclusions: Thus, poor air quality is associated with impaired visual cognition in the first two years of life, consistent with animal studies of early brain development. We demonstrate for the first time an association between air quality and cognition in the first year of life using direct measures of in-home air quality and looking-based measures of cognition. Because indoor air quality was linked to cooking materials in the home, our findings suggest that efforts to reduce cooking emissions should be a key target for intervention. [ABSTRACT FROM AUTHOR]

The Air Quality Index (AQI) dataset contains information on measurements of pollutants and ambient air quality conditions at certain location that can be used to predict air quality. Unfortunately, this dataset often has many missing observations and imbalanced classes. Both of these problems can affect the performance of the prediction model. In particular, predictions for the minority class are very important because inaccurate predictions can be fatal or cause big losses. Moreover, the missing data may lead to biased results. This paper proposes the single imputation of the median and the multiple imputations of the k -Nearest Neighbor (KNN) regressor to handle missing values of less than or equal to 10% and more than 10%, respectively. At the same time, the SMOTE-Tomek Links address the imbalanced class. These proposed approaches to handle both issues are then used to assess the air quality prediction of the India AQI dataset using Naive Bayes (NB), KNN, and C4.5. The five treatments show that the proposed method of the Median-KNN regressor-SMOTE-Tomek Links is able to improve the performance of the India air quality prediction model. In other words, the proposed method succeeds in overcoming the problems of missing values and class imbalance. [ABSTRACT FROM AUTHOR]

Stubble burning in Punjab, India, poses significant environmental challenges, particularly impacting air quality. This study aims to examine the spatial and temporal patterns of stubble burning events and their potential effect on ambient air quality from 2019 to 2022. High-resolution Sentinel-2 satellite imagery was employed to delineate the spatial extent of stubble burning. Burnt areas were identified using the Normalised Burn Ratio (NBR). Air quality was evaluated based on PM2.5 and PM10 concentrations data obtained from the Punjab Pollution Control Board. The Inverse Distance Weighting (IDW) interpolation technique was used to estimate pollution values in areas lacking direct monitoring. The study revealed significant year-to-year variations in areas affected by stubble burning. The smallest burnt areas were recorded in October 2019 and 2021 (209 sq km), while the largest was in 2020 (755.38 sq km). In every year studied, the burnt area in November consistently exceeded that in October, with the largest area (10315 sq km) observed in 2021. PM2.5 and PM10 concentrations also showed annual fluctuations, with the highest recorded in 2020 and 2021. In particular, in October 2020, higher PM2.5 and PM10 levels were detected in the eastern region of Punjab. November consistently exhibited higher PM2.5 and PM10 concentrations than October for all years analysed, peaking in 2021. The spatial and temporal variations of stubble burning events and their relationship with air quality highlight the need for targeted interventions. Understanding these patterns is crucial for mitigating the adverse effects of stubble burning on air quality in Punjab, India. Future research should focus on evaluating the effectiveness of various mitigation strategies. [ABSTRACT FROM AUTHOR]

Severe air pollution in China and India threatens the health of over one-third of the global population. When it comes to air pollution governance, the two countries have vastly different approaches. China's approach features centralized target-setting and implementation, with policies enforced primarily through top-down administrative lines. While India's approach reflects the division of power among central and state governments and relies substantially on legislative and judiciary systems for accountability. We conduct a detailed review of the trends and sources of six air pollutants in China and India, and make a structured comparison of China and India's air pollution policy frameworks. An extensive literature review is conducted to evaluate the effectiveness of policies in each country. The studies show that China's policy efforts have achieved significant improvements in air quality, while India's policies have been largely ineffective. Nevertheless, both approaches have the potential to lead to effective air pollution governance, if a country can build strong political will and clearly defined accountability systems. We further identify key lessons learned from the two countries for air pollution policymaking in developing countries, including taking good advantage of "post-crisis policy window", and targeting "low-hanging fruits" to make incremental improvements in a cost-effective way. • We systematically reviewed and compared air pollution governance in China and India. • China and India have similar policy frameworks but different implementation approaches. • Strong political will and clearly defined accountability systems are key to success. • We provide insights into effective air pollution control in developing countries. [ABSTRACT FROM AUTHOR]