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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]

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]

Diwali has become an occasion of air and noise pollution, and the release of particulate matter and toxic gases has chronic and acute effects on people and their environment. Thus, an air quality assessment study was done by CSIR-CSMCRI covering the pre-to-post Diwali 2021 period (5 days) in the three locations (traffic, residential, and control) of Bhavnagar. The average 24-h concentration of PM10 (380 µg/m3), PM2.5 (182.2 µg/m3), and SPM (403 µg/m3) was significantly higher during Diwali, exceeding the National Ambient Air Quality Standards (NAAQS). The concentrations of SO2 and NO2 were 121.8 µg/m3 and 102.1 µg/m3. Metals like Zn, Al, Pb, and Mn were found in higher concentrations during the study. The air quality index (AQI) was maximum on Diwali, resembling very poor air quality. More elements and oxides were detected in PM2.5 (S, Al, Mg, Ba, and Zn and their oxides) than in PM10 (Fe and S) through WDXRF. Water-soluble anions like SO42−, Cl−, and NO3− were observed during the study, with a higher SO42− (64%) on Diwali. The PM10 morphology and mapping of elements were done using SEM–EDX. Emerging contaminants, specifically phthalate groups, were detected through GCMS. The enrichment factor (EF) showed Zn and Pb originating from anthropogenic activities. The air quality data was validated using a variance test, least significance difference (LSD), correlation, and principal component analysis (PCA). This paper is the first to highlight the air quality assessment during Diwali for a western coastal place in India. It is time to implement regulations on burning firecrackers for pollution reduction, aiming to achieve a sustainable atmosphere. [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]

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]

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]

The related literature reveal that the papers on environmental pollution do not sufficiently analyse the cement production which is an important determinant of air pollution and health problems by using econometric methods. To fill this gap, this paper aims to examine the relationship between cement production, air pollution, mortality rate, and economic growth by employing MS Bayesian Vector Autoregressive (MScBVAR) and Markov Switching Bayesian Granger causality (MScBGC) approaches from 1960 to 2017 for China, Brazil, India, Turkey and the USA. MSIA(2)-BVAR(1) model for China, MSIAH(2)-BVAR(3) models for India, MSIAH(3)-BVAR(2) for Brazil, and MSIAH(3)-BVAR(1) for Turkey, and MSIAH(2)-BVAR(2) for the USA were selected. The MScBGC results revealed that the cement production is granger cause of mortality rate, air pollution, and economic growth in all regimes for China, India, Brazil, Turkey, and the USA. [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]

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India's economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere's natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World. [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 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]

In this paper, we have evaluated the bioaccumulation of metals by Atrichum angustatum, which is a readily available moss species in the Brahmaputra valley, India. A systematic investigation of metallic pollutants in the atmosphere was carried out using A. angustatum as a biomonitor collected from representative locations during three seasons viz. winter, pre-monsoon, and monsoon (n = 99) during the year 2018. The study was done in four unique habitations of the Brahmaputra Valley, which were further divided into three landuse areas: residential, roadside, and industrial. The highest accumulations were seen against Ca, Mg, Zn, and Fe. The calculated contaminant factors and ecological risk indices suggest that the Brahmaputra Valley is mostly contaminated by Cr, Zn, Cu, Ni, and Pb, and these metals pose a maximum ecological risk. The accumulation trend of metallic pollutants was site-specific, but most metals showed positive seasonal accumulation. A significant difference in spatial and seasonal accumulation patterns was specific to metal species. Principal component analysis (PCA) and inter-species correlations revealed that the air quality of Brahmaputra valley was greatly affected by coal burning, vehicular emission, biomass burning, road dust, and crustal dust. Finally, the study led us to the conclusion that A. angustatum can serve as a potential biomonitor for metallic pollutants. [ABSTRACT FROM AUTHOR]

The current nationally determined contributions, pledged by the countries under the Paris Agreement, are far from limiting climate change to below 2 ∘C temperature increase by the end of the century. The necessary ratcheting up of climate policy is projected to come with a wide array of additional benefits, in particular a reduction of today's 4.5 million annual premature deaths due to poor air quality. This paper therefore addresses the question how climate policy and air pollution–related health impacts interplay until 2050 by developing a comprehensive global modeling framework along the cause and effect chain of air pollution–induced social costs. We find that ratcheting up climate policy to a 2 ∘ compliant pathway results in welfare benefits through reduced air pollution that are larger than mitigation costs, even with avoided climate change damages neglected. The regional analysis demonstrates that the 2 ∘C pathway is therefore, from a social cost perspective, a "no-regret option" in the global aggregate, but in particular for China and India due to high air quality benefits, and also for developed regions due to net negative mitigation costs. Energy and resource exporting regions, on the other hand, face higher mitigation cost than benefits. Our analysis further shows that the result of higher health benefits than mitigation costs is robust across various air pollution control scenarios. However, although climate mitigation results in substantial air pollution emission reductions overall, we find significant remaining emissions in the transport and industry sectors even in a 2 ∘C world. We therefore call for further research in how to optimally exploit climate policy and air pollution control, deriving climate change mitigation pathways that maximize co-benefits. [ABSTRACT FROM AUTHOR]

Background: Crop residue burning adversely affects air quality and consequently human health. India, being one of the largest agro-economies of the world, produces around 500 Million tonnes of crop residue annually most of which is burnt on-farm. However, integrated studies that simultaneously quantify the effects of crop residue burning while exploring the subjective determinants of the practice are lacking in India. This paper describes the protocol for a longitudinal mixed methods research study employing a community-based participatory approach to fill this gap.Methods: Both quantitative and qualitative data will be collected in a rural setting of the central Indian province of Madhya Pradesh, over 1 year. A steering committee comprising of the research team and community representatives will be formed. The proportion of cultivable land burnt in one crop burning season will be estimated. The association between crop residue burning, level of ambient air pollutants, and pulmonary function of village residents will be determined. Focus groups, interviews, and participatory rural appraisal methods will be used to explore stakeholder perspectives about crop residue burning. Potential barriers and opportunities for substituting burning with an alternative crop residue management technique will be ascertained as the basis for future interventions. Ethics approval has been obtained from the Institutional Ethics Committee of the National Institute for Research in Environmental Health (No: NIREH/BPL/IEC/2019-20/1494, dt 06/01/2020).Discussion: This manuscript describes the protocol for a novel community-based participatory study to investigate thoroughly the phenomenon of crop residue burning from the perspective of the agricultural community through their active collaboration. The lack of comprehensive evidence regarding the factors responsible for crop residue burning in India underlines the importance of implementing this study protocol to fill in this critical gap in knowledge. While acknowledging that findings of this study will be not generalizable to agricultural communities other than the one studied, it is expected that the study will generate baseline evidence that might be beneficial in developing and implementing an appropriate intervention strategy. [ABSTRACT FROM AUTHOR]

This paper presents a summary of PM2.5, PM10 and gaseous pollutant concentrations measured during each season of the year from March 1, 2018 to February 28, 2019 in Visakhapatnam city (17.6868°N, 83.2185°E) located on the east coast of India. The city is studded with 14 major industries and surrounded on three sides by mountains and the Bay of Bengal on the fourth side. The monthly variations of mass concentrations of PM2.5, PM10 and gaseous pollutants SO2, NO2 and CO recorded revealed the impact of atmospheric pollutants originating from industry, urbanization and increased automobile traffic. The seasonal variability of PM concentrations, highest in winter and lowest in summer, is observed. The annual averages for 2018 in Visakhapatnam are 103.5 ± 55.1 μg/m³ and 111.5 ± 29.1 μg/m³ for PM2.5 and PM10 respectively. To establish the causal relationship between PM2.5, PM10 and the gaseous pollutants we used Pearson correlation and regression statistical methods. The Pearson correlation coefficients between PMs and gaseous pollutants were either high or moderate. Regression results further confirmed that NO2 and SO2 significantly impacted PM2.5 and PM10 in Visakhapatnam city. [ABSTRACT FROM AUTHOR]

In the past few decades, many urban areas around the world have suffered from severe air pollution and the health hazards that come with it, making gathering real-time air quality and air quality forecasting very important to take preventive and corrective measures. This paper proposes a scalable architecture to monitor and gather real-time air pollutant concentration data from various places and to use this data to forecast future air pollutant concentrations. Two sources are used to collect air quality data. The first being a wireless sensor network that gathers and sends pollutant concentrations to a server, with its sensor nodes deployed in various locations in Bengaluru city in South India. The second source is the real-time air quality data gathered and made available by the Government of India as a part of its Open Data initiative. Both sources provide average concentrations of various air pollutants on an hourly basis. Due to its proven track record of success with time-series data, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model was chosen to perform the task of air quality forecasting. This paper critically analyses the performance of the model in two regions that exhibit a significant difference in temporal variations in air quality. As these variations increase, the model suffers performance degradation necessitating adaptive modelling. [ABSTRACT FROM AUTHOR]

Urbanization and Industrialization during the last few decades have increased air pollution causing harm to human health. Air pollution in metro cities turns out to be a serious environmental problem, especially in developing countries like India. The major environmental challenge is, to predict accurate air quality from pollutants. Envisaging air quality from pollutants like PM2.5, using the latest deep learning technique (LSTM timer series) has turned out to be a significant research area. The primary goal of this research paper is to forecast near-time pollution using the LSTM time series multivariate regression technique. The air quality data from Central Pollution Control Board over Hyderabad station has been used for the present study. All the processing is done in real-time and the system is found to be functionally very stable and works under all conditions. The Root Mean Square Error (RMSE) and R2 have been used as evaluation criteria for this regression technique. Further, the time series regression has been used to find the best fit model in terms of processing time to get the lowest error rate. The statistical model based on machine learning established a relevant prediction of PM2.5 concentrations from meteorological data. [ABSTRACT FROM AUTHOR]

Air pollution is an emerging public health concern as there are increasing evidences that the quality of air adversely affects human health due to the presence of various toxic pollutants. Linking air pollution from its source to adverse human health effects is a complicated phenomenon that requires a multidisciplinary approach for better understanding. Decision-makers need relevant, comprehensive estimates of the disease burden attributable to different risk factors. Many statistical models have become very relevant for estimating atmospheric concentrations by analysis of complex datasets to produce inferences and predictions that can lead to better management of air pollution. This paper focuses on the Indian scenario as a case study and presents the current status of air quality in India with special reference to particulate matter. The study suggest that air-quality networks need to be developed that can depict and forecast pollution levels with health advisories for public and for pollution emergencies measures. Development of statistical models, and methods for Big Data Analytics, can yield a wide array of actionable insights to facilitate policy decisions. Models may also be used to predict the cost of the air-pollution control measures as well as the benefits in terms of the control of acute and chronic diseases caused by air pollution. This study concludes that the application of statistical models and algorithms can act as an important tool to bridge the gap between science and policy. [ABSTRACT FROM AUTHOR]

Air pollution refers to the release of pollutants into the air that jeopardises human health and also results in serious environmental problems. Many countries are suffering from heavy air pollution. The inhalation of pollutant will lead to acute conditions like asthma, chronic lung diseases, and cancer. The Air Pollution not only harms the health of both humans and animals but also abolish the life of the plants. Hence the estimation of Air Quality is an essential. This paper employs air quality estimation using Particle Swarm Optimization. Multilayer perceptron type of back-propagation Neural Network is used to analyse the status of air pollution at various locations in India. The efficiency of the proposed model is evaluated with K-Nearest Neighbor, Random Forest and Support Vector Machine algorithms by classifying the data into six classes of pollution levels, which can be simply understood by the public as Good, Satisfactory, Moderately Polluted, Poor, Very Poor and Severe. [ABSTRACT FROM AUTHOR]

The chaotic structure of air pollution, human health, the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and mortality rate were explored for China, India, and Turkey in the period of 1975-2018 by using four different methods. Firstly, Lyapunov and Shannon tests were applied to determine chaotic dynamics. The maximum Lyapunov test, for the selected variables, found the evidence of chaotic dynamics. Secondly, Fourier ADF and NL tests were applied. Fourier unit root test determined stationary of the variables. Thirdly, bootstrapping autoregressive distributed lag with Fourier transformation (FBARDL) was used to determine the evidence of cointegration between the variables with two different models. FBARDL test determined that the manufacturing, tourism, transport, and construction industries, air pollution, and mortality rate have evidence of cointegration in different two models. Lastly, the causality test with Fourier transformation was used to determine the direction of causality between the variables. Granger causality test determined that there is evidence of one-way causality running from transportation, tourism, construction, and manufacturing industries to air pollution and mortality rates. Accordingly, the results of this paper suggest air pollution and human health have chaotic behaviors. Air pollution has a complex, multi-variable, and multi-coupling system. Air pollutants influencing factors and air pollution itself have adverse effects on human health. [ABSTRACT FROM AUTHOR]

Air quality monitoring (PM2.5, CO, NOx, SO2 and EC/OC) was carried out at highway toll plazas, municipality toll plazas and control sites (n = 23) in the National Capital Region of Delhi, to determine the exposure of toll plaza workers to air pollution and its effect on lung function. Lung function indices (n = 45) were also measured for these workers. The results reveal the high level of air pollution at almost all locations with PM2.5 values exceeding the national permissible limit except at a few control sites. Observed reduction in lung function indices was significant over years of occupational exposure even after adjusting for age, amongst non-smoking outdoor workers. This study found pollutant concentrations were highest at municipality toll plazas with minimum protective work area. The paper suggests measures to reduce the exposure of outdoor workers. [ABSTRACT FROM AUTHOR]

Rapid industrialization, urbanization, and motorization lead to hiking of air pollution in various mega cities including Delhi, India. Studies done to analyze pollution suggests that vehicle exhaust emissions are major contributor to the urban air quality along with many other comparable sources. Hence to check instantaneous pollution increment, government aims to reduce vehicle emission. Traditional policies are working at times for holding sudden increase but long-term effects are not seen which need meso-spatial and small temporal policy. For optimizing meso-spatial policies implementation, many intricacies related to moving and stationary sources in near real-time have to be monitored including vehicular emission, itself. Analyzing these moving source emissions on the meso-spatial level is challenging. Therefore, the present study is an attempt to first understand the need of meso-spatial and near real-time checking policy and then leveraging the usage of previous region specific pollutant survey reports to map real-time pollution monitoring parameters. For that, a real-time vehicle monitoring strategy is proposed to map city-wide traffic congestion and emission using crowd-source data of Google Maps. For comparative study of different meso-spatial regions real-time moving source pollutant parameter 'μ' is calculated using a factor 'F'. Also, the different effects of congestion on vehicular emission in two different meso-spatial areas of Delhi is presented using comparative case study. Analysis of case study revealed that Anand Vihar area is more prone to vehicle emission variability in case of congestion fluctuation. Further the discussion of short temporal and spatial emission calculation, for weekly pollution evaluation is presented in the paper. This will be helpful to the authorities in formulating technological, institutional and traffic management policies. • Traditional pollution checking policy and different emission models are discussed for Delhi region. • A methodology to map real-time congestion for city-wide area on small tempo-spatial basis is provided. • A factor 'F' is introduced for relating congestion to vehicular and dust emission. • Vehicle proportionate area specific moving source emission 'μ' is calculated and analyzed by comparative emission study. • Suggestions on checking policy and the methodology to judge their effectiveness for immediate action is provided. [ABSTRACT FROM AUTHOR]

Building an effective clean air action plan for a city's air pollution problem requires an extensive network of monitoring stations to represent spatial and temporal patterns and an understanding of the sources contributing to the problem. In India, the National Clean Air Programme (NCAP) was launched in 2019 to develop clean air action plans for 131 non-attainment cities, which includes conducting source apportionment studies and establishing emission baselines. As of February 2023, only 39 cities have completed the apportionment studies. In this paper, we present the essential resources needed to strengthen the ambient air monitoring networks, for designing a representative airshed size, sampling size, and sampling frequency, to effectively track the progress made in the cities for better air quality. The NCAP cities were grouped into 104 airsheds (5.3% of the national area), collectively representing a total of 164 cities and a total population of 295 million (21% of the national total). Of these airsheds, 73 contain only one city; 18 contain two cities, and nine contain three cities. Four airsheds – Delhi, Mumbai, Indore, and Chandigarh contain 10, 8, 5, and 5 cities respectively. To measure and analyse particulate matter pollution, a total of 2118 sampling sites are recommended for the 104 airsheds. Cities could consider hybrid monitoring networks by complementing existing regulatory monitoring network with a high-density network of low-cost/sensor-grade monitors. An airshed level air quality management plan, an enhanced monitoring network, and consolidation of information on emission sources, are crucial for optimizing the clean air efforts under NCAP. [Display omitted] • Defined 104 airsheds to represent 131 non-attainment cities under NCAP. • Defined the minimum number of monitors required in the airsheds. • Conducted high-resolution meteorological modelling to determine minimum filter sampling frequency per airshed. • Examples of hybrid networks to reduce monitoring costs. [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]

The enormity of air pollution has always been a matter of concern due to rapid development and urbanization over a long period. The increasing level of pollutants in ambient air in 2016-2017 has deteriorated the air quality of Delhi at an alarming rate. This brought us to focus our study on air quality in Delhi region. The prediction of future air quality has been carried out by analyzing the pollutants using data analysis techniques. In our previous study, we had analyzed the data from 2011-2015. Detailed analysis from 2009-2017 of air pollutants has been proposed in this extended paper along with the critical observation of 2016-2017 air pollutants trend in Delhi. Descriptive analysis and predictive analysis have been used to study the trends of various air pollutants like sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), suspended particulate matter (PM), ozone (O 3 ) carbon monoxide (CO), benzene, and forecast the future trend. We have observed through data analytics techniques that SO 2 is likely to increase by 1.24ug/m 3 , NO 2 is likely to increase by 16.77ug/m 3 , O3 is likely to increase by 6.11 mg/m 3 , benzene is likely to reduce by 1.33 mg/m 3 and NO 2 is predicted to reduce by 0.169 mg/m 3 in the coming years. [ABSTRACT FROM AUTHOR]

Purpose: The current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution. Design/methodology/approach: The air quality parameters under study are particulate matter10 (PM10), PM2.5, SO2 and NO2. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results. Findings: The findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO2, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib. Research limitations/implications: Future research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India. Originality/value: Very few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors. [ABSTRACT FROM AUTHOR]

This world has a wide range of technologies and possibilities that are available to control air pollution. Still, finding the best solution to control the contamination of the air without having any impact on humans is a complicated task. This proposal helps to improve the air quality using the multi-criteria decision making method. The decision to improve air quality is a challenging problem with today's technology and environmental development level. The multi-criteria decision making method is quite often faced with conditions of uncertainty, which can be tackled by employing fuzzy set theory. In this paper, based on an objective weighting method (CCSD), we explore the improved fuzzy MULTIMOORA approach. We use the classical Interval-Valued Triangular Fuzzy Numbers (IVTFNs), viz. the symmetric lower and upper triangular numbers, as the basis. The triangular fuzzy number is identified by the triplets; the lowest, the most promising, and the highest possible values, symmetric with respect to the most promising value. When the lower and upper membership functions are equated to one, we get the normalized interval-valued triangular fuzzy numbers, which consist of symmetric intervals. We evaluate five alternatives among the four criteria using an improved MULTIMOORA method and select the best method for improving air quality in Tamil Nadu, India. Finally, a numerical example is illustrated to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. A study conducted by the Swiss organization IQAir indicated that 22 of the top 30 most polluted cities in the world are in India. This creates the problem of air pollution, which is very relevant to India as well. Exposure to air pollutants has both acute (short-term) and chronic (long-term) impacts on health. Among the major air pollutants, particulate matter 2.5 (PM2.5) is the most harmful, and its long-term exposure can impair lung functions. Pollutant concentrations vary temporally and are dependent on the local meteorology and emissions at a given geographic location. PM2.5 forecasting models have the potential to develop strategies for evaluating and alerting the public regarding expected hazardous levels of air pollution. Accurate measurement and forecasting of pollutant concentrations are critical for assessing air quality and making informed strategic decisions. Recently, data-driven machine learning algorithms for PM2.5 forecasting have received a lot of attention. In this work, a spatiotemporal analysis of air quality was first performed for Hyderabad, indicating that average PM2.5 concentrations during the winter were 68% higher than those during the summer. Following that, PM2.5 modelling was done using three different techniques: multilinear regression, K-nearest neighbours (KNN), and histogram-based gradient boost (HGBoost). Among these, the HGBoost regression model, which used both pollution and meteorological data as inputs, outperformed the other two techniques. During testing, the model acquired an amazing R² value of 0.859, suggesting a significant connection with the actual data. Additionally, the model exhibited a minimum Mean Absolute Error (MAE) of 5.717 μg/m³ and a Root Mean Square Error (RMSE) of 7.647 μg/m³, further confirming its accuracy in predicting PM2.5 concentrations. In our investigation, we discovered that the HGBoost3 model beat other PM2.5 modelling models by having the lowest error and the highest R² value. This study made a substantial addition by incorporating the spatiotemporal relationship between air pollutants and meteorological variables in predicting air quality. This method has the potential to improve the creation of more precise air pollution forecast models. [ABSTRACT FROM AUTHOR]

Introduction: Policy decisions on containment of commercial activity to control the spread of COVID-19 SARS may influence urban air quality. Methodology: A methodological approach has been proposed to explore the relationship that exists between the policy decisions on six criteria pollutants (PM10, PM2.5, SO2, Ozone, NO2, and CO). This study also explores the knowledge/association that exists between the climatic variables, criteria pollutants, inhalation exposure dose levels and its vulnerable population age group and gender. The methodology explores a mixed urban cluster, Coimbatore, Tamilnadu, INDIA. Results: From the results, PM10, PM2.5, SO2 and CO has reduced to about 31%, 42%, 26% and 29% during phase-I, whereas NO2 (66%) and Ozone (14%) has increased. It is inferred that the reduced criteria pollutant load (during the phase I, II, and III) gets dispersed taking the advantage of geographical location. Conclusion: It is also observed that gender has an influence on the inhalation dose due to variability in inflow rate. [ABSTRACT FROM AUTHOR]

There is extensive evidence linking air pollution exposure to physical health. Less is known about the mental health impacts of poor air quality, especially in developing countries. We use data from India and estimate the causal impact of air pollution exposure on feeling sad, experiencing cognitive difficulties, and feeling unable to control and cope with important things in life. We instrument for air pollution exposure using the annual number of nighttime thermal inversions and show that air pollution exposure in the previous calendar year significantly worsens mental health in the current year. We examine potential mechanisms and find that air pollution exposure negatively impacts self-reported physical health, worsens respiratory conditions, and increases the likelihood of experiencing sleeping difficulties. Accounting for mental health impacts of pollution exposure is critical to accurately estimating the true health costs of air pollution and designing optimal environmental policy. [ABSTRACT FROM AUTHOR]

Over the past two decades, smoke aerosols from crop residue burning have increasingly degraded postmonsoon (October‐November) air quality in north India. We use satellite data and atmospheric modeling to investigate whether cascading delays in monsoon rice growth and postmonsoon fires over 2003–2019 have exacerbated the already poor urban air quality downwind of the fires. Beginning in 2008, a government effort to combat groundwater depletion in Punjab mandated rice sowing until closer to the arrival of monsoon rains. We find evidence of district‐level delays in the timing of both monsoon rice growth and postmonsoon fires, which vary from 1 to 4 weeks with largely an east‐west gradient. These delays are correlated spatially (r = 0.51–0.77), with northern and western districts in Punjab, which rely less on groundwater for irrigation, tending to have the greatest delays. Had the delays in fire activity not occurred, we estimate that cities downwind and near the fire source would have consistently seen less smoke‐related fine particulate matter (PM2.5), on average ranging from 11% to 21% for New Delhi, Bathinda, and Jind during 2008–2019. This net benefit of earlier postmonsoon burning could have been even larger given that (a) a longer rice‐to‐wheat transition could incentivize farmers to find alternatives to burning crop residues; and (b) background PM2.5 is less abundant earlier in the season, decreasing the likelihood of extreme pollution episodes. Strategies aiming to mitigate air pollution while conserving groundwater may be more effective by promoting an earlier monsoon growing season in districts with less groundwater depletion. Plain Language Summary: During the postmonsoon period from October to November, farmers in northwest India have increasingly burned rice residues to quickly clear fields and prepare to plant winter wheat. As seen by satellites, these agricultural fires emit large amounts of smoke that travel to nearby rural areas and populous urban centers, such as New Delhi, contributing to severe air pollution episodes. Beginning in 2008, a government effort to combat groundwater depletion in the state of Punjab mandated rice planting until closer to the arrival of monsoon rains. However, delays in rice planting have led to delays in the timing of postmonsoon agricultural burning. Our modeling results show consistently lower air quality in nearby cities as delayed fires coincide with meteorological conditions that are more favorable for trapping smoke near the surface. Delays in the fire season also shorten the transition period from rice to wheat, thus increasing fire activity further. Strategies aiming to mitigate air pollution in north India may be more effective by stemming the delays in the postmonsoon fire season. Key Points: Punjab, India experienced cascading delays in the monsoon rice growing season and postmonsoon crop residue burning from 2003 to 2019District‐level delays vary from 1 to 4 weeks with a longitudinal east‐west gradient, where western districts experienced the largest delaysDelays in postmonsoon fires have consistently led to increased air quality degradation across north India from 2008 to 2019 [ABSTRACT FROM AUTHOR]

Environmental sustainability is the rate of renewable resource harvesting, pollution control, and non-renewable resource exhaustion. Air pollution is a significant issue confronted by the environment particularly by highly populated countries like India. Due to increased population, the number of vehicles also continues to increase. Each vehicle has its individual emission rate; however, the issue arises when the emission rate crosses the standard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop prediction approaches to monitor and control pollution using real time data. With the development of the Internet of Things (IoT) and Big Data Analytics (BDA), there is a huge paradigm shift in how environmental data are employed for sustainable cities and societies, especially by applying intelligent algorithms. In this view, this study develops an optimal AI based air quality prediction and classification (OAI-AQPC) model in big data environment. For handling big data from environmental monitoring, Hadoop MapReduce tool is employed. In addition, a predictive model is built using the hybridization of ARIMA and neural network (NN) called ARIMA-NN to predict the pollution level. For improving the performance of the ARIMA-NN algorithm, the parameter tuning process takes place using oppositional swallow swarm optimization (OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS) classifier is used to classify the air quality into pollutant and non-pollutant. A detailed experimental analysis is performed for highlighting the better prediction performance of the proposed ARIMA-NN method. The obtained outcomes pointed out the enhanced outcomes of the proposed OAI-AQPC technique over the recent state of art techniques. [ABSTRACT FROM AUTHOR]

The study evaluates the impacts of India's COVID-19 lockdown and unlocking periods on the country's ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and satellite data of NO2 emissions for several Indian cities to assess the impacts of the lockdowns in India. Additionally, we analyzed NO2 data acquired from the Sentinel-5P TROPOMI sensor over a few districts of the Punjab state, as well as the National Capital Region. The comparisons between the in situ and satellite NO2 emissions were performed for the years 2019, 2020 and up to July 2021. Further analysis was conducted on the satellite data to map the NO2 emissions over India during March to July for the years of 2019, 2020 and 2021. Based on the in situ and satellite observations, we observed that the NO2 emissions significantly decreased by 45–55% in the first wave and 30% in the second wave, especially over the Northern Indian cities during the lockdown periods. The improved air quality over India is indicative of reduced pollution in the atmosphere due to the lockdown process, which slowed down the industrial and commercial activities, including the migration of humans from one place to another. Overall, the present study contributes to the understanding of the trends of the ambient air quality over large geographical areas using the Sentinel-5P satellite data and provides valuable information for regulatory bodies to design a better decision support system to improve air quality. [ABSTRACT FROM AUTHOR]

As a result of extreme modifications in human activity during the COVID-19 pandemic, the status of air quality has recently been improved. This bibliometric study was conducted on a global scale to quantify the impact of the COVID-19 pandemic on air pollution, identify the emerging challenges, and discuss the future perspectives during the course of the ongoing COVID-19 pandemic. For this, we have estimated the scientific production trends between 2020 and 2021 and investigated the contributions of countries, institutions, authors, and most prominent journals metrics network analysis on the topic of COVID-19 combined with air pollution research spanning the period between January 01, 2020, and June 21, 2021. The search strategy retrieved a wide range of 2003 studies published in scientific journals from the Web of Sciences Core Collection (WoSCC). The findings indicated that (1) publications on COVID-19 pandemic and air pollution were 990 (research articles) in 2021 with 1870 citations; however, the year 2020 witnessed only 830 research articles with a large number 16,600 of citations. (2) China ranked first in the number of publications (n = 365; 18.22% of the global output) and was the main country in international cooperation network, followed by the USA (n = 278; 13.87% of the global output) and India (n = 216; 10.78 of the total articles). (3) By exploring the co-occurrence and links strengths of keywords "COVID-19" (1075; 1092), "air pollution" (286; 771), "SARS-COV-2" (252; 1986). (4) The lessons deduced from the COVID-19 pandemic provide defined measures to reduce air pollution globally. The outcomes of the present study also provide useful guidelines for future research programs and constitute a baseline for researchers in the domain of environmental and health sciences to estimate the potential impact of the COVID-19 pandemic on air pollution. [ABSTRACT FROM AUTHOR]

The global burden of disease study estimated 695,000 premature deaths in 2010 due to continued exposure to outdoor particulate matter and ozone pollution for India. By 2030, the expected growth in many of the sectors (industries, residential, transportation, power generation, and construction) will result in an increase in pollution related health impacts for most cities. The available information on urban air pollution, their sources, and the potential of various interventions to control pollution, should help us propose a cleaner path to 2030. In this paper, we present an overview of the emission sources and control options for better air quality in Indian cities, with a particular focus on interventions like urban public transportation facilities; travel demand management; emission regulations for power plants; clean technology for brick kilns; management of road dust; and waste management to control open waste burning. Also included is a broader discussion on key institutional measures, like public awareness and scientific studies, necessary for building an effective air quality management plan in Indian cities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Air pollution is a severe environmental problem in the Indian subcontinent. Largely caused by the rapid growth of the population, industrialization, and urbanization, air pollution can adversely affect human health and environment. To mitigate such adverse impacts, the Indian government launched the National Clean Air Programme (NCAP) in January 2019. Meanwhile, the unexpected city-lockdown due to the COVID-19 pandemic in March 2020 in India greatly reduced human activities and thus anthropogenic emissions of gaseous and aerosol pollutants. The NCAP and the lockdown could provide an ideal field experiment for quantifying the extent to which various levels of human activity reduction impact air quality in the Indian subcontinent. Here, we study the improvement in air quality due to COVID-19 and the NCAP in the India subcontinent by employing multiple satellite products and surface observations. Satellite data shows significant reductions in nitrogen dioxide (NO2) by 17% and aerosol optical depth (AOD) by 20% during the 2020 lockdown with reference to the mean levels between 2005–2019. No persistent reduction in NO2 nor AOD is detectable during the NCAP period (2019). Surface observations show consistent reductions in PM2.5 and NO2 during the 2020 lockdown in seven cities across the Indian subcontinent, except Mumbai in Central India. The increase in relative humidity and the decrease in the planetary boundary layer also play an important role in influencing air quality during the 2020 lockdown. With the decrease in aerosols during the lockdown, net radiation fluxes show positive anomalies at the surface and negative anomalies at the top of the atmosphere over most parts of the Indian subcontinent. The results of this study could provide valuable information for policymakers in South Asia to adjust the scientific measures proposed in the NCAP for efficient air pollution mitigation. [ABSTRACT FROM AUTHOR]

The impact of air pollution on the roadside plants in the Hadapsar suburban was determined using six different plant species. To assess the effects of air pollutants on roadside plants of Hadapsar area in Pune city, APTI (Air Pollution Tolerance Index) was determined of six plant species. Plants species selected for the study were Polyalthia long folia, Ficus racemosa, Terminalia catappa, Ficus benghalensis, Ficus religiosa, Samanea saman of nearly the same age size and were found in abundance. Physiological and biochemical parameters such as pH, Relative water content (RWC), Total Chlorophyll (T.C.), Ascorbic acid (A.A.) contents were determined to calculate APTI. Results indicated that all of the plants selected for the study were found to be sensitive & heavily impacted due to air pollution. Thus, these plants can be used as Bio-indicators in monitoring air pollution. [ABSTRACT FROM AUTHOR]

The emergence of COVID-19 has brought the entire planet to a halt. Many countries, including India, were compelled to shut down most urban, industrial, social and other activities as a result of the pandemic. Due to a series of complete lockdowns imposed in India from March 24 to May 17, 2020, and state-wise local level restrictions afterward, have resulted in significant reduction of emissions of numerous atmospheric pollutants. The objective of this study is to analyse the change in concentration of various pollutants such as nitrogen oxide (NO2), carbon monoxide (CO) and aerosol optical depth (AOD) due to lockdown and also to quantify the contribution of crop stubble burning to air pollution. The Sentinel-5P based NO2 and CO observations for 2019 and 2020 and Moderate Resolution Imaging Spectroradiometer (MODIS)-based AOD observations for 2016–2020 were used for detecting the variations. The obtained results showed a significant decrease in NO2 levels during various stages of lockdown. Small decrease in CO levels was observed across most part of the India. With a few exceptions, such as coastal and desert regions, there was a moderate decrease in AOD levels. Furthermore, to study the contribution of NO2, CO and AOD from crop stubble burning, MODIS observations on active fire events were obtained from Visible Infrared Imaging Radiometer Suite (VIIRS). The burning of crop stubble increased NO2 emissions by 22 to 80%. CO levels, on the other hand, have risen by 7 to 25%. A considerable variation in AOD was reported, ranging from 1 to 426%. [ABSTRACT FROM AUTHOR]

India has recently experienced severe air quality problems and related mortality compared to other countries. Particulate matter (PM) is identified as a major health risk factor. Aerodynamic diameter of PM less than 2.5 µm (PM2.5) and that of PM less than 10 µm (PM10) are the two regulatory sizes in India. Nonetheless, size-segregated PM measurements are also crucial for understanding the formation, size distribution, sources, deposition in the human respiratory tract, and health effects. Size-segregated PM is commonly referred to simultaneous measurement of PM in multiple size ranges using cascade impactors or aerosol monitors. Here, we review 106 investigations on size-segregated PM in India. We found that bimodal distribution was common among size-segregated PM and chemical components. Secondary inorganic anions were dominant in all ions analysed. Enrichment factor analysis indicated a strong contribution from anthropogenic sources. Source apportionment data from 33 fine size fractions and 26 coarse size fractions revealed that the major source was vehicular followed by crustal origin, biomass or refuse burning, industrial, coal burning, mixed, and secondary sources. Coarse and fine PM sizes were deposited mainly in the head and alveolar regions of the human respiratory tract, respectively. Bioaerosols contained more fungi than bacteria. Episodic events significantly altered the characteristics of size-segregated PM. Overall, rural areas and indoor environments were less explored. [ABSTRACT FROM AUTHOR]

Vehicular pollution is one of the major sources of air pollution in urban locales that have reportedly elevated concentrations of air pollutants. This study aims to examine the performance of two air quality dispersion models, STREET and CALINE 4 to predict pollutant concentrations for an urban monitoring location that is en route to the high traffic volumes in Shimla, Himachal Pradesh, India. This study will compare the predicted and observed concentrations (for the urban monitoring location) using both quantitative and statistical methods for the 2 years of the study. The pollutant selected for the study is PM10. It was observed from the modeling studies that the performance of CALINE4 was slightly better than the STREET model. The models selected to a certain extent are defined by the available parameters for successful run completions. The application of detailed modeling studies is the first of its kind for the study location, to the best of the authors' knowledge. Hence, the application of basic and simplistic models and the examination of their performance could potentially find the best fit model to predict approximately precise concentrations. Further scope of this study should include the use of advanced air quality dispersion models for the improved prediction of concentrations. [ABSTRACT FROM AUTHOR]

To mitigate the impacts of the pandemic of coronavirus disease 2019 (COVID-19), the Indian government implemented lockdown measures on 24 March 2020, which prohibited unnecessary anthropogenic activities, thus leading to a significant reduction in emissions. To investigate the impacts of this lockdown measure on air quality in India, we used the Community Multi-Scale Air Quality (CMAQ) model to estimate the changes of key air pollutants. From pre-lockdown to lockdown periods, improved air quality is observed in India, indicated by the lower key pollutant levels such as PM 2.5 (- 26 %), maximum daily 8 h average ozone (MDA8 O 3) (- 11 %), NO 2 (- 50 %), and SO 2 (- 14 %). In addition, changes in these pollutants show distinct spatial variations with the more important decrease in northern and western India. During the lockdown, our results illustrate that such emission reductions play a positive role in the improvement of air quality. Significant reductions of PM 2.5 concentration and its major components are predicted, especially for secondary inorganic aerosols that are up to 92 %, 57 %, and 79 % for nitrate (NO 3-), sulfate (SO 42-), and ammonium (NH 4+), respectively. On average, the MDA8 O 3 also decreases 15 % during the lockdown period although it increases slightly in some VOC-limited urban locations, which is mainly due to the more significant reduction of NO x than VOCs. More aggressive and localized emission control strategies should be implemented in India to mitigate air pollution in the future. [ABSTRACT FROM AUTHOR]

India responded to the severe acute respiratory syndrome (SARS) coronavirus disease 2019 (COVID-19) pandemic through a three-phase nationwide lockdown: 25 March–14 April, 15 April–3 May, and 4–17 May 2020. We utilized this unique opportunity to assess the impact of restrictions on the air quality of Indian cities. We conducted comprehensive statistical assessments for the air quality index (AQI) and criteria pollutant concentrations for 91 cities during the lockdown phases relative to the preceding seven days (prelockdown phase of 18–24 March 2020) and to corresponding values from the same days of the year in 2019. Both comparisons show statistically significant countrywide mean decrease in AQI (33%), PM2.5 (36%), PM10 (40%), NO2 (58%), O3 (5%), SO2 (25%), NH3 (28%), and CO (60%). These reductions represent a background or the lower bound of air quality burden of industrial and transportation sectors. The northern region was most impacted by the first two phases of the lockdown, whereas the southern region was most affected in the last phase. The northeastern region was least affected, followed by the eastern region, which also showed an increase in O3 during the lockdown. Analysis of satellite-retrieved aerosol optical depth (AOD) shows that effects of restrictions on particulate pollution were variable—locally confined in some areas or having a broader impact in other regions. Anomalous behavior over the eastern region suggests a differing role of regional societal response or meteorological conditions. The study results have policy implications because they provide the observational background values for the industrial and transportation sector's contribution to urban pollution. [ABSTRACT FROM AUTHOR]

The overlap in sources of greenhouse gas and local air pollutant emissions creates scope for policy measures to limit global warming and improve air quality simultaneously. In a first step, we derive estimates for the air pollution mortality-related component of the social cost of atmospheric release for 6 pollutants and 56 regions in the world. Combining these estimates with emission inventory data highlights that sector contributions to greenhouse gas emissions and air pollution health impacts differ widely across regions. Next, simulations of future emission pathways consistent with the 2 °C and 1.5 °C targets illustrate that strengthening climate policy ambition raises the total value of air quality co-benefits despite lower marginal co-benefits per tonne of greenhouse gas emissions abated. Finally, we use results from a multi-model ensemble to quantify and compare the value of health-related ambient air quality co-benefits of climate policy across sectors and regions. On the global level, overall air quality co-benefits range from $8 to $40 per tonne of greenhouse gases abated in 2030, with median across models and scenarios of $18/tCO2e. These results mask strong differentiation across regions and sectors, with median co-benefits from mitigation in the residential and service sectors in India exceeding $500/tCO2e. By taking a sector- and region-specific perspective, the results presented here reveal promising channels to improve human health outcomes and to ratchet up greenhouse gas reduction efforts to bridge the gap between countries' pledges and the global targets of the Paris Agreement. [ABSTRACT FROM AUTHOR]

Climate change and air pollution have important societal consequences, especially in emerging economies, wherein transitions from polluting technologies to cleaner alternatives coincide with high population vulnerability to environmental threats. India is home to a fifth of the world's population and a gamut of human activities, employing a far ranging spectrum of technologies and fuels, with consequent emissions. Atmospheric fine particles or aerosols in the region predominate in carbonaceous constituents and dust. Multi-institutional studies in the region have earlier focused on natural and anthropogenic climate forcing by aerosols and feedbacks on regional and global climate. Important gaps remain in understanding human activities influencing emissions, emission aerosol properties, and regional atmospheric processes, specifically those related to carbonaceous aerosol impacts on climate and air quality. With an aim to address these gaps, the COALESCE (Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts) project was launched on 7 July 2017. The project adopts integration of scientific methods developed by both the climate and air quality research communities. New fundamental knowledge from the project and strong links to India's policy framework would enable climate and clean-air action in the region. The article describes the scientific rationale, objectives, and planned activities under COALESCE to explore engagement with the international climate and air quality research communities and to enable eventual dissemination of research findings, knowledge products, and decision-support tools. [ABSTRACT FROM AUTHOR]

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM 2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM 2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM 2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µ g m -3 , and, on occasion, exceeded 180 µ g m -3. PM 2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM 2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM 2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM 2.5 during the periods studied at the four locations averaged about 30 µ g m -3 , representing approximately 30 % and 20 % of total PM 2.5 levels in the rural and urban stations, respectively. [ABSTRACT FROM AUTHOR]