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China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

The COVID-19 has affected and threatened the world health system very critically throughout the globe. In order to take preventive actions by the agencies in dealing with such a pandemic situation, it becomes very necessary to develop a system to analyze the impact of environmental parameters on the spread of this virus. Machine learning algorithms and artificial Intelligence may play an important role in the detection and analysis of the spread of COVID-19. This paper proposed a twinned gradient boosting machine (GBM) to analyze the impact of environmental parameters on the spread, recovery, and mortality rate of this virus in India. The proposed paper exploited the four weather parameters (temperature, humidity, atmospheric pressure, and wind speed) and two air pollution parameters (PM2.5 and PM10) as input to predict the infection, recovery, and mortality rate of its spread. The algorithm of the GBM model has been optimized in its four distributions for best performance by tuning its parameters. The performance of the GBM is reported as excellent (where R2 = 0.99) in training for the combined dataset comprises all three outcomes i.e. infection, recovery and mortality rates. The proposed approach achieved the best prediction results for the state, which is worst affected and highest variation in the atmospheric factors and air pollution level. [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]

Coal is the major source of power in India and world over. Coal mining is an essential industry which has a major role in the economic development of the country. Most major mining activities contribute directly or indirectly to air pollution. Coal dust is a major air pollutant which affects the personal working in the mines and also people residing in villages near the mines. Air pollution due to coal particulates can affect human health and cause damages to the environment. Hence effective pollution control mechanisms are needed to keep the pollution levels within permissible levels. The easiest and most common method employed for dust suppression worldwide is sprinkling of water. In majority of mines, water sprinklers are operated manually and can lead to wastage of water due to over sprinkling. It can also prove to be ineffective in dust suppression if sprinkling is not done properly. The paper proposes a system which can be deployed to automate the dust suppressions sprinklers. The system will monitor the concentration of PM10 and PM2.5 in the air and initiate sprinkling operation when the particulate matter content exceeds preconfigured limits. [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]

Mountain glaciers are of immense importance for the balanced natural ecosystem. Billions of human life depend on this natural reserve. However, there is an alarming situation worldwide to save this resource. The melting rate of glaciers is a matter of serious concern and one of the driving forces is air pollution. Many research studies have suggested that the deposition of aerosol particles is closely associated with the intensification of glacier retreat. We are witnessing the imperative necessity to understand the regional role of aerosol pollutants in glaciers melting. The third largest glacier occupier, the Himalayan mountain glacier has also unexceptionally countersigned many calamities such as glacial lake outburst floods, flash floods, landslides and biodiversity loss. Since the geography, climate, population, human activities, culture and pollution patterns are divergent at different mountain glaciers, thus regional studies should be encouraged to understand the role of pollutants on glaciers. On-site monitoring requires studying the regional aerosols behavior in mountain glaciers specially the most sensitive regions such as Sikkim Himalaya region. Sikkim Himalaya region has experienced a rise in temperature and an increase in glacial lakes number as well as area. This review paper suggests that the in-situ study of aerosols in ecologically sensitive mountain glaciers region is our utmost need in the current research time. As many glaciers are on the verge of disappearing and different types of aerosols deposition blamed for it at different places. The regional aerosol information may help in saving this precious natural reserve. [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]

In this paper, we analyzed two decadal long term (2003–2022) data of AOD and cloud parameters to understand the spatial and temporal variations in aerosol radiative characteristics over a highly polluted urbanized region of Northern India. The aerosol and cloud data were derived at spectral wavelength of 550 nm from the MODIS based Terra/Aqua satellites. Detailed AOD quantitative analysis considering monthly, seasonal and interannual changes was investigated in relation to cloud characteristics and zonal synoptic climatic disturbances. HYSPLIT back trajectory 5-day analysis was made to know the pathways of air masses ending at Delhi region of India. Moreover, we applied Student's t-test to mean Terra AOD and cloud properties to examine and further validate the relationship of aerosol-cloud interaction over the urban tropical Delhi zone close to Himalaya range of Northern India. Results indicated that AODs were found to be increased significantly (> 68%) during the 20 years period of 2003–2022, which might be accounted to continuously increased aerosol pollution in atmosphere. On an annual basis, mean Terra AOD exhibited an increasing trend of ~ 0.0009 per year, whereas seasonal mean AOD was observed to be maximum for winter (~ 0.96) and minimum for summer (~ 0.55), respectively. The correlation coefficient was highest (+ 0.73) between AOD and precipitable water vapor, whereas lowest (− 0.46) between AOD and cloud top pressure (CTP). The findings of AOD and cloud parameters have been compared with the earlier findings made on different Indian cities. [ABSTRACT FROM AUTHOR]

The burning of agricultural residue from previous season's rice crop, primarily in the states of Punjab and Haryana, is a key contributor to poor air quality during the winter across North India. Air quality can deteriorate to catastrophic levels during the Agricultural Waste Burning (AWB) season in October–November, when fine particulate matter (PM2.5) concentrations can exceed WHO daily maxima over a sustained period by an order of magnitude or more, over a large swathe of the Indo-Gangetic plain. Over the past decade, attempts by Indian governments to change farmer behavior by incentivizing the use of novel technologies for managing rice residue without burning it have been met with little success. This paper uses farmer and expert interviews, as well as secondary data, to examine the barriers to adoption of these technologies in the state of Punjab. We analyze how operational factors (such as farm size, timing, technology availability, and choice) affect a farmers' decision to choose (or not) a rice residue management practice. We develop a financial model for analyzing the costs of residue management technologies that are consistent with the decision-making process of both small and large farmers. We find that more sustainable residue management practices can be cost effective relative to residue burn, especially when existing subsidies are applied. However, difficulties in accessing technological alternatives to AWB and subsidies for their use and a lack of trust in the government's ability to deliver the full benefits of subsidies, all contribute the low adoption of technological alternatives to AWB. [ABSTRACT FROM AUTHOR]

The paper contributes the literature in the following ways: (a) It establishes negative sole impact of urban outdoor air-pollution and its interaction-effect with growth, poverty, and urbanisation on female life-expectancy (FLE), hence on female labour force participation rate (FLFPR); proving the positive two-way relation between these two through estimating a simultaneous panel-model comprising equations of FLFPR and FLE, for Indian-states over the period 2017–2022. The significant impact of the interaction-effect shows partial-effect of a change in the concerned variable on FLE and on FLFPR depends on air-pollution. Thus, measures to raise growth, urbanisation or reduce poverty can improve FLE and FLFPR, provided air-pollution is mitigated. (b) Moreover, significant impact of interaction-effect of household-size and growth (Lnnsdp × HHsz) on FLFPR is supported, i.e., the impact of growth on FLFPR depends on household-size and vice-versa. A critical-level of Lnnsdp × HHsz exists; FLFPR increases/falls with Lnnsdp × HHsz according as actual Lnnsdp × HHsz is lesser/higher than critical-level. The higher sample-mean of Lnnsdp × HHsz than critical-value implies FLFPR falls with both growth and household-size, i.e. given household-size, income-effect dominates and women value leisure more. It adds the earlier inverted-U feminisation hypothesis for India investigating only sole-impact of growth on FLFPR. (c) Further, the relationship between interaction-effect of female education and household-size (Hedu × HHsz) on FLFPR is U-type, i.e., a critical level of Hedu × HHsz exists, FLFPR falls/rises with Hedu × HHsz according as actual Hedu × HHsz is lesser/higher than critical-level. The lower sample-mean of Hedu × HHsz than critical-value implies, FLFPR falls with education and household-size, i.e., female education does not necessarily imply higher FLFPR. [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]

Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt–Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available. [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]

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities. [ABSTRACT FROM AUTHOR]

Due to the lack of timely data on socioeconomic factors (SES), little research has evaluated if socially disadvantaged populations are disproportionately exposed to higher PM2.5 concentrations in India. We fill this gap by creating a rich dataset of SES parameters for 28,081 clusters (villages in rural India and census-blocks in urban India) from the National Family and Health Survey (NFHS-4) using a precision-weighted methodology that accounts for survey-design. We then evaluated associations between total, anthropogenic and source-specific PM2.5 exposures and SES variables using fully-adjusted multilevel models. We observed that SES factors such as caste, religion, poverty, education, and access to various household amenities are important risk factors for PM2.5 exposures. For example, we noted that a unit standard deviation increase in the cluster-prevalence of Scheduled Caste and Other Backward Class households was significantly associated with an increase in total-PM2.5 levels corresponding to 0.127 μg/m3 (95% CI 0.062 μg/m3, 0.192 μg/m3) and 0.199 μg/m3 (95% CI 0.116 μg/m3, 0.283 μg/m3, respectively. We noted substantial differences when evaluating such associations in urban/rural locations, and when considering source-specific PM2.5 exposures, pointing to the need for the conceptualization of a nuanced EJ framework for India that can account for these empirical differences. We also evaluated emerging axes of inequality in India, by reporting associations between recent changes in PM2.5 levels and different SES parameters. [ABSTRACT FROM AUTHOR]

Black carbon, which is a by-product of incomplete combustion of carbon containing fuels, can alter atmospheric radiation property and make adverse impacts on human health. This paper shows the comparative study of particles and associated gaseous pollutants measurements at five observatories dispersed across central part of megacity Delhi, India, with high-temporal resolution (5 min) measurements. The mean ± SD concentrations of black carbon (BC), fine particles (PM), carbon monoxide (CO), and oxides of nitrogen (NO) of the all five stations were 13.4 ± 10, 149.6 ± 109 µg m, 3.4 ± 2 ppm, and 81.8 ± 79 ppb, respectively. The mean mass concentration of PM was approximately four times and ten times in excess of annual average standards of Indian air quality (40 µg m) and USEPA (15 µg m) levels. Highest BC mass concentrations were recorded during winter (December) due to consistent with a shallower atmospheric boundary layer, lower wind speeds, and possibly larger biofuel burning in the coldest period. Also, the strong diurnal dynamics in boundary layer height had a large impact on the ground-level concentrations of all the pollutants. A significant difference in the emissions of atmospheric pollutants was observed over Delhi during weekdays and weekends during study period. There was a strong diurnal effect on BC and the other pollutants, presumably with mixing height as a strong driver. The 24-h cycle is characterized throughout the study by maximum concentrations around midnight and lowest concentrations at 1500-1700 hours local time (LT), with a local maximum between 0800 and 1000 hours LT, presumably due to morning rush hour. While the amplitude of the BC excursion was a factor of 2-3, the mixing height decreased by a factor of 12-14 from day to night. This monitoring program contributed to insights into the levels and dynamics of atmospheric pollutants in the New Delhi megacity over a 5-month period. The concentrations are varying over several timescales, consistent with both atmospheric mixing and variations in presumed emission source strengths. [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]

Hydrophobic organic contaminated polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and CHNS (carbon, hydrogen, nitrogen and sulphur species) are explosively associated with road dust particles. A few organic contaminants are toxic in nature and have an unpleasant effect on human health. The International Agency for Research on Cancer (IARC), the US Department of Health and Human Services (HHS) and the United States–Environmental Protection Agency has considered several PAHs and PCBs as carcinogens for human beings. In the proposed study, the anthropogenic contaminants present in road dust were assessed in six representative diversified sites i.e. industrial, commercial, office, residential, construction and traffic intersection in Delhi NCR, India. Roadside dust samples were gathered in premonsoon, monsoon and postmonsoon seasons and characterized for PAHs, PCBs and CHNS. The concentration of total PAHs (16 Nos) and PCBs (6 Nos) of the selected sites ranged from 0.27 µg/kg to 605.80 µg/kg and 0.01 µg/kg to 41.26 µg/kg, respectively. The Fourier transform infrared spectroscopy-attenuated total reflectance study suggested that the presence of O = C = O, Si–O, carbonyl, acidic or aliphatic esters group were associated with road dust particles. Hydrogen and sulphur concentrations were not detected in the selected road dust samples. Carbon and nitrogen concentrations varied from 2.24% to 16.82% and 0.69% to 14.5%, respectively, seasonally. In the premonsoon season, road dust was distinguishably contaminated as compared to monsoon and postmonsoon season, which might be due to movement of contaminated road dust from adjacent locations. It was perceived that Delhi NCR organic contamination in road dust was much below as compared to other countries. It may be concluded that due to the presence of significant amounts of carbon and nitrogen concentrations in the road dust, to a greater extent, road dust can be fertile and might be advantageous for green belt development to mitigate air pollution. The utilization of road dust will further bring down the burden of landfill sites and may lead towards sustainability. [ABSTRACT FROM AUTHOR]

Methane directly contributes to air pollution, as an ozone precursor, and to climate change, generating physical and economic damages to different systems, namely agriculture, vegetation, energy, human health, or biodiversity. The methane-related damages to climate, measured as the Social Cost of Methane, and to human health have been analyzed by different studies and considered by government rulemaking in the last decades, but the ozone-related damages to crop revenues associated to methane emissions have not been incorporated to policy agenda. Using a combination of the Global Change Analysis Model and the TM5-FASST Scenario Screening Tool, we estimate that global marginal agricultural damages range from ~ 423 to 556 $2010/t-CH4, of which 98 $2010/t-CH4 occur in the USA, which is the most affected region due to its role as a major crop producer, followed by China, EU-15, and India. These damages would represent 39–59% of the climate damages and 28–64% of the human health damages associated with methane emissions by previous studies. The marginal damages to crop revenues calculated in this study complement the damages from methane to climate and human health, and provides valuable information to be considered in future cost-benefits analyses. [ABSTRACT FROM AUTHOR]

Real-time measurement of black carbon (BC) aerosol contaminated air has potential for human health and climate change. A comprehensive investigation of BC concentration and its source apportionment distribution in the atmosphere of three traffic sites in East India was conducted using the portable Aethalometer (AE-33). The results showed that concentrations of BC ranged from 2.48 to 47.57 μg m−3 were minimum in Ranchi (RNC, 10.39 ± 2.48 μg m−3) and maximum in Jamshedpur (JSR, 18.22 ± 10.76 μg m−3). The average BC mass concentrations were reported as 18.22 ± 10.76 μg m−3, 14.18 ± 3.14 μg m−3, and 10.39 ± 2.48 μg m−3 at JSR, DHN (Dhanbad), and RNC, respectively. This was attributed to a high volume of traffic and a dense population in JSR compared to other sites. The airborne particulate particles arrived from various directions, as evidenced by backward trajectories. According to the diagnostic ratio analysis of BC, it was observed that the highest contribution of fossil fuel was reported at DHN (57%) and the highest contribution of biomass burning was reported at RNC (53%). Apart from explaining BC's atmospheric impacts, the health risk measurement of BC during various traffic sites over East India is also demonstrated in order. [ABSTRACT FROM AUTHOR]

Open burning of agricultural residue adversely impacts climate change, air quality, and public health. Considering this, the current study examines the public and farmer's perceptions about crop residue burning (CRB), including the obstacles to sustainable crop residue management practices and health implications in Haryana, India. A questionnaire-based survey was conducted with 418 study participants, having 56.1% of respondents as male and 43.4% as female. Results indicate a statistically significant association between CRB, socio-demographic factors, and respiratory issues. The study found that respiratory problems caused by CRB were more severe in the 60 + age group (93.3%) and the < 18 age group (70%). There is a mixed response from males (64%) and females (67.6%) regarding the impact of CRB on respiratory health. The study cites a variety of reasons for not adopting alternative technologies for crop residue management which include high cost (48%), followed by the unavailability of alternative technology (22%) and the belief that alternative technologies are not beneficial for timely clearing crop residues (13%). The minimum compensation required by farmers to avoid CRB varies significantly among respondents, with 45% citing INR 10,000 ($120) per acre as the minimum amount. The study also appraises behavioral and societal aspects of adopting new technologies. It was reported that government schemes have implementation challenges, but recycling crop residues for commercial use has a considerable scope. Considering this, the study also discusses the way ahead in light of the various initiatives the government has taken up for sustainable crop residue management. [ABSTRACT FROM AUTHOR]

The present study investigates the reduction in nitrogen dioxide (NO2) levels using satellite-based (Sentinel-5P TROPOMI) and ground-based (Central Pollution Control Board) observations of 2020. The lockdown duration, monthly, seasonal and annual changes in NO2 were assessed comparing the similar time period in 2019. The study also examines the role of atmospheric parameters like wind speed, air temperature, relative humidity, solar radiation and atmospheric pressure in altering the monthly and annual values of the pollutant. It was ascertained that there was a mean reduction of ~ 61% (~ 66.5%), ~ 58% (~ 51%) in daily mean NO2 pollution during lockdown phase 1 when compared with similar period of 2019 and pre-lockdown phase in 2020 from ground-based (satellite-based) measurements. April month with ~ 57% (~ 57%), summer season with ~ 48% (~ 32%) decline and an annual reduction of ~ 20% (~ 18%) in tropospheric NO2 values were observed (p < 0.001) compared to similar time periods of 2019. It was assessed that the meteorological parameters remained almost similar during various parts of the year in 2019 and 2020, indicating a negligent role in reducing the values of atmospheric pollution, particularly NO2 in the study area. It was concluded that the halt in anthropogenic activities and associated factors was mainly responsible for the reduced values in the Delhi conglomerate. Similar work can be proposed for other pollutants to holistically describe the pollution scenario as an aftermath of COVID-19-induced lockdown. [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]

Agroforestry, a sustainable land use practice adopted as a strategy under Kyoto Protocol, plays a crucial role to mitigate the inevitable climate change with a promising potential of carbon sequestration in their biomass and utilization of their numerous resource. Agroforestry is crucial for reducing greenhouse gas emissions, sustaining livelihoods, and partial solutions for biodiversity conservation. Worldwide, agroforestry is practiced by more than 1.2 billion people, on around 1 billion hectares (ha) of land area, while in India, around 25.32 million hectares area comes under agroforestry. Agroforestry system is the enhancement of overall farm productivity, soil enrichment through litter fall, above and below ground carbon sequestration, maintaining environmental services. Different agroforestry systems are adapted at the global level and periodic monitoring and estimation of area under agroforestry, monitoring of tree and soil carbon stocks is still a challenging task due to the lack of uniform methodology. The review analyzes the potential of agroforestry systems for climate adaptation and mitigation as well as their implications for the livelihood of human well-being. [ABSTRACT FROM AUTHOR]

Mining is a significant part of the transforming economy, which is generally considered as essential as well as social evil at the same time. It is one of the potential contributors to air and water pollution and possesses long-term impact on their quality. Keeping in view the exponential mining activities, we have selected an iron mine area in Bailadila, Chhattisgarh, India, as a sampling site and investigated the impact of mining activities on the air as well as water quality by setting up seven air quality and thirty water quality monitoring stations. From the results obtained, it was observed that concentration of air pollutants such as SO2, NO2, PM2.5 and PM10 for the year 2015 lies in the range of 11.5–13.0 µg/m3, 11.5–13.0 µg/m3, 24.9–33.4 ppm and 61.6–74.2 ppm, respectively, while for the year 2018, it lies in the range of 10.3–11.7 µg/m3, 10.5–14.7 µg/m3, 18.3–50.8 ppm and 23.7–60.7 ppm, respectively. Furthermore, results obtained revealed that air pollutants such as SO2, NO2, PM2.5 and PM10 were within the permissible limits but they contributed towards the light air pollution (air pollution index: 25–50) at all the air monitoring stations. Moreover, PM10 was considered as criterion pollutant in the Bailadila, Chhattisgarh region. On the other hand, it was observed that groundwater quality was deteriorated in the subsequent years. Most of the water quality parameters were in the permissible limits except iron (Fe). Moreover, on the basis of water quality indexing, water quality was classified as "poor" in ~ 30% of the sites and "very poor" in ~ 34% sites. The water quality was "unhealthy for drinking" in 3% and 6% sites in the year 2015 and 2018, respectively. [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]

Most governmental initiatives in India, to leash down urban air pollution, have yielded little results till date, largely due to purely technocratic vision, which is shrouded by technological, economic, social, institutional, and political hardships. We present this reflective article on urban greenery, as a proposition to urban authorities (e.g., pollution regulators, environmental systems' managers, urban landscape planners, environmental policy makers), shift from purely technocratic way of thinking to thinking with nature, by strategic greening of urban spaces, for long-term air pollution prevention and control measures. To that end, we offer a meta-analysis of recent (post 2005) global literature using four-stage PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. We open the narrative by briefing about main pollutant filtration mechanisms by trees, followed by cognitive aspects of species selection (e.g., deciduous vs. evergreen, air pollution tolerance index, environmental stressors). Till date, most Indian studies on urban greenery mostly but focused on physiological aspects of trees. Here, we draw attention of urban authorities to an equally compelling, but yet less explored, aspect: design criteria, with reference to two most common urban configurations, namely, street canyon and open road. With pictorial depictions, we enumerate various categories of street canyons and discuss aspect ratio (building height to street width) and various wind flow regimes (isolated roughness, wake interface, and skimming), that the urban authorities should be cognizant about to maximize pollutant removal efficiency. For open road, we discuss vegetation barriers, with special emphasis on canopy porosity/density functions. In the final sections, we reflect on a potential systems' thinking approach for on-ground implementation, comprising of revamping of urban forestry programs, research and development, community mobilization and stakeholder engagement, and strategic outreach. In addition, we emphasize on means to harness co-benefits of urban greenery, beyond mere pollutant removal, to garner support from urban residents' communities. Last but not the least, we also caution the urban authorities about the undesirable outcomes of urban greenery that will require more process-level research. [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]

Biomass combustion in indoor kitchens contributes a lot to the PM2.5 exposure burden among millions of people worldwide. Emitted potential pollutants from the biomass combustion oven may enormously degrade indoor air quality during cooking time and impact crowded rooms with inadequate indoor ventilation. The concept of developing a "ventilation index" may be a valuable alternative to assess indoor air quality. A new empirical formula of the ventilation index has been proposed recently, in which multiple studies need to be validated by various studies. This study is a part of the validation process and has ended up with supportive evidence-based insight into residential indoor air quality. Ninety-three households were selected from 25 villages of Tamil Nadu, India, to monitor the parameters required to apply in the empirical formula. However, the association between indoor air velocity and PM2.5 (considered a surrogate component of indoor air quality) was focused on emphatically. Other important variables, e.g., used amount of biomass cooking fuel, kitchen room index, room temperature, and relative humidity, were also monitored to observe the ventilation index. Indoor PM2.5 data were compared categorically with variable air velocities. Those who were using only biomass for cooking in indoor kitchens were recruited in the study. The air pollution exposure burden among the community population is expected to be minimized when the desired ventilation index is optimized and applied in configured kitchens and living rooms. Then, it may attract government policy to accept the ventilation index as a critical parameter for green housing schemes. [ABSTRACT FROM AUTHOR]

Industrialization, urbanization, and rapid socioeconomic growth are the prime factors regarding development of an urban city. These activities deteriorate the environment slowly by increasing harmful pollutants and the impact of which will be observed in near future in terms of several natural calamities, disease, and ecological consequences. Various urban ecosystems like urban forestry, wetland, grassland, parks/gardens etc. are the main criteria of urban health in respect to delivering multiple of benefits to the surrounding biota. But these regulatory systems have been affected severely through different industrial pollution stresses resulting into deterioration of their physicochemical quality and biotic diversity. The study revealed that the annual average air pollution index (API) exceeded the higher threshold value (i.e. >100) during the study period and recognized Durgapur as the severely polluted region in West Bengal, India. The characteristics of major air pollutants like SO2, NOx, and suspended particulate matter (SPM) and their temporal trends have been evaluated. The objective of this study was to detect the effect of industrial pollution on the aforesaid urban ecosystems in terms of variable ecological services such as purification of polluted air, atmospheric regulation, reduction of noise, proper drainage of rainwater, and sewage treatment. The outcome of this review work was to evaluate thoroughly the physiological, biochemical, and morphological symptoms of plant species under air pollution stress. On the other hand, the effect of air pollution on human and animal populations regarding various diseases (like bronchitis, cardiopulmonary arrest, cancer, liver and kidney dysfunction) has also been discussed. [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]

Almost half of the world's population still practice biomass fuel burning for various domestic purposes. About 1.6 million deaths are attributed to acute exposure to indoor air pollutants in India. A very few attempts have been made in the eastern part of India for remediation from household air pollution through the intervention of improved cookstoves. In this study, an attempt has been made to curb the pollution level by substituting traditional biomass cookstoves with an improved cookstove. The concentrations of pollutants (CO, CO2, O3, NO2, SO2, PM2.5, PM10 and TSPM) were measured, and health parameters were assessed before and after installation of the improved cookstove. Results revealed that the pollution was reduced and health parameters were improved significantly (p < 0.001) after installation of an improved cookstove. Toxicological risk analysis and percentage of blood carboxyhemoglobin results confirmed that higher health risk was associated with the use of the traditional cookstove. However, the health risk of women was gradually decreased after using an improved cookstove. The Monte Carlo probabilistic simulation model was run to assess the uncertainty factors on the health risk, and it was observed that exposure time and duration of exposure were two important factors that contributed most to the health risk assessment. Therefore, it may be concluded that the present improved cookstoves could be an alternative for the rural people besides clean energy. [ABSTRACT FROM AUTHOR]

The present work was undertaken in order to detect some pollution responsive variables such as ascorbic acid, pH, total chlorophyll, relative water content, total soluble sugar, amino acid and protein of four selected plant species, namely Ficus religiosa, Anthocephalus cadamba, Lagerstroemia speciosa and Cassia siamea, at nine different sites of Durgapur, West Bengal, India. The spatial variability analyses of Air Pollution Tolerance Index (APTI) along with Anticipated Performance Index (API) were also examined on each plant species. In this study, the highest APTI was recorded in L. speciosa (183.54 mg/g) during 2015 at site S5 (CCR), whereas the lowest APTI was reported in C. siamea (11.25 mg/g) during 2014 at site S3 (DGC). The API gradation revealed that L. speciosa was categorised as a best performer followed by A. cadamba and F. religiosa; in contrast, C. siamea showed poor performance among all the sites. One-way ANOVA (at p < 0.05, with Dunnett's post hoc) was conducted for spatial variability analysis both on biochemical parameters and air pollutants (SO2, NOx and SPM) with respect to control site, while two-way ANOVA also operated for the detection of spatio-temporal interaction on concerned biochemical parameters of each tree species. A significant positive correlation was observed both in ascorbic acid and APTI of A. cadamba and L. speciosa with the air pollutants. So it would be said that, for varied environmental situations, different biochemical responses have been reflected by vegetation of the same species. Thus, the present study has tremendous potentiality to screening out tree species on the basis of APTI with pooling their API assessment category and spatial variability detection of biochemical parameters. Biochemical plasticity and adaptability were better revealed on L. speciosa, F. religiosa and A. cadamba which will be suitable for green belt development in air pollution–affected areas. [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]

In this study, we attempt to mitigate household air pollution (HAP) through improved kitchen design. Field surveys were conducted in ten kitchens of rural western India, which were then modelled and simulated for dynamic indoor airflow network analysis. The simulated results were statistically clustered using principal component analysis and hierarchical agglomerative clustering, to construct a cumulative built environment parameter called 'Built Factor' for each kitchen, and subsequently a derivative matrix was developed. Categorization of better performing kitchens from this derivative matrix enabled in deriving the built parameter thresholds for a 'better' kitchen design. This derived kitchen showed 60 % reduction in PM2.5 peak concentration during cooking hours. The evaluation described here is essentially a 'proof of concept', that effective building design can be an alternative way to reduce HAP without the introduction of chimneys, improved cookstoves or shifting to cleaner fuel. [ABSTRACT FROM AUTHOR]

To better understand the sources as well as characterization of regional aerosols at a rural semi-arid region Kadapa (India), size-resolved composition of atmospheric particulate matter (PM) mass concentrations was sampled and analysed. This was carried out by using the Anderson low-pressure impactor for a period of 2 years during March 2013-February 2015. Also, the variations of organic carbon (OC), elemental carbon (EC) and water-soluble inorganic ion components (WSICs) present in total suspended particulate matter (TSPM) were studied over the measurement site. From the statistical analysis, the PM mass concentration showed a higher abundance of coarse mode particles than the fine mode during pre-monsoon season. In contrast, fine mode particles in the PM concentration showed dominance over coarse mode particle contribution during the winter. During the post-monsoon season, the percentage contributions of coarse and fine fractions were equal, whereas during the monsoon, coarse mode fraction was approximately 26 % higher than the fine mode. This distinct feature in the case of fine mode particles during the studied period is mainly attributed to large-scale anthropogenic activities and regional prevailing meteorological conditions. Further, the potential sources of PM have been identified qualitatively by using the ratios of certain ions. A high sulphate (SO) concentration at the measurement site was observed during the studied period which is caused by the nearby/surrounding mining activity. Carbon fractions (OC and EC) were also analysed from the TSPM, and the results indicated (OC/EC ratio of ~4.2) the formation of a secondary organic aerosol. At last, the cluster backward trajectory analyses were also performed at Kadapa for different seasons to reveal the origin of sources from long-range transport during the study period. [ABSTRACT FROM AUTHOR]

In the present study, we have analyzed the spatial patterns of seasonal-scale diurnal cycle of three main pollutants, NO, SO, and O, across five stations in Delhi. We used hourly data collected by the local pollution monitoring agencies for the time period, October 2010 to September 2013. Harmonic analysis was used to detect the diurnal cycle in the observed patterns of pollutants across Delhi. The results of our study indicated a consistently higher level of concentrations of all the pollutants at two of the stations, IGI Airport and Civil Lines, while lower concentration levels were observed in the mixed use residential areas of Shadipur and NSIT Dwarka. A clockwise gradual progression in the time of maximum from east to west was observed in the case of SO during each season. The time of maximum for O for each season was in the early evening hours, 1800 local standard time (LST). Moreover, there were little spatial variations in the peak time of occurrence of NO and O at the seasonal scale. [ABSTRACT FROM AUTHOR]

The present study is aimed to explore the relationship between coal consumption, industrial production, and CO emissions in China and India for the period of 1971-2011. The structural break unit root test and cointegrating approach have been applied. The direction of causal relationship between the variables is investigated by applying the VECM Granger causality test. Our results validate the presence of cointegration among the series in both countries. Our results also validate the existence of inverted U-shaped curve between industrial production and CO emissions for India, but for China, it is a U-shaped relationship. Coal consumption adds in CO emissions. The causality analysis reveals that industrial production and coal consumption Granger cause CO emissions in India. In the case of China, the feedback effect exists between coal consumption and CO emissions. Due to the importance of coal in China and India, any reduction in coal consumption will negatively affect their industrial value added as well as economic growth. [ABSTRACT FROM AUTHOR]

In this study, we characterize the impacts of COVID-19 on air pollution using NO2 and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO2 reduction during the lockdown (March 25–May 3rd, 2020) compared to the pre-lockdown (January 1st–March 24th, 2020) period. Also, a 19% reduction in NO2 was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO2 reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO2 for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO2 levels decreased exponentially. In contrast, to the decreased NO2 observed for most of the cities, we observed an increase in NO2 for cities in Northeast India during the 2020 lockdown period and attribute it to vegetation fires. The NO2 temporal patterns matched the AOD signal; however, the correlations were poor. Overall, our results highlight COVID-19 impacts on NO2, and the results can inform pollution mitigation efforts across different cities of India. [ABSTRACT FROM AUTHOR]

Air pollution is a very serious current environmental issue of human society. Large parts of countries, especially the densely populated cities, having high vehicular movement, industries, and factories, are worst affected. Biomarkers are changes in plant parameters that help in easy assessment of the environmental quality of an area at a certain time. Plants can react to different environmental stresses with the most evident responses shown by the leaves. In the present work, we have studied changes in biochemical parameters in the leaves of a mango plant (Mangifera indica), a very common plant in West Bengal, India, which were collected from four different locations in the city of Kolkata which has a high concentration of air pollutants and one control area from a rural region having a low concentration of air pollutants. It was observed that leaves which were exposed to high amounts of harmful air pollutants showed higher accumulation of molecules such as phenol, proline, malondialdehyde, and cellulose with lower amounts of chlorophyll. From this, we can observe that common environmental stress such as air pollution leads to a change in the synthesis of bioactive molecules to resist the effect of stress on the plant. Thus, from these data, it can be concluded that biochemical parameters can serve as efficient biomarkers of the air quality of an area. [ABSTRACT FROM AUTHOR]

The ambient air quality (AAQ) assessment was undertaken in Sukinda Valley, the chromite hub of India. The possible correlations of meteorological variables with different air quality parameters (PM10, PM2.5, SO2, NO2 and CO) were examined. Being the fourth most polluted area in the globe, Sukinda Valley has always been under attention of researchers, for hexavalent chromium contamination of water. The monitoring was carried out from December 2013 through May 2014 at six strategic locations in the residential and commercial areas around the mining cluster of Sukinda Valley considering the guidelines of Central Pollution Control Board (CPCB). In addition, meteorological parameters viz., temperature, relative humidity, wind speed, wind direction and rainfall, were also monitored. The air quality data were subjected to a general linear model (GLM) coupled with one-way analysis of variance (ANOVA) test for testing the significant difference in the concentration of various parameters among seasons and stations. Further, a two-tailed Pearson's correlation test helped in understanding the influence of meteorological parameters on dispersion of pollutants in the area. All the monitored air quality parameters varied significantly among the monitoring stations suggesting (i) the distance of sampling location to the mine site and other allied activities, (ii) landscape features and topography and (iii) meteorological parameters to be the forcing functions. The area was highly polluted with particulate matters, and in most of the cases, the PM level exceeded the National Ambient Air Quality Standards (NAAQS). The meteorological parameters seemed to play a major role in the dispersion of pollutants around the mine clusters. The role of wind direction, wind speed and temperature was apparent in dispersion of the particulate matters from their source of generation to the surrounding residential and commercial areas of the mine. [ABSTRACT FROM AUTHOR]