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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]

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]

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]

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]

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]

The paper aims to quantify the changes in the pollutants level in the ambient air of Madhya Pradesh during COVID-19 induced 21 days lockdown. The data of 16 continuous ambient air quality monitoring stations (CAAQMS) for six pollutants, namely PM2.5, PM10, SO2, NOx, CO, and Ozone for pre-lockdown (1–21 March 2020) and lockdown (25 March-14 April 2020) period was compiled and appropriate statistical analysis was done. The result showed a reduction of 13%, 20%, 37%, 39% and 25% in the ambient air level of PM2.5, PM10, SO2, NOx and CO, respectively, during the lockdown. No such declining trend was observed in the ambient air ozone level. Ambient air ozone level changed between −22 and +30% compared to pre-lockdown concentrations. The mean ambient air ozone concentration was reduced at the residential/commercial stations (−5%), but was increased at the industrial stations (+19%) concentration. [ABSTRACT FROM AUTHOR]

To reduce the pollution from the atmosphere or polluted cities like the capital city Delhi of India, use of artificial rain is a solution. In this paper, we have proposed and analyzed a nonlinear mathematical model to reduce the pollution level by rain making. In the proposed model five variables are considered, namely; (i) number density of water vapor, (ii) number density of cloud drops, (iii) number density of raindrops, (iv) cumulative concentration of aerosols, and (v) concentration of pollutant particles suspended in the region of consideration. The effect of environmental fluctuations has been studied with the help of Lyapunov functionals. The model is analyzed in the presence of white noise and proved that if rain persists, the pollutants can be totally washed out. It has been observed that the environmental disturbances are not much favorable in such experiments as the presence of environmental disturbance may destabilize the system. It is found that to remove pollutants completely, it is necessary to prevent the formation of pollutants. The simulation is performed to support the analytical findings. [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]

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]

BACKGROUND: High quality personal exposure data is fundamental to understanding the health implications of household energy interventions, interpreting analyses across assigned study arms, and characterizing exposure–response relationships for household air pollution. This paper describes the exposure data collection for the Household Air Pollution Intervention Network (HAPIN), a multicountry randomized controlled trial of liquefied petroleum gas stoves and fuel among 3,200 households in India, Rwanda, Guatemala, and Peru. OBJECTIVES: The primary objectives of the exposure assessment are to estimate the exposure contrast achieved following a clean fuel intervention and to provide data for analyses of exposure–response relationships across a range of personal exposures. METHODS: Exposure measurements are being conducted over the 3-y time frame of the field study. We are measuring fine particulate matter [PM < 2.5 μm in aerodynamic diameter (PM2.5)] with the Enhanced Children’s MicroPEM™ (RTI International), carbon monoxide (CO) with the USB-EL-CO (Lascar Electronics), and black carbon with the OT21 transmissometer (Magee Scientific) in pregnant women, adult women, and children < 1 year of age, primarily via multiple 24-h personal assessments (three, six, and three measurements, respectively) over the course of the 18-month follow-up period using lightweight monitors. For children we are using an indirect measurement approach, combining data from area monitors and locator devices worn by the child. For a subsample (up to 10%) of the study population, we are doubling the frequency of measurements in order to estimate the accuracy of subject-specific typical exposure estimates. In addition, we are conducting ambient air monitoring to help characterize potential contributions of PM2.5 exposure from background concentration. Stove use monitors (Geocene) are being used to assess compliance with the intervention, given that stove stacking (use of traditional stoves in addition to the intervention gas stove) may occur. CONCLUSIONS: The tools and approaches being used for HAPIN to estimate personal exposures build on previous efforts and take advantage of new technologies. In addition to providing key personal exposure data for this study, we hope the application and learnings from our exposure assessment will help inform future efforts to characterize exposure to household air pollution and for other contexts. [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]

Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM 2.5 removed natural dust (PM 2.5_No Dust) concentrations and the corresponding population's PM 2.5_No Dust exposure (PPM 2.5 E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM 2.5_No Dust -polluted regions, whose spatial relative magnitude of PM 2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM 2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM 2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM 2.5_NoDust concentrations and PPM 2.5 E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM 2.5_NoDust concentrations and PPM 2.5 E were greater in the medium stage than in the early and later stages. The local trends of PM 2.5_NoDust concentration and PPM 2.5 E in the two higher PM 2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM 2.5_NoDust concentration and PPM 2.5 E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM 2.5 E to Europe. The PPM 2.5 E trend in Africa generally increased during the study period. • First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions. • Firstly applies a developed Bayesian space-time model in investigating the global population's exposure to PM 2.5. • Deeply analyses the spatiotemporal trends of the global continental PM 2.5 pollution removed natural dust. • Details the spatiotemporal trends of the global population's exposure to anthropogenic PM 2.5. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We monitored PM levels pre and post odd-even driving scheme in Delhi city, India. • We find that the scheme has reduced the PM 2.5 and PM 1 levels at traffic corridors. • The reduction of PM 1 is found higher than that of PM 2.5 during the days of scheme. • We find gross non-conformance towards NAAQS values for PM at traffic corridors. • Timely implementation of more stringent emission norms need to be ensured in Delhi. Abstract According to a global survey of 1600 cities by WHO in 2015, Delhi was found to be the most polluted city in the world, highlighting immediate need to bring in appropriate and effective measures to ensure a healthy air quality throughout the city. In a bid to do so, the odd-even driving scheme was implemented a few years ago in Delhi city on a trial basis requiring the vehicles bearing odd and even registration numbers to run on alternative days with effect from 1st January 2016. Applied for the very first time in India and lasting on a pilot basis for 15 days and also referred to as Phase 1 of implementation, the scheme ended on 15th January 2016 with primary goal of assessing the extent to which such a scheme could serve as an effective measure to bring down and control the excessively high ambient air pollution levels. Other associated benefits, which the study considered, were decongestion of city roads, and promoting use of public transport system and carpooling, besides other alternative modes of transport. The paper presents a comparative analysis of particulate matter concentrations recorded before and during the implementation of odd-even scheme of Phase 1 for three key traffic corridors of the city. The study reports an average reduction of PM 2.5 of 5.73% and that of PM 1.0 of 4.70% in the ambient air across all the corridors during the days of implementation of scheme, which depicts a small but positive impact of the measure adopted towards abatement of urban air pollution in the megacity of Delhi. Capsule: The study finds that the odd-even driving scheme can help to reduce particulate matter levels compared to non-scheme days at traffic corridors. [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 effects of greenhouse gas emissions, urban air pollution, and rising transport fuel prices, on the low-income capita in India, necessitate appropriate measures to ensure commodities are sustainably available. This paper presents a study whereby biodiesel blends produced from waste cooking oil are optimised for use in urban medium-duty commercial vehicles, accounting for factors of production capacity, economics, and approximated engine characteristics. An artificial neural network model, trained with experimental data, is used to predict performance, combustion, and emission parameters. The results for various biodiesel blends are applied to a standard driving cycle to obtain variations in key factors. A multi objective optimisation is carried out with engine operation parameters to arrive at optimised biodiesel blends varying between 25% and 81% based on different criteria such as minimising fuel cost, enhancing engine efficiency, and reducing emissions. Considerations when choosing blend concentration are discussed in light of different governmental targets. • Artificial neural network model for predicting engine performance using biodiesel. • Consideration of production, performance, economic, and emission criteria. • Optimisation of biodiesel blends for transport in India considering life cycle. • Blends between 25% and 81% achieved depending on optimisation parameters. [ABSTRACT FROM AUTHOR]

Solar energy is the primary resource for all biological, chemical and physical processes. The amount of global solar radiation is an important parameter for solar energy applications. It is common to estimate a monthly average of daily global solar radiation using different regression models. These models in turn exploit the correlation between solar radiation and various atmospheric factors. These factors are commonly derived from meteorological, geographical and climatological data that are readily available for majority of weather stations across the world. In this paper, a novel regression model that can predict location-independent daily global solar radiation is presented. The proposed exponential quadratic model captures the correlation between measured global solar radiation values, sunshine hour and Air Pollution Index for Indian cities. In addition to this, an extended study of several other regression models (e.g. linear, quadratic, exp.-linear and exp.-quadratic) is also presented. This analysis with real data from Indian cities suggests that air pollution is a more significant factor than location when predicting solar radiation. Finally, the model parameters (regression coefficients) for each model are listed out. Additionally, the generalised model equation for the best performing model is also presented. [ABSTRACT FROM PUBLISHER]

This paper assesses comparable urban transport scenarios for China and India. The assessment methodology uses AIM/End-use model with a detailed characterization of technologies to analyze two scenarios for India and China till the year 2050. The first scenario assumes continuation and enhancement, in both countries, of policies under a typical business-as-usual dynamics, like constructing metros, implementing national fuel economy standards, promoting alternate fuel vehicles and implementing national air quality standards. The alternative, low carbon scenario assumes application, in both countries, of globally envisaged measures like fuel economy standards as well as imposition of carbon price derived from a global integrated assessment modeling exercise aiming to achieve global 2 °C temperature stabilization target. The modeling results for both countries show that decarbonizing transport sector shall need a wide array of measures including fuel economy, low carbon fuel mix including low carbon electricity supply. The comparison of China and India results provides important insights and lessons from their similarities and differences in the choice of urban transport options. India can benefit from China’s experiences as it lags China in urbanization and income. Modeling assessments show that both nations can contribute to, as well as benefit by aligning their transport plans with global climate stabilization regime. [ABSTRACT FROM AUTHOR]

Background: More than 3 billion of the world's population are affected by household air pollution from relying on unprocessed solid fuels for heating and cooking. Household air pollution is harmful to human health, climate, and environment. Sustained uptake and use of cleaner cooking technologies and fuels are proposed as solutions to this problem. In this paper, we present our study protocol aimed at understanding multiple interacting feedback mechanisms involved in the dynamic behavior between social, ecological, and technological systems driving sustained use or abandonment of cleaner cooking technologies among the rural poor in India. Methods/Design: This study uses a comparative case study design to understand the dynamics of sustained use or abandonment of cleaner cooking technologies and fuels in four rural communities of Rajasthan, India. The study adopts a community based system dynamics modeling approach. We describe our approach of using community based system dynamics with rural communities to delineate the feedback mechanisms involved in the uptake and sustainment of clean cooking technologies. We develop a reference mode with communities showing the trend over time of use or abandonment of cleaner cooking technologies and fuels in these communities. Subsequently, the study develops a system dynamics model with communities to understand the complex sub-systems driving the behavior in these communities as reflected in the reference mode. We use group model building techniques to facilitate participation of relevant stakeholders in the four communities and elicit a narrative describing the feedback mechanisms underlying sustained adoption or abandonment of cleaner cooking technologies. Discussion: In understanding the dynamics of feedback mechanisms in the uptake and exclusive use of cleaner cooking systems, we increase the likelihood of dissemination and implementation of efficacious interventions into everyday settings to improve the health and wellbeing of women and children most affected by household air pollution. The challenge is not confined to developing robust technical solutions to reduce household air pollution and exposure to improve respiratory health, and prevent associated diseases. The bigger challenge is to disseminate and implement cleaner cooking technologies and fuels in the context of various social, behavioral, and economic constraints faced by poor households and communities. Trial registration: The Institutional Review Board of Washington University in St. Louis has exempted community based system dynamics modeling from review. [ABSTRACT FROM AUTHOR]

Open-burning of municipal solid waste (MSW) is a major source of PM emissions in developing world cities, but few studies have characterized this phenomenon at the city and intracity (neighborhood) scale relevant to human health impacts. This paper develops a consistent field method for measuring the spatial frequency of the incidence of MSW-burning and presents results in three neighborhoods of varying socioeconomic status (SES) in Delhi, India, observed in winter and summer over 2 years. Daily MSW-burning incidents ranged from 24 to 130/km²-day during winter and 5-87/km²-day during summer, with the highest intensity in low SES neighborhoods. Distinct seasonal and diurnal patterns are observed. The daily mass of MSW-burned was also estimated at 90-1170 kg/km²-day and 13-1100 kg/km²-day in highest to low SES neighborhoods, in winter and summer, respectively. The scaled-up estimate of total MSW-burned for Delhi city ranged from 190 to 246 tons/day, about 2%-3% of total generated MSW; morning-burning contributed >65% of the total. MSW composition varied systematically across neighborhoods and season. Agra had much higher MSW-burning (39-202 incidents/km²-day; 672-3485 kg/km²-day) in the summer. The field method thus captures differences in MSW-burning across cities, neighborhoods, diurnally and seasonally, important for more fine grained air pollution modeling, and for tracking/monitoring policy effectiveness on-ground. [ABSTRACT FROM AUTHOR]

Recently, some high-skilled workers tend to escape from areas with severe air pollution. The phenomenon is more prominent in two emerging economies, China and India, and they are facing the brain drain caused by air pollution. This paper establishes a partial equilibrium model about the relationship of air pollution and the flow of technological innovative professionals (TIP), and for the first time, examines the effect of air pollution on the accumulation of technological innovative human capital based on cross-country data from China and India. We find that air pollution will reduce the stock of TIP in some regions. The estimation results show that a 1% increase in the PM 2.5 concentration of China's cities leads to an approximate 146 people decrease in the stock of TIP, while a 1% increase in the PM 10 concentration of Indian states leads to a 0.127% decrease in the stock of TIP. Further analysis of regional differences indicates that air pollution has a significant negative effect on the stock of TIP in economically developed areas of China, but has little effect on that in underdeveloped areas of China. Moreover, the results of robustness tests prove the stability of the findings in this study. This paper concludes with suggestions on how the country, local governments, enterprises, and research institutions could better deal with air pollution, attract and retain TIP, and effectively reduce brain drain. • Air pollution negatively affects the accumulation of innovative human capital. • Air pollution will reduce the stock of technological innovative professionals (TIP). • A 1% increase in PM 2.5 in China's cities decreases the stock of TIP by 146 people. • A 1% increase in PM 10 in Indian states reduces the stock of TIP by 0.127%. • TIP in economically developed areas are more sensitive to air pollution. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of air pollution on child growth in India. We rely on wind direction to capture quasi-random variation in three main criteria air pollutants. We show that an increase in the average concentration of fine particulate matter by one standard deviation is accountable for almost 5 and 2.4 percentage points of stunting and severe stunting rates, respectively. We also find that ozone and carbon monoxide impact weight-related outcomes. Stunting has critical long-term health and economic consequences; through its impact on stunting, pollution exacerbates the height premium in earnings, with girls being more adversely affected than boys in India. [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]

India has more than 100,000 brick kilns producing around 250 billion bricks annually. Indian brick industry is often a small scale industry and third largest consumer of coal in the country. With the growing demand for building materials and characterised by lack of pollution control measures the brick industry has a potential to cause adverse effects on the environment. This paper presents assessment of five brick making technologies based on the measurements carried out at seventeen individual brick kilns. Emissions of PM, SO 2 , CO and CO 2 were measured and these emissions were used to estimate the emission factors for comparing the emissions across different fuel or operating conditions. Estimated emission from brick kilns in South Asia are about 0.94 million tonnes of PM; 3.9 million tonnes of CO and 127 million tonnes of CO 2 per year. Among various technologies that are widely used in India, Zig zag and vertical shaft brick kilns showed better performance in terms of emissions over the traditional fixed chimney Bull's trench kilns. This suggests that the replacement of traditional technologies with Zig zag, vertical shaft brick kilns or other cleaner kiln technologies will contribute towards improvements in the environmental performance of brick kiln industry in the country. Zig zag kilns appear to be the logical replacement because of low capital investment, easy integration with the existing production process, and the possibility of retrofitting fixed chimney Bull's trench kilns into Zig zag firing. [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]

Coal-fired energy generation is the backbone of India's power sector and considered a driver of its economic development. However, it is associated with detrimental environmental and health impacts in India and its fleet is currently struggling with overcapacity and inefficiency problems. One solution to address these challenges is the early retirement of some of India's coal-fired power plants. In this paper, we introduce multidimensional indices that identify plants for retirement based on comprehensive criteria that include technical and economic characteristics of plants as well as their environmental impacts. We implement an ensemble approach, where we formulate 8008 indices based on all possible combination of seven relevant parameters and rank plants accordingly. This approach facilitates a comprehensive analysis of the plants' performance on different parameters and provides a new outlook on plant retirements that differs from the common approach of retiring plants based solely on technical characteristics such as age, capacity, and heat rate. Our results show that top plants recommended for early retirement are typically 7 years older, 13% more expensive and have around 40% higher population exposure to emissions compared to an average plant in India. We estimate the potential costs saved from the retirement of the worst-performing 50 GW of generating capacity to be $21 billion resulting from shifting ownership towards a cheaper cost of capital and replacing coal by more competitive sources such as solar power. • Identify plants for early retirement based on technical, economic and environmental indicators. • Worst performing 50 GW capacity are older, more expensive to operate and have higher population exposure. • Costs saved from retiring 50 GW capacity and replacing them with solar are $21 billion. • A multidimensional approach is essential for a politically feasible phaseout of coal. [ABSTRACT FROM AUTHOR]

Although lockdown of the industrial and transport sector and stay at home advisories to counter the COVID-19 pandemic have shown that the air quality has improved during this time, very little is known about the role of ambient air pollutants and meteorology in facilitating its transmission. This paper presents the findings from a study that was conducted to evaluate whether air quality index (AQI), three primary pollutants (PM 2.5 , PM 10 and CO), Ground level ozone (O 3) and three meteorological variables (temperature, relative humidity, wind speed) have promoted the COVID-19 transmission in five megacities of India. The results show significant correlation of PM 2.5 , PM 10 , CO, O 3 concentrations, AQI and meteorological parameters with the confirmed cases and deaths during the lockdown period. Among the meteorological variables considered, temperature strongly correlated with the COVID-19 cases and deaths during the lockdown (r = 0.54 ; 0.25) and unlock period (r = 0.66 ; 0.25). Among the pollutants, ozone, and among the meteorological variables, temperature, explained the highest variability, up to 34% and 30% respectively, for COVID-19 confirmed cases and deaths. AQI was not a significant parameter for explaining the variations in confirmed and death cases. WS and RH could explain 10–11% and 4–6% variations of COVID-19 cases. A GLM model could explain 74% and 35% variability for confirmed cases and deaths during the lockdown and 66% and 19% variability during the unlock period. The results suggest that meteorological parameters may have promoted the COVID-19 incidences, especially the confirmed cases. Our findings may encourage future studies to explore more about the role of ambient air pollutants and meteorology on transmission of COVID-19 and similar infectious diseases. [Display omitted] • During lockdown for COVID-19, air quality improved in five megacities of India. • PM 2.5 , PM 10 , CO concentration across the megacities reduced during the lockdown. • Delhi and Kolkata experienced rise in O 3 concentration during lockdown. • PM 2.5 , PM 10 , CO, O 3 , AQI and T correlated with COVID-19 confirmed cases and deaths. • Temperature explained up to 30% variability for COVID-19 cases during the lockdown. [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]