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The global problem of climate change and difficulties related to energy access, air pollution, and water pollution in India (and other developing countries) necessitate the need to look for alternative clean, local, and low-carbon energy sources for displacing fossil fuels. This paper analyses the existing waste-to-biomethane potential of the sericulture, fisheries, and agro-industrial sectors in India, which have mostly been neglected previously in India's waste-to-energy assessments. In the process, the volume of wastewaters generated from India's agro-industries are also estimated, data on which are scarce in the academic and grey literature. India's annual biomethane potentials (for 2018–19) were found to be 88 million m3, 207 million m3, and 3514 million m3 from the three sectors, respectively. To assess the energy generation potential of these wastes, a comprehensive literature review was conducted and data on residue-to-product ratios, results of proximate analyses, and specific biomethane yields were either compiled or calculated. The gross thermal energy and electricity generation potentials are 132 PJ and 22 TWh respectively; between 19 and 95 million people can benefit from such waste-to-energy production, depending on the end use of the biomethane for electricity generation or as a domestic cooking fuel. The corresponding mean removal of organic pollution from India's surface waters can be between 5.5 and 9.3 million tonnes of chemical oxygen demand. For the end uses of biomethane in electricity generation and cooking, the cumulative GHG abatements attributable to these industries are 11.4 million tonnes and 6.8 million tonnes of carbon dioxide-equivalent respectively. Besides the numeric results reported, this paper also contains a comprehensive compilation of data on waste characteristics and biomethane productions. The present status of biogas generation and related policy implementation in India are reviewed and discussed. Overall, our findings indicate that biomethane from these wastes and wastewaters can generate substantial energy for India, is in line with recent government policies, and can aid India in achieving six Sustainable Development Goals of the United Nations. • Annual estimates of wastes from India's sericulture, fisheries and agro-industries • The corresponding annual biomethane generation potential is 3810 million m3. • Clean electricity or cooking gas for 19 and 95 million people respectively • Quantitative estimates of atmospheric GHG and surface water COD removals • Biomethane data compilation and a review of present status and policies in India [ABSTRACT FROM AUTHOR]

A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO 2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006–2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%–43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia. [Display omitted] • Sulfate emission reduces Indian summer monsoon precipitation through aerosol-radiation and aerosol-cloud interactions. • Over 27% of the summer monsoon rainfall deficit in a normal year are linked to Asian anthropogenic sulfate emissions. • Sulfate-induced rainfall reduction is the maximum for La Niña year. • SO 2 loading increases atmospheric stability and weakens moisture transport to India. [ABSTRACT FROM AUTHOR]

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

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]

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]

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

How are citizens experiencing the increase in Air pollution? How can the Social media data be explored and used to develop deeper insights to study citizen’s observations, opinions and experiences? In current research paper, by utilizing micro-blog social media application – Twitter data, these questions are examined. A suitable framework, He a l th P ractice - “ HELP ”, is emerged based on computational analysis and visualizations of the content of the Tweets. [ABSTRACT FROM AUTHOR]

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]

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]