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Anthropogenic methane (CH4) emissions increases from the period 1850–1900 until 2019 are responsible for around 65% as much warming as carbon dioxide (CO2) has caused to date, and large reductions in methane emissions are required to limit global warming to 1.5°C or 2°C. However, methane emissions have been increasing rapidly since ~2006. This study shows that emissions are expected to continue to increase over the remainder of the 2020s if no greater action is taken and that increases in atmospheric methane are thus far outpacing projected growth rates. This increase has important implications for reaching net zero CO2 targets: every 50 Mt CH4 of the sustained large cuts envisioned under low-warming scenarios that are not realized would eliminate about 150 Gt of the remaining CO2 budget. Targeted methane reductions are therefore a critical component alongside decarbonization to minimize global warming. We describe additional linkages between methane mitigation options and CO2, especially via land use, as well as their respective climate impacts and associated metrics. We explain why a net zero target specifically for methane is neither necessary nor plausible. Analyses show where reductions are most feasible at the national and sectoral levels given limited resources, for example, to meet the Global Methane Pledge target, but they also reveal large uncertainties. Despite these uncertainties, many mitigation costs are clearly low relative to real-world financial instruments and very low compared with methane damage estimates, but legally binding regulations and methane pricing are needed to meet climate goals.

The differential diagnosis of cystic echinococcosis should be considered in the suspected case of a pelvic mass presenting with vague symptoms. Primary pelvic hydatid cyst is rare, and the achievement of early diagnosis is important, as the involvement of bone in the pelvis is difficult to treat, and the probability of recurrences is greater. This case report presents a rare case of a primary pelvic hydatid cyst in a male in his 30s. The patient presented vague symptoms of difficulty in micturition and chronic pelvic pain. First-line radiological imaging techniques diagnosed it as a neoplastic mass and tuberculosis, while MRI revealed it as a cystic disease, probably a hydatid cyst. Surgical excision and histopathological examination confirmed the diagnosis of a primary pelvic hydatid cyst. Acquaintance with the varied presentation of cystic echinococcosis helps to obtain an accurate diagnosis and thus decreases the severe complication associated with the disease. This case report highlights the importance of considering parasitic infections in the differential diagnosis of cystic pelvic masses, even in regions with low endemicity.

India experiences some of the highest levels of ambient PM2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMBModified: 0.19; NMBBase: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMBModified: 0.08; NMBBase: 1.64) and ammonium simulation (NMBModified: 0.49; NMBBase: 0.90). We use this validated simulation to estimate a population-weighted annual PM2.5 exposure of 61.4 μg m-3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM2.5 aerosol source attribution for health assessments and air quality management in India.

Biaxial tensile tests were carried out on cruciform specimens of composite solid propellants. Digital Image Correlation (DIC) was used to measure the strain in the gage section. Experiments were conducted at displacement rates of 1, 50 and 1000 mm/min (Y direction) to study the effect of the displacement rate on the material response. Two displacement rate ratios of 1:1 and 0.5:1 (X:Y directions) are considered to understand the influence of biaxiality of loading on the mechanical response. The biaxial experiments were also conducted at temperatures of 20 °C and 55 °C to understand the effect of temperature on the material response. The stress–strain response under biaxial loading was observed to be non-linear and dependent on the displacement rate. The slope of the stress–strain response in the linear region and the yield stress increased with an increase in the displacement rate for all the loading situations. There was no significant change observed in the slope of the stress–strain response in the linear region and yield stress with variation in temperature from 20 to 55 °C. However, a significant decrease in failure strain was observed with an increase in temperature from 20 °C to 55 °C. The mechanical response under equi-biaxial (displacement rate ratio — X:Y of 1:1) loading is compared with the uniaxial loading responses. The yield stress was observed to be 25%–47% higher during equi-biaxial tests as compared to uniaxial tests. However, the failure strain was 50%–70% lower in the equi-biaxial loading compared to uniaxial loading. The fractography and elemental mapping using Energy Dispersive Spectroscopy (EDS) of the fractured surfaces were carried out. A large number of cavities which are bigger in size and depth were observed on the fractured surface of the uniaxially loaded specimens as compared to equi-biaxially loaded specimens.

Abstract Quantifying natural geological sources of methane (CH4) allows to improve the assessment of anthropogenic emissions to the atmosphere from fossil fuel industries. The global CH4 flux of geological gas is, however, an object of debate. Recent fossil (14C-free) CH4 measurements in preindustrial-era ice cores suggest very low global geological emissions (~ 1.6 Tg year−1), implying a larger fossil fuel industry source. This is however in contrast with previously published bottom-up and top-down geo-emission estimates (~ 45 Tg year−1) and even regional-scale emissions of ~ 1–2 Tg year−1. Here we report on significant geological CH4 emissions from the Lusi hydrothermal system (Indonesia), measured by ground-based and satellite (TROPOMI) techniques. Both techniques indicate a total CH4 output of ~ 0.1 Tg year−1, equivalent to the minimum value of global geo-emission derived by ice core 14CH4 estimates. Our results are consistent with the order of magnitude of the emission factors of large seeps used in global bottom-up estimates, and endorse a substantial contribution from natural Earth’s CH4 degassing. The preindustrial ice core assessments of geological CH4 release may be underestimated and require further study. Satellite measurements can help to test geological CH4 emission factors and explain the gap between the contrasting estimates.

Coal is a principal source of energy and the combustion of coal supplies around one-third of the global electricity generation. Coal mines are also an important source of CH4 emissions, the second most important greenhouse gas. Monitoring CH4 emissions caused by coal mining using earth observation will require the exact location of coal mines. This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task. This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks. With a list of known coal mine locations from various countries, a training dataset of “Coal Mine” and “No Coal Mine” image patches is prepared using Sentinel-2 satellite images with 13 spectral bands. Various pre-trained CNN network architectures (VGG, ResNet, DenseNet) are trained and validated with our prepared coal mine dataset of 3500 “Coal Mine” and 3000 “No Coal Mine” image patches. After several experiments with the VGG network combined with transfer learning is found to be an optimal model for this task. Classification accuracy of 98% has been achieved for the validation dataset of the pre-trained VGG architecture. The model produces more than 95% overall accuracy when tested on unseen satellite images from different countries outside the training dataset and evaluated against visual classification.

The lack of a comprehensive, up-to-date emission inventory for the Himalayan region is a major challenge in understanding the extensive regional air pollution, including its causes, impacts and mitigation pathways. This study describes a high-resolution (1 km × 1 km) present-day emission inventory for Nepal, developed with a higher-tier approach. The complete study is divided into two parts; this paper covers technologies and combustion sources in residential, industrial, commercial, agricultural diesel-use and transport sectors as Part I (NEEMI-Tech), while emissions from the open burning of municipal waste and agricultural residue in fields and fugitive emissions from waste management, paddy fields, enteric fermentation and manure management for the period 2001–2016 will be covered in Part II (NEEMI-Open). The national total energy consumption (except hydropower, solar and wind energy) estimated in the base year 2011 was 374 PJ, with the residential sector being the largest energy consumer (79 %), followed by industry (11 %) and the transport sector (7 %). Biomass is the dominant energy source, contributing to 88 % of the national total energy consumption, while the rest is from fossil fuel. A total of 8.9 Tg of CO2, 110 Gg of CH4, 2.1 Gg of N2O, 64 Gg of NOx, 1714 Gg of CO, 407 Gg of NMVOCs, 195 Gg of PM2.5, 23 Gg of BC, 83 Gg of OC and 24 Gg of SO2 emissions were estimated in 2011 from the five energy-use sectors considered in NEEMI-Tech. The Nepal emission inventory provides, for the first time, temporal trends of fuel and energy consumption and associated emissions in Nepal for a long period, 2001–2016. The energy consumption showed an increase by a factor of 1.6 in 2016 compared to 2001, while the emissions of various species increased by a factor of 1.2–2.4. An assessment of the top polluting technologies shows particularly high emissions from traditional cookstoves and space-heating practices using biomass. In addition, high emissions were also computed from fixed-chimney Bull's trench kilns (FCBTKs) in brick production, cement kilns, two-wheeler gasoline vehicles, heavy-duty diesel freight vehicles and kerosene lamps. The monthly analysis shows December, January and February as periods of high PM2.5 emissions from the technology-based sources considered in this study. Once the full inventory including open burning and fugitive sources (Part II) is available, a more complete picture of the strength and temporal variability in the emissions and sources will be possible. Furthermore, the large spatial variation in the emissions highlights the pockets of growing urbanization, which emphasize the importance of the detailed knowledge about the emission sources that this study provides. These emissions will be of value for further studies, especially air-quality-modeling studies focused on understanding the likely effectiveness of air pollution mitigation measures in Nepal.

Abstract The annual premature mortality in India attributed to exposure to ambient particulate matter (PM2.5) exceeds 1 million (Cohen et al., 2017, https://doi.org/10.1016/S0140‐6736(17)30505‐6). Studies have estimated sector‐specific premature mortality from ambient PM2.5 exposure in India and shown residential energy use is the dominant contributing sector. In this study, we estimate the contribution of PM2.5 and premature mortality from six regions of India in 2012 using the global chemical‐transport model. We calculate how premature mortality in India is determined by the transport of pollution from different regions. Of the estimated 1.1 million annual premature deaths from PM2.5 in India, about ~60% was from anthropogenic pollutants emitted from within the region in which premature mortality occurred, ~19% was from transport of anthropogenic pollutants between different regions within India, ~16% was due to anthropogenic pollutants emitted outside of India, and ~4% was associated with natural PM2.5 sources. The emissions from Indo Gangetic Plain contributed to ~46% of total premature mortality over India, followed by Southern India (13%). Indo Gangetic Plain also contributed (~8%) to the most premature mortalities in other regions of India through transport. More than 50% of the premature mortality in Northern, Eastern, Western, and Central India was due to transport of PM2.5 from regions outside of these individual regions. Our results indicate that reduction in anthropogenic emissions over India, as well as its neighboring regions, will be required to reduce the health impact of ambient PM2.5 in India.

Enrichment and immobilization of analytes by chemical bonding or physical adsorption is typically the first step in many commonly used analytical techniques. In this paper, we discuss a permeation drag based technique as an alternative approach for carrying out location-specific immobilization of macromolecular analytes. Fluorescein isothiocyanate (FITC) labeled macromolecules and their complexes were enriched near the surface of ultrafiltration membranes and detected by direct visual observation and fluorescence imaging. The level of macromolecule enrichment at the immobilization sites could be controlled by manipulating the filtration rate and thereby the magnitude of permeation drag. Higher enrichment as indicated by higher fluorescence intensity was observed at higher filtration rates. Also, larger macromolecules were more easily enriched. The feasibility of using this technique for detecting immunocomplexes was demonstrated by carrying out experiments with FITC labeled bovine serum albumin (FITC-BSA) and its corresponding antibody. This permeation drag based enrichment technique could potentially be developed further to suit a range of analytical applications involving more sophisticated detection methods. Keywords: Macromolecule, Enrichment, Immobilization, Concentration polarization, Permeation drag, Immunoassay, Membrane, Ultrafiltration

India is currently experiencing degraded air quality, and future economic development will lead to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015–2050, under specific pathways of diffusion of cleaner and more energy-efficient technologies. The impacts of individual source sectors on PM2.5 concentrations were assessed through systematic simulations of spatially and temporally resolved particulate matter concentrations, using the GEOS-Chem model, followed by population-weighted aggregation to national and state levels. We find that PM2.5 pollution is a pan-India problem, with a regional character, and is not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 annual standard (40 µg m−3). Sources related to human activities were responsible for the largest proportion of the present-day population exposure to PM2.5 in India. About 60 % of India's mean population-weighted PM2.5 concentrations come from anthropogenic source sectors, while the remainder are from other sources, windblown dust and extra-regional sources. Leading contributors are residential biomass combustion, power plant and industrial coal combustion and anthropogenic dust (including coal fly ash, fugitive road dust and waste burning). Transportation, brick production and distributed diesel were other contributors to PM2.5. Future evolution of emissions under regulations set at current levels and promulgated levels caused further deterioration of air quality in 2030 and 2050. Under an ambitious prospective policy scenario, promoting very large shifts away from traditional biomass technologies and coal-based electricity generation, significant reductions in PM2.5 levels are achievable in 2030 and 2050. Effective mitigation of future air pollution in India requires adoption of aggressive prospective regulation, currently not formulated, for a three-pronged switch away from (i) biomass-fuelled traditional technologies, (ii) industrial coal-burning and (iii) open burning of agricultural residue. Future air pollution is dominated by industrial process emissions, reflecting larger expansion in industrial, rather than residential energy demand. However, even under the most active reductions envisioned, the 2050 mean exposure, excluding any impact from windblown mineral dust, is estimated to be nearly 3 times higher than the WHO Air Quality Guideline.

The use of conventional membrane adsorbers such as radial flow devices is largely restricted to flow-through applications, such as virus and endotoxin removal, as they fail to give acceptable resolution in bind-and-elute separations. Laterally-fed membrane chromatography or LFMC devices have been specifically developed to combine high-speed with high-resolution. In this study, an LFMC device containing a stack of strong cation exchange membranes was compared with an equivalent resin packed column. Preliminary characterization experiments showed that the LFMC device had a significantly greater number of theoretical plates per metre than the column. These devices were used to separate a ternary model protein mixture consisting of ovalbumin, conalbumin and lysozyme. The resolution obtained with the LFMC device was better than that obtained with the column. For instance, the LFMC device could resolve lysozyme dimer from lysozyme monomer, which was not possible using the column. In addition, the LFMC device could be operated at lower pressure and at significantly higher flow rates. The devices were then compared based on an application case study, i.e., preparative separation of monoclonal antibody charge variants. The LFMC device gave significantly better separation of these variants than the column.

Background: Institutional birth and Emergency Obstetric Care (EmOC) are important strategies of the National Rural Health Mission (NRHM). While the Community Health Center (CHC) is expected to serve EmOC needs in NRHM, the CHCs are hamstrung due to chronic shortage of specialist doctors. Alternative strategies are therefore needed for ensuring EmOC. Objectives: This study aims to estimate the EmOC needs in a private rural hospital from case records and find some useful predictors for caesarian section (C-section) and to assess C-section needs in the context of on-call specialist support. Materials and Methods: We analyzed a two-decade series of 2587 obstetric cases in a private rural hospital for normal deliveries and EmOC including C-section. Results: About 80% of the obstetric cases were normal deliveries. Of the remaining 20% cases that required EmOC, nearly one-third required C-section. In the series, two maternal deaths occurred due to hemorrhage. About 13% case records showed past abortion, which adds to EmOC workload. Primipararous mothers with higher age had a greater incidence (23%) of C-section. The C-section rate shows a steady rise from 3% to above 10% in the series. Conclusions : This rural hospital required C-section in 6.4% cases. This C-section workload was managed with the help of on-call specialists. The local hospital team could manage 93.6% of the cases and abortions with only two maternal deaths. This strategy of an on-call specialist team can be an option for CHCs till resident specialists are adequately available.

Background: Low birth weight remains a major reason behind childhood malnutrition. The NFHS findings show no dent in this problem. Objective: This study was undertaken to explore change in birth weights in a period from 1989 to 2007 and any associations thereof. Materials and Methods: All birth records of a private rural hospital spanning two decades (1989-2007) were analyzed for birth weight, age of mother, gender, birth order of the baby, proportion of pre-term babies and low birth weight babies. Results: No change was observed in the average birth weights (average 2.71 kg) over the period. Although the birth weight shows some expected variance with the age of mother, it was found to have no relation with the baby′s birth order and gender. The low birth weight proportion is about 24% and shows little difference before and after the series midpoint of year 1998. Conclusion: The birth weights have hardly changed in this population in the two decades.

Coal is a principal source of energy and the combustion of coal supplies around one-third of the global electricity generation. Coal mines are also an important source of CH4 emissions, the second most important greenhouse gas. Monitoring CH4 emissions caused by coal mining using earth observation will require the exact location of coal mines. This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task. This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks. With a list of known coal mine locations from various countries, a training dataset of "Coal Mine" and "No Coal Mine" image patches is prepared using Sentinel-2 satellite images with 13 spectral bands. Various pre-trained CNN network architectures (VGG, ResNet, DenseNet) are trained and validated with our prepared coal mine dataset of 3500 "Coal Mine" and 3000 "No Coal Mine" image patches. After several experiments with the VGG network combined with transfer learning is found to be an optimal model for this task. Classification accuracy of 98% has been achieved for the validation dataset of the pre-trained VGG architecture. The model produces more than 95% overall accuracy when tested on unseen satellite images from different countries outside the training dataset and evaluated against visual classification. [ABSTRACT FROM AUTHOR]

Enrichment and immobilization of analytes by chemical bonding or physical adsorption is typically the first step in many commonly used analytical techniques. In this paper, we discuss a permeation drag based technique as an alternative approach for carrying out location-specific immobilization of macromolecular analytes. Fluorescein isothiocyanate (FITC) labeled macromolecules and their complexes were enriched near the surface of ultrafiltration membranes and detected by direct visual observation and fluorescence imaging. The level of macromolecule enrichment at the immobilization sites could be controlled by manipulating the filtration rate and thereby the magnitude of permeation drag. Higher enrichment as indicated by higher fluorescence intensity was observed at higher filtration rates. Also, larger macromolecules were more easily enriched. The feasibility of using this technique for detecting immunocomplexes was demonstrated by carrying out experiments with FITC labeled bovine serum albumin (FITC-BSA) and its corresponding antibody. This permeation drag based enrichment technique could potentially be developed further to suit a range of analytical applications involving more sophisticated detection methods. [ABSTRACT FROM AUTHOR]

The "online" meteorological and chemical transport Weather Research and Forecasting/Chemistry (WRF-Chem) model has been implemented over the Indian subcontinent for three consecutive summers in 2008, 2009 and 2010 to study the aerosol properties over the domain. The model simulated the meteorological parameters, trace gases and particulate matter. Predicted mixing ratios of trace gases (Ozone, carbon monoxide and sulfur dioxide) are compared with ground based observations over Kan-pur. Simulated aerosol optical depth are compared with those observed at nine Aerosol Robotic Network stations (AERONET). The simulations show that the aerosol optical depth of the less polluted regions is better simulated compared to that of the locations where the aerosol loading is very high. The vertical profiles of extinction coefficient observed at the Kanpur Micropulse Lidar Network (MPLNET) station is underpredicted by the model by 10 to 50 % for altitudes greater than 1.5 km and qualitatively simulate the elevated layers of aerosols. The simulated mass concentration of black carbon shows a correlation coefficient of 0.4 with observations. Vertical profiles of black carbon at various locations have also been compared with observations from an aircraft campaign held during pre-monsoon period of 2008 and 2009. This study shows that WRF-Chem model captures many important features of the observed atmospheric composition during the pre-monsoon season in India. [ABSTRACT FROM AUTHOR]

Use of natural fibres with enhanced mechanical performance, as reinforcement in cement-based matrix has shown to be a promising opportunity. Advantages associated with natural fibres include widespread availability from renewable sources, high fibre tensile strength, high fibre modulus of elasticity, relatively low cost, and well-developed technology to extract the fibres [1]. [ABSTRACT FROM AUTHOR]

India experiences some of the highest levels of ambient PM 2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM 2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMB Modified : 0.19; NMB Base : 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMB Modified : 0.08; NMB Base : 1.64) and ammonium simulation (NMB Modified : 0.49; NMB Base : 0.90). We use this validated simulation to estimate a population-weighted annual PM 2.5 exposure of 61.4 μg m -3 , with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM 2.5 -attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM 2.5 aerosol source attribution for health assessments and air quality management in India., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world's most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a -1 , representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data-based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Methane emissions due to accidents in the oil and natural gas sector are very challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions.

The annual premature mortality in India attributed to exposure to ambient particulate matter (PM 2.5 ) exceeds 1 million (Cohen et al., 2017, https://doi.org/10.1016/S0140-6736(17)30505-6). Studies have estimated sector-specific premature mortality from ambient PM 2.5 exposure in India and shown residential energy use is the dominant contributing sector. In this study, we estimate the contribution of PM 2.5 and premature mortality from six regions of India in 2012 using the global chemical-transport model. We calculate how premature mortality in India is determined by the transport of pollution from different regions. Of the estimated 1.1 million annual premature deaths from PM 2.5 in India, about ~60% was from anthropogenic pollutants emitted from within the region in which premature mortality occurred, ~19% was from transport of anthropogenic pollutants between different regions within India, ~16% was due to anthropogenic pollutants emitted outside of India, and ~4% was associated with natural PM 2.5 sources. The emissions from Indo Gangetic Plain contributed to ~46% of total premature mortality over India, followed by Southern India (13%). Indo Gangetic Plain also contributed (~8%) to the most premature mortalities in other regions of India through transport. More than 50% of the premature mortality in Northern, Eastern, Western, and Central India was due to transport of PM 2.5 from regions outside of these individual regions. Our results indicate that reduction in anthropogenic emissions over India, as well as its neighboring regions, will be required to reduce the health impact of ambient PM 2.5 in India., Competing Interests: The authors declare no conflicts of interest relevant to this study., (©2018. The Authors.)

India is currently experiencing degraded air quality, and future economic development will lead to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015-2050, under specific pathways of diffusion of cleaner and more energy-efficient technologies. The impacts of individual source sectors on PM 2.5 concentrations were assessed through systematic simulations of spatially and temporally resolved particulate matter concentrations, using the GEOS-Chem model, followed by population-weighted aggregation to national and state levels. We find that PM 2.5 pollution is a pan-India problem, with a regional character, and is not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM 2.5 annual standard (40 μg m -3 ). Sources related to human activities were responsible for the largest proportion of the present-day population exposure to PM 2.5 in India. About 60 % of India's mean population-weighted PM 2.5 concentrations come from anthropogenic source sectors, while the remainder are from "other" sources, windblown dust and extra-regional sources. Leading contributors are residential biomass combustion, power plant and industrial coal combustion and anthropogenic dust (including coal fly ash, fugitive road dust and waste burning). Transportation, brick production and distributed diesel were other contributors to PM 2.5 . Future evolution of emissions under regulations set at current levels and promulgated levels caused further deterioration of air quality in 2030 and 2050. Under an ambitious prospective policy scenario, promoting very large shifts away from traditional biomass technologies and coal-based electricity generation, significant reductions in PM 2.5 levels are achievable in 2030 and 2050. Effective mitigation of future air pollution in India requires adoption of aggressive prospective regulation, currently not formulated, for a three-pronged switch away from (i) biomass-fuelled traditional technologies, (ii) industrial coal-burning and (iii) open burning of agricultural residue. Future air pollution is dominated by industrial process emissions, reflecting larger expansion in industrial, rather than residential energy demand. However, even under the most active reductions envisioned, the 2050 mean exposure, excluding any impact from windblown mineral dust, is estimated to be nearly 3 times higher than the WHO Air Quality Guideline., Competing Interests: Competing interests. The authors declare that they have no conflict of interest.

Tropospheric aerosol optical depth (AOD) over India was simulated by Goddard Earth Observing System (GEOS)-Chem, a global 3-D chemical-transport model, using SMOG (Speciated Multi-pOllutant Generator from Indian Institute of Technology Bombay) and GEOS-Chem (GC) (current inventories used in the GEOS-Chem model) inventories for 2012. The simulated AODs were ~80% (SMOG) and 60% (GC) of those measured by the satellites (Moderate Resolution Imaging Spectroradiometer and Multi-angle Imaging SpectroRadiometer). There is no strong seasonal variation in AOD over India. The peak AOD values are observed/simulated during summer. The simulated AOD using SMOG inventory has particulate black and organic carbon AOD higher by a factor ~5 and 3, respectively, compared to GC inventory. The model underpredicted coarse-mode AOD but agreed for fine-mode AOD with Aerosol Robotic Network data. It captured dust only over Western India, which is a desert, and not elsewhere, probably due to inaccurate dust transport and/or noninclusion of other dust sources. The calculated AOD, after dust correction, showed the general features in its observed spatial variation. Highest AOD values were observed over the Indo-Gangetic Plain followed by Central and Southern India with lowest values in Northern India. Transport of aerosols from Indo-Gangetic Plain and Central India into Eastern India, where emissions are low, is significant. The major contributors to total AOD over India are inorganic aerosol (41-64%), organic carbon (14-26%), and dust (7-32%). AOD over most regions of India is a factor of 5 or higher than over the United States.

The difference in land surface temperature (LST) between an urban region and its nearby non-urban region, known as surface urban heat island intensity (SUHII), is usually positive as reported in earlier studies. India has experienced unprecedented urbanization over recent decades with an urban population of 380 million. Here, we present the first study of the diurnal and seasonal characteristics of SUHII in India. We found negative SUHII over a majority of urban areas during daytime in pre-monsoon summer (MAM), contrary to the expected impacts of urbanization. This unexpected pattern is associated with low vegetation in non-urban regions during dry pre-monsoon summers, leading to reduced evapotranspiration (ET). During pre-monsoon summer nights, a positive SUHII occurs when urban impacts are prominent. Winter daytime SUHII becomes positive in Indo-Gangetic plain. We attribute such diurnal and seasonal behaviour of SUHII to the same of the differences in ET between urban and non-urban regions. Higher LST in non-urban regions during pre-monsoon summer days results in intensified heatwaves compared to heatwaves in cities, in contrast to presumptions made in the literature. These observations highlight the need for re-evaluation of SUHII in India for climate adaptation, heat stress mitigation, and analysis of urban micro-climates.