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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.

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]

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.)