CO₂ Emissions from Direct Energy Use of Urban Households in India

India hosts the world’s second largest population and offers the world’s largest potential for urbanization. India’s urbanization trajectory will have crucial implications on its future GHG emission levels. Using household microdata from India’s 60 largest cities, this study maps GHG emissions patterns and its determinants. It also ranks the cities with respect to their household actual and “counter-factual” GHG emissions from direct energy use. We find that household GHG emissions from direct energy use correlate strongly with income and household size; population density, basic urban services (municipal water, electricity, and modern cooking-fuels access) and cultural, religious, and social factors explain more detailed emission patterns. We find that the “greenest” cities (on the basis of household GHG emissions) are Bareilly and Allahabad, while the “dirtiest” cities are Chennai and Delhi; however, when we control for socioeconomic variables, the ranking changes drastically. In the control case, we find that smaller lower-income cities emit more than expected, and larger high-income cities emit less than expected in terms of counter-factual emissions. Emissions from India’s cities are similar in magnitude to China’s cities but typically much lower than those of comparable U.S. cities. Our results indicate that reducing urban heat-island effects and the associated cooling degree days by greening, switching to modern nonsolid cooking fuels, and anticipatory transport infrastructure investments are key policies for the low-carbon and inclusive development of Indian cities

Velocity profiles for different values of with and .

<p>Velocity profiles for different values of with and .</p

Comparison between values obtained with (21) and (38) for , , , .

<p>Comparison between values obtained with (21) and (38) for , , , .</p

Variations of the skin friction for different values of and with and .

<p>Variations of the skin friction for different values of and with and .</p

Physical system and coordinate axes.

<p>Physical system and coordinate axes.</p

Comparison of velocity in Eq. (37) with Eq. (18) in Chandran <i>et al.</i>[9].

<p>Comparison of velocity in Eq. (37) with Eq. (18) in Chandran <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088766#pone.0088766-Chandran3" target="_blank">[9]</a>.</p

Absolute errors of velocity calculated by Eqs. (21) and (38).

<p>Absolute errors of velocity calculated by Eqs. (21) and (38).</p

Synthesis of 21,23-Selenium- and Tellurium-Substituted 5‑Porphomethenes, 5,10-Porphodimethenes, 5,15-Porphodimethenes, and Porphotrimethenes and Their Interactions with Mercury

The 3+1 condensation of symmetrical 16-Selena/telluratripyrranes with symmetrical selenophene-2,5-diols/tellurophene-2,5-diols in the presence of BF₃-etheratre or BF₃-methanol followed by oxidation with DDQ gave 5,10-porphodimethenes, whereas the process with unsymmetrical selenophene-2,5-diols/tellurophene-2,5-diols gave 5-porphomethenes. In addition, the reaction of unsymmetrical 16-Selena/telluratripyrranes with symmetrical selenophene-2,5-diols/tellurophene-2,5-diols gave the corresponding porphotrimethenes, whereas the process with unsymmetrical selenophene-2,5-diols/tellurophene-2,5-diols gave the 5,15-porphodimethenes. The structures of different products were characterized by IR, ¹H and ¹³C NMR, ¹H–¹H COSY, CHN analysis, and mass spectrometry. The binding of mercury with the calix[4]­phyrins mentioned above had been observed in the decreasing order of porphodimethenes > porphomethenes > porphotrimethenes by UV–vis and ¹H NMR spectroscopy

Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BackgroundRegularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels.MethodsWe applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level.FindingsIn 2019, there were 12·2 million (95% UI 11·0-13·6) incident cases of stroke, 101 million (93·2-111) prevalent cases of stroke, 143 million (133-153) DALYs due to stroke, and 6·55 million (6·00-7·02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11·6% [10·8-12·2] of total deaths) and the third-leading cause of death and disability combined (5·7% [5·1-6·2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70·0% (67·0-73·0), prevalent strokes increased by 85·0% (83·0-88·0), deaths from stroke increased by 43·0% (31·0-55·0), and DALYs due to stroke increased by 32·0% (22·0-42·0). During the same period, age-standardised rates of stroke incidence decreased by 17·0% (15·0-18·0), mortality decreased by 36·0% (31·0-42·0), prevalence decreased by 6·0% (5·0-7·0), and DALYs decreased by 36·0% (31·0-42·0). However, among people younger than 70 years, prevalence rates increased by 22·0% (21·0-24·0) and incidence rates increased by 15·0% (12·0-18·0). In 2019, the age-standardised stroke-related mortality rate was 3·6 (3·5-3·8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3·7 (3·5-3·9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62·4% of all incident strokes in 2019 (7·63 million [6·57-8·96]), while intracerebral haemorrhage constituted 27·9% (3·41 million [2·97-3·91]) and subarachnoid haemorrhage constituted 9·7% (1·18 million [1·01-1·39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79·6 million [67·7-90·8] DALYs or 55·5% [48·2-62·0] of total stroke DALYs), high body-mass index (34·9 million [22·3-48·6] DALYs or 24·3% [15·7-33·2]), high fasting plasma glucose (28·9 million [19·8-41·5] DALYs or 20·2% [13·8-29·1]), ambient particulate matter pollution (28·7 million [23·4-33·4] DALYs or 20·1% [16·6-23·0]), and smoking (25·3 million [22·6-28·2] DALYs or 17·6% [16·4-19·0]).InterpretationThe annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.FundingBill & Melinda Gates Foundation