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The world has achieved massive economic progress in the past few decades, along with population explosion and rapid urbanization/industrialization, the effects of which are being realized across the world in the form of global climate change with huge environmental impact. Though climate change is a natural phenomenon, the release of several greenhouse gases (GHGs) into the atmosphere through human activities has made a greater contribution to global warming. The present paper is a review of Indian climate features/trends and their consequences across the country. Recent countrywide trends in some selected climatic parameters like rainfall and temperature have been depicted. India is an agrarian country and is exposed to various hydro-ecological vulnerabilities, particularly successively occurring, extreme, climatic hazards like drought, flood and cyclone. Based on some indices, possible vulnerable areas due to the effect of climate change have also been depicted. Although the analyses are based on national level secondary data on selected parameters, some region-specific micro-level studies may better reveal the significance of these vulnerabilities. The present paper an attempt to map the climate variability across the nation. [ABSTRACT FROM AUTHOR]

Background: Almost 70% of women residing in urban areas and 48% of women in rural areas use sanitary napkins in India. According to menstrual health alliance India (MHAI), single sanitary pad will take about 500-800 years to decompose as the plastic used in manufacturing is nonbiodegradable and causes severe noxious effects contributing to global warming through the production of planet warming fuels which eventually have severe impact on environment sustainability. Hence, the study was undertaken to contribute the evidence for the "Clean and Green India". Aim: To know the perception and practice of disposal of sanitary napkins among young college-going females in India. Materials and Methods: A cross-sectional study was conducted throughout the country employing a self-administered questionnaire using a survey link sent through social media. Data collected were analyzed and interpreted using SPSS version 20.0. Result: The study population comprised 484 young college girls with a mean age of 20.92 ± 1.86 years and 96.9% of them are using sanitary napkins as menstrual absorbent aids. The most common method employed for the disposal of sanitary napkins was dumping them in the bin (87.4%). About 63.2% of them had no knowledge about sanitary napkin-burning machines. Around 92% think that improper disposal of sanitary napkins can cause health problems. Conclusion: The findings from the study revealed that a significant number of women were practicing noneco-friendly disposal methods and menstrual hygiene aids which are a bane to the ecosystem. Study warrants the education and training of females to achieve a green and clean sustainable India. [ABSTRACT FROM AUTHOR]

Solar drying systems are becoming a popular alternative to traditional energy-based drying systems for agricultural products due to their effectiveness and reduced fuel consumption. Although the efficiency of solar drying systems has been thoroughly investigated, their sustainability has not been studied enough. This study aims to fill that gap by conducting a life-cycle assessment of two new solar drying systems built in Udaipur, Rajasthan, India. The environmental implications of an innovative cylindrical solar-assisted drying system and a phase-change material-based solar drying system were evaluated using cradle-to-gate life-cycle analysis. The study uses the ReCiPe 2016 endpoints (H) technique to calculate various aspects such as midpoint, endpoint, single score, normalization result, and network diagram for phase-change material-based solar drying and cylindrical solar-assisted drying. Results show that phase-change material-based solar drying has an average of 40% more impact on the environment than cylindrical solar-assisted drying, with a high impact found in human non-carcinogenic toxicity, mainly due to the production of phase-change materials. However, cylindrical solar-assisted drying system crossover phase-change material based solar drying in terms of its impact on human carcinogenic toxicity and fossil resource scarcity. The contribution to global warming of phase-change material-based solar drying is 13.7% more than that of cylindrical solar-assisted drying. The endpoint characterization indicates that phase-change material-based solar drying exceeds in terms of human health (40%) and ecosystem (37.04%), whereas cylindrical solar-assisted drying surpasses phase-change material-based solar drying in terms of impacts on resources, at 14%. The early drying in phase-change material-based solar drying makes up for its higher impact than that in cylindrical solar-assisted drying, which takes 3 hours longer to dry. This study offers guidance and methods for making the best choice of solar-powered dryers. [ABSTRACT FROM AUTHOR]

Introduction: The increase in atmospheric CO2 concentration, which mainly is attributed to fossil-fuel combustion and deforestation, is often suggested as one of the prime causative factors toward accelerated global warming. This commends for sequestration of atmospheric carbon under terrestrial systems to partially offset fossil-fuel emissions. Concerning the same, agricultural sector presents an extensive opportunity, especially for countries such as India where over 55% of the population is engaged in the agriculture sector. Methods: Sequestering atmospheric carbon in agriculture requires the adoption of climate-resilient alternative agriculture practices without compromising food security. The deliberated study highlights the options of alteration in current conventional farming practices and its economic evaluation for sequestrating carbon under two Climate Change (CC) scenarios, viz., RCP 4.5 and 8.5, over three temporal scales, i.e., 2020, 2030, and 2050. Considering the current landuse pattern and existing growth rate in land-use shifting, three land-use policies, namely, Business as Usual (BaU), Optimistic, and Pessimistic scenario, integrated with CC scenarios were contemplated. Six possible futuristic scenarios were generated for the assessment of carbon sequestration and its valuation following the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model. Results: The results suggested that across the studied region adopting an optimistic policy over BaU and pessimistic scenario, carbon can sequestrate an additional 0.64 to 1.46 Mt. (2.35 to 5.36 million ton CO2e) having an economic value of 193.4 to 504.8 million USD. Results: Moreover, the outcomes of the study are advocated for the policy of carbon credit in the agriculture sector, which shall contribute toward meeting various nationally determined contributions (NDCs) and sustainable development goals (SDGs) as well. [ABSTRACT FROM AUTHOR]

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region's agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10–20% massive decrease during pre-monsoon (March–May) and earlier ISM season months (i.e., June and July). A significant 40–70% decline in mean monsoon rainfall during the June–July months in the near future (NF; 2041–2060) has been projected compared to the historical period (1986–2005). An abrupt increase of 80–170% in mean monsoon rainfall during the post-monsoon (October–December) in the far future (FF; 2080–2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5–10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9–4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP. [ABSTRACT FROM AUTHOR]

Global Warming has become an emerging threat to mankind. Sudden changes caused in the environment are making survival, a difficult task. The flora and fauna, not only of the country India but also the whole world is disturbed; hence, the ecosystems are not easily adapting to the changes. Many hands-on methods adopted by the people do not contribute to the solution needed. To resolve this issue, prediction of the extent of Global Warming to name the solutions is needed and the proposed system suggests the same. The system proposed uses the technique of Machine Learning and based upon the hypothesis proposed, the prediction of global warming for an upcoming decade is made. Also, the formula to calculate the extent of global warming is derived. [ABSTRACT FROM AUTHOR]

Land use and land cover changes have greatly contributed to global warming and increased urbanization during the last two decades. It is crucial to track alterations of the globe's surface's usage of land as well as cover in order to obtain accurate information about the study region for all development planning scenarios. Land cover categorization is critical for identifying changes, managing development, and tracking and assessing the ground's surface area. Along with RS and GIS methodologies, machine learning algorithms have recently gained popularity in land use and land cover change detection studies. This present study was conducted to detect land use and land cover (LULC) in a heterogeneous landscape of Pauri Garhwal district of Uttarakhand, India by using different machine learning classifiers, i.e. (i) Random forest. (ii) SmileCart (iii) SmileGradientTreeBoost and (iv) SVM. Overall accuracy and kappa coefficient found for RF, SVM, CART and GTB were (0.98, 0.96), (0.97, 0.95), (0.80, 0.75) and (0.89, 0.80) respectively. Results show that Random Forest (RF) provided highest classification accuracy among all four classifier. [ABSTRACT FROM AUTHOR]