To become the fastest-growing large economy in the world, India has set a target growth rate of 9%, reaching an economy of $5 trillion by 2024-25. It is an immense challenge to meet the growth target and keep the CO2 emissions under control. The present paper aims to discover the determinants for explaining CO2 emissions in India by conducting a complete decomposition analysis, where the residuals are fully distributed to the determinants for the country from 1990-2018. The analysis reveals that the biggest contributor to the rise in CO2 emissions in India is the expansion of the economy (scale effect). The intensity of CO2 and the change in the composition of the economy, which nearly move in tandem, also contribute to the rise in CO2 emissions, although more slowly. A declining energy intensity of the Indian economy is responsible for a considerable reduction in CO2 emissions. As a typical result for an upcoming economy, this paper did not find evidence for an environmental Kuznets curve. This implies that continued economic growth will lead to increased CO2 emissions.

This first-of-its-kind study systematically assesses the abundance and characteristics of Microplastics (MPs) in different categories of informal open drains (nallas) carrying different liquid waste streams from different functional areas of an Indian city. Such drains are part of the informal urban drainage system that carries wastewater, stormwater, industrial effluent, and rural runoff. Logistical and locational limitations of traditional wastewater (WW) sampling methods severely limit their application in open drains. To overcome sampling challenges owing to complex geography, vast drainage network spread across different functional areas of the entire city, and local challenges, appropriately modified sampling strategies were adopted to collect samples from 35 open WW drains (small/local, intermediatory, and large). MPs (50μm-5mm) were present in a bucket, and net samples obtained from all 35 WW drains. The average MP concentration in WW drains was 4.20 ± 1.40 particles/L (bucket samples) and 5.19 ± 1.32 particles/L (net samples). A declining trend of MPs abundance was observed from larger to smaller drains, confirming that smaller and intermediatory drains (carrying WW from different functional areas of the city) are discharging their MP loads into larger drains. Intermixing different WW streams (municipal WW, stormwater surface runoff, agricultural runoff, and industrial WW) increases MP levels in drains. The local riverine ecosystem is being put at risk by a daily MPs load of 12.6 x 108 particles discharged from 9 larger drains into the local river Kharun. To protect the riverine ecosystem, controlling the high daily MPs load from such drains is important. Diversion of WW drains through constructed wetlands built near river banks can be a cost-effective solution. Because the entire Indian subcontinent and parts of Africa rely mainly on such drains having similar characteristics and local conditions, the findings of this study reflect the status and pattern of MPs pollution in informal drains of the entire Indian subcontinent and can be used by stakeholders and governments to take mitigative and preventive measures to manage the MPs pollution and protect the local riverine ecosystem.

We develop an economic model to derive the conditions under which individuals will invest in human capital and move on to adopt sustainable technology instead of natural resource-intensive technology. For this purpose, we extend the overlapping generation model developed by Ikefuji & Horii as our analytical framework. Unlike Ikefuji & Horii who developed an overlapping generation model (OLG) in the context of local pollution, the authors adopted it in the context of renewable natural resources. To do this, we have introduced the production sector that relies on natural resource-intensive technology. This research extends beyond the Ikefuji & Horii model by assuming that an individual derives utility by investing in his child’s education apart from utility derived from consumption when young and adult. Human capital accumulation enables individuals to participate in human capital-intensive production, which produces output through sustainable production technology. As the main result of our theoretical analysis, we find that more educated individual is less dependent on the natural resource endowment for earning their income. We also find that sustainable consumption growth requires that individuals assign a certain positive weight to investment in their child’s education. A long-run steady-state equilibrium level of human capital accumulation is higher and higher than the weight assigned by the parents to the child’s education. In this overlapping generation’s economy, sustainable consumption growth requires that individuals assign a certain weight or give some importance to human capital accumulation. This follows from the fact that the long-run steady-state value of the income earned by an individual depends positively on the expenditure on education.

Rice milling involves shelling and polishing paddy grains to produce rice- both raw and parboiled. Parboiled rice production requires a massive quantity of freshwater for soaking, which, in turn, generates a large amount of wastewater. If this wastewater is not properly ameliorated, it can cause tremendous troubles of surface water pollution, land pollution, and, ultimately, groundwater pollution. Therefore, proper treatment of polluted water from rice mills (PWRM) as per the effluent discharge norms is necessary to protect the surface and subsurface water resources for sustainable development. There are two methods for remediating rice mill wastewater- physicochemical and biological. The biological methods produce comparatively less sludge and are cost-effective. Moreover, these processes are capable of retrieving green energy in the form of biomethane, biohydrogen, and bioelectricity to augment bio-fuel production, aiming to meet the ever-increasing fuel demands caused by rapid industrialization, motorization, and urbanization. The focus on green energy production is gaining momentum day by day due to the adverse effects of conventional energy derived from fossil fuel combustion in terms of enhanced Air Pollution Index (API) in the ambient atmosphere. In this paper, anaerobic biodegradation, phytoremediation, phyco-remediation, and microbial fuel cell techniques adopted by various researchers for remediating the polluted water from rice mills have been well addressed and critically discussed. The pros and cons of these biological methods have been well addressed to assess the socio-technoeconomic feasibility of each method.

The area around Qinghai Lake is one of the most serious desertification areas on the Qinghai-Tibet Plateau. In this paper, combined with field investigation and indoor analysis, the classification and grading system of desertification around Qinghai Lake was established. On this basis, through remote sensing data processing and parameter inversion, the desertification monitoring index model was established. Based on the analysis of Landsat-5/TM remote sensing data from 1990 to 2020, the dynamic change characteristics of desertification land around Qinghai Lake in recent 30 years were obtained. The results show that the desertification area around Qinghai Lake was 1,359.62 km2, of which the light desertification land was the main one. The desertification spread in a belt around Qinghai Lake, concentrated in Ketu sandy area in the east, Ganzi River sandy area in the northeast, Bird Island sandy area in the northwest, and Langmashe sandy area in the southeast. From 1990 to 2000, the annual expansion rate of desertification around Qinghai Lake was 2.68%, the desertification spread rapidly, and light desertification land was the main part of desertification expansion. From 2000 to 2010, the annual expansion rate of desertification was only 0.83%, but severe desertification land and moderate desertification land developed more rapidly than in the previous period. From 2010 to 2020, the annual expansion rate of desertification was 2.66%, and the desertification was spreading rapidly, mainly with moderate desertification land and light desertification land. In the process of desertification land transfer around Qinghai Lake, the transfer of desertification land and non-desertification land was the main, accompanied by the mutual transformation of different levels of desertification land. The process of desertification around Qinghai Lake was essentially the result of natural and human factors. The special geographical location, climate changes, rodent damage, and human factors around Qinghai Lake were the main causes of desertification.

Air quality has recently been a huge concern as it directly affects people’s lives. An air quality level assessment and prediction system is essential to keep track of air quality. Therefore, developing an efficient air quality assessment and prediction system has become one of the most important concerns. In the present work air quality level of Surat city, India is assessed and predicted for the period from 2020 to 2023 using the Autoregressive integrated moving average (ARIMA) model. Experimental results show that the ARIMA model outperforms the other models. According to the findings, the maximum quantity of SO2 and NO2 present in the air in 2020 is 37 mm and 18 mm, respectively, with a maximum of 27 mm and 31 mm in 2021. Thus, we can observe that even though SO2 has reduced a bit, the amount of NO2 has increased, thus degrading the quality of air.

Environmental damage due to the discharge of organic pollutants and heavy metal toxins has become a major topic of concern for the past couple of years. Using just a natural adsorbent to solve wastewater concerns has lately gained popularity as an ecologically acceptable solution that encourages long-term growth. A range of approaches, including adsorption to the surface of agricultural leftovers, have been used to minimize heavy metals in an aqueous medium. Lead is amongst the most hazardous and widely discovered toxic substances in industrial waste. Citrus limetta peel powder, Banana peel powder, and Betel leaf powder were chosen as adsorbents in this study to absorb synthetic lead from an aqueous solution since they are low-cost materials. Our research aims to find natural bio-sorbents that can remove highly hazardous Pb2+ ions from aqueous solutions. The importance of contact time, concentrations, adsorbent-based dose, and pH in the adsorption process is investigated. The adsorption rate for betel leaves, Citrus limetta peel, and banana peel was 5, 10, 15, 20, and 25 g.L-1. Citrus limetta peel (10 g.L-1), banana peel (5 g.L-1), and betel leaf (5 g.L-1) provide the highest lead adsorption. Material characterization is used to determine the lead nitrate process in lead adsorption. The capacity of the lead-adsorbing substances to achieve adsorption equilibrium was assessed and estimated using linear Freundlich and Langmuir isotherms, with the experimental data fitting the Freundlich isotherm models.

Evapotranspiration (ET) plays a significant role in climatic studies, directly influencing the hydrological cycle, energy balance equation, and surface vegetation. ET comprises three components: bare soil or ground evaporation, evaporation, and transpiration, in which vegetation removes water influenced by food grain production. In turn, soil moisture availability depends on precipitation characteristics over land, surface net radiation, and wind speed are the major climatic factors that together determine the magnitude of ET. This controls moisture availability in the lower troposphere, hence atmospheric stability, chances of cloud formation, and precipitation. Though the study of evapotranspiration is important for determining agricultural water consumption and analyzing drought situations, there is a lot of uncertainty in its accurate estimation. Land use/Land cover changes (LULCC) occurring throughout the Indian subcontinent have been found to affect the characteristics of low to moderate rainfall events and surface temperature extremes (Halder et al. 2016). A global warming scenario will change the hydrological cycle, and the impact of anthropogenic factors has also necessitated the need to understand the mechanisms that control changes in ET over India. In this study, we want to analyze the relationship between transpiration and the Normalized Difference Vegetation Index (NDVI) and investigate the relationship between canopy interception with respect to NDVI all over the Indian region. Attempts have been made to assess the impact of changes in climate and LULC on ET and its three components over the Indian region from 1981 till the present time. The monsoon season increases precipitation, and soil evaporation is found to increase at first, along with an increase in NDVI followed by canopy evaporation and transpiration. It is noted that changes in precipitation and LULCC across the Indian subcontinent have contributed significantly to changes in ET in different seasons. As variability in surface net radiation also plays an important role in controlling changes in total ET, it is being investigated.

Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

The goal of this study was to examine the water quality for drinking and domestic purposes in the Korba coalfield region of Chhattisgarh, India. The Korba Coalfield region has seen the collection of fifteen groundwater samples from different places. The content of eight metals was determined using ICP-MS instruments: aluminum (Al), barium (Ba), cadmium (Cd), iron (Fe), magnesium (Mn), lead (Pb), nickel (Ni), and zinc (Zn). Spatial distribution maps were produced using GIS software to make it simple to understand the groundwater’s quality. The groundwater samples were collected during the pre-monsoon season and the amount of Al, Ba, Cd, Fe, Mn, Pb, Ni, and Zn exceeded the ideal drinking water standards in a few sites. The elevated metal concentrations in the study region’s groundwater could be hazardous to the quality of water. The HPI value based on mean concentration was calculated to be 21.64, which is significantly lower than the reference pollutant index score of 100. The HPI calculation revealed that 73.33% of groundwater samples had low HPI values, 6.67% had medium HPI values, and the remaining 20% had high HPI values. The correlation between heavy metals and HPI was calculated; HPI is positively correlated with Fe (r > 0.9471), Pb (r > 0.9666), and Zn (r > 0.9634), indicating that these elements contribute significantly more to heavy metal concentration in the various samples examined than the other selected elements. The box plot seems to be a graphical representation of the outcomes of the different parameter concentrations which show the mean, maximum, and minimum metal values. The cluster analysis was performed and it was classified into two clusters. Cluster-1 comprises 14 members (93.33%) of the water samples examined and is distinguished by relatively low Ba (<700 μg.L-1), pH, TDS, Al, Fe, Cd, Mn, Pb, and Zn concentrations. Cluster-II is made up of 1 member (6.67%), which is primarily made up of groundwater samples (GW-10) taken in the KCF region, India. High values of HPI are found in the eastern portion of Chhattisgarh’s KCF region, reflecting the spatial distribution of metals. Heavy metal leaching from open-pit mining and transit routes was observed to have contaminated groundwater in the eastern section of the research region.