Ocean acidification poses a growing global threat to marine ecosystems and biodiversity, with significant implications for ecological balance and human livelihoods. The Palk Strait, a vital ecological corridor between India and Sri Lanka, is particularly vulnerable to acidification due to anthropogenic influences and its unique hydrography. This study employs CO2sys and the R package Seacarb to predict acidification trends in the Palk Strait by analyzing key carbonate system parameters, including partial pressure of CO2 (pCO2), pH, total alkalinity, temperature, and salinity. Using the Seacarb function “pgas”, projected trends in pH, aragonite saturation state (ΩAr), and calcite saturation state (ΩCa) were evaluated. The findings reveal critical insights, indicating that the Bay of Bengal region is nearing undersaturation for both aragonite and calcite, signaling a concerning trajectory if mitigation efforts are not implemented. This research identifies potential acidification hotspots and underscores the urgency for targeted conservation and sustainable management strategies to safeguard the fragile marine ecosystem of the Palk Strait.

As photovoltaic (PV) power generation assumes a central role in the global energy transition, a systematic assessment of its full life-cycle environmental impact is crucial. This study conducts a comprehensive life-cycle assessment (LCA) of crystalline silicon photovoltaic modules in China, aiming to quantify resource consumption and environmental emissions across all stages, from manufac- turing, transportation, and operation to end-of-life (EoL) recycling. Adopting a “cradle-to-grave” system boundary in accordance with ISO 14040/44 standards, the research places a specific focus on comparing the environmental performance of two mainstream EoL treatment routes: Pyrolytic recycling and mechanical recycling. The results reveal that: (1) The environmental burdens of PV modules are highly concentrated in the upstream manufacturing and transportation stages, which dominate impacts across multiple categories, including acidification, eutrophication, ecotoxicity, and human toxicity; (2) At the EoL stage, the mechanical recycling route demon- strates overwhelming environmental superiority over pyrolytic recycling, exhibiting a global warming potential (GWP) over 100 times lower and a significantly reduced human toxicity potential (HTP), establishing it as a low-carbon, non-toxic pathway; (3) The primary drivers of the environmental impacts are the high energy consumption of manufacturing processes like polysilicon purification (espe- cially when powered by a fossil-fuel-dominated grid) and the intensive use of chemicals such as chlorosilanes and cutting fluids. The study concludes that physical recycling is the preferred pathway for achieving a green and closed-loop PV industry under current technological conditions. By identifying environmental hotspots and optimizing recycling strategies, this work provides a solid scientific foundation for advancing the sustainable development of China’s photovoltaic industry and informing related circular economy policies.

We report a framework for calculating the exergy and energy losses in the hydrochlorination of the methanol process for a methyl chloride production unit. Machine learning-based predictive maintenance models are identified to assess plant toxicity. The proposed novel framework integrates Hyprotech Systems (HYSYS) and machine learning models. It optimizes the operating conditions of the chloromethane plant. Both supervised and unsupervised machine learning models such as Bayesian Ridge regression (BRR), Nearest Neighbors regression (KNR), and Stochastic gradient descent regression (SGD) are used to forecast energy and exergy destruc¬tion. Among these models, the BRR exhibits exceptional accuracy in predicting thermodynamic losses, notably achieving an R2 value of 0.998. Thereby, an integrated framework could serve as a valuable diagnostic tool for assessing real-time plant data to enhance plant opera¬tional efficiency.

With the rapid development of Central China, the temperature in this region is continuously increasing. Extreme weather events (e.g., high-temperature weather for many consecutive days) are becoming frequent. In order to provide future theoretical guidance on the direction of local development and the prevention of extreme natural disasters, the daily datasets of 12 meteorological stations in three provinces were collected. The corresponding predictors from 25 large-scale climatic factors were then screened using stepwise regression. A stepwise regression and statistical downscaling (SRSD) approach was developed to establish the statistical relationship. The future temperature results were projected by the weather generator, and the probability of extreme weather occurrence was analyzed by extreme values. The results indicate that future temperature in Central China shows an increasing trend from 2036 to 2065 and 2066 to 2095, with the representative concentration pathway 4.5 (RCP4.5) scenario showing a greater increase in temperature than the representative concentration pathway 8.5 (RCP8.5) scenario. Hunan Province has the largest temperature increase, followed by Hubei Province and Henan Province. The average annual duration of heat waves in Central China is 74.7 days.

In view of increasing population size and energy consumption, greenhouse gas (GHG) emissions are increasing and are one of the main causes of climate change. The regional electric power system is one of the main sources of carbon emissions, so there is an urgent need to optimize the regional electric power system to meet the Paris Agreement's long-term temperature goal. Therefore, this study provided a linear chance-constrained mixed-integer programming (LCMI) model with the objective of maximizing the total system profit and applying it to the regional electric power system. Chance-constrained programming and mixed-integer programming were integrated into the LCMI model to address input uncertainties. Including five commonly used power generation technologies, namely coal-fired, natural gas-fired, hydropower, wind power, and solar power, the model can provide the optimal electricity generation schemes and capacity expansion plans for different technologies at the regional level to meet the end-user’s needs while meeting the carbon dioxide emission targets under different risk levels. The outcomes of the research will offer decision-makers a framework for optimizing conventional regional electric power systems for their long-term sustainability in environmental and economic development.

Rainfall trend analysis is very important in the economic and sociocultural context of the country, like the development of irrigation and water resource management practices. In this paper, Mann-Kendall (M-K), Sen’s slope estimator, and wavelet analysis were used to analyze long-term (1901 ~ 2017) annual and seasonal rainfall trends in India. The sequential Mann-Kendall (SQMK) test is used to estimate the temporal variation of rainfall. The analysis shows monsoon precipitation in “Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Uttar Pradesh, Chhattisgarh, Sikkim, Arunachal Pradesh, Mizoram, Assam, Karnataka, Nagaland, Kerala, Meghalaya, and Goa” have significant trends. The above-mentioned states were subjected to a discrete wavelet transform (DWT) analysis using Daubechies (db5-db10) and Symlet (sym5-sym10) mother wavelet families. The analysis revealed that the trends of subdivisions had a short-term periodicity of less than a decade. Therefore, the present study indicates significant changes over time. By utilizing wavelet techniques, it is possible to gain insight into both short- and long-term tendencies, making it simpler to spot patterns and sudden changes. Therefore, the present study by combining non-parametric tests with wavelet synopsis, offers a thorough comprehension of rainfall trends in India. The collaborative use of these techniques improves the precision of trend identification, offering valuable insights to make well-informed decisions for water resource management and climate adaptation strategies.

Informal waste workers (IWWs) play a vital role in recovery, disposal and recycling of waste materials. This study examines the current solid waste management practices in Nigeria and Nepal, with a particular focus on the roles of IWWs in effective waste management. Through comprehensive secondary literature reviews, including journal databases, books, reports, and websites, this study highlights the inadequate solid waste management systems in both countries, resulting in limited reuse of generated waste. Common challenges related to waste collection, transportation, sorting, processing, and final disposal were identified in Nigeria and Nepal. The important role played by IWWs in waste management activities, such as collection, sorting, recovery, and recycling, is evident. However, the lack of appropriate legislation addressing their involvement serves as a major hindrance. This study also presents key findings on Nigeria’s waste generation, recycling rates, and reliance on the informal sector, as well as Nepal’s municipal solid waste composition, recycling rates, and the significant role played by informal waste workers. The study emphasizes the need for inclusive waste management policies, strengthened waste collection and segregation systems, investment in recycling infrastructure, and enhanced collaboration among government agencies, waste management companies, and informal waste workers. Enforcing existing legislation and providing support to IWWs, including improved working conditions and social protection measures, are essential steps toward improving waste management practices. The study concludes by highlighting the importance of research, innovation, and international cooperation in developing sustainable waste management solutions for Nigeria and Nepal.

Connectivity among the resource patches that provide the wildlife with essential habitat is critical to their survival, but highways and other anthropogenic developments commonly impede wildlife movement. The purpose of this study was to identify suitable locations for highway crossing structures for wildlife movement in a fragmented landscape. Functional connectivity was modeled using human footprint data over a regional landscape in western Canada. A graph-theoretic approach was employed to identify corridors, link-age zones, and the locations where wildlife species cross the highways. A betweenness centrality model was used to compute the shortest path, current flow, and network flow of movements across various landscape lattices. The shortest path model identified a set of geodesic paths to connect the resource patches, the current flow model identified a number of movement zones around the resource patches, and the network flow model identified linkage zones in the network. Finally, a composite of the outputs was used to identify suitable locations for highway crossing for maintaining wildlife movement on the landscape.

Hurricane Harvey demonstrated how natural disasters and changing environmental conditions can substantially increase pollutant releases from industrial facilities. Releases from such facilities in already flood-vulnerable communities will further exacerbate existing negative public health outcomes. In this study, we focus on the TX neighborhood of Galena Park, an underserved, fenceline community in the Greater Houston Area that suffers from frequent flooding and environmental justice issues, as well as being the location of the largest reported chemical spill after Hurricane Harvey in 2017. To help improve the resilience of this community to hurricane/flood-induced releases of contaminants, we analyzed geospatial data on flood risk related to storm surge as well as the current and future 100-year flood plain using advanced ArcGIS Dashboard operations. We then link these flood risks to vulnerabilities to flood-induced contamination releases through proximity to environmentally-regulated facilities.

China is a large economy being troubled by excessive energy consumption, serious environmental pollution and carbon emission problems. To reduce energy consumption, pollutant and carbon emission, understanding their trend and their relationships with the socioeconomic development is essential. Among various sectors, transportation sector is energy-intensive and emits a large amount of air toxics and CO2, and therefore deserves primary attention. This study took carbon emission as a proxy of environmental degradation and employed an analytical framework composed of input-output analysis, ecological network analysis and structural decomposition analysis to scrutinize production- and consumption-based energy consumption and carbon emission (ECCE), to analyze effects of final demand elasticity on them, mutualism relationships between transportation sector and other sectors, and pulling/ driving force of trans- portation sector on the ECCE of the whole economy, and to explore the drivers affecting ECCE of transportation sector. Results comprise the increase trend of ECCE of transportation sector, the noticeable relevance of transportation sector to ECCE, the domination of control relationship and the increase of competition relationship between transportation sector and other sectors, the significant effects of final demand structure, per capita final demand, production structure and sectoral carbon emission density on ECCE. According to these results, as for transportation sector, adjusting the energy structure, improving the transportation efficiency and coordinating the relations between the transportation and its relevant sectors are suggested. The analytical framework facilitates ECCE policy devising in transportation sector for China's target of energy conservation and emission reduction and are instructive for other countries' ECCE actions.