Development of a Comprehensive Environmental Guidance and Sustainability Assessment for Different Organic Fraction of Municipal Solid Waste-to-Biofuel Scenarios based on Life Cycle Analysis

Energy, Год журнала: 2024, Номер 304, С. 132073 - 132073

Опубликована: Сен. 1, 2024

Язык: Английский

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A comprehensive review of landfill leachate treatment technologies

Frontiers in Environmental Science, Год журнала: 2024, Номер 12

Опубликована: Сен. 2, 2024

The management of landfill leachate presents a significant environmental challenge, necessitating comprehensive and dynamic treatment approach. This review delves into the critical issue treatment, exploring its impact, technologies, regulatory frameworks, path towards sustainable practices. explores complexities leachate, emphasizing need for waste practices to safeguard health. Our analysis highlights evolution conventional advanced technologies designed mitigate these risks, focusing on membrane oxidation processes, promising potential emerging techniques such as adsorption biological nutrient removal. These are evaluated their efficiency, cost implications, sustainability impacts, underscoring challenges opportunities within current landscape treatment. aims provide insights designing efficient effective systems through detailed methods. By examining case study in Changsha City, effectiveness system integrating various is demonstrated. underscores interconnectedness human activities, health, management, importance holistic It stresses continuous improvement adoption reduce footprint landfills. Ultimately, it calls multiple economic considerations, readiness address future contributing advancement

Язык: Английский

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Comparison and prediction of shallow groundwater nitrate in Shaying River basin based on urban distribution using multiple machine learning approaches

Water Environment Research, Год журнала: 2025, Номер 97(2)

Опубликована: Фев. 1, 2025

Groundwater, a pivotal water resource in numerous regions worldwide, confronts formidable challenges posed by severe nitrate pollution. Traditional research methodologies aimed at addressing groundwater contamination frequently struggle to accurately depict the intricate conditions of environment, particularly when dealing with high variability and nonlinear data. However, advent machine learning (ML) has heralded an innovative approach simulating dynamics. In this study, six ML algorithms were deployed model concentrations shallow nitrates Shaying River Basin. The efficacy each was assessed through comprehensive metrics including coefficient determination (R2), mean absolute error (MAE), root square (RMSE), gauging alignment between observed predicted levels. Subsequently, discern principal environmental factors influencing NO3-N concentrations, most proficient selected. Among array models, XGB algorithm, renowned for its capacity handle extreme values, demonstrated superior performance (R2 = 0.773, MAE 7.625, RMSE 11.92). Through in-depth analysis across major urban centers, Fuyang city identified as heavily contaminated locale, attributing phenomenon potential sources such domestic sewage agricultural activities (feature importance Cl- 78.64%). Conversely, Zhengzhou emerged least polluted city, notable influences from K+ NO2 - 52.06% 18.41%), indicative prevailing reducing environment compared other cities. summation, study explores methodology amalgamating diverse variables investigation contamination. Such insights hold profound implications effective management mitigation Basin, offering demonstration similar endeavors analogous regions. PRACTITIONER POINTS: Six models utilized simulate pollution prediction outperformed models. Relative using model. Impact main on discussed.

Язык: Английский

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Microplastics in freshwater and marine ecosystems: Occurrence, characterization, sources, distribution dynamics, fate, transport processes, potential mitigation strategies, and policy interventions

Ecotoxicology and Environmental Safety, Год журнала: 2025, Номер 294, С. 118036 - 118036

Опубликована: Март 18, 2025

Язык: Английский

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Optimization of an eco-friendly municipal solid waste-to-multi-generation energy scheme integrated by MSW gasification and HSOFC: Regression analysis and machine learning study

Process Safety and Environmental Protection, Год журнала: 2023, Номер 182, С. 166 - 175

Опубликована: Ноя. 26, 2023

Язык: Английский

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