Environmental Quality Management最新文献

Peat soils in Malaysia are characterized by low nutrient content and very high acidity, which can pose challenges to agricultural productivity. This study developed a proof-of-concept biofertilizer to address the low nutrient content and high acidity of Malaysian peat soils. A low-methoxyl (LM) pectin (2.97 ± 0.86% methoxyl content) was extracted from durian rind waste to create a stable, controlled-release matrix. The microalga Chlorella vulgaris was then encapsulated within this pectin matrix using sodium alginate to observe its impact on chili plant growth and soil quality. After 30 days, chili plants grown in peat soil treated with the biofertilizer demonstrated significantly improved growth, reaching a height of 6 cm with five leaves, which is a 100% increase compared to the 3 cm height and three leaves of untreated controls. FTIR analysis of the treated soil showed distinct peaks representing hydroxyl, aliphatic, carbonyl, and polysaccharide groups (3951, 2926, 1625, and 1047 cm⁻¹), suggesting increased organic matter input and enhanced microbial activity. An improved soil chemical profile was confirmed by the higher spectral intensity in treated samples. This result underscores the success of our innovative approach, which uses durian rind pectin as a natural carrier for C. vulgaris. The findings, supported by both plant growth data and FTIR analysis, reveal a significant dual-action benefit. Biofertilizer not only enhances plant vitality but also directly improves the fundamental organic structure of soil, thus providing an effective method for rehabilitating acidic peat soil compared to conventional approaches.

A vast amount of food (about 1.3 billion tons) is wasted every year. Food waste ends up in landfills and rots, producing methane, the most powerful greenhouse gas. This is the reason why several attempts were carried out to valorize food waste into new derived compounds. In this context, the present study aims to prepare microbial media from food waste samples, including potato peel, pumpkin peel, tomato residue, egg shells, chicken bones, bread, and wheat bran. Three different food waste-based culture media were formulated to support the growth of mesophilic bacteria, lactic acid bacteria (LAB), and fungi. These media were compared to the commercial ones. Results demonstrate similar or better performance for microbial growth. Additionally, specialized tests, including antibiogram analysis, lipolytic and proteolytic activities assays, and bacterial interaction tests, confirmed the efficacy of the formulated media for advanced microbiological applications. Unlike previous studies that focused on single waste sources or microbial types, this work demonstrates the feasibility of developing multi-purpose, low-cost media from diverse household food wastes, validated across bacterial, fungal, and LAB cultures. Such transformation of food waste contributes to reducing environmental impact and advancing scientific research.

This study explores how responsibilities are shared among stakeholders in reverse supply chains (RSCs), using the lens of extended producer responsibility (EPR) and its evolution in the literature. Through a systematic literature review (SLR), it identifies how producers, importers, distributors, retailers, consumers, and governments contribute to RSC waste management under an expanded responsibility concept. Key responsibilities were found in areas such as education, waste monitoring, infrastructure, and recovery promotion. Although some duties are marked by cooperation, the study highlights gaps—particularly in stakeholder participation in critical functions like waste storage. A proposed framework illustrates stakeholder interactions and offers insights into how government and corporate actions can enhance waste management and environmental sustainability. The research advances theoretical understanding of shared responsibilities in RSC and provides practical guidance by identifying strategic priorities for improving reverse logistics systems.

Urban green space (UGS) is vital for sustaining the urban environment. It provides diverse ecosystem services and offers multiple health benefits to residents. However, UGS is increasingly threatened by developmental activities and urbanization. This study aims to assess the role of UGS in supporting biodiversity conservation and facilitating climate change adaptation, with a specific focus on their capacity for carbon (C) sequestration and mitigation of the urban heat island (UHI) effect. The study combines field-based ecological measurements with geospatial analysis (NDVI and LST) to evaluate ecosystem services across three UGS types: an urban park, a university campus, and a plantation site. We show that the university campus supports greater tree diversity and an urban park stores significantly higher biomass C (105.92 ± 14.88 Mg C ha⁻¹) across the UGSs. The study demonstrates considerable variations in ecosystem C storage among the selected UGSs, which can sequester an average of 306 Mg CO₂ ha⁻¹. Moreover, UGS helps increase vegetation cover that could substantially reduce the UHI effect. Overall, the university campus serves as a biodiversity hotspot within an urban environment, while urban parks act as the largest C sink and potentially help achieve climate change mitigation goals. The results of this study could be used to develop evidence-based policies for investment in nature-based solutions (NbS) and to protect C-rich and biodiverse UGS from urbanization.

Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) are persistent organic pollutants (POPs) that pose significant environmental and health risks due to their persistence, bioaccumulation, and toxicity. In urbanizing regions like Ado-Ekiti, Nigeria, these contaminants, stemming from industrial activities, vehicular emissions, and waste disposal, accumulate in soils, threatening ecosystems and human health. This study investigates the concentrations, spatial distribution, sources, and ecological risks of PAHs and PCBs in sediment samples from three sites (A, B, and C) in Ado-Ekiti, Nigeria. Sixteen priority PAHs and 13 PCB congeners were analyzed to characterize contamination levels and patterns. The total PAH concentrations ranged from 29.12 µg/kg at Site B to 55.44 µg/kg at Site A, with high molecular weight PAHs (5–6 rings) dominating, comprising over 70% at Site A and over 50% at Sites B and C. Diagnostic ratios indicated a mix of pyrogenic and petrogenic sources. Total PCB concentrations varied from 7.65 µg/kg at Site B to 40.50 µg/kg at Site A, with dioxin-like PCBs (e.g., PCB 114, PCB 77) prevalent at Site A (>70% of total PCBs) and indicator PCBs (e.g., PCB 28, 44, 101) elevated at Site C. Spatial variability was moderate with a coefficient of variation (CV%) of 63.59%. However, individual congeners like PCB 114 exhibited CVs exceeding 170%, indicating localized contamination region. Toxic equivalency (TEQ) estimates highlighted potential carcinogenic risks, particularly at Sites A and C, surpassing international sediment quality guidelines. Ecological Risk Index values revealed moderate to high risks at Sites A and C, driven by local industrial activities, waste disposal, and land use practices. These findings underscore the need for ongoing monitoring and regulatory measures to mitigate POP contamination and protect environmental and public health in Ado-Ekiti.

The present study investigates the quality of river water of Prayagraj region, which is a prominent holy pilgrimage site of India. A total of 100 river water samples were collected from five selected sites during January 2020–November 2021 and examined for 12 physicochemical parameters. The mean values of turbidity (6 ± 3.65 NTU), total hardness (220 ± 25.69 mg L⁻¹), and dissolved oxygen (8.2 ± 2.74 mg L⁻¹) exceeded the BIS permissible limits. The physicochemical parameter dataset was employed to evaluate the weighted arithmetic water quality index (WQI). River water of the region was found to have an overall WQI of 66.39 ± 23.35, suggesting that it was unfit for human consumption, although it may be utilized for agricultural and industrial applications. Recognizing the importance of computational modeling for rapid WQI evaluation, 18 machine learning algorithms were investigated, which revealed PCA-ANN (principal component analysis-artificial neural network) as the most appropriate model for WQI prediction. PCA reduced the dimensionality of water quality data by identifying 7 PCs that explained >90% variance. The model ANN-A directly incorporated all 12 physicochemical parameters as input, achieving an exceptionally high accuracy (R² = 0.998, RMSE = 0.829, A_f = 1.088, MAPE = 0.847%). The model ANN-B utilized PC scores of only 7 significant components and could attain a commendable accuracy (R² = 0.950, RMSE = 5.193, A_f = 1.289, MAPE = 7.854%). Consequently, PCA-ANN may serve as a significant and reliable tool for policymakers for modeling WQI, since it not only reduced the data load but also generated a decent prediction accuracy.