Probabilistic ecological risk characterizing and source-specific risk apportionment of antibiotics in rivers in the sub-center of Beijing, China.

Presence and sources of antibiotics in Dongzhai Harbor, Hainan Island.

Exploring microbial drivers of DOM removals and greenhouse gas emissions in a centralized wastewater treatment plant of a large-scale coal industrial park.

Advancing sustainable nutrient recycling and carbon neutrality: urea recovery and hydrolysis inhibition via a novel urea electro-forward osmosis system (UEFOS) from source-separated urine.

An isotope-spectral fingerprint coupling approach for dynamic tracer optimization in DOM source apportionment within highly anthropogenically disturbed watersheds.

High-throughput suspect screening of priority hormone-disrupting chemicals in megacity aquatic ecosystems using a domain-specific database framework.

Production of hydroxyl radicals by the oxygenation of FeS and the effect on sulfide removal in sewer systems.

The objective of this paper is to investigate the performance evaluation assessment of the wastewater treatment plant (WWTP) at Omotosho Power Plant, Ondo State, Nigeria by collecting wastewater samples at the inlet and outlet of the plant. Standard procedures for key water quality parameters such as BOD₅, COD, TSS, and DO were employed. The Biodegradability Index (BI) of raw and treated sewage was also assessed to determine reductions in organic load. Average removal efficiencies were 49.39% for BOD₅, 53.14% for COD, and 62.44% for TSS. The BI values of 0.50 (raw) and 0.54 (treated) indicate minimal change in organic content, suggesting the effluent still retains a significant organic load. Seeding the raw sewage is recommended to enhance microbial activity and treatment performance. Although the plant meets NESREA standards and does not pollute the Oluwa River, its overall performance is moderate compared to other reviewed STPs. Further investigation is required to identify performance limitations and optimize treatment efficiency.

Novel insights into sources identification of fluvial dissolved organic matter in intricate plain river networks using optical indices with grey influence analysis and Bayesian model.

This study assessed the presence and concentration of crAssphage, pepper mild mottle virus (PMMoV) and human mastadenovirus (HAdV) as potential indicators of human fecal contamination in different aquatic matrices. A total of 75 stream and seawater samples were collected between 2020 and 2021 across four watersheds displaying varying urbanization degrees in Brazil. Viral particles were concentrated employing the adsorption-elution method followed by ultrafiltration and quantified by qPCR/RT-qPCR. The findings indicate that crAssphage was the most frequently detected (65.33%), followed by PMMoV (45.33%) and HAdV (44.00%). CrAssphage also exhibited the highest viral loads in stream water (median of 6.49 log₁₀ GC L⁻¹), reflecting a strong association with domestic sewage. Comparatively, PMMoV showed similar detection rates in both matrices, whereas HAdV exhibited higher detection rates in stream water. Higher crAssphage and PMMoV concentrations were observed in watersheds located in higher population density areas when compared to lower density areas. No viruses were detected upstream of the urbanized regions, confirming the specificity of the selected viral indicators. In this sense, crAssphage and HAdV are more suitable for monitoring stream water bodies affected by human activities, while PMMoV is suited for both matrices. The findings suggest that the three viruses emerged as promising indicators for tracking human fecal contamination in aquatic matrices with different characteristics.

This paper explores ‘utopia as method’ to envision solid waste and sewage management systems which are equitable and sustainable. Global solid waste management is largely sustained by millions of waste pickers who collect, sort and manage waste. Sanitation workers play a vital role in maintaining sewage systems. These workers are exposed to dangerous, toxic workspaces, and they often belong to marginalised socio-economic groups. Labour market segregation on the basis of gender, racialisation and caste is particularly visible in the case of waste-related occupations as workers face stigmatisation. In South Asia, Dalits (lowest caste groups) are significantly overrepresented as sanitation workers and waste pickers. Thus, this conceptual paper draws upon studies from India, to explore and imagine possibilities of anti-caste futures within visions of sustainable waste management. The paper highlights the need to incorporate worker-centric approaches and anti-caste politics to build ecologically sustainable and socio-economically equitable waste infrastructures.

Water pollution monitoring data typically exhibit characteristics of complexity, high dimensionality, and non-linearity. However, traditional receptor models-including PCA, PMF, and APCS-MLR-struggle to handle nonlinear high-dimensional water quality data and are susceptible to interference from outliers and missing values, resulting in inaccurate pollution source apportionment. To address this issue, this study aimed to establish a precise pollution source apportionment model to determine the quantity of potential pollution sources, identify their types, and quantify their contribution rates. The proposed method follows three key steps: first, PCA is applied to determine the number of potential pollution sources based on the cumulative variance contribution rate of principal components; second, an AE model is used for dimensionality reduction and pollution source identification; third, a CatBoost model is employed to quantify the contribution rate of each identified source. Taking the Qinhuai New River as a case study, four types of pollution sources were identified: organic pollution or domestic sewage discharge sources, industrial pollution sources, urban runoff or soil erosion sources, and agricultural pollution sources, with their respective contribution rates being 31.1%, 21.5%, 21.7%, and 25.7%. Model evaluation results demonstrate that the AE model achieves a reconstruction 0.95 and a MSE 0.05; Meanwhile, the CatBoost model for contribution rate quantification also yields an 0.95 and MSE 0.05, indicating high fitting accuracy. Overall, the PCA-AE-CatBoost model outperforms the PCA-APCS-MLR and PCA-CatBoost models, providing a more accurate technical basis for pollution control.

Response to microplastics exposure and changes in system performance: a stark contrast between domestic sewage and landfill leachate.

Occurrence, distribution, and ecological risks of organic pollutants in global mangrove sediments.

Polycyclic aromatic hydrocarbons (PAHs) in surficial sediments of Vembanad wetland ecosystem, southwest coast of India: An integrated assessment of dissemination, source apportionment, ecological and public health implication.

Surveillance of urban river environment by quantifying distributions of water quality parameters using hyperspectral remote sensing-based ripple propagation graph network.

Characterization and recognition of three-dimensional excitation-emission matrix spectra of wastewater from six typical categories

Identification of pollution properties in typical tropical island watershed and their significant for source apportionment: A case study of Sanya, China.

Characterization of microbial communities in the rhizosphere and water of a field-scale artificial floating island system for nutrient removal.

Euphrates water quality in the Babil Governorate has suffered a decline with addition of industrial waste, raw sewage, and agricultural effluent. Lab data and review of core variables depict the situation in some parts of governorate such as Mahmoudiyah, Hillah and Al-Kifl. Water quality evaluation and implications to the local environment and human health were determined by determining turbidity, pH, DO in water total dissolved solids TDS and COD.