This study is the first attempt to comprehensively investigate deca-BDE and alternative flame retardants in a wastewater treatment plant in such a long term in China (2009-2016). Influent, effluent and sludge samples were collected. The mean concentration of deca-BDE, ΣNBFRs and ΣDPs in influent were 311.5, 76.0 and 1.4ng/L, respectively, which were at the low end of the global range. The levels of deca-BDE, ΣNBFRs and ΣDPs in effluent were range from 9.5-68.6, 4.1-38.5 and BLD-1.6ng/L, respectively. In sludge samples, the mean concentrations were 406.7, 510.5 and 6.9ng/g dw for deca-BDE, ΣNBFRs and ΣDPs. The concentration of temporal trends in this study may reflected the release of those compounds. Compared to the beginning year of this study, the usage of deca-BDE was decreased but the usage of total NBFRs and DPs presented sustained increase over the sampling period. There were no significant variation of deca-BDE, NBFRs and DPs in the wastewater treatment plant in Harbin was observed in the four seasons except for NBFRs in influents, which the ΣNBFRs mean concentration in influents in the summer was statistically significantly higher than that in winter indicating that NBFRs was easier impacted by temperature compared to deca-BDE and DPs. In addition, sorption and accumulation to sludge was the major removal mechanism for those compounds, accounting for 73.3% to 89.0%.
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http://dx.doi.org/10.1016/j.scitotenv.2017.12.346 | DOI Listing |
Food Environ Virol
December 2024
Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi, 400-8511, Japan.
Wastewater surveillance for pathogens is important to monitor disease trends within communities and maintain public health; thus, a quick and reliable protocol is needed to quantify pathogens present in wastewater. In this study, a method using a commercially available magnetic carbon bead-based kit, i.e.
View Article and Find Full Text PDFSci Rep
December 2024
MTA-DE "Momentum" Ecology, Evolution & Developmental Biology Research Group, Dept. of Evolutionary Zoology, University of Debrecen, Debrecen, Hungary.
The objective to study the influence of microbiome on host fitness is frequently constrained by spatial and temporal variability of microbial communities. In particular, the environment serves as a dynamic reservoir of microbes that provides potential colonizers for animal microbiomes. In this study, we analyzed the microbiome of Hydra oligactis and corresponding water samples from 15 Hungarian lakes to reveal the contribution of environmental microbiota on host microbiome.
View Article and Find Full Text PDFJ Environ Manage
December 2024
Department of Chemistry and Biology "Adolfo Zambelli", University of Salerno, via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy.
This study evaluates the combined use of H₂O₂ and thermally activated S₂O₈⁻ (T-PDS) for the degradation of phenolic compounds (PhOH) in wastewater, aiming to limit or eliminate sludge production. Phenolic compounds are common in industrial effluents, and their effective removal is crucial for reducing environmental impact. The study employs Response Surface Methodology (RSM) and Principal Component Analysis (PCA) to optimise critical variables such as temperature, pH, and oxidant concentrations.
View Article and Find Full Text PDFWater Res
December 2024
GEMMA - Group of Environmental Engineering and Microbiology, Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya-BarcelonaTech, c/ Jordi Girona 1-3, Building D1, Barcelona 08034, Spain. Electronic address:
Cyanobacterial wastewater-based biorefineries are a sustainable alternative to obtain high-value products with reduced costs. This study aimed to obtain phycobiliproteins and carotenoids, along with biogas from a wastewater-borne cyanobacterium grown in secondary effluent from an urban wastewater treatment plant, namely treated wastewater. For the first time, the presence of contaminants of emerging concern in concentrated pigment extracts was assessed.
View Article and Find Full Text PDFWater Res
December 2024
State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China. Electronic address:
Wastewater treatment plants (WWTPs) generate vast amounts of water quality, operational, and biological data. The potential of these big data, particularly through machine learning (ML), to improve WWTP management is increasingly recognized. However, the costs associated with data collection and processing can rise sharply as datasets grow larger, and research on determining the optimal data volume for effective ML application remains limited.
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