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Polybrominated diphenyl ethers in water, suspended particulate matter, and sediment of reservoirs and their tributaries in Shenzhen, a mega city in South China.

Tingting ZhuYouchang ZhuYunlang LiuChen DengXiujuan QiJinling WangZhizhi ShenDonggao YinYihong LiuRuohan SunWeiling SunNan Xu
Published in: Environmental science and pollution research international (2023)
Urban reservoirs serve many purposes including recreation and drinking water, and larger bodies of water can alter the surrounding air temperatures, making urban areas cooler in summer and warmer in winter. However, reservoirs may also be sinks for contaminants. One such group of contaminants, the polybrominated diphenyl ethers (PBDEs), are persistent organic pollutants known to accumulate in sediments and suspended particulate matter (SPM). Few studies have been conducted on PBDEs in water, SPM, and sediment from reservoirs of Shenzhen which is a mega city in South China. To this end, 12 PBDEs were measured in water, SPM, and sediment samples during the dry season (DS) and wet season (WS), to explain the spatiotemporal distribution, congener profiles, sources, and risks of pollutants in four reservoirs (A-D) and their tributaries in the study region. The concentration of ∑ 12 PBDEs during the DS was found to be significantly higher than that during the WS. Source apportionment suggested that commercial penta-, octa-, and deca-BDEs are the major components of PBDEs, resulting mainly from atmospheric deposition, wastewater discharge, and external water-diversion projects. Further, attention should be paid to electronic equipment manufacturing factories in the study area. Risk assessment indicated risk of PBDEs (especially BDE-209) in sediment and SPM to be of concern. This study provides important data support for the control of PBDEs in natural drinking water sources.
Keyphrases
  • drinking water
  • particulate matter
  • heavy metals
  • health risk assessment
  • air pollution
  • health risk
  • risk assessment
  • polycyclic aromatic hydrocarbons
  • quality improvement
  • machine learning
  • big data
  • deep learning