Levels of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in sediments from the sandy flat system of Shuangtaizi Estuary, the highest-latitude estuary in China, were investigated to identify their possible sources and potential ecological risk. The concentrations of 28 PCBs and 18 OCPs ranged from 1.83 to 36.68 ng g(-1) dw (mean 10.53 ng g(-1) dw) and from 0.02 to 14.57 ng g(-1) dw (mean 5.65 ng g(-1) dw), respectively. Generally, these organic pollutants showed an obvious spatial distribution, and relatively high levels were found at the high-tidal zone near river mouths. Compositional analyses indicated that tetra-PCBs were dominant for PCBs, whereas heptachlor was identified to be prevalent for OCPs in surficial sediment in the sand flats of Shuangtaizi Estuary. Overall, Shuangtaizi Estuary had moderate PCB and OCP levels in the sand flat sediments and posed a low ecological hazard to aquatic biota. Our results indicated that the sediment PCBs came from nonpoint deposition, such as atmospheric contribution and river input, for light chlorinated congeners and point source deposition, such as the industrial sources along river flow, for highly chlorinated congeners, whereas OCPs originate mainly from old residuals and new usage of pesticides in agriculture and aquaculture.

No abstract

No abstract

No abstract

This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration of water quality. A prediction system based on support vector machine (SVM)-particle swarm optimization (PSO) (SVM-PSO) algorithm is proposed under the background of deep learning. SVM-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary, so is the difference in water pollution of different sea consuming types. Based on the analysis results for causes of pollution, the control countermeasures of water pollution in Liaohe estuary are put forward. The results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%, the average error of 1.24% in predicting the samples, the root mean square error (RMSE) of 5.36 × 10 −4 , and the square of correlation coefficient of 0.91. Therefore, the prediction system in this study is feasible. At present, the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication, and the water pollution status basically remains at the level of mild pollution. The general trend is from phosphorus moderate restricted eutrophication to phosphorus restricted potential eutrophication. To sum up, the SVM-PSO algorithm shows good sewage prediction ability, which can be applied and promoted in water pollution control and has reliable reference significance.