The levels of metals in sediments of urban river ecosystems are crucial for aquatic environmental health and pollution assessment. Yet little is known about the interaction of nutrients with metals for environmental risks under contamination accumulation. Here, we combined hierarchical cluster, correlation, and principal component analysis with structural equation model (SEM) to investigate the pollution level, source, toxicity risk, and interaction associated with metals and nutrients in the sediments of a river network in a city area of East China. The results showed that the pollution associated with metals in sediments was rated as moderate degree of contamination load and medium-high toxicity risk in the middle and downstream of urban rivers based on contamination factor, pollution load index, and environmental toxicity quotient. The concentration of mercury (Hg) and zinc (Zn) showed a significant correlation with toxic risks, which had more contribution to toxicity than other metals in the study area. Organic nitrogen and organic pollution index showed heavily polluted sediments in south of the study area. Though correlation analysis indicated that nutrients and metals had different input zones from anthropogenic sources in the urban river network, SEM suggested that nutrient accumulation indirectly intensified toxicity risk of metals by 13.6% in sediments. Therefore, we suggested the combined consideration of metal toxicity risk with nutrient accumulation, which may provide a comprehensive understanding to identify sediment pollution.
Toxicity rate of metals in sediments from urban river network indirectly intensified by nutrients accumulation
An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO2 and NOx, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM10, SO2, and NO2. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied. 相似文献
