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The Fuyang River system (FRS) in north China, for a long time, is seriously polluted with organic compounds and heavy metals due to industrialization. However, the information on heavy metal pollution in this area is still limited, and health risks raised by trace elements are neglected up to now. To characterize the heavy metal pollutants and assess their potential ecological risks scientifically in FRS, surface sediments were collected from 66 sampling sites selected according to the hydrological and anthropogenic conditions along the river. A total of twelve metal pollutants (e.g., Cr, As, and Hg) in the sediments were detected among the distributaries. A combining application of geoaccumulation index (I geo) , ratio of secondary phase and primary phase (RSP) , and the ecological risk factor (Er i ) in this study gave systematic assessment results of single or combined pollution status raised by heavy metals in this area. The results show that Cr, Ni, Cu, As, Cd, Co, and Sn are mainly dispersed in the river reaches of Xingtai City and pose potential health risks in midstream, as per the geoaccumulation index and Pearson's correlation analyses. In particular, Cd accumulates strongly in sediments of Ming River and Aixinzhuang dam from Xingtai City. In upstream and downstream of FRS, the potential ecological risk is low, except in Yongnian County where high ecological risk was caused by Cd and Hg. These findings provide new insights into the pollution characteristics and assessment of the potential ecological risks induced by heavy metals along FRS, which suggest new directions should strategically tend to typical pollutants control by policy formulation and taking effective measures to prevent and manage heavy metal pollution in North China. [Display omitted] • Multiple heavy metals pollution in FRS in North China was surveyed and assessed. • Distribution and pollution status of heavy metals in the FRS were characterized. • The health risk raised by heavy metals along the FRS was systematically evaluated. • The correlations between pollution and human activities along the river was built. Heavy metal pollution in Fuyang river system in North China are systematically characterized and the ecological risks are assessed through I geo , RSP, and Er i . [ABSTRACT FROM AUTHOR]

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset. There are two alternative cross attention models: (1) using the CNN features as the key–value pair, and the Bi-LSTM features as the query, this we called a XA model, and (2) using the Bi-LSTM features as the key–value pair, and the CNN features as the query, this we called a XA_I model; we found empirically for a properly chosen sliding window length, whichever CNN model or Bi-LSTM model performs well in the 1-step ahead prediction, then its features should be used as the query in the cross attention mechanism, thus, the XA achieves better results than XA_I on the East-China dataset, while XA_I achieves better results than XA on the Yalova dataset, and on the 16 MW dataset. Where comparable results using other state-of-the-art models are available on the Yalova dataset and the 16 MW dataset, we found that our XA_I results are superior. Moreover, it is found empirically, that the cross attention mechanism model produces stable k -step ahead predictions, for k ≤ W 2 , where W is the sliding window length. • A novel prediction model for wind generated power based on cross attention mechanism has been proposed. • The proposed method demonstrated on three practical datasets to have superior performance. • The cross attention mechanism based models give a stable multi-step ahead prediction. [ABSTRACT FROM AUTHOR]