Multiple isotopes decipher the nitrogen cycle in the cascade reservoirs and downstream in the middle and lower Yellow River: Insight for reservoir drainage period.

Intensive anthropogenic activities, such as excessive nitrogen input and dam construction, have altered the nitrogen cycle in the global river system. However, the focus on the source, transformation and fate of nitrogen in the Yellow River is still scarce. In this study, the multiple isotopes (δ15N-NO3-, δ18O-NO3-, δ15N-NH4+ and δ15N-PN) were deciphered to explore the nitrogen cycling processes and the driving factors in the thermally stratified cascade reservoirs (Sanmenxia Reservoir: SMXR and Xiaolangdi Reservoir: XLDR) and Lower Yellow River (LYR) during the drainage period of the XLDR. In the SMXR, algal bloom triggered the assimilation process in the upper layer before the SMX Dam, followed by remineralization and subsequent nitrification processes in the lower water layers. The nitrification reaction in the XLDR progressively increased along both longitudinal and vertical directions to the lower layer of the XLD Dam, which was linked to the variation in the water residence time of riverine, transition and lentic zones. The robust nitrification rates in the lower layer of the lentic zone coincided with the substantial depletion of nitrate isotopic composition and enrichment of both δ15N-PN and δ15N-NH4+, indicating the longer water residence time not only promoted the growth of the nitrifying population but also facilitated the remineralization to enhance NH4+ availability. In the LYR, the slight nitrate assimilation, as indicated by nitrate isotopic composition and fractionation models, was the predominant nitrogen transformation process. The Bayesian isotope mixing model results showed that manure and sewage was the dominant nitrate source (50 %) in the middle and lower Yellow River. Notably, the in-reservoir nitrification was a significant nitrate source (27 %) in the XLDR and LYR. Our study deepens the understanding of anthropogenic activities impacting the nitrogen cycle in the river-reservoir system, providing valuable insight into water quality management and nitrogen cycle mechanisms in the Yellow River.

Nutrient changes in the Bohai Sea over the past two decades.

With the growing problem of eutrophication in the Bohai Sea, actions have been taken to reduce nutrient inputs, but it remains to be seen whether nutrient levels and structure have been ameliorated. In this study, the nutrient trends in the Bohai Sea are re-examined based on observations from 2000 to 2019. The results suggest that dissolved inorganic nitrogen (DIN) concentrations and DIN/DIP (dissolved inorganic phosphate) ratios gradually increased from 2000 to 2013 but dramatically decreased from 2013 to 2019. The increase and decrease rates of DIN concentrations decreased with increasing water depth, indicating that DIN concentrations in nearshore waters responded more rapidly to changes in human activities. However, DIP concentrations responded weakly to nutrient inputs, with their trends uncoupled. The DIN/DIP ratios have declined close to and in some seasons even below the canonical Redfield ratio in areas with water depths >20 m recently, implying that relative nutrient limitation in these areas may be shifting from relative phosphorus (P) limitation to absence of relative nutrient limitation or relative nitrogen (N) limitation. Atmospheric deposition, wastewater discharge, and riverine input were responsible for 66 %, 21 %, and 13 % of the variance in the decline of DIN concentration, respectively. Several environmental indicators responded positively to the decrease in DIN concentrations and DIN/DIP ratios, with varying degrees of recovery recently. Our study proves the phased success of various nutrient reduction measures taken by the Chinese government to improve the environment of the Bohai Sea over the past decade.

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.