Sulfate sources constrained by sulfur and oxygen isotopic compositions in the upper reaches of the Xijiang River,China

While it is critical to accurately understand the sources and transformation of sulfate based on time-series analysis, there are limited studies on temporal variation of sulfate in rivers and on rock weathering by sulfuric acids.We conducted a monthly sampling campaign in the Beipan, Nanpan, and Hongshui Rivers over the course of one hydrological year. This study examined seasonal variations in riverine sulfate impacted by the monsoon climate in the upper reaches of the Xijiang River basin. In general, the SO_4^(2-) contents in these rivers dropped from relatively high levels to low values during the high-flow season, in response to increasing discharge. The sulfate was generally enriched in heavy isotopes during the low-flow season compared to the high-flow season. The calculated results indicate that the riverine sulfate was mainly derived from sulfide oxidation, but that evaporite dissolution could be an important source during the low-flow season, based on isotopic evidence. Mine drainage is likely an important source of sulfate to these rivers during the high-flow season due to contributions from fast surface flow, which responds to frequent heavy rain in monsoonal climate regions. Arelatively high proportion of HCO_3^- was found to be derived from rock weathering by sulfuric acid during the high-flow season when compared to that observed during the low-flow season. The results suggest that approximately one quarter of the HCO_3^- in the Hongshui River originated from carbonate weathering by sulfuric acid derived from the oxidation of sulfide. Such information on the specific dual isotopic characteristics of riverine sulfate throughout a hydrological year can provide unique evidence for understanding the temporal variability of sulfate concentrations and weathering processes in rivers.

Adaptive electricity theft detection method based on load shape dictionary of customers

With the application of the advanced measurement infrastructure in power grids,data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves.However,owing to anomaly submergence,which shows that the usage patterns of electricity thieves may not always deviate from those of normal users,the performance of the existing usage-pattern-based method could be affected.In addition,the detection results of some unsupervised learning algorithm models are abnormal degrees rather than“0-1”to ascertain whether electricity theft has occurred.The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users.To address these issues,this study proposes a new electricity theft detection method based on load shape dictionary of users.A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft,and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.