Chromophoric dissolved organic matter (CDOM) is a key component with a critical role in the littoral zones of eutrophic shallow lakes; yet the characteristics of CDOM in these zones remain seldom systematically reported. In this study, the differences in sources, biogeochemical characteristics, and fates of CDOM between the littoral zones of eutrophic lakes Taihu (LLT; frequently occurring algal blooms and longer lake residence time) and Hongze (LLH; no obvious algal blooms and shorter residence time) were compared during the algal bloom season using ultraviolet-visible spectra and excitation and emission matrix spectroscopy combined with parallel factor analysis. Three humic-like fluorescent dissolved organic matter (FDOM) components (C1, C3, and C4) and one protein-like component (C2) were identified. Results showed that FDOM components were dominated by protein-like fluorescent substances in LLT, and humic-like materials in LLH, respectively. The CDOM in LLT had a lower relative aromaticity and molecular weight, humification degree and a higher autotrophic productivity because of algal blooms. Furthermore, CDOM depletion rates in LLT were higher than those in LLH due to a longer lake residence time in LLT. In addition, CDOM shifted from high molecular weight to low molecular weight as the humification degree decreased during the CDOM depletion process. This comparative study showed that algal blooms and lake residence time were the significant factors for distinguishing characteristics of CDOM between littoral zones of shallow lakes on a similar trophic level. This study provides field-based knowledge for remote sensing CDOM measurement and serves as a reference for lakeshore aquatic environmental management.
A sudden increase in passenger flow can primitively lead to continuous congestion of a subway network and thus have a profound impact on the subway system. To prevent the risk caused by sudden overcrowding, the prediction of passenger flow is a daily task of the rail transit management. Most current short-term passenger flow forecasts rely only on inbound passenger flow, which cannot accurately characterize the total impact of sudden passenger flow. To enhance the prediction accuracy, we propose a sudden passenger flow prediction model with two factors, the outbound and inbound passenger flows. The wavelet neural network (WNN) model was used to detect the sudden passenger flow, and subsequently, it is optimized by the genetic algorithm (GA), according to two-factor data characteristics. Sudden passenger flow events from 2014 to 2016 in the Beijing Dongsishitiao Station (DS) were used to train and verify the reliability of the prediction model. The optimized WNN results proved better than the conventional WNN, and the error of models based on two factors was significantly smaller than the models with a single-factor.
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