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To explore the influence of storm runoff on reservoir organic matter during the flood season, the Lijiahe Reservoir was selected to analyze variations in the content and components of dissolved organic matter (DOM) during four periods (before runoff, flood peak period, 1 week after runoff, and 6 weeks after runoff) using three-dimensional fluorescence spectroscopy parallel factor analysis (EEMs-PARAFAC) and ultraviolet-visible (UV-Vis) spectra. The results showed that:① the turbidity and DOC content of the reservoir increased significantly during the flood peak period ( P <0.01) and gradually decreased thereafter; ② the UV-Vis spectrum characteristics showed that a (254) and a (355) were significantly increased in the flood peak period ( P <0.01) while E2/E3 and E3/E4 were significantly decreased ( P <0.01), indicating that the concentration, relative molecular weight, and degree of DOM humification in the reservoir were increased by storm runoff; ③ four DOM components were identified as terrestrial humus (C1 and C2), microbial humus (C3), and a tryptophan-like component (C4). The fluorescence intensity of the C1-C3 components increased significantly during the flood peak period ( P <0.05), indicating that the increase in the DOM humic-like component was caused by the storm runoff. At the same time, a decrease in the fluorescence intensity of the C1-C4 components was observed after the flood peak period, indicating that DOM continuously settled and degraded after runoff; and ④ Pearson's correlation analyses showed that DOM fluorescence intensity and turbidity were significantly correlated ( r >0.467, P <0.05), indicating that the observed decrease in DOM content was related to the sedimentation of suspended solids. A principal component analysis (PCA) showed that the water quality in the reservoir reflected the observed characteristics during the different runoff periods. Overall, this study reveals the effects of the storm runoff on DOM content and its components over the short and long term, providing scientific support for the management of drinking water quality.

UV-visible absorption spectroscopy, fluorescence spectroscopy, and parallel factor analysis were used to analyze the composition of chromophoric dissolved organic matter (CDOM) in the waters of the Wangchuan River in summer, and the source of this CDOM was explored. The redundant analysis method and Pearson correlation were used to analyze the correlation between optical parameters and water quality parameters. The results showed that the CDOM of the Wangchuan River is composed of the tryptophan-like component C1 (245, 300/335 nm), the short-wave humus component C2 (240, 320-340/405 nm), and the long-wave humus component C3 (270, 350-370/470 nm), in which components C1 and C2 have some homology ( r =0.859, P <0.001). CDOM absorption coefficient α (355) indicates that the CDOM concentration in the water body of the Wangchuan River is at a low level, and the correlation between α (355) and DOC concentration is significant ( r =0.850, P <0.001), which is conducive to the establishment of a DOC inversion model. Water fluorescence index FI (2.36±0.20), HIX (3.66±2.47), BIX (1.56±0.82), and freshness index ( β : α ) (1.33±0.62), and the spectral slope ratio S R (0.76±0.25) indicate that the CDOM of the Wangchuan River has strong self-generated characteristics, weak humification characteristics, and more new CDOM. Redundancy analysis showed that the humic components (C2, C3) are affected by algae metabolism and microbial action, while tryptophan-like components (C1) are related to land-based input, and negatively correlated with dissolved total nitrogen. The humic components C2 and C3 are positively correlated with total phosphorus, dissolved total phosphorus, and dissolved organic carbon. This paper clarifies the characteristics and influencing factors of CDOM in the Qinling valley, and provides a theoretical basis for water body management in the Qinling valley.

To understand the temporal and spatial succession characteristics of algae functional communities and water quality changes in a drinking water reservoir, the Lijiahe Reservoir was selected to monitor variations in water quality and algae from September 2018 to June 2019. The algae community was classified into functional groups following the method proposed by Reynolds and Padisák. The relationship between algal functional community and water quality was discussed, and the water quality was assessed using the water quality index (WQI) method. The results showed that 56 species of algae were obtained, belonging to 4 families and 28 genera, which were divided into 15 functional groups. The dominant algae functional communities were B, D, G, J, L 0 , Mp, P, W 1 , and X 1 . The algae structure of Lijiahe Reservoir showed obvious seasonal characteristics. The algal density in the mixed period was significantly lower than that in the stratification period. The main functional algae in the mixed period were Chlorella and Cyclotella, but Navicula and Synedra were the dominant functional algae in the stratification period. Redundancy analysis showed that the water temperature, mixing depth, and relative water column stability index were the main factors driving algae succession. WQI analysis indicated that the water quality of Lijiahe Reservoir was "good", and the water quality during the mixed period was slightly better than that in the stratification period. This study demonstrates that water-lifting aerators can change the succession characteristics of algae functional groups, and effectively contribute to improvement in water quality in a drinking water reservoir.

To explore the effects of water quality improvement by artificial destratification and artificially-induced mixing, as well as realize the conditions of artificial mixing and natural mixing, the Lijiahe Reservoir was selected to monitor the indexes of water quality and hydrometeorology from June 2017 to April 2019 and to analyze the characteristics of variations in water temperature, dissolved oxygen, and pollutants during the natural and induced-mixing processes. The results demonstrated that:① The natural process had the features of a long period of thermal stratification and only a short period of mixing of about 2.5 months. Through the operation of water-lifting aerators (WLAs) during the induced-mixing process, the water body was completely mixed and entered the cooling period at the end of September, achieving the conditions of induced-natural mixing. The surface water temperature and average air temperature were 20.17℃ and 16.5℃, respectively, and the water body continued to be naturally mixed after the WLA system was shut down, which led to a natural mixing cycle of 5.5 months. ② During the natural-mixing process, the concentration of pollutants was relatively high in the whole period, and the concentration of surface pollutants in the mixing stage presented a trend of increasing first and then decreasing. The oxycline appeared with thermal stratification, and the anaerobic cycle in the bottom of water column reached 6 months. ③ Compared with the natural-mixing process, the hypolimnetic anaerobic condition was eliminated and the control effects of pollutants dominated during the induced-mixing process. Simultaneously, the concentrations of NH 4 + -N, TP, Fe, and Mn in the bottom of the water column were cut by 76.2%, 75.5%, 82.2%, and 82.1%, respectively, during the same period from October to March of the following year, and met the "Environmental Quality Standards for Surface Water". This study shows that the artificially-induced mixing process contributes to water quality improvement and mixing-period prolongation.

An in-depth understanding of the source of pollutants and the evolution of water quality is a prerequisite for water pollution control in source water reservoirs. Lijiahe Reservoir is an important water source for Xi'an. To strengthen the protection of Lijiahe Reservoir, water quality monitoring was carried out monthly from December 2016 to December 2017. The results showed that the main pollutant source was upstream water, and its percentage contribution to the bulk pollutants was 99.52% for permanganate index, 99.41% for TN, and 99.23% for TP. The main source of pollution in the upstream Dongcaiyu was the upper reach with Gepai Primary School and the turf service area. The main source of pollution in Xicaiyu was the upper reach with a chicken farm and fishery. In summer and autumn, Lijiahe Reservoir exhibited thermal stratification. During the stable stratification period, the maximum concentrations of TN, TP, permanganate index, TOC, Fe, and Mn in the water body were as high as 3.32, 0.177, 5.21, 3.01, 0.21, 0.235 mg·L -1 , respectively, under the dual effects of exogenous pollution and bottom anaerobic release. The maximum concentrations of TN, TP, Fe, and Mn exceeded the Class Ⅲ water limit of Surface Water Environmental Quality Standards (GB 3838-2002). The higher nutrient levels promoted large-scale reproduction of algae. The density of algae in the upper water reached 2.18×10 8 cells·L -1 , and the dominant algae species were Microcystis aeruginosa and Aphanizomenon flosaquae , which pose a significant threat to water quality. Therefore, the primary goal for water pollution control in Lijiahe Reservoir is to take effective measures to control algae blooms, and at the same time, reduce the input of upstream pollutants.

In this paper,the generalized additive model (GAM) was introduced to analyze the interactive effects of the influencing factors on the change of PM 2.5 concentration during 2013-2015 in Nanjing city.The results showed as follows:PM 2.5 and its influencing factors appeared to follow normal distribution.There were strong correlations among the influencing factors,especially among the temperature (TEM),pressure (PRS) and water vapor pressure (VAP).For the single influencing factor GAM models of PM 2.5 concentration,all influencing factors passed the significance test.Moreover,the equation fitting degrees of SO 2 ,CO,and NO 2 were much higher.In the multiple influencing factors GAM models of PM 2.5 concentration,the contribution of the SO 2 ,CO,NO 2 ,O 3 ,precipitation (PRE),wind and relative humidity (RHU) to the change of PM 2.5 concentration was 73.9% with significant impacts on the change of PM 2.5 concentration.Based on the diagnostic analysis of the effect of multi factors on the change of PM 2.5 concentration,there were linear relationship between PM 2.5 and SO 2 ,NO 2 and wind,and non-linear relationship between PM 2.5 and CO,O 3 ,PRE and RHU.The GAM models,which considered the interaction of SO 2 respectively with CO,PRE and RHU,the interaction of CO respectively with NO 2 ,O 3 ,PRE,Wind and RHU,and the interaction of NO 2 respectively with Wind、PRE and RHU,all passed the significance test ( P <0.01 or P <0.05).The interaction of SO 2 ,CO and NO 2 respectively with other factors such as meteorological factors had the most important influence on the change of PM 2.5 concentration.At last,through the visualized three-dimensional map of the GAM models considering the interaction of the influencing factors on the PM 2.5 concentration,the interactive effects of the influencing factors on PM 2.5 concentration were quantitatively modeled.Our results demonstrated that GAM could be used to quantitatively analyze the interactive effect of the influencing factors on the change of PM 2.5 concentration.Therefore,the research method is innovative and important for PM 2.5 pollution and control.