Dissolved organic matter (DOM) has a great impact on the main pollution indicators of lakes (such as chemical oxygen demand, COD). Therefore, DOM is the research basis for understanding the meaning of the water environment and the laws of the migration and transformation of pollutants. Qinghai Lake is one of the world’s typical inland plateau lake wetlands. It plays important roles in improving and regulating the climate and in promoting a virtuous regional ecological cycle. In recent years, with the acceleration of urbanization and the rapid development of tourism, under the background of climate change, and with grassland degradation and precipitation change, the whole basin of Qinghai Lake has been facing great ecological pressure. In order to comprehensively explore the water environment of Qinghai Lake and to protect the sustainable development of the basin, a systematic study was carried out on the whole basin of Qinghai Lake. The results show the following: (1) from 2010 to 2020, the annual average value of COD_Cr in Qinghai Lake fluctuated in the range from class III to class V according to the surface water environmental quality standard, showing first a downward trend and then an upward trend. (2) The concentration of CDOM in Qinghai Lake had obvious temporal and spatial changes. (3) The spatial distribution of the total fluorescence intensity of FDOM in water was also different in different seasons. However, in the three surveys, the area with the highest total fluorescence intensity of FDOM in the water body appeared near Erlangjian in the south of Qinghai Province, indicating that anthropogenic sources are the main controlling factors of dissolved organic matter in the lake.

In this study, a comprehensive research framework coupled with electric power demand forecasting, a regional electric system planning model, and post-optimization analysis is proposed for electric power system management. For dealing with multiple forms of uncertainties and dynamics concerning energy utilization, capacity expansions, and environmental protection, the inexact two-stage stochastic robust programming optimization model was developed. The novel programming method, which integrates interval parameter programming (IPP), stochastic robust optimization (SRO), and two-stage stochastic programming (TSP), was applied to electric power system planning and management in Harbin, China. Furthermore, the Gray-Markov approach was employed for effective electricity consumption prediction, and the forecasted results can be described as interval values with corresponding occurrence probability, aiming to produce viable input parameters of the optimization model. Ten scenarios were analyzed with different emissions reduction levels and electricity power structure adjustment modes, and the technique for order of preference by similarity to ideal solution (TOPSIS) was selected to identify the most influential factors of planning decisions by selecting the optimal scheme. The results indicate that a diversified power structure that dominates by thermal power and is mainly supplemented by biomass power should be formed to ensure regional sustainable development and electricity power supply security in Harbin. In addition, power structure adjustment is more effective than the pollutants emission control for electricity power system management. The results are insightful for supporting supply-side energy reform, generating an electricity generation scheme, adjusting energy structures, and formulating energy consumption of local policies.