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At present, China’s air pollution and its treatment effect are issues of general concern in the academic circles. Based on the analysis of the development stages of air pollution in China and the development history of China’s air quality standards, we selected 17 cities of Shandong Province, China as the research objects. By expanding China’s existing Air Quality Index System, the air quality of six major pollutants including PM2.5 and PM10 in 17 cities from February 2017 to January 2020 is comprehensively evaluated. Then, with a forecast model, the air quality of the above cities in the absence of air pollution control policies since June 2018 was simulated. The results of the error test show that the model has a maximum error of 4.67% when simulating monthly assessment scores, and the maximum mean error of the four months is 3.17%. Through the comparison between the simulation results and the real evaluation results of air quality, we found that since June 2018, the air pollution control policies of six cities have achieved more than 10% improvement, while the air quality of the other 11 cities declined. The different characteristics of pollutants and the implementation of governance policies are perhaps the main reasons for the above differences. Finally, policy recommendations for the future air pollution control in Shandong and China were provided.
Bowen Jiang; Yuangang Li; Weixin Yang. Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China. International Journal of Environmental Research and Public Health 2020, 17, 9476 .
AMA StyleBowen Jiang, Yuangang Li, Weixin Yang. Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China. International Journal of Environmental Research and Public Health. 2020; 17 (24):9476.
Chicago/Turabian StyleBowen Jiang; Yuangang Li; Weixin Yang. 2020. "Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China." International Journal of Environmental Research and Public Health 17, no. 24: 9476.
In this paper, a novel multi-population parallel co-evolutionary differential evolution, named MPPCEDE, is proposed to optimize parameters of photovoltaic (PV) models and enhance conversion efficiency of solar energy. In the MPPCEDE, the reverse learning mechanism is employed to generate the initial several subpopulations to enhance the convergence velocity and keep the population diversity. A new multi-population parallel control strategy is developed to maintain the search efficiency in subpopulations. The co-evolutionary mutation strategy with elite population and three mutation strategies is proposed to reduce computing resources and balance the exploration and exploration capability through the cooperative mechanism, improve the convergence speed, realize the information exchange. Then the MPPCEDE is employed to effectively optimize parameters of PV models under various conditions and environments to obtain a parameter values of PV models. Finally, the effectiveness of the proposed method is tested by different PV models and manufacturer's datasheet. The experimental and comparative results demonstrate that the MPPCEDE exhibits higher accuracy and reliability, and has fast convergence speed by comparing with several methods in extracting parameters of PV models.
Yingjie Song; Daqing Wu; Wu Deng; Xiao-Zhi Gao; Taiyong Li; Bin Zhang; Yuangang Li. MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization. Energy Conversion and Management 2020, 228, 113661 .
AMA StyleYingjie Song, Daqing Wu, Wu Deng, Xiao-Zhi Gao, Taiyong Li, Bin Zhang, Yuangang Li. MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization. Energy Conversion and Management. 2020; 228 ():113661.
Chicago/Turabian StyleYingjie Song; Daqing Wu; Wu Deng; Xiao-Zhi Gao; Taiyong Li; Bin Zhang; Yuangang Li. 2020. "MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization." Energy Conversion and Management 228, no. : 113661.
As a developing country with insufficient water resources, China’s water environment management and performance evaluation have important research value. The three provinces (Henan, Hubei, and Hunan) in central China with typical significance in geographical location and water resources governance were selected as research objects in this paper. Based on the principal component analysis (PCA) method and the pressure-state-response (PSR) model, a comprehensive evaluation system for the water environment in those three provinces during 2011–2017 was established in this paper. The evaluation results show that: (1) The water environment management and performance evaluation of the three provinces in central China were generally poor in 2011–2012, but the overall trend was rising; (2) in 2013–2014, the situation was improved compared to the previous two years, but needed further enhancement; (3) in 2015–2017, the water environment management and performance of the three provinces showed significant improvement. Among them, the Hubei Province had the highest water environment evaluation value (1.692), and the Henan Province had the most significant progress (from 0.043 to 1.671). The contributions of this paper are: (1) The comprehensive evaluation model based on PCA and the PSR model was constructed to analyze the sustainable development of water environment in central China; (2) the performance evaluation system for water environment management, which could comprehensively evaluate the performance of water environment treatment and effectively reveal the correlation between various indicators, was established. The principal factors in water environment management can be obtained by this evaluation system. Based on the analysis of the reasons underlying the above changes, the corresponding policy recommendations for improving water environment management and performance in central China were suggested in order to provide a reference for further improvement of water environment management in developing countries.
Yuangang Li; Weixin Yang; Xiaojuan Shen; Guanghui Yuan; Jiawei Wang. Water Environment Management and Performance Evaluation in Central China: A Research Based on Comprehensive Evaluation System. Water 2019, 11, 2472 .
AMA StyleYuangang Li, Weixin Yang, Xiaojuan Shen, Guanghui Yuan, Jiawei Wang. Water Environment Management and Performance Evaluation in Central China: A Research Based on Comprehensive Evaluation System. Water. 2019; 11 (12):2472.
Chicago/Turabian StyleYuangang Li; Weixin Yang; Xiaojuan Shen; Guanghui Yuan; Jiawei Wang. 2019. "Water Environment Management and Performance Evaluation in Central China: A Research Based on Comprehensive Evaluation System." Water 11, no. 12: 2472.
Focusing on the topic of water environment safety of China, this paper has selected the three northeast provinces of China as the research object due to their representativeness in economic development and resource security. By using the Entropy Weight Method, the Grey Correlation Analysis Method, and the Principal Component Analysis Method, this paper has first constructed a water environment safety evaluation system with 17 indicators from the economic, environmental, and ecological aspects. Furthermore, this paper has screened the initially selected indicators by the Principal Component Analysis Method and finally determined 11 indicators as the evaluation indicators. After indicator screening, this paper has adopted the improved Fuzzy Comprehensive Evaluation Method to evaluate the water environment safety of the three northeast provinces of China and obtained the change in water environment safety of different provinces from 2009 to 2017. The results show that the overall water environment safety of the region had improved first but worsened afterward, and that in terms of water safety level, Jilin Province ranked first, followed by Heilongjiang Province and Liaoning Province. The three factors that have the greatest impact on the water environment safety of the three provinces are: Liaoning—Chemical Oxygen Demand (score: 17.10), Per Capita Disposable Income (score: 13.50), and Secondary Industry Output (score: 11.50); Heilongjiang—Chemical Oxygen Demand (score: 18.64), Per Capita Water Resources (score: 12.75), and Concentration of Inhalable Particles (score: 10.89); Jilin—Per Capita Water Resources (score: 15.75), Chemical Oxygen Demand (score: 14.87), and Service Industry Output (score: 11.55). Based on analysis of the evaluation results, this paper has proposed corresponding policy recommendations to improve the water environment safety and promote sustainable development in the northeast provinces of China.
Yuangang Li; Maohua Sun; Guanghui Yuan; Yujing Liu. Evaluation Methods of Water Environment Safety and Their Application to the Three Northeast Provinces of China. Sustainability 2019, 11, 5135 .
AMA StyleYuangang Li, Maohua Sun, Guanghui Yuan, Yujing Liu. Evaluation Methods of Water Environment Safety and Their Application to the Three Northeast Provinces of China. Sustainability. 2019; 11 (18):5135.
Chicago/Turabian StyleYuangang Li; Maohua Sun; Guanghui Yuan; Yujing Liu. 2019. "Evaluation Methods of Water Environment Safety and Their Application to the Three Northeast Provinces of China." Sustainability 11, no. 18: 5135.
In order to evaluate the atmospheric environment sustainability in the provinces of Northeast China, this paper has constructed a comprehensive evaluation model based on the rough set and entropy weight methods. This paper first constructs a Pressure-State-Response (PSR) model with a pressure layer, state layer and response layer, as well as an atmospheric environment evaluation system consisting of 17 indicators. Then, this paper obtains the weight of different indicators by using the rough set method and conducts equal-width discrete analysis and clustering analysis by using SPSS software. This paper has found that different discrete methods will end up with different reduction sets and multiple indicators sharing the same weight. Therefore, this paper has further introduced the entropy weight method based on the weight solution determined by rough sets and solved the attribute reduction sets of different layers by using the Rosetta software. Finally, this paper has further proved the rationality of this evaluation model for atmospheric environment sustainability by comparing the results with those of the entropy weight method alone and those of the rough set method alone. The results show that the sustainability level of the atmospheric environment in Northeast China provinces has first improved, and then worsened, with the atmospheric environment sustainability level reaching the highest level of 0.9275 in 2014, while dropping to the lowest level of 0.6027 in 2017. Therefore, future efforts should focus on reducing the pressure layer and expanding the response layer. Based on analysis of the above evaluation results, this paper has further offered recommendations and solutions for the improvement of atmospheric environment sustainability in the three provinces of Northeast China.
Yuangang Li; Maohua Sun; Guanghui Yuan; Qi Zhou; Jinyue Liu. Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method. Sustainability 2019, 11, 3793 .
AMA StyleYuangang Li, Maohua Sun, Guanghui Yuan, Qi Zhou, Jinyue Liu. Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method. Sustainability. 2019; 11 (14):3793.
Chicago/Turabian StyleYuangang Li; Maohua Sun; Guanghui Yuan; Qi Zhou; Jinyue Liu. 2019. "Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method." Sustainability 11, no. 14: 3793.
In recent years, China's urban air pollution has caused widespread concern in the academic world. As one of China's economic and financial centers and one of the most densely populated cities, Shanghai ranks among the top in China in terms of per capita energy consumption per unit area. Based on the Shanghai Energy Statistical Yearbook and Shanghai Air Pollution Statistics, we have systematically analyzed Shanghai’s atmospheric pollutants from three aspects: Primary pollutants, pollutants changing trends, and fine particulate matter. The comprehensive pollution index analysis method, the grey correlation analysis method, and the Euclid approach degree method are used to evaluate and analyze the air quality in Shanghai. The results have shown that Shanghai's primary pollutants are PM2.5 and O3, and the most serious air pollution happens during the first half of the year, particularly in the winter. This is because it is the peak period of industrial energy use, and residential heating will also lead to an increase in energy consumption. Furthermore, by studying the particulate pollutants of PM2.5 and PM10, we clearly disclosed the linear correlation between PM2.5 and PM10 concentrations in Shanghai which varies seasonally.
Ying Yan; Yuangang Li; Maohua Sun; Zhenhua Wu. Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai. Sustainability 2019, 11, 2319 .
AMA StyleYing Yan, Yuangang Li, Maohua Sun, Zhenhua Wu. Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai. Sustainability. 2019; 11 (8):2319.
Chicago/Turabian StyleYing Yan; Yuangang Li; Maohua Sun; Zhenhua Wu. 2019. "Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai." Sustainability 11, no. 8: 2319.