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Li Y, Zhou S, Jia Z, Ge L, Mei L, Sui X, Wang X, Li B, Wang J, Wu S. Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15122628. [PMID: 30477150 PMCID: PMC6313624 DOI: 10.3390/ijerph15122628] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 11/10/2018] [Accepted: 11/19/2018] [Indexed: 02/08/2023]
Abstract
In order to quantitatively study the effect of environmental protection in China since the twenty-first century and the environmental pollution projected for the next ten years (under the model of extensive economic development), this paper establishes a Bayesian regulation back propagation neural network (BRBPNN) to analyze the typical pollutants (i.e., cadmium (Cd) and benzopyrene (BaP)) for Taihu Lake, a typical Chinese freshwater lake. For the periods 1950–2003 and 1950–2015, the neural network model estimated the BaP concentration for the database with Nash-Sutcliffe model efficiency (NS) = 0.99 and 0.99 and root-mean-square error (RMSE) = 3.1 and 9.3 for the total database and the Cd concentration for the database with NS = 0.93 and 0.98 and RMSE = 45.4 and 65.7 for the total database, respectively. In the model of extensive economic development, the concentration of pollutants in the sediments of Taihu reached the maximum value at the end of the twentieth century and early twenty-first century, and there was an inflection point. After the early twenty-first century, the concentration of pollutants was controlled under various environmental policies and measures. In 2015, the environmental protection ratio of Cd and BaP reached 52% and 89%, respectively. Without environmental protection measures, the concentrations of Cd and BaP obtained from the neural network model is projected to reach 2015.5 μg kg−1 and 407.8 ng g−1, respectively, in 2030. Based on the results of this study, the Chinese government will need to invest more money and energy to clean up the environment.
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Affiliation(s)
- Yan Li
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
- Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210008, China.
| | - Shenglu Zhou
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
- Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing 210008, China.
| | - Zhenyi Jia
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
| | - Liang Ge
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
| | - Liping Mei
- School of Chemistry and Chemical Engineering, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
| | - Xueyan Sui
- Jiangsu Land Consolidation and Rehabilitation Center, Nanjing 210023, China.
| | - Xiaorui Wang
- Jiangsu Land Consolidation and Rehabilitation Center, Nanjing 210023, China.
| | - Baojie Li
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
| | - Junxiao Wang
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
| | - Shaohua Wu
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China.
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