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1.INTRODUCTIONOver the past 40 years, the ecological environment system has gradually established a relatively complete statistical system of the ecological environment1. Its purpose is to grasp the quantity, industry and regional distribution of various pollution sources, understand the generation, emission and treatment of major pollutants, and provide a basis for social and economic development and environmental protection2. Maintaining harmony between urbanization, the economy and the ecological environment is central to achieving Sustainable Development Goals in any complex geographical region3. Water plays an important role in supporting and sustaining human health and sustainable ecosystem development4. Through wastewater ecological and environmental statistics, targeted policies for environmental protection were implemented, which promoting economic and social development is of great significance5. 2.MATERIALS AND METHODS2.1Data filtering principlesBased on the data in the ecological environment statistical database of Hunan Province from 2020 to 2022 in Hunan Province, the key industrial sources and centralized pollution control facilities were investigated6. The chemical oxygen demand, ammonia nitrogen, total nitrogen and other pollution factors in industrial wastewater were analysed. 2.2Data analysis methodsThe comparison method, graphical method (line chart, histogram, curve chart, etc.)7, regression analysis, normal distribution and other relevant analysis methods were used. 3.RESULTS AND DISCUSSION3.1General information about statistical objectsThe emission situation of major pollutants from key industrial enterprises in Hunan Province from 2020 to 2022 is shown in Table 1. All pollution sources showed a downward trend in 2020-2022. Table 1.Emission situation of major pollutants (wastewater) from key industrial enterprises in Hunan Province from 2020 to 2022.
3.2Comprehensive analysis of major pollutants in wastewater3.2.1Comprehensive analysis of pollutants by region.(1) Production and emission of various pollutants in industrial wastewater in 2022. In 2022, Yueyang produced the largest number of chemical oxygen demand (COD) in industrial wastewater (48882.42 tons) and Changsha emitted the largest number of COD (2367.29 tons). Chenzhou produced the smallest number of COD (3884.96 tons), and Xiangxi Autonomous Prefecture emitted the smallest number of COD (134.24 tons). The reduction rate of COD in industrial wastewater was 95.40%, of which Yongzhou had the largest reduction rate (98.36%), and Chenzhou had the smallest reduction rate (77.94%). Loudi produced the largest number of ammonia nitrogen (AN) in industrial wastewater (3238.38 tons), and Changsha emitted the largest amount of AN (113.52 tons). Zhangjiajie produced and emitted the smallest number of AN (34.27, 4.21 tons). The reduction rate of AN in industrial wastewater was 96.64%, of which Loudi had the largest reduction rate (98.48%), and Zhuzhou had the smallest reduction rate (80.45%). Yueyang produced the largest number of total nitrogen (TN) in industrial wastewater (6304.72 tons), and Changsha emitted the largest number of TN (543.57 tons). Zhangjiajie produced and emitted the smallest number of TN (67.44, 24.36 tons). The reduction rate of TN was 91.36%, of which Yueyang had the largest reduction rate (95.38%), and Shaoyang had the smallest reduction rate (50.39%). Changsha produced and emitted the largest number of total phosphorus (TP) in industrial wastewater (158.73, 24.99 tons). Chenzhou produced the smallest number of TP (18.73 tons), and Xiangxi Autonomous Prefecture emitted the smallest number of TP (2.26 tons). The reduction rate of TP was 89.25%, of which Changde had the largest reduction rate (88.96%), and Shaoyang had the smallest reduction rate (76.56%). Hengyang produced the largest number of heavy metals (HM, including total arsenic, total lead, total cadmium, total mercury, and total chromium) in industrial wastewater (114345.54 kg), and Xiangtan emitted the largest number of HM (2050.54 kg). Yiyang produced the smallest number of HM (321.80 kg), and Changde emitted the smallest number of HM (5.65 kg). The reduction rate of HM was 98.33%, of which Changde had the largest reduction rate (99.89%), and Xiangtan had the smallest reduction rate (86.27%). (2) Spatio-temporal distribution of pollutants from 2020 to 2022 Figure 1 shows the reduction rate of pollutants in industrial wastewater in 2022. Compared with 2020, the COD emissions in wastewater of all cities in Hunan decreased significantly for Xiangtan, Yiyang, and Zhuzhou, which decreased by 61.67%, 56.11%, and 46.87% in 2022 respectively. The AN emission in wastewater of all cities decreased significantly for Huaihua, Yiyang, and Yueyang, which decreased by 80.97%, 44.16% and 41.62% in 2022 respectively. The TN emissions in wastewater of all cities decreased significantly for Huaihua, Yueyang and Hengyang, which decreased by 47.80%, 46.63% and 38.24% in 2022 respectively. The TP emissions in wastewater of all cities decreased significantly for Hengyang, Yiyang and Yueyang, which decreased by 56.26%, 40.1% and 39.04% in 2022 respectively. The HM emissions in wastewater of all cities decreased significantly for Yueyang, Hengyang, and Yiyang, which decreased by 92.95%, 88.16%, and 85.37% in 2022 respectively. From 2020 to 2022, the cities with the largest average reduction rate of COD were Changde, Yiyang, and Zhuzhou, which were 96.14%, 95.98%, and 95.70%, respectively. The cities with the largest average reduction rate of AN were Loudi, Yueyang, and Hengyang, which were 98.48%, 98.33% and 97.49% respectively. The cities with the largest average reduction rate of TN were Loudi, Yueyang and Hengyang, which were 95.68%, 94.60% and 93.02%, respectively. The cities with the largest average reduction rate of TP were Changde, Zhuzhou and Yueyang, which were 93.15%, 92.12% and 90.81% respectively. The cities with the largest average reduction rate of HM were Changde, Changsha and Zhuzhou, which were 99.85%, 99.68%, and 99.47% respectively. 3.2.2Comprehensive analysis of pollutants by industry.(1) Production and emission of various pollutants in industrial wastewater in 2022. In 2022, the industry with the largest number of COD production was the paper and paper products industry (22 industries, 37272.17 tons), accounting for 15.79% of the province’s total8. Followed by the chemical fiber manufacturing industry (28 industries), chemical Industry (26 industries), petroleum, coal and other fuel processing industry (25 industries), agricultural and sideline food processing industry (13 industries), accounting for 14.48%, 12.04%, 10.14%, 6.73% of the province’s total. The 5 major industries accounted for 59.19% of the total COD produced by key industries in Hunan. The industry with the largest number of COD emissions was the chemical fiber manufacturing (28 industries, 1860.3980 tons), accounting for 18.00% of the province’s total. Followed by the paper and paper products industry (22 industries), agricultural and sideline food processing industry (13 industries), food manufacturing industry (14 industries), and chemical industry (26 industries), accounting for 11.07%, 9.31%, 8.80%, 6.12% of the province’s total. The 5 major industries accounted for 53.39% of the total COD emissions by key industries in Hunan. In 2022, the industry with the largest number of AN production was the chemical industry (26 industries, 7095.7230 tons), accounting for 52.89% of the province’s total. Followed by petroleum, coal and other fuel processing industry (25 industries), electricity, heat production and supply industry (44 industries), food manufacturing industry (14 industries), non-ferrous metal smelting and rolling processing industry (32 industries), accounting for the 9.65%, 6.99%, 5.71%, 5.41% of the province’s total. The 5 major industries accounted for 80.64% of the total AN by key industries in Hunan. The industry with the largest AN emission was the Food manufacturing industry (14 industries, 74.6840 tons), accounting for 17.81% of the province’s total. Followed by the chemical industry (26 industries), agricultural and sideline food processing industry (13 industries), computer, communication and other electronic equipment manufacturing industry (39 industries), pharmaceutical manufacturing industry (27 industries), accounting for 14.94%, 11.76%, 6.75%, 6.66% of the province’s total. The 5 major industries accounted for 57.91% of the total AN emission by key industries in Hunan. In 2022, the industry with the largest number of TN production was the chemical industry (26 industries, 11160.539 tons), accounting for 44.76% of the province’s total. Followed by the paper and paper products industry (22 industries), petroleum, coal and other fuel processing industry (25 industries), pharmaceutical manufacturing industry (27 industries), and food manufacturing industry (14 industries), accounting for 10.27%, 10.00%, 5.22%, 4.29% of the province’s total. The 5 major industries accounted for 74.53% of TN production by key industries in Hunan. The industry with the largest TN emissions was the chemical industry (26 industries, 337.6740 tons), accounting for 18.09% of the province’s total. Followed by the ferrous metal smelting and rolling processing industry (31 industries), pharmaceutical manufacturing industry (27 industries), food manufacturing industry (14 industries), agricultural and sideline food processing industry (13 industries), accounting for 10.99%, 10.19%, 9.42% and 8.47% of the province’s total, respectively. The 5 major industries accounted for 57.16% of TN emissions by key industries in Hunan. In 2022, the industry with the largest number of TP production was the petroleum, coal, and other fuel processing industry (25 industries, 118.7390 tons), accounting for 15.31% of the province’s total. Followed by the agricultural and sideline food processing industry (13 industries), wine, beverage and refined tea manufacturing industry (15 industries), food manufacturing industry (14 industries), chemical industry (26 industries), accounting for 14.63%, 12.10%, 11.49% and 9.35% of the province’s total, respectively. The 5 major industries accounted for 62.87% of TP production. The industry with the largest TP emission was the agricultural and sideline food processing industry (13 industries, 22.0520 tons), accounting for 28.76% of the province’s total. Followed by the food manufacturing industry (14 industries), Water production and supply industry (46 industries), wine, beverage and refined tea manufacturing industry (15 industries), Chemical industry (26 industries), accounting for 16.11%, 8.18%, 7.48%, 6.33% of the province’s total. The 5 major industries accounted for 66.85% of TP emission in Hunan. In 2022, the industry with the largest number of HM production was the non-ferrous metal mining and selection industry (09 industry, 145609.614 kg), accounting for 53.89% of the province’s total. Followed by non-ferrous metal smelting and rolling processing industry (32 industries), chemical industry (26 industries), accounting for 34.67% and 3.53% of the province’s total, respectively. The 3 major industries accounted for 91.21% of the total. The industry with the largest emission of HM was ferrous metal smelting and rolling processing industry (31 industries, 2035.193 kg), accounting for 45.12% of the province’s total. Followed by non-ferrous metal mining and selection industry (09 industries), non-ferrous metal smelting and rolling industry (32 industries), accounting for 39.07% and 6.80% of the total, respectively. The 3 major industries accounted for 91.00% of the total in Hunan. (2) Key industries in 2020-2022 Compared with 2020, the top 3 industries with a decrease in COD emissions were Instrumentation manufacturing (40 industries)9, other manufacturing (41 industries), and furniture manufacturing (21 industries), which decreased by 100%, 99.70%, and 94.21% in 2022 respectively. In AN Emissions were Furniture manufacturing (21 industries), instrumentation manufacturing (40 industries), and other manufacturing (41 industries), all decreased by 100%. In TN emissions were furniture manufacturing (21 industries), instrumentation manufacturing (40 industries), metal products, machinery and equipment repair (43 industries), all decreased by 100%. In TP emissions were furniture manufacturing (21 industries), instrumentation manufacturing (40 industries), and other manufacturing industries (41 industries), all decreased by 100%. In HM emissions were printing and recording media reproduction industry (23 industries), other manufacturing industry (41 industries), metal products, machinery and equipment repair industry (43 industries), all decreased by 100%. From 2020 to 2022, the industries with the largest average reduction rate of COD were the electricity and heat production and supply industry (44 industries), wood processing and wood, bamboo, rattan, brown and grass products industry (20 industries), and petroleum, coal and other fuel processing industry (25 industries), which were 97.64%, 97.59% and 97.34% respectively. The industries with the largest average reduction rate of AN were chemical industry (26 industries), petroleum, coal and other fuel processing industries (25 industries), power and heat production and supply industries (44 industries), which were 99.06%, 98.68% and 97.74% respectively. The industries with the largest average reduction rate of TN were power and heat production and supply industry (44 industries), chemical industry (26 industries), non-ferrous metal smelting and rolling processing industry (32 industries), 97.11%, 97.05% and 95.12%, respectively. The industries with the largest average reduction rate of TP were petroleum, coal and other fuel processing industries (25 industries), textile and apparel industries (18 industries) and textile industries (17 industries), which were 97.04%, 95.70% and 94.72%, respectively. The industries with the largest average reduction rate of HM were the general equipment manufacturing industry10 (34 industries), metal products industry (33 industries), non-ferrous metal smelting and rolling industry (32 industries), which were 99.91%, 99.72%, 99.54% respectively. 4.CONCLUSIONCompared with the ecological and environmental statistics in 2020 and 2021, the emissions of COD and AN from industrial sources and domestic sources had continued to decrease, indicating that the emission reduction work had achieved remarkable results. From the perspective of regional distribution, the top cities of COD emissions were Shaoyang, Yongzhou and Zhuzhou, respectively. Compared with 2020, the ranking of COD emissions of Changsha and Changde had moved down, mainly because some cities had built, renovated and expanded urban sewage treatment plants in recent years, resulting in a decrease in the emissions of domestic pollutants. Chenzhou, Yueyang and Zhuzhou City were the top cities in AN emission, mainly because the total number of urban domestic water in some cities had changed. The suggestions were put forward: (1) to optimize the functional layout of industrial parks and accelerate the transformation and upgrading of industrial parks; (2) to integrate into the overall situation of national development; (3) to strengthen support for basic industrial capabilities. REFERENCESJing, R.,
“Coordination degree of marine biological ecological resources based on multi-source monitoring data,”
Arabian Journal of Geosciences, 14
(7), 654
(2021). https://doi.org/10.1007/s12517-021-06993-9 Google Scholar
Du, P., Li, P. Y., Huang, H. G.,
“Inexact Chance-Constrained Waste-Load Allocation Model for Water Quality Management of Xiangxihe River,”
Journal of Environmental Engineering, 139
(9), 1178
–1197
(2013). https://doi.org/10.1061/(ASCE)EE.1943-7870.0000724 Google Scholar
Han, Z. W., Jiao, S., Zhang, X., et al.,
“Seeking sustainable development policies at the municipal level based on the triad of city, economy and environment: Evidence from Hunan province, China,”
Journal of Environmental Management, 290 112554
(2021). https://doi.org/10.1016/j.jenvman.2021.112554 Google Scholar
Sun, Y., Chen, Z., Wu, G. X., et al.,
“Characteristics of water quality of municipal wastewater treatment plants in China: Implications for resources utilization and management,”
Journal of Cleaner Production, 131 1
–9
(2016). https://doi.org/10.1016/j.jclepro.2016.05.068 Google Scholar
Wang, X., Shen, C. Q., Wei, J., et al.,
“Study of ecological compensation in complex river networks based on a mathematical model,”
Environmental Science and Pollution Research International, 25
(23), 22861
–22871
(2018). https://doi.org/10.1007/s11356-018-2316-4 Google Scholar
Qin, X., Sun, C., Han, Q., et al.,
“Grey Water Footprint Assessment from the Perspective of Water Pollution Sources: A Case Study of China,”
Water Resources, 46
(3), 454
–465
(2019). https://doi.org/10.1134/S0097807819030187 Google Scholar
Lukichev, A.,
“Graphical method for the Debye-like relaxation spectra analysis,”
Journal of Non-Crystalline Solids, 358
(3), 447
–453
(2012). https://doi.org/10.1016/j.jnoncrysol.2011.10.022 Google Scholar
Han, D. C., Huang, G., Liu, L. R., et al.,
“Multi-regional industrial wastewater metabolism analysis for the Yangtze River Economic Belt, China,”
Environmental Pollution, 284 117118
(2021). https://doi.org/10.1016/j.envpol.2021.117118 Google Scholar
Song, J.N., Dong, X.C., Yang, W., et al.,
“Environmental co-benefits of energy recovery from wastewater of typical industrial sectors from life cycle perspective: Regional potentials in China,”
Energy Conversion and Management, 293 117450
(2023). https://doi.org/10.1016/j.enconman.2023.117450 Google Scholar
Li, Q. G., Liu, G. H., Qi, L., et al.,
“Heavy metal-contained wastewater in China: Discharge, management and treatment,”
Science of the Total Environment, 808 152091
(2022). https://doi.org/10.1016/j.scitotenv.2021.152091 Google Scholar
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