1. Introduction
In recent years, with the rapid development of the social economy, environmental problems have become increasingly severe, especially water pollution. Along with the rapid economic and social developments and urbanization processes, water-quality issues caused by water pollution has become an increasingly severe threat to people’s survival and development. The global environment includes water, atmospheric, soil, biological, and other natural environments. In these environments, the water environment is the basis and guarantee of human production and life. On the one hand, with economic development, social development, and industrialization process acceleration, more and more wastewater and toxic substances from the production process are entering the environment without adequate treatment. On the other hand, high urbanization has caused excessive population density rates, and the detergent in domestic wastewater has also had a severe impact on the water environment. People’s requirements for high environmental quality have also improved with the improvement of material living standards.
However, the water environmental quality at present not only restricts the development of the social economy, but also has an important impact on the quality of the daily life of residents [
1,
2]. Water environmental quality restricts the high degree of urbanization and directly affects environmental water quality. At present, both developed and developing countries are facing different degrees of environmental water pollution. In China, the amount of sewage discharged from production and living scenarios is seriously threatening the water environment’s safety. Although, in recent years, all regions have actively conducted watershed management, pollution levels are still high. The water quality of the Yellow, Yangtze, Huaihe, and other water systems is polluted to varying degrees [
3]. The deterioration of water quality has become a significant problem for the water environment, at present. Eutrophication, hypoxia, and toxic algae outbreaks have become urgent problems to be solved in water bodies, such as rivers, lakes, reservoirs, and estuaries. Based on this practical background, the management and control of the water environment in China have gradually changed from concentration and target total amount control to capacity complete amount control. Water environmental management is also listed as an essential factor in urban development planning.
In this research, the water environmental capacity for different seasons is calculated by using the WASP model according to the water function zoning and water quality of the Lushui River. Meanwhile, the Gini coefficient method is applied to the allocation of discharge permits. It selects four representative criteria chosen in the EGC method, population, land area, gross domestic production (GDP), and water environmental capacity to represent social, economic, water, and other factors, respectively. Based on GIS spatial analysis technology, the distribution of unfair factors that cause pollution inequity is investigated. Then, according to the pollution load status of each management unit for the Lushui River at present, its reduction is analyzed and discussed, and the corresponding pollution control measures are proposed, promising scientific suggestions for water-quality management [
4,
5,
6].
The Lushui River Basin is chosen as our research area, located in the Hunan-Jiangxi provinces in China. Its area is approximately 5675 km
2, of which 2278 km
2 is located in Jiangxi and 3397 km
2 is in Hunan. Water-quality model development can quantitatively describe the transformation and migration processes of pollutants in water bodies, providing water environmental planning and management tools [
7,
8,
9,
10].
Therefore, based on the Zhuzhou City Plan of Yangtze River and Ecological Environmental Protection Technology program project number 2019-LHYJ-01-0212-31, and the Risk Assessment of Ecological Environment Problems and Investigation in Key Cities of Yangtze River in Economic Zone program project number 2018CJA030301-036, this paper uses the Lushui River Basin as the research object. After investigating and analyzing the situation of the main rivers to date, the environmental Gini coefficient allocation method is used to allocate the COD, AN, and TP discharge permits. Based on GIS spatial analysis technology, the distribution of unfair factors that cause pollution inequity are investigated to provide a theoretical basis and decision-making reference for the environmental water management and protection of the Lushui River [
11,
12,
13,
14].
4. Discussion
Table 6,
Table 7 and
Table 8 show the EGCs for each indicator before and after optimizations. After the optimization, the EGC of COD by land area was 0.30, EGC by population was 0.21, EGCs by environmental capacity was 0.02, and EGC by GDP was 0.45. After the optimization, the sum of the EGCs was 0.962. This result shows that the Gini coefficient does not change considerably. Therefore, the most significant change in the Gini coefficient was for land area, which presented a greater rate of change than the other indicators.
Table 10 shows the distributions of COD, AN, and TP emission permits for each control unit. It considers each control unit’s natural, economic, and other objective factors. The environmental Gini coefficient method is the most equitable scheme after allocating units (units of population, GDP, land area, environmental capacity) based on the index system.
Figure 6a–c shows the Lorenz curves for the optimized multi-indicator system. The optimized EGC and Lorenz curves did not change much because the indicators, such as population and GDP, were unequal for each control unit. The order of the control units in the Lorenz curves was different. In addition, each control unit had an upper and lower limit for reducing wastewater discharge. Considering the constraints in the EGC method, the shape of the Lorenz curve could only be adjusted progressively, which was why there was no significant change in the EGC after optimization.
Based on the population, GDP, and the reciprocal of pollution intensity, in the distribution of wastewater discharge permits using the Gini coefficient allocation method, each control unit’s total allocation of COD, AN, and TP discharge permits under the present pollution load conditions was analyzed. The water environmental capacity was obtained by calibrating the parameters on the WASP model using the trial-and-error method; however, some researchers used the formula to calculate the water environmental capacity. Based on the environmental Gini coefficient method, we performed the optimization after the calculation of the EGC for each indicator to construct the discharge permit allocation, and we used four indicators, such us population, land area, GDP, and environmental capacity, to represent the social, economic, and environmental factors, respectively. In the other commonly used method, three indicators were used to construct the discharge permit allocation.
It can be observed from
Table 6,
Table 7 and
Table 8 that most Gini coefficients are lower than 0.4, except for COD based on GDP after optimization; therefore, it is considered that the distribution results are reasonable. It can be observed from
Table 10 that the total discharge permits allocated of COD, AN, and TP to the Lushui River Basin were 51,483.304, 843.119, and 340.926 tons/year, respectively.
Comparing the allocation scheme and present status of pollution control, the allocation scheme was consistent with the actual pollution control capacity. The total amount of pollutants discharged in the Lushui watershed was not the highest. However, pollution control facilities are imperfect due to the relatively slow economy. Therefore, the pollution control efficiency was low and the environmental capacity was relatively small. Consequently, it was more important for us to cut a large proportion of this result. When using COD as an example, it can be observed that the calculation showed that the most apparent distribution inequity (
Table 6) appeared in the land area where EGC equaled 0.64 and GDP equaled 0.63. The high Gini coefficient indicated that COD emission source distributions were incompatible with the natural conditions.
With the pollution control division as the primary statistical unit, we calculated the contribution coefficient of the six pollution control divisions in the area to investigate the spatial differences of the contribution coefficients for each division (see
Table 11). Additionally, based on GIS spatial analysis technology, the distribution of unfair factors that caused pollution inequity was investigated. The results are shown in
Table 11 and
Figure 7,
Figure 8 and
Figure 9.
For COD, the contribution coefficients based on the EC values in CU-1, CU-2, CU-3, and CU-6 were less than 1; only CU-3 was greater than 1. The contribution coefficients based on land in CU-1, CU-3, CU-5, and CU-6 were greater than 1, and CU-2 and CU-4 were less than 1. The contribution coefficients based on the GDP in CU-1, CU-3, and CU-6 were greater than 1; CU-2, CU-4, and CU-5 were less than 1. Additionally, the contribution coefficients based on the population in CU-2, CU-3, and CU-5 were greater than 1, and in CU-1, CU-4, and CU-6, they were less than 1.
For AN, the contribution coefficients based on the EC in CU-3 and CU-4 were greater than 1; CU-1, CU-2, CU-5, and CU-6 were less than 1. The contribution coefficients based on land in CU-3, CU-4, CU-5, and CU-6 were greater than 1, and CU-1 and CU-2 were less than 1. The contribution coefficients based on the GDP in CU-1, CU-3, CU-4, and CU-6 were greater than 1; CU-2 and CU-5 were less than 1. Additionally, the contribution coefficients based on the population in CU-2, CU-3, CU-4, and CU-5 were more significant than 1; CU-1 and CU-6 were less than 1.
For TP, the contribution coefficients based on the EC in CU-1. CU-2, CU-3, and CU-6 were greater than 1, and CN-4 and CN-5 were less than 1. The contribution coefficients based on land in CU-1, CU-2, CU-3, and CU-5 were greater than 1, and only CU-4 was less than 1. The contribution coefficients based on the GDP in CU-1, CU-2, CU-3, and CU-6 were greater than 1; CU-2 and CU-5 were less than 1. Additionally, the contribution coefficients based on the population in CU-1, CU-2, CU-3, and CU-6 were greater than 1, and CU-4 and CU-5 were less than 1.
All the areas with the highest contribution coefficients for COD, AN, and TP were mainly concentrated in the pollution control areas and exhibited a green development model. Additionally, some areas presented the lowest contribution coefficients for COD, AN, and TP, the main factor causing inequality. The economic contribution coefficients of some control divisions for each indicator were the lowest, while their land area contribution coefficients were the largest. It can be seen that the unfair factors based on GDP in the area were mainly concentrated in the mountainous and hilly ecological conservation areas. In contrast, the unfair factors based on the land area primarily focused on central urban pollution control areas.
By analyzing the contribution coefficients of different indicators, it was necessary to adjust the region’s industrial structure as soon as possible, improve resource utilization efficiency, reduce pollution emissions, and take the path of sustainable development.
Natural self-purifying capacity is not effectively used in some districts and is overused in others; among other indicators, it is the most inequitable of all factors. The EGC based on the land area would be smaller, and the COD distribution would be more reasonable. Lushui has some old industrial land in the transition period of industrialization and urbanization, which can realize the transformation of industrial structures and reduce industrial pollution to a greater extent. According to the magnitude of the environmental Gini coefficient of each control unit, the inequitable factors of regional development were analyzed from the four indicators presented. This made it possible to complete some measures to increase the environmental capacity, thus increasing the amount of environmental health risk zones in Lushui. In this way, it was possible to interact with overall urban and industrial planning schemes. Additionally, improving the environment and controlling pollution without affecting urban construction and economic development is possible.
Based on the above analysis, for the present situation of pollution sources and water quality in the area where the lower reaches of the Lushui River are located, we focused more on the water quality during the seasons presenting high pollution levels for COD, AN, and TP, and strengthened the pollution reduction and control measures in the urban section of Lushui River. This was performed to improve the prevention, management, and supervision of industrial pollution sources; speed up the construction of sewage pipe networks and improve the quality of sewage pipes and network coverage; strictly investigate enterprise pollution sources; eliminate the occurrence of stealing, mixing, and missing discharge; strengthen the prevention and control of surface pollution; promote ecological and healthy breeding; optimize the industrial structure; conduct the comprehensive management of the water environment; improve the self-purification capacity of water bodies and environmental capacity; strengthen public participation and social supervision; and strictly enforce environmental regulations and strengthen environmental water management. Applying the Gini coefficient allocation method to the allocation of water environmental capacity is still not the optimal method; although, the Gini coefficient calculation results are more equitable. Therefore, subsequent studies should determine a more appropriate allocation method. Additionally, by analyzing the contribution coefficients of different indicators, it was necessary to adjust the industrial structure of the region as soon as possible; therefore, the Gini coefficient method needed to improve the efficiency of resource utilization. This will support the evaluation of pollution emission reductions and promote sustainable development.
This research focused on analyzing water environmental capacity, investigating pollution reduction, and distributing unfair factors for pollution inequity analysis. It provided us with references for water environmental protection and management practices for the Lushui River as well as other areas.
The method used in this study can also be used in the other areas and can use the results as a reference for subsequent research.