Time and Space Differences of Water Environmental Quality of the Mangrove Wetland Park in Nansha:Based on the Improved Twice-slope Clustering Method
Yan Song1,2(),Songjun Xu1(),Xianzhao Liu2,Yong Zhang2,Penghua Qiu3,Anyi Niu1,Guanchang Xu1
1. College of Geography Science,South China Normal University,Guangzhou 510631, Guangdong,China 2. Department of Geography Science,Hunan University of Science and Technology,Xiangtan 411201, Hunan, China 3.College of Geography and Tourism,Hainan Normal University,Haikou 571158, Hainan,China
This study constructs the wider twice-slope clustering membership functions of the clustering index — dissolved oxygen (DO), based on the nondimensionalization of the monitoring results of water environment and the pollution classification standard. The physical chemistry indexes (e.g., temperature, pH, DO, COD, BOD5, TP and TN) of eight sampling sites were measured in the Nansha Mangrove Wetland Park Ⅰ, Ⅱ and the brook nearby at the abundant, medium and dry seasons in 2007 and 2014. The comparison of the monitoring and evaluation results of the eight sampling points in 2007 and 2014 shows the following findings: on the whole, the water quality in the study area is in the health or sub-health state (level Ⅱ-Ⅲ or level Ⅱ-Ⅳ). Since its opening to outside as a tourist scenic spot, the Mangrove Wetland Park Ⅰ has shown some changes in the indexes of water environment in 2008-2014. The monitoring results demonstrate that the change in pH is very small. At the same time, the DO has decreased, while the BOD5 has increased. A decrease is also observed in the COD content, by contrast, the TP and TN contents have shown a trend of increase. In 2007, the water quality of the sampling points W1, W2 and W3 in the Nansha Mangrove Wetland Park Ⅰ was better than that of the sampling points W7 and W8 in the brook. The water quality of the sampling points W7 and W8 in the brook was better than that of the sampling points W4, W5 and W6 in the Nansha Mangrove Wetland Park Ⅱ. In 2014, the water quality of the sampling points W4, W5 and W6 showed a dramatic improvement and was better than that of the sampling points W1, W2 and W3 in the Nansha Mangrove Wetland Park Ⅰ and the sampling points W7 and W8 in the brook. Among the eight sampling sites, W6 in the Nansha Mangrove Wetland Park Ⅱ showed the greatest improvement, while the sampling point W3 in the Nansha Mangrove Wetland Park Ⅰ observed the greatest declining in water quality. Although the water quality of the brook was improved at abundant season in 2014, the water quality of the Mangrove Wetland Park, at either abundant or dry seasons, is at least not less than and in most cases superior to the water quality of the brook. The general rule of the time and space differences of the water environmental quality in the study area is that the water quality of the Mangrove Wetland Park Ⅱ has been improved, while that of the Mangrove Wetland Park Ⅰ shows declining a trend in 2007-2014.
. 南沙红树林湿地公园水环境质量时空差异分析——基于改进后倍斜率聚类分析的视角[J]. 地理科学,
2016, 36(2): 303-311.
Xianzhao Liu et al
. Time and Space Differences of Water Environmental Quality of the Mangrove Wetland Park in Nansha:Based on the Improved Twice-slope Clustering Method[J]. SCIENTIA GEOGRAPHICA SINICA,
2016, 36(2): 303-311.
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<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">A grey fuzzy optimization model is developed for water quality management of river system to address uncertainty involved in fixing the membership functions for different goals of Pollution Control Agency (PCA) and dischargers. The present model, Grey Fuzzy Waste Load Allocation Model (GFWLAM), has the capability to incorporate the conflicting goals of PCA and dischargers in a deterministic framework. The imprecision associated with specifying the water quality criteria and fractional removal levels are modeled in a fuzzy mathematical framework. To address the imprecision in fixing the lower and upper bounds of membership functions, the membership functions themselves are treated as fuzzy in the model and the membership parameters are expressed as interval grey numbers, a closed and bounded interval with known lower and upper bounds but unknown distribution information. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for different membership functions, specified for different imprecise goals are interval grey numbers in place of a deterministic real number. In the final solution optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. Application of the GFWLAM is illustrated with case study of the Tunga–Bhadra river system in India.</p>
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Abstract A previously developed fuzzy waste load allocation model (FWLAM) for a river system is extended to address uncertainty involved in fixing the membership functions for the fuzzy goals of the pollution control agency (PCA) and the dischargers using the concept of grey systems. The model provides flexibility for the PCA and the dischargers to specify their goals independently, as the parameters for membership functions are considered as interval grey numbers instead of deterministic real numbers. An inexact or a grey fuzzy optimization model is developed in a multiobjective framework, to maximize the width of the interval valued fractional removal levels for providing latitude in decision-making and to minimize the width of the goal fulfillment level for reducing the system uncertainty. The concept of an acceptability index for order relation between two partially or fully overlapping intervals is used to get a deterministic equivalent of the grey fuzzy optimization model developed. The improvement of the optimal solutions over a previously developed grey fuzzy waste load allocation model (GFWLAM) is shown through an application to a hypothetical river system. The fuzzy multiobjective optimization and fuzzy goal programming techniques are used to solve the deterministic equivalent of the GFWLAM.
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The species composition and characteristics of mangrove flora in the world were reviewed and discussed. The results suggested that the world's mangrove plants have 84 species (including 12 varieties) in 24 genera and 16 families. Of which, true mangrove plants have 70 species (including 12 varieties) in 16 genera and 11 families, and semi_mangrove plants 14 species in eight genera and five families. The Eastern Group has 74 species (including 12 varieties) in 18 genera and 14 families, characterized by the genera Aegiceras , Osbornia , Aegialitis , Bruguiera , Ceriops , Kandelia , Scyphiphora and Nypa etc. The Western Group has only 10 species in six genera and five families, characterized by the endemic one_species family, Pelliceraceae, and the genus Laguncularia . The mangrove flora of China is composed of 26 species (including one variety) in 15 genera and 12 families, four of which are endemic. Hainan is most rich in mangrove species, making up about 96.2% of the Chinese total; Guangdong ranks second, making up about 42.3%. It has been demonstrated that Rhizophora stylosa was mistaken for R. mucronata in Taiwan by previous authors.