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Allocation of Marine Environmental Carrying Capacity in the Xiamen

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Allocation of Marine Environmental Carrying Capacity in the Xiamen Marine Pollution Bulletin 75 (2013) 21–27 Contents lists available at ScienceDirect Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul Allocation of marine environmental carrying capacity in the Xiamen Bay ⇑ Enhui Liao a,b, Yuwu Jiang a, , Xiao-Hai Yan a,b, Zhaoyun Chen a, Jia Wang a, Luoping Zhang a a State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361005, China b College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA article info abstract Keywords: Three optimization methods are employed to allocate Marine Environmental Carrying Capacity (MECC) in Marine environmental carrying capacity the Xiamen Bay. The hydrodynamic and pollutant fields are first simulated by the Princeton Ocean Model. Classical linear optimization Taking phosphorus as an index of the water quality, the response fields are then calculated. These Fuzzy optimization response fields represent the relationship between the concentration of the sea zone and the pollution Grey fuzzy optimization sources. Finally, MECC is optimized and distributed in the Xiamen Bay by three optimization methods. Numerical model The results show classical linear optimization can only maximize the satisfaction level for one of the stake Xiamen Bay holders’, e.g., dischargers or environmental protection bureau, satisfaction level. However, the fuzzy and grey fuzzy optimizations can provide a compromise, and therefore a fairer result, by incorporating the conflicting goals of all of the different stakeholders. Compared with fuzzy optimization, the grey fuzzy optimization provides a more flexible choice for the decision-makers. Ó 2013 Elsevier Ltd. All rights reserved. 1. Introduction it predicts that the water quality standards can be met (Lee et al., 2008, 2005; Nikolaidis et al., 2006). Unfortunately, this approach Xiamen, located in semi-closed Xiamen Bay, is known for its is time-consuming and may ignore some feasible combinations beautiful sea beaches. Over the past 30 years, the economy has of load reductions (Jia and Culver, 2006). As a result, an optimiza- been growing rapidly in China. Xiamen, being the major city in tion method with the capability to efficiently determine the best southeast China, has benefitted from this rapid economic develop- allocation scenario is adopted. Han et al. (2011) and Deng et al. ment, but it also has suffered from significant environmental dete- (2010), using a water quality model, employed classical linear opti- rioration. For example, the concentration of Active Phosphate (AP, mization to allocate MECC based on the response fields. However, PO4-P) reaches a level of 0.06 mg/L, which is 2 times larger than the this method cannot incorporate the conflicting objectives of the Chinese sea water quality standards allows, 0.03 mg/L. This serious dischargers and Environmental Protection Bureau (EPB). In addi- problem indicates that significant aquatic pollution management is tion, objectives such as specifying water quality criteria and frac- urgently required. Estimation and allocation of Marine Environ- tional removal levels are generally vague. Therefore, fuzzy mental Carrying Capacity (MECC), defined as the greatest pollutant optimization was proposed to incorporate these conflicting goals load that marine environment can receive without violating water and quantify the vagueness using a membership function (Sasiku- quality goal, is very important for aquatic pollution management. mar and Mujumdar, 1998). Furthermore, in order to best address As a first step, it is necessary to assess environmental water quality the uncertainty, Karmakar and Mujumdar (2006) proposed using and collect pollution source data. The National Fund Project, the grey fuzzy optimization. The fixed upper and lower bounds of study of marine environmental quality assessment and marine the membership functions (fuzzy membership parameters) could environmental carrying capacity in the Xiamen Bay, is carried out be relaxed by treating them as fuzzy and the membership param- by Xiamen University (Cui and Zhang, 2009; Tu et al., 2009; Wang eters are expressed as interval grey numbers, a closed and bounded et al., 2010; Zhang et al., 2010). Once enough data has been col- interval with known lower and upper bounds but unknown distri- lected, numerical ocean model is conducted to study the transport bution information (Karmakar and Mujumdar, 2006). The fuzzy and fate of pollutant and MECC is allocated without violating water optimization and grey fuzzy optimization have been successfully quality goals. used in river watersheds to allocate pollutant discharge amounts There are many approaches to allocate MECC. The trial-and-er- (e.g. Karmakar and Mujumdar, 2006; Sasikumar and Mujumdar, ror approach (load sensitive testing) would be to reduce the pollu- 1998). However, these methods have yet to be applied in a coastal tant loads of various sources in an eco-hydrodynamic model until area, such as Xiamen Bay, which is controlled by both river and ti- dal processes. In order to solve the environmental problem in Xiamen Bay, ⇑ Corresponding author. Tel.: +86 592 2185510; fax: +86 592 2880108. E-mail address: [email protected] (Y. Jiang). these optimization methods are applied in this paper to allocate 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.08.023 22 E. Liao et al. / Marine Pollution Bulletin 75 (2013) 21–27 MECC to each pollution source and their utility will be evaluated. plants and electric power plants) and the others are generated The paper is organized as follows: The model and method are from merging non-point sources. The Jiulong River (Source 22) introduced in Section 2, the water quality simulation and response and Dongxi River (Source 3) provide the largest pollutant loads fields are outlined in Section 3 and the results of the different opti- to the system. mization methods are discussed and compared in Section 4. 2.2. Response fields 2. Model and methods The response field is the concentration field due to a pollution source with a unit load of 1.0 g/s (Deng et al., 2010; Han et al., 2.1. Numerical ocean model 2011). The concentration caused by the total load of the pollution source equals the product of the response field and the magnitude An improved 3D ocean model based on the Princeton Ocean of the total load. Therefore, the total pollutant concentration field Model (Blumberg and Mellor, 1987) is used in this paper. First, is the sum of the concentration of all pollution sources under total the Alternating Direction Implicit method, which enhances the load. The relationship between concentration and load of pollution model’s numerical stability, is applied in the external part of the sources can be built with response field as model (Chau and Jiang, 2001, 2003; Jiang et al., 1997; Leendertse, 1970). Second, a two-way nested-grid strategy (Oey and Chen, Xn C P Q 2 1992) is adopted to allow for increased resolution in specific sub- i ¼ ði;jÞ j ð Þ i¼1 domains. Third, in order to study tidal areas in the Xiamen Bay, the model employs a dry and flood technology (Jiang and Wai, where i is the sea zone number, j is the source number, Qj (g/s) is the 3 3 2005) which allows some grid cells to go dry during a portion of pollutant load of source j, P(i,j) ((g/m )/(g/s), that is s/m ) is pollutant the tidal cycle. In addition, a Geographic Information System is concentration per unit load and can be defined as the response field 3 adopted for pre- and post-processing (Jiang et al., 2004). The im- of source j in sea zone i, and Ci (g/m or mg/L) is the concentration in proved model has been successfully used in the Taiwan Strait sea zone i. and its adjacent regions including Xiamen Bay (Jiang et al., 2011,1997; Lin et al., 2007). In this application, because Xiamen 2.3. Optimization methods Bay is 70 km-long and 50 km-wide, the averaged depth is about 10 m, and the bay is dominated by tidal processes with an average The water quality management is viewed as a multiple objec- tidal range of 4 m. The stratification in the Xiamen Bay is weak and tives optimization problem accounting for the conflicting goals of there are little differences between surface and bottom distribu- the different stakeholders (e.g., EPB and dischargers). The aspira- tions of pollutants, temperature, and salt (Wang et al., 1998). In tion of EPB is to improve water quality by imposing higher stan- addition, only horizontal response coefficients and distribution of dards, while the dischargers prefer to minimize waste treatment pollutant is needed in the study. Therefore, the two-dimensional costs by using the assimilative capacity of the environment (Sas- version of the improved model is chosen for modeling efficiency. ikumar and Mujumdar, 1998). In this study, three types of optimi- The equation for the pollutant/tracer transport can be written: zation (classic linear optimization, fuzzy optimization and grey fuzzy optimization) are applied and their relative merits are @C @C @C @ @C @ @C evaluated. þ u þ v ¼ Dx þ Dy þ KC þ Sc ð1Þ @t @x @y @x @x @y @y Because of serious pollution problem in Xiamen Bay, if higher water quality standard was set as the goal, the reduction of pollu- where C is the pollutant concentration; t is the time; x and y are the tant load is too large to achieve. As a result, classic linear optimiza- latitudinal and longitudinal coordinates; u and are the velocity v tion is set to maximize the total pollutant load subject to strict components in the x and y directions, respectively; D and D are x y constraints in the paper. In another word, while the method can the spatially varying diffusion coefficients; K is the linear, compre- represent the dischargers’ interests, it cannot fully represent EPB’s hensive degradation coefficient; and S represents all pollution c aspiration of simultaneously imposing higher standards to im- sources.
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