An Integrated Methodology Using Extended Swara and Dea for the Performance Analysis of Wastewater Treatment Plants: Turkey Case

Influencing Factors of Stereotypes on Wastewater Treatment Plants- Case Study of 9 Wastewater Treatment Plants in Xi'an, China

As an indispensable part of cities, wastewater treatment plants play an important role in environmental protection and urbanization. However, the promotion of wastewater treatment plants has been consistently hindered by residents' negative stereotypes and rejections, which is called "Not-In-My-Back-Yard" (NIMBY) effect. This study collected the first-hand data with the residents residing within 3 kilometers of 9 wastewater treatment plants in Xi'an, China through a survey. Keyword co-occurrence network analysis was conducted and the results illustrate that residents have stereotypes toward wastewater treatment plants. There are two types of residents' stereotypes toward wastewater treatment plants: positive and negative. The positive stereotypes of wastewater treatment plants in turn can be subdivided into the three categories of treatment technologies, treatment results, and social impacts. But the negative stereotypes didn't demonstrate meaningful categories. We also tried to identify the influencing factors that cause residents' stereotypes. The distance from residents' residence to the wastewater treatment plants has impacts on the stereotypes of residents' who reside within 1000 meters of the wastewater treatment plant: the farther from the wastewater treatment plants their residence is, the more positive their stereotypes are. We also found that the more educated the participants are, the more positive stereotypes of wastewater treatment plants they have. Moreover, residents' stereotypes toward wastewater treatment plants are more influenced by formal education. Non-formal education and informal learning probably have less influence on the promotion of wastewater treatment plants. Therefore, we propose to incorporate environmental education for sustainable development into formal education to increase residents' acceptance of wastewater treatment plants.

A modified dynamic DEA model to assess the wastewater treatment efficiency: perspective from Yangtze River and Non-Yangtze River Basin

The wastewater treatment efficiency is crucial to constructing a livable ecological environment and promoting the sustainable development of economy and society. The differences in natural conditions, economic development and local policies between the Yangtze River Basin (YRB) and the Non-Yangtze River Basin (NYRB) increase the difficulty of wastewater treatment in governance. This study uses a modified Dynamic Data Envelopment Analysis (DEA) model to assess the wastewater treatment from 2013 to 2020, and divides the study period into two stages: the first stage (2013–2017) assesses the wastewater treatment efficiency of 18 provinces and cities in YRB and 12 provinces and cities in NYRB; the second stage (2018–2020) conducts statistical analysis of wastewater discharge pollutants in YRB and NYRB. The results conclude that the total wastewater treatment efficiency is generally low, but polarization is quite prominent. Among total wastewater treatment efficiency, NYRB scored 0.504, or slightly higher than YRB (0.398). In terms of expense efficiency, both NYRB and YRB scored below 0.4. In terms of chemical oxygen demand (COD) output efficiency, YRB (0.488) is better than NYRB (0.420). The second stage of statistical analysis presents that pollutant emissions are still high; the regions need to increase wastewater treatment investment and improve wastewater treatment efficiency.

Predictive modeling of the primary settling tanks based on artificial neural networks for estimating TSS and COD as typical effluent parameters

A predictive model based on artificial neural networks (ANNs) for modeling primary settling tanks' (PSTs) behavior in wastewater treatment plants was developed in this study. Two separate ANNs were built using input data, raw wastewater characteristics, and operating conditions. The output data from the ANNs consisted of the total suspended solids (TSS) concentration and chemical oxygen demand (COD) as predictions of PSTs' typical effluent parameters. Data from a large-scale wastewater treatment plant was used to illustrate the applicability of the predictive model proposal. The ANNs model showed a high prediction accuracy during the training phase. Comparisons with available empirical and statistical models suggested that the ANNs model provides accurate estimations. Also, the ANNs were tested using new experimental data to verify their reproducibility under actual operating conditions. The predicted values were calculated with satisfactory results, having an average absolute deviation of <20%. The model could be adapted to any large-scale wastewater plant to monitor and control the operation of primary settling tanks, taking advantage of the ANNs' learning capacity.

Towards Sustainable Management of Anchoring on Mediterranean Islands—Concession Support Concept

The focus of this paper is to define anchorage management model for concession planning purposes to provide quality support to experts in spatial planning when developing maritime spatial plans. The research aim is to develop an anchorage management model that includes decision and concession support concept. Decision support concept is defined in order to support the processes of identifying potential anchorage locations, their evaluation and comparison, and finally, the priority ranking and selection of locations for their construction. The final step is modelling the concession support concept that includes financial analysis to concession parameters definition. The problem of decision making and concession of the anchorage location selection is complex and ill-structured because of the unsystematic and ad-hoc decisions by all included stakeholders. Additionally, the involvement of several stakeholders’ groups with different preferences and background knowledge, a large amount of conflicting and seemingly incomparable information and data, and numerous conflicting goals and criteria impact final decisions. The proposed concepts overcome the above obstacles in order to enable the construction of anchorages in a way of optimal use of maritime space. The model is tested on the island of Brač, Croatia. The methods used to solve the task are SWARA (The Stepwise Weight Assessment Ratio Analysis) for defining the criteria weights and ELECTRE (Elimination and Choice Expressing Reality) for ranking anchorage locations.