In this study, a customized WQI (Seoul water quality index, S-WQI) for urban rivers that can ultimately reflect their characteristics was developed by modifying and supplementing the existing Bascarόn WQI calculation method through linkage with statistical methods such as factor analysis. We used the water quality data generated monthly at 17 water quality monitoring networks (WQMNs) in Seoul for 18 years, from 2002 to 2019. Result of a research, the monthly S-WQI showed an average 70 out of 100, ‘good (II)’ grades, whereas the average water quality grade according to the environmental standards was ‘slightly good (II),’ with an R2 value of 0.8298. The annual S-WQI was found to be 39 (bad) to 97 (very good), with an average of 72 (good). Through this study, S-WQI, a customized WQI for urban rivers, was judged to be a reasonable index that can represent the characteristics of urban river water quality. This is because it is easy to apply and is a calculation method that uses relatively fewer water quality items than the WQI calculated in the past, and it is highly likely to be linked to the currently implemented water quality grade system. In addition, to extend the application of WQI to various water quality survey points, based on the calculation methodology performed to derive the indices in this study, such as modified S-WQI (MS-WQI), by adding new water quality items and changing some items, it is also possible to develop an advanced customized WQI for urban rivers considering watershed characteristics and measurement items.

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Objectives : In this study, a learning-based optimization method is proposed and implemented for determining new monitoring sites when expanding the roadside air pollution monitoring network. Utilizing the bigdata available in Seoul, this decision-making tool is developed that takes into account the objectives of selecting new monitoring sites and incorporates social, economic, and environmental characteristics. The optimized results can suggest potential locations for new roadside air pollution monitoring sites. Additionally, the capability of this tool to facilitate objective decision-making processes is evaluated by determining the influence range providing reliable air pollution information with the addition of the new monitoring sites.Methods : The proposed learning-based optimization algorithm is a new approach for selecting the new optimal monitoring sites by comprehensively considering social, economic, and environmental factors aligned with the installation purpose of the monitoring system in Seoul. The algorithm starts with genetic algorithms to select candidate locations for new monitoring sites that maximize the influence area of the expanded monitoring network compared to the existing monitoring network, capture a high overall level of air pollution, and do not overlap with the existing monitoring network. After that, PROMETHEE method is applied to evaluate the solutions generated by the genetic algorithm and choose the final solution that best fits six evaluation factors (Information entropy, number of new monitoring sites, distance from point sources, wind speed, traffic volume, and population) to be considered when installing new monitoring sites.Results and Discussion : The learning-based optimization algorithm selects 10 potential new monitoring sites adding to the existing roadside air pollution monitoring network having 15 monitoring sites. The explainable spatiotemporal range of the air pollution information that can be expected after the installation of the new monitoring sites is quantified to cover 84.33% of Seoul, reducing the uncertainty of the air pollution information of existing monitoring network by 26.15%. The final solution, selected from several solutions, can get new optimal roadside air pollution monitoring sites reflecting the regional characteristics of Seoul and the installation purpose of the monitoring system by having a small number of newly established monitoring locations, being close to air pollution emissions facilities, and having a high population and traffic volume.Conclusion : The proposed learning-based optimization method, using relevant variables for the installation purpose of the monitoring system, can derive the objective solution for deciding new monitoring locations of the roadside air pollution monitoring network, considering additional social factors as opposed to urban air pollution monitoring network. The final solution obtained through the optimization algorithm has great potential for future use, as it can guide to determine practical and feasible new monitoring sites with additional on-site verification. Furthermore, this optimized approach can be applied widely during the decision-making process for the expansion of other environmental monitoring networks.