Optimizing Green-Gray Infrastructure for Non-Point Source Pollution Control under Future Uncertainties

Multi-objective optimization of the spatial layout of green infrastructures with cost-effectiveness analysis under climate change scenarios

Green infrastructure (GI) plays a significant role in alleviating urban flooding risk caused by urbanization and climate change. Due to space and financial limitations, the successful implementation of GI relies heavily on its layout design, and there is an increasing trend in using multi-objective optimization to support decision-making in GI planning. However, little is known about the hydrological effects of synchronously optimizing the size, location, and connection of GI under climate change. This study proposed a framework to optimize the size, location, and connection of typical GI facilities under climate change by combining the modified non-dominated sorting genetic algorithm-II (NSGA-II) and storm water management model (SWMM). The results showed that optimizing the size, location, and connection of GI facilities significantly increases the maximum reduction rate of runoff and peak flow by 13.4 %-24.5 % and 3.3 %-18 %, respectively, compared to optimizing only the size and location of GI. In the optimized results, most of the runoff from building roofs flew toward green space. Permeable pavement accounted for the highest average proportion of GI implementation area in optimal layouts, accounting for 29.8 %-54.2 % of road area. The average cost-effectiveness (C/E) values decreased from 16 %/105 Yuan under the historical period scenario to 14.3 %/105 Yuan and 14 %/105 Yuan under the two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, respectively. These results can help in understanding the optimization layout and cost-effectiveness of GI under climate change, and the proposed framework can enhance the adaptability of cities to climate change by providing specific cost-effective GI layout design.

Optimization and trade-off framework for coupled green-grey infrastructure considering environmental performance

Implementing runoff control infrastructure has been regarded as an efficacious measure in stormwater management. The issue of its cost-effectiveness is a primary concern for decision makers since it is an exorbitant investment. However, most of existed studies only concentrated on the cost-effectiveness optimization of runoff control infrastructure, especially green infrastructure, between hydrological and economic aspects, and therefore, the potential layout scenarios with high extra environmental benefits could be neglected in the traditional two-dimensional frameworks. In this study, a novel carbon dioxide equivalent-based index was quantified to represent the extra environmental benefits of runoff control infrastructure besides stormwater management and was further integrated into the assessment framework. The effectiveness of green and grey infrastructure was comprehensively evaluated and traded off between hydrological, environmental and economic aspects. The results demonstrated that grey infrastructure is a better measure than green infrastructure when only hydrological (HF index) and economic (CI index) performances were considered. Nevertheless, the environmental performance (EROI index) of green infrastructure prevails over grey infrastructure, and when optimizing green and grey infrastructure simultaneously in the three-dimensional framework considering environmental effectiveness, green infrastructure is comparable with grey infrastructure. Furthermore, an appropriate composition of coupled green-grey infrastructure is requisite, which could achieve an optimal trade-off between hydrological and environmental effectiveness. The sources of environmental benefits were also identified and analyzed from three representative preference scenarios. The findings of the study could serve as a trade-off basis between green and grey infrastructure, as well as between EROI and HF.

Determination of Pollution and Environmental Risk Assessment of Stormwater and the Receiving River, Case Study of the Sudół River Catchment, Poland

Changes in the land use of urban catchments and the discharge of stormwater to rivers are causing surface water pollution. Measurements were taken of the quality of discharged stormwater from two areas with different types of development: a residential area and a residential-commercial area, as well as the quality of the Sudół River water below the sewer outlets. The following indicators were studied: TSS, COD, N-NO₃, N-NO₂, TKN, TN, TP, Zn, Cu, Hg, HOI, and PAHs. The influence of land use on the magnitudes of flows in the river was modeled using the SCS-CN method and the Snyder Unit Hydrograph Model. The results showed an increase in sealing and a resulting increase in surface runoff. Concentrations of pollutants in stormwater and analysis of the potential amounts of loadings contributed by the analyzed stormwater outlets indicate that they may be responsible for the failure to meet environmental targets in the Sudół River. Environmental risk assessment shows that the aquatic ecosystem is at risk. A risk factor indicating a high risk of adverse environmental effects was determined for N-NO₃, Zn, and Cu, among others.

A new LID spatial allocation optimization system at neighborhood scale: Integrated SWMM with PICEA-g using MATLAB as the platform

Despite the growing interest, limited studies have been conducted on LID spatial allocation optimization (SAO) at neighborhood scale, and no study has applied modifications to the optimization algorithm to improve its performance. In this study, such a new LID SAO system was proposed, which integrated a hydrological computing engine (SWMM) with an optimization algorithm (PICEA-g) using a programming language (MATLAB) as the platform. The specific modifications to the PICEA-g algorithm include: new methodologies for initializing candidate solutions, defining goal vector boundaries and enhanced genetic operators. The new LID SAO system was tested in a typical urban residential neighborhood in western Canada, and optimal solutions for LID implementation (bioretention, rain garden, permeable pavement and green roof) were obtained. The results showed that promising hydrologic benefits of reducing peak flow rate and total volume of stormwater runoff from the catchment, can be achieved with a relatively low cost. The LID SAO system provides users with flexibility and feasibility for a variety of drainage locations, scales and objectives (e.g., water quality).