Model predictive control of stormwater basins coupled with real-time data assimilation enhances flood and pollution control under uncertainty

Urban stormwater quality: A review of methods for continuous field monitoring

Urban stormwater is contaminated by a wide range of substances whose concentrations vary greatly between locations, as well as between and during rain events. This literature review evaluates advantages and limitations of current methods for using continuous water quality monitoring for stormwater characterization and control. High-temporal-resolution measurements have been used to improve the understanding of stormwater quality dynamics and pollutant pathways, facilitate the performance evaluation of stormwater control measures and improve operation of the urban drainage system with real-time control. However, most sensors used to study stormwater were developed for either centralized water treatment or natural water contexts and adaptation is necessary. At present, the primary application of interest in stormwater - characterization of pollutant concentrations - can only be achieved through the use of indirect measurements with site-specific relationships of pollutants to basic physical-chemical parameters. In addition, various problems arise in the field context, associated with intermittent or variable flow rates, the accumulation of debris and sediment, adverse conditions for electrical equipment and human factors. Obtaining reliable continuous stormwater quality data requires the adoption of best practices, including the calibration and regular maintenance of sensors, verification of data and accounting for the considerable uncertainties in data; however, the literature review showed that improvement is needed among the scientific community in implementing and documenting these practices.

A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models' parameters on their performance.

Effects analysis and probability forecast (EAPF) of real-time management on urban flooding: A novel bidirectional verification framework

Government departments usually prepare and implement contingency plans to address frequent urban flooding caused by short-term heavy rainfall. Previous studies focused on the evaluation of the static impact of the policies on urban floods, while there is a lack of research on the effect of off-design conditions, real-time feedback and treatments of the flood events on urban flood mitigation, which is detrimental to the optimization of management strategies of the cities. To quantify the effects of real-time management on flood mitigation in Fuzhou City, China, this study proposed a framework (EAPF) for evaluation and risk prediction. First, we collected data on the locations, rainfall intensity, inundation time, and the triggers of the waterlogging events from 2017 to 2021. Second, based on the vigilance analyses, a structural equation model (SEM) was constructed to quantitatively evaluate the mitigation effects of management on waterlogging. Finally, a probability prediction model of dynamic drainage capacity was proposed for flood simulation caused by the rainwater grate blockage. The results indicate that the environmental factors were the decisive triggers affecting the severity of waterlogging, and increasing the frequency of management events effectively reduced the probability of blocking. The correlation between the number of management events and blocking flood events was -0.42, while a decrease in vigilance increased the possibility of flooding caused by overdue treatment. The proposed hydrological waterlogging model, which considered blockages, exhibited a Nash-Sutcliffe efficiency (NSE) coefficient exceeding 0.9 under deterministic conditions. The probability prediction model verified the mitigating effect of management on the blockages and urban flooding, and its results were consistent with those of the SEM. Our study contributes to improving the reliability of waterlogging prediction and optimizing the management flow in the developing cities.

Plants' emission behaviors under dual control of pollutant concentration and quantity

Quantity-based and concentration-based limits are two common environmental permitting approaches utilized by government worldwide in environmental management. While existing literature is still unambiguous about roles played by quantity-based versus concentration-based limits in environmental management, it becomes evident that relying exclusively on concentration-based or quantity-based limits to control plant emissions may not necessarily result in improved environmental quality. This paper leverages a unique opportunity arising from a recent reform in China's Pollutant Emission Permit System (PEPS) initiated in 2016 to analyze how the introduction of quantity-based limits in addition to concentration-based limits through the PEPS reform impact emissions at the plant level. Utilizing a unique plant-level continuous emission monitoring system data collected from Shaanxi Province (located in western China), the paper finds a significant reduction in air pollutant emissions as a result of the PEPS reform (nitrogen oxides (NOx) by 39%, sulfur dioxide (SO2) by 15% and particulate matter (PM) by 13%). The heterogeneity analyses show emission reductions in plants differ across those with varying quantity limits specified in their permits, distinct emission ratios and diverse ownership structures. Furthermore, plants that fall under the classified management system with more stringent regulations imposed, especially those operating in high-pollution sectors, situated within industrial parks, or classified as large-sized plants, attain higher pollutant quantity limits. Findings of the paper carry important implications for effective environmental management, particularly within developing countries, and shed some light on carbon emission reduction policies in China.