Effluent trading in river systems through stochastic decision-making process: a case study

Water quality modeling-based assessment for the scope of wastewater treatment of the urban reach of River Yamuna at Delhi, India

River water quality management is a tedious job as it comprises multiple variables. The River Yamuna, Delhi's urban reach, is highly contaminated with very low or zero freshwater flow during the non-monsoon periods. The river quality has been appraised for pre- and post-monsoon periods of 10 years of data collected from the Delhi Pollution Control Council (DPCC). A sharp declination of dissolved oxygen and acceleration of BOD have been observed after the outfalling of drain 1. The wastewater treatment plants data for 2020-2022 have been analyzed, and the removal efficiencies of BOD and COD were found between 65 and 94%. The BIOFORE technology has shown maximum removal efficiencies, around 94% and 89% for BOD and COD, respectively. The level of treatment has been evaluated by developing a water quality model with the existing QUAL2kw framework. Twelve strategies have been generated for four levels of treatment and three different flow conditions. The severely polluted reach of Yamuna has low self-purification capacity; flow augmentation has little effect on the existing load. Hence, the level of treatment required to increase. After the introduction of outfalling drain D13-D16, a sag of DO has been observed; to improve the DO concentration, external aeration is suggested before wastewater disposal into the river. The study is a novel effort to manage the river quality by developing scenarios, including the level of treatments and flow augmentation, and keeping up the desired DO concentration assigned for this river reach.

Optimal design of two-dimensional water trading based on risk aversion for sustainable development of Daguhe watershed, China

Water related problems, including water scarcity and pollution, have become increasingly urgent challenges especially in arid and semiarid regions. Two-dimensional water trading (2DWT) mechanism has been designed to unify the quantity and quality of water for relieving the water crisis. This study aims to develop a risk aversion optimization-two dimensional water trading model (RAO-2DWTM) for planning the regional-scale water resources management system (RWMS). This is the first attempt on planning RWMS through risk aversion optimization within the two-dimensional water trading framework. RAO-2DWTM cannot only support in-depth analysis regarding the effect of decision maker's preferences on system risk in different trading scenarios, but also reflect the interaction between water right trading and effluent trading, as well as disclose the optimal scheme of water resource management under uncertainties. Twenty four scenarios associated with different trading scenarios and robust levels are analyzed. The optimization scheme under the optimal risk control level is determined based on TOPSIS. Results revealed that 2DWT would bring high benefit with reduced risk cost, water deficit and emissions, implying the effectiveness of 2DWT mechanism. The results also disclosed that risk aversion behavior can mitigate water scarcity and pollution, as well as reduce risk cost, but may lead to some losses of system benefit. Consequently, decision makers should make trade-offs between system benefit and risk in identifying desired trading schemes.