Towards sustainable water resources planning and pollution control: Inexact joint-probabilistic double-sided stochastic chance-constrained programming model

An integrated simulation-optimization approach for combined allocation of water quantity and quality under multiple uncertainties

Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

Modeling Cu removal from aqueous solution using sawdust based on response surface methodology

Copper (Cu), as one of the heavy metals widely used in industrial and agricultural activities, has a fundamental role in the pollution of water resources. Therefore, removing Cu from the aqueous solutions is considered an important challenge in the purification of water resources. Thus, in this study, sawdust with a diameter of 260-600 μm was used to remove Cu from the aqueous solutions. At first, sawdust was washed using distilled water and dried at laboratory temperature. Cu absorption experiments in closed conditions were performed based on the central composite design (CCD) model and with a range of initial Cu concentrations equal to 1-25 mgl-1. The amount of changes for other variables, including pH, time, and amount of sawdust, was equal to 2-10, 5-185 (min), and 5-25 (gl-1), respectively. After the completion of each test, the remaining Cu concentration in the solution was measured using atomic absorption, and the percentage of Cu removed was determined from the difference between the initial and final concentrations. The results showed that the CCD model has a favorable ability to predict Cu removal from the aqueous solutions (R2=0.90 and RSME=3.34%). Based on the Pareto analysis, contact time, the amount of sawdust, pH, and the Cu concentration had the most significant effect on removing Cu from the solution. Contact time, amount of sawdust, and pH were directly related, and the amount of dissolved Cu was proportional to the removal of Cu from the solution. Therefore, sawdust is desirable as a natural adsorbent, and the removal efficiency of Cu from solutions with low Cu concentration is very high (94%). In this regard, it is advised to use sawdust in the process of targeting Cu and heavy metals due to its low cost and availability.

A nexus approach engaging water rights transfer for addressing water scarcity in energy and food production under uncertainty

Water rights transfer is significantly required for alleviating the ever-intensive water crisis, particularly for arid watersheds with abundant farmland and fossil fuels. However, focusing solely on the re-allocation of water rights and disregarding agricultural water saving potential imperil the security of Water-Energy-Food (WEF) nexus. Furthermore, randomness in water availability leads to water shortage risks and subsequent impact on the whole system. In this study, a risk-based optimization model (RWEF) was proposed to promote inter-sectoral water rights transfer through encouraging energy sector to invest in agricultural water-saving works and get paid back in water rights. Chance-constrained programming is incorporated to analyze the trade-offs between system benefits and water-shortage risks. The developed model was applied to the Inner Mongolia section of the Yellow River Basin, China to verify its effectiveness, considering different development levels of food and energy industries. Results indicated that 488 million m³ of water could be transformed from agriculture to energy, without compromising agricultural production. The main recipients of transferred water rights would be traditional coal-based industries, while it would be difficult for thermal power and most modern coal chemical industries to participate. The construction of water-saving works would help safeguard agricultural production under risks. Compared against two alternative models without water rights transfer mechanism, the average benefit acquired from RWEF under varied water-shortage risks would be at least 68% higher. Particularly, when confronted with extreme water-shortage risk and increased production demands, RWEF would still be able to support agricultural and energy production, while the alternative models being incapable.

A dual-randomness bi-level interval multi-objective programming model for regional water resources management

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions. It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 10⁸ CNY to [22,290.84, 27,492.57] × 10⁸ CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios.

Inexact inventory-theory-based optimization of oily waste management system in shoreline spill response

The oily waste generated from the cleanup operations during shoreline spill response can result in challenging environmental and socioeconomic problems. In this study, an inexact inventory-theory-based optimization model (ITOM) for oily waste management during shoreline spill response was developed to support the spill management team. The most appropriate facilities and optimal waste allocation scheme under uncertainty can be selected to achieve minimum total system cost. To satisfy the demand of oily waste treatment, these oily waste management facilities can be selectively opened depending on the situation. In the combination with the economic order quantity model of inventory theory, the developed model can provide the optimal solutions of batch size and order cycle for treatment facilities to minimize the inventory cost. A case study was used to demonstrate the application of ITOM. The obtained solutions include the facilities selection and waste allocation for waste collection and destocking stages under different risk levels. These solutions can provide a good guideline with managers to analyze the trade-offs between system cost and constraint-violation risks. The developed model has high application potential as a job-aid tool to manage the oily waste generated from oiled shoreline cleanup operations.

Distributed treatment systems

This section presents a review of the scientific literature published in 2019 on topics relating to distributed treatment systems. This review is divided into the following sections: constituent removal, treatment technologies, planning and treatment management, and other topics. PRACTITIONER POINTS: Highlights changes and innovation in removal techniques and technologies in water treatment. Reviews management systems of distributed treatment systems. Discusses point-of-use treatment systems.

Assessing the Effect of the Chinese River Chief Policy for Water Pollution Control under Uncertainty-Using Chaohu Lake as a Case

The River Chief Policy (RCP) is an innovative water resource management system in China aimed at managing water pollution and improving water quality. Though the RCP has been piloted in some river basins of China, few scholars have studied the effects of the policy. We built a differential game model under random interference factors to compare the water pollution in Chaohu Lake under the RCP and without the RCP, and we explored the conditions to ensure the effectiveness of the RCP. The results showed that: (1) The average effect of water pollution control under the RCP was greater than under non-RCP; (2) the higher the rewarding excellence and punishing inferiority coefficient (θ) was, the better the water pollution control effect under the RCP; (3) the greater the random interference coefficient (σ) and rewarding excellence and punishing inferiority coefficient (θ) were, the bigger the fluctuation of the water pollution control effect was; (4) when using the stochastic differential game, when σ≤0.0403, θ≥0.0063, or σ>0.0403, θ≥0.268, the RCP must be effective for water pollution control. Therefore, we can theoretically adjust the rewarding excellence and punishing inferiority coefficient (θ) and the random interference coefficient (σ) to ensure the effective implementation of the RCP and achieve the purpose of water pollution control.

A bi-level chance-constrained programming method for quantifying the effectiveness of water-trading to water-food-ecology nexus in Amu Darya River basin of Central Asia

Issues of water scarcity, food crisis, and ecological degradation pose great challenges to the sustainable development of Central Asia. In this study, a bi-level chance-constrained programming (BCCP) method is developed for planning water-food-ecology (WFE) nexus system of the Amu Darya River basin, where the efficiency of water-trading mechanism and the impact of uncertain water-availability are examined. This is the first attempt for planning WFE nexus system by incorporating chance-constrained programming (CCP) within a bi-level optimization framework. BCCP can reflect the risk of violating probabilistic constraint under uncertainty as well as balance the tradeoff between two-level decision makers in the WFE nexus system. Under trading scheme, multiple scenarios in association with different food demand, ecological-water requirement, and water availability are examined. Major findings are: (i) compared with that under non-trading, system benefits would increase [3.9, 20.4]% under trading scenarios, disclosing that water trading is an effective mechanism for the study basin; (ii) when food demand increases 10.5%, water allocated to ecological use would decrease [0.9, 2.7]% under all scenarios, revealing that agriculture can squeeze ecological water; (iii) both system benefit and water allocation would increase with p level, implying there is a tradeoff between system benefit and system-failure risk. These findings can gain insight into the interaction between two-level stakeholders and objectives as well as provide decision support for WFE nexus synergetic management.