Application of calcium peroxide in water and soil treatment: A review

Assessing hydrological performance for optimized integrated grey-green infrastructure in response to climate change based on shared socio-economic pathways

Optimizing the layout of coupled grey-green stormwater infrastructure with multi-objective oriented decision making

Drainage facilities such as drainage pumping systems and Low Impact Development (LID) practices are effective measures to reduce urban flood risk. The quantitative identification of their influence on the reduction of urban flood susceptibility (UFS) is of great significance in providing scientific references for urban flood control. In this study, we constructed a conceptual method to investigate the spatial variation of UFS based on the machine learning models (i.e., Convolution Neural Network (CNN) and Support Vector Machine (SVM)), which has been tested in Wuhan City of China with good performances. After model evaluation, we have quantitatively studied the impact of two flood mitigation measures (pumping stations and LID practices) on the UFS. In particular, we evaluated the UFS mitigation efficiency of several designed scenarios using different combinations of pump discharges and LID area fractions by comparing them against default scenarios. We found a nonlinear negative response relation between the reduction of UFS with either the increase in pump discharge or LID area fractions. The proportion of the area of highest susceptibility (PAH) decreases as the pumping capacity increases, and when the pumping capacity is 2.5 times the default condition, the PAH reduces to 45% from 73.7% of no pump stations and reaches its minimum value. When the LID layout area is 100% of the whole region, the PAH can reduce to 51% from 67.7% of no LID. The findings can be beneficial for the design of optimal preventative strategy to sufficiently reduces UFS. 

The quantitative assessment of impact of pumping capacity and LID on urban flood susceptibility based on machine learning

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

The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm.

In situ laboratory studies were carried out in simulated reactors to evaluate the potential of calcium nitrate, Polyaluminium Chloride (PAC) and calcium peroxide for remediation municipal river sediment. Calcium peroxide could increase the dissolved oxygen concentration (DO) of water for long time (8 weeks). It would optimize the anoxia environment of sediment and be propitious for remediation of sediment. The disadvantage of calcium peroxide was that pH (8.6) would rise slightly and accelerate NH3-N release. Spraying poly-aluminum chloride also could prohibit phosphorus release from sediment. But the remediation effect might be weakened due to disorganization of the aluminium floc after period of time. Phosphorus release from sediment injected calcium nitrate was almost stopped during the experiment. Calcium nitrate could greatly improve the ability of bio-treatment properties for sediment. After 28 weeks of treatment, the TN concentration with dose of 70g per square meter NO3-N could reduce to common level compared to blank reactor by denitrification. It makes in-situ chemical remediation technology by calcium nitrate injection become possibly.

Municipal River Sediment Remediation with Calcium Nitrate, Polyaluminium Chloride and Calcium Peroxide Compound

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The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm.

Municipal River Sediment Remediation with Calcium Nitrate, Polyaluminium Chloride and Calcium Peroxide Compound