Environmental Sustainability in Water Treatment Systems and Water Resource Management

1.研究背景：水是受当前资源使用率和资源退化影响最大的自然资源之一。到2050年，估计世界人口将增加，这将改变降雨量、频率和质量。如果没有适当的水资源管理计划，雨水径流可能携带大量的颗粒物和其他污染物。其目的是评估城市化集水区的雨水水质状况，并探索改善水质的处理方案的适用性。因此，目标是分析受频繁洪水影响的城市地区的水质状况，并评估有效处理径流和保护水资源的可持续方法和管理策略。研究结果有助于减少污染径流对人类生活质量和生态系统的影响。2.方法学：2.1生物过滤器的开发：生物滞留系统等过滤技术的设计将结合不同的组成部分，包括最佳植被和基质。还将考虑通过嵌入内部储水层和/或使用生物炭改良的沙子来创造厌氧环境以促进脱氮过程，以改善有机物的去除。2.2数据分析：对于水样，将使用红外光谱等分析仪器进行多次扫描。然后，将采用化学计量学技术，使用主成分分析（PCA）和偏最小二乘（PLS）回归或其他回归模型分析数据，以推导模型。2.3实验设计：在设计和分析数据时，将考虑实验设计（DOE）方法。它可以大大减少所需的实验总数，因为它有助于获得最大量的“信息”，同时限制所需的观察次数。

1. Research background: Water is one of the natural resources most affected by the current rate of use and deterioration of resources. By 2050, the world's population is estimated to increase which changes the amounts, frequency and quality of rainfall. Without proper water resource management schemes, storm water runoff could carry high amounts of particulate matters and other pollutants. The aim is to assess the stormwater quality state of urbanized catchments and explore suitability of treatment scenarios for improving the quality of water. Therefore the objectives are to analyze the water quality status of urban locations highly affected by frequent flooding, and evaluate sustainable approaches and management strategies effective in treating the runoff and protecting the water resource. The results can aid in the reduction of the impact of contaminated runoff on the quality of human life and ecosystem. 2. Methodology: 2.1 Development of biofilters: Filtration techniques such as bioretention systems will be designed by incorporating different components including optimum vegetation, and substrates. Artificially creating an anaerobic environment to facilitate the denitrification process by imbedding Internal Water Storage Layer, and/or using sand amended with biochar to improve removal of organics will be taken into account as well. 2.2 Data analysis: For water samples, several scans from analytical instruments such as Infrared spectroscopy will be made. Then, chemometric techniques will be employed for analysing the data using Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression or other regression models to derive the models. 2.3 Design of Experiments: In designing and analysing the data, a Design of experiment (DOE) approach will be considered. It could significantly reduce the total number of experiments needed, as it facilitates obtaining the maximum amount of "information" while limiting the number of observations needed.