Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Meguro-Ku, Tokyo 152-8552, Japan E-mail:
Graduate School of Science, Nagoya University, Furo-Cho, Chikusa-Ku, Nagoya 464-8602, Japan.
J Water Health. 2022 Jun;20(6):972-984. doi: 10.2166/wh.2022.022.
Sewage comprises multifarious information on sewershed characteristics. For instance, influent sewage quality parameters (ISQPs) (e.g., total nitrogen (TN)) are being monitored regularly at all treatment plants. However, the relationship between ISQPs and sewershed characteristics is rarely investigated. Therefore, this study statistically investigated relationships between ISQPs and sewershed characteristics, covering demographic, social, and economic properties in Tokyo city as an example of a megacity. To this end, we collected ISQPs and sewershed characteristic data from 2015 to 2020 in 10 sewersheds in Tokyo city. By principal component analysis, spatial variability of ISQPs was aggregated into two principal components (89.8% contribution in total), indicating organics/nutrients and inorganic salts, respectively. Concentrations of organics/nutrients were significantly correlated with the population in sewersheds (daytime population density, family size, age distribution, etc.). Inorganic salts are significantly correlated with land cover ratios. Finally, a multiple regression model was developed for estimating the concentration of TN based on sewershed characteristics (R=0.97). Scenario analysis using the regression model revealed that possible population movements in response to the coronavirus pandemic would substantially reduce the concentration of TN. These results indicate close relationships between ISQPs and sewershed characteristics and the potential applicability of big data of ISQPs to estimate sewershed characteristics and vice versa.
污水包含有关污水流域特征的多种信息。例如，所有处理厂都定期监测进水污水质量参数（ISQPs）（例如总氮（TN））。然而，ISQPs 与污水流域特征之间的关系很少被研究。因此，本研究以东京市为例，从统计学上调查了 ISQPs 与污水流域特征之间的关系，涵盖了人口统计学、社会和经济特性。为此，我们从 2015 年到 2020 年在东京市的 10 个污水流域中收集了 ISQPs 和污水流域特征数据。通过主成分分析，ISQPs 的空间变异性被聚集到两个主成分中（总贡献 89.8%），分别表示有机物/养分和无机盐。有机物/养分的浓度与污水流域中的人口显著相关（污水流域中的白天人口密度、家庭规模、年龄分布等）。无机盐与土地覆盖比率显著相关。最后，根据污水流域特征建立了一个用于估计 TN 浓度的多元回归模型（R=0.97）。使用回归模型进行的情景分析表明，由于冠状病毒大流行而可能发生的人口流动将大大降低 TN 的浓度。这些结果表明 ISQPs 与污水流域特征之间存在密切关系，并且 ISQPs 的大数据有可能用于估计污水流域特征，反之亦然。
