This study investigates the correlations between key wastewater parameters – water level, turbidity, and electrical conductivity – under varying weather conditions, including extreme rainfall events such as the May 2023 flood event in Bologna, Italy. Data collected via IoT-based sensors are analyzed using Detrended Cross-Correlation Analysis and Autoregressive Distributed Lag (ARDL) models. The results highlight significant correlations between water level and other parameters, with distinct patterns emerging during extreme and regular weather periods. Notably, water level correlates negatively with electrical conductivity, particularly during flood events, due to the dilution effect of rainwater. Turbidity shows a complex relationship with water level, influenced by weather conditions and the opposing effects of different factors. ARDL models further demonstrate the potential to predict turbidity and electrical conductivity from water level data, offering valuable insights for wastewater management in urban areas.

中文翻译：

---

在极端和常规天气条件下建立废水参数时间序列之间的相关性


本研究调查了不同天气条件下关键废水参数（水位、浊度和电导率）之间的相关性，包括极端降雨事件，例如 2023 年 5 月意大利博洛尼亚的洪水事件。通过基于 IoT 的传感器收集的数据使用 Detrended Cross-Correlation Analysis 和 Autoregressive Distributed Lag （ARDL） 模型进行分析。结果突出了水位和其他参数之间的显著相关性，在极端和常规天气期间会出现不同的模式。值得注意的是，由于雨水的稀释效应，水位与电导率呈负相关，尤其是在洪水事件期间。浊度与水位呈复杂关系，受天气条件和不同因素的相反影响。ARDL 模型进一步证明了从水位数据中预测浊度和电导率的潜力，为城市地区的废水管理提供了有价值的见解。