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与SARS-CoV-2和胡淡斑病毒污水度相关的水文和物理化学参数,用于大型联合下水道系统的废水度
Luan Nguyen Thanh1, Mounia Hachad2, Natasha McQuaid2
1Département des génies civil, géologique et des mines, Polytechnique Montréal, Montréal, Canada
Journal of water and health
|March 29, 2025
概括
对SARS-CoV-2 (严重急性呼吸系统综合征冠状病毒2) 的废水监测对于社区跟踪至关重要. 温度和电导率 (EC) 等环境因素影响病毒度,EC规范化改善了与临床数据的相关性.
科学领域:
- 环境科学 环境科学
- 病毒学 病毒学
- 公共卫生 公共卫生
背景情况:
- 废水监测为追踪SARS-CoV-2 (严重急性呼吸系统综合征冠状病毒2) 感染提供了社区一级的工具.
- 环境因素显著影响原始废水中的SARS-CoV-2度,影响监测的准确性.
- 了解这些因素对于完善基于废水的流行病学至关重要.
研究的目的:
- 调查废水特征对SARS-CoV-2和胡温和斑点病毒 (PMMoV) 量化的影响.
- 评估不同的规范化方法 (EC,pH,流量,PMMoV) 以改善SARS-CoV-2废水数据与临床病例的相关性.
- 利用物理化学参数和确诊病例数据,开发废水中SARS-CoV-2度的预测模型.
主要方法:
- 主要成分分析 (PCA) 用于确定病毒度与环境因素 (温度,流量,EC) 之间的相关性.
- 将正常化技术 (EC,pH,流量,PMMoV) 与SARS-CoV-2原始数据进行比较,以与临床数据相关联.
- 应用多线性回归和随机森林 (RF) 模型来利用确诊病例和物理化学参数预测SARS-CoV-2度.
主要成果:
- 水温与SARS-CoV-2和PMMoV都有负相关性.
- 流速和EC在春季和冬季与SARS-CoV-2高度相关.
- 使用EC的规范化与其他方法相比,显著改善了与临床数据的相关性.
- 随机森林回归模型在预测SARS-CoV-2度方面实现了高精度 (R2=0.8),确认病例和水温是最有影响力的变量.
结论:
- 废水的物理化学参数,特别是温度和EC,在SARS-CoV-2量化中发挥着重要作用.
- 欧盟标准化是将废水病毒数据与临床监测数据协调的更有效方法.
- 随机森林建模为预测废水中的SARS-CoV-2水平提供了强大的方法,提高了社区爆发监测的可靠性.
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