Yu Han, Shao Xue-Ting, Liu Si-Yu, Pei Wei, Kong Xiang-Peng, Wang Zhuang, Wang De-Gao
College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China.
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, No. 219 Ningliu Road, Nanjing, 210044, China.
Environ Geochem Health. 2021 Nov;43(11):4627-4635. doi: 10.1007/s10653-021-00954-7. Epub 2021 Apr 30.
Wastewater-based epidemiology is a useful approach to estimate population-level exposure to a wide range of substances (e.g., drugs, chemicals, biological agents) by wastewater analysis. An important uncertainty in population normalized loads generated is related to the size and variability of the actual population served by wastewater treatment plants (WWTPs). Here, we built a population model using location-based services (LBS) data to estimate dynamic consumption of illicit drugs. First, the LBS data from Tencent Location Big Data and resident population were used to train a linear population model for estimating population (r = 0.92). Then, the spatiotemporal accuracy of the population model was validated. In terms of temporal accuracy, we compared the model-based population with the time-aligned ammonia nitrogen (NH-N) population within the WWTP of SEG, showing a mean squared error of < 10%. In terms of spatial accuracy, we estimated the model-based population of 42 WWTPs in Dalian and compared it with the NH-N and design population, indicating good consistency overall (5% less than NH-N and 4% less than design). Furthermore, methamphetamine consumption and prevalence based on the model were calculated with an average of 111 mg/day/1000 inhabitants and 0.24%, respectively, and dynamically displayed on a visualization system for real-time monitoring. Our study provided a dynamic and accurate population for estimating the population-level use of illicit drugs, much improving the temporal and spatial trend analysis of drug use. Furthermore, accurate information on drug use could be used to assess population health risks in a community.
基于废水的流行病学是一种通过废水分析来估计人群对多种物质（如药物、化学品、生物制剂）暴露水平的有用方法。所产生的人群归一化负荷中的一个重要不确定因素与污水处理厂（WWTPs）实际服务的人口规模和变异性有关。在此，我们利用基于位置的服务（LBS）数据构建了一个人口模型，以估计非法药物的动态消费情况。首先，使用来自腾讯位置大数据的LBS数据和常住人口数据来训练一个用于估计人口的线性人口模型（r = 0.92）。然后，对人口模型的时空准确性进行了验证。在时间准确性方面，我们将基于模型的人口与SEG污水处理厂内时间对齐的氨氮（NH-N）人口进行了比较，平均平方误差<10%。在空间准确性方面，我们估计了大连42个污水处理厂基于模型的人口，并将其与NH-N人口和设计人口进行了比较，总体显示出良好的一致性（比NH-N人口少5%，比设计人口少4%）。此外，基于该模型计算出的甲基苯丙胺消费量和患病率分别平均为111毫克/天/1000居民和0.24%，并动态显示在一个可视化系统上进行实时监测。我们的研究提供了一个动态且准确的人口数据，用于估计非法药物的人群水平使用情况，大大改善了药物使用的时空趋势分析。此外，准确的药物使用信息可用于评估社区中的人群健康风险。
