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Estimating dynamic population served by wastewater treatment plants using location-based services data

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Abstract

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 (r2 = 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 (NH4-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 NH4-N and design population, indicating good consistency overall (5% less than NH4-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.

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Data availability

The data and material are available from the corresponding author by request.

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The codes are available from the corresponding author by request.

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Acknowledgements

This study was supported by the Dalian Science and Technology Innovation Foundation (2019J13SN123) and the National Key R&D Program of China (2019YFC1407700).

Funding

This study was supported by the Dalian Science and Technology Innovation Foundation (2019J13SN123) and the National Key R&D Program of China (2019YFC1407700).

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Authors and Affiliations

  1. College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China

    Han Yu, Xue-Ting Shao, Si-Yu Liu, Wei Pei, Xiang-Peng Kong & De-Gao Wang

  2. 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

    Zhuang Wang

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  1. Han Yu
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  2. Xue-Ting Shao
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  5. Xiang-Peng Kong
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  6. Zhuang Wang
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  7. De-Gao Wang
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Contributions

HYu Methodology, Writing—original draft. XTS Writing—review & editing, Supervision,Validation. SYL Formal analysis and investigation. WP Writing—review & editing. XPK Writing—review & editing. ZW Writing—review & editing. DGW Writing—review & editing.

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Correspondence to De-Gao Wang.

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Yu, H., Shao, XT., Liu, SY. et al. Estimating dynamic population served by wastewater treatment plants using location-based services data. Environ Geochem Health 43, 4627–4635 (2021). https://doi.org/10.1007/s10653-021-00954-7

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  • DOI: https://doi.org/10.1007/s10653-021-00954-7

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