Assessing Alternative Population Size Proxies in a Wastewater Catchment Area Using Mobile Device Data

Area of Science:

• Environmental Science
• Public Health
• Data Science

Background:

• Accurate population data is crucial for wastewater-based epidemiology (WBE).
• Traditional population estimates can be inaccurate, introducing uncertainty in WBE.
• Dynamic population monitoring is needed for effective public health and environmental management.

Purpose of the Study:

• To develop and validate low-cost methods for modeling and predicting daily dynamic population.
• To assess the utility of mobile device data and wastewater markers for population estimation.
• To improve population normalization for wastewater-based epidemiology.

Main Methods:

• Utilized mobile device activity data from Oslo, Norway.
• Trained linear models using drinking water production, electricity consumption, and wastewater ammonium levels.
• Validated models against independent mobile phone data and compared results with official population data.

Main Results:

• The wastewater ammonium model demonstrated the highest accuracy (R² = 0.88).
• Ammonium-based population estimates for Oslo in 2017 were within 4% of official figures.
• Drinking water and electricity consumption models showed significant under- and overestimations, respectively.

Conclusions:

• Wastewater ammonium mass loads serve as a reliable anthropogenic proxy for dynamic population monitoring.
• This approach offers a simple and effective method for population size estimation in catchment areas.
• The findings support improved accuracy in wastewater-based epidemiology and other research areas.