Quantifying Groundwater Infiltration into Sewers with Chemical Markers Measurements and Bayesian Chemical Mass Balance Model: Methodology and Verification

Urban sewer conditions assessment is important for the proper conveyance of sanitary water to wastewater treatment plants prior to environmental discharge. An effective approach to address this important process needs to be developed. This paper presents a data-driven methodology for sewer condition assessment with gridding-based chemical markers measurement in combination with a Bayesian chemical mass balance (CMB) model. A field study was performed in an urban sewer in Nanjing, China, to test the robustness of the developed methodology. In this site, data library of chemical markers (total nitrogen, phosphate, chloride, and total hardness) for source flows, including domestic wastewater, commercial wastewater and groundwater, was established. Meanwhile, a gridding-based measurement of these chemical markers in sewer flows was performed along the assessed sewer. Then, the CMB model with Bayesian inference and parallel Markov Chain Monte Carlo simulations was developed to quantify source contributions in sewer flows based on the chemical markers data of source and sewer flows. Accordingly, the proportion of clean water infiltration into the sewer and associated sewer defect level can be assessed. The Bayesian CMB model presented that groundwater contributed 11~14% of the sewer flow, indicating a neglectable sewer defect condition. The sewer assessment result was further verified by on-site physical inspection with distributed temperature sensing of in-sewer flows, proving the reliability of the developed methodology. Using this data-driven approach, a preliminary screening of the high-risk sub-catchments with severe sewer defect levels can be made for the following targeted sewer defects locations, optimizing the labor-intensive, system-wide physical inspections. Therefore, the proposed approach offers a cost-effective solution for system-wide sewer inspections.