Key Takeaways & Executive Findings
- •• • The SAM algorithm exhibited a linear response (R² > 0.88) between spectral angle values and the volume ratio of enterprise wastewater in mixed samples, enabling quantitative estimation of pollution source contributions. • • Field monitoring over 12 hours revealed that enterprises D and F showed significant increases in SAM values (up to 1.04 and 1.13, respectively) during peak pollution periods, correlating with elevated DOC, TN, and UV254 levels, identifying them as primary contributors. • • Untreated industrial raw water samples showed low similarity (SAM values typically < 0.5) to municipal sewer water, indicating no direct discharge of raw wastewater, whereas treated effluents exhibited higher similarity (SAM values up to 1.13), confirming proper discharge pathways. • • The EEM+SAM technique provides a non-invasive, high-throughput method for real-time monitoring and source tracing, capable of distinguishing multiple pollution sources and quantifying their relative contributions, which is critical for proactive management of sewer networks.
Abstract
The increasing complexity of pollutant sources in municipal wastewater networks, driven by unauthorized industrial discharges, poses significant risks to the stable operation of wastewater treatment plants. This study, conducted in an industrial park in Ningbo, Zhejiang Province, developed a source apportionment method using excitation-emission matrix (EEM) fluorescence spectroscopy combined with spectral angle mapping (SAM). A pollution fingerprint database was constructed from wastewater samples of six representative enterprises (A–F) and municipal sewer samples. The SAM algorithm demonstrated high sensitivity and stability in detecting changes in water composition, with spectral angle values showing a strong linear correlation (R² > 0.88) with the volume ratio of enterprise wastewater in mixed samples. This enabled both qualitative identification and quantitative estimation of pollution sources. Field application over a 12-hour monitoring period identified two enterprises as major contributors to organic matter and nitrogen during critical pollution episodes, consistent with trends in DOC, TN, and UV254. The proposed EEM+SAM approach offers a non-invasive, high-throughput method for real-time monitoring and source tracing of multi-source pollution in complex sewer systems, providing a scientific basis for pollution accountability and precise enforcement.
1. Introduction
Municipal wastewater networks are increasingly vulnerable to unauthorized industrial discharges, which introduce high-strength organic pollutants, heavy metals, and recalcitrant compounds that can overwhelm biological treatment processes. Such events cause biochemical system failures, as evidenced by two incidents at a Ningbo wastewater treatment plant in 2023 that led to temporary shutdowns. Traditional monitoring methods are reactive and lack the specificity to identify pollution sources in complex, mixed-wastewater environments, creating an urgent need for rapid, accurate, and proactive source-tracing technologies.
This study addresses this bottleneck by integrating three-dimensional fluorescence spectroscopy (EEM) with spectral angle mapping (SAM). EEM provides high-throughput fingerprinting of dissolved organic matter, while SAM enables quantitative comparison of spectral shapes without relying on specific peaks. This combination allows for the identification and relative quantification of multiple pollution sources in real time, offering a practical solution for regulatory compliance and operational stability in municipal sewer systems.
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JI Yuxi, HU Wei, ZHU Huifeng, ZHAO Zhonghua, ZHANG Guanyi, YU Xubiao (2026). Rapid Identification of Industrial Wastewater Discharges in Municipal Sewer Networks Based on Three-Dimensional Fluorescence Spectroscopy and Spectral Angle Mapping. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202507055
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Frequently Asked Questions
What is the detection limit of the SAM algorithm for quantifying industrial wastewater contributions in mixed samples?
The study demonstrated a linear correlation (R² > 0.88) between spectral angle values and the volume ratio of enterprise wastewater, but the exact detection limit was not specified. However, the method successfully identified contributions from enterprises D and F during peak pollution events, suggesting sensitivity to contributions that significantly alter the organic matter composition.
How does the EEM+SAM method perform under varying background organic matter concentrations typical of municipal sewage?
The method was validated in a real industrial park with mixed domestic and industrial wastewater. The SAM algorithm effectively distinguished between treated industrial effluents and background sewage, as evidenced by higher similarity values for treated effluents (e.g., up to 1.13 for enterprise F) compared to raw sewage. This indicates robustness in complex matrices, though further studies are needed to assess performance under extreme variability.
Can this technique be scaled for continuous online monitoring in large municipal networks?
The EEM+SAM approach is non-invasive and high-throughput, making it suitable for online implementation. However, the current study used discrete sampling over 12 hours. For continuous monitoring, automated sampling and real-time spectral analysis would be required, which may involve significant instrumentation costs. The linear response and rapid matching capability suggest feasibility, but pilot-scale trials are necessary to address engineering challenges.
What are the limitations of the SAM algorithm in distinguishing between similar industrial processes that produce comparable fluorescence signatures?
The study included enterprises from diverse industries (pharmaceutical, electronics, stationery), and SAM successfully differentiated their fingerprints. However, if two enterprises discharge similar organic compounds (e.g., both using similar solvents), their spectral signatures may overlap, reducing discrimination. The method's specificity depends on the uniqueness of the fluorescence fingerprints, which should be validated for each application scenario.
How does the method account for temporal variability in wastewater composition due to production cycles or batch discharges?
The 12-hour monitoring captured temporal variations, and SAM values fluctuated accordingly. For instance, enterprise D showed SAM values increasing from 0.82 to 1.04 between 9:30 and 20:30, likely reflecting discharge patterns. To account for production cycles, longer monitoring periods and correlation with production schedules would be needed. The method provides a snapshot of similarity, but continuous monitoring would be required to fully capture dynamic changes.
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