Review charts path toward AI‑supported nitrogen source tracking in watersheds

A new review sets out a framework for how scientists identify and quantify the sources of excess nitrogen entering rivers, lakes and groundwater, and maps a path toward integrated, AI‑supported watershed monitoring systems.

Fertilisers, livestock waste, domestic sewage and atmospheric deposition can all contribute to nitrogen pollution but determining exactly where nitrogen originates and how it moves through a watershed remains difficult.

The review, led by corresponding author Yongqiu Xia, classifies existing watershed nitrogen source apportionment approaches into four categories: qualitative methods, quantitative methods, spatiotemporally refined approaches and intelligent technologies.

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"Effective nitrogen pollution control begins with knowing where the pollution originates, how much each source contributes, and how those contributions change across space and time," Xia said.

"The next generation of source apportionment should move beyond isolated measurements toward integrated, intelligent systems that can support practical watershed management."

From chemical tracers to intelligent systems

Earlier approaches relied largely on chemical tracers and microbial indicators, later supplemented by stable nitrate isotopes and statistical and Bayesian models to estimate source contributions.

Each has limitations: chemical tracers can be altered by rainfall and biogeochemical reactions, microbial indicators are sensitive to temperature, and isotopic signatures can overlap between sources.

Process‑based models such as SWAT and HSPF have since been used to simulate nitrogen generation, transport and transformation, helping identify critical source areas across timescales from days to decades.

The review highlights an emerging shift toward intelligent source apportionment, combining satellite remote sensing, uncrewed aerial vehicles and in‑situ sensors with machine learning and process‑based modelling to deliver higher‑resolution, more timely information than conventional field sampling alone.

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Priorities for future watershed monitoring

The authors propose three priorities: building integrated space‑air‑ground monitoring systems; more closely coupling statistical, isotopic and process‑based models to improve understanding of nitrogen transport under extreme weather; and incorporating artificial intelligence and large language models into decision‑support platforms for watershed managers.

"Connecting source identification directly with management decisions is the key goal," Xia said. "By integrating monitoring, modelling and intelligent analysis, we can provide more precise information about where mitigation efforts are most urgently needed."

The review is published in Nitrogen Cycling.

Read the full paper here.