aceBeyond concentration-based analysis: explainable AI identifies environmental settings influencing urban PM 10 variability
This study applies an explainable artificial intelligence framework to investigate PM10 variability using routine regulatory air-quality data from a single monitoring station, targeting data-limited conditions. A four-year dataset (2020-2023) of PM10, PM2.5, NO2, SO2, O3, and meteorological predictors was analyzed using ensemble machine-learning models with metaheuristic hyperparameter optimization. The best-performing model achieved high predictive performance (R2 = 0.913), supporting model-based interpretation…
Ensemble ML with SHAP clustering achieved R2=0.913 on 2020-2023 regulatory data and identified ten recurrent environmental settings driving PM10, enabling interpretable analysis without chemical speciation.
Findings are from a single monitoring station and broader transferability requires validation across additional sites and environmental conditions.
Evidence
- Peer-reviewedScience of The Total Environment2026-08-15
How should this claim be treated?
Truvace Impact Record TRV-2026-0803, v1: “Beyond concentration-based analysis: explainable AI identifies environmental settings influencing urban PM 10 variability.” Truvace, 2026-08-17. /record/TRV-2026-0803 (accessed at citation time). sha256 288f7adfa641e15d…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0803 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.