Feature-Based Machine Learning Framework for Multi-Source NH3 Dataset Analysis
TL;DR
A feature-based machine-learning framework with train–test leakage-controlled preprocessing to evaluate relative NH3 classification consistency across three datasets over China, finding that CAMS GEI provided the broadest spatial–temporal coverage, while MEIC showed comparatively stable classifier behavior.
Abstract
Accurate monitoring of atmospheric ammonia (NH3) is important for air-quality assessment and sustainable agriculture, but available datasets differ in spatial resolution, temporal coverage, units, and physical meaning. This study presents a feature-based machine-learning framework with train–test leakage-controlled preprocessing to evaluate relative NH3 classification consistency across three datasets over China: CAMS GEI, CAMS EAC4, and MEIC. Statistical descriptors were extracted for four spatial–temporal cases: Province–Year, Zone–Year, Province–Season–Year, and Zone–Season–Year. Six classifiers were evaluated using chronological testing and cross-validation. CAMS GEI provided the broadest spatial–temporal coverage, while MEIC showed comparatively stable classifier behavior. The best-performing ensemble models commonly achieved accuracies between 0.97 and 0.99 in the main province-level cases. Mean Absolute Value (MAV) was the leading feature in several province-level analyses, with a maximum reported contribution of 64.26%. These scores describe the separability of threshold-derived reference classes and should not be interpreted as an independent physical prediction of NH3 from external atmospheric drivers.