High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003-2021) with multi-stage data reconstruction and ensemble machine learning methods.

ABSTRACT: In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO₂) and particulate matter (PM₁₀ and PM_2.5) at a 1 km² resolution over Great Britain for the period 2003-2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM_2.5 using co-occurring PM<

SUBMITTER: Libardi AC 

PROVIDER: S-EPMC7616380 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature