evalPM: a framework for evaluating machine learning models for particulate matter prediction

Research output: Contribution to journalResearch articleContributedpeer-review



Air pollution through particulate matter (PM) is one of the largest threats to human health. To understand the causes of PM pollution and enact suitable countermeasures, reliable predictions of future PM concentrations are required. In the scientific literature, many methods exist for machine learning (ML)-based PM prediction, though their quality is difficult to compare because, among other things, they use different data sets and evaluate the resulting predictions differently. For a new data set, it is not apparent which of the existing prediction methods is best suited. In order to ease the assessment of said models, we present evalPM, a framework to easily create, evaluate, and compare different ML models for immission-based PM prediction. To achieve this, the framework provides flexibility regarding data sets, input features, target variables, model types, hyperparameters, and model evaluation. It has a modular design consisting of several components, each providing at least one required flexibility. The individual capabilities of the framework are demonstrated using 16 different models from the related literature by means of temporal prediction of PM concentrations for four European data sets, showing the capabilities and advantages of the evalPM framework. In doing so, it is shown that the framework allows fast creation and evaluation of ML-based PM prediction models.


Original languageEnglish
Article number1491
JournalEnvironmental monitoring and assessment
Issue number12
Publication statusPublished - Dec 2023

External IDs

ORCID /0000-0001-8107-2775/work/147143269
PubMed 37979062
Scopus 85176966098


Research priority areas of TU Dresden

DFG Classification of Subject Areas according to Review Boards

Subject groups, research areas, subject areas according to Destatis

Sustainable Development Goals


  • Humans, Particulate Matter/analysis, Air Pollutants/analysis, Environmental Monitoring/methods, Air Pollution/analysis, Machine Learning