Predicting ambient PM_2.5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches.

ABSTRACT:

Background

Accurately assessing individual ambient air pollution exposure is a crucial part of epidemiological studies looking at the adverse health effect of poor air quality. This is particularly challenging in developing countries with high levels of air pollution, mostly due to sparse monitoring networks with a lack of consistent data.

Methods

We evaluated the performance of six different machine learning algorithms in predicting fine particulate matter (PM_2.5) concentrations in Ulaanbaatar, Mongolia using data between 2010 and 2018. We found that the algorithms produce robust results based on performance metrics.

Results

Random forest (RF) and gradient boosting models performed the best with leave-one-location-out cross-validated R² of 0.82

SUBMITTER: Enebish T 

PROVIDER: S-EPMC9871862 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature