edoc

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

Arowosegbe, O. O. and Röösli, M. and Künzli, N. and Saucy, A. and Adebayo-Ojo, T. C. and Schwartz, J. and Kebalepile, M. and Jeebhay, M. F. and Dalvie, M. A. and de Hoogh, K.. (2022) Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa. Environmental pollution, 310. p. 119883.

[img] PDF - Published Version
Available under License CC BY (Attribution).

6Mb

Official URL: https://edoc.unibas.ch/90369/

Downloads: Statistics Overview

Abstract

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM10) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM10 data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM10 concentrations at 1 km x 1 km spatial resolution across the four provinces. An out-of-bag R(2) of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM10 with a spatial CV R(2) of 0.48 and temporal CV R(2) of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Chronic Disease Epidemiology > Air Pollution and Health (Künzli)
03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Air Pollution and Health (Künzli)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Environmental Exposures and Health Systems Research > Physical Hazards and Health (Röösli)
UniBasel Contributors:Arowosegbe, Oluwaseyi Olalekan and Röösli, Martin and Künzli, Nino and Saucy, Apolline and Adebayo, Temitope and de Hoogh, Kees
Item Type:Article, refereed
Article Subtype:Research Article
ISSN:0269-7491
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Related URLs:
Identification Number:
edoc DOI:
Last Modified:21 Dec 2022 15:35
Deposited On:21 Dec 2022 15:35

Repository Staff Only: item control page