Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda
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Date
2020
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Abstract
here are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the
region to conduct air monitoring in the region can help estimate exposure to air pollution for
epidemiology research. The purpose of our study is to develop a land use regression (LUR)
model using low-cost air quality sensors developed by a research group in Uganda (AirQo).
Methods
Using these low-cost sensors, we collected continuous measurements of fine particulate matter
(PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban
municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo
sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in
Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine
Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean
squared error (RMSE) to evaluate model performance.
Results
Monthly PM2.5 concentration was 60.2 μg/m3 (IQR: 45.4-73.0 μg/m3; median= 57.5 μg/m3). For
the ML LUR models, RMSE values ranged between 5.43 μg/m3 - 15.43 μg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized a 46 dditive models explained the
largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 μg/m3) in
the held-out test set. The most important predictors of monthly PM2.5 concentrations included
monthly precipitation, major roadway density, population density, latitude, greenness, and
percentage of households using solid fuels.
Conclusion
To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in
sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for
LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our
analysis suggests that locally produced low-cost air quality sensors can help build capacity to
conduct air pollution epidemiology research in the region.
Description
Keywords
Land use regression, Low-cost sensors, Machine learning, Particulate matter
Citation
Coker, E. S., Joel, S., & Bainomugisha, E. (2020). Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2. 5 in Urban Uganda. doi:10.20944/preprints202006.0158.v1