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Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya
- Source :
- Indoor Air. 31:1441-1457
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM2.5 , black carbon, CO); household air pollution (PM2.5 , CO); personal exposure (PM2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM2.5 (R2 = 0.45) and CO (R2 = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R2 = 0.76) and a root mean-squared error of 85 µg/m3 , and the survey-only-based regression model was able to predict PM2.5 exposures with an R2 of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R2 of 0.57 and RMSE of 98 µg/m3 .
- Subjects :
- Rural Population
Environmental Engineering
010504 meteorology & atmospheric sciences
Mean squared error
Air pollution
010501 environmental sciences
medicine.disease_cause
01 natural sciences
law.invention
Soot
law
Linear regression
Statistics
medicine
Humans
Cooking
Household Articles
0105 earth and related environmental sciences
Air Pollutants
Family Characteristics
Models, Statistical
Public Health, Environmental and Occupational Health
Statistical model
Regression analysis
Environmental Exposure
Building and Construction
Kenya
Ensemble learning
Ventilation
Air Pollution, Indoor
Stove
Ventilation (architecture)
Environmental science
Particulate Matter
Environmental Monitoring
Subjects
Details
- ISSN :
- 16000668 and 09056947
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- Indoor Air
- Accession number :
- edsair.doi.dedup.....e51142a77d1a1f9090f9e0bb94c5bf82