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In low-resource settings, there is a need to develop models that can address contributions of household and outdoor sources to population exposures. The aim of the study was to model indoor PM<sub>2.5</sub> using household characteristics, activities, and outdoor sources. Households belonging to participants in the Mother and Child in the Environment (MACE) birth cohort, in Durban, South Africa, were randomly selected. A structured walk-through identified variables likely to generate PM<sub>2.5</sub> . MiniVol samplers were used to monitor PM<sub>2.5</sub> for a period of 24 hours, followed by a post-activity questionnaire. Factor analysis was used as a variable reduction tool. Levels of PM<sub>2.5</sub> in the south were higher than in the north of the city (P < .05); crowding and dwelling type, household emissions (incense, candles, cooking), and household smoking practices were factors associated with an increase in PM<sub>2.5</sub> levels (P < .05), while room magnitude and natural ventilation factors were associated with a decrease in the PM<sub>2.5</sub> levels (P < .05). A reasonably robust PM<sub>2.5</sub> predictive model was obtained with model R<sup>2</sup> of 50%. Recognizing the challenges in characterizing exposure in environmental epidemiological studies, particularly in resource-constrained settings, modeling provides an opportunity to reasonably estimate indoor pollutant levels in unmeasured homes.

Original publication

DOI

10.1111/ina.12432

Type

Journal

Indoor air

Publication Date

03/2018

Volume

28

Pages

228 - 237

Addresses

Discipline of Occupational and Environmental Health, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa.

Keywords

Humans, Factor Analysis, Statistical, Models, Statistical, Family Characteristics, Air Pollution, Indoor, Environmental Exposure, Environmental Monitoring, Social Class, Poverty, Adult, Child, South Africa, Female, Male, Particulate Matter