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Open AccessArticle

Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

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Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
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Faculty of Science, University of Basel, CH-4003 Basel, Switzerland
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Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195 USA
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Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa
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Department of Environmental and Occupational Studies, Cape Peninsula University of Technology (CPUT), 8001 Cape Town, South Africa
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Discipline of Occupational and Environmental Health, School of Nursing and Public Health, University of KwaZulu-Natal, 4041 Durban, South Africa
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(7), 1452; https://doi.org/10.3390/ijerph15071452
Received: 23 May 2018 / Revised: 29 June 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies. View Full-Text
Keywords: air pollution; informal settlements; modelling; environmental exposure; exposure assessment; land use regression; nitrogen dioxide; particulate matter; South Africa; Western Cape air pollution; informal settlements; modelling; environmental exposure; exposure assessment; land use regression; nitrogen dioxide; particulate matter; South Africa; Western Cape
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MDPI and ACS Style

Saucy, A.; Röösli, M.; Künzli, N.; Tsai, M.-Y.; Sieber, C.; Olaniyan, T.; Baatjies, R.; Jeebhay, M.; Davey, M.; Flückiger, B.; Naidoo, R.N.; Dalvie, M.A.; Badpa, M.; De Hoogh, K. Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa. Int. J. Environ. Res. Public Health 2018, 15, 1452. https://doi.org/10.3390/ijerph15071452

AMA Style

Saucy A, Röösli M, Künzli N, Tsai M-Y, Sieber C, Olaniyan T, Baatjies R, Jeebhay M, Davey M, Flückiger B, Naidoo RN, Dalvie MA, Badpa M, De Hoogh K. Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa. International Journal of Environmental Research and Public Health. 2018; 15(7):1452. https://doi.org/10.3390/ijerph15071452

Chicago/Turabian Style

Saucy, Apolline; Röösli, Martin; Künzli, Nino; Tsai, Ming-Yi; Sieber, Chloé; Olaniyan, Toyib; Baatjies, Roslynn; Jeebhay, Mohamed; Davey, Mark; Flückiger, Benjamin; Naidoo, Rajen N.; Dalvie, Mohammed A.; Badpa, Mahnaz; De Hoogh, Kees. 2018. "Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa" Int. J. Environ. Res. Public Health 15, no. 7: 1452. https://doi.org/10.3390/ijerph15071452

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