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Land Use Regression Modelling of Outdoor NO 2 and PM 2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa

Author

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  • Apolline Saucy

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Martin Röösli

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Nino Künzli

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Ming-Yi Tsai

    (Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195 USA)

  • Chloé Sieber

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Toyib Olaniyan

    (Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa)

  • Roslynn Baatjies

    (Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa
    Department of Environmental and Occupational Studies, Cape Peninsula University of Technology (CPUT), 8001 Cape Town, South Africa)

  • Mohamed Jeebhay

    (Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa)

  • Mark Davey

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland)

  • Benjamin Flückiger

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland)

  • Rajen N. Naidoo

    (Discipline of Occupational and Environmental Health, School of Nursing and Public Health, University of KwaZulu-Natal, 4041 Durban, South Africa)

  • Mohammed Aqiel Dalvie

    (Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, Rondebosch, 7700 Cape Town, South Africa)

  • Mahnaz Badpa

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

  • Kees De Hoogh

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland
    Faculty of Science, University of Basel, CH-4003 Basel, Switzerland)

Abstract

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 NO 2 and PM 2.5 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 NO 2 and PM 2.5 were 22.1 μg/m 3 and 10.2 μg/m 3 , respectively. The NO 2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R 2 ). The PM 2.5 annual models had lower explanatory power (R 2 = 0.36, 0.29, and 0.29). The best predictors for NO 2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM 2.5 , together with population density. This study demonstrates that land-use-regression modelling for NO 2 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 PM 2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO 2 and PM 2.5 seasonal exposure estimates and maps for further health studies.

Suggested Citation

  • Apolline Saucy & Martin Röösli & Nino Künzli & Ming-Yi Tsai & Chloé Sieber & Toyib Olaniyan & Roslynn Baatjies & Mohamed Jeebhay & Mark Davey & Benjamin Flückiger & Rajen N. Naidoo & Mohammed Aqiel Da, 2018. "Land Use Regression Modelling of Outdoor NO 2 and PM 2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa," IJERPH, MDPI, vol. 15(7), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1452-:d:157161
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    References listed on IDEAS

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    1. Marie-Eve Héroux & H. Anderson & Richard Atkinson & Bert Brunekreef & Aaron Cohen & Francesco Forastiere & Fintan Hurley & Klea Katsouyanni & Daniel Krewski & Michal Krzyzanowski & Nino Künzli & Inga , 2015. "Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(5), pages 619-627, July.
    2. Eric Coker & Samuel Kizito, 2018. "A Narrative Review on the Human Health Effects of Ambient Air Pollution in Sub-Saharan Africa: An Urgent Need for Health Effects Studies," IJERPH, MDPI, vol. 15(3), pages 1-15, March.
    3. Chloé Sieber & Martina S. Ragettli & Mark Brink & Olaniyan Toyib & Roslyn Baatjies & Apolline Saucy & Nicole Probst-Hensch & Mohamed Aqiel Dalvie & Martin Röösli, 2017. "Land Use Regression Modeling of Outdoor Noise Exposure in Informal Settlements in Western Cape, South Africa," IJERPH, MDPI, vol. 14(10), pages 1-10, October.
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    1. Yan Zhang & Hongguang Cheng & Di Huang & Chunbao Fu, 2021. "High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM 2.5 Distribution in Beijing, China," IJERPH, MDPI, vol. 18(11), pages 1-19, June.

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