Sheena Muttoo1, Lisa Ramsay2, Bert Brunekreef3, Rob Beelen4, Kees Meliefste3, Rajen N Naidoo5. 1. Discipline of Occupational and Environmental Health, School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa. Electronic address: muttoos@ukzn.ac.za. 2. School of Agricultural, Earth and Environmental Sciences, University of Kwa-Zulu Natal, Durban, South Africa. 3. Institute for Risk Assessment Sciences, Utrecht University, The Netherlands. 4. National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. 5. Discipline of Occupational and Environmental Health, School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.
Abstract
BACKGROUND: The South Durban (SD) area of Durban, South Africa, has a history of air pollution issues due to the juxtaposition of low-income communities with industrial areas. This study used measurements of oxides of nitrogen (NOx) to develop a land use regression (LUR) model to explain the spatial variation of air pollution concentrations in this area. METHODS: Ambient NOx was measured over two two-week sampling periods at 32 sites using Ogawa badges. Following the ESCAPE approach, an annual adjusted average was calculated for these results and regressed against pre-selected geographic predictor variables in a multivariate regression model. The LUR model was then applied to predict the NOx exposure of a sample of pregnant women living in South Durban. RESULTS: Measured NOx levels ranged from 22.3-50.9μg/m3 with a median of 36μg/m3. The model developed accounts for 73% of the variance in ambient NOx measurements using three input variables (length of minor roads within a 1000m radius, length of major roads within a 300m radius, and area of open space within a 1000m radius). Model cross validation yielded a R2 of 0.59. Subsequent participant exposure estimates indicated exposure to ambient NOx ranged from 19.9-53.2μg/m3, with a mean of 39μg/m3. DISCUSSION AND CONCLUSION: This is the first study to develop a land use regression model that predicts ambient concentrations of NOx in a South African context. The findings of this study indicate that the participants in the South Durban are exposed to high levels of NOx that can be attributed mainly to traffic.
BACKGROUND: The South Durban (SD) area of Durban, South Africa, has a history of air pollution issues due to the juxtaposition of low-income communities with industrial areas. This study used measurements of oxides of nitrogen (NOx) to develop a land use regression (LUR) model to explain the spatial variation of air pollution concentrations in this area. METHODS: Ambient NOx was measured over two two-week sampling periods at 32 sites using Ogawa badges. Following the ESCAPE approach, an annual adjusted average was calculated for these results and regressed against pre-selected geographic predictor variables in a multivariate regression model. The LUR model was then applied to predict the NOx exposure of a sample of pregnant women living in South Durban. RESULTS: Measured NOx levels ranged from 22.3-50.9μg/m3 with a median of 36μg/m3. The model developed accounts for 73% of the variance in ambient NOx measurements using three input variables (length of minor roads within a 1000m radius, length of major roads within a 300m radius, and area of open space within a 1000m radius). Model cross validation yielded a R2 of 0.59. Subsequent participant exposure estimates indicated exposure to ambient NOx ranged from 19.9-53.2μg/m3, with a mean of 39μg/m3. DISCUSSION AND CONCLUSION: This is the first study to develop a land use regression model that predicts ambient concentrations of NOx in a South African context. The findings of this study indicate that the participants in the South Durban are exposed to high levels of NOx that can be attributed mainly to traffic.
Authors: Arvind Tiwari; Prashant Kumar; Richard Baldauf; K Max Zhang; Francesco Pilla; Silvana Di Sabatino; Erika Brattich; Beatrice Pulvirenti Journal: Sci Total Environ Date: 2019-03-26 Impact factor: 7.963
Authors: Hasheel Tularam; Lisa F Ramsay; Sheena Muttoo; Rajen N Naidoo; Bert Brunekreef; Kees Meliefste; Kees de Hoogh Journal: Int J Environ Res Public Health Date: 2020-07-27 Impact factor: 3.390
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