Abstract
Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), and nitrogen dioxide (NO2) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively). Furthermore, higher levels of NO2 and SO2 were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO2 models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO2 models were less robust with lower R2 annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R2 for PM10 models ranged from 52% to 80% while for PM2.5 models this range was 61–76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO2 concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.
Highlights
Quantifying an individual’s exposure to air borne pollutants remains a key challenge in epidemiological studies as the level of exposure depends on both the spatial-temporal dynamics of airInt
This study aims to characterize the spatial distribution of nitrogen dioxide (NO2 ), sulphur dioxide (SO2 ), particulate matter with an aerodynamic diameter of less than 10 μm (PM10 ) and of less
Since wind speed and direction is regarded as a key meteorological parameter in the dispersion of air pollution, this study investigated this phenomenon by assessing a wind trajectory in relation to industry location
Summary
Quantifying an individual’s exposure to air borne pollutants remains a key challenge in epidemiological studies as the level of exposure depends on both the spatial-temporal dynamics of airInt. Quantifying an individual’s exposure to air borne pollutants remains a key challenge in epidemiological studies as the level of exposure depends on both the spatial-temporal dynamics of air. Res. Public Health 2020, 17, 5406 pollution concentrations and the individual’s activities. Public Health 2020, 17, 5406 pollution concentrations and the individual’s activities Each individual has their own unique personal exposure to air pollution during their daily life, occurring both in indoor and outdoor environments, and the quantifying process is complex [1]. To determine the effect of these exposures on health, many of these studies have estimated individual air pollution exposure by making use of air quality monitoring datasets that are representative of the study area and have made use of more complex approaches such as spatial interpolation [2,3,4]
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