Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa

Hasheel Tularam; Lisa F Ramsay; Sheena Muttoo; Rajen N Naidoo; Bert Brunekreef; Kees Meliefste; Kees de Hoogh

doi:10.3390/ijerph17155406

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa

Int J Environ Res Public Health. 2020 Jul 27;17(15):5406. doi: 10.3390/ijerph17155406.

Authors

Hasheel Tularam¹, Lisa F Ramsay¹, Sheena Muttoo¹, Rajen N Naidoo¹, Bert Brunekreef², Kees Meliefste², Kees de Hoogh^{3

4}

Affiliations

¹ Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban 4041, South Africa.
² Institute for Risk Assessment Sciences, Utrecht University, 3508TD Utrecht, The Netherlands.
³ Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland.
⁴ Faculty of Science, University of Basel, CH-4003 Basel, Switzerland.

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 PM_2.5), 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 PM_2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, 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 PM_2.5 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.

Keywords: air pollution monitoring; exposure assessment; land use regression; ship emissions.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Cities
Environmental Monitoring
Industry
Particulate Matter / analysis
South Africa
Vehicle Emissions

Substances

Air Pollutants
Particulate Matter
Vehicle Emissions