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 and Kees de Hoogh
Additional contact information
Hasheel Tularam: Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban 4041, South Africa
Lisa F. Ramsay: Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban 4041, South Africa
Sheena Muttoo: Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban 4041, South Africa
Rajen N. Naidoo: Discipline of Occupational and Environmental Health, University of KwaZulu-Natal, Durban 4041, South Africa
Bert Brunekreef: Institute for Risk Assessment Sciences, Utrecht University, 3508TD Utrecht, The Netherlands
Kees Meliefste: Institute for Risk Assessment Sciences, Utrecht University, 3508TD Utrecht, The Netherlands
Kees de Hoogh: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute (Swiss TPH), Socinstrasse 57, CH-4002 Basel, Switzerland

IJERPH, 2020, vol. 17, issue 15, 1-16

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 (PM 10 and PM 2.5 ), sulphur dioxide (SO 2 ), and nitrogen dioxide (NO 2 ) 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/m 3 for NO 2 , SO 2 , PM 10 , and PM 2.5 , respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m 3 for NO 2 , SO 2 , PM 10 , and PM 2.5 , respectively). Furthermore, higher levels of NO 2 and SO 2 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 NO 2 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 SO 2 models were less robust with lower R 2 annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R 2 for PM 10 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 NO 2 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: exposure assessment; land use regression; ship emissions; air pollution monitoring (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/15/5406/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/15/5406/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:15:p:5406-:d:390622

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().