PM2.5 in Cape Town, South Africa: Chemical characterization and source apportionment using dispersion-normalised positive matrix factorization

Abstract

Understanding fine particulate matter (PM2.5) composition and sources is beneficial to improving visibility, addressing climate change, and mitigating poor air quality and related public health effects. Source apportionment techniques have been instrumental in evaluating the impact of sources and secondary processes on the ambient PM2.5 concentrations in receptor areas. Positive Matrix Factorization (PMF) is now the most commonly used tool due to its ability to provide mixture resolution based on available PM2.5 compositional data. Sampling and analysis of PM2.5 was conducted in Cape Town, South Africa from April 2017 to April 2018. The resulting data were dispersion normalized to address the modifications of the source concentrations resulting from the varying dispersion conditions and thereby permit dispersion normalized PMF (DN-PMF) to be employed. DN-PMF quantified the 6 sources as 2-stroke vehicles/galvanizing industries (16.8%); soil/road dust (12.3%); sulphate/marine diesel (3.6%), traffic (15.7%), sea salt (21.8%), and heating/biomass burning/cooking (15.7%). In addition, air mass back trajectory analysis using the Hybrid Single Particle Lagrangian Trajectory (HYSPLIT) model identified long-range transport pathways to Cape Town. The HYSPLIT results showed air masses from the Atlantic SSW (6%), Atlantic SW (24%), Indian Ocean (31%), and Atlantic WSW (39%) influence air quality. The primary sources affected by the transport clusters were heating, 2-stroke vehicles/galvanizing, road and soil dust, and traffic emissions. These results show that reducing emissions from the local sources will improve air quality.