Spatial variability of PM10, PM2.5 and PM chemical components in an industrialised rural area within a mountainous terrain

Cheledi Tshehla; Caradee Y. Wright

doi:10.17159/sajs.2019/6174

Authors

Cheledi Tshehla 1. Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa; 2. South African Weather Service, Pretoria, South Africa https://orcid.org/0000-0001-8670-5172
Caradee Y. Wright 1. Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa; 2. Environment and Health Research Unit, South African Medical Research Council, Pretoria, South Africa https://orcid.org/0000-0001-9608-818X

DOI:

https://doi.org/10.17159/sajs.2019/6174

Keywords:

complex terrain, passive samplers, mixing height, air pollution potential

Abstract

We describe the measurement and spatial variability of particulate matter (PM) chemical composition, PM₁₀ and PM_2.5 in the Greater Tubatse Municipality, South Africa. Monthly samples were collected over 12 months (July 2015 to June 2016) using the inexpensive and easy to operate passive samplers of the University of North Carolina. Sites for sample collection were located at private residences, a church, a hospital and a school. Concentrations of PM₁₀, PM_2.5 and PM chemical components were determined using computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy. The annual observed concentrations at all sites were below the South African National Ambient Air Quality Standards of 40 μg/m³ for PM₁₀ and 25 μg/m³ for PM_2.5. The Cr-rich and CrFe-rich particles showed substantial heterogeneity with high concentrations observed near the chrome smelters, and Si-rich particles were highest near the silicon mine. SiAl-rich particles were highest at sites close to busy roads, while SiAlFe-rich particles were less spatially distributed. The low spatial variability of SiAlFe-rich particles indicates that these elements are mainly found in crustal material. Using the synoptic meteorological parameters of The Air Pollution Model, we were unable to effectively determine correlations between PM₁₀ and mixing height, Monin–Obukhov length, air pollution potential, or coefficient of divergence.

Significance:

We have shown that the use of University of North Carolina passive samplers coupled with computer-controlled scanning electron microscopy is effective in determining the chemical composition of PM.
The use of passive samplers is a cheap and effective method to collect data in remote areas of South Africa which have limited or no electricity supply.
Assessment of the spatial distribution of PM and PM chemical components can assist in the development of effective air quality management strategies.