PM2.5 chemical composition, source apportionment and geographical origin of air masses in Pretoria, South Africa

Abstract

Background: Air pollution is one of the major problems being faced by most of the big and industrial cities of the world and has become a major environmental threat over the last few years. This environmental threat has gained more attention because of its increased health effects on humans which includes morbidity and mortality. It has various adverse effects, such as increased pulmonary infections, respiratory diseases, acute illnesses and hospitalizations and can eventually lead to death. The need for source identification and abatement strategies for air pollution is crucial in order to meet the SDG goal to reduce pollution by 2030. Therefore, this study investigated the characterisation of PM2.5, source apportionment, origin of air masses into Pretoria alongside the association between air pollutant and hospital admissions due to respiratory diseases.

Method: This study was divided into primary and secondary data collection phases. For the primary data collection, daily 24-hour PM2.5 samples were collected every third day between 18 April 2017 and 17 April 2018 at an urban background site in Pretoria. A total of 122 PM2.5 samples and 25 duplicate PM2.5 samples were collected and analysed for particulate mass, soot, black carbon (BC), organic carbon (OC) and 18 trace elements. Source apportionment analysis was conducted on this dataset using the positive matrix factorisation method. Air mass trajectories, as a surrogate for distant sources of PM2.5, were estimated using the HYSPLIT model (version 4.9). The daily average trajectories were calculated backwards for 72 h and used for cluster analysis. The clustering algorithm coupled in HYSPLIT was based on the distance between a trajectory endpoint and the corresponding cluster mean endpoint.

The secondary data, daily hospital admissions, PM10, NO2 and SO2 data, used for this project were obtained from a private hospital group and the South African Air Quality Information System, managed by the South African Weather Services. The time-stratified case-crossover epidemiology study design and conditional logistic regression models were applied to investigate the association between PM10, NO2, SO2 and respiratory disease (RD) hospital admissions during the study period 1 January 2011 to 30 November 2014.

Results: The annual mean for PM2.5 (n =122 days) was 21.1µg/m3 (range 0.7 - 66.8 µg/m3). The highest PM2.5 mean value was recorded during winter, which was significantly higher than autumn, spring, and summer (p<0.0001). No significant difference between weekdays and weekend (P>0.9567) was observed. Most exceedances of PM2.5, when compared with daily World Health Organization (WHO) guidelines and South African standards were observed in mid-autumn and winter. Soot, BC and OC followed the same trend as PM2.5 concentration. Average S (1480 ng/m3) concentration was the highest among elements detected, followed by Si, Fe, K and Ca, in that order. Seven sources and their contributions to the total PM2.5 were identified and quantified. These included vehicle exhaust – 8.6%, and base metal/ pyrometallurgical - 0%, soil dust -13.2%, secondary Sulphur – 31.4%, vehicle exhaust – 12.5%, road traffic – 7.3%, coal burning -27.2%, while the percentage of PM2.5 specie in the base metal/ pyrometallurgical factor was 0%. The identified source factors exhibited seasonal variations, coal burning and secondary Sulphur being the highest during winter while soil dust and road traffic were lowest during summer.

Five transport clusters were identified during the 1-year study period: National Limpopo (Nat-LP), transboundary (TB), Easterly-Indian Ocean, South Easterly-Indian Ocean and South Westerly-Atlantic Ocean. In addition to this, 85% of the transport clusters were of local and transboundary origin, 15% were long-range transport, while cluster 1 had the highest PM2.5 concentration. Cluster 1 can be attributed to main source of pollution contributing to the PM level at the sampling site due to the activities going on in the region, such as biomass burning, coal mining.

Of the 17,647 hospital admissions in Pretoria, 51.8% (n=9,147) were women and 61.6% (n=10,870) were 0-14-year old. In the unstratified analysis, a 10 g/m3 increase in PM10 was associated with statistically insignificant increase of 0.2% (-0.7%; 1.2%) in RD hospital admissions; no significant association was observed for NO2 during cold days. Significant association between SO2 and RD hospital admissions was observed for females and male patients during warm and cold days.

Conclusion: This project contributes to the very few source apportionments studies of PM2.5 in Africa and specifically South Africa. Coal burning remains one of the main sources that should be addressed. Late autumn and winter season recorded the highest concentration. The risks of RD hospital admission due to PM10 exposure in Pretoria were higher on warm days than on cold days. The apportioned sources and the origin of air masses from this study align with the known existing sources in the country. Oceanic influences, local and transboundary sources (Southern African countries) contribute to the air masses passing over Pretoria, therefore, abatement strategies are paramount to reduce the level of pollution during this time. The findings of the study can be of help to the government in the formulation of air pollution guidelines as a measure to mitigate the effect of air pollution on the environment. In addition to this, if there is strict compliance to the already formulated regulation on the identified sources, this will significantly reduce the effect of air pollution in our cities. Lastly, the outcome of this project will help the South African government in their air quality management plan that are reviewed regularly.