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There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM10) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM10 data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM10 concentrations at 1 km x 1 km spatial resolution across the four provinces. An out-of-bag R(2) of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM10 with a spatial CV R(2) of 0.48 and temporal CV R(2) of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.

Objective Children and adolescents living in agricultural areas are likely to be exposed to mixtures of pesticides during their daily activities, which may impair their neurodevelopment. We investigated various such activities in relation to headache severity and neurodevelopment of school-children living in rural agricultural areas in the Western Cape of South Africa. Method We used baseline date from 1001 school-children of the Child Health Agricultural Pesticide Cohort Study in South Africa (CapSA) aged 9–16 from seven schools and three agriculture areas in the Western Cape. Questionnaires were administrated to assess activities related to pesticide exposure and health symptoms addressing four types of activities: 1) child farm activities related to pesticide handling, 2) eating crops directly from the field, 3) contact with surface water around the field, and 4) seen and smelt pesticide spraying activities. Neurocognitive performance across three domains of attention, memory and processing speed were assessed by means of an iPad-based cognitive assessment tool, Cambridge Automated NeuroPsychological Battery (CANTAB). Headache severity was enquired using a standard Headache Impact Test (HIT-6) tool. Cross-sectional regression analysis was performed. Results About 50% of the cohort report to have ever been engaged in activities related to pesticide exposure including farm activities, eating crops directly from the field and leisure activities. Headache severity score was consistently increased in relation to pesticide-related farm activities (score increase of 1.99; 95% CI: 0.86, 3.12), eating crops (1.52; 0.41, 2.67) and leisure activities of playing, swimming or bathing in nearby water (1.25; 0.18, 2.33). For neurocognitive outcomes, an overall negative trend with pesticide exposure-related activities was observed. Among others, involvement in pesticide-related farm activities was associated with a lower multi-tasking accuracy score (−2.74; −5.19, −0.29), while lower strategy in spatial working memory (−0.29; −0.56; −0.03) and lower paired associated learning (−0.88; −1.60, −0.17) was observed for those who pick crops off the field compared to those who do not pick crops off the field. Eating fruits directly from the vineyard or orchard was associated with a lower motor screening speed (−0.06; −0.11, −0.01) and lower rapid visual processing accuracy score (−0.02; −0.03, 0.00). Conclusions Children who indicate activities related to pesticide exposure may be at higher risk for developing headaches and lower cognitive performance in the domains of attention, memory and processing speed. However, self-reported data and cross-sectional design are a limitation. Future research in CapSA will consider pesticide exposure estimations via urinary biomarkers and longitudinal assessment of cognitive functions.

Particulate matter less than or equal to 10 μm in aerodynamic diameter (PM10 µg/m3) is a priority air pollutant and one of the most widely monitored ambient air pollutants in South Africa. This study analyzed PM10 from monitoring 44 sites across four provinces of South Africa (Gauteng, Mpumalanga, Western Cape and KwaZulu-Natal) and aimed to present spatial and temporal variation in the PM10 concentration across the provinces. In addition, potential influencing factors of PM10 variations around the three site categories (Residential, Industrial and Traffic) were explored. The spatial trend in daily PM10 concentration variation shows PM10 concentration can be 5.7 times higher than the revised 2021 World Health Organization annual PM10 air quality guideline of 15 µg/m3 in Gauteng province during the winter season. Temporally, the highest weekly PM10 concentrations of 51.4 µg/m3, 46.8 µg/m3, 29.1 µg/m3 and 25.1 µg/m3 at Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape Province were recorded during the weekdays. The study results suggest a decrease in the change of annual PM10 levels at sites in Gauteng and Mpumalanga Provinces. An increased change in annual PM10 levels was reported at most sites in Western Cape and KwaZulu-Natal.

Few studies have investigated the relationship between ambient air pollution and cardiorespiratory outcomes in Africa. A cross-sectional study comprising of 572 adults from four informal settlements in the Western Cape, South Africa was conducted. Participants completed a questionnaire adapted from the European Community Respiratory Health Survey, and the National Health and Nutrition Examination Survey questionnaire. Exposure estimates were previously modelled using Land-Use Regression for Particulate Matter (PM2.5) and Nitrogen Dioxide (NO2) at participants’ homes. The median age of the participants was 40.7 years, and 88.5% were female. The median annual NO2 level was 19.7 µg/m3 (interquartile range [IQR: 9.6–23.7]) and the median annual PM2.5 level was 9.7 µg/m3 (IQR: 7.3–12.4). Logistic regression analysis was used to assess associations between outcome variables and air pollutants. An interquartile range increase of 5.12 µg/m3 in PM2.5 was significantly associated with an increased prevalence of self-reported chest-pain, [Odds ratio: 1.38 (95% CI: 1.06–1.80)], adjusting for NO2, and other covariates. The study found preliminary circumstantial evidence of an association between annual ambient PM2.5 exposure and self-reported chest-pain (a crude proxy of angina-related pain), even at levels below the South African National Ambient Air Quality Standards.