Air pollution exposure assessment in sparsely monitored settings; applying machine-learning methods with remote sensing data in South Africa.

Air pollution is one of the leading environmental risk factors to human health – Both short and long-term exposure to air pollution impact human health accounting for over 4 million deaths. Although the risk of exposure to air pollution has been quantified in different settings and countries of the world. The majority of these studies are from high-income countries with historical air pollutant measurement data and corresponding health outcomes data to conduct such epidemiological studies. Air pollution exposure levels in these high-income settings are lower than the exposure levels in low-income countries. The exposure level in sub-Saharan Africa (SSA) countries has continued to increase due to rapid industrialization and urbanization. In addition, the underlying susceptibility profile of SSA population is different from the profiles of the population in high-income settings. However, a major limitation to conducting epidemiological studies to quantify the exposure-response relationship between air pollution and adverse health outcomes in SSA is the paucity of historical air pollution measurement data to inform such epidemiological studies. South Africa an SSA country with some air quality monitoring stations especially in areas classified as air pollution priority areas have historical particulate matter less than or equal to 10 micrometres in aerodynamic diameter (PM10 μg/m3) measurement data. PM10 is one of the most monitored criteria for air pollutants in South Africa. The availability of satellite-derived aerosol optical depth (AOD) at high spatial and temporal resolutions provides information about how particles in the atmosphere can prevent sunlight from reaching the ground. This satellite product has been used as a proxy variable to explain ground-level air pollution levels in different settings. This thesis main objective was to use satellite-derived AOD to bridge the gap in ground-monitored PM10 across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape). We collected PM10 ground monitor measurement data from the South Africa Weather Services across the four provinces for the years 2010 – 2017. Due to the gaps in the daily PM10 across the sites and years. In study I, we compared methods for imputing daily ground-level PM10 data at sites across the four provinces for the years 2010 – 2017 using random forest (RF) models. The reliability of air pollution exposure models depends on how well the models capture the spatial and temporal variation of air pollution. Thus, study II explored the spatial and temporal variations in ground monitor PM10 across the four provinces for the years 2010 – 2017. To explore the feasibility of using satellite-derived AOD and other spatial and temporal predictor variables, Study III used an ensemble machine-learning framework of RF, extreme gradient boosting (XGBoost) and support vector regression (SVR) to calibrate daily ground-level PM10 at 1 × 1 km spatial resolution across the four provinces for the year 2016. In conclusion, we developed a spatiotemporal model to predict daily PM10 concentrations across four provinces of South Africa at 1 × 1 km spatial resolution for 2016. This model is the first attempt to use a satellite-derived product to fill the gap in ground monitor air pollution data in SSA

Spatial and Temporal Variations in PM10 Concentrations between 2010–2017 in South Africa

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

Short-Term Effects of PM; 10; , NO; 2; , SO; 2; and O; 3; on Cardio-Respiratory Mortality in Cape Town, South Africa, 2006-2015

The health effect of air pollution is rarely quantified in Africa, and this is evident in global systematic reviews and multi-city studies which only includes South Africa.; A time-series analysis was conducted on daily mortality (cardiovascular (CVD) and respiratory diseases (RD)) and air pollution from 2006-2015 for the city of Cape Town. We fitted single- and multi-pollutant models to test the independent effects of particulate matter (PM; 10; ), nitrogen dioxide (NO; 2; ), sulphur dioxide (SO; 2; ) and ozone (O; 3; ) from co-pollutants.; daily average concentrations per interquartile range (IQR) increase of 16.4 µg/m; 3; PM; 10; , 10.7 µg/m; 3; NO; 2; , 6 µg/m; 3; SO; 2; and 15.6 µg/m; 3; O; 3; lag 0-1 were positively associated with CVD, with an increased risk of 2.4% (95% CI: 0.9-3.9%), 2.2 (95% CI: 0.4-4.1%), 1.4% (95% CI: 0-2.8%) and 2.5% (95% CI: 0.2-4.8%), respectively. For RD, only NO; 2; showed a significant positive association with a 4.5% (95% CI: 1.4-7.6%) increase per IQR. In multi-pollutant models, associations of NO; 2; with RD remained unchanged when adjusted for PM; 10; and SO; 2; but was weakened for O; 3; . In CVD, O; 3; estimates were insensitive to other pollutants showing an increased risk. Interestingly, CVD and RD lag structures of PM; 10; , showed significant acute effect with evidence of mortality displacement.; The findings suggest that air pollution is associated with mortality, and exposure to PM; 10; advances the death of frail population

Short-Term Joint Effects of PM; 10; , NO; 2; and SO; 2; on Cardio-Respiratory Disease Hospital Admissions in Cape Town, South Africa

In sub-Sahara Africa, few studies have investigated the short-term association between hospital admissions and ambient air pollution. Therefore, this study explored the association between multiple air pollutants and hospital admissions in Cape Town, South Africa.; Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework to estimate the cumulative effects of PM; 10; , NO; 2; , and SO; 2; up to a lag of 21 days. We further conducted multi-pollutant models and stratified our analysis by age group, sex, and season.; The overall relative risk (95% confidence interval (CI)) for PM; 10; , NO; 2; , and SO; 2; at lag 0-1 for hospital admissions due to respiratory disease (RD) were 1.9% (0.5-3.2%), 2.3% (0.6-4%), and 1.1% (-0.2-2.4%), respectively. For cardiovascular disease (CVD), these values were 2.1% (0.6-3.5%), 1% (-0.8-2.8%), and -0.3% (-1.6-1.1%), respectively, per inter-quartile range increase of 12 µg/m; 3; for PM; 10; , 7.3 µg/m; 3; for NO; 2; , and 3.6 µg/m; 3; for SO; 2; . The overall cumulative risks for RD per IQR increase in PM; 10; and NO; 2; for children were 2% (0.2-3.9%) and 3.1% (0.7-5.6%), respectively.; We found robust associations of daily respiratory disease hospital admissions with daily PM; 10; and NO; 2; concentrations. Associations were strongest among children and warm season for RD

Short-term joint effects of PM10, NO2 and SO2 on cardio-respiratory disease hospital admissions in Cape Town, South Africa

BACKGROUND/AIM : In sub-Sahara Africa, few studies have investigated the short-term association between hospital admissions and ambient air pollution. Therefore, this study explored the association between multiple air pollutants and hospital admissions in Cape Town, South Africa. METHODS : Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework to estimate the cumulative effects of PM10, NO2 , and SO2 up to a lag of 21 days. We further conducted multi-pollutant models and stratified our analysis by age group, sex, and season. RESULTS : The overall relative risk (95% confidence interval (CI)) for PM10, NO2 , and SO2 at lag 0–1 for hospital admissions due to respiratory disease (RD) were 1.9% (0.5–3.2%), 2.3% (0.6–4%), and 1.1% (−0.2–2.4%), respectively. For cardiovascular disease (CVD), these values were 2.1% (0.6–3.5%), 1% (−0.8–2.8%), and −0.3% (−1.6–1.1%), respectively, per inter-quartile range increase of 12 µg/m3 for PM10, 7.3 µg/m3 for NO2 , and 3.6 µg/m3 for SO2 . The overall cumulative risks for RD per IQR increase in PM10 and NO2 for children were 2% (0.2–3.9%) and 3.1% (0.7–5.6%), respectively. CONCLUSION : We found robust associations of daily respiratory disease hospital admissions with daily PM10 and NO2 concentrations. Associations were strongest among children and warm season for RD.DATA AVAILABILITY STATEMENT : Exposure data are available for download on the South African Air Quality Information System (SAAQIS) https://saaqis.environment.gov.za/; (accessed on 22 April 2019) however, restrictions apply to the health outcome data.SUPPLEMENTARY MATERIAL : This document describes the air pollution data by station for each year and outlines the imputation analysis. In addition, it tabulates the estimates for age groups, sex, and season per interquartile range and 10 µg/m3.https://www.mdpi.com/journal/ijerphSchool of Health Systems and Public Health (SHSPH

A new global air quality health index based on the WHO air quality guideline values with application in Cape Town

This Original Article is part of the IJPH Special Issue “Science To Foster the WHO Air Quality Guideline Values.”OBJECTIVES : This study developed an Air Quality Health Index (AQHI) based on global scientific evidence and applied it to data from Cape Town, South Africa. METHODS : Effect estimates from two global systematic reviews and meta-analyses were used to derive the excess risk (ER) for PM2.5, PM10, NO2, SO2 and O3. Single pollutant AQHIs were developed and scaled using the ERs at the WHO 2021 long-term Air Quality Guideline (AQG) values to define the upper level of the “low risk” range. An overall daily AQHI was defined as weighted average of the single AQHIs. RESULTS : Between 2006 and 2015, 87% of the days posed “moderate to high risk” to Cape Town’s population, mainly due to PM10 and NO2 levels. The seasonal pattern of air quality shows “high risk” occurring mostly during the colder months of July–September. CONCLUSION : The AQHI, with its reference to the WHO 2021 long-term AQG provides a global application and can assist countries in communicating risks in relation to their daily air quality.https://www.ssph-journal.org/journals/international-journal-of-public-healtham2024School of Health Systems and Public Health (SHSPH)SDG-11:Sustainable cities and communitie

Spatial and Temporal Variations in PM10 Concentrations between 2010–2017 in South Africa

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

Spatial and Temporal Variations in PM10 Concentrations between 2010–2017 in South Africa

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.</jats:p

Short-Term Effects of PM10, NO2, SO2 and O3 on Cardio-Respiratory Mortality in Cape Town, South Africa, 2006–2015

Background: The health effect of air pollution is rarely quantified in Africa, and this is evident in global systematic reviews and multi-city studies which only includes South Africa. Methods: A time-series analysis was conducted on daily mortality (cardiovascular (CVD) and respiratory diseases (RD)) and air pollution from 2006–2015 for the city of Cape Town. We fitted single- and multi-pollutant models to test the independent effects of particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3) from co-pollutants. Results: daily average concentrations per interquartile range (IQR) increase of 16.4 µg/m3 PM10, 10.7 µg/m3 NO2, 6 µg/m3 SO2 and 15.6 µg/m3 O3 lag 0–1 were positively associated with CVD, with an increased risk of 2.4% (95% CI: 0.9–3.9%), 2.2 (95% CI: 0.4–4.1%), 1.4% (95% CI: 0–2.8%) and 2.5% (95% CI: 0.2–4.8%), respectively. For RD, only NO2 showed a significant positive association with a 4.5% (95% CI: 1.4–7.6%) increase per IQR. In multi-pollutant models, associations of NO2 with RD remained unchanged when adjusted for PM10 and SO2 but was weakened for O3. In CVD, O3 estimates were insensitive to other pollutants showing an increased risk. Interestingly, CVD and RD lag structures of PM10, showed significant acute effect with evidence of mortality displacement. Conclusion: The findings suggest that air pollution is associated with mortality, and exposure to PM10 advances the death of frail population.</jats:p

Short-Term Joint Effects of PM10, NO2 and SO2 on Cardio-Respiratory Disease Hospital Admissions in Cape Town, South Africa

Background/Aim: In sub-Sahara Africa, few studies have investigated the short-term association between hospital admissions and ambient air pollution. Therefore, this study explored the association between multiple air pollutants and hospital admissions in Cape Town, South Africa. Methods: Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework to estimate the cumulative effects of PM10, NO2, and SO2 up to a lag of 21 days. We further conducted multi-pollutant models and stratified our analysis by age group, sex, and season. Results: The overall relative risk (95% confidence interval (CI)) for PM10, NO2, and SO2 at lag 0–1 for hospital admissions due to respiratory disease (RD) were 1.9% (0.5–3.2%), 2.3% (0.6–4%), and 1.1% (−0.2–2.4%), respectively. For cardiovascular disease (CVD), these values were 2.1% (0.6–3.5%), 1% (−0.8–2.8%), and −0.3% (−1.6–1.1%), respectively, per inter-quartile range increase of 12 µg/m3 for PM10, 7.3 µg/m3 for NO2, and 3.6 µg/m3 for SO2. The overall cumulative risks for RD per IQR increase in PM10 and NO2 for children were 2% (0.2–3.9%) and 3.1% (0.7–5.6%), respectively. Conclusion: We found robust associations of daily respiratory disease hospital admissions with daily PM10 and NO2 concentrations. Associations were strongest among children and warm season for RD.</jats:p