A5048607323- Air Quality and Health Impacts
- Air Quality Monitoring and Forecasting
- Global Health Care Issues
- Climate Change and Health Impacts
- Vehicle emissions and performance
- Healthcare cost, quality, practices
- Cardiac Imaging and Diagnostics
- Pulmonary Hypertension Research and Treatments
- Atmospheric chemistry and aerosols
- Cardiac, Anesthesia and Surgical Outcomes
- Health Systems, Economic Evaluations, Quality of Life
MRC Centre for Environment and Health
2023
Swiss Tropical and Public Health Institute
2020-2022
University of Basel
2020-2022
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 multiple pollutants in Cape Town, South Africa. Methods: Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework to estimate cumulative effects of PM10, NO2, SO2 up 21 days. We further conducted multi-pollutant stratified our analysis by age group, sex, season....
There is a paucity of air quality data in sub-Saharan African countries to inform science driven management and epidemiological studies. We investigated the use available remote-sensing aerosol optical depth (AOD) develop spatially temporally resolved models predict daily particulate matter (PM10) concentrations across four provinces South Africa (Gauteng, Mpumalanga, KwaZulu-Natal Western Cape) for year 2016 two-staged approach. In stage 1, Random Forest (RF) model was used impute...
Background: The health effect of air pollution is rarely quantified in Africa, and this evident global systematic reviews multi-city studies which only includes South Africa. Methods: A time-series analysis was conducted on daily mortality (cardiovascular (CVD) respiratory diseases (RD)) from 2006–2015 for the city Cape Town. We fitted single- multi-pollutant models to test independent effects particulate matter (PM10), nitrogen dioxide (NO2), sulphur (SO2) ozone (O3) co-pollutants. Results:...
Good quality and completeness of ambient air monitoring data is central in supporting actions towards mitigating the impact pollution. In South Africa, however, availability continuous ground-level pollution scarce incomplete. To address this issue, we developed compared different modeling approaches to impute missing daily average particulate matter (PM10) between 2010 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used explore relationship...
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 two systematic reviews meta-analyses were used derive the excess risk (ER) for PM2.5, PM10, NO2, SO2 O3. Single pollutant AQHIs scaled using ERs at WHO 2021 long-term Guideline (AQG) values define upper level of "low risk" range. An overall daily AQHI was defined as weighted average single AQHIs. Results:...
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 pollutants South Africa. This study analyzed PM10 from monitoring 44 sites across four provinces Africa (Gauteng, Mpumalanga, Western Cape KwaZulu-Natal) aimed present spatial temporal variation concentration provinces. In addition, potential influencing factors variations around three site categories (Residential, Industrial Traffic)...
Background Air pollution is a major environmental risk to health, responsible for one in every nine deaths globally. Currently, limited spatiotemporal air data available conduct large-scale epidemiological studies investigating the adverse health effects of South Africa. Here we aim model Africa's daily average PM10 concentrations years 2010 2016 using spatial and temporal predictor variables including from Moderate Resolution Imaging Spectroradiometer (MODIS) Copernicus Atmosphere...
Background: South Africa currently uses a single-pollutant based air quality index (AQI) to communicate the levels. However, this does not fully capture risk associated with pollution related illness as is reported using highest concentration level of all pollutants. Therefore, revision AQI multipollutant approach could better reflect combined health effect exposure. Methods: Using time-series analysis we derived coefficient, daily values for PM10, NO2, SO2 and O3 were regressed against...
BACKGROUND AND AIM: There is limited understanding on the short-term association between hospital admissions and ambient air pollution in sub-Sahara African countries. Therefore, this study investigated of with daily counts due to respiratory cardiovascular diseases Cape Town, South Africa. METHODS: Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework estimate cumulative effects PM10, NO2 SO2 up 14 days. We further conducted multi-pollutant...