Defining the South African Acute Respiratory Infectious Disease Season

First-time calculation of the spatial distribution of concentration and air quality index over South Africa using TROPOMI data

The release of toxic gases into the atmosphere may reach concentrations that can cause undesirable health, economic, or aesthetic effects. It is therefore important to monitor the amounts of pollutants injected into the atmosphere from various sources. Most countries have a ground network with multiple measuring sites and instruments, that can measure the air quality index (AQI). However, the main challenge with the networks is the low spatial coverage. In this work, satellite data is used to calculate for the first time the spatial distribution of AQI and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. The results that the month of June has the worst air quality distribution throughout the country, while March has the best air quality distribution. Overall, the results clearly show that TROPOMI has the capability to measure air quality at a country and city level.Implications: In this work, satellite data is used to calculate for the first time the spatial distribution of the air quality index (AQI) and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. Currently, South Africa has a ground network of instruments that measure AQ, however, the network does not cover the whole country. In this work, we show that the use of TROPOMI can compliment the current network and provide data for the areas not covered.

Assessing the impact of climatic variability on acute respiratory diseases across diverse climatic zones in South Africa

Acute respiratory diseases are a significant public health concern in South Africa, with climatic variables such as temperature and rainfall being key influencers. This study investigates the associations between these variables and the prevalence of acute respiratory diseases in Johannesburg, Cape Town, and Gqeberha (Port Elizabeth), representing distinct climatic zones. Spearman's correlation analyses showed negative correlations in Johannesburg for respiratory disease claims with maximum temperature (r = -0.12, p < 0.0001) and mean temperature (r = -0.13, p < 0.0001), and a negative correlation with daily rainfall (r = -0.12, p < 0.0001). Cape Town demonstrated a negative correlation with maximum temperature (r = -0.18, p < 0.0001) and a positive correlation with rainfall (r = 0.08, p < 0.0001). Utilizing Distributed Lag Non-linear Models (DLNM), the study revealed that in Johannesburg, the relative risk (RR) of respiratory claims increases notably at temperatures below 12 °C, and again at a Tmax between 16 and 23 °C. The risk escalates further at >30 °C, although with a considerable error margin. For Cape Town, a stable level of moderate RR is seen from Tmax 15-24 °C, with a significant increase in RR and error margin above 30 °C. In Gqeberha, the DLNM results are less definitive, reflecting the city's moderate climate and year-round rainfall. The RR of acute respiratory diseases did not show clear patterns with temperature changes, with increasing error margins outside the 22 °C threshold. These findings emphasize the imperative for region-specific public health strategies that account for the complex, non-linear influences of climate on respiratory health. This detailed understanding of the climate-health nexus provides a robust basis for enhancing public health interventions and future research directed at reducing the impacts of climate factors.