Statistical analysis of factors driving surface ozone variability over continental South Africa

Abstract

Statistical relationships between surface ozone (O 3 ) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and -regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O 3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain~50% of the variability in daily maximum O 3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O 3 variability. In summer, daily O 3 variances were mostly associated with relative humidity, while winter O 3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O 3 . Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O 3 ,while the relationship with relative humidity and CO is probably linear. An intercomparison between O 3 levels modelled with the three statistical models compared to measured O 3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O 3 precursors coupled with meteorological conditions in daily variances of O 3 levels in continental South Africa.

Measurement report: Long-term measurements of ozone concentrations in semi-natural African ecosystems

“…Indeed, Swartz et al (2020b) attributed the decrease in O3 concentrations from 1995 to 2001 to the decrease in NO2 concentrations over this period at LT. The importance of humidity and temperature in O3 photochemistry observed at almost all South African sites has been highlighted by Balachov et al (2014), Laban et al (2018Laban et al ( , 2020. Laban et al (2018) added that emissions from domestic combustion (in winter) and regional biomass combustion (in spring) are also responsible for the increase of O3 levels.…”

Non-CO2 greenhouse gases: the underrepresented, complex side of the climate challenge

“…This Special Issue contains a selection of contributions to the symposium. It consists of this overview and seven topical papers as illustrative examples of the scientific results presented at the symposium (Wilson and Scholes 2020;O'neill et al 2020;Godwin and Ferenchiak 2020;Howarth 2020;Laban et al 2020;Verchot et al 2020;Zou and Osborne 2020). In the following section, we give an impression of the seven contributions.…”

“…The last two papers are focused on policy, mitigation measures and emission accounting (i.e. more related to the 3rd and 4th theme of NCGG8) Laban et al (2020) determined statistical relationships between surface ozone (O 3 ), its precursor species and meteorological conditions in South Africa. Multiple linear regression analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting daily O 3 variability.…”