The Concentration, Transport and Fate of Nitrogen Oxides in the Highveld Atmosphere

Abstract

The Highveld region of South Africa is a major source of emissions from large industries, including electricity utility power stations, petrochemical plants, and smelting industries. This study investigates the sources, concentrations, and transport of nitrogen dioxide (NO2) emissions in Wakkerstroom, a town located downwind of major NO2 emission sources in the Mpumalanga Highveld. Near-surface and tropospheric vertical column NO2 concentrations were measured using ground-based Pandora-2s and TROPOMI-Sentinel-5P satellite instruments. The Pandora-derived diurnal NO2 concentrations at Wakkerstroom ranged between 0.2 ppb, with a standard deviation of ± 0.04 ppb and 7.5 ppb, with a standard deviation of 5.7 ppb, with the highest concentrations recorded in September. Air quality monitors near power stations measured peak weekly averaged concentrations in winter (27.6 ppb) and summer (31.5 ppb) of 2020. The annual mean Pandora-derived tropospheric vertical column (TVC) NO2 concentrations were 5.1 x 10-5 mol/m2 with a standard deviation of 9.0 x 10-5 mol/m2 in 2020 and 5.03 × 10-5 mol/m2 with a standard deviation of 9.40 × 10-5 mol/m2 in 2021, while TROPOMI measured 21% and 40% higher concentrations, respectively. Theil-Sen regression analyses showed that TROPOMI overestimates Pandora-derived TVC-NO2 concentrations by over 50%. Backward trajectory analyses revealed that air mass transport from the east of Wakkerstroom, passing over eSwatini and southern Mozambique, contributed significantly to the observed NO2 concentrations due to biomass burning. The findings of this study highlight the complex dynamics of NO2 pollution in the Highveld region, revealing the influence of both local and regional sources on Wakkerstroom's concentrations. This research emphasises the fundamental role of continuous long-term ground-based data of pollutants in heavily polluted areas like the Highveld, which is characterised by unique synoptic meteorological patterns. Such data serves to bridge the information gaps to corroborate and augment high-spatial resolution satellite observations. Consequently, it helps to confirm the reliability and use of satellite data as a proxy for larger geographical areas with scarce ground-based instruments.