Measurement report : Statistical modelling of long-term 1 atmospheric inorganic gaseous species trends within 2 proximity of the pollution hotspot in South Africa 3

12 South Africa is considered an important source region of atmospheric pollutants, which is 13 compounded by high populationand industrial growth. However, this region is understudied, 14 especially with regard to evaluating long-term trends of atmospheric pollutants. The aim of this 15 study was to perform statistical modelling of SO2, NO2 and O3 long-term trends based on 21-, 16 19and 16-year passive sampling datasets available for three South African INDAAF 17 (International network to study Atmospheric Chemistry and Deposition in Africa) sites located 18 within proximity of the pollution hotspot in the industrialised north-eastern interior in South 19 Africa. The interdependencies between local, regional and global parameters on variances in 20 SO2, NO2 and O3 levels were investigated in the model. Long-term temporal trends indicated 21 seasonal and inter-annual variability at all three sites, which could be ascribed to changes in 22 meteorological conditions and/or variances in source contribution. Local, regional and global 23 parameters contributed to SO2 variability, with total solar irradiation (TSI) being the most 24 significant factor at the regional background site, Louis Trichardt (LT). Temperature (T) was 25 the most important factor at Skukuza (SK), located in the Kruger National Park, while 26 population growth (P) made the most substantial contribution at the industrially impacted 27 Amersfoort (AF) site. Air masses passing over the source region also contributed to SO2 levels 28 at SK and LT. Local and regional factors made more substantial contributions to modelled NO2 29 levels, with P being the most significant factor explaining NO2 variability at all three sites, 30 while relative humidity (RH) was the most important local and regional meteorological factor. 31 The important contribution of P on modelled SO2 and NO2 concentrations was indicative of 32 https://doi.org/10.5194/acp-2020-166 Preprint. Discussion started: 6 April 2020 c © Author(s) 2020. CC BY 4.0 License.


47
Although Africa is regarded as one of the most sensitive continents with regard to air pollution 48 and climate change, it is the least studied (Laakso et al., 2012). South Africa is considered an 49 important source region of atmospheric pollutants within the African continent, which is 50 attributed to its highly industrialised economy with the most significant industrial activities 51 including mining-, metallurgical-and petrochemical activities, as well as large-scale coal-fired 52 electricity generation (Rorich and Galpin, 1998;Tiitta et al., 2014). Atmospheric pollution 53 associated with South Africa is compounded by high population growth that, in turn, drives 54 further economic and industrial growth leading to an ever-increasing energy demand (Tiitta et 55 al., 2014). The extent of air pollution in South Africa is illustrated by the well-known NO2 56 pollution hotspot revealed by satellite data over the Mpumalanga Highveld, where 11 coal-57 fired power stations are located (Lourens et al., 2011), which was also recently indicated by 58 the newly launched European Space Agency Sentinel 5P satellite (Meth, 2018).

59
The importance of long-term atmospheric chemical measurements has been indicated by 60 numerous studies on atmosphere-biosphere interactions (Fowler et al., 2009) and air quality 61 (Monks et al., 2009). These long-term assessments are crucial in identifying relevant policy 62 requirements on local and global scales, as well as the most topical atmospheric chemistry 63 research questions (Vet et al., 2014;IPCC, 2014). In 1990, the International Global The predominant anticyclonic air mass circulation over the interior of South Africa is reflected 145 by the overlay back trajectories at each site, while it also indicates that AF is frequently 146 impacted by air masses passing over the major sources in the north-eastern interior. In addition, 147 it is also evident that the rural background sites (LT and SK) are also impacted by the regional 148 circulation of air masses passing over the major sources. Overlaid hourly arriving 96-hour back-trajectories for air masses arriving at (a) (Ferm, 1991;Dhammapala, 1996;Martins et al., 2007;Adon et al., 2010). In addition, the 161 passive samplers utilised in this study have been substantiated through a number of inter-162 comparison studies (Martins et al., 2007;He and Bala, 2008).
163 Samplers were exposed in duplicate sets for each gaseous species at each measurement site 164 (1.5 m above ground level) for a period of approximately one month and returned to the 165 laboratory for analysis. Blank samples were kept sealed in the containers for each set of The trend was parameterised as linear: Trend (t) = α0 + α1.t, where t denotes the time range, α0 195 is a constant, α1 is the slope of Trend(t) line that estimates the trend over the time scale.

196
The significance of each of the independent variables on the calculated C(t) was evaluated by 197 the relative importance weights (RIW) approach, which examines the relative contribution that 198 each independent variable makes to the dependent variable and ranks independent variables in 199 order of significance (Nathans et al., 2012;Kleynhans et al., 2017 (Kaufman et al., 2003). Fire events were separated into local fire events (LFE), occurring 231 within a 100 km radius from a respective site, and regional fire events (DFE), taking place 232 between 100 km and 1 000 km from each site.

233
Hourly arriving back trajectories (as discussed above) were also used to calculate the Igneous Complex, as well as a region of anticyclonic recirculation (Fig. 1).

240
Since data was not available for certain local and regional factors considered in the model for 241 the entire sampling periods at AF, LT and SK, and, in an effort to include the optimum number 242 of local and regional factors available for each site, modelled concentrations could not be 243 calculated for the entire sampling periods when global, regional and local factors were included 244 in the MLR model.  (1997 -2015), LT (1995LT ( -2015 and SK (2000SK ( -2015. Seasonal and inter-249 annual variability associated with changes in the prevailing meteorology and source  The inter-annual variability of SO2, NO2 and O3 levels is presented in Fig. 6 observed periods of decreased and increased SO2 and NO2 levels are also indicated by the 308 three-year moving averages of the annual mean SO2 and NO2 concentrations at all three sites.

309
Since these trends are observed at all three sites, located several kilometres apart in the north-310 eastern interior, these inter-annual trends seem real and not merely a localised artefact.  after which all factors (local, regional and global) were incorporated in the model (ii and iii).

355
In Table 1  contribution. Although TSI was the second most significant factor at AF and SK, local and 407 regional parameters were more important to variances in modelled SO2 levels at these sites. African commodity-based producers (e.g. platinum group metal, base metal, ferrochromium, 427 ferromanganese, ferrovanadium and steel smelters) to completely discontinue production.

428
Ferrochromium production in South Africa, for instance, decreased by approximately 35%  Furthermore, these variances in source contribution associated with anthropogenic activities 432 are also observed at LT and SK distant from the major sources due to these sites also being 433 impacted by the regional circulation of air masses passing over major sources, as indicated in  between measured and modelled NO2 are also significantly improved when local, regional and 492 global factors are included in the model at all three sites (Pane iii in Fig. 10a, b and (Sheskin, 2003). In general, modelled NO2 concentrations   of local and regional factors on measured NO2 concentrations (Pane iii in Fig. 10a, b and c).

549
The RMSE differences between the modelled and measured NO2 concentrations (Pane i Fig.   550 10a, b and c) indicated that the linear combination between most of the global force factors, 551 i.e. IOD, TSI, QBO and ENSO, resulted in the largest decrease in RMSE when only global 552 force factors were included. The RIW% listed in Table 2 for the optimum MLR equation,   553 including only global factors, indicates that IOD (65.3% and 49.4%, respectively) was the most 554 significant parameter at AF and SK, while TSI (52.4%) was the most important factor at LT.

555
The inclusion of local, regional and global factors in the MLR model indicated that the (i.e. NO2 levels) were P (53.7%) and IOD (17.8%) at AF, P (29.9%), RH (16.6%) and IOD 561 (15.5%) at LT, and P (29.8%) and RH (20.6%) at SK. It is evident from these interdependencies 562 of the dependent variable and RIW% of parameters included in the MLR model that local and 563 regional factors were more significant to NO2 variability at AF, LT and SK, while global 564 meteorological factors also contributed to variances in NO2 levels.

565
Population growth made the most significant contribution to modelled NO2 concentrations at 566 all three sites, and not only at AF, as observed for SO2. Therefore, the influence of increased 567 population growth and associated anthropogenic activities is reflected in ambient NO2 568 concentrations modelled for the entire north-eastern interior region. Therefore, the periods variability can be attributed to similar variances in source contribution, as discussed above for 571 SO2, with regional circulation of air masses passing over major sources also influencing LT 572 and SK (Fig. 2). However, the significant contribution of population growth to the modelled 573 https://doi.org/10.5194/acp-2020-166 Preprint. Discussion started: 6 April 2020 c Author(s) 2020. CC BY 4.0 License.
NO2 levels at two rural background sites (LT and SK) also points to increased household 574 combustion associated with enlarged populations within rural communities being a major 575 source of NO2 in this part of South Africa. The influence of increased seasonal household 576 combustion is also indicated by higher NO2 concentrations determined in June and July at SK 577 (Fig. 4), which also signifies the impacts of the growing rural communities in proximity of SK.

578
RH made the second most important contribution in explaining variances in modelled NO2 579 concentrations at LT and SK, while it was the third most important factor at AF as indicated 580 by RIW%. Therefore, RH can be considered the factor representing the influence of changes 581 in local and regional meteorology at these sites. Although T was indicated as a factor included 582 in the linear combination of parameters yielding the largest decrease in RMSE at AF and SK, 583 its relative importance in explaining modelled variances is not indicated by its RIW% in Table   584 2. The strong negative correlation with RH is indicative of increased NO2 corresponding with 585 months (or years) when dry meteorological conditions prevail, i.e. winter and early spring 586 months in the north-eastern interior of South Africa. As indicated in Fig. 4   at AF, and the second most important factor at LT and SK when local, regional and is a period associated with increased rainfall and less stable meteorological conditions (Fig. 5).

660
The influence of regional open biomass burning during late winter and spring (August to growth, as discussed above (section 3.2.2).

678
The comparisons between modelled and measured O3 concentrations (Pane iii in Fig. 11a, b   679 and c) also indicated, as observed for SO2 and NO2, that the correlations are significantly especially ENSO, as indicated above, on O3 variability (Pane iii in Fig. 11a, b and c).  source region as previously indicated. However, the regional O3 problem in the South Africa  Carmichael et al., 2003). Similar O3 concentrations were determined at the South American 814 regional sites, except for Petit Saut that had lower O3 concentrations (Carmichael et al., 2003).

815
Average O3 levels determined at some of the regional Asian sites were in the same range as O3 global parameters contributed to variances in NO2 concentrations, local and regional factors 844 made more substantial contributions to modelled NO2 levels. The most significant factor 845 explaining NO2 variability at all three sites was population growth, while RH was the most 846 important local and regional meteorological factor. Therefore, similar to SO2, the influence of 847 population growth and associated increases in anthropogenic activities in the north-eastern 848 interior is also reflected in NO2 levels, while the impacts of increased household combustion 849 associated with growing rural communities are also evident, especially at SK. The negative 850 correlation to RH indicates higher NO2 levels associated with drier months, i.e. winter, which 851 contribute to seasonal variances. ENSO was shown to make a significant contribution to 852 modelled O3 levels at all three sites, while the important influence of local and regional 853 meteorological factors was also evident, especially through significant negative correlations 854 with T and RH at SK and LT. Inter-annual O3 variability in this part of South Africa can 855 therefore most likely be attributed to ENSO cycles, while seasonal patterns are attributed 856 changes in local and regional meteorology.

857
The decreases in SO2 and NO2 concentrations from 1995 were attributed to the implementation 19-, 21-and 16-year sampling periods at AF, LT and SK, respectively.

868
As expected, SO2 and NO2 concentrations were higher at AF compared to levels thereof at the 869 rural background sites LT and SK. SO2 levels at AF were similar to levels of these species 870 determined with passive samplers at other sites within the Mpumalanga Highveld with the 871 exception of sites closer to the major industrial sources. NO2 levels at AF were generally lower 872 than NO2 concentrations determined at sites within the source region, as well as than regional 873 sites in China. SO2 and NO2 concentrations determined at LT and SK were similar to levels 874 thereof determined with passive samplers at regional and rural sites in Africa and other parts 875 of the world. The regional problem of O3 in the interior of South Africa was also evident, with 876 similar O3 levels determined at all three sites.