Visibility as a proxy for air quality in East Africa

The hourly horizontal visibility data along with other meteorological parameters, such as RH, wind speed (ws) and wind direction (wd), were downloaded from National Oceanic and Atmospheric Administration (NOAA), Integrated Surface Database (ISD) system using the worldmet package in R (https://github.com/davidcarslaw/worldmet) (Lott et al 2008, Smith et al 2011, Carslaw 2017). The study sites: Kampala (Entebbe International Airport), Addis Ababa (Bole International Airport), and Nairobi (Jomo Kenyatta International Airport) are shown in figure 1. Data were downloaded for 45 years (1974 to 2018). For the same time period, the population data for study sites were obtained from the United Nations, Department of Economic and Social Affairs, Population Division (http://www.un.org/en/development/desa/population/), and national GDP data was obtained from the World Bank (https://data.worldbank.org/).

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Hourly mean PM_2.5 (PM with diameter less than 2.5 μm) mass concentration data were obtained from Airnow (airnow.gov) observing stations, located at the US Embassy locations in Kampala (Ggaba Road) and Addis Ababa (Entoto Street) for the (available) period of 2017–2018. Airnow observational stations at Kampala and Addis Ababa are located approximately 34 and 10 km from the meteorological sites, respectively. Airnow is run by the U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration (NOAA), National Park Service, NASA, Centers for Disease Control, and tribal, state, and local air quality agencies (White et al 2004, White 2010).

2.2.1. Nairobi (kenya)

2.2.2. Kampala (uganda)

2.2.3. Addis ababa (ethiopia)

PM and light absorbing gases can influence the visibility via the scattering (sca) and absorption (abs) of radiation at specific wavelengths (Huang et al 2009). In general, visibility (V) can be represented as a function of the extinction coefficient (β_ext) using equation (1), where visibility is inversely proportional to the β_ext (Koschmieder 1924).

$$\begin{equation}{V = K /\beta _{ext}}\end{equation} \tag{ 1 }$$

Here, is the Koschmieder constant, equal to 3.912, which assumes a visual contrast threshold of 2%. β_ext is the total extinction coefficient and can be explained via equation (2).

$$\begin{equation}{\beta _{ext}} = {\beta _{gas,sca}} + {\beta _{gas,abs}} + {\beta _{particle,sca}} + {\beta _{particle,abs}}\end{equation} \tag{ 2 }$$

Typically, the contribution of aerosol particle extinction to the visibility loss far outweighs the contribution of gases (Singh and Dey 2012, Singh et al 2017). Nitrogen dioxide (NO₂) is the only gas with a significant visible absorption coefficient (Groblicki et al 1981, Singh et al 2017), but its contribution to the extinction coefficient/visibility is typically minor compared to the extinction caused by PM.

Trend analysis is performed upon the visibility and meteorology data set. The visibility along with other meteorological parameters at 12:00 noon for each day was averaged (mean) to determine trends over annual, decadal, seasonal and monthly cycles. The seasonal periods of all three sites are slightly different, as defined in table 1.

To understand the influence of hygroscopic growth of aerosol particle on visibility change, the data set were disaggregated into 5% RH bins. Data with RH > 97.5% is excluded because of increased likelihood of visibility disrupting fog and mist (Singh et al 2017).

45 years (1974–2018) of visibility data for the three study locations is investigated. Overall, the lowest mean yearly visibility (± 1 σ), over the whole study period, was in Kampala (15.2 ± 7.4 km) compared to Nairobi (18.6 ± 6.7 km) and Addis (19.8 ± 8.7 km). Figures 2 and 3 provide the annual and seasonal visibility for the three sites. Individual regressions for yearly average visibility versus year are shown in figure S1(avaliable online at stacks.iop.org/ERL/15/084002/mmedia). Clear downward trends in annual mean visibility is observed for all study sites (figure 2), which is understood to be due to increasing concentrations of PM. Visibility in Kampala, Nairobi and Addis Ababa decreased at a rate of 0.45, 0.52, and 0.26 km yr⁻¹, respectively, see figure S1. Nairobi has the greatest visibility loss (60%) over the study period compared to Kampala (56%), and Addis Ababa (34%).

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Clear trends in seasonal visibility are observed for Kampala and Addis Ababa, where relatively high visibility is observed during wet/rainy periods (figures 3(a) and (c)). In particular, lower visibility with higher RH is observed during summers in Addis Ababa, while in winter higher visibility with lower RH was found. In Kampala, overall visibility was higher in both the wet seasons as compared to dry seasons. Negligible seasonal changes in Nairobi (figures 3(a) and (b)) were observed. Overall, average visibilities for all seasons have shown a general decline for all sites from 1970s to 2010s (figures S2 and S3).

Monthly and day-of-week averaged trends of visibility and RH were determined at each study site over the whole time period. Figure 4(a) provides the monthly averaged values of visibility, where a clear and strong monthly cycle is observed for all three sites. In particular, monthly visibility trend patterns were nearly the same for Kampala and Addis Ababa, wherein visibility during the wet seasons were higher than dry months. However, due to its distinct seasons, monthly visibility trends in Nairobi were different from Kampala and Addis Ababa but the wet seasons show relatively higher visibilities compared to dry months. This suggests that wet deposition of PM is a significant factor in improving visibility and hence air quality in the region. In a previous study, Gaita et al (2014) found similar results in Nairobi, where air quality improved during wet months compared to dry months.

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Day-of-the-week visibility is analysed using the whole study period. No significant day-of-week effects are observed for all three study sites. However, visibilities on Sundays are relatively higher compared to weekdays, which is likely due to lower traffic and industrial emissions on Sundays.

RH and wind are key meteorological parameters to understand the seasonal visibility variation. Wind can influence PM concentrations by generating and depositing PM, windy conditions also lead to dilution of pollution by bringing fresh air into the city. RH influences the aerosol scattering efficiency via the hygroscopicity effect (Zhao et al 2011, Li et al 2014, Singh et al 2017). Relationships between visibility and RH and wind speed are shown in figure 5. Overall, a strong negative correlation between monthly visibility and RH was observed in Nairobi and Addis Ababa (figure 5, panels b2 and c2). However, low correlation between monthly visibility and RH was noted in Kampala, which is likely due to the small observed range of RH, which makes it difficult to observe trends above noise. Kampala's complex topographical and geographical situation may also contribute. The monitoring station in Kampala is located at the shore of the very large Lake Victoria (surface area of 68 800 km²), which influences the local meteorology of Kampala. In figure 5, panels: a3, b3 and c3 show the correlation between wind speed and visibility with good correlations for Nairobi and Addis Ababa observed, while a poor correlation was observed for Kampala, which is again likely due to smaller range of wind speed and geographical complexities of Kampala.

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4.3.1. Particle hygroscopicity effect upon visibility

RH influences visibility by changing particle properties (size, volume and weight). Figure 6 shows the visibility variations at different RH bins for the three study sites. Overall, a similar pattern was observed for all three sites, with visibility showing a clear dependence upon RH (figure 6(a)), indicating the bulk PM composition is hygroscopic. Figure 6(b) shows the normalized visibility trends, it indicates that the PM hygroscopicty of the bulk aerosol are similar at each site. The bulk hygroscopicity is found to be lower in East Africa when compared to sites in the UK (Singh et al 2017). Following the approach of Singh et al (2017), a hygroscopicity parameter (γ) for the bulk aerosol can be derived, see discussion and figure S4 in the supplementary material for more information. In general, the aerosol hygroscopicity, as parameterized by γ, at the three sites decreases over time indicating that the aerosol is becoming less hygroscopic. This could indicate an increasingly greater fraction of the aerosol coming from resuspension of relatively non-hygroscopic dust from traffic emissions. Other sources of low hygroscopicity PM could be biomass burning.

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To understand the distribution of wind speed and directions at the three study sites, wind rose plots are provided in figure 1. All three sites have distinct dominant wind directions, where the predominant wind directions in Kampala, Nairobi and Addis Ababa are from the southwest, northeast and eastern directions, respectively.

Bivariate polar plots of visibility, relative humidity and PM_2.5 mass concentration were generated using the openair tool (Carslaw and Ropkins 2012) in RStudio (Allaire 2012) to explore the effect of both wind speed and wind direction upon PM concentration. In general, bivariate polar plots provide an effective graphical presentation of emission sources with wind speed and wind direction and allows for simple PM source apportionment. Figure 7 provides the graphical information on the variation of visibility and RH (and PM mass concentration) with wind speed and direction for all three study sites using two years of data (2017–2018). The length of the time period analysed here was constrained by the availability of the PM_2.5 data. Bivariate polar plots of PM_2.5 mass concentration were performed only for Kampala and Addis Ababa since no long-term continuous data were available for Nairobi to undertake a similar analysis.

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4.4.1. Kampala

4.4.2. Addis Ababa

4.4.3. Nairobi

The paucity of historic air pollution data in East African sites makes it is difficult to assess the evolution of urban air quality in the region. However, in this paper we have shown that visibility can be used as a proxy for PM pollution since visibility depends on particle scattering and absorption and is inversely related to the extinction coefficient (β_ext) via the Koschmeider equation (1) (i.e. low visibility = high particle loading). To understand the changes in long term air quality, a decadal pollution index was calculated using visibility data for all three sites, see figure 8. The index is referenced to the visibility levels observed in the 1970s, using a simple mean over all data. Significant increases in the pollution index are observed for all three study sites. The index, suggests that PM pollution levels have increased by 162 %, 182% and 62% from the 1970s to 2010s at the Kampala, Nairobi, and Addis Ababa sites, respectively. Changes in pollution levels in these cities are likely due to a combination of increasing population, fuel use, motorization, industrialization and construction.

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Socio-economic factors contribute to the causes of PM air pollution and hence they influence visibility. The much debated Environmental Kuznets Curve (EKC) hypothesis suggests a relationship between environmental degradation and economic development (Stern et al 1996, Li et al 2007). The inverted U-shaped EKC hypothesis suggests that environmental degradation initially increases with economic growth, until an inflexion point is reached, and then further economic growth results in reduced environmental degradation, see figure S5 for a hypothetical EKC. Thus, the EKC implies economic development can be a significant and powerful factor for the environment throughout a region's development. Previous studies mention that EKC hypothesis can be explained factors such as adoption/diffusion of clean technologies, globalisation, foreign trade and investment (Copeland and Taylor 1995, Reppelin-Hill 1999, Dasgupta et al 2002). The inverted U-shape predicted by the EKC can be approximated by a lognormal curve (Gangadharan and Valenzuela 2001, Kahuthu 2006, Halkos 2011).

To investigate the influences of socio-economic factors upon visibility, the relationship between study city population, countrywide GDP and visibility for the study sites are analysed (figures 9 and S6). Major increases in population growth and national GDP are observed over the last five decades in all three study cities. GDP data for the individual cities were not available, but it is noted that the country GDP is predominantly driven by capital cities in the countries of interest. A clear decline in visibility is found to coincide with population growth over the last five decades (figure 9(a)), with a significant anti-correlation between city population and visibility in all three cities observed: Kampala (R² = 0.81), Nairobi (R² = 0.91), and Addis Ababa (R² = 0.98). The growth in population and GDP results in increased rates of fuel use and motorization (Sachs et al 2004, Floater et al 2014), which provides a direct and causal link to increased PM emissions and thus visibility loss. In future studies, if the data becomes available, it would be good to investigate the role of population density in addition to total population on air pollution. In figure 9(b) (and S6b), β_ext is compared to GDP using decadal means. For all three study cities, an increasing trend of β_ext with GDP is observed in which the relationship over the available data range is well modelled using a natural log function. Overall, the results suggest that the observed shape of the curves are consistent with EKC hypothesis with East African urban areas entering a phase of a weakening positive relationship between outdoor air quality and GDP. However, these results do not definitively prove the EKC hypothesis for the region and pollutant studied.

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