A periodical of the Faculty of Natural and Applied Sciences, UMYU, Katsina
ISSN: 2955 – 1145 (print); 2955 – 1153 (online)
ORIGINAL RESEARCH ARTICLE
*Bello Sa’adu1, Usman M. Gana2
1Department of Physics Umaru Musa Yaradua University Katsina, Nigeria
2Department of Physics, Bayero University Kano, Nigeria
Corresponding email: bellosaadu5222@gmail.com
West Africa has experienced rapid urbanization, economic growth, population increase, and industrialization, with no serious attention paid to aerosol emissions in the past decade. Thus, understanding the spatial and temporal variation of aerosol optical depth (AOD) is urgently needed to evaluate the effectiveness of aerosol control activities. Based on the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite, AOD was retrieved over West Africa from 2012–2024. The monthly data were utilized for the analysis. The ground-based AOD from AERONET at a 550 nm wavelength was interpolated for inter-comparison with VIIRS satellite AOD data over the study period. Statistical parameters such as mean, maximum, minimum, and standard deviation were calculated. The results show that the monthly average mean AOD value in Nigeria ranges from 0.25–0.84, in Benin from 0.35–0.91, and in Niger from 0.24–0.84. The annual average AOD in Nigeria and Niger varied between 0.41–0.55, while in Benin it varied between 0.44–0.58. In terms of season, Nigeria has the highest mean AOD value in spring (0.66) and the lowest in winter (0.37), Niger has the highest mean AOD value in summer (0.74) and the lowest in winter (0.26), and Benin has the highest mean AOD value in spring (0.70) and the lowest in autumn (0.39). In addition, the AOD fluctuated greatly in 2015 and 2022 in Nigeria with the highest standard deviation (0.54 and 0.47), while the AOD fluctuated in 2016 and 2015 with the highest standard deviation (0.55 and 0.45) in Niger and Benin, respectively. These results provide a robust basis for assessing regional climate–aerosol interactions, and the validation of satellite observations with ground-based measurements shows good agreement.
KEYWORDS; AOD; VIIRs; AERONET; Spatial; Temporal
Aerosols are solid and liquid particles suspended in air, with diameters of up to several micrometers, such as smoke, haze, dust, particulate matter, and liquid droplets (Payra et al., 2023; Choi et al., 2019). These particles are emitted from both natural and anthropogenic sources (Wang et al., 2017). Aerosol particles scatter and absorb sunlight, and the scattering of light from the Earth’s surface affects the atmospheric radiation balance (Lang-Yona et al., 2010). These particles can persist in the atmosphere from days to weeks and can be transported from one region to another (Winker et al., 2013). The optical parameters of aerosols, such as Aerosol Optical Depth (AOD), Ångström Exponent (AE), and Single Scattering Albedo (SSA), indicate important characteristics of aerosols, including column-integrated aerosol load, particle size, and light-absorbing properties, which depend on their chemical composition. Specifically, SSA measures the ratio of the scattering coefficient to the total extinction coefficient (Xinyu et al., 2022). These parameters are also important factors in climate change analysis (Deng et al., 2012). AOD is a primary aerosol parameter representing the amount of aerosol loading in the atmosphere, while AE is generally used to estimate aerosol particle size. Aerosols are classified by particle size: coarse-mode aerosols with diameters greater than 1 µm are dominant when the AE value is smaller than 0.7, whereas a higher AE (>1.8) indicates the dominance of fine-mode aerosols with diameters smaller than 0.1 µm. An AE value between 0.7 and 1.8 is typical of the accumulation mode, where aerosol diameters vary between 0.1 and 1 µm (Kaufman et al., 1995).
Aerosol Optical Depth (AOD) is an important indicator and key parameter for understanding atmospheric physics and regional air quality by quantifying the aerosol load in the atmosphere (Bhatia et al., 2018; Filonchyk et al., 2018; Li et al., 2019), and it is generally derived and estimated using satellite observations. There are two main methods to measure AOD in the atmosphere: ground-based observations using sun photometer measurements and remote sensing inversion, which relies on sensor data from satellite platforms (Bello et al., 2025). Moreover, aerosols increase the rate of mortality and morbidity and may lead to accidents on roads as they reduce visibility levels. At present, different ground-based monitoring networks have been established to provide AOD data worldwide, such as the AERosol RObotic NETwork (AERONET) (Holben et al., 1998), the Sun-Sky Radiometer Observation Network (SONET) (Ma et al., 2016), and the Sky Radiometer Network (SKYNET) (Pilahome et al., 2022). There are also many remote sensors that can monitor AOD, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) (Pilahome et al., 2022), the Visible Infrared Imaging Radiometer Suite (VIIRS) (He et al., 2018), the Advanced Very High Resolution Radiometer (AVHRR) (Xue et al., 2017), and the Ozone Monitoring Instrument (OMI) (Stammes et al., 2002). Among them, MODIS, developed by NASA and carried on the Terra and Aqua satellites, is widely used for studying regional AOD due to its advantages, such as easy accessibility and excellent spatial and temporal resolution.
Ground-based observation networks such as the Aerosol Robotic Network (AERONET) provide accurate time-series AOD observations at different sites around the world (Estellé et al., 2012; Cesnulyte et al., 2014). However, due to the limited number of observation sites, it is difficult to produce AOD data that cover large geographical areas (Liu et al., 2016; Aklesso et al., 2018). Satellite-based observations, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products, have recently proved useful in ensuring the spatiotemporal continuity of AOD observations (Liu et al., 2016; Nichol et al., 2016). The MODIS AOD products provide daily operational retrievals over land based on the Dark Target (DT) or Deep Blue (DB) algorithms (Ma et al., 2014; He et al., 2018). The DT algorithm was developed to retrieve AOD only over dark surfaces (e.g., water and dense vegetation), whereas the DB algorithm works over both dark and bright surfaces (e.g., arid, semi-arid, and urban areas) (Tao et al., 2015; Fan et al., 2017).
In this paper, we used Visible Infrared Imaging Radiometer Suite (VIIRS) satellite remote sensing data over a long time period, together with ground-based sun photometer observations from AERONET, to verify the accuracy of the VIIRS AOD product. The aims of the study were to investigate the spatial and temporal distribution of AOD over the West African sub-region (Benin, Niger, and Nigeria) during the 12-year period from 2012 to 2024, to discuss the monthly, seasonal, and annual differences, and to analyze the spatial patterns of AOD, aerosol types, and development trends in the region.
The study area is located in West Africa, covering Nigeria, Niger, and Benin. The combined territory of the three countries spans approximately from latitudes 4°N to 23°N and longitudes 1°E to 17°E, representing a complete transitional zone from the humid coastal areas of the Gulf of Guinea to the semi-arid Saharan desert. The study area comprises the entirety of Nigeria (923,769 km²), Niger (1,267,000 km²), and Benin (112,622 km²), making it one of the most populous regions on the continent, with a resident population of over 270 million, as shown in Figure 1.
Figure 1: An over view map of the study area
VIIRS is an instrument onboard the joint NASA and NOAA Suomi NPP mission (Zhou et al., 2016). It collects global observations across the visible and infrared wavelengths over the ocean, land, and atmosphere using a whiskbroom radiometer design. It was launched into space on 28 October 2011 into a sun-synchronous, polar orbit around the Earth (Xiong et al., 2013). The VIIRS sensor operates with a total of 22 spectral channels ranging from 0.412 µm to 12 µm (Moyer et al., 2018). The spatial resolution of VIIRS varies from 375 m to 750 m, and it covers the entire Earth twice daily (Oudrari et al., 2014). Suomi NPP crosses the equator at 1:30 a.m. (nighttime overpass) and 1:30 p.m. (daytime overpass) at the same local time (Payra et al., 2023).
AERONET (Aerosol Robotic Network) is a global ground-based network that provides long-term measurements of aerosol optical properties at a wide range of wavelengths from 340 to 1640 nm with high temporal resolution. AERONET uses the CE-318 sun photometer to measure and provide data on aerosol optical, microphysical, and radiometric properties, and has been in operation for more than 25 years (Wang et al., 2023). It provides globally distributed observations of spectral aerosol optical depth (AOD), inversion products, and precipitable water across diverse aerosol regimes. Version 3 AOD data are computed for three data quality levels: Level 1.0 (unscreened data), Level 1.5 (cloud-screened and quality-controlled data), and Level 2.0 (cloud-screened and quality-assured data) (de Leeuw et al., 2018), corresponding to different sites.
Preprocessing is the initial step in obtaining reliable AOD data for analysis. This cleaning process ensures consistency of the data and addresses potential issues. Daily AOD data were obtained from LAADS Web and aggregated into monthly, annual, and seasonal datasets. Missing values and outliers were discarded during preprocessing to ensure data integrity. AOD measurements that significantly deviated from the expected range of values in a dataset were considered outliers. Because AOD data have a fine resolution, they were aggregated to match the AERONET data by calculating the average AOD values in each grid cell. This step ensured that the spatial resolution of the AOD data aligned with that of AERONET, enabling accurate comparisons and analyses. The retrieved VIIRS AOD data were then averaged to present a comprehensive view of air quality across monthly, seasonal, and annual timescales using Python and Excel.
To validate the reliability of VIIRS data in the West African region, three stations—Ilorin (Nigeria), Banizoumbou (Niger), and Djougou (Benin)—were selected for validation. Based on the inversion of VIIRS, the AOD value at 550 nm was obtained. However, only two stations matched the timescale of the VIIRS satellite, as the Djougou site ceased operation in 2007, while VIIRS was launched in 2011. To compare and analyze the sun photometer data with VIIRS remote sensing AOD products, and to better match the AOD values of the products at the corresponding wavelength bands and spatial and temporal scales, the AOD value at 550 nm was obtained from the sun photometer data (AERONET) using the angstrom exponential formula. By applying AOD data at wavelengths close to 550 nm, typically 500 nm and 675 nm, the AOD at 550 nm was calculated for the sun photometer data (Zhu et al., 2023). According to Angstrom’s formula:
\[\tau_{550} = \beta \times 500^{- \alpha}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (1)\]
\[\alpha = - \frac{\ln\left( \frac{\tau_{500}}{\tau_{675}} \right)}{\ln\frac{500}{675}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (2)\]
\[\beta = \frac{\tau_{550}}{500^{- \alpha}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (3)\]
where α is the Angstrom exponent(500-675nm), estimated from the slope of the spectral AOD plots in logarithmic scales, β is the turbidity coefficient, \(\tau_{500}\)means AOD at 550 nm, and \(\tau_{675}\)means AOD at 675 nm
\[x = \frac{1}{n}\sum_{i = 1}^{n}{x_{i}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (4)}\]
\[\sigma = \sqrt{\frac{1}{n}\sum_{i = 1}^{n}{\left( x - \overline{x} \right)^{2}\ \ }}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (5)\]
Where \(\overline{x}\) is the arithmetic mean of VIIR AOD, and \(x_{i}\) is the ith AOD sample value with n number of samples.
\[R = \frac{\sum_{i = 1}^{n}{\left( x_{i} - \overline{x} \right)\left( y_{i} - \overline{y} \right)}}{\sqrt{\sum_{i}^{n}\left( x_{i} - \overline{x} \right)^{2}} \times \left( y_{i} - \overline{y} \right)^{2}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (6)\]
R is the correlation between the variable x and the variable y; \(x_{i}\)is the AOD for a year i; \(y_{i}\)is the value of another AOD variable in year i; x is the average AOD over the study period; y is the average of the other variables over the study period.
The VIIRS satellite AOD was compared with ground-based AOD observations over AERONET stations (Ilorin, Banizoumbou, and Djougou), as shown in Figures 3 and 4. Figures 3(a) and 4(c) demonstrate strong agreement between VIIRS and AERONET measurements, with correlation coefficients (R2) of 0.91 for Nigeria and 0.81 for Niger, consistent with previous studies (Wang et al., 2023). Figures 3(c) and 4(d) further show that the highest VIIRS AOD occurred in 2016 and the lowest in 2012, while AERONET recorded its maximum in 2022 and minimum in 2024.
Monthly mean AOD for Nigeria, Niger, and Benin over the last decade is presented in Figures 5, 6 and 7. AOD fluctuated seasonally, peaking in February for Nigeria and Benin, and in June for Niger. The monthly mean exceeded 0.8 in Nigeria and Benin, and 0.7 in Niger. These seasonal maxima are attributed to enhance Saharan dust transport and biomass burning, which increase aerosol loading during these months.
Figures 8, 9 and 10 show interannual variation of mean AOD and its standard deviation (STD) for 2012–2024. The highest AOD values were observed in 2015–2017 and 2022, when mean AOD exceeded 0.5 across the countries. These peaks reflect intense dust outbreaks and biomass burning episodes, whereas declines in other years correspond to reduced dust activity and more efficient aerosol removal by precipitation. The STD followed the same trend, with greater variability during years of high AOD, indicating more heterogeneous aerosol conditions. In contrast, years with lower AOD were characterized by smaller STD, suggesting more stable and less extreme aerosol concentrations.
Figure 3: Comparison of VIIR data with AERONET data observations for Banizoumbou
Figure 4: Comparison of VIIR data with AERONET data observations for Ilorin
Figure 5: Variation of the monthly mean of AOD over Nigeria
Figure 6: Variation of the monthly mean of AOD over Niger
Figure 7: Variation of the monthly mean of AOD over Benin
Figure 8: Variation of the annual mean of AOD Red curve indicate a standard deviation over Nigeria
Figure 9: Variation of the annual mean of AOD Red curve indicate a standard deviation over Niger
Figure 10: Variation of the annual mean of AOD over Benin
The monthly minimum, maximum, mean, and standard deviation of AOD recorded in Nigeria, Niger, and Benin from 2012 to 2022 are summarized in Tables 1, 2, and 3. The annual mean AOD values were 0.49, 0.48, and 0.50, with corresponding standard deviations of 0.43, 0.42, and 0.28, respectively, indicating moderate variability. The minimum AOD dropped as low as 0.0490 for Nigeria and Niger, and 0.0182 for Benin, while the maximum reached up to 5 across the countries. In terms of seasonal variability, January exhibited the lowest mean AOD (0.25 for Nigeria, 0.24 for Niger, and 0.34 for Benin), whereas June showed the highest mean AOD in Nigeria (0.84) and Niger (0.84), and February in Benin (0.94).
The higher mean AOD values observed in Niger are primarily driven by Sahelian and Bodélé Depression dust transport, reinforced by frequent storm events (Prospero et al., 2020; Ridley et al., 2014). In Benin, elevated mean AOD values occur during the dry-season months, particularly in February. This pattern reflects Benin’s coastal location, where Harmattan dust transported from the north, combined with widespread regional biomass burning, dominates the aerosol regime (Jury & Santiago, 2010; Malavelle et al., 2011).
Table1: Monthly observation of Nigeria from 2012-2024
| Month | Observation | Minimum | Mean | Maximum | Std |
|---|---|---|---|---|---|
| January | 31248 | 0.0495 | 0.2473 | 3.0018 | 0.2137 |
| February | 28476 | 0.0490 | 0.3465 | 4.9991 | 0.3505 |
| March | 33600 | 0.0493 | 0.4419 | 5.0000 | 0.4772 |
| April | 32676 | 0.0499 | 0.4819 | 4.9577 | 0.4411 |
| May | 33600 | 0.0546 | 0.6690 | 5.0000 | 0.5908 |
| June | 32760 | 0.0577 | 0.8437 | 5.0000 | 0.6981 |
| July | 33012 | 0.0563 | 0.7082 | 5.0000 | 0.5697 |
| August | 33096 | 0.0545 | 0.6597 | 5.0000 | 0.5463 |
| September | 32760 | 0.0555 | 0.4953 | 4.9940 | 0.3976 |
| October | 33684 | 0.0527 | 0.3983 | 5.0000 | 0.3628 |
| November | 31332 | 0.0523 | 0.2792 | 4.4334 | 0.2179 |
| December | 33684 | 0.0499 | 0.2784 | 4.7296 | 0.2527 |
| Annual mean | 0.4875 | 0.4265 | |||
Table 2: Monthly observation of Niger from 2012-2024
| Month | Observation | minimum | Mean | maximum | Std |
|---|---|---|---|---|---|
| January | 28145 | 0.0495 | 0.2430 | 3.0018 | 0.2107 |
| February | 26257 | 0.0490 | 0.3408 | 4.9991 | 0.3453 |
| March | 30933 | 0.0493 | 0.4349 | 5.0000 | 0.4687 |
| April | 30118 | 0.0499 | 0.4732 | 4.9578 | 0.4373 |
| May | 30917 | 0.0546 | 0.6501 | 5.0000 | 0.5613 |
| June | 31151 | 0.0577 | 0.8389 | 5.0000 | 0.6963 |
| July | 30752 | 0.0563 | 0.7078 | 5.0000 | 0.5707 |
| August | 27806 | 0.0545 | 0.6698 | 5.0000 | 0.5616 |
| September | 29672 | 0.0555 | 0.4918 | 4.9940 | 0.4030 |
| October | 31999 | 0.0527 | 0.3920 | 5.0000 | 0.3541 |
| November | 28829 | 0.0523 | 0.2752 | 4.4334 | 0.2172 |
| December | 30608 | 0.0499 | 0.2727 | 4.7296 | 0.2468 |
| Annual mean | 0.4825 | 0.4228 |
Table 3: Monthly observation of Benin from 2012-2024
| Month | Observation | Minimum | Mean | Maximum | Std |
|---|---|---|---|---|---|
| January | 13117 | 0.0906 | 0.7203 | 4.8724 | 0.3211 |
| February | 10575 | 0.1551 | 0.9411 | 4.0145 | 0.4351 |
| March | 9190 | 0.0293 | 0.6512 | 5.0000 | 0.4564 |
| April | 7628 | 0.0275 | 0.5223 | 5.0000 | 0.3634 |
| May | 6759 | 0.0200 | 0.3675 | 3.6078 | 0.2016 |
| June | 4482 | 0.0267 | 0.3769 | 5.0000 | 0.2439 |
| July | 2577 | 0.0242 | 0.3825 | 4.9991 | 0.1805 |
| August | 2350 | 0.0288 | 0.4109 | 1.5634 | 0.1684 |
| September | 3563 | 0.0284 | 0.3446 | 1.8979 | 0.1965 |
| October | 7210 | 0.0325 | 0.3781 | 4.3341 | 0.2682 |
| November | 11004 | 0.0182 | 0.3580 | 1.7617 | 0.1730 |
| December | 14377 | 0.0246 | 0.5621 | 4.4186 | 0.3585 |
| Annual mean | 0.5013 | 0.2806 | |||
The analysis of seasonal AOD over Nigeria, Niger, and Benin (Table 4, 5 and 6) reveals distinct temporal variations that reflect the combined influence of dust transport, biomass burning, and rainfall seasonality. Niger shows pronounced variability consistent with regional climatic and emission dynamics. The highest mean AOD values for Nigeria and Benin are observed during spring (0.66; SD = 0.58 and 0.73; SD = 0.46, respectively), while Niger records its highest mean AOD during summer (0.73; SD = 0.62). In contrast, autumn (mean = 0.37; SD = 0.30) and winter (mean = 0.37; SD = 0.34) exhibit relatively lower AOD values. The spring maximum is particularly significant, corresponding to the onset of the West African biomass burning season in Nigeria and reflecting the combined influence of Saharan dust outbreaks and enhanced convective activity that uplift mineral particles into the atmosphere. In Niger, spring coincides with the peak dust season in the Sahel.
Table 4: Seasonal statistic of average AOD for Nigeria during 2012-2024
| Season | Observation | Minimum | Mean | Maximum | Std |
|---|---|---|---|---|---|
| Autumn | 1607 | 0.0545 | 0.3710 | 3.0196 | 0.2977 |
| Spring | 2017 | 0.0714 | 0.6556 | 4.9885 | 0.5795 |
| Summer | 1790 | 0.0746 | 0.6230 | 3.1204 | 0.4019 |
| Winter | 2105 | 0.0545 | 0.3674 | 4.9514 | 0.3383 |
Table 5: Seasonal statistic of average AOD for Niger during 2012-2024
| Season | Observation | Minimum | Mean | Maximum | Std |
|---|---|---|---|---|---|
| Autumn | 89477 | 0.0527 | 0.5114 | 5.0000 | 0.4580 |
| Spring | 87308 | 0.0490 | 0.4198 | 5.0000 | 0.4273 |
| Summer | 92820 | 0.0546 | 0.7326 | 5.0000 | 0.6179 |
| Winter | 87582 | 0.0495 | 0.2640 | 4.7296 | 0.2265 |
Table 6: Seasonal statistic of average AOD for Benin during 2012 -2024
| Season | Observation | Minimum | Mean | Maximum | Std |
|---|---|---|---|---|---|
| Autumn | 13123 | 0.028437 | 0.374862 | 4.334073 | 0.235738 |
| Spring | 27393 | 0.027503 | 0.727219 | 5 | 0.459396 |
| Summer | 13818 | 0.020035 | 0.37338 | 5 | 0.212805 |
| Winter | 38498 | 0.018177 | 0.557645 | 4.872363 | 0.334826 |
The spatial distribution of the average AOD across the study period is depicted in Figures 11, 12, and 13, revealing distinct patterns with concentrations of high AOD values exceeding 0.5, 1.5, and 0.6 in Nigeria, Niger, and Benin, respectively. Particularly hazy conditions were observed in January and February for Nigeria, and in July and August for Niger and Benin. Various factors, including increased anthropogenic activities and meteorological conditions such as low wind speeds, elevated air temperatures, and high humidity in the region, likely contributed to this spatial variability. The inter-annual spatial distribution further reveals significant variability from 2012 to 2024, with annual average AOD values fluctuating under the combined influence of natural and anthropogenic drivers. Consistently high AOD concentrations were recorded in 2024 and 2023 for Nigeria and Benin, whereas Niger showed relatively stable values throughout the study period. The seasonal spatial distributions of AOD, also presented in the figures, illustrate consistent patterns across the countries, with winter and autumn showing the highest average AOD values in Nigeria, summer and autumn in Niger, and spring and winter in Benin. Notably, winter exhibited high concentrations of coarse particles in Nigeria and spring in Benin, owing to increased Saharan dust transport over the area. The elevated AOD in winter is likely attributable to enhanced aerosol loading in the atmospheric column, particularly in the upper atmosphere, driven by long-range Saharan dust transport (Torabi et al., 2024).
Figure 11: Spatial distribution of AOD from 2012 to 2024 over Nigeria
Figure 12: Spatial distribution of AOD from 2012 to 2024 over Niger
Figure 13: Spatial distribution of AOD from 2012 to 2024 over Benin
The Angstrom Exponent (AE), derived from AERONET, is used to characterize aerosol size and types, as shown in Figures 14, 15 and 16 for the VIIRS satellite data (a–c) over the three regions. AE exploits the different responses of two wavelengths to aerosol loading and characterizes the size of aerosol particles. This parameter is particularly useful for distinguishing between fine aerosols (biomass burning, BB; vehicular emissions, VE), coarse aerosols (desert dust, DD; sea salt, SS), and mixed aerosols (BB/DD and VE/SS) for the purpose of identifying aerosol clusters. When AOD concentrations are low, there is a non-negligible uncertainty in the calculated AE; therefore, AE parameters are compared only when the AERONET 550 nm AOD is greater than 0.2 (Li et al., 2022). Figures 14, 15 and 16 indicate that desert dust (DD) and sea salt (SS) are dominant in the region (AE < 1), reflecting the prevalence of coarse particles. This is consistent with the geographical setting of the study area, which is strongly influenced by long-range Saharan dust transport and marine aerosols from the Atlantic Ocean. In particular, Niger shows persistent dust dominance linked to the Bodélé Depression, one of the world’s most active dust sources, while coastal Benin reflects both desert dust intrusions and contributions from marine aerosols. Nigeria, located in the transitional zone, shows signatures of both dust outbreaks and local fine aerosols.
A moderate level of mixed particles (BB/DD) is also evident in the region (cyan clusters), especially during the dry season, when biomass burning emissions coincide with dust outbreaks. This mixture highlights the complexity of aerosol sources in West Africa, where natural processes such as dust uplift interact with anthropogenic activities such as agricultural residue burning, traffic emissions, and industrial activities. The dominance of coarse particles underscores their major role in modulating regional radiative forcing, visibility reduction, and health impacts, while the seasonal variability of mixed aerosols indicates a dynamic interplay between human-driven and natural aerosol sources.
Figure 14: Plot of AOD against AE to Characterized the aerosol mode over Nigeria
Figure 15: Plot of AOD against AE to Characterized the aerosol mode over Niger
Figure 16: Plot of AOD against AE to Characterized the aerosol mode over Benin
This study reveals that aerosol optical depth (AOD) over West Africa exhibits marked spatial and temporal variability, shaped by biomass burning, Saharan dust transport, and meteorological factors. The results show that Nigeria and Niger record their highest AOD values in spring and summer, while Benin peaks in spring due to the combined influence of dust and biomass burning. Seasonal cycles consistently highlight a summer maximum and winter minimum, in agreement with Saharan dust climatology, while interannual variations were most pronounced in 2015, 2016, and 2022. The correlation between VIIRS and AERONET data demonstrates strong consistency (R² = 0.91 and 0.81 for Nigeria and Niger), confirming the reliability of satellite observations. Analysis of aerosol modes indicates that Niger is strongly dust-dominated, Benin is influenced by biomass burning and rainfall, and Nigeria represents a transitional zone between Sahelian and coastal climates. These findings not only improve understanding of aerosol sources and transport mechanisms in the sub-region but also have important implications for air quality monitoring, climate modeling, and policymaking aimed at mitigating the impacts of atmospheric aerosols on health and the environment.
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