This paper investigates the Impact of relative humidity, varying the concentrations of water-soluble aerosol particle concentrations (WASO), Mineral Nuclei Mode Aerosols Particle Concentration (MINN), mineral accumulation mode, nonspherical (MIAN) aerosol particles concentrations and Mineral Coarse Mode Aerosols Particle Concentration (MICN) on the visibility and particles size distribution of desert aerosols based on microphysical properties of desert aerosols. The microphysical properties (the extinction coefficients, volume mix ratios, dry mode radii and wet mode radii) were extracted from Optical Properties of Aerosols and Clouds (OPAC 4.0) at eight relative humidities, RHs (00 to 99%) and at the spectral visible range of 0.4-0.8mm, the concentrations were varied to obtain five different models for each above-mentioned component. Regression analysis of some standard equations were used to determine the Angstrom exponent (α), the turbidity coefficient (β), the curvature (α2), humidification factor (), the mean exponent of aerosol growth curve (µ) and the mean exponent of aerosol size distributions (n). The values of angstrom exponent (α) were observed to be less than 1 throughout the five models at all RHs for the four studied components, and this signifies the dominance of coarse mode particles over fine mode particles. But the magnitude of the angstrom exponent (α) fluctuates all through the studied components except for WASO which increased with the increase in RH across the models and this also signifies the dominance of coarse mode particles with some traces of fine mode particles. The investigation also revealed that the curvature (α2) has both monomodal (negative signs) and bimodal (positive signs) types of distributions all through the five models and this also signifies the dominance of coarse mode particles with some traces of fine mode particles across the individual models for all the studied components. it was also found that the visibility decreased with the increase in RH and increased with the increase in wavelength. The investigation further revealed that the turbidity coefficient (β) fluctuates with the increase in RH and the particles concentrations, and this might be due to major coagulation and sedimentation. The analysis further found that there is a direct inverse power relation between the humidification factor and the mean exponent of aerosols size distribution with the mean exponent of aerosols growth curve. It was also found that as the magnitude of µ increased for MIAN, MINN and MICN, the effective hygroscopic growth decreased. For WASO, it was found that as the magnitude of µ decreased, the effective hygroscopic growth increased with the increase in particles concentrations and RH. The decreased in the magnitude of µ for WASO might be due to the fact that as we increase the non-hygroscopic particles, we decrease the deliquescence. The mean exponent of aerosol size distribution (n) being less than 3 shows foggy condition of the desert atmosphere the four investigated components and five studied models.
onboard satellite, then a second instrument was launched onboard EOS-Aqua in May 2002. Both satellites are sun-synchronous with Equatorial overpass at 10:30 AM for Terra and 1:30 PM for Aqua, local time. They measure angular dependence of radiance at multi ranging from UV to infrared. As they are able to provide spectral optical depth, and aerosol size information in the form of Angstrom exponent (Remer et al., 2005). MODIS also measures the properties of aerosols-tiny liquid or solid particles in the atmosphere. Aerosols enter the atmosphere from manmade sources like pollution and biomass burning and natural sources like dust storms, volcanic eruptions, and forest fires (Kaufman et al., 2000). Aerosol Robotic Network (AERONET) is a federated ground-based remote sensing ABSTRACT In this Paper, Empirical Orthogonal Function (EOF) was used to assess the MODIS C006 LV2 aerosol AOD and AE products, and compared the data with AERONET AOD and AE observations. The data were taken from an AERONET station at Ilorin, Nigeria which were obtained from (MAPSS) and wer data period for the two satellite data were from Dec 2004 to May 2015 It was observed from the graphical representation that the seasonal variation of AOD peaks during the dry season from Dec to Feb and reaches minimum during summer in August 2008.The comparisons showed both underestimations in MODIS AOD and considerable overestimations in the AE. On the EOF analysis it was observed that a good correlation between MODIS & AERONET AOD are observed on the correlation matrices in all the data. Lower correlation is only observed at Ilorin AERONET wavelength at 470nm (ILA470) with Ilorin 660nm (ILM660).On the total variance explained table for MODIS and AERONET AOD, it was observed that out of the 6 com with a very high percentage of 81.57% observed with a percentage of 83.95% for MODIS and AERONET AOD,and little variation was observed for MODIS and AERONET AE.
The spatial and temporal variations of aerosol optical depth at 550 nm (τ550) and Angstrom exponent derived from 470 and 660 nm (α470-660) over Nairobi (NAI), Skukuza (SKU) and Ilorin (ILO) Aerosol Robotic Network (AERONET) stations in sub-Saharan Africa, as recorded by Moderate Resolution Imaging Spectroradiometer (MODIS) satellites for fifteen years (2000-2015), were examined in relation to their climatologies and prediction. The MODIS measurements of τ550 and α470-660 from aqua (MYD04) and terra (MOD04) satellites were used in this study. Retrievals of τ550 for both satellites were validated with AERONET τ550 for the same period. The validation results showed that they compare favourably over the three stations, but MOD04 performed better than MYD04 data in NAI and ILO for τ550. This shows that the τ550 of NAI and ILO are best captured using the MOD04 data while that of SKU is best with MYD04. It was also discovered that MODIS underestimated AERONET τ550 data over NAI and SKU. The most polluted station is ILO while the least polluted one is NAI. Similarly, the station with the highest concentration of absorbing aerosols is NAI and the least was observed in ILO. The aerosol climatology shows that the most polluted months in NAI, SKU and ILO are October, June and March respectively. On the other hand, February, November and March has the highest amount of scattering aerosols in the atmosphere for NAI, SKU and ILO respectively. The highest amount of absorbing aerosols was found, respectively, in the months of June, June and August. The generated time series (TS) models are all good, though a general underestimation of the parameters by the models was also observed. Keywords: Aerosol optical depth, Angstrom exponent, MODIS, Time series, sub-Saharan Africa
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