Sensitivity analysis of aerosols' effect in UVB transmission to solar zenith angle at subtropical location (Qena, Egypt)

摘要

In this work, the impact of aerosols over ultraviolet-B at the Earth's surface (UVB) was studied. The estimations of aerosols radiative effects on UVB are significant to UVB estimations, air quality studies, as well as assessments of the impact of regional environmental change. The study used simultaneously hourly values of UVB, global solar radiation (G) at the horizontal surface and direct normal solar radiation (). In addition, other meteorological parameters such as air temperature (T), relative humidity (R) and cloudiness were collected. These data were measured at Qena, Egypt (26.20°N, 32.75°E, and 96 m amsl) through the period from 2001 to 2004. To analyze the relationship between UVB and aerosols, the dimensionless parameter UVB transmission (K_(tUVB)) and Angstrom turbidity coefficient (β) in cloudless conditions were considered. The results showed that there is no correlation between K_(tUVB) and β in the wide range of SZA (3° > SZA < 75°). The change of K_(tUVB) to change β was equal to -0.078 (the correlation coefficient, R = -0.22, i.e. 5% of the variability of K_(tUVB) was explained by β). So, sensitivity analysis of aerosol effect in K_(tUVB) to SZA was employed. The relationship between K_(tUVB) and β was determined for a narrow ranges of SZA (the range is equal to 1°) and a linear regression was fitted for each range of SZA. The △K_(K_(tUVB))/△β, accordingly the correlation coefficient (R), increases with the increasing SZA, which means K_(tUVB) becomes more sensitive to β as SZA increase. Datasets for each narrow range of SZA, which showed a correlation between K_(tUVB) and 0 (-R > 0.50), were selected to quantify the relationship between both parameters. These selected datasets just show the effect of aerosols in K_(tUVB) when their UVB penetrating influence is more than the influence of other atmospheric factors such as ozone, i.e. the effect of aerosols is to induce a notable reduction in K_(tUVB). For the selected datasets, △K_(tUVB)/△β varied from -0.05 to -0.21 and its average value was equal to -0.12. The resulting regression analysis showed that the determination coefficients of linear fit vary from 0.25 to 0.77, i.e. 25% to 75% of the variance in K_(tUVB) was explained by β.