Spatial and temporal variability of aerosol: size distribution and optical properties

Masmoudi, M.; Chaâbane, M.; Tanré, D.; Gouloup, P; Blarel, Luc; Elleuch, F

doi:10.1016/s0169-8095(02)00174-6

Cited by 99 publications

(51 citation statements)

References 20 publications

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“…The fact that high convective activity makes the sand particles to lift into the atmosphere, anthropogenic aerosols produced by crop biomass burning (Lee et al, 2006;Tao et al, 2009;Li et al, 2010), and also the dust storm events initiates at the end of the spring season (transport coarse dust particles from north down to the east) which drastically changes the AOD distribution during the summer . Another reason for high values of AOD in summer is higher air temperatures which tend to hold abundant atmospheric water vapor facilitated the hygroscopic growth of aerosols (Masmoudi et al, 2003;Xin et al, 2005;Li et al, 2007). Similar results were also reported by several authors over China that increase in aerosol concentration which changes from season to season by regional anthropogenic and natural pollution (Pan et al, 2010;He et al, 2012;.…”

Section: Seasonal Variations In Aodsupporting

confidence: 85%

Correlation Analysis between AOD and Cloud Parameters to Study Their Relationship over China Using MODIS Data (2003-2013): Impact on Cloud Formation and Climate Change

Kumar

Yin

et al. 2015

Aerosol Air Qual. Res.

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In the present study, we examined the spatial and temporal variations in aerosol optical depth (AOD) at 550 nm and its relationship with various cloud parameters derived from the Moderate resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra satellite. The data have been analyzed for the period of 10-years between March 2003 and February 2013 over 12 major cities in China. The results revealed that high AOD noticed over low latitude regions influenced with high anthropogenic activities and the low AOD observed for the high altitude and mountainous areas, since AOD accounts for the slant path which reduces the aerosol emissions. In addition, the aerosol variations in the atmosphere are complicated by several factors in emissions (natural and anthropogenic) as well as stagnant synoptic meteorology. From the temporal studies, it is clear that the maximum AOD was found during summer followed by spring and autumn with a minimum AOD in winter season for all the regions of study in China. Furthermore, we studied the relationship between AOD versus water vapor (WV), cloud fraction (CF), cloud optical thickness (COT), cloud effective radius (CER), cloud top pressure (CTP), and cloud top temperature (CTT) for the selected regions in China. Additionally, regression analysis and one paired student's t-Test were applied to represent the probability of data significant at 95% confidence for the derived AOD values and cloud parameters in order to provide a better understanding of aerosol-cloud interaction.

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Section: Seasonal Variations In Aodsupporting

confidence: 85%

Correlation Analysis between AOD and Cloud Parameters to Study Their Relationship over China Using MODIS Data (2003-2013): Impact on Cloud Formation and Climate Change

Kumar

Yin

et al. 2015

Aerosol Air Qual. Res.

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“…These τ patterns have been observed at several locations and for different aerosol types (e.g., biomass smoke, anthropogenic aerosols, desert dust) (Masmoudi et al, 2003;Kim et al, 2004;Ogunjobi et al, 2004;Eck et al, 2010). Different aerosol types have been identified earlier over Indian landmass surrounding the oceanic regions e.g.…”

Section: Identification Of Aerosols Typesmentioning

confidence: 88%

A New Classification of Aerosol Sources and Types as Measured over Jaipur, India

Verma

Prakash

Ricaud

et al. 2015

Aerosol Air Qual. Res.

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The aerosol properties retrieved from the AErosol RObotic NETwork (AERONET) measurements during the period 2009 to 2012 over Jaipur (26.9°N, 75.8°E, 450m asl) in Northwestern India are used for the first time to identify the types of aerosols. In order to consider the appropriate threshold of aerosol optical thickness (τ) at 500 nm (τ 500 ) and Angstrom exponent (α) in the spectral band 440-870 nm, a novel approach has been conducted and applied for the identification process. Five prevailing aerosol classes are identified: desert dust, biomass, maritime, arid background and mixed aerosols. Arid background and desert dust type aerosols are the most common at Jaipur (34.7% and 13.6%, respectively), with a wide variability in both τ and α. In about 8.4% of the cases, aerosols can be classified as maritime, although mixing with other aerosols (33.6%) is substantial. The ground-based spectral optical thickness and the refractive index estimated at visible and near-infrared wavelengths are used to account for the type of atmospheric aerosols. They are compared with four more AERONET sites located in India based upon their geographical distribution and extensive data availability. Simultaneously, single scattering albedo of dust is also inferred for all the available AERONET sites for the same period over India. The comparison results suggest that Jaipur arid background is more scattering in nature than Northern and Western regions in India. Finally, the absorption is less in summer than in winter over the Jaipur site.

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“…Early research mainly focused on a group of cities that hold an Aerosol Robotic Network (AERONET) site (equipped with a sunphotometer). For instance, Masmoudi et al (2003) found higher spatial variability of AOD and AE for the central African sites than the Mediterranean ones. The central African sites showed a lower variation with the smallest values of their measured AE due to the presence of very large dust particles.…”

Section: Introductionmentioning

confidence: 94%

“…Extensive snow cover is the main reason causing the inability to retrieve AOD in winter (Power et al, 2006). In addition, higher air temperatures tend to hold more water vapor that feeds aerosol to grow (Masmoudi et al, 2003). This is another reason causing the higher AOD levels in the summer time.…”

Section: Air Quality 70mentioning

confidence: 99%

Monitoring Spatial and Temporal Variability of Air Quality Using Satellite Observation Data: a Case Study of MODIS-Observed Aerosols in Southern Ontario, Canada

Chen¹,

Tian²

2010

Air Quality

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Spatial and temporal variability of aerosol: size distribution and optical properties

Cited by 99 publications

References 20 publications

Correlation Analysis between AOD and Cloud Parameters to Study Their Relationship over China Using MODIS Data (2003-2013): Impact on Cloud Formation and Climate Change

Correlation Analysis between AOD and Cloud Parameters to Study Their Relationship over China Using MODIS Data (2003-2013): Impact on Cloud Formation and Climate Change

A New Classification of Aerosol Sources and Types as Measured over Jaipur, India

Monitoring Spatial and Temporal Variability of Air Quality Using Satellite Observation Data: a Case Study of MODIS-Observed Aerosols in Southern Ontario, Canada

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