Identification of aerosol types over an urban site based on air-mass trajectory classification

Pawar, Ganesh; Devara, P. C. S.; Aher, G. R.

doi:10.1016/j.atmosres.2015.04.022

Cited by 29 publications

(14 citation statements)

References 110 publications

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“…They form after different physical and chemical processes of natural dust, volcanic dust, mist, fog, sea salts, oceanic sulfates, urban/industrial sulfates, anthropogenic aerosols, pollen, soot (back carbon), organic particles, and some toxic pollutants Pawar et al, 2015). The current research on this issue mainly focuses on the role of aerosol in the Earth's climate system (Misra et al, 2015;Kolhe et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The current research on this issue mainly focuses on the role of aerosol in the Earth's climate system (Misra et al, 2015;Kolhe et al, 2016). Aerosols attenuate the incoming solar radiation that reaches the Earth's surface by absorbing and scattering the radiation (Haywood and Ramaswamy, 1998;Hatzianastassiou et al, 2007;Pawar et al, 2015;Kolhe et al, 2016). Atmospheric aerosols can cause climate change through their direct, indirect, and semi-direct effects on the radiative energy budget of the Earth-Atmosphere system (Papadimas et al, 2008;Pawar et al, 2015;Kolhe et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal Aerosol Optical Depth (AOD) Variability Using Satellite Data and its Comparison over Saudi Arabia for the Period 2002?2013

Ali

Assiri

Dambul

2017

Aerosol Air Qual. Res.

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This study analyzes the spatiotemporal variations of seasonal Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) AOD at 550 nm from the Aqua satellite over Saudi Arabia for the period 2002-2013. Satellite retrieved AOD is also compared with AERONET AOD over the Solar Village and KAUST station. The result of the seasonal AOD spatial distribution shows that the peak AOD value of 0.6 is observed over Hafr Al Batin, Riyadh, and the Rub Al Khali desert during spring, whereas the Gizan area shows the peak AOD during summer. In contrast, the autumn shows the peak AOD value of 0.5 over Dhahran and in the proximity of Jeddah, whereas Hafr Al Batin, Al Khafji, Al Jubail, and the Rub Al Khali desert show the peak AOD value of 0.4 in winter. Regression analysis shows the AOD increasing trends during spring, summer, and autumn (except for winter) over the entire Saudi Arabia. Over the Solar Village, the AOD increasing trends are also noted during spring and summer, whereas autumn and winter display the AOD decreasing trends. The AOD increasing trends are displayed in all seasons over KAUST. Hence, the AOD increasing/decreasing trends indicate that the number of dust storms either increases or decreases over these regions. Over the Solar Village, the correlation values for MODIS DB AOD versus AERONET AOD are 0.77 (spring), 0.62 (summer), 0.65 (autumn), and 0.75 (winter). Likewise, over KAUST, the correlation values for the same pairing are 0.85 (spring), 0.71 (summer), 0.81 (autumn), and 0.89 (winter). The incorrect aerosol model selection and imperfect surface reflectance calculation are responsible for reducing the correlation. Therefore, this study recommends that the DB algorithm can be used effectively to detect AOD over Saudi Arabia, which will further help to improve the MODIS DB AOD product utilizing the next version of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal Aerosol Optical Depth (AOD) Variability Using Satellite Data and its Comparison over Saudi Arabia for the Period 2002?2013

Ali

Assiri

Dambul

2017

Aerosol Air Qual. Res.

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“…This indicates a significant change in the monthly aerosol type and columnar aerosol size distribution. Interestingly, for all the three spectral intervals, AE ≥ 1 during December-March with large standard deviations while AE < 1 for April-May months with comparatively less standard deviations indicating the nature of the prevalent relationship between spectral dependence of AE and aerosol type (Kaskaoutis et al, 2007b;Kumar et al, 2013;Pawar et al, 2015). Similar kind of Ångström exponent variation as a function of different wavelength ranges used for its estimation had been the subject of many aerosol measurement studies (Eck et al, 1999(Eck et al, , 2005Sinha et al, 2012).…”

Section: Frequency Distribution Of Ae β and α′mentioning

confidence: 99%

“…Air mass back trajectory analysis is a useful tool to recognize the possible aerosol source regions and to trace back their pathways along which they reach the receptor site (Pawar et al, 2012;2015) For this, a 5-day back trajectory analysis has been carried out at 0600 UTC for individual days for a period of 2008-15 over NWC, Pune using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) meteorological model (Draxler and Hess, 1998). Air-mass trajectories are computed at three different heights viz., 500 m (mixed layer height), 1500 m (boundary layer height) and 4000 m (free troposphere) for the present location.…”

Section: Air Mass Back Trajectory Analysismentioning

confidence: 99%

“…The past studies have indicated gradual increase in aerosol load due to upward growth in population, urbanization, industrialization over Indian sub-continent exhibiting large spatio-temporal variability in aerosol trends (Dani et al, 2012;Ramachandran et al, 2012a, b;Moorthy et al, 2013). Aerosols can be a mixture of various types, like anthropogenic aerosols produced due to urbanization/industrialization, crop residue burning and seasonal forest fires (Jain et al, 2014;Pawar et al, 2015), dust generated in the Thar Desert or transported from Arabian Desert, Saudi Arabia (SA), and the United Arab Emirates (UAE) (Aher et al, 2014), and marine aerosols from adjoining oceans (Sinha et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Year Analysis of Aerosol Properties Retrieved from the Ångström Parameters for Different Spectral Ranges over Pune

Kolhe

Pawar

Varpe

et al. 2016

Aerosol Air Qual. Res.

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The present study evaluates the temporal variation of aerosol optical depth (AOD 500 nm ) and the Ångström parameters [viz., Ångström exponent (AE, α), Ångström turbidity coefficient (β) and second order Ångström exponent (α′)] at a tropical observing site, Pune (18°32′N; 73°49′E, 559 m AMSL) during 2008-15. Six-year means for winter and premonsoon seasons together are found to be 0.534 ± 0.13, 1.054 ± 0.27, 0.254 ± 0.08 and 0.167 ± 1.33 for AOD 500 nm , AE, β and α′ respectively. Average month-to-month variability of AOD 500 nm , AE, β and α′ during 2008-15 depicts seasonal cycle with strong departures with respect to long-term means. Frequency distributions for AOD, AE and β are positively skewed (skewness = 0.77, 0.32 and 1.14 respectively) while it is negatively skewed for α′ (skewness = -0.18). Analysis of AE difference, curvature parameter difference (α 2 -α 1 ) and AOD 500 nm -AE 440-870 nm contour density map reveals that the aerosol ensemble at Pune consists of four aerosol types viz., UI (urban/industrial), CM (clear maritime), DD (desert dust) and MT (mixed type). Their relative magnitudes, however, differ during winter and pre-monsoon seasons. Thus, the contour density map shows dominance of UI and relatively less occurrence of MT type aerosols during winter. In pre-monsoon, however, the aerosol scenario is driven by MT type aerosol although UI and DD type aerosols show their remarkable existence.

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Air pollution in the Gobi Desert region: Analysis of dust‐storm events

Filonchyk

Peterson

Hurynovich

2021

Quart J Royal Meteoro Soc

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Air pollutants in nine cities of the Gobi Desert region are examined in this study. Mean concentrations of PM2.5 and PM10, retrieved from ground‐based air quality monitoring stations, were 36.2 ± 23.7 and 97.3 ± 84.5 μg·m−3, respectively. The highest concentrations of pollutants were in spring and winter. This can be explained by wind‐borne dust in the spring and emissions from domestic sources during the winter heating season. The highest PM2.5 and PM10 concentrations were registered in a period of dust‐storm activity with values of 343 μg·m−3 and 1,642 μg·m−3. Generally, clean continental (CC) was the dominant aerosol type (73.9%), followed by mixed (MX) aerosol type (20.4%) with insignificant contribution of clean marine (CM) (2.1%), urban/industrial and biomass burning (UI/BB) (0.9%) and desert dust (DD) (2.7%) aerosol types. The various measures of pollution retrieved from AERONET stations, including aerosol optical depth (AOD), Ångström exponent (AE), asymmetry parameter (AP), single scattering albedo (SSA) and aerosol volume size distribution (AVSD), specify the content of coarse aerosol fractions in the period of dust activity. The aerosol radiative forcing at the bottom of the atmosphere (ARFBOA) and the top of the atmosphere (ARFTOA) during dust days were estimated to be −115.76 and −241.31 W·m−2, with a corresponding heating rate of 3.52 K·day−1. According to the backward and forward trajectories of air masses, the dust cloud moved to the east and southeast from the epicentre, affecting the eastern and the central parts of the country. This study may advise environmental policy, as it showed how controlling causes of pollution can improve air quality.

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Identification of aerosol types over an urban site based on air-mass trajectory classification

Cited by 29 publications

References 110 publications

Seasonal Aerosol Optical Depth (AOD) Variability Using Satellite Data and its Comparison over Saudi Arabia for the Period 2002?2013

Seasonal Aerosol Optical Depth (AOD) Variability Using Satellite Data and its Comparison over Saudi Arabia for the Period 2002?2013

Multi-Year Analysis of Aerosol Properties Retrieved from the Ångström Parameters for Different Spectral Ranges over Pune

Air pollution in the Gobi Desert region: Analysis of dust‐storm events

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