Monitoring spatio-temporal variations in aerosols and aerosol–cloud interactions over Pakistan using MODIS data

Alam, Khan; Iqbal, Muhammad Jawed; Blaschke, Thomas; Qureshi, Salman; Khan, Gulzar

doi:10.1016/j.asr.2010.06.025

Cited by 109 publications

(83 citation statements)

References 40 publications

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“…Their ability to absorb or scatter solar radiations have direct impact to increase or decrease the temperature of the atmosphere (Ichuku et al, 2004;IPCC, 2013). In addition, their indirect affect modifies the cloud lifetime, albedo, and the precipitation by changing the size and density of cloud droplets in the atmosphere (Lohmann., 2002;Alam et al, 2010;Balakrishnaiah et al, 2012). In addition, as smaller cloud droplets are less efficient to produce precipitation, an increased aerosol population will cause to a longer cloud life.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, as smaller cloud droplets are less efficient to produce precipitation, an increased aerosol population will cause to a longer cloud life. Cloud formation requires super-saturation with aerosol that also explains the importance of aerosols in our atmosphere (Alam et al, 2010). These particles also emitted and reflected radiation from the earth in different directions depending on their physical and chemical properties (Ichuku et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Aerosols and clouds play a very significant role in the climatic system (Balakrishnaiah et al, 2012;Alam et al, 2014). Aerosols demonstrate both spatial and temporal variations, which may lead to variations in the cloud optical properties (Alam et al, 2010). Their ability to absorb or scatter solar radiations have direct impact to increase or decrease the temperature of the atmosphere (Ichuku et al, 2004;IPCC, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Interactions between aerosols and clouds have become a key topic of scientific research due to the importance of clouds in controlling climate (Mahowald and Kiehl, 2003;Alam et al, 2010Alam et al, , 2014. Aerosols and clouds play a very significant role in the climatic system (Balakrishnaiah et al, 2012;Alam et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…A wide range of studies has shown aerosol and cloud interactions with large uncertainties in the magnitude of radiative forcing (e.g., Twomey et al, 1984;Coakley et al, 1987;Kaufman and Nakajima, 1993;Kaufman and Fraser, 1997a, b;Ackerman et al, 2000;Rosenfeld, 2000;Ramanathan et al, 2001;Rosenfeld et al, 2002;Schwartz et al, 2002;Kim et al, 2003;Andreae et al, 2004;Koren et al, 2004;Penner et al, 2004;Koren et al, 2005;Alam et al, 2010;Balakrishnaiah, 2012;Alam et al, 2014). Alam et al (2010) and Balakrishnaiah et al (2012) have examined the spatial and temporal variations in aerosol particles and the impact of these variations over different locations in Pakistan and India. They found highest AOD values during the monsoon period (June-August).…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Sharif

Alam

Afsar

2015

Aerosol Air Qual. Res.

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In this study, aerosols spatial, seasonal and temporal variations over Sindh, Pakistan were analyzed which can lead to variations in the microphysics of clouds as well. All cloud optical properties were analyzed using Moderate Resolution Imaging Spectroradiometer (MODIS) data for 12 years from 2001 to 2013. We also monitored origin and movements of air masses that bring aerosol particles and may be considered as the natural source of aerosol particles in the region. For this purpose, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to make trajectories of these air masses from their sources. Aerosol optical depth (AOD) high values were observed in summer during the monsoon period (June-August). The highest AOD values in July were recorded ranges from 0.41 to 1.46. In addition, low AOD values were found in winter season (December-February) particularly in December, ranges from 0.16 to 0.69. We then analyzed the relationship between AOD and Ångström exponent that is a good indicator of the size of an aerosol particle. We further described the relationships of AOD and four cloud parameters, namely water vapor (WV), cloud fraction (CF), cloud top temperature (CTT) and cloud top pressure (CTP) by producing regional correlation maps of their data values. The analyses showed negative correlation between AOD and Ångström exponent especially in central and western Sindh. The correlation between AOD and WV was throughout positive with high correlation values > 0.74 in whole Sindh except eastern most arid strip of the Thar Desert in the region. The correlation between AOD and CF was positive in southern Sindh and goes to negative in northern Sindh. AOD showed a positive correlation with CTP and CTT in northern Sindh and a negative correlation in southern Sindh. All these correlations were observed to be dependent on the meteorological conditions for all of the ten sites investigated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Sharif

Alam

Afsar

2015

Aerosol Air Qual. Res.

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Seasonal assessment and classification of aerosols transported to Lahore using AERONET and MODIS deep blue retrievals

Zafar¹,

Zafar

Holben

2017

Intl Journal of Climatology

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Daily measurements of aerosol optical depth ( ) and Ångstrom wavelength exponent ( ) acquired from Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectrometer (MODIS) are analysed over Lahore -an urban city of Pakistan (period: 2010Pakistan (period: -2014 to investigate contribution of different types of aerosols originating from both local and regional source locations. The obtained annual averages (mean ± standard deviation) for AERONET retrievals of (500 nm) and (440-870 nm) are 0.68 ± 0.37 (0.08-2.91) and 0.99 ± 0.33 (0-1.8), respectively. Of all the sources, 61% are found within Pakistan, 11% in India, 19% in Afghanistan, 6% in Iran and 2% in Saudi Arabia with seasonal contributions of 35, 25, 23 and 17% in pre-monsoon, monsoon, winter and post-monsoon, respectively. The bimodal distributions of show dominance of coarse-mode particles during pre-monsoon, fine-mode particles during post-monsoon and presence of both coarse-mode and fine-mode particles during winter and monsoon with winter showing more fine-mode particles. Two broad classes of aerosols namely desert dust (DD) and biomass burning/urban industrial (BU) are identified with criteria, e.g. ≥ 0.3 and ≤ 0.75 indicating presence of DD while ≥ 0.2 and ≥ 1.15 indicating BU. The frequency of occurrence (FOO) of DD and BU aerosols is further identified by applying classification criteria over Aqua-MODIS deep blue retrievals. The FOO identifies anthropogenic activity on-going throughout the year, disrupted with DD aerosols only during pre-monsoon and monsoon. The maximum dust activity is seen over Indo-Gangetic plains (IGP) (localized maxima: 35-45%) and the Arabian peninsula (>55%) during pre-monsoon, while maximum BU aerosols are found over IGP, central and south-eastern plains of India and the state of Gujarat (localized maxima: >70%) in winter and post-monsoon. KEY WORDS aerosol optical depth; Ångström wavelength exponent; long-range transport; AERONET; HYSPLIT; MODIS deep blue; desert dust/urban-industrial aerosols

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Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China

Zhang

et al. 2020

Intl Journal of Climatology

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Along with the rapid urbanization and economic development, the Yangtze River Middle‐Reach urban agglomeration (YRMR) has suffered from increasingly serious environmental problems. Characterizing the long‐term dynamics of aerosol optical depth (AOD) in YRMR is of important significance for scientific control of aerosol pollutants over this region. Previously, numerous researches have analysed the temporal and spatial characteristics of AOD in different regions through statistical values of AOD time series or using trend analysis methods based on linear regression models. It is challenging to approximate nonlinear trends and identify change points of AOD time series. This paper proposed a new methodology for investigating the dynamics of AOD over the YRMR region on a monthly basis by using the Terra MODIS Collection 6.1 Level 2 aerosol products (MOD04_L2) during 2001–2017. A three‐dimensional spatio‐temporal interpolation method was used to generate gap‐filled monthly AOD maps, and then a generic Bayesian time series decomposition algorithm was applied to analyse the trends and change points of monthly AOD time series. The results showed that the areas with high AOD values were mainly located in the core cities and the connecting belts. Strong positive AOD anomalies had been observed during 2006–2011, whereas the positive anomalies were typically intermingled with negative anomalies and it indicated that the evolution patterns of AOD for different regions were different and influenced by fluctuations. Trend analysis of the monthly mean AOD values indicated that AOD in YRMR increased first, then fluctuated, and finally decreased. Although it was difficult to separate the increasing and fluctuating periods from each other due to the fluctuation, the decreasing trend after 2011 was significant, and a significant drop of AOD had been observed since 2014. Long‐term AOD variations were closely related to national policies, and the results could provide useful references for regional environmental assessment and decision‐making.

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Monitoring spatio-temporal variations in aerosols and aerosol–cloud interactions over Pakistan using MODIS data

Cited by 109 publications

References 40 publications

Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

Seasonal assessment and classification of aerosols transported to Lahore using AERONET and MODIS deep blue retrievals

Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China

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