Aerosol and Air Quality Research, 15: 657–672, 2015 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2014.09.0200
Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model
Fozia Sharif1, Khan Alam2,3*, Sheeba Afsar1
1 Department of Geography, University of Karachi, Karachi 75270, Pakistan 2 Institute of Space and Planetary Astrophysics, University of Karachi, Karachi 75270, Pakistan 3 Department of Physics, University of Peshawar, Peshawar 25120, Pakistan
ABSTRACT
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.
Keywords: AOD; CTT; CTP; CF; Ångström exponent.
INTRODUCTION significant role in the climatic system (Balakrishnaiah et al., 2012; Alam et al., 2014). Aerosols demonstrate both Aerosols are minute solid and liquid particles suspended spatial and temporal variations, which may lead to variations in the atmosphere. These may occur in the form of natural in the cloud optical properties (Alam et al., 2010). Their dust particles, volcanic dust, mist, clouds, fog, sea salts, ability to absorb or scatter solar radiations have direct impact anthropogenic aerosols, oceanic sulphates, industrial to increase or decrease the temperature of the atmosphere sulphates, soot (Black carbon), organic particles and some (Ichuku et al., 2004; IPCC, 2013). In addition, their indirect toxic pollutants after various physical and chemical processes affect modifies the cloud lifetime, albedo, and the that have several effects on our atmosphere ( Gupta et al., precipitation by changing the size and density of cloud 2013). droplets in the atmosphere (Lohmann., 2002; Alam et al., Interactions between aerosols and clouds have become a 2010; Balakrishnaiah et al., 2012). In addition, as smaller key topic of scientific research due to the importance of cloud droplets are less efficient to produce precipitation, an clouds in controlling climate (Mahowald and Kiehl, 2003; increased aerosol population will cause to a longer cloud life. Alam et al., 2010, 2014). Aerosols and clouds play a very 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 * Corresponding author. on their physical and chemical properties (Ichuku et al., Tel.: +92-91-9216727 2004). Therefore, the description of atmospheric aerosols E-mail address: [email protected] is important to understand global solar heat budgets, water
658 Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015 cycle and various dynamics of climate change (Ichuku et al., year i.e., 2008 with highest AOD concentrations in the 2004). Increasing anthropogenic aerosol concentrations lead atmosphere. Alam et al. (2014) examined AOD and cloud to form cloud condensation nuclei (CCN) that ranges from parameters such as water vapor, cloud top temperature, cloud smaller effective radii to longer-lived clouds, affecting the top pressure and cloud fraction over five cities in Pakistan hydrological cycle (Ramanathan et al., 2001; Balakrishnaiah during the period (2001–2011). The AOD and cloud optical et al., 2012). A wide range of studies has shown aerosol and properties correlations changes with changing seasons in cloud interactions with large uncertainties in the magnitude Pakistan e.g., AOD showed a positive correlation with CTP of radiative forcing (e.g., Twomey et al., 1984; Coakley et and CTT for the spring season and a negative correlation al., 1987; Kaufman and Nakajima, 1993; Kaufman and was observed in summer in all investigated regions. Fraser, 1997a, b; Ackerman et al., 2000; Rosenfeld, 2000; This study focused on three objectives. The first objective Ramanathan et al., 2001; Rosenfeld et al., 2002; Schwartz was to understand the spatio-temporal distribution of AOD et al., 2002; Kim et al., 2003; Andreae et al., 2004; Koren over Sindh, Pakistan. The second objective was to investigate et al., 2004; Penner et al., 2004; Kaufman et al., 2005; Koren the relationship between AOD and cloud parameters (i.e., et al., 2005; Alam et al., 2010; Balakrishnaiah, 2012; Alam Cloud fraction, cloud-top pressure, cloud-top temperature et al., 2014). and water vapor). The third objective was to analyze the Alam et al. (2010) and Balakrishnaiah et al. (2012) have influence of air mass trajectory on AOD concentrations. examined the spatial and temporal variations in aerosol Aerosols spatio-temporal distributions, seasonal variations particles and the impact of these variations over different and physical properties may be determined within a short locations in Pakistan and India. They found highest AOD span of time from land, aircraft, or satellite remote sensing values during the monsoon period (June–August). Their (Ichoku et al., 2004). The analysis of air mass trajectories analysis also showed a strong positive correlation for AOD helps to determine natural source of aerosols in southern and cloud parameters in some regions and a negative Pakistan. The months of January (winter), July (summer), correlation in other regions. Ramachandran and Jayaraman April (spring) and October (autumn) were chosen as these (2003) investigated AOD values for February–March 2001 are considered as representatives of four seasons in the over the Bay of Bengal. They found higher AODs on 21 study region. In particular, AOD-cloud parameter correlation February influenced by the air masses originating either study acquired for thirteen years (2001–2013) suggests new from India or Bangladesh at different heights, indicating findings in a region. the anthropogenic sources. They concluded the difference in AOD values over Chennai that is a coastal urban station MATERIALS AND METHODS of India with that of clean continental land. Reasons may be due to the difference of anthropogenic activities and Site Description meteorological conditions, wind patterns and subsequently Sindh (23°42′–28º29′N; 67°24′–71°05′E) is the third loading of aerosol particles. Kaufman et al. (2002) observed largest province of Pakistan with a population of strong link of anthropogenic aerosols with the climate approximately 42 million. It is bounded by Balochistan to system and the hydrological cycle. A region with a clean the west, Punjab to the north, Indian states (Gujarat and atmosphere (with no pollutant) has relatively large cloud Rajasthan) to the east and the Arabian Sea to the south of its droplets to form clouds for precipitation, although natural location (Fig. 1(b)). The landscape of Sindh is diversified, aerosols are required to condense upon; a cloud does not having Kirthar Mountains to the west, Thar Desert to the form otherwise. In contrast, urban smoke and pollutants east, and Piedmont Plains to the north of Sindh. The affect cloud radiative properties and reduce precipitation at climate of Sindh is hot and semi-arid. It has a variation in the same time. Thus, anthropogenic aerosols impact the temperature as inland extreme temperatures to maritime hydrological cycle with a change in cloud cover, cloud moderate temperatures. For an inland Sindh, the temperature property and rainfall patterns in a region. Gupta et al. (2013) rises above 46°C in May and falls up to 2°C in January. In examined the quantity and quality of MODIS data over addition, the coastal region has temperature not more than urban areas of Pakistan. They found a lack of availability 40°C and less than 10°C for summer and winter months, of MODIS data over Karachi due to the presence of bright respectively. Mainly rainfalls during monsoon season (July– surfaces that increases surface reflection. They further September) that is about 7 inches in a year. Dust Storms hit added in their findings that high AOD values are shown frequently, mainly during April–May and October–November near the city center and decrease as we move out of that to the region. city. The population is more concentrated inside the city In Sindh, there are ten industrial estates, commonly named with more anthropogenic activities impacts atmospheric as SITE (Sindh Industrial and Trading Estates). Karachi, aerosols. Biomass burning, industrial activities and transport Hyderabad, Kotri, Sukkur, Tando Adam, Nooriabad, Larkana, might also have significant impact on this analysis. Ashraf Nawabshah and Shikarpur are common stations of Sindh et al. (2014a) found high AOD values in two seasons of industries. The main industries include cement industries, Pakistan i.e., monsoon and pre-monsoon. They associated oil refineries, chemical industries, heavy machinery and these higher values of coarse particles with a high frequency pharmaceutical industries, automobiles, shipbuilding, fish of a dust storm in the region as well as fine particles from factories, sugar and flour mills, fertilizer industries and a burning activities in the agricultural fields of Pakistan and steel mill. Their industrial emissions contribute to increase India (Indian Gigantic Plain). They found a highly polluted anthropogenic aerosols in the region. Similarly, vehicular
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015 659 emissions from the roads of Sindh, like Super Highway uncertainty in the ocean state and 0.05AOD is another due and National Highway and sea salt emissions from Arabian to uncertainty in aerosol characteristics). Over the land, Sea are the major local sources of aerosol in the study area. MODIS measures AOD from the reflection of residual at All the ten sites selected for the study are covering almost the top of the atmosphere and uses the 2.1-μm channel for whole Sindh with some geographical characteristics. For it. Over the ocean, MODIS distinguishes fine particles of example, most of the stations chosen for study are cities pollutants to coarse particles of dust and sea-salt with and industrial centers like Karachi, Hyderabad, Rohri, aerosol spectral signatures of 0.47–2.1 μm range (Kaufman Larkana, Nawabshah, Jacobabad and Sukkur. Other sites et al., 2002). The MODIS aerosol retrieval algorithm has are small cities with some topographical characteristics recently been improved in order to correct systematic biases either mountain or desert like Dadu, Ghotki, Umerkot and in the MODIS algorithm used previously (Remer et al. Tharparkar. 2005; Levy et al. 2007). MODIS Level 3 collection 5.1 deep blue (DB) data are The Moderate Resolution Imaging Spectroradiometer available from 2002 to present from Aqua, and 2000 to 2007 (MODIS) Instrument from Terra due to the known calibration issues. Therefore, MODIS (Moderate Resolution Imaging Spectroradiometer) in the present study MODIS DB Terra and Aqua data were acquires data at 36 spectral bands to give atmospheric, used from 2001–2002 and 2003–2013, respectively. This oceanic and terrestrial information (King et al., 2003; Ichoku DB algorithm of the MODIS aerosol product provides more et al., 2004). These bands range from visible to the thermal reliable and accurate data over bright surfaces such as infrared wavelengths, which are very useful to collect and deserts and snow or ice covered regions. While, the summarize large statistical and visual impacts of aerosols standard MODIS AOD relies on finding dark targets. Deep on climatic conditions (Ichoku et al., 2004). MODIS provides blue AODs data are more accurate over Sindh due to the observations at moderate spatial resolution of 250 m (Band advantage of taking dark-surface properties at blue channels 1 & Band 2), 500 m (Bands 3 to Band 7) and 1 km (Band 8 (0.412, 0.47 μm) and less absorption of dust at the red to Band 36) or temporal resolution (1–2 days) over different channel (0.65 μm). portions of the electromagnetic spectrum (King et al., 2003; The monthly MODIS Level 3 data with a 1° × 1° degree Ichoku et al., 2004; Alam et al., 2010). MODIS data include spatial resolution was obtained from the NASA DAAC daily, 8-day or 16-day composite images and products with website http://disc.sci.gsfc.nasa.gov/giovanni. AOD was used different time scales. Several climatic parameters also as a surrogate for aerosol concentration and calculated the retrieved at a spatial resolution of 5 km and 10 km from correlation coefficient for each set of parameters on a monthly MODIS (Alam et al., 2010; Balakrishnaiah et al., 2010; Alam basis, averaged annually, for the period from 2001 to 2013. et al., 2011c; Alam et al., 2014). MODIS provides global All data sets were interpolated using software ArcGIS coverage every 1 to 2 days. This source is also beneficial to (version 10.1). Spline was found as best technique used to represent detailed local, regional and global distributions of do interpolation of MODIS data. MODIS Level 3 atmosphere several climatic parameters over land as well as over the products contain no runtime QA flags. Therefore, Level 2 ocean. These MODIS instruments are equally beneficial to runtime QA flags are used in Level 3 to calculate (aggregate look at atmospheric, terrestrial and ocean phenomenology to and QA weight) statistics in Level 3. To identify cloud free- monitor various changes and collect statistics. These statistics pixel, the cloud mask algorithm is used. This algorithm are especially helpful to analyze aerosol concentrations and classifies each pixel as either confident clear, probably clear, the impacts on cloud formation (Alam et al., 2011c, 2014). probably cloudy, or cloudy (Hsu et al., 2004). In order to MODIS aerosol retrievals are expected to be more minimize the cloud contamination problem, DB uses AOD accurate over the ocean than land (Kaufman et al., 1997; spatial variance method to the radiance at 412 nm at every 3 Tanre et al., 1997). Therefore, data accuracy is found to be × 3 pixel spatial domain (Hsu et al., 2004). The uncertainty more over the oceans than it is over land (Remer et al., of DB AOD retrievals is expected as ± 0.05 ± 20% × 2005). All bright surfaces such as deserts and snow covered AODAERONET (Hsu et al., 2006, Huang et al., 2011, Shi et areas cause problems for the MODIS instrument, as do al., 2013). MODIS detailed information on algorithms for complex terrains, leading to a large bias in models and needs the retrieval of aerosol data is available at http://modis- to have ground-based observations (De Meij et al., 2006; atmos.gsfc.nasa.gov. Alam et al., 2010;). There is a difference in the usage of the visible and the thermal bands of MODIS datasets, as The Hybrid Single Particle Lagrangian Integrated the visible bands are used during the day and the thermal Trajectory (HYSPLIT) Model bands are used during the day and night both to measure In this study, we used the HYSPLIT model to archive bright surfaces, in particular. MODIS determine the physical data of an air parcel at several localities of Sindh named as properties of cloud as the cloud top pressure and cloud top ‘backward trajectories’. This model computes air mass temperature using infrared bands; optical and microphysical trajectories with several meteorological parameters like cloud properties using visible and near-infrared bands (Jin rainfall, relative humidity, ambient temperature and solar and Shepherd, 2008; Alam et al., 2010). radiation flux (Alam et al., 2011c). This meteorological MODIS calculates AOD with an estimated error of ± 0.03 input for the trajectory model was processed as the FNL ± 0.05AOD over the ocean and ± 0.05 ± 0.02AOD over the dataset and reprocessed from NOAA (National Oceanic land (it has been learned that 0.03AOD is an offset due to and Atmospheric Administration) by ARL (Air Resource
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Laboratory) to understand air masses (Alam et al., 2010). from 2001 to 2013. The air mass HYSPLIT trajectories Furthermore, several different pre-processor programs demonstrate a source of dust aerosols in the mid-months of convert NOAA, ECMWF (European Centre for Medium- all four seasons in Sindh. range Weather Forecasts), MM5 (The fifth generation NCAR/Penn State Mesoscale Model), NCAR (National Spatial Distribution of Annual Mean Aerosol Optical Centre for Atmospheric Research) re-analysis, and WRF Depth (weather research and forecasting) model output fields in a The spatial distribution of annual mean AOD at a format compatible for direct input into the model (Alam et wavelength of 550 nm has been plotted for the period 2001– al., 2010). 2013 (see Fig. 1(a)). The results from Fig. 1 demonstrates that HYSPLIT has the ability to determine almost 40 forward aerosols had a marked impact on ten stations in Sindh, or backward trajectories at different altitudes of 500, 1000, namely Karachi, Hyderabad, Nawabshah, Larkana, Sukkur, 1500, 2000, and 2500 (Alam et al., 2010, 2011c). All Tharparkar, Umerkot, Dadu, Jacobabad and Ghotki. This backward trajectories in this study were calculated at 500, study includes most of the cities due to their geo-strategic 1500 and 2500 m heights above ground level by the model locations, heavy industrial emissions, vehicular emissions archived from the website (http://ready.arl.noaa.gov/HYSP and other anthropogenic activities. The spatial gradient of LIT.php). We analyze the origin of air masses with aerosol AOD for thirteen years shows high AOD values in central such as dust, sea salt, pollutants, etc. at various sites of and northern Sindh. This region has Indus River with dense Sindh. With some limitations of web version all registered urban populations, residences, commercial and industrial PC has no computational restrictions with the user to obtain estates that are well connected by the highways of Sindh the necessary meteorological data files in advance. In i.e., Indus Highway and National Highway. High AOD addition, all unregistered version is similar to the registered values (> 0.74) are observed over the areas with intense version except not work with forecasted meteorological anthropogenic activities. This includes an industrial region of data files (Draxler and Rolph, 2003; Alam et al., 2010). Jacobabad, Rohri (Sukkur), Larkana, Nawabshah, Hyderabad and Karachi. Some other areas with spatial geomorphologic RESULTS AND DISCUSSIONS locations such as Dadu and Ghotki has high AOD which may consider a result of wind masses and weather conditions that The spatial distribution of annual mean AOD at 550 nm affects the aerosol loads in each locality. This study also wavelength has been plotted for the period from 2001 to examined a very different case of Karachi. Karachi shows 2013. We also used mean spatial seasonal distribution of AOD values ranges 0.30 to 0.65 instead to show highest AOD for the whole period during 2001–2013. The correlation AOD values due to continuous sea breezes from Arabian maps of aerosol and cloud parameters named water vapor Sea that keeps AOD in motion. Balakrishnaiah et al. (2012). (WV), cloud fraction (CF), cloud top temperature (CTT), and determined aerosols marked impact on six southern India cloud top pressure (CTP) have been plotted for the period sites, namely TVM, BLR, ATP, HYD, PUNE and VSK
Fig. 1(a). Spatial distributions of annual mean AOD at 550 nm for the period 2001–2013.
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Fig. 1(b). Geographical location of Sindh.
They observed increased aerosol particles from southern than 0.98 were reported. Similar increases in AOD during India to northern India up to the Himalayas. Prasad et al. the summer season have previously been reported in other (2004) also examined the spatial gradient of AOD with cities of Pakistan (Alam et al., 2010, 2011a, b, c, 2012, high AOD values over areas with intense anthropogenic 2014, 2015). In contrast, very low AOD values were observed activities. Alam et al. (2010) analyzed AOD distribution in almost whole region during the winter season. During for eight cities of Pakistan named Peshawar, Rawalpindi, the winter season low AODs values were observed. During Zhob, Lahore, Multan, D.G. Khan, Rohri and Karachi. It the winters, very low AOD values (0.29–0.47) were found was found that during summers AOD values were very in the southeastern and the southwestern parts of Sindh. The high in all selected cities, particularly in southern Pakistan, reasons for these differences are the arrival of air masses these values were recorded more than 0.9. They also observed with dust in the region from the southern coast of the an increase in AOD from north to south of Pakistan. Middle East and the monsoon winds during the summer Similarly, high AOD in the summer months was reported season. The monsoon winds blow from the Indian Ocean by Munir and Zareen (2006) between 24°–25°N and 66°– after crossing the Indian peninsula, these winds with a very 71°E location of Pakistan. Alam et al. (2014) found high large amount of moisture enters in the northeastern Pakistan. AOD values specifically over Karachi, Lahore and D.G. The tail end of these winds enters in the regions of Khan. They attributed these variations to the reason that southeastern Pakistan that is specifically Sindh Province. these are urban, industrial and coastal areas. The dust from All the northern and the central areas of Sindh are Cholistan and Arabian Sea also contributed persistence of considered as the hotspots of dust aerosols in the summer dust aerosols along with local vehicular emission. months. Karachi, which is the coastal city, has a relatively high AOD value (1.17) in the summer and low AOD value Seasonal Variations in Aerosol Optical Depth (AOD) (0.38) in the winter season. During the autumn season, Seasonal variations in AOD were demonstrated and Dadu had specifically the highest AOD value (0.72) of the calculated for the period 2001–2013 (Fig. 2). Very high season while other areas of Sindh had AOD values range AOD values were found in almost whole Sindh during the from 0.54 to 0.66. In addition, the spring months are the summer season, particularly in the northern and central period of dust storms in the region that’s why after winters parts of Sindh where AODs greater than 1.27 were calculated. again AOD had shown increased values (0.71–0.86). The In the central parts of Sindh AOD ranges from 1.17 to southeastern and southwestern Sindh had low AOD values, 1.27. Likewise, in the southern Sindh AODs values greater which are greater than 0.49 during the autumn season.
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Fig. 2. Monthly mean AOD retrieved from MODIS for the period 2001–2013 over ten sites in Sindh, Pakistan.
Pre-monsoon period prevails in the month of June, marks a spot of high AOD values and spring shows a similar monsoon in the months of July and August and post- spatial distribution with little difference in extent. The monsoon in the month of September (Alam et al., 2010; similar seasonal variations were studied by Balakrishnaiah Balakrishnaiah et al., 2012). Pre-monsoon shows high et al. (2012) in India and Alam et al. (2010, 2012, 2014, AOD in northern Sindh including sites, like Jacobabad, 2015) in Pakistan. They found low AOD values in summer Sukkur, Ghotki, and Larkana which experience air masses and high AOD values in winter season. Alam et al. (2014) from the Punjab that impacts on aerosol concentration. found a high value of 2.3 in summer and low values of 0.2 Monsoon shows high AOD value in northern, northwestern in winter for coastal areas of Pakistan. Sindh due to air masses through Punjab and southern The spatial distribution of aerosol is much higher in Sindh, which have maximum water droplets and sea sprays extreme east and west of Sindh that are remote arid areas by tropical cyclones of coast. The AOD seasonal variations experiencing dust plumes from Middle-east, Iran and Arabian are maximum in Sindh in all four seasons, i.e., summer, Sea, or north to south central Sindh because of increases in winter, autumn and spring (Fig. 3). During the winter and urbanization and industrialization, dust storms, and salt summer seasons AOD distribution shifts from low AOD particles in Sindh. Similar studies have been conducted over values (winter) to high AOD values (summer). Autumn northern China by Qi et al. (2012), who concluded that the
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Fig. 3. Spatial correlation of AOD vs. Alpha for the period 2001–2013.
AOD values have significant seasonal variation, specifically ranges from –1.00 to –0.32 over Sindh. This decrease in small and high in summer. Tiwari and Singh (2013) studies correlation may be associated with dust loading for most of shown that coarse-mode aerosol particles are dominant in the the Sindh sites except the coastal area, where sea salt aerosols pre-monsoon, while fine-mode aerosol particles are dominant are dominant. In order to investigate the origins of the air in the winter season. Other studies have been conducted by masses arriving at the studied sites we performed back Kuniyal et al. (2009) and Gupta et al. (2013), concluded trajectory analyses based on the NOAA HYSPLIT (National AOD variations were highly dependent on both the travel Oceanic and Atmosphere Administration Hybrid Single pathways and the sources (e.g., sea salt, desert dust, smoke Particle Particle Langrangian Integrated Trajectory) model from burning biomass, and industrial pollution) of the air shown in Fig. 4. Most of the air masses reached Sindh from masses. the Dasht (Iran), Hamoun Wetlands (Afghanistan), Cholistan (Pakistan), Thar (India) deserts, and also from the Arabian Relationship between AOD and Ångström Exponent Sea. Ångström exponent (Alpha) is the exponent that Alam et al. (2012) found high
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Environmental Prediction (NCEP) of the Air Resource 25°00′N, 67°01′E (Karachi), 26°14′N, 68°24′E (Nawabshah), Laboratory. The NOAA HYSPLIT model is helpful to 27°40′N, 68°53′E (Rohri), 24°74′N, 69°80′E (Tharparkar), understand the origins of the air masses arriving in the 25°22′N, 68°22′E (Hyderabad), 28°16′N, 68°27′E (Jacobabad). studied region (Draxler and Rolph, 2003). We found similar All the back trajectories are calculated at 0000 h UTC. The trajectories for other time periods, but this study includes only trajectories originate from different sources, mostly from selective back trajectories as these are the representatives Hamoun Wetlands of Afghanistan, Dasht Desert of Iran, of the entire season. Cholistan Desert of Pakistan and Thar Desert of Pakistan and The cruise locations on these days of months were India and Arabian Sea that increase in aerosol concentrations
Fig. 4. Back trajectories of different heights in meters ending at 2300 UTC for six stations in Sindh, Pakistan dated January 15, July 15, April 15, and May 15, 2013.
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Fig. 4. (continued). could be observed over this region. Sindh is considered as also seen to have contributed to an increase in the aerosol lower Indus Plain that is influenced by four seasons: summer concentrations in the studied region (Gupta et al., 2006; with monsoon time period (June–August), winter under the Alam et al., 2011c). influence of western depressions (December–February), On 15 January the air parcels at 2500 m originated from spring (March–May) and autumn (September–November). UAE and Oman arid regions and before reaching Karachi, Dust storms and thunderstorms are characteristics of April, Hyderabad, Nawabshah and Tharparkar traveled through May, October and November (Prasad et al., 2004, 2005, the Arabian Sea. For Rohri and Jacobabad 2500 m trajectory 2007). In addition, local pollution in cities and industrial originated from the Dasht Desert of Iran. A short distance estates, like Karachi, Hyderabad, Nawabshah and Rohri has been traveled from Thar to Nawabshah, Makran for
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Rohri, Coast of Iran for Hyderabad, and Persian Gulf for (India), Arabian Sea, Afghanistan, and Sahara desert (Alam Karachi and Tharparkar at 1500 m. The 500 m air trajectory et al., 2010, 2011c, 2015). Therefore, both local sources originates from the Arabian Sea and Thar Desert of Pakistan and long-range transported aerosol lead to high AOD in and India. On 15 July the 2500 m air trajectories had north, the region. northeast, and east origin of Pakistan. The 500 m air trajectories originated from the Arabian Sea and spent about Relationship between Aerosol Optical Depth and Cloud 12 hours in the sea before reaching Karachi, Hyderabad and Parameters Tharparkar. The 1500 m air parcels originating within The MODIS provides an enormous valuable data to Sindh had little journeyed with zigzag air mass pattern. It understand how aerosols relate cloud parameters (Myhre et is therefore analyzed that air masses in January traveled al., 2007; Alam et al., 2010; Alam et al., 2014). This section long distances mostly from south-west before reaching deals with the relationships between AOD and cloud Sindh whereas, in June traveled shorter distances mostly parameters for the ten sites of Sindh through the spatial from the northeast of Sindh. The air trajectories spent more correlation map. We used the correlation maps between time over sea in January; in contrast, in June these trajectories AOD and cloud parameters for the time period from 2001 spent more time over land that is the reason why AOD to 2013. For the entire study area correlation maps used for values are recorded highest in summer months and lowest a real comparison between AOD and WV, AOD and CF, in winter season. Most of the locations from where air AOD and CTT, and AOD and CTP. It has been noticed masses originated are arid or semi-arid in all seasons thus that higher aerosol loading favors the distributions of higher there is a great chance of dust particles blown with air masses clouds and larger cloud fractions, either the MODIS AOD to increase aerosols concentrations in Sindh. A similar source data is calculated from all-data or the stringently less-cloudy was found by Ramachandran and Jayaraman (2003) that data sets. Most of the data are noisier when recorded high most of the air masses with the altitude of 500, 1000 and 5000 clouds datasets with a significant deficiency in the sample m originated from North Atlantic Ocean passes through size (Balakrishnaiah et al., 2012). These relationships are Africa having world's largest Sahara Desert and the Arabian discussed in the following sub-sections. peninsula with the states like Yemen, Oman and Saudi Arabia traveled through Afghanistan and southern Pakistan Relationship between Aerosol Optical Depth and Water before reaching India. The possibility of the air masses Vapor getting mixed with the anthropogenic particles during travel In order to understand the aerosol impact and the process cannot be eliminated. From air mass trajectories it has been of the hydrological cycle, we investigated the change in clear that aerosol concentrations are influenced by arid and column water vapor in relation to aerosols (Platnick et al., semi-arid regions of middle-east, the Arabian Sea, the Indian 2003; Myhre et al., 2007; Balakrishnaiah et al., 2012). The Ocean and industrial estates in Sindh (Ramachandran and spatial correlation between AOD and WV (Fig. 5) reveals, Jayaraman, 2003; Alam et al., 2011c). Most of the air masses that AOD and WV have a stronger positive correlation over reached to various cities of Pakistan from Dasht (Iran), Thar western and central Sindh than on eastern Sindh. The highest
Fig. 5. Spatial correlation for AOD vs. Water Vapor for the period 2001–2013.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015 667 positive correlations (correlation coefficient greater than 0.67) season, most of the air masses come from Arabian Sea shown were found for most of the eastern Sindh sites including in Fig. 4, that is the source of water vapor and higher Karachi, Hyderabad, Nawabshah, Larkana, Sukkur, Ghotki values of AOD in the region. IPCC (2013) describes the and Dadu. In addition relatively lower correlations (greater role of water vapor on the scattering behavior of aerosols. than 0.62) was found for other site, such as Umerkot. The All hydrophilic aerosols are important to form clouds with lowest levels of correlation coefficient (greater than 0.22) the availability of water vapors to change in both direct were found on eastern most strip of Sindh that’s the part of and indirect radiative forcing on climate. Therefore, the Tharparkar. Similar results were found by Alam et al. (2010, correlation of aerosol and water vapor is an important topic 2014) that shows a strong positive correlation between for modeling of aerosol and for remote sensing to understand. AOD and WV at higher latitudes than at lower latitudes of Pakistan. They analyzed highest positive correlation greater Relationship between Aerosol Optical Depth and Cloud than 0.8 at Peshawar and Rawalpindi and lower levels of Fraction correlations greater than 0.7 over other studied sites of The spatial-temporal correlation between AOD and CF relatively lower latitudes of Pakistan. In addition, Alam et has been plotted for various locations over Sindh during the al. (2014) found no significant relationship between AOD period 2001–2013 as shown in Fig. 6. Overall the correlation and WV during the winter season due to absence of dust for AOD and CF was found higher at lower Sindh and aerosols consequently lowers the WV in several cities of lower/negative at upper Sindh. For example, the correlation Pakistan. They related these results with the fact that aerosols between AOD and CF was greater than 0.23 in Karachi might be removed by monsoon rains of summer and the AOD whereas, in Dadu, Larkana, and Sukkur it was less than values reduced by colder ground conditions. An elevated WV Karachi and greater than 0.08 and in some cases less than values was founded along the coast like Karachi where zero or negative. Such areas are biomass and dust prone industrial and vehicular emissions is also very high. areas that affect the correlation between two parameters. The water absorbing ability of aerosols may vary for Kaufman et al. (2005a, b) examined the influence of AOD different types of aerosol. Shi et al. (2008) and Alam et al. on meteorological parameters such as cloud cover and air (2011) found during their studies that coarse fractions of pressure and temperature differences. They found that various aerosols i.e., dust particles are mostly insoluble but may cloud properties and aerosol concentrations are being affected become hygroscopic when coated with sulphate or other by changing in atmospheric circulations for instance, a low soluble inorganic aerosols during transport. In addition, Li pressure region may tend to accumulate aerosol particles and Shao (2009) found that when these dust particles well and water vapors that lead generating favorable atmospheric coated with a nitrate may become hygrophobic. Indeed, the conditions to increase cloud cover. concentration of aerosols with their types and nature changes The increased aerosol exposure to solar radiation will result the climate with meteorological parameters such as in stronger aerosol absorption effect on clouds. Physically, precipitation, temperature, wind speed and wind direction aerosol absorption of solar radiation can evaporate or thin (Aloysius et al., 2009; Alam et al., 2011). During the summer cloud optically (Hoeve et al., 2011), and that the decrease of
Fig. 6. Spatial correlation for AOD vs. Cloud fraction for the period 2001–2013.
668 Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015
CF with AOD. Thus absorption effects suppress the formation AOD and CF were positively correlated as both values and growth of clouds. Some desert clouds are dust formed were in increasing trend. In contrast, the correlation was clouds made with dust and tiny droplets in a case of presence found negative during winter months. of humidity in surroundings as studied by Rosenfeld et al. (2001). Similar results were found by Balakrishnaiah et al. Relationship between Aerosol Optical Depth and Cloud (2012) that the correlation between AOD and CF was found Top Temperature to be higher than that of coastal sites compared to continental. In order to retrieve many properties of the atmosphere Nakajima et al. (2001) found that most of the global regions closest to surface that is important to know the atmospheric adjacent to the coasts of the continents, have shown positive temperature at the level of the cloud top (Balakrishnaiah et correlation between AOD and CF. In addition, most of the al., 2012). In Fig. 7, the spatial correlation of AOD and CTT tropical continental regions have shown a negative correlation has shown over the period 2001 to 2013. This demonstrates with small CF and large AOD value due to the total absence that CTT increased as AOD increased in almost all northern or a very few low clouds interacting with aerosols or may Sindh sites, whereas, started to decrease and shown a negative be due to the presence of dust particles that are not very correlation towards southern Sindh. Similarly, Alam et al. effective as CCN. (2010) found a positive correlation between AOD and CTT For most of the southern Sindh sites AOD and cloud at higher latitudes like Peshawar, Zhob, and Rawalpindi and fraction are positively correlated such as in Karachi, a negative correlation at lower latitudes like Karachi. This Hyderabad, Nawabshah, Thatta, Badin, Tharparkar, and is the opposite case as we discussed earlier for AOD and Umerkot. As we move towards the north of Sindh correlation CF correlation map in which CF was observed positively starts to reduce for the rest of the Sindh sites like for Dadu, correlated with AOD over southern Sindh, whereas, negatively Larkana, Khairpur, Jacobabad and Ghotki especially the correlated over northern Sindh. There was a positive negative correlation has been noticed in eastern and western correlation between AOD and CTT at Ghotki, Jacobabad, Sindh region. In these areas negative correlation was found Dadu, Sukkur, Larkana and Nawabshah indicate a consistent in the range from –0.56 to –0.60. Similarly, Alam et al. positive correlation at higher latitudes of Sindh (High values (2010) found the correlation between CF and AOD higher are up to 0.17). In addition, Karachi, Hyderabad, Umerkot at lower latitudes and lower at higher latitudes of Pakistan. and Tharparkar following a negative correlation at lower They mentioned all those regions of Pakistan dominated latitudes of Sindh (Low values are not less than –0.58). by biomass and dust aerosols have the marked increase in Therefore, mainly a positive correlation between AOD and the correlation. All meteorological factors influenced that CTT has been observed for most of the stations of Sindh may relationship of AOD and CF as well. Alam et al. (2014) be due to large-scale meteorological situations in MODIS analyzed the correlation between AOD and CF, and found data. This correlation might be involved in a very complex lower in urban areas and higher in arid and coastal stations interplay among convection, layer of boundary and large- of Pakistan. During the summer months, it was found that scale cloud parameterizations in the region (Quass et al.,
Fig. 7. Spatial correlation for AOD vs. Cloud Top Temperature for the period 2001–2013.
Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015 669
Fig. 8. Spatial correlation for AOD vs. Cloud Top Pressure for the period 2001–2013.
2009; Alam et al., 2010). Several atmospheric and surface at continental sites such as Hyderabad, Nawabshah, Larkana, properties may retrieve by accurate information on cloud Sukkur, Dadu, Jacobabad and Ghotki this decrease was top temperature. It plays an essential role in the net earth only moderate. The correlation was found lower in Karachi radiation budget studies (Balakrishnaiah et al., 2012). (–0.45), Tharparkar (–0.35), Hyderabad (–0.25), Nawabshah Furthermore, Alam et al. (2014) found a negative correlation (–0.16) and more than –0.16 at Ghotki. In addition, Alam et between AOD and CTT in the winter season, except for al. (2014) found a consistent positive correlation between Lahore and D G Khan. On the other hand, a consistent AOD and CTP at Peshawar (higher latitudes) and a negative correlation was observed at coastal sites of lower negative correlation at Karachi (lower latitudes). Furthermore, latitudes like Karachi due to the presence of fine mode they have investigated a positive correlation of CTP with aerosol particles that may couple within the water column AOD for the entire region of Pakistan in the spring season. to show decreased CTT. In addition, anthropogenic aerosols These correlation facts may be caused by the large-scale may change the cloud-top temperature by their induced meteorological variations. secondary circulations in the atmosphere (Balakrishnaiah A similar study has been conducted over different cities et al., 2012). of Pakistan by Alam (2010) in which they showed a It has been found that the aerosol concentration influence correlation of AOD and CTT or CTP. Overall, they found CCN for most of Sindh areas as aerosol acts on clouds to a negative correlation of CTP with AOD in the southern change the size and composition of cloud particles with a region of Pakistan as there was a significant decrease in change in nearby temperature conditions in the atmosphere. CTP relative to AOD, moderately decreases at mid latitudes Some southern most sites are insensitive to changes in CTT and increase at high-latitudes. Kaufman (2005) reported with changing aerosol concentrations. The anthropogenic that CTP decreased with an increased of AOD due to the aerosols increase CTT by the induced secondary circulation, suppression by increasing the cloud lifetime thus affecting and thus change the cloud top temperature (Santer et al., the cloud albedo and CTP. Balakrishnaiah (2012) and 1995; Sekiguchi et al., 2009; Balakrishnaiah et al., 2012). Myhre (2007) investigated the same correlation in order to understand atmospheric characteristics that changes with Relationship between Aerosol Optical Depth and Cloud time. Top Pressure Tripathi et al. (2007) analyzed reduced updrafts of vertical The spatial correlation between AOD and CTP over the winds in the winter seasons and very strong drafts in the period 2001 to 2013 indicates overall a negative correlation monsoon seasons of Indo- Gangetic Plain. They suggest in Sindh (see Fig. 8). It was observed that as CTP decreased that all these vertical winds of the region are strongly with an increased in AOD especially at lower latitudes, correlated with changing CTP and CF. Koren et al. (2005) whereas in relatively mid-latitude this decrease has been also found the vertical wind velocity as a very important observed moderate. Overall, at coastal sites such as Karachi meteorological parameter to influence cloud properties. They there was a significant decrease in CTP with AOD whereas investigated the Atlantic convection clouds are restructured
670 Sharif et al., Aerosol and Air Quality Research, 15: 657–672, 2015 and invigorated by vertical winds. These vertical updrafts A.J., Ramanathan, V. and Welton, E.J. (2000). Reduction at 500 mb are correlated to the CTP and CF larger than 0.9 for of Tropical Cloudiness by Soot. Science 288: 1042–1047. almost the entire studied region, 0.6 for some regions and Andreae, M.O., Rosenfeld, D., Artaxo, P., Costa, A.A. and –0.7 for other regions. All these variations suggest associations Frank, G.P. (2004). Smoking Rain Clouds over the between meteorology and aerosol in these regions. Amazon. Science 303: 1337–1342. Alam, K., Iqbal, M.J., Blaschke, T., Qureshi, S. and Khan, CONCLUSION G. (2010). Monitoring Spatiotemporal Variations in Aerosols and Aerosol-cloud Interactions over Pakistan All aerosol characteristics based on satellite data were Using MODIS Data. Adv. Space Res. 46: 1162–1176. not deeply studied over Sindh. To the best of our knowledge Alam, K., Trautmann, T. and Blaschke, T. (2011a). Aerosol this is the first attempt to analyze and assist aerosol Optical Properties and Radiative Forcing over Mega characteristics over this region. Thus, this paper fills the City Karachi. Atmos. Res. 101: 773–782. scientific gap in understanding of aerosols and its Alam, K., Blaschke, T., Madl, P., Mukhtar, A., Hussain, distributions, concentrations and variations over the region. M., Trautmann, T. and Rahman, S. (2011b). Aerosol Size AOD spatial and temporal distributions were analyzed Distribution and Mass Concentration Measurements in through the use of the MODIS satellite data. The various Various Cities of Pakistan. J. Environ. Monit. 13: 1944– sources of aerosols arriving to Sindh were identified 1952. through the HYSPLIT model. The Angstrom exponent was Alam, K., Qureshi, S. and Blaschke, T. (2011c). Monitoring determined through the MODIS dataset that may change if Spatio-temporal Aerosol Patterns over Pakistan Based on the concentration of small and large aerosol particles changes. MODIS, TOMS and MISR Satellite Data and a HYSPLIT A correlation of AOD products with Cloud parameters such Model. Atmos. Environ. 45: 4641–4651. as CF, WV, CTT, and CTP is necessary for climatological Alam, K., Trautmann, T., Blaschke, T. and Majid, H. (2012). studies and to improve the accuracy and the coverage Aerosol Optical and Radiative Properties during Summer achievable with MODIS sensor. Cloud parameters were and Winter Seasons over Lahore and Karachi. Atmos. investigated in order to represent MODIS information that Environ. 50: 234–245. was rarely studied for the understanding of atmospheric Alam, K., Khan, R., Blaschke, T. and Mukhtiar, A. (2014). conditions over this region of Pakistan. Variability of Aerosol Optical Depth and Their Impact In this study of aerosol spatio-temporal distribution, on Cloud Properties in Pakistan. J. Atmos. Sol. Terr. maximum AOD was found in the summer season and Phys. 107: 104–112. minimum AOD was recorded in the winter season. The Alam, K., Khan, R., Ali, S., Ajmal, M., Khan, G., Wazir, highest AOD values were observed in cities of Sindh where M. and Azmat, M. (2015). Variability of Aerosol industrial and vehicular emissions with various anthropogenic Optical Depth over Swat in Northern Pakistan Based on activities increases the number of concentration and Satellite Data. Arabian J. Geosci. 8: 547–555. distribution over Sindh. Similarly, AOD concentration is Aloysius, M., Mohan, M., Babu, S.S., Parameswaran, K. also influenced by water cycle of the southern humid and Moorthy, K.K. (2009). Validation of MODIS regions that converts particles in hygroscopic aerosols. The Derived Aerosol Optical Depth and an Investigation on desert of Sindh has high seasonal concentrations mostly Aerosol Transport over the South East Arabian Sea from the Arabian Sea during the monsoon windy season. It during ARMEX-II. Ann. Geophys. 27: 2285–2296. has also been found that the water vapor increased as AOD Ångström, A. (1961). Techniques of Determining the increased over most of Sindh except the desert, CF Turbidity of the Atmosphere. Tellus 8: 214–223. increased with AOD, where as CTP and CTT have shown Ashraf, A., Aziz, N. and Ahmed, S.S. (2013). Spatio a positive correlation in the northern Sindh, a negative in Temporal Behavior of AOD over Pakistan Using MODIS the central Sindh and a highly negative in the southern Data. IEEE Inter. Conf. Aerospace Science & Engineering Sindh. The observed relationship between AOD and CF, (ICASE). the co-variation of AOD with CTP, CTT and WV may be Balakrishnaiah, G., Raghavendra Kumar, K., Kumar, Suresh, attributed to the large-scale meteorological variation. Reddy, B., Rama Gopal, K., Reddy, R.R., Reddy, L.S.S., Swamulu, C., Nazeer Ahammed, Y., Narasimhulu, K., ACKNOWLEDGEMENTS Krishna Moorthy, K. and Suresh Babu, S. (2012). Spatio- temporal Variations in Aerosol Optical and Cloud We are highly grateful to Meritorious Prof. Dr. Syed Parameters over Southern India Retrieved from MODIS Jamil Hasan Kazmi, Department of Geography, University Satellite Data. Atmos. Environ. 47: 435–445. of Karachi for his support and assistance. We are Coakley, J.A., Bernstein, R.L. and Durkee, P.A. (1987). extremely grateful to Dean, Faculty of Science, University Effects of Ship Stack Effluence on Cloud Reflectance. of Karachi for the financial support extended which make Science 237: 953–1084. this research study possible. 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