Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

Salman Tariq; Zia Ul-Haq

doi:10.1155/2018/3582191

Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

Tariq, Salman;Ul-Haq, Zia 2018-05-13 00:00:00 Hindawi Advances in Meteorology Volume 2018, Article ID 3582191, 12 pages https://doi.org/10.1155/2018/3582191 Research Article Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan Salman Tariq and Zia Ul-Haq Remote Sensing and GIS Group, Department of Space Science, University of the Punjab, New Campus, Lahore, Pakistan Correspondence should be addressed to Salman Tariq; salmantariq_pu@yahoo.com Received 20 July 2017; Revised 10 December 2017; Accepted 12 April 2018; Published 13 May 2018 Academic Editor: Andreas Matzarakis Copyright © 2018 Salman Tariq and Zia Ul-Haq. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Atmospheric aerosols are considered to be an important constituent of Earth’s atmosphere because of their climatic, environmental, and health eﬀects. /erefore, while studying the global climate change, investigation of aerosol concentrations and properties is essential both at local and regional levels. In this paper, we have used relatively long-term Aerosol Robotic Network (AERONET) data during September 2006–August 2014 to analyze aerosol properties such as aerosol optical depth at 500 nm (AOD), Angstro¨m exponent (440–870 nm) (AE), refractive index (RI), and asymmetry parameter over Karachi, a coastal megacity of Pakistan. /e average annual values of AOD and AE were found to be 0.48± 0.20 and 0.59± 0.29, respectively. /e peak (0.88 ± 0.31) AOD was recorded in July with corresponding AE of 0.30± 0.22 representing reasonably higher concentration of coarse size particles over Karachi. /e cluster analysis using the scatter plot between absorption AE and extinction AE revealed that desert dust prevailed in the atmosphere of Karachi in spring and summer, while biomass burning aerosols dominate in autumn and winter. /e peak values of volume concentrations of coarse and ﬁne-mode particulate matter were found in summer and autumn, respectively. Also, we found signiﬁcant growing trend in single-scattering albedo with wavelength, indicating the domination of dust particles during summer and spring. /e peak value of the real part of the RI was observed in spring (1.53) and modest in winter (1.50). On the contrary, the peak value of the imaginary part of the RI was observed to be constantly elevated in winter and lesser in spring. /e satellite remote sensing (SRS) technique provides global 1. Introduction coverage of aerosol properties for longer periods. However /e main component of Earth’s atmosphere consists of ground-based remote sensing is ideal for obtaining authentic a mixture of gases. However, it also contains particles of some and continuous aerosol characteristics over megacities all over solid or liquid material. /is particulate matter is released into the world [3]. the atmosphere from natural- and human-induced activities. As far as aerosol concentrations and their properties are Aerosol particles are considered to be an important con- concerned, Karachi is not a well-studied location. In par- ticular, very few studies have used the remote sensing stituent of Earth’s atmosphere because of their climatic, environmental, and health eﬀects. /e net eﬀect of aerosols on techniques to monitor aerosols over Pakistan (e.g., [4–7]). Alam et al. [5] tried to characterize the aerosol properties by climate is usually quantiﬁed in terms of radiative forcing. However, due to our poor knowledge regarding the spatial using the data for small duration (1 year). However, mul- and temporal distribution of aerosols and their characteristics, tiyear averages are necessary for characterizing a truly large uncertainties exist in quantifying the aerosol radiative representative climatological seasonal cycle [8]. In this pa- forcing [1]. Keeping in view the role of aerosols, climate per, we have used relatively long-term AERONET data models consider both direct and indirect inﬂuences of these ranging from September 2006 to August 2014 to analyze particles on radiative forcing [2]. /erefore, while studying the aerosol properties over Karachi, the biggest city of Pakistan. global climate change, investigation of aerosol concentrations In terms of aerosol characteristics, the location of Karachi is and properties is essential both at local and regional levels. of special interest because it is a heavily industrialized city in 2 Advances in Meteorology algorithm developed by Dubovik and King [15] is employed to retrieve the wide number of aerosol optical properties from the measurements of direct and diﬀuse radiation. /e inversion algorithm provides both retrieved aerosol properties such as size distribution, complex refractive index, and partition of spherical/nonspherical particles and is computed on the basis of the retrieved aerosol properties such as single- scattering albedo (SSA), asymmetry parameter, and broad- band ﬂuxes. /e inversion algorithm developed by Dubovik and King [15] is used to retrieve single-scattering albedo (SSA), refractive index (RI), asymmetry parameter, and size dis- Figure 1: Map showing location of Karachi (data source: Google tributions of aerosols. /e retrieval error in SSA is estimated Earth). to be in the range of 0.03–0.05. /e uncertainty in the size distribution of aerosols within the radius of 0.1–7μm lies in the neighbor of /ar desert and the only costal AERONET the range 15–35%, while for very small aerosols (0.05–0.1μm) site in Pakistan. and very large aerosols (7–15 μm) it goes down consid- erably (greater than 35%) due to very low sensitivity of 2. Study Area and Methodology little sensitivity of aerosol scattering at AERONET wave- lengths [5, 6]. 2.1. Site Description. /e megacity of Karachi is located Detailed description of retrieval errors of the CIMEL sky ° ° (24 51′ N, 67 00′ E) in southern Pakistan on the coast of radiometer is given by Dubovik and King [15] and Smirnov Arabian Sea. It is the capital city of province Sindh having et al. [16]. In the current study, we have used level 2.0 the densest population. /e location map of the study area (quality assured) AOD at 500 nm, ﬁne/coarse AOD, ﬁne- has been shown in Figure 1. With a population of 23.5 mode fraction and AE data from direct sun algorithm, and million and covering an area of 3,527 km , it has one of the absorption AE, extinction AE, SSA, RI, asymmetry pa- strenuous ports of the region and is known as a center of rameter, and size distribution data from inversion algo- trade and industrial activities in Pakistan. Emissions from rithm. /e data can be obtained via the NASA website at industries and automobile and dust are the main sources of http://aeronet.gsfc.nasa.gov/. air pollution in Karachi. High temperature and low pressure during spring and summer seasons lift dust particles from 3. Results and Discussion nearby arid areas and cause dust storms in Indo-Gangetic Basin (IGB) [9–11]. On the contrary, low temperature and 3.1. Variability in AOD and AE. /e intensity of radiation, high pressure along with intense biomass burning during while travelling through Earth’s atmosphere is attenuated by winter and autumn seasons are responsible for the formation aerosols and gases. /e AOD is a measure of extinction by of dense haze and fogs [12, 13]. the aerosols in the intensity of solar radiation during tra- Figure 2 shows variation in monthly average temper- versing through the terrestrial atmosphere. It is the most ature, dew point, relative humidity, mean sea level (MSL) important parameter that represents the atmospheric pressure, wind speed, and wind direction over Karachi aerosol burden. It is also signiﬁcant for the identiﬁcation of during the study period. It can be seen in Figure 2 that the aerosol source regions and aerosol evaluation [17]. /e AOD highest value (76.7%) of monthly average relative humidity and AE are generally used to describe the atmospheric is observed in August and lowest (46.2%) in January. Mean aerosol burden and the aerosol size distribution [18]. AE sea level (MSL) pressure decreases gradually from January provides fundamental information about the aerosol size, (1017.9 hPa) to its lowest value (998 hPa) in July and then and it is computed by spectral variation of AOD. Singh et al. increases afterwards. /e highest monthly average tem- [19], while analyzing variation of AE over central India, ° ° perature is found to be 32.1 C in June and lowest (19.7 C) in showed that coarse-mode and ﬁne-mode aerosols are ap- January. Dew point temperature increases gradually from proximately equal in the regions where AE is close to 1. /ey ° ° January (6.5 C) and reaches its maximum value (25.7 C) in further reported that the value of AE steadily grows as the July and then decreases afterwards. Maximum monthly ﬁne-mode aerosol contribution increases. average wind speed is observed in August (16.8 km/h) and Figure 3(a) shows the time series and trend of monthly minimum in November (3.7 km/h). /e winds from the mean values of AOD at 500 nm over Karachi for the period southeast direction have prevailed over Karachi during the September 2006–August 2014. We observe a strong sea- study period. sonality in AOD variations which is the speciﬁc feature of IGB that observes four distinct seasons during the year. /e 2.2. AERONET. /e AERONET instrument, a ground-based average values of AOD over Karachi are generally higher CIMEL sky radiometer, was established in Karachi (Pakistan) during spring and summer than autumn and winter. /e by NASA in August 2006. It operates in the wavelength ranges slope and y-intercept of the linear trend line are found to be of 340 nm–1020 nm and 440 nm–1020 nm to record direct sun 0.0002 and 0.474, respectively. Figure 3(b) shows the his- and sky radiances, respectively [14]. AERONET inversion togram of AOD at 500 nm over Karachi during the study Advances in Meteorology 3 90 1020 0 985 (Months) Temperature (celsius) Relative humidity Dew point (celsius) MSL pressure (hPa) (a) 18 300 12 200 6 100 0 0 (Months) Wind speed (km/h) Wind direction (degree) (b) Figure 2: Variability of monthly average (a) temperature, dew point, relative humidity, and mean sea level (MSL) pressure, and (b) wind speed and wind direction over Karachi during the study period. 1.2 y = 0.0002x + 0.474 0.8 0.6 0.4 0.2 (Months) (a) Figure 3: Continued. AOD (500 nm) Temperature, dew point, relative 2006-SEP Wind speed (km/h) humidity 2006-DEC 2007-MAR 2007-JUN January January 2007-SEP 2007-DEC February February 2008-MAR 2008-JUN March March 2008-SEP 2008-DEC April April 2009-MAR 2009-JUN May May 2009-SEP 2009-DEC June June 2010-MAR 2010-JUN July July 2010-SEP 2010-DEC August August 2011-MAR 2011-JUN September September 2011-SEP 2011-DEC October October 2012-MAR 2012-JUN November November 2012-SEP 2012-DEC December December 2013-MAR 2013-JUN 2013-SEP Wind direction (degree) 2013-DEC MSL pressure 2014-MAR 2014-JUN 4 Advances in Meteorology 12,000 Series: AOD_500 Observations 45625 10,000 Mean 0.48 Median 0.37 8,000 Maximum 3.64 Minimum 0.03 Std. dev. 0.24 6,000 Skewness 2.38 4,000 2,000 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 (b) Figure 3: (a) Time series and linear trend of monthly mean AOD over Karachi during September 2006–August 2014. Broken line means no data. (b) All points AOD data at 500 nm over Karachi during September 2006–August 2014. Table 1: Descriptive statistics of monthly average AOD, ﬁne-mode AOD, coarse-mode AOD, and ﬁne-mode fraction at 500 nm over Karachi during September 2006–August 2014. AOD Fine-mode AOD Coarse-mode AOD Fine-mode fraction Mean 0.48 0.18 0.30 0.42 Standard error 0.02 0.01 0.02 0.02 Median 0.42 0.17 0.24 0.39 Standard deviation 0.20 0.05 0.20 0.16 Range 0.86 0.28 0.88 0.59 Minimum 0.25 0.10 0.04 0.19 Maximum 1.11 0.37 0.91 0.79 1.4 1.2 0.8 0.6 0.4 0.2 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (Months) (a) 1.5 0.5 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (Months) (b) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (Months) (c) FIGURE 4: Continued. PW (cm) AE AOD Advances in Meteorology 5 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Jan 13 Backward trajectories ending at 0800 UTC 15 Feb 13 GDAS meteorological data GDAS meteorological data 55 60 65 70 40 45 50 55 60 65 70 75 25 25 1000 1000 1000 1000 Job ID: 195257 Job start: u May 18 08:50:44 UTC 2017 Job ID: 197564 Job start: u May 18 09:37:53 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Jan 2013 - GDAS1 Meteorology: 0000Z 15 Feb 2013 - GDAS1 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Mar 13 Backward trajectories ending at 0800 UTC 15 Mar 13 GDAS meteorological data GDAS meteorological data 60 62 64 66 68 70 72 56 58 60 62 64 66 68 2000 2000 1000 1000 1000 Job ID: 195902 Job start: u May 18 09:03:11 UTC 2017 Job ID: 195929 Job start: u May 18 09:04:05 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Mar 2013 - GDAS1 Meteorology: 0000Z 15 Mar 2013 - GDAS1 (d) FIGURE 4: Continued. Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E 6 Advances in Meteorology NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 May 13 Backward trajectories ending at 0800 UTC 15 May 13 GDAS meteorological data GDAS meteorological data 50 55 60 65 70 60 65 70 75 80 1000 1000 Job ID: 196102 Job start: u May 18 09:07:47 UTC 2017 Job ID: 196208 Job start: u May 18 09:09:32 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 May 2013 - GDAS1 Meteorology: 0000Z 15 Jun 2013 - GDAS1 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Jul 13 Backward trajectories ending at 0800 UTC 15 Jul 13 GDAS meteorological data GDAS meteorological data 45 50 55 60 65 70 75 55 60 65 70 75 1000 1000 1000 1000 500 500 Job ID: 196125 Job start: u May 18 09:08:34 UTC 2017 Job ID: 196358 Job start: u May 18 09:12:44 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Jul 2013 - GDAS1 Meteorology: 0000Z 15 Aug 2013 - GDAS1 (d) FIGURE 4: Continued. Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Advances in Meteorology 7 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Sep 13 Backward trajectories ending at 0800 UTC 15 Oct 13 GDAS meteorological data GDAS meteorological data 60 62 64 68 58 60 62 64 66 68 3000 3000 2500 2500 2000 2000 1500 1500 1000 1000 1000 1000 500 500 Job ID: 196950 Job start: u May 18 09:22:20 UTC 2017 Job ID: 196847 Job start: u May 18 09:21:23 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Sep 2013 - GDAS1 Meteorology: 0000Z 15 Oct 2013 - GDAS1 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Nov 13 Backward trajectories ending at 0800 UTC 15 Nov 13 GDAS meteorological data GDAS meteorological data 66 68 70 72 74 60 62 64 66 68 2000 2000 1500 1500 1000 1000 1000 500 500 Job ID: 196994 Job start: u May 18 09:24:13 UTC 2017 Job ID: 197153 Job start: u May 18 09:28:22 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Nov 2013 - GDAS1 Meteorology: 0000Z 15 Dec 2013 - GDAS1 (d) Figure 4: Monthly average variations in the (a) AOD (500 nm), (b) AE (440/870 nm), and (c) PW over Lahore during September 2006–August 2014 along with the standard deviations. (d) HYSPLIT model backward trajectories during year 2013. period. It can be observed that highest frequency is observed 2014 have been presented in Table 1. /e mean monthly at a value of 0.3. AOD is found to be 0.48± 0.20 during the study period. It Descriptive statistics of monthly average AOD, ﬁne- can be further observed from Table 1 that the monthly mean mode AOD, coarse-mode AOD, and ﬁne-mode fraction value (0.30) of coarse-mode AOD is greater than the at 500 nm over Karachi during September 2006–August monthly mean value (0.18) of ﬁne-mode AOD. /e coarse- Source ∗ at 24.90 N 67.13 E Source ∗ at 24.90 N 67.13 E Meters AGL Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL 8 Advances in Meteorology to the fact that Alam et al. [5] utilized the data of just mode AOD is observed to have the highest range (0.88). Figures 4(a)–4(c) show the monthly average AOD (at twelve months (August 2006–July 2007), while in present study, we have used long-term data of more than seven years 500 nm), AE, and precipitable water (PW) along with their standard deviations over Karachi during the period (September 2006–August 2014). September 2006–August 2014. /e error bars show the Figure 4(c) shows notable variations in mean monthly corresponding standard deviations of each monthly mean PW over Karachi during the study period. It can be noted value. In order to understand the movement of air masses from Figure 4(c) that PW starts to increase from January over Karachi, HYSPLIT model backward trajectories have (1.29 cm) and becomes maximum in August (4.68 cm) and been computed (Figure 4(d)). We note that, in spite of then decreases afterwards. /e occurrence of the highest value of PW in August is attributed to the high amounts of natural changes or ﬂuctuations in the meteorological con- ditions and aerosol transport, the variations in the three rainfall received by Karachi during the monsoon season. An overall mean value of PW is observed to be 2.48± 1.24 cm. parameters (i.e., AOD, AE, and PW) over Karachi are almost gradual and steady. We also ﬁnd strong seasonal variabilities Due to high values of PW, water is attracted by hygroscopic particles such as ammonium sulfate, ammonium nitrate, and in all the three parameters. /e average values of AOD are, in general, higher during spring and summer than those during sodium chloride leading to increased size of aerosol parti- autumn and winter. A similar trend has been found by Ali cles. During pollution episodes, high values of PW further et al. [20], while analyzing the AERONET data over Lahore. worsen the situation by reducing the visibility. Furthermore, However, a comparison of monthly average AOD values in the presence of aerosol and gaseous pollution, high PW is over Karachi (present study) and Lahore [20] reveals that responsible for increase in the speed of process of smog Karachi carries only about 80% aerosol burden than that of formation. /e scattering and absorption of light by aerosols in the lower atmosphere deteriorate the visibility [21]. In Lahore. Furthermore, we notice that the highest value of monthly particular, ﬁne-mode aerosols are recognized as the main cause of poor visibility [22]. Temperature inversion and low mean AOD (0.88± 0.31) is recorded in July along with the AE value of 0.30± 0.22, indicating the dominance of coarse wind speeds limit the dispersion of aerosol pollution and result in low visibility. High water vapor content further particles. Ali et al. [20], while studying aerosol properties over Lahore, also found the highest value of monthly mean reduces the visibility due to hygroscopic growth of aerosols. AOD (1.02± 0.41) in July with the AE value of 0.84± 0.28. High hygroscopic growth increases the particulate matter /us, the peak AOD and the corresponding AE values concentration and scattering ability of aerosols [23]. observed over Karachi are found to be smaller than those measured over Lahore. /is diﬀerence suggests that the atmosphere over Karachi is less polluted as compared to 3.2. Identiﬁcation of Aerosol Types. Optical and physical properties of aerosols are dependent upon their origins. A Lahore, while it contains comparatively elevated amount of coarse-mode particles. /ese particles basically originate number of methodologies have been used for the classiﬁcation of aerosols. Kaskaoutis et al. [24], Sharma et al. [25], and Tariq from /ar, Sistan, and Sahara regions. We observe the lowest monthly mean AOD (0.33± 0.15) in February along with the et al. [13] used correlation of AOD and AE, while Russell et al. AE value of 0.68± 0.29, representing that Karachi has the [26] and Giles et al. [27] used correlation between absorption lowest aerosols burden during this month. In an earlier study and extinction AE to discriminate aerosol types. Russell et al. over Karachi, Alam et al. [5] found the maximum average [26] used absorption Angstro¨m exponent (440–870) (AAE) AOD value of 0.92± 0.28 in July, whereas the minimum and extinction Angstrom exponent (440–870) (EAE) from monthly average AOD value was recorded to be 0.31± 0.11 AERONET to classify dominant aerosol types. /e cluster in February. /ese diﬀerences arise probably because Alam analysis using the scatter plot of AAE and EAE reduces the et al. [5] utilized data for only one year, that is, August ambiguities in aerosol classiﬁcation [26]. Diﬀerent threshold values of aerosol properties are considered for aerosol subtype 2006–July 2007. In another study, Alam et al. [6] tried to analyze the AERONET data over Karachi and Lahore for classiﬁcation. /e selection of threshold values is dependent upon location, range of AOD, and aerosol characteristics [28]. a period of only six months (April–June 2010 and December 2010–February 2011). However, in terms of climatic eﬀects, In this study, we have used the scatter plot of AAE and EAE we need long-term data to analyze the situation reliably. to categorize dominant aerosol types over Karachi. We have Alam et al. [5] also analyzed aerosol optical properties considered EAE< 1.0 and 0.7< AAE< 1.3, EAE> 0.8 and such as AOD and AE over Karachi by using AERONETdata. AAE> 1.3, and EAE> 1 and AAE> 1.2 to represent urban/ We note some diﬀerences in AOD and AE values found in industrial aerosols, desert dust aerosols, and biomass burning the present study and that of Alam et al. [5]. /e diﬀerences aerosols, respectively [7, 29]. /e remaining cases that do not have been calculated by subtracting the AOD and AE values belong to any of the aforementioned groups are referred to as found by Alam et al. [5] from AOD and AE values found in mixed type of aerosols. Seasonal classiﬁcation of diﬀerent aerosol types using the scatter plot between daily mean AAE the present study, respectively. We observe notable diﬀer- ences in AOD values such as 0.3, −0.12, 0.11, and 0.1 in June, and EAE values over Karachi during the study period has been shown in Figure 5. It can be observed from Figure 5 that desert November, March, and September, respectively. As far as AE is concerned, we observe diﬀerences of 0.35, 0.24, 0.16, and dust aerosols remained present in the atmosphere of Karachi 0.14 in January, November, October, and February, re- during all the seasons, but they are more dominating in the spectively. /e inconsistencies in the results are possibly due seasons of spring and summer. Figure 5(b) exhibits dust storm Advances in Meteorology 9 3.5 0.35 Desert dust 0.3 Biomass burning 0.25 2.5 0.2 0.15 1.5 0.1 0.05 0.5 Urban/industrial 0.01 0.1 1 10 Radius (µm) 0 0.5 1 1.5 2 EAE Summer Winter Spring Summer Autumn Spring Autumn Winter Figure 6: Seasonal averaged volume size distribution over Karachi (a) during the study period. of volume concentration of ﬁne-mode aerosols is observed during autumn and lowest during spring. /e high ﬁne- 15 mode volume concentration in autumn is attributed to increased emissions from biomass burning activities. /e peak value of ﬁne-mode volume concentration is observed to be 0.039, 0.034, 0.037, and 0.021 during autumn, winter, summer, and spring, respectively. We ﬁnd the highest coarse-mode volume concentration in summer and lowest during winter season. /e coarse-mode volume concen- (Months) tration in summer is seen to be about ﬁve times greater than that of winter. /is diﬀerence in concentration is attributed (b) to the high temperatures and wind speeds which eject dust Figure 5: (a) Classiﬁcation of aerosols during seasons of spring aerosols from the surface. For coarse aerosols, the peak (MAM), summer (JJA), autumn (SON), and winter (DJF) using the volume concentration is found to be 0.313, 0.171, 0.114, and scatter plot of daily average absorption Angstrom ¨ exponent 0.067 in summer, spring, autumn, and winter, respectively. (440–870) (AAE) versus extinction Angstrom ¨ exponent (440–870) (EAE) over Karachi and (b) dust storm frequency over central region of Pakistan during the study period. 3.4. Single Scattering Albedo. Single-scattering albedo (SSA) is one of the key parameters that explain the contribution of aerosols towards radiative forcing. Increasing trend of SSA frequency over the central region of Pakistan during the study with wavelength represents the presence of desert dust period. It can be noted from the ﬁgure that higher (greater than aerosols, while its decreasing trend indicates the presence of 10%) dust storm frequencies were observed during April–July. urban/industrial and biomass burning aerosols [30]. Sea- Urban/industrial aerosols are also observed to contribute to sonal variations in average SSA with respect to wavelength aerosol pollution over Karachi during all the seasons. It can (440 nm–1020 nm) over Karachi are shown in Figure 7. further be noted that during winter and autumn, biomass It can be noted that SSA increases with wavelength burning aerosols also contribute to total aerosol burden over during all the seasons, but it shows a maximum increase Karachi. during spring season. Yu et al. [28] also reported an increase in SSA values caused by dust aerosols in spring season. /is indicates that dust aerosols are consistently present in the 3.3. Size Distribution. Size distribution of aerosols is re- trieved from the inversion algorithm [15] in the range atmosphere of Karachi during all the seasons, but they became more dominating in spring. /e SSA increased in 0.05–15μm. Figure 6 shows the aerosol volume size dis- tribution over Karachi during diﬀerent seasons. A bimodal the range 0.897–0.960, 0.925–0.968, 0.897–0.930, and size distribution of aerosols is observed over Karachi 0.900–0.914 during spring, summer, autumn, and winter, (Figure 6). /e two-mode distribution of aerosols is caused respectively. SSA values during the summer season are by various factors such as combination of two air masses found to be the highest at all the wavelengths. Additionally, carrying diverse types of aerosols. /e maximum volume during summer, hygroscopic growth of aerosols causes SSA concentration of ﬁne-mode aerosols is found to occur at values to increase [9]. /e mean SSA values are found to be 0.905, 0.929, 0.938, and 0.942 at the wavelength of 440 nm, a radius of∼0.15µm in winter and autumn and, at a radius of ∼0.11µm, in summer and spring seasons. /e highest value 675 nm, 870 nm, and 1020 nm, respectively. Dust storm frequency (%) AAE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 3 –2 dVr/dlnr (µm µm ) 10 Advances in Meteorology 0.98 1.55 1.54 0.97 1.53 0.96 1.52 0.95 1.51 0.94 1.5 1.49 0.93 1.48 0.92 1.47 0.91 1.46 0.9 1.45 0.89 400 500 600 700 800 900 1000 1100 400 600 800 1000 1200 Wavelength (nm) Wavelength (nm) Summer Winter Summer Winter Autumn Spring Autumn Spring (a) Figure 7: Variations in seasonal averaged SSA with respect to 0.012 wavelength over Karachi during September 2006–August 2014. 0.01 3.5. Refractive Index. /e refractive index (RI) of aerosols is 0.008 an important optical property that depends on chemical 0.006 composition and SSA of aerosol particles [30, 32]. /e RI 0.004 being a complex quantity has real and imaginary parts. /e real part of the RI exhibits elevated values for high scattering, 0.002 while the imaginary part increases with absorption [3]. Numerous studies have been reported that the RI to be 1.53 400 500 600 700 800 900 1000 1100 for dust aerosols [33–35], but uncertainty of ±0.05 in this Wavelength (nm) value exists due to diﬀerent methods of measurements and chemical composition of dust [36, 37]. Summer Winter Cheng et al. [38, 39] and Alam et al. [6] showed that the Autumn Spring real part of the RI is larger at higher wavelengths because of (b) high absorption due to coarse-mode aerosols. Seasonal variations of real and imaginary parts of the RI Figure 8: Seasonal variations in (a) real part and (b) imaginary in the wavelength range of 440–1020 nm over Karachi part of the RI (refractive index) over Karachi during September 2006–August 2014. during September 2006–August 2014 have been shown in Figure 8. It can be observed in Figure 8(a) that during autumn and winter seasons, the real part of the RI shows considerable increasing trend in the wavelength range of 3.6. Asymmetry Parameter. /e asymmetry parameter is 440–870 nm. During spring, the real part of the RI is found frequently used to illustrate the angular distribution of to be consistently high (>1.52) at all the wavelengths in- scattered light in various radiative transfer models. /e dicating the dominance of desert dust aerosols. Yu et al. [3] asymmetry parameter and SSA are reported to have positive also presented a similar result over Beijing. correlation likely due to the scavenging of aerosol particles It can be seen in Figure 8(b) that the imaginary part of by clouds [41]. So far, there is no direct method for the the RI shows a declining trend in the range 440 nm–675 nm, measurement of the asymmetry parameter, but several and then, it becomes almost stable in the range 675 nm– methods are used to retrieve the asymmetry parameter from 1020 nm. Yu et al. [3] also found low wavelength dependence aerosol and radiation measurements. /e asymmetry pa- rameter reports the direction of light after scattering by of the imaginary part of the RI over Beijing. /e high value of the imaginary part of the RI at each wavelength during aerosol particles and hence calculated by taking cosine of the winter indicates the dominance of absorbing aerosols in the scattering direction weighted by the phase function. It atmosphere over Karachi. During summer, decreased values ranges from +1 for complete forward scattering to −1 for of the imaginary part of the RI at each wavelength represent complete backward scattering. For a symmetric scattering, the dominance of nonabsorbing aerosols. /e hygroscopic its value is zero. Zege et al. [42] showed that the asymmetry growth of aerosols during summer season also leads to lower parameter ranges from ∼0.1 to ∼0.75 for very clean atmo- values of the imaginary part of the RI [40]. /e maximum spheres to heavily polluted conditions. value of the imaginary part of the RI is found in winter Figure 9 shows the variations in the spectral asymmetry (0.0803) and minimum in summer (0.0243). During spring parameter in diﬀerent seasons over Karachi during and autumn, moderate mean values of 0.0643 and 0.0333 of September 2006–August 2014. It can be seen in Figure 9 that the asymmetry parameter remains higher at all the the imaginary part of the RI, respectively, show the nearly equal amounts of diﬀerent types of aerosols over Karachi. wavelengths during summer as compared to other seasons. SSA Real part of RI Imaginary part of RI Advances in Meteorology 11 0.76 Additional Points 0.74 (i) /e mean annual AOD and AE were found to be 0.48 ± 0.20 and 0.59± 0.29, respectively. (ii) Cluster analysis revealed 0.72 that desert dust prevails over coastal megacity Karachi in 0.7 spring and summer. (iii) /e highest volume concentration of coarse-mode aerosols was observed in summer. 0.68 0.66 Conflicts of Interest 0.64 /e authors declare that there are no conﬂicts of interest 400 500 600 700 800 900 1000 1100 regarding the publication of this paper. Wavelength (nm) Summer Winter Acknowledgments Autumn Spring /e authors are thankful to NASA for setting up Karachi Figure 9: Variations in the seasonal averaged asymmetry pa- AERONET site and for providing the data. /e authors are rameter over Karachi during September 2006–August 2014. also thankful to Pakistan Meteorological Department for providing the meteorological data. It can be further noted that the asymmetry parameter References shows a decreasing trend in the wavelength range 440– 870 nm during each season and then depicts a little increase [1] O. Torres, P. K. Bhartia, J. R. Herman, Z. Ahmad, and J. 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Publisher: Hindawi Publishing Corporation
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Abstract

Hindawi Advances in Meteorology Volume 2018, Article ID 3582191, 12 pages https://doi.org/10.1155/2018/3582191 Research Article Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan Salman Tariq and Zia Ul-Haq Remote Sensing and GIS Group, Department of Space Science, University of the Punjab, New Campus, Lahore, Pakistan Correspondence should be addressed to Salman Tariq; salmantariq_pu@yahoo.com Received 20 July 2017; Revised 10 December 2017; Accepted 12 April 2018; Published 13 May 2018 Academic Editor: Andreas Matzarakis Copyright © 2018 Salman Tariq and Zia Ul-Haq. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Atmospheric aerosols are considered to be an important constituent of Earth’s atmosphere because of their climatic, environmental, and health eﬀects. /erefore, while studying the global climate change, investigation of aerosol concentrations and properties is essential both at local and regional levels. In this paper, we have used relatively long-term Aerosol Robotic Network (AERONET) data during September 2006–August 2014 to analyze aerosol properties such as aerosol optical depth at 500 nm (AOD), Angstro¨m exponent (440–870 nm) (AE), refractive index (RI), and asymmetry parameter over Karachi, a coastal megacity of Pakistan. /e average annual values of AOD and AE were found to be 0.48± 0.20 and 0.59± 0.29, respectively. /e peak (0.88 ± 0.31) AOD was recorded in July with corresponding AE of 0.30± 0.22 representing reasonably higher concentration of coarse size particles over Karachi. /e cluster analysis using the scatter plot between absorption AE and extinction AE revealed that desert dust prevailed in the atmosphere of Karachi in spring and summer, while biomass burning aerosols dominate in autumn and winter. /e peak values of volume concentrations of coarse and ﬁne-mode particulate matter were found in summer and autumn, respectively. Also, we found signiﬁcant growing trend in single-scattering albedo with wavelength, indicating the domination of dust particles during summer and spring. /e peak value of the real part of the RI was observed in spring (1.53) and modest in winter (1.50). On the contrary, the peak value of the imaginary part of the RI was observed to be constantly elevated in winter and lesser in spring. /e satellite remote sensing (SRS) technique provides global 1. Introduction coverage of aerosol properties for longer periods. However /e main component of Earth’s atmosphere consists of ground-based remote sensing is ideal for obtaining authentic a mixture of gases. However, it also contains particles of some and continuous aerosol characteristics over megacities all over solid or liquid material. /is particulate matter is released into the world [3]. the atmosphere from natural- and human-induced activities. As far as aerosol concentrations and their properties are Aerosol particles are considered to be an important con- concerned, Karachi is not a well-studied location. In par- ticular, very few studies have used the remote sensing stituent of Earth’s atmosphere because of their climatic, environmental, and health eﬀects. /e net eﬀect of aerosols on techniques to monitor aerosols over Pakistan (e.g., [4–7]). Alam et al. [5] tried to characterize the aerosol properties by climate is usually quantiﬁed in terms of radiative forcing. However, due to our poor knowledge regarding the spatial using the data for small duration (1 year). However, mul- and temporal distribution of aerosols and their characteristics, tiyear averages are necessary for characterizing a truly large uncertainties exist in quantifying the aerosol radiative representative climatological seasonal cycle [8]. In this pa- forcing [1]. Keeping in view the role of aerosols, climate per, we have used relatively long-term AERONET data models consider both direct and indirect inﬂuences of these ranging from September 2006 to August 2014 to analyze particles on radiative forcing [2]. /erefore, while studying the aerosol properties over Karachi, the biggest city of Pakistan. global climate change, investigation of aerosol concentrations In terms of aerosol characteristics, the location of Karachi is and properties is essential both at local and regional levels. of special interest because it is a heavily industrialized city in 2 Advances in Meteorology algorithm developed by Dubovik and King [15] is employed to retrieve the wide number of aerosol optical properties from the measurements of direct and diﬀuse radiation. /e inversion algorithm provides both retrieved aerosol properties such as size distribution, complex refractive index, and partition of spherical/nonspherical particles and is computed on the basis of the retrieved aerosol properties such as single- scattering albedo (SSA), asymmetry parameter, and broad- band ﬂuxes. /e inversion algorithm developed by Dubovik and King [15] is used to retrieve single-scattering albedo (SSA), refractive index (RI), asymmetry parameter, and size dis- Figure 1: Map showing location of Karachi (data source: Google tributions of aerosols. /e retrieval error in SSA is estimated Earth). to be in the range of 0.03–0.05. /e uncertainty in the size distribution of aerosols within the radius of 0.1–7μm lies in the neighbor of /ar desert and the only costal AERONET the range 15–35%, while for very small aerosols (0.05–0.1μm) site in Pakistan. and very large aerosols (7–15 μm) it goes down consid- erably (greater than 35%) due to very low sensitivity of 2. Study Area and Methodology little sensitivity of aerosol scattering at AERONET wave- lengths [5, 6]. 2.1. Site Description. /e megacity of Karachi is located Detailed description of retrieval errors of the CIMEL sky ° ° (24 51′ N, 67 00′ E) in southern Pakistan on the coast of radiometer is given by Dubovik and King [15] and Smirnov Arabian Sea. It is the capital city of province Sindh having et al. [16]. In the current study, we have used level 2.0 the densest population. /e location map of the study area (quality assured) AOD at 500 nm, ﬁne/coarse AOD, ﬁne- has been shown in Figure 1. With a population of 23.5 mode fraction and AE data from direct sun algorithm, and million and covering an area of 3,527 km , it has one of the absorption AE, extinction AE, SSA, RI, asymmetry pa- strenuous ports of the region and is known as a center of rameter, and size distribution data from inversion algo- trade and industrial activities in Pakistan. Emissions from rithm. /e data can be obtained via the NASA website at industries and automobile and dust are the main sources of http://aeronet.gsfc.nasa.gov/. air pollution in Karachi. High temperature and low pressure during spring and summer seasons lift dust particles from 3. Results and Discussion nearby arid areas and cause dust storms in Indo-Gangetic Basin (IGB) [9–11]. On the contrary, low temperature and 3.1. Variability in AOD and AE. /e intensity of radiation, high pressure along with intense biomass burning during while travelling through Earth’s atmosphere is attenuated by winter and autumn seasons are responsible for the formation aerosols and gases. /e AOD is a measure of extinction by of dense haze and fogs [12, 13]. the aerosols in the intensity of solar radiation during tra- Figure 2 shows variation in monthly average temper- versing through the terrestrial atmosphere. It is the most ature, dew point, relative humidity, mean sea level (MSL) important parameter that represents the atmospheric pressure, wind speed, and wind direction over Karachi aerosol burden. It is also signiﬁcant for the identiﬁcation of during the study period. It can be seen in Figure 2 that the aerosol source regions and aerosol evaluation [17]. /e AOD highest value (76.7%) of monthly average relative humidity and AE are generally used to describe the atmospheric is observed in August and lowest (46.2%) in January. Mean aerosol burden and the aerosol size distribution [18]. AE sea level (MSL) pressure decreases gradually from January provides fundamental information about the aerosol size, (1017.9 hPa) to its lowest value (998 hPa) in July and then and it is computed by spectral variation of AOD. Singh et al. increases afterwards. /e highest monthly average tem- [19], while analyzing variation of AE over central India, ° ° perature is found to be 32.1 C in June and lowest (19.7 C) in showed that coarse-mode and ﬁne-mode aerosols are ap- January. Dew point temperature increases gradually from proximately equal in the regions where AE is close to 1. /ey ° ° January (6.5 C) and reaches its maximum value (25.7 C) in further reported that the value of AE steadily grows as the July and then decreases afterwards. Maximum monthly ﬁne-mode aerosol contribution increases. average wind speed is observed in August (16.8 km/h) and Figure 3(a) shows the time series and trend of monthly minimum in November (3.7 km/h). /e winds from the mean values of AOD at 500 nm over Karachi for the period southeast direction have prevailed over Karachi during the September 2006–August 2014. We observe a strong sea- study period. sonality in AOD variations which is the speciﬁc feature of IGB that observes four distinct seasons during the year. /e 2.2. AERONET. /e AERONET instrument, a ground-based average values of AOD over Karachi are generally higher CIMEL sky radiometer, was established in Karachi (Pakistan) during spring and summer than autumn and winter. /e by NASA in August 2006. It operates in the wavelength ranges slope and y-intercept of the linear trend line are found to be of 340 nm–1020 nm and 440 nm–1020 nm to record direct sun 0.0002 and 0.474, respectively. Figure 3(b) shows the his- and sky radiances, respectively [14]. AERONET inversion togram of AOD at 500 nm over Karachi during the study Advances in Meteorology 3 90 1020 0 985 (Months) Temperature (celsius) Relative humidity Dew point (celsius) MSL pressure (hPa) (a) 18 300 12 200 6 100 0 0 (Months) Wind speed (km/h) Wind direction (degree) (b) Figure 2: Variability of monthly average (a) temperature, dew point, relative humidity, and mean sea level (MSL) pressure, and (b) wind speed and wind direction over Karachi during the study period. 1.2 y = 0.0002x + 0.474 0.8 0.6 0.4 0.2 (Months) (a) Figure 3: Continued. AOD (500 nm) Temperature, dew point, relative 2006-SEP Wind speed (km/h) humidity 2006-DEC 2007-MAR 2007-JUN January January 2007-SEP 2007-DEC February February 2008-MAR 2008-JUN March March 2008-SEP 2008-DEC April April 2009-MAR 2009-JUN May May 2009-SEP 2009-DEC June June 2010-MAR 2010-JUN July July 2010-SEP 2010-DEC August August 2011-MAR 2011-JUN September September 2011-SEP 2011-DEC October October 2012-MAR 2012-JUN November November 2012-SEP 2012-DEC December December 2013-MAR 2013-JUN 2013-SEP Wind direction (degree) 2013-DEC MSL pressure 2014-MAR 2014-JUN 4 Advances in Meteorology 12,000 Series: AOD_500 Observations 45625 10,000 Mean 0.48 Median 0.37 8,000 Maximum 3.64 Minimum 0.03 Std. dev. 0.24 6,000 Skewness 2.38 4,000 2,000 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 (b) Figure 3: (a) Time series and linear trend of monthly mean AOD over Karachi during September 2006–August 2014. Broken line means no data. (b) All points AOD data at 500 nm over Karachi during September 2006–August 2014. Table 1: Descriptive statistics of monthly average AOD, ﬁne-mode AOD, coarse-mode AOD, and ﬁne-mode fraction at 500 nm over Karachi during September 2006–August 2014. AOD Fine-mode AOD Coarse-mode AOD Fine-mode fraction Mean 0.48 0.18 0.30 0.42 Standard error 0.02 0.01 0.02 0.02 Median 0.42 0.17 0.24 0.39 Standard deviation 0.20 0.05 0.20 0.16 Range 0.86 0.28 0.88 0.59 Minimum 0.25 0.10 0.04 0.19 Maximum 1.11 0.37 0.91 0.79 1.4 1.2 0.8 0.6 0.4 0.2 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (Months) (a) 1.5 0.5 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (Months) (b) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC (Months) (c) FIGURE 4: Continued. PW (cm) AE AOD Advances in Meteorology 5 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Jan 13 Backward trajectories ending at 0800 UTC 15 Feb 13 GDAS meteorological data GDAS meteorological data 55 60 65 70 40 45 50 55 60 65 70 75 25 25 1000 1000 1000 1000 Job ID: 195257 Job start: u May 18 08:50:44 UTC 2017 Job ID: 197564 Job start: u May 18 09:37:53 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Jan 2013 - GDAS1 Meteorology: 0000Z 15 Feb 2013 - GDAS1 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Mar 13 Backward trajectories ending at 0800 UTC 15 Mar 13 GDAS meteorological data GDAS meteorological data 60 62 64 66 68 70 72 56 58 60 62 64 66 68 2000 2000 1000 1000 1000 Job ID: 195902 Job start: u May 18 09:03:11 UTC 2017 Job ID: 195929 Job start: u May 18 09:04:05 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Mar 2013 - GDAS1 Meteorology: 0000Z 15 Mar 2013 - GDAS1 (d) FIGURE 4: Continued. Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E 6 Advances in Meteorology NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 May 13 Backward trajectories ending at 0800 UTC 15 May 13 GDAS meteorological data GDAS meteorological data 50 55 60 65 70 60 65 70 75 80 1000 1000 Job ID: 196102 Job start: u May 18 09:07:47 UTC 2017 Job ID: 196208 Job start: u May 18 09:09:32 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 May 2013 - GDAS1 Meteorology: 0000Z 15 Jun 2013 - GDAS1 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Jul 13 Backward trajectories ending at 0800 UTC 15 Jul 13 GDAS meteorological data GDAS meteorological data 45 50 55 60 65 70 75 55 60 65 70 75 1000 1000 1000 1000 500 500 Job ID: 196125 Job start: u May 18 09:08:34 UTC 2017 Job ID: 196358 Job start: u May 18 09:12:44 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Jul 2013 - GDAS1 Meteorology: 0000Z 15 Aug 2013 - GDAS1 (d) FIGURE 4: Continued. Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Advances in Meteorology 7 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Sep 13 Backward trajectories ending at 0800 UTC 15 Oct 13 GDAS meteorological data GDAS meteorological data 60 62 64 68 58 60 62 64 66 68 3000 3000 2500 2500 2000 2000 1500 1500 1000 1000 1000 1000 500 500 Job ID: 196950 Job start: u May 18 09:22:20 UTC 2017 Job ID: 196847 Job start: u May 18 09:21:23 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Sep 2013 - GDAS1 Meteorology: 0000Z 15 Oct 2013 - GDAS1 NOAA HYSPLIT model NOAA HYSPLIT model Backward trajectories ending at 0800 UTC 15 Nov 13 Backward trajectories ending at 0800 UTC 15 Nov 13 GDAS meteorological data GDAS meteorological data 66 68 70 72 74 60 62 64 66 68 2000 2000 1500 1500 1000 1000 1000 500 500 Job ID: 196994 Job start: u May 18 09:24:13 UTC 2017 Job ID: 197153 Job start: u May 18 09:28:22 UTC 2017 Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Source 1 Lat.: 24.9 Lon.: 67.13 Height: 1000 m AGL Trajectory direction: backward Duration: 48 hrs Trajectory direction: backward Duration: 48 hrs Vertical motion calculation method: Model vertical velocity Vertical motion calculation method: Model vertical velocity Meteorology: 0000Z 15 Nov 2013 - GDAS1 Meteorology: 0000Z 15 Dec 2013 - GDAS1 (d) Figure 4: Monthly average variations in the (a) AOD (500 nm), (b) AE (440/870 nm), and (c) PW over Lahore during September 2006–August 2014 along with the standard deviations. (d) HYSPLIT model backward trajectories during year 2013. period. It can be observed that highest frequency is observed 2014 have been presented in Table 1. /e mean monthly at a value of 0.3. AOD is found to be 0.48± 0.20 during the study period. It Descriptive statistics of monthly average AOD, ﬁne- can be further observed from Table 1 that the monthly mean mode AOD, coarse-mode AOD, and ﬁne-mode fraction value (0.30) of coarse-mode AOD is greater than the at 500 nm over Karachi during September 2006–August monthly mean value (0.18) of ﬁne-mode AOD. /e coarse- Source ∗ at 24.90 N 67.13 E Source ∗ at 24.90 N 67.13 E Meters AGL Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL Source ∗ at 24.90 N 67.13 E Meters AGL 8 Advances in Meteorology to the fact that Alam et al. [5] utilized the data of just mode AOD is observed to have the highest range (0.88). Figures 4(a)–4(c) show the monthly average AOD (at twelve months (August 2006–July 2007), while in present study, we have used long-term data of more than seven years 500 nm), AE, and precipitable water (PW) along with their standard deviations over Karachi during the period (September 2006–August 2014). September 2006–August 2014. /e error bars show the Figure 4(c) shows notable variations in mean monthly corresponding standard deviations of each monthly mean PW over Karachi during the study period. It can be noted value. In order to understand the movement of air masses from Figure 4(c) that PW starts to increase from January over Karachi, HYSPLIT model backward trajectories have (1.29 cm) and becomes maximum in August (4.68 cm) and been computed (Figure 4(d)). We note that, in spite of then decreases afterwards. /e occurrence of the highest value of PW in August is attributed to the high amounts of natural changes or ﬂuctuations in the meteorological con- ditions and aerosol transport, the variations in the three rainfall received by Karachi during the monsoon season. An overall mean value of PW is observed to be 2.48± 1.24 cm. parameters (i.e., AOD, AE, and PW) over Karachi are almost gradual and steady. We also ﬁnd strong seasonal variabilities Due to high values of PW, water is attracted by hygroscopic particles such as ammonium sulfate, ammonium nitrate, and in all the three parameters. /e average values of AOD are, in general, higher during spring and summer than those during sodium chloride leading to increased size of aerosol parti- autumn and winter. A similar trend has been found by Ali cles. During pollution episodes, high values of PW further et al. [20], while analyzing the AERONET data over Lahore. worsen the situation by reducing the visibility. Furthermore, However, a comparison of monthly average AOD values in the presence of aerosol and gaseous pollution, high PW is over Karachi (present study) and Lahore [20] reveals that responsible for increase in the speed of process of smog Karachi carries only about 80% aerosol burden than that of formation. /e scattering and absorption of light by aerosols in the lower atmosphere deteriorate the visibility [21]. In Lahore. Furthermore, we notice that the highest value of monthly particular, ﬁne-mode aerosols are recognized as the main cause of poor visibility [22]. Temperature inversion and low mean AOD (0.88± 0.31) is recorded in July along with the AE value of 0.30± 0.22, indicating the dominance of coarse wind speeds limit the dispersion of aerosol pollution and result in low visibility. High water vapor content further particles. Ali et al. [20], while studying aerosol properties over Lahore, also found the highest value of monthly mean reduces the visibility due to hygroscopic growth of aerosols. AOD (1.02± 0.41) in July with the AE value of 0.84± 0.28. High hygroscopic growth increases the particulate matter /us, the peak AOD and the corresponding AE values concentration and scattering ability of aerosols [23]. observed over Karachi are found to be smaller than those measured over Lahore. /is diﬀerence suggests that the atmosphere over Karachi is less polluted as compared to 3.2. Identiﬁcation of Aerosol Types. Optical and physical properties of aerosols are dependent upon their origins. A Lahore, while it contains comparatively elevated amount of coarse-mode particles. /ese particles basically originate number of methodologies have been used for the classiﬁcation of aerosols. Kaskaoutis et al. [24], Sharma et al. [25], and Tariq from /ar, Sistan, and Sahara regions. We observe the lowest monthly mean AOD (0.33± 0.15) in February along with the et al. [13] used correlation of AOD and AE, while Russell et al. AE value of 0.68± 0.29, representing that Karachi has the [26] and Giles et al. [27] used correlation between absorption lowest aerosols burden during this month. In an earlier study and extinction AE to discriminate aerosol types. Russell et al. over Karachi, Alam et al. [5] found the maximum average [26] used absorption Angstro¨m exponent (440–870) (AAE) AOD value of 0.92± 0.28 in July, whereas the minimum and extinction Angstrom exponent (440–870) (EAE) from monthly average AOD value was recorded to be 0.31± 0.11 AERONET to classify dominant aerosol types. /e cluster in February. /ese diﬀerences arise probably because Alam analysis using the scatter plot of AAE and EAE reduces the et al. [5] utilized data for only one year, that is, August ambiguities in aerosol classiﬁcation [26]. Diﬀerent threshold values of aerosol properties are considered for aerosol subtype 2006–July 2007. In another study, Alam et al. [6] tried to analyze the AERONET data over Karachi and Lahore for classiﬁcation. /e selection of threshold values is dependent upon location, range of AOD, and aerosol characteristics [28]. a period of only six months (April–June 2010 and December 2010–February 2011). However, in terms of climatic eﬀects, In this study, we have used the scatter plot of AAE and EAE we need long-term data to analyze the situation reliably. to categorize dominant aerosol types over Karachi. We have Alam et al. [5] also analyzed aerosol optical properties considered EAE< 1.0 and 0.7< AAE< 1.3, EAE> 0.8 and such as AOD and AE over Karachi by using AERONETdata. AAE> 1.3, and EAE> 1 and AAE> 1.2 to represent urban/ We note some diﬀerences in AOD and AE values found in industrial aerosols, desert dust aerosols, and biomass burning the present study and that of Alam et al. [5]. /e diﬀerences aerosols, respectively [7, 29]. /e remaining cases that do not have been calculated by subtracting the AOD and AE values belong to any of the aforementioned groups are referred to as found by Alam et al. [5] from AOD and AE values found in mixed type of aerosols. Seasonal classiﬁcation of diﬀerent aerosol types using the scatter plot between daily mean AAE the present study, respectively. We observe notable diﬀer- ences in AOD values such as 0.3, −0.12, 0.11, and 0.1 in June, and EAE values over Karachi during the study period has been shown in Figure 5. It can be observed from Figure 5 that desert November, March, and September, respectively. As far as AE is concerned, we observe diﬀerences of 0.35, 0.24, 0.16, and dust aerosols remained present in the atmosphere of Karachi 0.14 in January, November, October, and February, re- during all the seasons, but they are more dominating in the spectively. /e inconsistencies in the results are possibly due seasons of spring and summer. Figure 5(b) exhibits dust storm Advances in Meteorology 9 3.5 0.35 Desert dust 0.3 Biomass burning 0.25 2.5 0.2 0.15 1.5 0.1 0.05 0.5 Urban/industrial 0.01 0.1 1 10 Radius (µm) 0 0.5 1 1.5 2 EAE Summer Winter Spring Summer Autumn Spring Autumn Winter Figure 6: Seasonal averaged volume size distribution over Karachi (a) during the study period. of volume concentration of ﬁne-mode aerosols is observed during autumn and lowest during spring. /e high ﬁne- 15 mode volume concentration in autumn is attributed to increased emissions from biomass burning activities. /e peak value of ﬁne-mode volume concentration is observed to be 0.039, 0.034, 0.037, and 0.021 during autumn, winter, summer, and spring, respectively. We ﬁnd the highest coarse-mode volume concentration in summer and lowest during winter season. /e coarse-mode volume concen- (Months) tration in summer is seen to be about ﬁve times greater than that of winter. /is diﬀerence in concentration is attributed (b) to the high temperatures and wind speeds which eject dust Figure 5: (a) Classiﬁcation of aerosols during seasons of spring aerosols from the surface. For coarse aerosols, the peak (MAM), summer (JJA), autumn (SON), and winter (DJF) using the volume concentration is found to be 0.313, 0.171, 0.114, and scatter plot of daily average absorption Angstrom ¨ exponent 0.067 in summer, spring, autumn, and winter, respectively. (440–870) (AAE) versus extinction Angstrom ¨ exponent (440–870) (EAE) over Karachi and (b) dust storm frequency over central region of Pakistan during the study period. 3.4. Single Scattering Albedo. Single-scattering albedo (SSA) is one of the key parameters that explain the contribution of aerosols towards radiative forcing. Increasing trend of SSA frequency over the central region of Pakistan during the study with wavelength represents the presence of desert dust period. It can be noted from the ﬁgure that higher (greater than aerosols, while its decreasing trend indicates the presence of 10%) dust storm frequencies were observed during April–July. urban/industrial and biomass burning aerosols [30]. Sea- Urban/industrial aerosols are also observed to contribute to sonal variations in average SSA with respect to wavelength aerosol pollution over Karachi during all the seasons. It can (440 nm–1020 nm) over Karachi are shown in Figure 7. further be noted that during winter and autumn, biomass It can be noted that SSA increases with wavelength burning aerosols also contribute to total aerosol burden over during all the seasons, but it shows a maximum increase Karachi. during spring season. Yu et al. [28] also reported an increase in SSA values caused by dust aerosols in spring season. /is indicates that dust aerosols are consistently present in the 3.3. Size Distribution. Size distribution of aerosols is re- trieved from the inversion algorithm [15] in the range atmosphere of Karachi during all the seasons, but they became more dominating in spring. /e SSA increased in 0.05–15μm. Figure 6 shows the aerosol volume size dis- tribution over Karachi during diﬀerent seasons. A bimodal the range 0.897–0.960, 0.925–0.968, 0.897–0.930, and size distribution of aerosols is observed over Karachi 0.900–0.914 during spring, summer, autumn, and winter, (Figure 6). /e two-mode distribution of aerosols is caused respectively. SSA values during the summer season are by various factors such as combination of two air masses found to be the highest at all the wavelengths. Additionally, carrying diverse types of aerosols. /e maximum volume during summer, hygroscopic growth of aerosols causes SSA concentration of ﬁne-mode aerosols is found to occur at values to increase [9]. /e mean SSA values are found to be 0.905, 0.929, 0.938, and 0.942 at the wavelength of 440 nm, a radius of∼0.15µm in winter and autumn and, at a radius of ∼0.11µm, in summer and spring seasons. /e highest value 675 nm, 870 nm, and 1020 nm, respectively. Dust storm frequency (%) AAE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 3 –2 dVr/dlnr (µm µm ) 10 Advances in Meteorology 0.98 1.55 1.54 0.97 1.53 0.96 1.52 0.95 1.51 0.94 1.5 1.49 0.93 1.48 0.92 1.47 0.91 1.46 0.9 1.45 0.89 400 500 600 700 800 900 1000 1100 400 600 800 1000 1200 Wavelength (nm) Wavelength (nm) Summer Winter Summer Winter Autumn Spring Autumn Spring (a) Figure 7: Variations in seasonal averaged SSA with respect to 0.012 wavelength over Karachi during September 2006–August 2014. 0.01 3.5. Refractive Index. /e refractive index (RI) of aerosols is 0.008 an important optical property that depends on chemical 0.006 composition and SSA of aerosol particles [30, 32]. /e RI 0.004 being a complex quantity has real and imaginary parts. /e real part of the RI exhibits elevated values for high scattering, 0.002 while the imaginary part increases with absorption [3]. Numerous studies have been reported that the RI to be 1.53 400 500 600 700 800 900 1000 1100 for dust aerosols [33–35], but uncertainty of ±0.05 in this Wavelength (nm) value exists due to diﬀerent methods of measurements and chemical composition of dust [36, 37]. Summer Winter Cheng et al. [38, 39] and Alam et al. [6] showed that the Autumn Spring real part of the RI is larger at higher wavelengths because of (b) high absorption due to coarse-mode aerosols. Seasonal variations of real and imaginary parts of the RI Figure 8: Seasonal variations in (a) real part and (b) imaginary in the wavelength range of 440–1020 nm over Karachi part of the RI (refractive index) over Karachi during September 2006–August 2014. during September 2006–August 2014 have been shown in Figure 8. It can be observed in Figure 8(a) that during autumn and winter seasons, the real part of the RI shows considerable increasing trend in the wavelength range of 3.6. Asymmetry Parameter. /e asymmetry parameter is 440–870 nm. During spring, the real part of the RI is found frequently used to illustrate the angular distribution of to be consistently high (>1.52) at all the wavelengths in- scattered light in various radiative transfer models. /e dicating the dominance of desert dust aerosols. Yu et al. [3] asymmetry parameter and SSA are reported to have positive also presented a similar result over Beijing. correlation likely due to the scavenging of aerosol particles It can be seen in Figure 8(b) that the imaginary part of by clouds [41]. So far, there is no direct method for the the RI shows a declining trend in the range 440 nm–675 nm, measurement of the asymmetry parameter, but several and then, it becomes almost stable in the range 675 nm– methods are used to retrieve the asymmetry parameter from 1020 nm. Yu et al. [3] also found low wavelength dependence aerosol and radiation measurements. /e asymmetry pa- rameter reports the direction of light after scattering by of the imaginary part of the RI over Beijing. /e high value of the imaginary part of the RI at each wavelength during aerosol particles and hence calculated by taking cosine of the winter indicates the dominance of absorbing aerosols in the scattering direction weighted by the phase function. It atmosphere over Karachi. During summer, decreased values ranges from +1 for complete forward scattering to −1 for of the imaginary part of the RI at each wavelength represent complete backward scattering. For a symmetric scattering, the dominance of nonabsorbing aerosols. /e hygroscopic its value is zero. Zege et al. [42] showed that the asymmetry growth of aerosols during summer season also leads to lower parameter ranges from ∼0.1 to ∼0.75 for very clean atmo- values of the imaginary part of the RI [40]. /e maximum spheres to heavily polluted conditions. value of the imaginary part of the RI is found in winter Figure 9 shows the variations in the spectral asymmetry (0.0803) and minimum in summer (0.0243). During spring parameter in diﬀerent seasons over Karachi during and autumn, moderate mean values of 0.0643 and 0.0333 of September 2006–August 2014. It can be seen in Figure 9 that the asymmetry parameter remains higher at all the the imaginary part of the RI, respectively, show the nearly equal amounts of diﬀerent types of aerosols over Karachi. wavelengths during summer as compared to other seasons. SSA Real part of RI Imaginary part of RI Advances in Meteorology 11 0.76 Additional Points 0.74 (i) /e mean annual AOD and AE were found to be 0.48 ± 0.20 and 0.59± 0.29, respectively. (ii) Cluster analysis revealed 0.72 that desert dust prevails over coastal megacity Karachi in 0.7 spring and summer. (iii) /e highest volume concentration of coarse-mode aerosols was observed in summer. 0.68 0.66 Conflicts of Interest 0.64 /e authors declare that there are no conﬂicts of interest 400 500 600 700 800 900 1000 1100 regarding the publication of this paper. Wavelength (nm) Summer Winter Acknowledgments Autumn Spring /e authors are thankful to NASA for setting up Karachi Figure 9: Variations in the seasonal averaged asymmetry pa- AERONET site and for providing the data. /e authors are rameter over Karachi during September 2006–August 2014. also thankful to Pakistan Meteorological Department for providing the meteorological data. It can be further noted that the asymmetry parameter References shows a decreasing trend in the wavelength range 440– 870 nm during each season and then depicts a little increase [1] O. Torres, P. K. Bhartia, J. R. Herman, Z. Ahmad, and J. 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Tripathi et al., “Aerosol Pakistan, 2017. properties over the Indo-Gangetic plain: a mesoscale perspective International Journal of The Scientific Advances in Advances in Geophysics Chemistry Scientica World Journal Public Health Hindawi Hindawi Hindawi Hindawi Publishing Corporation Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 http://www www.hindawi.com .hindawi.com V Volume 2018 olume 2013 www.hindawi.com Volume 2018 Journal of Environmental and Public Health Advances in Meteorology Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 Submit your manuscripts at www.hindawi.com Applied & Environmental Journal of Soil Science Geological Research Hindawi Volume 2018 Hindawi www.hindawi.com www.hindawi.com Volume 2018 International Journal of International Journal of Agronomy Ecology International Journal of Advances in International Journal of Forestry Research Microbiology Agriculture Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 International Journal of Journal of Journal of International Journal of Biodiversity Archaea Analytical Chemistry Chemistry Marine Biology Hindawi Hindawi Hindawi Hindawi Hindawi www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018 www.hindawi.com Volume 2018

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Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

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Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

Ground-Based Remote Sensing of Aerosol Properties over a Coastal Megacity of Pakistan

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