Characterization of aerosol type based on aerosol

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Arabian Journal of Geosciences (2018) 11:633 https://doi.org/10.1007/s12517-018-3944-1

ORIGINAL PAPER

Characterization of aerosol type based on aerosol optical properties over Baghdad, Iraq Ali M. Al-Salihi 1 Received: 22 September 2017 / Accepted: 24 September 2018 # Saudi Society for Geosciences 2018

Abstract Aerosol optical depth (AOD), Angstrom exponent (AE), and ozone monitoring instrument aerosols index (OMI-AI) data, derived from MODerate Resolution Imaging Spectroradiometer (MODIS) and OMI sensor on board NASA’s Aqua satellite and NASAAura satellite platforms, have been analyzed and classified over Baghdad, Iraq, for an 8-year period (2008–2015). In order to give an obvious understanding of temporal inconsistency in the characteristics and classification of aerosols during each season separately, PREDE POM-02 sky radiometer measurements of AOD, carried out during a 2-year period (2014–2015), were compared with MODIS–Aqua AODs. On seasonal bases, MODIS–Aqua AODs corroborate well with ground-based measurements, with correlation coefficients ranging between 0.74 and 0.8 and RMSE ranging from 0.097 to 0.062 during spring and autumn seasons respectively. The overall satellite- and ground-based measurement comparisons showed a good agreement with correlation coefficients of 0.78 and RMSE of 0.066. These results suggest that MODIS–Aqua gives a good estimate of AOD. Analysis of MODIS– Aqua data for the 8-year period showed that the overall mean AOD, AE, and OMI-AI over Baghdad were 0.44 ± 0.16, 0.77 ± 0.29, and 1.34 ± 0.33 respectively. AOD records presented a unique peak which was extended from mid-spring (April) to mid-summer (July) while the AE annual variability indicated a more complicated behavior with minimum values during the period from late spring (May) to early autumn (September). The maximum AOD and OMI-AI values occurred during summer while their minimum values occurred during winter. The AE showed an opposite behavior to that of AOD such that the highest AE values occurred during autumn and winter and the lowest values happened during spring and summer. This behavior may be attributed to the domination of coarse aerosol particles during autumn and winter seasons and fine aerosol particles during spring and summer seasons. A Hybrid Single-Particle Lagrangian Integrated Trajectory model was utilized to determine the source of air mass transport and to recognize the variability of aerosol origin regions. Finally, AOD, AE, and OMI-AI values have been employed to identify several aerosol types and to present seasonal heterogeneity in their contribution based on their origins. Keywords Aerosol . OMI . MODIS . HYSPLIT . Baghdad

Introduction Atmospheric aerosols are airborne particles made up of liquid or solid materials ranging in size from a few nanometers to a few hundred micrometers in diameter with complicated composition and instability in their physical–chemical property (Xin et al. 2014). Due to high spatial and temporal variability, atmospheric aerosols are considered one of the actual reasons of uncertainty in several processes influencing climate

* Ali M. Al-Salihi [email protected] 1

Department of Atmospheric Sciences, College of Science, Mustansiriyah University, Baghdad, Iraq

(Solomon 2007), and this mentioned suspicion is associated with considerable uncertainty in an individual radiative forcing for different components of aerosol, such as black carbon and desert dust, and they also have impacts on air quality (AlSaadi et al. 2005), Furthermore, aerosols play a significant role in human health (Anderson et al. 2012; Lave and Seskin 2013). Besides all these mentioned impacts, aerosols also play significant roles in the Earth’s radiation budget. They have indirect effect through interacting with different atmospheric constituents, such as water vapor, to influence the formation and lifetime of clouds. Aerosols’ direct effect involves reflecting, scattering, and absorbing of atmospheric radiation. Absorbing aerosols also have semi-direct effect through heating the atmospheric layer, reducing cloud fraction, and suppressing convection. However, due to their highly variable

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spatial and temporal distribution, their direct, indirect, and semi-direct effects, and the counteracting effect between absorbing and non-absorbing aerosols, they have been identified as the largest single source of uncertainty in the anthropogenic contribution to global forcing of climate change (Field et al. 2014). Therefore, obtaining accuracy in evaluating the aerosol effect on radiative transfer is a difficult issue, because several aerosol types cause diverse impacts on the spectral distribution of solar radiation (Cazorla et al. 2013; Kaskaoutis and Kambezidis 2008). Thus, deep and detailed understanding of aerosol properties and types at local and global scales, their spatiotemporal variation, and their influence on atmospheric variables are of great significance for atmospheric aerosol research (Che et al. 2011; Liu et al. 2007; Maghrabi et al. 2011; Smirnov et al. 2002; Wang et al. 2010). Aerosol types, based on their source regions, can be classified as dust, maritime, sea salt, urban, biomass burning, carbon aerosol, and sulfate released by both anthropogenic and natural sources (Giannakaki et al. 2010). Numerous studies have been conducted on the classification of aerosol types and the characterization of aerosol properties in surrounding countries and worldwide regions. These research works were based on (i) dependence of aerosol type on air mass origins (Cao et al. 2015; Estellés et al. 2007; Kaskaoutis et al. 2009; Pace et al. 2006), (ii) Lidar aerosol backscatter measurements (Giannakaki et al. 2010; Kim et al. 2007b), (iii) ground-based observations of aerosol optical depth (AOD) and Angstrom exponent (AE) from Aerosol Robotic Network (AERONET) (El-Metwally et al. 2008; García et al. 2008; Karaca and Alagha 2008; Kaskaoutis et al. 2007; Masoumi et al. 2013), and (iv) remote sensing and satellite-derived aerosol optical properties (Carmona and Alpert 2009; Eck et al. 2008; Farahat 2016; Gupta et al. 2013; Levy et al. 2007; Sreekanth 2014). The geography of Iraq extends between latitudes 29° N– 37° N and longitudes 39° E–48° E, in subtropical zone of the Southwestern Asia. Baghdad city, the capital of the country, is characterized by relatively high aerosol concentrations due to the increase in population and urbanization. Iraq also includes regions that have been identified as major sources of aerosols (Cao et al. 2015). This study has aimed on three main objectives: The first one is to compare MODerate Resolution Imaging Spectroradiometer (MODIS)–Aqua AODs with groundbased measurement carried out by PREDE POM-02 sky radiometer in order to evaluate the quality of MODIS–Aqua AOD retrievals. The second is to analyze the seasonal behavior of aerosol optical properties, including AOD, AE, and ozone monitoring instrument aerosols index (OMI-AI) for the 8year period (January 2008 to December 201). The third objective is to employ two classification approaches and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model in order to determine aerosol origins and types over Baghdad city.

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Data and methodology Study location and data acquisition As shown in Fig. 1, Baghdad (33° 36′ N; 44°40′ E, 34 m) is located in the central region on a vast plain, on the banks of Tigris. It is within a large strip of flat sedimentary plain covering an area of 204 km2. Baghdad city is the major center for air, road, and rail transportation, and it has several oil refineries, power plants, food-processing plants, tanneries, textile, and other industrial activities. The population of Baghdad, according to more recent statistics of 2016, is about 7.6 million, which represents approximately 20% of the country population. This makes it the largest populated city in Iraq. Also Baghdad is ranked as the second most populated city in the Arab world (after Cairo, Egypt) and in the Southwestern Asia (after Tehran, Iran). High levels of atmospheric pollution have been reported over Baghdad during the past 10 years (Rabee 2015). In this paper, aerosol types over Baghdad city for the 8-year period (2008–2015) were classified using daily data obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) Aqua and OMI sensors. The data consist of level 3 1° × 1° daily gridded MODIS AODs (550 nm), AE, and OMI-AI. AOD values give information on the aerosol loading, AE is used to characterize the spectral dependence of the aerosol, and AI values provide the total column absorbing aerosol within the ultraviolet bands. MODIS instrument was launched on 4 May 2002 onboard the Aqua platform as part of the NASA’s Earth Observing System (EOS) mission. Aqua passes over the equator at 13:30 LT during its diurnal time. These outstanding designed systems (broad spectral extent, high spatial resolution, and daily global coverage) are powerful instruments for monitoring the variability of the Earth’s

Fig. 1 The geographic location of Baghdad and countries surrounding Iraq

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atmosphere. MODIS with its 2330-km observation swatch provides continuous daily global coverage. OMI is a nadirviewing imaging spectrometer that measures the TOA upwelling radiances in the ultraviolet and a part of the visible region (270–500 nm) of the solar spectrum. Detailed specifications of MODIS and OMI sensors are available in Chu et al. (2002) and Dobber et al. (2006). "

Ground-based measurements The ground-based aerosol optical depth measurements were measured on the roof of the Department of Atmospheric Sciences, College of Science, Mustansiriyah University, Baghdad, Iraq (33° 36′ N; 44° 40′ E), employing PREDE POM-02 sky radiometer as illustrated in Fig. 2 that measures a narrow wavelength band of direct solar irradiance at 550 nm. Aerosol optical properties of PREDE POM-02 sky radiometer were retrieved by using SKYRAD.PACK 4.2 (the newest version), which is a software to extract PREDE POM-02 sky radiometer data and the sky radiance developed by (Nakajima et al. 1996) and (Dubovik and King 2000). The measurements were available for 2 years (2014–2015) on daily basis. Some measurements were missing due to weather conditions and lag of electric power. These available ground-based measurements were used to evaluate MODIS– Aqua AODs retrievals. For the purpose of comparisons, the

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four seasons considered are winter (December to February inclusive), spring (March to May inclusive), summer (June to August inclusive), and autumn (September to November inclusive) for the years of 2014 and 2015.

Aerosol optical properties (AOD, AE, and OMI-AI) Aerosol optical depth (AOD) Bis a measure of radiation extinction due to the interaction of radiation with aerosol particles in the atmosphere, primarily owing to the processes of scattering and absorption, and it is dimensionless. The AOD (τ) is defined as the integrated extinction coefficient (fractional depletion of radiance per unit path length) over a vertical column of unit cross section.^ In other words, AOD is an important parameter related to aerosol concentrations (Liu 2005). The attenuation of solar radiation passing through the atmosphere is given by the Bouguer–Beer–Lambert law: I λ ¼ I oλ e−mτ tot ;λ

ð1Þ

where Iλ is the observed spectral direct beam irradiance at wavelength λ, I oλ is the extraterrestrial solar spectrum corrected for the actual Sun–Earth distance, m is the optical air mass, and τtot, λ, is the wavelength-dependent total optical depth (Kaskaoutis and Kambezidis 2006; Masoumi et al. 2010). The spectral dependence of AOD is frequently parameterized by the Angstrom exponent (AE), which is computed from the (Ångström 1929) empirical formula: τ αλ ¼ βλ−AE

ð2Þ

where ταλ is the AOD measured at wavelength λ(μ) and β is turbidity coefficient (related to the total aerosol content (Pedrós et al. 2003)). Because the wavelength dependence of the AOD does not follow Eq. (2) exactly, AE can be computed for any region using the following expression:   τ AE2 ln dlnτ AE τ AE ¼ − ¼ −  AE1 λ2 dlnλ ln λ1

Fig. 2 PREDE POM-02 sky radiometer installed on the roof of the Department of Atmospheric Sciences, College of Science, Mustansiriyah University, Baghdad, Iraq (33° 36′ N; 44° 40′)

ð3Þ

where τAE1 and τAE2 are the AODs at wavelengths λ1 and λ2. From the above expression, AE is the negative of the slope, or the negative of the first derivative of τα versus wavelength in logarithmic scale. As particles increase in size, the value of AE decreases. Typical values of AE are ~ 0 for coarse mode such as soil particles, while it varies from 1 to 3 for fine-mode anthropogenic pollutants.

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The value of AE depends strongly on the wavelength region used for its determination and on the method used in any particular case. Different AE values determined in various spectral bands have been reported by various authors (Eck et al. 1999; Reid et al. 1999). In some cases, negative AE values have also been obtained (Adeyewa and Balogun 2003; Vergaz et al. 2001) when its determination takes place in the visible (VIS) and near-infrared spectrum. In addition to being an important tool for estimating AOD within the shortwave spectral region, the value of AE is also a necessary qualitative indicator of aerosol particle size (Ångström 1929); values of AE ≤ 1 indicate size distributions dominated by coarse mode aerosols (radii ≥ 0.5 μm) that are typically dominated by dust and sea salt, and values of AE ≥ 2 indicate size distributions dominated by fine mode aerosols (radii ≤ 0.5 μm) that are usually associated with urban pollution and biomass burning (Eck et al. 1999). Aerosol index (OMI-AI) is a robust qualitative indicator of near-UV-absorbing aerosol for monitoring and evaluation of the existence of absorbing aerosols in the atmosphere. It separates the absorbing from non-absorbing aerosols in UV region; the absorbing aerosol can be characterized by OMI-AI with focus on aerosol optical properties, concentration, and altitude (Buchard et al. 2015),which make it powerful parameter to measure UV-absorbing aerosol such as biomass burning and dust using spaceborne observation techniques (De Graaf et al. 2005). Among all types of UV-absorbing aerosols, dust is the main contributor to the AI signal (Li 2011). OMIAI is based on a spectral contrast method in a UV region where ozone absorption is very small. It is the difference between the observations and model calculations of absorbing and non-absorbing spectral radiance ratios. For version 2 Aura OMI (2004–present) algorithms, quantitatively, OMI-AI can be defined as:

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Rolph 2005). A backward trajectory of air mass for 7 days by cluster analysis method was employed to identify the sources of aerosol types over Baghdad city during the study period. The simulations of the backward trajectories of air mass are performed by running the HYSPLIT model (http:// ready.arl.noaa.gov/HYSPLIT.php) based on Global Data Assimilation System (GDAS) data archive. The endpoint files of GDAS data were downloaded for the period (2008–2015). The cluster analysis was performed using a multivariate statistical techniques (Harris and Kahl 1990). In this procedure, the air mass trajectories are determined as a cluster and the SPatial VARiance (SPVAR) are calculated for every trajectory groups. SPVAR is defined as the summation of the squared distances between the endpoints of the cluster’s component trajectories and the mean of the trajectories in the same cluster. In the next step, the total spatial variance (TSV) (the sum of all the cluster spatial variances) is calculated and those pair of clusters for which an increase in TSV is the lowest are combined. Using this technique, the data were classified into four seasons with cluster mean trajectories and their percentage contributions were estimated at 1500 m above ground level over Baghdad city.

Classification methods of aerosol types

where Imeasured is the measured backscattered radiance at a given wavelength and I calculated is calculated is the backscattered radiance calculated at that wavelength for a pure Rayleigh atmosphere (Ahmad et al. 2006).

The identification of aerosol types can be achieved by several means; in this work, the first approach, which is the most important one, depends on a method of relating aerosol load (i.e., AOD) and particle size (i.e., AE). This method is utilized by different earlier researchers (Bayat et al. 2011; Eck et al. 1999; Kalapureddy et al. 2009; Kaskaoutis et al. 2007; Pace et al. 2006; Pathak et al. 2012; Pawar et al. 2015; Sreekanth 2014), and all these previous works were based on Eck et al. (1999). Aerosol classification in four different aerosol types includes maritime, dust, urban, and biomass burning as tabulated in Table 1. A second approach was employed for further subclassification of aerosol types (into absorbing and nonabsorbing types), using MODIS–OMI algorithm developed by (Kim et al. 2007a). They have identified aerosols based on two basic properties of aerosols, which are its size and radiation absorptance (Higurashi and Nakajima 2002) using

Air mass trajectory simulation model

Table 1 The values of thresholds for aerosol classification based on AOD and AE

      I 360 I 360 AI ¼ 100 log10 −log10 I 331 measured I 331 calculated

ð4Þ

The aerosol characteristic is significantly influenced by the synoptic-scale atmospheric transport patterns from different origin sources. In this consideration, a commonly used trajectory model is the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory (ARL) (Draxler and

Aerosol types

Aerosol optical depth (AOD)

Angstrom exponent (AE)

Maritime Dust Urban Biomass burning

< 0.3 > 0.4 0.2–0.4 > 0.7

0.5–1.7 < 1.0 > 1.0 > 1.0

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AODs, FMF (fine mode fraction), and OMI-AI values. In this work, instead of using FMF values (which are quantitative indicators of aerosol size), AE values were employed. The modified criterion thresholds are shown in Table 2. Finally, in both of the abovementioned processes, the aerosol classification procedures based on AOD and AE (method 1) and AOD, AE, and OMI-AI (method 2) were performed by the following steps: first, the AOD and AE have been put on Microsoft Excel work sheet then filtering technique has been applied on AOD and AE data by the first method considering threshold tabulated in Table 1, and the same procedures were applied on AOD, AE, and OMI-AI by the second classification method according to threshold values shown in Table 2.

(correlation coefficient 0.80 and RMSE 0.062) while during spring season, the comparisons show the lowest correlation coefficient 0.74 and highest RMSE 0.097. For winter and summer seasons, the statistical indicators are 0.79 and 0.78, and 0.090 and 0.067 respectively. Figure 3 also includes a plot for the comparison of the overall available measurements of the radiometer for the period of 2014–2015 and MODIS– Aqua AOD retrievals. It is obvious that Aqua retrievals of AOD are well compared with the ground-based measurements. As seen in Table 3, the correlation coefficient, the RMSE, slope, and the corresponding intercept of linear regression fit are 0.78, 0.066, 0.845, and 0.018 respectively. These comparisons suggest that MODIS–Aqua provide good estimates of AODs over Baghdad.

Results and discussion

Aerosol optical properties (AOD, AE, and OMI-AE) climatology

Inter-comparison of AOD measurements In validation tests, linear regression parameters (slope and intercept) of the correlation plot of the collocated data to be compared are assumed utmost importance (Levy et al. 2010; Misra et al. 2008). This is to determine the deviation of slope of the correlation plot from unity which represents systematic biases in the MODIS–Aqua AOD retrieval. An additional testing for the comparison between MODIS–Aqua AOD retrieval and PREDE POM-02 sky radiometer AODs a rootmean-square error (RMSE) was employed. Figure 3 shows the correlation plots of the MODIS–Aqua AOD retrievals versus PREDE POM-02 sky radiometer during winter, spring, summer, and autumn for the years of 2014 and 2015; the blue colored lines represent the linear regression of the scatter plot and the red colored lines are the 1:1 lines; Table 3 summarizes the results of comparisons for all seasons. It is seen that during autumn, the MODIS–Aqua AODs are relatively better compared with ground-based measurements Table 2 The values of thresholds for aerosol type classification using AOD, AE, and AI algorithm AE

OMI-AI ≤ 0.7

< 0.8

Sea salt Sea salt and sulfate (if AOD < 0.2)

< 0.7 >1 > 1.2 0.8 < AE < 1.0 0.8 < AE < 1.2

OMI-AI