Observations of black carbon aerosols characteristics over an urban environment: Radiative forcing and related implications

Observations of black carbon aerosols characteristics over an urban environment: Radiative forcing and related implications

Science of the Total Environment 603–604 (2017) 319–329 Contents lists available at ScienceDirect Science of the Total Environment journal homepage:...

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Science of the Total Environment 603–604 (2017) 319–329

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Observations of black carbon aerosols characteristics over an urban environment: Radiative forcing and related implications Samina Bibi a, Khan Alam a,⁎, Farrukh Chishtie b, Humera Bibi a, Said Rahman c a b c

Department of Physics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan Theoretical Research Institute, Pakistan Academy of Sciences, Islamabad, Pakistan Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), P.O. Box 8402, Off University Road, Karachi 75270, Pakistan




• Net BC ARF at ToA and in ATMOS were positive while negative at SUR in all months. • Significant correlations between AODabs and BC ARF were observed. • Contribution of BC to the total ARF was found to be greater than 84%.

a r t i c l e

i n f o

Article history: Received 5 February 2017 Received in revised form 9 June 2017 Accepted 10 June 2017 Available online xxxx Editor: D. Barcelo Keywords: Black carbon AOD OPAC SBDART Absorbing aerosol Forcing

⁎ Corresponding author. E-mail address: [email protected] (K. Alam).

http://dx.doi.org/10.1016/j.scitotenv.2017.06.082 0048-9697/© 2017 Elsevier B.V. All rights reserved.

a b s t r a c t With observations of black carbon (BC) aerosol concentrations, optical and radiative properties were obtained over the urban city of Karachi during the period of March 2006–December 2008. BC concentrations were continuously measured using an Aethalometer, while optical and radiative properties were estimated through the Optical Properties of Aerosols and Clouds (OPAC) and Santa Barbra DISORT Atmospheric Radiative Transfer (SBDART) models, respectively. For the study period, the measured BC concentrations were higher during January, February and November, while lower during May, June, July and August. A maximum peak value was observed during January 2007 while the minimum value was observed during June 2006. The Short Wave (SW) BC Aerosol Radiative Forcing (ARF) both at Top of the Atmosphere (ToA) and within ATMOSphere (ATMOS) were positive during all the months, whereas negative SW BC ARF was found at the SurFaCe (SFC). Overall, SW BC ARF was higher during January, February and November, while relatively lower ARF was found during May, June, July and August. Conversely, the Long Wave (LW) BC ARF at ToA and SFC remained positive, whereas within ATMOS it shifted towards positive values (heating effect) during June–August. Finally, the net (SW + LW) BC ARF were found to be positive at ToA and in ATMOS, while negative at SFC. Moreover, a systematic increase in Atmospheric Heating Rate (AHR) was found during October to January. Additionally, we found highest correlation between Absorption Aerosol Optical Depth (AODabs) and SW BC ARF within ATMOS followed by SFC and ToA. Overall, the contribution of BC to the total ARF was found to greater than 84% for the whole observational period while contributing up to 93% during January 2007. © 2017 Elsevier B.V. All rights reserved.


S. Bibi et al. / Science of the Total Environment 603–604 (2017) 319–329

1. Introduction Carbonaceous aerosols originating mainly from the south/south East Asian region are considered as significant entities responsible for atmospheric heating and associated variations in atmospheric thermodynamics and the precipitation cycle over the region (Menon et al., 2002). These aerosols can be considered as an aggregate of inadequately characterized elements having a wide range of chemical, physical and optical properties (Cheng et al., 2011). Among the carbonaceous aerosol species, atmospheric black carbon (BC) aerosol is a significant component of particulate matter which is inert in the atmosphere due to its small size and chemical structure (Babu et al., 2002; Begam et al., 2016). BC aerosols are generally produced in the atmosphere due to incomplete combustion of biomass burning and fossil fuel (Novakov et al., 2000). BC absorbs solar radiation in the visible and infrared wavelengths changing the temperature of atmosphere and its stability (Satheesh et al., 2010). Out of the many types of aerosols, BC participates strongly in the perturbation of radiative and climatic impacts (Ramachandran and Kedia, 2010) through direct and indirect effects that extend to several spatio-temporal scales. Previously, it has been shown that the presence of absorbing aerosols can even change the sign of Aerosol Radiative Forcing (ARF) at Top of Atmosphere (ToA) from negative to positive (Kim and Ramanathan, 2008; Satheesh et al., 2010). The presence of BC in the troposphere increases absorption significantly, resulting in a positive radiative forcing at the tropopause, whereas a negative forcing at the surface occurs (Kahnert and Devasthale, 2011). Hence, these aerosols deserve greater attention due to their contribution to regional and global ARF and climate change (Das and Jayaraman, 2011). Heintzenberg et al. (1997) reported that black carbon has a significant positive forcing even if there is only a small fraction of black carbon present in the aerosol mass. Together with BC aerosol concentration, inaccuracies in aerosol optical properties are found to be largest source of uncertainty in the estimation of local and global climate variation (Dubovik et al., 2000; Hansen et al., 2000). Thus, accurate measurement of BC aerosols is important for the prediction of ARF due to BC aerosols as they are the second strongest absorber after carbon dioxide in contributing to global warming (Surendran et al., 2013). BC aerosol levels cannot be diminished under regular atmospheric conditions and hence dry and wet deposition processes are the only main sinks (Begam et al., 2016). Therefore, the life span of BC aerosols ranges from a few days to weeks, depending on local atmospheric conditions. Despite short atmospheric lifetimes, BC aerosols play a significant role in climatic system, making it crucial to consider their effects in the short term climate mitigation strategy (Cheng et al., 2016). Consequently, in recent years, the impact of BC on the optical and earth radiative balance has been extensively examined in south Asian regions, due to its light absorbing properties (Babu et al., 2004; Sreekanth et al., 2007; Ramachandran and Kedia, 2010; Satheesh et al., 2010; Das and Jayaraman, 2011; Srivastava et al., 2012; Biswas et al., 2014; Bisht et al., 2015; Aruna et al., 2016; Kalluri et al., 2016; Li et al., 2016). While studies of spatial and temporal BC variation are sparse across the Pakistan region, (Husain et al., 2007; Dutkiewicz et al., 2009), however, to the best of our knowledge, the impact of BC on major aerosol optical properties and ARF estimation have not been studied before. Therefore, the present study, for the first time, provides a detailed analysis of BC aerosols and their impacts on optical as well as radiative properties over the urban city of Karachi, Pakistan. This study focuses on the monthly variations of atmospheric parameters such as surface reflectance, columnar water vapor and columnar ozone, which were further utilized towards accurate prediction of radiative fluxes. To achieve study objectives, monthly variations in BC concentrations measured using Aethalometer were carried out. Moreover, BC aerosol optical properties namely, the Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (AP) were calculated through the Optical Properties of Aerosols and

Clouds (OPAC) model using measured BC concentrations. Then, these optical properties were inserted in the Santa Barbra DISORT Atmospheric Radiative Transfer (SBDART) model to calculate the Short Wave (SW), Long Wave (LW) and net (SW + LW) BC ARF at ToA, SurFaCe (SFC) and within ATMOSphere (ATMOS). In addition, this study also reports the Atmospheric Heating Rate (AHR) and Absorption Forcing Efficiency (AFE) due to BC aerosols, as well as its relative contribution to the total ARF over the observational site for the study period. 2. Data and methods 2.1. Site description Karachi (Lat 24° 51′N; Lon 67° 02′E; 8 m amsl), is situated on the coast of Arabian Sea and has a large industrial base. The sampling site for this study is located on the northwest of Karachi Airport, which is positioned at University of Karachi campus. The main sources of BC emissions in the urban environment of Karachi are due to rapid urbanization, growing number of factories, vehicles, power plants as well as domestic cooking and seasonal agricultural biomass burning. The months of each year are classified into four seasons: December–January (winter), March–May (premonsoon), June–August (summer) and October–November (postmonsoon). Inside the campus, vehicular traffic is moderate; however, the study area is adjacent to highways which are typically loaded with traffic. 2.2. Aethalometer Instantaneous measurements of BC aerosol mass concentration were carried out using the multi-channel Aethalometer (AE-21). It works on the principle of attenuation of an incoming incident light beam which is transmitted through the aerosol constantly and deposited on a quartz fiber filter (Hansen, 1996). The measurement of the attenuation of light rays is linearly proportional to the BC concentration on the filter strip. The BC concentration is calculated by measuring the change in the transmittance through the quartz filter at which the BC particles are deposited (Hansen et al., 1984). This is done using a light beam of high intensity at wavelengths of 370, 440 and 880 nm respectively (Dutkiewicz et al., 2009). A vacuum pump sucks air continuously for the particles accumulated on the filter strip of the Aethalometer. The light beam passes through the unloaded filter strip first and is then compared with the loaded filter. The manufacturer indicates that the two channels are calibrated to record the same BC concentration, unless strongly UV-absorbing organic compounds are present. For example, some polycyclic aromatic hydrocarbons found in tobacco smoke, or fresh diesel exhaust, or those due to fresh aerosols from biomass burning can contribute to visible light absorption. In this study, the Aethalometer was placed on the second-floor window of the HEJ Research Institute of Chemistry building on the University of Karachi campus to measure concentrations at 880 nm channel. The mass concentrations were measured at 880 nm because BC is a major absorber at this wavelength and this signifies an accurate value of BC in the atmosphere (Ganguly et al., 2005). It was operated with a cyclone inlet (BGI corporation) equipped with an insect and rain guard at a flow rate of 4 l/min making the cut-point ~3.2 μm. We collected instantaneous BC data having a temporal resolution of five-minute interval in the time period from March 2006 till December 2008 for this study. Due to a temporary breakdown of the instrument, data for December 2006 was not available for analysis. It is noted that there are several systematic errors in filter based absorption methods used for the measurement of BC concentration that need to be corrected (Bond et al., 1999). The uncertainty in the measurement of BC concentration is ~10% (Babu et al., 2002) and particularly for the absorption coefficient used for the 880 and 370 nm, are 16.6 and 39.5 m2/g respectively (Dutkiewicz et al., 2009). Uncertainties in measurement of BC concentration are due to changes in filter scattering,

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depending on concentration levels and observed conversion from optical absorption to BC concentration (Bond and Bergstrom, 2006). Additional details of the Aethalometer and its methodology as well as its uncertainties are discussed by Nair et al. (2007). 2.3. Methods OPAC is a software package developed in 1998, as an aerosol and cloud model, comprising of a microphysical basis of aerosols as a mixture of variable components with consideration of different types of cloud (Hess et al., 1998). Atmospheric aerosols are typically a mixture of different components such as water-soluble and insoluble compounds, and more specifically substances such as soot, sulfates, sea salt and mineral aerosols. The OPAC model is suitable for calculating optical properties such as AOD, SSA and AP for aerosols comprising of a different mixture of basic aerosol components. The data is provided in each case for 1 particle c/m3 that defines the effective properties of the mixture of all particles for each size distribution. This model also includes user defined aerosol type, aerosol vertical height, wavelength and relative humidity. In this study, for the estimation of optical properties, the urban aerosol model of OPAC was chosen, which is appropriate for the strongly polluted study area of Karachi. The urban model comprises of water soluble (28,000 per cm3), insoluble (1.5 per cm3) and BC number density (130,000 per cm3) components. In this model, the BC number density gives rise to a BC mass of 7.8 μg/m3. However, the number concentrations of these components were iteratively adjusted with the aim of attaining the best fit between the measured and modeled spectral AODs. We have thus used the actual measurements of soot particles using the Aethalometer observations in the model to calculate the optical properties of the aerosols. A number density of 209,540 particles per cm3 of BC in the urban aerosol model results in the highest monthly averaged BC mass concentration measured over observational site, while 37,238 particles per cm3 correspond to the lowest. To set the aerosol vertical profile in OPAC 4.0, the tropopause height is taken as 17 km, which is more suitable than the default value set at 12 km. Towards deriving BC optical properties such as AOD, SSA and AP at the wavelength of 500 nm and relative humidity of 50%, BC number density derived from BC mass fraction was used in OPAC. The OPAC estimated AOD at 500 nm were then compared with the AErosol RObotic NETwork (AERONET) derived AOD for a given aerosol component and its concentration and when the two AODs match (within 5%), the output as optical properties were used in SBDART for ARF calculations. Subsequently, for deriving direct radiative forcing solely due to BC fraction, the modeled BC optical properties (AOD, SSA and ASP), surface reflectance derived from the MODerate resolution Imaging Spectroradiometer (MODIS), columnar ozone from Ozone Monitoring Instrument (OMI) and columnar water vapor from AERONET were used in SBDART. This model is a parallel plane discrete ordinate model developed by Ricchiazzi et al. (1998) for calculating radiative fluxes with and without aerosols. In our work, we used it for estimating the ARF at the ToA, SFC and within ATMOS (top-surface) in the spectral range of 0.2–4.0 μm (SW range) and 4.0–40 μm (LW range). Further, BC ARF at ToA, SFC and within the ATMOS for SW, LW and net (SW + LW) were calculated over Karachi for the period 2006–2008. According to meteorological conditions in Karachi, October to February were adopted as mid-latitude winter and the remaining months were integrated as mid-latitude summer for the estimation of BC ARF in SBDART model. A very effective and typically used geophysical quantity for estimating radiative effects due to different aerosol component is ARF. It is generally identified as direct forcing due to scattering and absorption by aerosols and indirect forcing which relates to the influence of aerosols on albedo and cloud lifetimes. In the direct forcing effect, solar radiation reaching the surface reduces through scattering or absorption. The absorption leads to a warming effect, whereas scattering produces a


cooling effect (Satheesh, 2002). Absorbing aerosols can also scatter back solar radiation and their net resultant forcing also depends on SSA along with surface reflectance (Aruna et al., 2016). ARF is the net (down up) radiative flux at any level of the atmosphere either at the ToA or at SFC with and without aerosols (Satheesh et al., 2010). The difference between ARF at ToA and SFC has been taken as ARF which describes the amount of energy captured by aerosols within the ATMOS and gets transformed into heat. As stated earlier, the absorption and emission procedure in the longwave at different layers in the atmosphere, while integrated over all the wavelength, can lead to either a net increment (heating) or decrement (cooling) of the radiative budget, whereas solar radiation always produces a heating effect in the atmosphere (Surendran et al., 2013). The atmospheric heating rate due to the aerosol absorption contains a proportional amount of atmospheric ARF (Das and Jayaraman, 2011; Srivastava et al., 2012) and can be expressed as: ∂T g ΔFATMOS ¼ ΔP: ∂t Cp is the atmospheric heating rate in K/day, g is the acceleration where ∂T ∂t due to gravity (9.8 m/s2), Cp is the specific heat capacity of air at constant pressure (i.e. 1006 Jkg−1 K−1) and ΔP (300 hPa) is the atmospheric pressure difference between top and bottom layers of the atmosphere where most aerosols are present which contribute to local heating (Pathak et al., 2010). The variations in ARF at ToA for the same aerosol type may be related to different scattering or absorption capacities (Hansen et al., 1997; Russell et al., 2002; Santos et al., 2008). The uncertainties in calculating ARF may arise from differences in the mid-latitude atmospheric dynamics and the real atmosphere, uncertainties in the surface reflectance, modeled AOD and SSA measurements (Singh et al., 2010). The ARF provides the total radiative effect of atmospheric aerosols, however in order to find a consistent relationship between them, ARF efficiency is more appropriate (Sreekanth et al., 2007). The measured AOD and radiative flux can be used to estimate the forcing efficiency (Satheesh et al., 2010). In clear sky condition, the ARF efficiency is obtained by unit change in AOD. In case of absorbing BC, BC ARF is strongly related to AODabs and BC AFE (ratio of BC ARF per unit AODabs) (Schulz et al., 2006; He et al., 2014). BC AFE can be expressed as: AFE ¼ BC ARF

. BC AODabs

and AODabs provides a formulation to obtain the absorption AOD from SSA and AOD at a given wavelength (AODabs(λ) = [1 − SSA(λ)]AOD), where λ is wavelength. 3. Results and discussion 3.1. Atmosphere parameters and surface reflectance The ARF is also influenced by lower boundary parameters. The time series of monthly averaged surface reflectance, water vapor and columnar ozone for the study period of 2006–2008 over Karachi are presented graphically in Fig. 1(a–c). Surface reflectance indicates the amount of radiation that is scattered and dispersed towards the space and the atmosphere. It is one of the most important parameter in estimating ARF (Menon et al., 2014). Garcıa et al. (2012) recommended that the surface reflectance is important factor for radiative balance at ToA as it depends on amount of aerosol and their absorptive capability. The maximum surface reflectance was recorded in July 2006 (0.21), while the minimum was noticed in January 2007 (0.16) with an average of 0.18 for the entire study period. In general, surface reflectance was observed to be relatively higher during summer (0.20) and premonsoon (0.19) as compared with postmonsoon (0.17) and winter (0.16) seasons. Similar


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observed higher surface reflectance during premonsoon and winter seasons and lower levels during postmonsoon season over Pune. The monthly averaged columnar water vapor was in the range of 0.72–4.8 cm over the observational site. The columnar water vapor was found to be higher in June to September while lower in November to May. Generally, columnar water vapor was found to be relatively higher during summer (4.48 cm) and postmonsoon (2.32 cm) followed by premonsoon (2.14 cm) and winter (1.14 cm) seasons. Analogously, Ramachandran and Kedia (2010) found greater surface reflectance during premonsoon and monsoon while these were observed to be lower during winter and postmonsoon seasons over Ahmedabad. They also found similar variation in columnar water vapor i.e., maximum from June to August and columnar ozone was low in winter and high in summer. Atmospheric ozone is one important component of earth's atmosphere system, contributing to global climate change phenomena with highest uncertainty (Pathak et al., 2010). The monthly averaged columnar ozone varied from a minimum value of 243 DU in October 2008 to a maximum of 286 DU in June 2006. It was observed that the seasonal averaged columnar ozone ranged from 251 to 277 DU with maximum levels in summer and minimum in winter. Previously, Srivastava et al. (2006) found comparable levels of total columnar ozone in the range of 251 to 308 DU over Nainital. Columnar ozone levels were found to be higher in summer months and lower in winter months whereas columnar water vapor were observed to have greater values during August to September 2010 and June to July 2011 over Delhi (Surendran et al., 2013). Numerous researchers have also reported the atmospheric parameters required for assessing the ARF (Singh et al., 2005; Ramachandran and Kedia, 2010; Latha et al., 2013; Aruna et al., 2016). Seasonal averaged surface reflectance, columnar water vapor, columnar ozone, AOD, SSA, AP and AAOD over Karachi for the period 2006–2008 are listed in Table 1. 3.2. Monthly variations of BC

Fig. 1. Monthly averaged variation of (a) surface reflectance, (b) water vapor and (c) ozone over Karachi during the period 2006–2008.

observations were also made by Aruna et al. (2016). Kalluri et al. (2016) who reported the highest (0.29) surface reflectance in April and lowest (0.20) in November over Anantapur. Kumar and Devara (2012)

The BC concentration data obtained every 5 min was used to get daily averaged BC value, which was used to calculate monthly averaged values at Karachi, as shown in Fig. 2. The monthly averaged BC concentration ranged from 2.2 to 12.5 μg/m3 with the maximum value recorded in the month of January 2007 and minimum in the month of June 2006, which is quite consistent with previous findings (Ramachandran and Kedia, 2010). Similarly, Surendran et al. (2013) reported the highest monthly averaged BC concentration (15.9 μg/m3) in December 2010 and lowest (2.44 μg/m3) in July 2011 over Delhi. Singh et al. (2010) also noted the extreme maximum of monthly averaged BC concentration occurred during December 2006 and extreme minimum during August having the values 16.7 and 3.26 μg/m3, respectively, over Delhi, which are higher than our result. Generally, the concentrations were relatively higher in winter and postmonsoon months while moderate in premonsoon and lower in summer months throughout the observational period, but with varying magnitudes. Similarly, Nair et al. (2007) found pronounced BC concentration levels in winter than other seasons over Kanpur. Our results are comparable with that of Tiwari et al. (2013), who observed over New Delhi, where BC concentration were highest during winter (10.8 μg/m3) and post-monsoon (9.4 μg/m3) while lowest during premonsoon (3.5 μg/m3) and monsoon (2.8 μg/m3) seasons. The increase in BC concentration during winter can be associated

Table 1 Seasonal averaged surface reflectance, columnar water vapor, columnar ozone, AOD, SSA, AP and AAOD over Karachi for the period 2006–2008. Season

Surface reflectance (660 nm)

Columnar water vapor (cm)

Columnar ozone (DU)

AOD (500 nm)

SSA (500 nm)

AP (500 nm)

AAOD (500 nm)

Winter Premonsoon Summer Postmonsoon

0.164 0.189 0.199 0.167

1.138 2.137 4.487 2.324

251 274 278 263

0.310 0.274 0.256 0.296

0.753 0.827 0.870 0.781

0.656 0.668 0.675 0.661

0.078 0.048 0.033 0.066

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Fig. 2. Monthly averaged variation of BC concentration over Karachi during the period 2006–2008.

with stable atmospheric conditions and lower mixing height (Panicker et al., 2013). One of the reasons for high BC concentration is indoor heating due to coal burning activities (Cao et al., 2009). Moreover, local sources such as industrial and vehicular emissions also lead to high BC concentration levels (Safai et al., 2007). It should be noted that the winter season is known for static meteorological conditions and man-made activities which lead to emission of large number of fine particles, which in turn cause increased impacts of aerosols including BC. On the other hand, the washout during monsoon rainfall is responsible for lower BC concentration levels (Zhang et al., 2010; Li et al., 2016). During postmonsoon, weak winds and less precipitation causes comparatively high BC concentration levels whereas low BC concentration levels during premonsoon could be attributed to the strong winds in this season (Ramachandran and Kedia, 2010). 3.3. Modeled optical properties Key optical parameters, namely the AOD, SSA and AP at 500 nm, were estimated using the OPAC model. In particular, AOD describes the radiation that passes through the aerosol profile and reaches the surface, which plays a vital role in the earth radiation budget. SSA is the ratio of scattered radiations to the sum of scattered and absorbed radiation, whereas AP provides information on the fraction of radiations which are scattered back to space. Fig. 3(a–c) reveals the monthly averaged variations in AOD, SSA and AP at 500 nm over Karachi for the observational period. The comparison of OPAC derived AOD values and those obtained from AERONET are shown in Fig. 3(a). The RMSE value was about 0.05 which are well within the retrieval uncertainties. OPAC estimated BC AOD at 500 nm was found to be in the range of 0.25–0.35 with an average of 0.28, contributing 4–40% to total AOD. It is notable that the contribution of BC AOD increases during winter and postmonsoon while this contribution decreases during summer and premonsoon depending on the level of BC concentration. The highest value of BC AOD (0.35) was observed during January 2007 and the lowest value (0.25) was observed during June 2006. Generally, relatively higher BC AOD was observed in winter (0.31) and postmonsoon (0.29) as compared to summer (0.26) and premonsoon (0.27) seasons, which is attributed to observing the highest concentration of BC aerosols during winter and postmonsoon seasons. In fact, monthly variations of BC AOD are significant and similar to that of BC aerosol monthly behaviour. Similarly, Panicker et al. (2013) reported that BC AOD at 500 nm ranged from 0.055 and 0.08 and the contribution of BC AOD was found to be 10–25% of the total AOD over Korea. Relatively less contribution of BC AOD to the total AOD were reported in previous findings (Babu et al., 2002; Panicker et al., 2010). Recently, Aruna et al. (2016) reported that BC aerosol concentration is

compatible with AOD values over Chennai. Our results are comparable to the findings of Das and Jayaraman (2011). They reported that the averaged AOD over Ahmedabad, Udaipur and Mt. Abu was observed to be 0.35, 0.31 and 0.28, respectively. The BC SSA was found to be in the range of 0.68–0.89 having maximum value in the month of June 2006 and minimum in January 2007, with an average value of 0.81 throughout the study period. Similarly, Ramachandran and Kedia (2010) found the OPAC derived SSA at 550 nm ranging from 0.64 during December to 0.95 during July over Ahmedabad. Decrease in BC SSA was observed during summer (0.87) and premonsoon (0.83) while an increase was found during winter (0.75) and postmonsoon (0.78) seasons. In general, the BC SSA decreases as the BC concentration increases showing the strong absorption of solar radiation due to larger contribution of BC as documented by Chung and Seinfeld (2005). Our measured results are lower than observations conducted over Manora Peak where the Pant et al. (2006) noted that BC concentration were found to be varied from 0.85 to 2.6 μg/m3 with associated SSA ranged from 0.87 to 0.94. The OPAC derived SSA values were 0.83, 0.87 and 0.88 during April 2007 over Ahmedabad, Udaipur and Mt. Abu, respectively showing the enhanced presence of BC aerosol over Ahmedabad (Das and Jayaraman, 2011). Similarly, Singh et al. (2010) found low (0.74) SSA values in January and high (0.89) during August with an average value of 0.79 during the whole study period over Delhi. They concluded that low value of SSA was mainly due to the enhanced presence of absorbing BC aerosols. It is to be noted that the monthly variations in BC AP was similar to that of SSA being lowest BC AP (0.64) during January 2007 and the highest (0.68) during June 2006. AP showed gradual increase from winter (0.66) to reach a maximum during summer (0.68) when BC concentration was also found to be at a minimum level. Aruna et al. (2016) has also reported that AP does not display remarkable seasonal variation having ranged from 0.69 to 0.71 over Chennai. The seasonal averaged AP were higher during summer and monsoon and low during winter and postmonsoon with maximum (0.70) in monsoon and (0.66) minimum in winter over Anantapur (Kalluri et al., 2016). Our estimation is also found to be consistent with AP values found by Singh et al. (2010) over Delhi. They reported maximum (0.73) AP during June and minimum (0.65) during January having an average value of 0.68 during the period of study. 3.4. Black carbon aerosol radiative forcing The monthly averaged variations of SW, LW and net BC ARF at ToA, SFC and within the ATMOS over Karachi for the period 2006–2008 is shown in Fig. 4(a–c). The SW BC ARF range from 3 to 17, −20 to – 69 and 23 to 86 W/m2 at ToA, SFC and within the ATMOS, respectively


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Fig. 3. (a) Monthly averaged variation of (a) AOD, (b) SSA and (c) AP at 500 nm over Karachi during 2006–2008.

whereas the LW BC ARF varies from 2 to 4, 2 to 9 and −5 to 1 W/m2, respectively. The net (SW + LW) BC ARF were in the range of 5 to 19 W/m2 at ToA, − 16 to − 54 W/m2 at SFC leading to the ATMOS BC

ARF from 22 to 73 W/m2. The averaged BC ARF for the whole observational period were found to be 8 and 3 W/m2 at ToA, − 36 and 4 W/m2 at SFC leading to 44 and −1 W/m2 within ATMOS at SW and

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Fig. 4. Monthly averaged variation of (a) SW ARF (0.3–4 μm), (b) LW BC ARF (4–100 μm), (c) net (SW + LW) at TOA, SFC and ATMOS over Karachi during 2006–2008.

LW, respectively. Generally, the SW BC ARF at ToA and within ATMOS were found to be positive during all the months leading to the warming produced by absorption by soot (absorbing BC) whereas, negative SW

BC ARF at SFC indicates a net cooling effect at the surface. It is clear from Fig. 4(a) that all the three ARF components at SW were higher during January, February and November mostly due the enhanced presence


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Table 2 Seasonal averaged variation of SW ARF, LW BC ARF and net (SW + LW) ARF and SW AFE at TOA, SFC and ATMOS along with AHR associated to SW BC ARF over Karachi during 2006–2008. BC ARF (W/m2) Season Winter Premonsoon Summer Postmonsoon

BC AFE (W/m2)















11 7 4 9

3 4 3 3

15 11 7 11

−51 −32 −23 −43

7 5 2 5

−44 −27 −21 −38

63 39 27 52

−3 −2 1 −2

59 38 28 49

144 147 127 129

of BC aerosols and higher BC AOD in combination with low SSA, though the surface reflectance was low. While lower values of SW BC ARF during May, June, July and August were observed because of the relatively lower BC concentration; and BC AOD coupled with higher SSA even though surface reflectance was much higher in these months as compared to other months. The increase in surface cooling due to negative ARF at SFC and robust atmospheric warming due to positive ARF within ATMOS display their relationship with various aerosol properties. Conversely, the LW BC ARF at ToA and SFC was noticed to be positive during all the months whereas, LW BC ARF within ATMOS was found to be either positive or negative. Negative values show the net cooling of the atmosphere or earth's system, while positive values indicate heating of the atmosphere. The magnitude of LW ToA and SFC BC ARF gradually decreases from January to June and increases up to December. The LW BC ARF at ToA remains positive but LW BC ARF at SFC was positive whereas, BC ARF within ATMOS shifts towards positive values (heating effect) during June–August when increase in water vapor content was found. Finally, the net (SW + LW) BC ARF at ToA and within ATMOS were found to be positive indicating the heating by absorption of BC aerosols, while net BC ARF at SFC was found to be negative representing the net cooling effect at surface during all months (see Fig. 4(c)). The seasonal averaged variation of SW ARF, LW BC ARF and net (SW + LW) ARF and SW AFE at ToA, SFC and ATMOS along with AHR associated to SW BC ARF over Karachi during 2006-2008are tabulated in Table 2. Fig. 5 depicts the monthly averaged AHR calculated for SW BC ARF within ATMOS over Karachi for the period 2006–2008. The AHR is the change in temperature which occurs at each level of the atmosphere due to energy stored by absorption. The AHR varied from 0.6 (June 2006) to 2.4 K/day (January 2007) with an average of 1.2 K/day for the whole observational period. It should be noted that a systematic increase in AHR was found from October to January as the absorbing BC



−668 −682 −689 −659

811 828 816 788

AHR 1.8 1.1 0.8 1.5

aerosol increased in this period. The seasonal averaged AHR was higher in winter and postmonsoon when compared with summer and postmonsoon. This behaviour indicates that absorbing aerosols were more dominant during winter and postmonsoon. Similarly, Das and Jayaraman, 2011 reported that the averaged SW BC ARF at ToA were 1.7, − 1.5 and − 1.5 W/m2, at SFC were − 46, − 35 and − 31 W/m2 and within ATMOS were 47.7, 33.5 and 29.5 W/m2 with corresponding values AHR of 1.3, 1.0 and 0.4 K/day over Ahmedabad, Udaipur and Mt. Abu, respectively. On the other hand, the LW BC ARF at ToA were 3, 3 and 3 W/m2, at SFC were 17, 14 and 16 W/m2 leading to the ARF within ATMOS of −14, −11, and −13 W/m2, respectively. As a result, they calculated the net (SW + LW) BC ARF values at ToA were 4.7, 1.5 and 1.5 W/m2, at SFC were −29, −17 and −19 W/m2 results to ARF within the ATMOS of 33.7, 18.5 and 20.5 W/m2 W/m2, respectively. Sreekanth et al. (2007) estimated the SW BC ARF through SBDART model in conjunction with OPAC model over Eastern India and found the higher SFC ARF during winter (− 35.78 W/m2), followed by summer (− 16.8 W/m2) whereas lower during monsoon (− 9.9 W/m2) and postmonsoon (− 2.81 W/m2) with corresponding AHR of 1.23, 0.58, 0.34 and 0.09, respectively. Recently, Aruna et al. (2016) conducted the study on BC ARF and reported the values of ARF at ToA were 5.75, − 6.00, − 4.34 and 5.37 W/m2, SFC were − 32.54, − 38.41, − 32.28 and − 35.33 W/m2 and ATMOS were 38.29, 32.41, 27.92 and 40.70 W/m2 during premonsoon, monsoon, postmonsoon over Chennai. Latha et al. (2014) noticed that the BC ARF at ToA and SFC ranged from 0 to 16 and 0 to − 33 W/m2, respectively over eastern India which are comparable to our estimated BC ARF. Surendran et al. (2013) found the highest BC ARF within the ATMOS during November (66 W/m2) and December (65 W/m2) and lowest during July (23 W/m2) over Delhi. Dumka et al. (2013) showed that BC ARF at SFC was always found to be negative, having higher negative values during January and March while lower values during summer months. They

Fig. 5. Monthly averaged variation AHR corresponding to the monthly averaged SW BC ARF over Karachi during 2006–2008.

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found the strong seasonal dependency in ARF within ATMOS with higher values during winter (33.49 W/m2) and spring (31.15 W/m2), moderate during autumn (18.94 W/m2) and lower during summer (13.15 W/m2) translating to an average AHR of 0.53 k/day over Hyderabad. Assessment of ARF over Hyderabad showed a positive BC ARF (9 W/m2) at ToA and negative (− 33 W/m2) and positive (42 W/m2) within ATMOS which translated to AHR of 1.1 K/day (Badarinath and Latha, 2006). In another study performed over Bangalore, Babu et al. (2002) reported the averaged BC ARF were 5, − 23 and 28 W/m2 at ToA, SFC and within the ATMOS, respectively. This SW ARF within ATMOS transforms to an AHR of 0.8 K/day. Earlier, the calculated SW BC ARF at SFC was as high as −62 W/m2 and at ToA was 9 W/m2 leading to the atmospheric absorption of 71 W/m2 which further translate to lower AHR of ~1.8 K/day over Kanpur (Tripathi et al., 2005). Recently, Kalluri et al. (2016) showed that the averaged seasonal variation in BC ARF at SFC were −20.2, −14.9, −7.5 and −13.22 W/m2 during winter, summer, monsoon and postmonsoon, respectively and at ToA were 2.91, 1.99, 1.06 and 1.89 W/m2, respectively and within the ATMOS were 23, 17.47, 8.55 and 15.11 W/m2, respectively over Anantapur. Over an Indian Arctic station namely Himadri, the BC ARF was found to be negative at ToA (−0.4 W/m2) and at SFC (−2.5 W/m2) while in the presence of BC in atmosphere, the ARF within ATMOS was found to be positive (2.1 W/m2) (Raju et al., 2015). Tiwari et al. (2016) observed that atmospheric SW BC ARF during June, July and August were 42.2, 35.4 and 34.3 W/m2 with corresponding AHR of 1.19, 0.99 and 0.96 K/day over Ballia. Likewise, Bisht et al. (2015) also determined very high atmospheric BC ARF reaching to 60–70 W/m2 during March and October to December which results in very high AHR of about 1.8–2.0 K/day over Delhi. The correlation of SW BC ARF at ToA, SFC and within ATMOS against AODabs with respective regression parameters are given in Table 3. The slopes of the linear fitting were interpreted as the averaged AFE. Overall, the highest correlation between AODabs and SW BC ARF was found within ATMOS followed by at SFC and then at ToA with correlation coefficient of 0.99, 0.99 and 0.95, respectively. The high degree of correlation between SW BC ARF and AODabs indicates that SW BC ARF is a strong function of AODabs and provides a reasonable confidence on calculated values. The monthly averaged values of BC AFE were found be in range of 96 to 170, −621 to −770 and 765 to 866 W/m2AODabs at ToA, SFC and ATOMS. The corresponding AODabs were ranged from 0.02 to 0.11 with an average of 0.05. Our estimated values of AFE were consistent with the previous computed AFE by Wang et al. (2016) and He et al. (2014) due to adopting a different radiative transfer scheme and vertical BC profile. The net heating efficiency decreases from 70 W/m2 during winter to 30 W/m2 during inter-monsoon and to 15 W/m2 during summer monsoon (Babu et al., 2004). Latha et al. (2014) showed that AODabs% representing the contribution of AOD into absorption, which is computed to relate with ARF at ToA due to its significant relationship with absorbing characteristics of aerosols. Haywood and Shine (1997) suggested that the AFE of BC aerosols increases when they are transported to higher altitude as compared to the estimated AFE values of BC aerosols near surface. To confirm atmospheric absorption due to BC aerosols, the contribution of BC to total atmospheric forcing can be obtained. For this purpose, model estimated BC ARF were compared with AERONET derived total ARF. Fig. 6 displays BC ARF and total ARF within ATMOS over Karachi

Table 3 Correlation parameters between SW BC ARF and AODabs at ToA, SFC and ATMOS over Karachi during 2006–2008. SW BC ARF vs. AODabs ToA






















Fig. 6. Monthly averaged variations of atmospheric forcing for BC and total aerosol over Karachi during 2006–2008.

for the period 2006–2008. On an average, the contribution of BC to the total ARF was found to be greater than 84% for the whole observational period and can contribute up to 93% during January 2007 when BC concentration was highest. However, the BC atmospheric absorption deceases and observed to be minimum (37%) during July 2007 due to lower concentration of BC aerosols. Similarly, Latha et al. (2013) concluded that BC atmospheric absorption ranged from 12% to 90% of total absorption with minimum in monsoon months and maximum in winter months over Jharkhand. Panicker et al. (2013) also reported that over Anmyeon, BC induced atmospheric ARF can contribute up to 88% to the total atmospheric ARF which is higher than the contribution of BC estimated in this study. The contribution of BC ARF to the total atmospheric forcing were reported earlier to about 65% over Visakhapatnam (Sreekanth et al., 2007), 55% over Pune (Panicker et al., 2010), 60% over Ahmedabad (Ramachandran and Kedia, 2010), 55% over Hyderabad (Dumka et al., 2013), 60–80% over Dibrugarh (Biswas et al., 2014), 60% over eastern India (Latha et al., 2014), 64% over Anantapur (Kalluri et al., 2016) and 57% over Himadri (Raju et al., 2015). 4. Conclusion Measurements were undertaken over Karachi, an urban environment, from March 2006 till December 2008 to study the temporal variation in BC concentration and their impact on the optical properties as well as SW, LW and net radiative forcing. The BC number density corresponding to BC concentration levels measured using Aethalometer was first determined, and were next incorporated in the OPAC model to derive BC optical properties. These model derived optical properties together with atmospheric parameters were used in SBDART model for the estimation of BC ARF. In light of the above analysis and discussion, the major findings of the study are: ▪ The observed BC concentration ranged from 2.2 to 12.5 μg/m3 with maximum values during January 2007 and minimum during June 2006. The BC AOD varied between 0.22 and 0.31 with an average of 0.25, contributing 4–40% to total AOD. Similarly, the highest value of BC AOD (0.31) was observed during January 2007 and the lowest value (0.22) was observed during June 2006. ▪ The SSA and AP were found to be in the range of 0.68–0.89 and 0.64– 0.89, respectively, reaching maximum value in the month of June 2006 (with minimum BC concentration) and minimum in January 2007 (with maximum BC concentration) with an average value of 0.81 and 0.66 throughout the study period.


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▪ Generally, BC was positively correlated with AOD while negatively correlated with SSA and AP. ▪ The averaged BC ARF for the whole observational period were found to be 7 and 3 W/m2 at ToA, −32 and 4 W/m2 at SFC leading to 40 and −1 W/m2 within ATMOS at SW and LW, respectively. ▪ The corresponding AHR varied from 0.6 (June 2006) to 2.2 K/day (January 2007) with an average of 1.1 K/day for the whole observational period. ▪ BC AFE were also found to be maximum during January 2007 having values of 15.5, −62 and 77.6 W/m2 at ToA, SFC and ATOMS with an average of 7.1, −32.4 and 39.5 W/m2 at ToA, SFC and ATOMS over the whole studied period. ▪ The contribution of BC to the total ARF was found to be greater than 84% during the observational period with 93% contribution during January 2007. Acknowledgement The authors would like to acknowledge the substantial contribution of NASA and Institute of Space Technology, Karachi for the provision of AERONET data (http://aeronet.gsfc.nasa.gov/). Ozone data was acquired using the GESDISC interactive online visualization and analysis infrastructure (Giovanni) as a part of the NASA Goddard Earth Sciences (GES) Data and Information Services Centre (DISC) (http://giovanni. gsfc.nasa.gov/). Finally, we thank the MODIS (http://lance.modaps. eosdis.nasa.gov) scientific teams for the provision of surface reflectance data utilized in this work. References Aruna, K., Kumar, T.L., Murthy, B.K., Babu, S.S., Ratnam, M.V., Rao, D.N., 2016. Short wave aerosol radiative forcing estimates over a semi urban coastal environment in southeast India and validation with surface flux measurements. Atmos. Environ. 125, 418–428. Babu, S.S., Satheesh, S., Moorthy, K.K., 2002. Aerosol radiative forcing due to enhanced black carbon at an urban site in India. Geophys. Res. Lett. 29:1880. http://dx.doi. org/10.1029/2002GL015826. Babu, S.S., Moorthy, K.K., Satheesh, S., 2004. Aerosol black carbon over Arabian Sea during intermonsoon and summer monsoon seasons. Geophys. Res. Lett. 31, L06104. http:// dx.doi.org/10.1029/2003GL018716. Badarinath, K., Latha, K.M., 2006. Direct radiative forcing from black carbon aerosols over urban environment. Adv. Space Res. 37, 2183–2188. Begam, G.R., Vachaspati, C.V., Ahammed, Y.N., Kumar, K.R., Babu, S.S., Reddy, R., 2016. Measurement and analysis of black carbon aerosols over a tropical semi-arid station in Kadapa, India. Atmos. Res. 171, 77–91. Bisht, D., Dumka, U., Kaskaoutis, D., Pipal, A., Srivastava, A., Soni, V., Attri, S., Sateesh, M., Tiwari, S., 2015. Carbonaceous aerosols and pollutants over Delhi urban environment: temporal evolution, source apportionment and radiative forcing. Sci. Total Environ. 521, 431–445. Biswas, J., Pathak, T.S.B., Bhuyan, P., 2014. Temporal Characterization of Black Carbon Aerosols and Their Contribution to Radiative Forcing Over Dibrugarh. Bond, T.C., Bergstrom, R.W., 2006. Light absorption by carbonaceous particles: an investigative review. Aerosol Sci. Technol. 40, 27–67. Bond, T.C., Anderson, T.L., Campbell, D., 1999. Calibration and intercomparison of filterbased measurements of visible light absorption by aerosols. Aerosol Sci. Technol. 30, 582–600. Cao, J.-J., Zhu, C.-S., Chow, J.C., Watson, J.G., Han, Y.-M., Wang, G.-H., Shen, Z.-X., An, Z.-S., 2009. Black carbon relationships with emissions and meteorology in Xi'an, China. Atmos. Res. 94, 194–202. Cheng, Y., He, K.-B., Zheng, M., Duan, F.-K., Du, Z.-Y., Ma, Y.-L., Tan, J.-H., Yang, F.-M., Liu, J.M., Zhang, X.-L., 2011. Mass absorption efficiency of elemental carbon and watersoluble organic carbon in Beijing, China. Atmos. Chem. Phys. 11, 11497–11510. Cheng, Y., Engling, G., Moosmüller, H., Arnott, W.P., Chen, A.L., Wold, C.E., Hao, W.M., He, K.-B., 2016. Light absorption by biomass burning source emissions. Atmos. Environ. 127, 347–354. Chung, S.H., Seinfeld, J.H., 2005. Climate response of direct radiative forcing of anthropogenic black carbon. J. Geophys. Res. Atmos. 110, D11102. http://dx.doi.org/10.1029/ 2004JD005441. Das, S., Jayaraman, A., 2011. Role of black carbon in aerosol properties and radiative forcing over western India during premonsoon period. Atmos. Res. 102, 320–334. Dubovik, O., Smirnov, A., Holben, B., King, M., Kaufman, Y., Eck, T., Slutsker, I., 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) sun and sky radiance measurements. J. Geophys. Res. Atmos. 105, 9791–9806. Dumka, U., Manchanda, R., Sinha, P., Sreenivasan, S., Moorthy, K.K., Babu, S.S., 2013. Temporal variability and radiative impact of black carbon aerosol over tropical urban station Hyderabad. J. Atmos. Sol. Terr. Phys. 105, 81–90.

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