Temporal variability and radiative impact of black carbon aerosol over tropical urban station Hyderabad

Journal of Atmospheric and Solar-Terrestrial Physics 105-106 (2013) 81–90

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Temporal variability and radiative impact of black carbon aerosol over tropical urban station Hyderabad U.C. Dumka a,b, R.K. Manchanda a, P.R. Sinha a,n, S. Sreenivasan a, K.Krishna Moorthy c, S. Suresh Babu c a

Tata Institute of Fundamental Research (TIFR), Balloon Facility, Hyderabad 500 062, India Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital 263 129, India c Space Physics Laboratory, Vikaram Sarabhai Space Center, Trivandrum 695 022, India b

art ic l e i nf o

a b s t r a c t

Article history: Received 1 November 2012 Received in revised form 5 July 2013 Accepted 3 August 2013 Available online 14 August 2013

Time variability of black carbon (BC) aerosols over different timescales (daily, weekly and annual) is studied over a tropical urban location Hyderabad in India using seven channel portable Aethalometer. The results for the 2-year period (January 2009–December 2010) show a daily-mean BC variability from
1.00 7 0.12 mg m  3 to 12.50 73.06 mg m  3, with a remarkable annual pattern of winter high and monsoon low. The BC values maximize during winter (December–January),
6.6770.22 mg m  3, and drop during summer (June–August),
2.36 70.09 mg m  3, which establishes a large seasonal variation. Furthermore, the BC mass concentration exhibits a well-defined diurnal variation, with a morning peak and early afternoon minimum. The magnitude of the diurnal variations is seasonal dependent, which maximizes during the winter months. Air mass back trajectories indicated several different transport pathways, while the concentration weighted trajectory (CWT) analysis reveals that the most important potential sources for BC aerosols are the Indo-Gangetic plain (IGP), central India and some hot spots in Pakistan, Arabian Peninsula and Persian Gulf. The absorbing Ångström exponent (αabs) estimated from the spectral values of absorption coefficient (sabs) ranges from 0.9 to 1.1 indicating high BC/OC ratio typical of fossil fuel origin. The annual average BC mass fraction to composite aerosols is found to be (10 7 3) % contributing to the atmospheric forcing by (55710) %. The BC radiative forcing at the atmosphere shows strong seasonal dependency with higher values in winter (33.49 7 7.01) and spring (31.78 712.89) and moderate in autumn (18.94 76.71) and summer (13.15 7 1.66). The BC radiative forcing at the top of the atmosphere (TOA) is positive in all months, suggesting an overall heating of the regional climate over Hyderabad. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Black carbon Air mass back-trajectory CWT Radiative forcing Hyderabad

1. Introduction Due to increase in population, industrialization and energy demands, atmospheric aerosols and pollutant emissions have gradually been increasing over south Asia (Lawrence and Lelieveld, 2010 and references therein). The outflow of aerosols and pollutants over the adjoining oceanic regions and Himalayan range has caused serious effects in regional climate, associated with the formation of atmospheric brownish clouds (Ramanathan et al., 2007), precipitation re-distribution (Lau et al., 2006), impact on monsoon onset (Gautam et al., 2009), mid-tropospheric heating (Gautam et al., 2009) and intensity of the solar dimming phenomenon (Badarinath et al., 2010; Kambezidis et al., 2012). Extensive long-term observations over India showed that the

n

Corresponding author. Tel.: þ 91 40 2712 2505; fax: þ 91 40 2712 3327. E-mail address: [email protected] (P.R. Sinha).

1364-6826/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jastp.2013.08.003

aerosol load and pollutants are higher over the densely-populated IGP (Habib et al., 2006; Dey and di Girolamo, 2010) and over major urban centers and power plants (Ramachandran and Rajesh, 2007). Particularly over Hyderabad, previous studies (e.g. Latha and Badarinath, 2005; Badarinath et al., 2007; Gummeneni et al., in press) showed high levels of aerosols, particulate matter (PM) and black carbon (BC) rendering it as one of the most polluted cities in India (Beegum et al., 2009). The enhanced presence of BC aerosols in the atmosphere has not only adverse health effects, but also modifies the radiation balance that may have potential impact on the hydrological cycle and, thereby, on the weather and climate (Satheesh and Ramanathan, 2000; Menon et al., 2002; Lau et al., 2006; Meehl et al., 2008). Due to the strong absorption of solar light, BC aerosols heat the atmosphere and contribute significantly to the global warming, since studies have shown that it is the second contributor (after CO2) to the greenhouse effect (Jacobson, 2000; Ramanathan and Carmichael, 2008). Large mass fraction of BC to

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Fig. 1. Terrain of Peninsular India and site map of the measurement location (NBF, TIFR) at the outskirts of Hyderabad.

the total composite aerosols can reverse the sign of aerosol radiative forcing (ARF) in top of atmosphere (TOA) from cooling to heating, thus leading to large atmospheric absorption (Keil et al., 2001; Babu et al., 2002) and offset of the whitehouse aerosol effect (Schwartz, 1996). Previous studies suggest that BC can also alter the cloud lifetime (Ackerman et al., 2000), reflectivity and melting of snow and ice (Hansen and Nazarenko, 2004; Chaubey et al., 2010). Modeling studies also suggest that the direct forcing of BC can cause a significant change in the atmospheric circulation and tropical convective precipitation (Wang et al., 2006). Recently, Ramanathan et al. (2007) demonstrated that the carbonaceous aerosols may contribute to the regional warming over the Indian Ocean as much as the greenhouse gases. Although several studies have modeled the global radiative effects of BC aerosols (Haywood and Shine, 1995; Jacobson, 2000; Reddy and Boucher, 1997), there are still exist large uncertainties due to the inadequacy of observed data. Due to significance of BC in modification of the regional climate over south Asia, systematic measurements of BC aerosols have been carried out at several locations in the Indian sub-continent, namely Dibrugarh [27.31N, 4.51E] (Pathak et al., 2010), Minicoy [8.31N, 73.041E] (Vinoj et al. 2010), Nainital [29.231N, 79.411E] (Dumka et al., 2010), Kullu valley [31.91N, 77.111E] (Guleria et al., 2011), Delhi [28.631N, 77.171E], Kanpur [26.471N, 80.331E] (Chinnam et al., 2006), Ahmedabad [23.031N, 72.551E] (Ganguly and Jayaraman, 2006), Pune [18.531N, 73.851E] (Panicker et al., 2010), Visakhapatnam [17.71N, 83.31E] (Sreekanth et al., 2007), Bangalore [13.01N, 77.01E] (Babu et al., 2002), among many others. Results from these studies reflect the large spatio-temporal variability of BC aerosols specific to the region. In the present study, systematic measurements of aerosol BC mass concentration for 2 years (January 2009–December 2010) have been performed over Hyderabad, which is a typical tropical urban industrial region. The data were obtained using a seven channel portable Aethalometer aiming to study the seasonal and diurnal variation of BC and their association with the prevailing atmospheric processes. The seasonal variation in the BC aerosol concentration as well as the identification of its potential pathways and assessment of BC radiative forcing constitute the main objectives of the present study that are examined for the first time over Hyderabad.

2. Experimental site description and methodology The study area pertains to Hyderabad (Fig. 1a), which is the fifth largest city in India and is also considered as one of the most polluted (Beegum et al., 2009). This is a direct result of the growth in population and associated activities that have been observed during the last decade. Greater Hyderabad area consists of twin cities, viz Hyderabad and Secunderabad, with its suburbs extending up to
26 km. The population of the city and surrounding regions according to 2010 census is
5.3 million inhabitants with a
18% decadal growth. The measurements have been carried out at the premises of National Balloon Facility (NBF), Tata Institute of Fundamental Research (TIFR), located at the outskirts of the urban center (17.471N and 78.581E) as seen in Fig. 1. 2.1. Aethalometer Continuous real-time measurements of the black carbon (BC) aerosol mass concentration and absorption coefficient have been carried out during January 2009–December 2010 using a seven wavelengths Aethalometer (model AE-42). A semi-continuous optical absorption method is applied to measure the attenuation of light by aerosols at the seven selected wavelengths (370, 470, 520, 590, 660, 880 and 950 nm). The instrument aspirates ambient air at a standard flow rate of 5 l per minute at an altitude of
10 m above the ground level. The measurement sampling interval was kept at 5 min. The particles in the incoming airflow are deposited on the quartz filter tape of the Aethalometer and the BC mass concentration is determined by measuring the change in transmittance through the quartz filter tape due to the particle deposition. It is noticed that filter-based absorption techniques for the measurement of BC aerosol encounter various systematic errors that need to be corrected (Bond et al., 1999). The overestimation of BC mass concentration due to multiple scattering (denoted by C) within the filter is partly compensated by higher particle loading called shadowing effect (denoted by R) in the filter, which decreases the optical path. Based on several experiments Weingartner et al. (2003) found that the shadowing effect and R factor are quite significant for pure soot particles, while almost negligible for aged aerosols (mixture of several components).

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Fig. 2. Monthly mean variation of ambient air temperature (temp, 1C), relative humidity (RH, %), wind speed (WS, m s  1) and wind direction (WD, θ1) during January 2009–December 2010.

In the present study, the values of R¼ 1 and C ¼2.355, 2.656, 2.677, 2.733, 2.827, 2.933, 2.925 were used for the seven wavelengths Aethalometer following Schmid et al. (2006). The maximum uncertainly for BC is up to 20% (ranging from
40 ng m  3 to 60 ng m  3) with higher percentage of error for low mass concentrations (Moorthy et al., 2007). 2.2. Multi-wavelength radiometer The spectral AOD measurements were carried out over Hyderabad using multi-wavelength radiometer (MWR) at 10 wavelength bands centered at 380, 400, 450, 500, 600, 650, 750, 850, 935 and 1025 nm. More details about instrumentation, method of analysis and uncertainties are discussed elsewhere (e.g. Sinha et al., 2012). The typical error in the retrievals of AOD is
0.01 excluding the variance of the Langley fit. The variance of the Langley intercept (typically 5%) along with other uncertainties, i.e. influence of Rayleigh scattering and absorption by ozone and trace gases, increases the uncertainty in AOD in the range of 0.02–0.03. In the present study, a total of 171 sets of MWR data spread over 2 years (January 2009–December 2010) have been analyzed to obtain the aerosol characteristics and radiative forcing over Hyderabad on monthly and seasonal basis. 2.3. Assessment of aerosol radiative forcing The Santa Barbara Discrete ordinate Atmospheric Radiative Transfer (SBDART) model (Ricchiazzi et al., 1998) was used for estimating the ARF over Hyderabad. The ARF calculations were performed in the shortwave (0.3–4.0 μm) solar spectrum, while the diurnally averaged ARF values are used to compute the monthly mean. The input parameters in the model are the AOD, α, single scattering albedo (SSA), asymmetry parameter (g), columnar water vapor content (WVC) and ozone. The spectral AOD and α values were obtained from MWR, while the columnar ozone and WVC were taken from ozone monitoring instrument (OMI) and atmospheric infrared sounder (AIRS), respectively. Externally mixed aerosol types (i.e. water soluble, insoluble, soot, mineral transported, sea salt) were used for continental/urban model as described in optical properties of aerosol and clouds (OPAC) model (Hess et al. (1998)). BC number concentration corresponding to BC mass concentration has been incorporated as soot aerosol types in OPAC. The number concentrations of the aerosol types were iteratively adjusted aiming to attain the best fit between measured and modeled spectral AODs (Sinha et al., 2012). The optical properties of aerosols (AOD, Ångström exponent, SSA and asymmetry parameter) have been computed with and without including the BC mass fraction in the OPAC model. These aerosol optical properties were then used

Fig. 3. Six hourly (open circle) and daily (filled circle with error bar) variation of BC mass concentration over Hyderabad during January 2009–December 2010. The horizontal line in the top panel shows the annual-mean value of BC mass concentration.

separately to estimate the BC contribution to the total radiative forcing in the short wavelength range (0.25–4.0 mm). Furthermore, 8-day MODIS-derived spectral reflectance values over Hyderabad at seven wavelength bands centered at 0.469, 0.555, 0.645, 0.859, 1.24, 1.64 and 2.13 mm were used to model the surface reflectance (using a combination of sand and vegetation) required as input to SBDART. The surface reflectance was found to be well below 0.2 (for short wavelengths) and 0.3 (for long wavelengths) (Sinha et al., 2012).

3. Synoptic and local meteorology The monthly mean variation of meteorological parameters (such as relative humidity, ambient temperature, wind speed and wind direction) over the observational site is shown in Fig. 2. The ambient air temperature is high varying from 20.77 1C 70.34 1C in January to 32.63 1C 70.13 1C in May. The surface-level wind follows variable directions, from southeasterly in the period October–March to southwesterly in the period of April–September (Fig. 2). Furthermore, the wind speed is generally low and increases during the period April to September. More details about the meteorological conditions over Hyderabad are given elsewhere (Beegum et al., 2009; Kaskaoutis et al., 2009).

4. Results and discussions 4.1. Temporal variation of BC mass concentration The temporal variation of six hourly (open circle) and dailymean (filled circle with line) BC mass concentration is shown in Fig. 3. The vertical bars express the standard error of the mean and the solid horizontal line denotes the annual-mean BC mass concentration. The results reveal a marked daily and annual variation in BC mass concentration, with high values (4 4 mg m  3) during winter and spring followed by lower values during summer. The daily-mean BC mass concentration varies from a minimum of 1.06 70.12 mg m  3 during summer to a maximum of 12.50 71.58 mg m  3 during winter exhibiting a mean value of 4.45 70.12 mg m  3. This strong seasonal variability could be associated with the variation in the nature of synoptic wind flow from Northwest to Northeast, which is associated with frequent crop reside and intense biomass burning during winter and early spring (Badarinath et al., 2008), and clean marine airflow from southwest

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Table 1 Mean BC mass concentration measured over Hyderabad, in comparison with BC mass concentration over other location over globe. Location

Environment

Study period

BC mass conc.

Reference

Nainital Kanpur Delhi

Rural (high altitude) Urban, industrial Urban, industrial

November 2004–December 2008 December 2004 January–December 2007

Ahmedabad Pune Sinhagad Hyderabad Bangalore Visakhapatnam Mumbai Trivandrum Hyderabad

Urban, industrial Urban, industrial Rural (high altitude) Urban Urban Urban coastal Urban, industrial, coastal Urban, coastal Tropical, urban

September 2003–June 2005 January–December 2005 November 2004–April 2005 January–December 2003 November 2001 December 2005–September 2006 January–March 1999 August 2000–October 2001 January 2000–December 2010

0.99 7 0.02 6–20 4–42 (147 12) 11–65 0.2–10 4.1 1.5 10 (dry o 4 (wet) 0.4–10.2 3.4 7.5–17.5 0.5–8.0 4.45 7 0.12

Dumka et al. (2010) Tripathi et al. (2005) Tiwari et al. (2013) Ganguly and Jayaraman (2006) Ramachandran and Rajesh (2007) Safai et al. (2007) Safai et al. (2007) Latha and Badarinath (2005) Babu et al. (2002) Sreekanth et al. (2007) Venkataraman et al. (2005) Moorthy and Babu, 2006 Present study

Table 2 Monthly mean values of aerosol black carbon mass concentration for full day (24 h), daytime (06:00–18:00 h) and nighttime (18:00–06:00 h) over Hyderabad. The monthly mean value of absorption Ångström coefficients is also given in the table.

Fig. 4. Diurnal variation of BC mass concentration for each month over Hyderabad during January 2009–December 2010.

accompanied with rain washout during summer. The local anthropogenic activities are not believed to contribute so much in the seasonal cycle of BC mass concentration, since the urban emissions are continuous throughout the year. Similar variation in BC mass concentration has been found over other urban locations in India, namely Kanpur (6–20 mgm  3 (Tripathi et al., 2005)), Ahmedabad (2–11 mgm  3 (Ramachandran and Kedia, 2010)), Pune (4.1 mgm  3 (Safai et al., 2007)), Bangalore (0.4–10.2 mgm  3 (Babu et al., 2002)). The BC mass concentrations at several locations in India are summarized in Table 1.

4.2.1Diurnal variation in BC mass concentration In addition to the annual changes BC mass concentration also exhibits a pronounced diurnal variation. The monthly average diurnal variation of BC mass concentration obtained from the hourly means is shown in Fig. 4. A well-defined diurnal variation is observed at Hyderabad with a primary peak at around 8:00 LST and the another moderate peak in the late evening around 20:00. The primary and secondary peaks in the morning and late evening appear consistently throughout the year but with varying magnitude. The diurnal variation of BC has shown a strong association with the local boundary layer dynamics as well as local anthropogenic activities (Moorthy and Babu, 2006). The primary morning peak might also be associated with the build-up of urban aerosol from anthropogenic activities and fumigation effect in the

Month

BC (full day)

Nighttime (BC)

Day time (BC)

Alpha

January February March April May June July August September October November December

7.08 7 0.37 6.92 7 0.22 6.107 0.39 4.55 7 0.18 4.167 0.23 2.28 7 0.09 1.88 7 0.09 2.78 7 0.18 3.337 0.20 4.107 0.24 3.03 7 0.21 4.98 7 0.24

7.91 70.43 6.88 70.34 6.43 70.52 5.12 70.29 5.79 70.47 2.40 70.13 1.92 70.13 3.0770.23 3.88 70.27 5.03 70.42 3.45 70.26 5.63 70.27

6.62 7 0.42 7.277 0.27 6.177 0.42 3.99 7 0.23 2.92 7 0.16 2.23 7 0.10 1.87 7 0.08 2.54 7 0.19 2.92 7 0.21 3.447 0.19 2.687 0.21 4.377 0.23

1.02 70.01 1.01 70.01 1.06 70.01 1.05 70.01 1.04 70.01 0.96 70.01 0.95 70.01 0.91 70.01 0.94 70.01 1.00 70.01 1.01 70.01 0.96 70.01

atmospheric boundary layer, which confines aerosols and pollutants from the nocturnal residual layer shortly after the sun rise (Stull, 1998). As the day progresses the BC mass concentration continuously decreases due to increasing solar heating favoring the uplift and dilution of pollutants and reaches the diurnal minima at the local noon hours. The BC mass concentration continues to be low until 17:00 h and thereafter it is slowly starting to increase and reach a secondary peak in the evening. The evening peak is again associated with the shallower nocturnal boundary layer, which leads to rapidly reduction in the ventilation effects. The monthly mean values of BC mass concentration for full day, day light (06:00–18:00 h) and nighttime (18:00–06:00 h) are presented in Table 2. The shape and nature of diurnal variation of BC is similar to the other continental, urban and coastal locations in India (Allen et al., 1998; Latha and Badarinath, 2005; Babu and Moorthy, 2002; Moorthy et al., 2007, Ramachandran and Rajesh, 2007; Safai et al., 2007; Sreekanth et al., 2007; Ganguly and Jayaraman, 2006; Ganguly et al., 2006; Tripathi et al., 2005). However, the scenario is quite different for high altitude stations, e.g. Nainital (
2 km above mean sea level) located in the central Himalayas, where a mid-day or late afternoon peak was observed (Pant et al., 2006; Dumka et al., 2010), and La Reunion Island,
2.5 km above mean sea level (Bhugwant et al., 2001).

4.2.2Weekly cycle The weekly cycle of BC mass concentration over Hyderabad is shown in Fig. 5. Very interestingly, BC mass concentration does not show significant weekly variation and the weekend minimum is almost absent. Recent investigations have shown weekly cycles in

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Fig. 5. Weekly cycle of aerosol BC (top panel) and contour plot showing the weekly diurnal variation of BC mass concentration (bottom panel) over Hyderabad during January 2009–December 2010.

the various aerosol parameters (e.g. aerosol optical depth, number/ mass concentration etc) (Murphy et al., 2008; Jin et al., 2005; Almeida et al., 2006) as well as the meteorological variables such as temperature, wind speed and precipitation (Gordon, 1994; Forster and Solomon, 2003; Gong et al., 2006; Bäumer and Vogel, 2007). This variability suggests continuous emissions of pollutants over urban Hyderabad throughout the year. 4.2. Spectral variation of absorption coefficients (sabs) The spectral absorption coefficient (sabs) obtained from Aethalometer measurements at 7 wavelengths was used to estimate the absorption Ångström exponent (AAE, αabs) using a power law relationship of the form: sabs ðλÞ ¼ βabs λαabs

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Fig. 6. Monthly variation of absorption Ångström exponent (αabs) over Hyderabad during the period January 2009–December 2010. The vertical bars express the standard error of the monthly mean.

Table 3 Ångström absorption coefficient during different weekdays and seasons. Season \weekday

Winter (DJF)

Spring (MAM)

Summer (JJA)

Autumn (SNO)

Annual

Sunday Monday Tuesday Wednesday Thursday Friday Saturday Total

0.99 7 0.05 0.99 7 0.04 0.98 7 0.06 0.98 7 0.06 1.017 0.08 1.017 0.07 1.017 0.04 1.007 0.02

1.067 0.11 1.067 0.08 1.067 0.12 1.05 7 0.11 1.067 0.07 1.05 7 0.08 1.03 7 0.08 1.057 0.04

0.977 0.16 0.92 7 0.08 0.92 7 0.08 0.95 7 0.15 0.96 7 0.11 0.95 7 0.12 0.93 7 0.09 0.947 0.04

1.02 7 0.10 0.99 7 0.10 0.98 7 0.08 0.95 7 0.10 0.977 0.13 1.02 7 0.14 1.017 0.10 0.997 0.04

1.02 7 0.07 1.007 0.06 0.99 7 0.06 0.99 7 0.07 1.017 0.07 1.02 7 0.07 1.007 0.07 0.99 7 0.00

ð1Þ

where αabs and βabs are the absorption Ångström exponent and absorption coefficient, respectively. The αabs is a measure of the spectral dependence of aerosol absorption and constitutes a useful parameter as it contains some characteristic features of the sources producing these absorbing particles (Kirchstetter et al., 2004). This fact is particularly important over India where contribution of BC from biomass/bio-fuel burning is as important as fossil–fuel combustion, while the same amount of BC from bio-fuel exhibits stronger absorption characteristics (Ganguly et al., 2005; Venkataraman et al., 2005). In order to explore the source characteristics of BC aerosols over Hyderabad, the monthly mean values of αabs were obtained using Eq. (1) by performing a linear regression of ln(sabs) vs. ln(λ) during the study period (January 2009–December 2010) (Fig. 6). It is observed that the αabs remains mostly
1 during all months over the studied period without exhibiting a strong annual variation; however, slightly lower values are observed during summer. Kirchstetter et al. (2004) have shown that the aerosols produced from the biomass burning exhibit stronger wavelength dependence (αabs Z2) in the absorption, while those produced from fossil–fuel burning, such as motor vehicle exhausts, etc., show a weaker dependence (αabs r1). Furthermore, those mixed with dust still have higher αabs values (Kirchstetter et al., 2004). Several other studies (e.g. Jacobson, 2000; Bond, 2001; Bergstrom et al., 2002) have shown that the atmospheric aerosol mixtures in which absorption is mainly due to BC exhibit a weak spectral dependence. Bond (2001) found that the spectral dependence of light absorption by aerosols is mostly determined by the size of graphite clusters present within the absorbing material, while Bergstrom et al. (2002) demonstrated that stronger absorption at shorter wavelengths could also arise due to the presence of mineral dust in the atmosphere. According

to the above, the results suggest that the BC aerosols over Hyderabad are mainly originated from fossil–fuel rather than bio-fuel combustion, while the contribution of dust in the mixture is rather low. Note that the higher αabs is indication of higher OC/BC ratio, while low αabs r
1 represents high BC/OC, typically of fossil– fuel combustion. Furthermore, during summer there is a larger possibility of increased fraction of non-absorbing aerosols, such as sulfate and sea salt that reduce the αabs values. The αabs values during different weekdays and seasons are given in Table 3.

4.3. BC emission patterns at Hyderabad and the potential sources It is well known that the concentration of any aerosol species in the atmosphere is a function of emission rates, atmospheric dispersion and removal processes. For a simple leaky box model in a given volume element, the observed flux can be written by the source term minus loss term as dS dL  ¼ F U dV dt dt

ð2Þ

where S is the source term in the volume, L is the loss term, F is the measured BC flux and dV is the volume element, F is the steady state flux, which may hold for short samples but may increase or decrease based on the source and loss term. The loss term depends not only on the diffusion from the boundaries of the enclosed volume, upwelling from the upper surface near the boundary layer and BC swept out of the volume element by the wind vector, but also on the quick settling of the large particles, which may become heavy by humidity absorption due to their hygroscopic nature which in turn depends on the surface area of the BC, temperature

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and the RH value and the morphology of the BC, which is highly irregular. The BC flux is the average at the surface, which consists of the BC aerosols emanating from the industrial activity around the city as well as BC transported from elsewhere. Then the above flux balance equation can be modified as dS dL  þ ∑ f ¼ F U dV dt dt n i

ð3Þ

where Σnfi represents the fractional flux from the external sources contributing to the target area. In the case of steady state conditions in local equilibrium we can use a simple approach proposed by Dutkiewicz et al. (2009) to estimate the BC mass emissions using the measured BC values. The mass of emitted BC is the mean concentration times the affected volume, which is taken as the area of city times the mixing height (MH), time a factor that account the vertical and horizontal dispersion rates as follows: Mass of BC emitted ¼ ½BC  area of city  MH fðK  dtÞ=MH þ ðV þ dtÞ=De g

ð4Þ

where K is the rate of vertical mixing, V is the surface wind speed and De is the effective diameter of the city. The last term is a dimensionless factor to count the vertical and horizontal dispersion, which represents the number of times the volume is filled during the time period dt. In this method, other factors, such as surface roughness and dry deposition, have been neglected as we are mainly interested in the trends of the emitted BC mass. The value of mixing height (MH) was extracted from the hourly intervals of the output off the HYSPLIT program (Dutkiewicz et al., 2009). The diurnal pattern of mixing height during winter, spring, summer and autumn is shown in Fig. 7 (top panel) by dotted lines. The nighttime mixing height is 100, 200, 550 and 160 m during the respective seasons, whereas the mid-day maximum value of mixing height is 2250, 3876, 1847 and 1225 m, respectively. In this simple approach we have set K as the mean volume of the city (area of city times mixing height) under calm conditions filled once per 24 h (1 ¼(K  24/MHmean)). The values of K were calculated on monthly basis and varied from 21 to 67 m h  1. Here we have used the area of Hyderabad proper city,
625 km2 for this calculation having an equivalent/effective diameter of
28 km. The true BC emission profile for the city is strongly dependent on the meteorological conditions, such as surface wind speed. Even though the BC values from a single site do not represent the mean (or true) value over the city, the mean diurnal variation of BC is expected to be fairly consistent over the city. Despite the strongly varying diurnal and monthly/seasonally mean BC mass concentration, the above equation gives us fairly consistent values of BC emission patterns over the city. The seasonal mean diurnal variation of BC emission is shown in Fig. 7 (top panel) by solid line with symbols whereas Fig. 7 (bottom panel) presents the monthly mean BC emission profiles. The diurnal variation of BC emission profile is consistent during all the four seasons with a strong prominent peak at local noon time. Although, the observed BC mass concentrations are relatively low during the mid-day, the BC emissions are relatively large throughout the day and seasons. The BC emissions are found to vary between 360 and 1941, 556 and 3689, 549 and 2240, and 214 and 984 kg h  1 during winter, spring, summer, and autumn, respectively. The daily-mean BC emissions are fairly consistent throughout the study period and vary from 2.65  103 to 1.41  105 kg per day, whereas the integrated BC emissions are 1.27  107 kg per year. The highest values of BC emissions are observed in May and lowest in November, whereas the measured BC mass concentrations are maximum during January and minimum during July. It is

Fig. 7. Diurnal variation of BC emissions for the four seasons based on the measured BC mass concentration and Eq. (4) (solid lines with symbols), and diurnal variation of mixing height for the four seasons by dotted lines (top panel). Monthly variation of the BC emissions (open bars) and BC mass concentration (filled bars) over Hyderabad during the period January 2009–December 2010 (bottom panel).

clearly seen that, in spite of the absolute values of measured BC being very different in different months, the nighttime monthly averaged BC flux is higher than the daytime throughout the year, which would be normal in the case of a box model due to the boundary layer dynamics. However, this simple explanation falters in the case of two anomalous months i.e. April and May. In the case of April, the nighttime flux is 25% higher than the daytime and in May, it is 50% higher. LIDAR data show that during the summer months, the boundary layer does not collapse during the nighttime. Even in the Asian region, the summer months do have forest fires and the farmers usually burn the paddy fields to clear land for agricultural practices. Agricultural burning normally coincides with the March–May northern tropical dry season and terminates in early June when the rainy season begins. In fact major fires had broken up in SW China and Myanmar in the month of March 2009. The above suggests that the analysis points out the long-range transport of BC aerosols over the continent. 4.4. Potential source regions of black carbon aerosol 4.4.1. Trajectory clustering Cluster analysis is a multivariate statistical technique, which is used to group trajectories, to ascertain the primary pathways through which the aerosol particles originated and how they are transported to the study location. The main criterion of the trajectory clustering is to minimize the variability among the trajectories within a cluster and maximize the variability between the clusters, as discussed elsewhere (Gogoi et al., 2009; Dumka et al., 2010; Vinoj et al., 2010).

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In this technique the trajectories reaching the studied location were weighted on the basis of the mean concentration measured at the site during the arrival of the trajectory. In CWT technique, each grid cell is assigned a concentration obtained by averaging trajectory associated concentrations that had crossed the grid cell

In the current study, six main trajectory clusters were identified using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003) and shown in Fig. 8. The percentage contribution of each trajectory clusters is also given in the figure. These clusters identify different aerosol transport pathways. The number of trajectories assigned to each cluster as well as the mean BC mass concentration for each clusters and the high BC trajectories are summarized in Table 4; the high BC trajectories correspond to BC mass concentrations above 4.0 mg m  3. The percentages of the trajectories assigned to the six clusters are not greatly different, ranging from a low 13% (cluster B) to a high of 27% (cluster C). The high BC mass concentration in Hyderabad is favored by clusters A, B, D, E and F (Table 4). The full day (i.e. all trajectories column), daytime and nighttime average BC mass concentration at Hyderabad for each cluster group are also given in Table 4. It is seen that the trajectories originating either from the Indo-Gangetic Plain (IGP) and/or passing through the IGP region or Indian land mass are associated with the higher BC mass concentration.

C ij ¼

M 1 ∑ C l τijl ∑M τ ijl l ¼ 1 l¼1

ð5Þ

where Cij is the average weighted concentration in the ijth cell, l is the index of the trajectory, M is the total number of trajectories, Cl is the concentration observed in the trajectory endpoint and τijl is the time spent in the ijth cell by the trajectory l (Seibert et al., 1994; Stohl, 1996). The time that a trajectory spends in each cell could be represented by the number of trajectory segments located in the cell. High values of Cij imply that air parcels traveling over the ijth cell would be, on an average, associated with high concentrations at the studied location. The arbitrary weighting function described above was also used in the CWT analysis to reduce the effect of the small values of nij. The CWT was applied for the BC mass concentrations measured over Hyderabad and the obtained map is shown in Fig. 9. The results reveal that the most important potential sources for BC concentrations are the central India and IGP and some hot spots in Pakistan. Note also the larger values in the eastern coastal India than the western one. Air masses originating from Arabian Peninsula and Persian Gulf traversing the northern AS can also contribute to medium BC mass concentrations, while the air masses from southern AS seem to be cleaner.

4.4.2. Concentration weighted trajectory analysis (CWT) Since it is difficult to extract information regarding the relative potential source regions of aerosol getting transported at the measurement site from simple air mass back trajectories, such information can be extracted by combining some additional statistical analysis of air-mass back trajectories with the aerosol measurements at the site. In this regard, CWT analysis (Seibert et al., 1994; Wang et al., 2006) is used to evaluate the potential sources contribution of BC mass concentration at Hyderabad.

4.5. Radiative impact of black carbon aerosol BC aerosols play an important role in Indian monsoon circulation by increasing the atmospheric heating and, consequently, altering the vertical as well as the meridional temperature gradient (Meehl et al., 2008). Fig. 10 shows the scatter plot

Fig. 8. Different mean advection pathways (trajectory clusters) that influence Hyderabad (open circle) during January 2009–December 2010. The error bars at each hourly point represent the latitudinal extent of trajectories in each cluster group. The percentage contribution for each cluster is also given in the figure.

Fig. 9. Concentration weighted trajectory (CWT) map for BC mass concentration (mg m  3) at Hyderabad January 2009–December 2010. The black circle shows the location of the observational site.

Table 4 Trajectory number and BC mass concentration of each cluster. Trajectories associated with concentration higher than mean concentration (4.0 mg m  3). Cluster

A B C D E F

All trajectories Number

Mean con (mg m

227 172 376 187 198 232

5.18 6.39 2.54 4.25 4.34 4.93

Polluted trajectories 3

)

Std. error

Number

Mean con (mg m

0.21 0.28 0.09 0.21 0.21 0.19

133 116 43 85 80 130

7.10 8.03 5.94 6.61 7.11 6.75

Nighttime 3

)

Std. error

Mean con (mg m

0.25 0.31 0.36 0.29 0.32 0.23

6.03 6.74 2.85 4.04 5.30 5.25

Day time 3

)

Std. error

Mean con (mg m  3)

Std. error

0.26 0.31 0.16 0.21 0.36 0.24

4.32 6.00 2.24 4.46 3.54 4.63

0.33 0.48 0.06 0.36 0.22 0.29

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Fig. 10. Scatter plot of MWR AOD500 and BC mass concentration over Hyderabad during January 2009–December 2010.

Fig. 11. Monthly mean MWR AOD500 in comparison with OPAC derived AOD500 over Hyderabad during January 2009–December 2010. The vertical bars in the MWR AOD500 denote the standard deviation of the monthly mean.

between AOD500 and BC mass concentration over Hyderabad for all the available datasets. It is shown that the BC and AOD are positively correlated but without presenting a statistically significant correlation, since the scatter of the data points is large and the correlation coefficient r ¼0.25. This indicates that the BC mass near the surface cannot be a representative measure for the aerosol burden in the whole atmospheric column due to multiple aerosol layers observed in the vertical (Sinha et al., 2013). This suggests a significant vertical heterogeneity in the atmosphere, which renders the calculation of the BC ARF in the vertical. The comparison between the MWR-derived AOD500 with those calculated using OPAC simulations is quite satisfactory, at least on monthly basis (Fig. 11). Thus, the MWR and OPAC AODs are very close during the period January–June 2009, while some discrepancies occur in the rest of the months. However, in all months the OPAC AODs are within the standard deviation of the monthly mean MWR AOD values, a fact that allows the OPAC retrievals to be used for the ARF calculations. The monthly mean variation of BC radiative forcing is shown in Fig. 12 for the period January 2009–December 2010. As expected,

Fig. 12. Monthly mean shortwave (0.3–4.0 mm) aerosol radiative forcing values at top of the atmosphere (TOA), in the atmosphere (ATM) and at the surface (SFC) over Hyderabad during January 2009–December 2010. The atmospheric heating rates are shown in parenthesis for each month.

due to strong absorption of solar light, the BC ARF at surface (FSUR) is always negative, with more negative values (
38.57 Wm  2) in January and March and lower (
 10 Wm  2) values during summer months. The BC radiative forcing within the atmosphere (FATM) shows strong seasonal and monthly dependence with high values in winter (33.49 77.01 Wm  2) and spring (31.78 7 12.89 Wm  2) and moderate during autumn (18.94 76.71) and summer (13.15 71.66). The overall heating of the atmosphere due to BC aerosols over Hyderabad during 2009 was translated into an average heating rate of 0.53 Kd  1. As expected, the atmospheric heating rate is higher in March (1.30) and January (1.14) and drops down (0.34) in July, close associated with the BC mass fraction. The larger atmospheric and surface forcing could be associated with the enhanced BC aerosol loading and higher AOD values during these months (January and March). The annual average BC mass fraction (not shown) was found to be 10 73% and contributes to the atmospheric forcing by 55%, which is similar to the reported values over Ahmedabad (60%) (Ramachandran and Kedia, 2010) and Pune (55%) (Panicker et al., 2010). The BC ARF at TOA is always positive, thus suggesting an overall heating of the regional climate over Hyderabad. This is in turn with the observations of several researchers (Charlson et al., 1992; Haywood and Boucher, 2000) who reported positive ARF values at TOA for the absorbing aerosols. The uncertainty in measured spectral AOD data acquired from the MWR is about 70.02–0.03, while for α it is between 0.04 and 0.12. The uncertainties for single scattering albedo and asymmetry parameter obtained from OPAC were taken 10% and 0.05, respectively. Estimation of ARF is also dependent on the aerosol vertical distribution. By implementing all the above, the overall uncertainty in the calculated ARF at TOA was found to be
10–25%

5. Conclusions Near real-time measurements of surface BC mass concentrations have been carried out using a seven channel Aethalometer at a tropical urban site Hyderabad, India, during January 2009– December 2010. The observed BC concentration exhibited a strong diurnal and seasonal variation and the main findings can be summarized as follows:

 BC mass concentration showed significant temporal variations, diurnal as well as monthly and seasonal. The BC mass

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concentration varied from a low value of 2.36 70.09 mg m  3 during summer to a high of 6.677 0.22 mg m  3 during winter season. BC mass concentration was found to show a well-defined diurnal variation with two peaks, one around 08:00 h and the other at 20:00 h. This diurnal variation is strongly associated with the variation in atmospheric boundary layer. The two peaks in the diurnal variation of BC mass concentration were consistent during the study period but with varying magnitude depending on season. Six distinct advection pathways were identified on the basis of back-trajectory clustering. The most important potential sources are found to be the central India and IGP while the concentration weighted trajectory (CWT) analysis reveals that the most important potential sources for BC aerosols are the Indo-Gangetic plain (IGP), central India and some hot spots in Pakistan, Arabian Peninsula and Persian Gulf. The Ångström exponent (αabs) estimated from the spectral values of absorption coefficients (sabs) was found to vary from 0.92 to 1.1 indicating high BC/OC ratio, typical of dominance of fossil–fuel combustion over Hyderabad. The BC radiative forcing at the atmosphere showed strong seasonal dependency with higher values in winter (33.49 7 7.01) and spring (31.78 712.89) and moderate in autumn (19.54 79.38) and summer (14.687 2.90). The annual average BC mass fraction was found to be (10 73) % and contributing to the atmospheric forcing by (55 710) %. The BC ARF at TOA is positive in all months suggesting an overall heating of the regional climate.

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