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Influence of aerosol composition on visibility in megacity Delhi
Atmospheric Environment 62 (2012) 367e373
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Influence of aerosol composition on visibility in megacity Delhi Ajit Singh, Sagnik Dey* Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
h i g h l i g h t s < Role of aerosols on visibility degradation in megacity Delhi is examined. < Sensitivity of visibility to individual aerosol species is examined. < Seasonally representative aerosol composition is derived. < The quantitative estimates can help addressing the visibility problem in this region.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 May 2012 Received in revised form 19 July 2012 Accepted 19 August 2012
Influence of aerosol composition on visibility degradation in megacity Delhi has been quantified using model-derived seasonal mean aerosol composition constrained by satellite-measured columnar aerosol optical depth spectrum. Aerosols contribute w90% to the observed visibility degradation in non-foggy condition, while its relative contribution decreases rapidly at RH 80% due to stronger relative influence of fog droplets on attenuation of radiation, especially during the winter season. Visibility is most sensitive to water-soluble particles and soot in all seasons. Sensitivity study shows that visibility does not respond strongly to reduction of mass concentration of insoluble, accumulation mode and coarse mode dust particles. Reduction of mass concentration of soot and water-soluble particles in the range of 10%e50% will lead to an increase in visibility by 2.4 0.1%e11.3 1.6% and 4.9 2%e29 12% respectively. Reduction of the last two anthropogenic components has co-benefits, as it may reduce fog formation and thus further enhance the visibility along with an improved air quality in terms of associated health and climatic effects. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Aerosol composition Visibility Sensitivity
1. Introduction Visibility is a critically important parameter, because low visibility can disrupt the traffic movement impacting business, public safety and tourism industry. Theoretically, visibility is inversely proportional to extinction coefficient, bext (Koschmieder, 1924):
VIS ¼ K=bext ;
(1)
where K, Koschmieder constant, is equal to 3.912 assuming a 2% contrast threshold to visually detect an object against the horizon sky and bext is the total attenuation of visible radiation due to scattering and absorption by gas molecules, aerosols and other components (e.g. fog and cloud droplets) in the atmosphere. Visibility may vary within a wide range (from few meters to few
* Corresponding author. E-mail address: [email protected] (S. Dey). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.08.048
hundred km) (Horvath, 1995) depending on the concentration of gaseous pollutants and aerosols and their optical and microphysical properties (Bäumer et al., 2008). Hence, visibility may also be utilized as a proxy for concentration of aerosols and trace gases. Many studies exist in the literature that connect visibility with aerosol properties (e.g. Bäumer et al., 2008 and the references therein), but very few studies were carried out in the Indian subcontinent, where the visibility has reduced rapidly over the last 30 years period (Wang et al., 2009) with an increase in number of low visibility days (De and Dandekar, 2001). This rapid decrease in visibility has been attributed to an increase in aerosol concentration due to rise in anthropogenic emission, which also leads to a reduction in surface reaching solar radiation (Wild et al., 2005). The problem is more critical in the winter season, particularly in the Indo-Gangetic Basin (IGB), where favorable meteorological conditions aided by high aerosol concentration (Tiwari et al., 2011) lead to frequent fog formation resulting in a very low visibility (De et al., 2005). However, since attenuation of visible radiation depends strongly on aerosol composition, the changes in visibility in
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response to the changes in various aerosol components and their mixture need to be quantified. In the present study, we focus on the megacity Delhi (28.38 N, 77.12 E), home to w14 million population. Delhi is one of the most polluted cities in the world (Goyal and Sidhartha, 2003), where the major sources of pollutants are vehicular emission and fossil-fuel burning. Besides these two sources, biomass burning in the nearby regions and dust transported from the Great Indian Desert (Singh et al., 2005) and nearby regions also have contributions on the aerosol characteristics in Delhi. The influences of air pollutants (both gaseous and aerosols) on visibility in Delhi have previously been assessed rather qualitatively and empirically (e.g. Goyal and Sidhartha, 2003; Tiwari et al., 2011) and most of the studies focused only on the winter season characterized by frequent fog (Tiwari et al., 2011; Mohan and Payra, 2009). It must be noted that the aerosol and gaseous pollutant concentrations remain high throughout the year in Delhi (Srivastava et al., 2012), yet the role of aerosol composition in visibility degradation has not been fully understood. Here, we examine and present the sensitivity of the visibility to seasonally representative aerosol composition observed in the megacity Delhi and quantify the influences of aerosol composition on visibility degradation as function of relative humidity (RH) using an aerosol microphysical model based on Mie theory constrained by observations. These results may help in formulating appropriate mitigation measures in dealing with the visibility problem in this region. 2. Data analysis Visibility is estimated (in km) by measuring the attenuation of light due to the atmosphere and theoretically represents the farthest distance any human observer can see through. The daily horizontal visibility data used in this study were measured at Safdarjung Airport, Delhi by automatic weather station that has a visibility sensor. Daily mean visibility along with eighteen other meteorological parameters are archived as Global Summary of Day database distributed by National Climatic Data Centre, USA (http:// www7.ncdc.noaa.gov/CDO/cdo) for 30 years (1980e2009) period (Wang et al., 2009). The data archive is maintained as part of data exchange under the World Meteorological Organization World Weather Watch Program according to WMO Resolution (WMO, 1996). Temperature and dew point temperature reported along with visibility were utilized to derive the RH of each day of observation. For the trend analysis, 30 years of data were used. However, the columnar aerosol optical depth (AOD) over Delhi is available for the period of 2000e2009 and hence visibility data during this period were only used to constrain the model simulations to examine the sensitivity of visibility to aerosol composition. Visibility data of first 20 years are only used for trend analysis. The analysis has been carried out for four seasons, winter (Dece Feb), pre-monsoon (MareJun), monsoon (JuleSep) and postmonsoon (OcteNov). As mentioned before, other than aerosols, visibility also depends on Rayleigh scattering and gaseous absorption. Columnar Rayleigh extinction coefficient at 550 nm wavelength has been calculated following Bodhaine et al. (1999). In brief, the scattering coefficient can be expressed as the product of total Rayleigh cross-section per molecule (s) and molecular number density Ns, where s can be written as:
2 24p3 n2s 1 6 þ 3r s¼ 4 : 2 6 7r l Ns2 n2s þ 2
(2)
Here l is the wavelength, r is the depolarization factor and ns is the refraction index for standard air at l, which can be calculated as:
ðn300 1Þ 108 ¼ 8060:51 þ
2480990 2
132:274 l 17455:7 þ : 2 32:32957 l
(3)
n300 in equation (3) is the refraction index at 300 ppm CO2 concentration, which was corrected for present-day CO2 concentration of 390 ppm using:
ðns 1Þ ¼ 1 þ 0:54ðCO2 0:0003Þ: ðn300 1Þ
(4)
Contribution of gaseous absorption on total bext has been estimated following Groblicki et al. (1981), where NO2 concentration over Delhi is taken from the measurements by Central Pollution Control Board (www.cpcb.nic.in). Total bext at surface has been calculated from measured visibility using Koschmieder equation and extinction coefficient due to only aerosols has been derived by subtracting the Rayleigh scattering and absorption coefficient from total bext at surface. Finally, mean observed visibility due to only aerosols (hereafter referred to as VISO) has been calculated for each season from the 10 years of daily data as a function of RH. This is carried out by averaging data of all days with observed RH within 50 2%, 70 2%, 80 2%, 90 2% and 95 2% within each season (the number of days are shown in Table 1). These RH ranges are considered because the model can simulate aerosol optical properties at these specific RH. A 2% RH range is considered to allow sufficient number of days for robust estimation of mean seasonal VISO in assessing model simulated visibility (hereafter denoted as VISM) with minimum error in computed bext due to hygroscopicity of aerosols. Rest of the days have RH other than these ranges and hence are not considered for the closure study. 90% of the observed visibility can be explained by aerosols, while w10% contribution is attributed to gaseous pollutants in non-foggy condition.
Table 1 Mean seasonal Na for individual aerosol components and total (in # cc1), thickness of aerosol layer (DZ) and scale height (H) as considered in OPAC for calculation of visibility. ‘Dusta’ and ‘Dustc’ represent accumulation and coarse mode mineral dust respectively. # of days with RH in the given ranges within each season are considered for closure studies.
Insoluble Water-soluble Soot Dusta Dustc Total Na DZ (in km) H (in km) # Days with RH 50 2% # Days with RH 70 2% # Days with RH 80 2% # Days with RH 90 2% # Days with RH 95 2%
Winter
Pre-monsoon
Monsoon
Post-monsoon
0.8 26,000 135,000 9.4 0.6 1.61 105 2.2 0.79 41
1.1 15,000 86,000 10.3 0.98 1.01 105 4.95 1.7 72
0.7 16,700 80,800 9.4 0.6 0.97 105 4.18 1.9 20
0.2 25,000 115,000 8.5 0.6 1.4 105 3.2 1.4 22
90
60
78
72
43
49
54
30
20
21
7
12
4
5
2
4
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3. Model description OPAC (Optical Properties of Aerosol and Clouds, Hess et al., 1998) model has been used for examining the impacts of aerosol composition on visibility. OPAC utilizes the microphysical properties of various aerosol components from Global Aerosol Data set (Koepke et al., 1997) to calculate composite columnar aerosol spectral optical properties following Mie theory assuming an external mixing state. In total, ten individual aerosol components are available and they are ‘insoluble’, ‘water-soluble’, ‘soot’, ‘sea-salt in accumulation mode’, ‘sea-salt in coarse mode’, ‘mineral dust in nuclei mode’, ‘mineral dust in accumulation mode’, ‘mineral dust in coarse mode’, ‘transported mineral dust’ and ‘stratospheric sulfate’. Several standard aerosol compositions have been pre-defined in OPAC (Hess et al., 1998). For example, ‘urban’ composition includes water-soluble scattering aerosols (formed as gas-to-particle conversion from industrial emissions), insoluble and soot (emitted from biomass and bio-fuel emissions), while ‘continental polluted’ composition has same components with a lower concentration of soot relative to ‘urban’ composition. It further allows formulation of a new aerosol composition to be defined by choosing maximum of five individual aerosol components. OPAC model has been used many times to derive aerosol properties in the Indian Subcontinent (e.g. Dey and Tripathi, 2008, 2007), including Delhi (Pandithurai et al., 2008; Srivastava et al., 2012). Here, the aerosol composition is considered to be composed of ‘insoluble’, ‘water-soluble’, ‘soot’, ‘accumulation mode dust’ and ‘coarse mode dust’ (i.e. a combination of ‘urban’ and ‘dust’) based on the previous studies on aerosol characteristics in Delhi (e.g. Singh et al., 2005; Srivastava et al., 2012). Two types of sensitivity studies have been carried out. First column-integrated bext (i.e. AOD) has been calculated as a function of varying number concentrations (Na) of each of these components individually, which has been converted to bext at surface using the mean seasonal aerosol scale height (H). CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization, onboard CALIPSO satellite) version 3.1 level 2 data for the period Jul 2006 to Dec 2010 (CALIOP retrieval started in Jul 2006) have been analyzed for aerosol vertical distribution over Delhi. H is defined as the height above ground level below which 63% (i.e. the height up to which bext reduces by a factor of e) of total columnar extinction (i.e. AOD) is present (Hayasaka et al., 2007):
ZH bext dz ¼
1 e1 AOD ¼ 0:63 AOD:
(5)
0
Although the scale height does not represent the detailed vertical structure, it is a good approximation of the aerosol vertical distribution on regional and global scales, particularly for satellite retrievals, model simulations, and modelemodel and modele satellite inter-comparison (Yu et al., 2010). Since most of the profiles do not show exponential decay of bext with altitude, this parameterized equation has been used to estimate H. Mean seasonal H and thickness of the aerosol layer (DZ) are used to convert columnar bext (computed from the seasonally representative aerosol composition) into surface bext. VISM has then been calculated using Koschmieder equation and sensitivity of visibility to changing Na of these components has been examined. Watersoluble particles are hygroscopic in nature and hence the change in visibility depends on the ambient RH, while for others, hygroscopic growth has been neglected (Srivastava et al., 2012). Since VISO depends on composite aerosol properties, aerosol composition representative of each season has been derived by fixing Na of each individual component through iteration following
369
Dey and Tripathi (2008). Na of each of the five aerosol species are varied until simulated AOD at four wavelengths closest to Multiangle Imaging SpectroRadiometer, MISR channels of 446, 558, 675 and 867 nm wavelengths match with MISR-AOD (Supplementary qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P Fig. S1). c2 (defined as 1=4 4i ¼ 1 ðAODiMISR AODiModel Þ2 ; where AODMISR and AODModel represent MISR-retrieved and modelderived AOD at four wavelengths, i) values are 0.017, 0.023, 0.011 and 0.024 for the winter, pre-monsoon, monsoon and postmonsoon seasons respectively. These values are smaller than the error in MISR-retrieved AOD and thus highlight the robustness of the method (Satheesh and Srinivasan, 2005) to estimate the mean seasonal aerosol composition for visibility closure study. Na of ‘insoluble’ and ‘water-soluble’ components are not allowed to reduce below Na of clean continental composition, while that of ‘soot’ is not allowed to reduce below Na of continental average composition during the iteration. Although, dust is not a common urban aerosol component, it is ubiquitously found in Delhi (e.g. Pandithurai et al., 2008; Srivastava et al., 2012) and their Na are varied from zero with an increment of 0.01 particles cc1. MISR data were considered because of availability of continuous measurements during the study period and the data have been extensively validated in the Indian region (Dey and Di Girolamo, 2010). In accordance with the previous studies (e.g. Singh et al., 2005; Srivastava et al., 2012) coarse mode dust increases during the premonsoon season, while water-soluble and soot concentrations are higher in the post-monsoon and winter seasons, when aerosols are confined below 3 km of the atmosphere with a smaller scale height (0.79 and 1.4 km respectively) relative to the other seasons. Columnar bext are converted to surface bext for each season using the scale height (Table 1) and visibility is calculated from surface bext using Koschmieder equation (1). In the second sensitivity experiment, mass concentration of one component has been reduced at 10% interval keeping the mass concentrations of other components fixed and response of VISM to this change has been examined. It must be noted that the aerosol retrieval (either by satellite or ground-based radiometer) is not possible under ‘cloudy-sky’ condition and hence VISM may be biased toward ‘clear-sky’ condition. 4. Results Visibility in Delhi decreased rapidly at a rate of 0.11 km year1 during 1980e2000 as shown in Fig. 1, but was found to be stabilized after the year 2000. A major air quality policy of transforming the regional transport sector from diesel/petrol to CNG has been implemented during the year 2001 (Goyal and Sidhartha, 2003) and this policy intervention may contribute to this stabilization of rapid decrease in visibility in the recent years. Since aerosol data
Fig. 1. Annual visibility trend over Delhi during last 30 years (1980e2009) period. Major air quality related policy of converting public transport sector from diesel/petrol to CNG has been implemented in the year 2001 (marked by the bold solid line).
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Fig. 2. Variability of mean monthly (1s) observed visibility in Delhi along with RH during the period 2000e2009. Correlation coefficient (R) between visibility and RH, number of samples (N) and the statistical significance following t-test are also provided.
are not available prior to the year 2000, any further attribution of this rapid decrease to pollutant concentrations cannot be quantified. Rather we focused on the last ten years (2000e2009) when visibility shows fluctuating trend. Mean (1 standard deviation, s) monthly observed visibility in Delhi during this period is shown in Fig. 2. As expected, visibility is lowest (1.13 0.4 km) during Dece Jan when fog is prevalent. It increases to 2.29 0.4 km in the month of April followed by a reduction to 2.03 0.35 km during the summer months. Although inter-annual variability exists, in general visibility is lowest in the winter, followed by postmonsoon, monsoon and pre-monsoon seasons. During winter, strong hygroscopic growth of particles at high RH (Dey and Tripathi, 2007) leads to large reduction in visibility due to enhanced scattering of radiation and thus VISO and RH are in opposite phase. The changes of visibility in response to an increase in Na of various individual aerosol components as simulated by OPAC are
shown in Fig. 3. The changes of VISM for hygroscopic water-soluble particles are shown at 50%, 70% and 80% RH, because maximum change in bext occurs in this range of RH (Dey and Tripathi, 2007). The figure clearly shows three regimes. Insignificant sensitivity of VISM in response to an increase in Na at low Na and high Na are defined as regime 1 and 3. In contrast, regime 2 shows a significant change in visibility with an increase in Na. It is noteworthy that the ranges of Na in the three regimes for various individual compositions are different. For example, for mineral dust (accumulation mode) regime 1 extends up to Na < 101 particles cc1, and regime 3 extends beyond Na > 103 particles cc1 (>5.54 103 mg m3 mass). Visibility is most sensitive to coarse mode mineral dust in the range 102 < Na < 101 particles cc1, while for insoluble and soot particles, regime 2 spans in the range 101 < Na < 102 and 101 < Na < 106 particles cc1 respectively. These differences are attributed to the size distributions of the individual components
Fig. 3. Changes of model-derived visibility (in km) with an increase in number concentrations of various aerosol components (in # cc1). Water soluble components are considered as hygroscopic and their influences on visibility depend on RH.
A. Singh, S. Dey / Atmospheric Environment 62 (2012) 367e373
(Hess et al., 1998). Visibility is affected only if the number concentration is large enough to influence attenuation of visible radiation significantly. Visibility shows significant decrease with an increase in Na in the range 101e105 particles cc1 for water soluble particles with higher rate of decrease at higher RH due to enhanced scattering of radiation by larger effective scattering cross-section area at higher RH (Dey and Tripathi, 2007). This implies that similar mass concentration of water-soluble particles may lead to lower visibility at higher RH. It must also be noted that this figure should be interpreted as the sensitivity of visibility to individual aerosol species and the influence of composite aerosol composition on visibility degradation in reality may be different depending on the relative proportions of Na of various individual components. However, the importance of this sensitivity experiment lies in the different regimes identified for the individual components. For example, if soot particle Na is less than 103 particles cc1, any further change in Na will not influence the visibility significantly. Furthermore, same amount of changes in Na of various individual components will not lead to same amount of changes in visibility due to different mass concentration. To quantify this statement, we derived seasonally representative aerosol composition by constraining the OPAC model with MISR-retrieved AOD spectrum as described earlier. Mean VISM at 50%, 70%, 80%, 90% and 95% RH are compared with VISO (Fig. 4). In general, the model captures the RH-dependence of VISO reasonably well in the pre-monsoon and post-monsoon seasons because all the points lie very close to 1:1 line. This suggests that the observed visibility degradation can be explained by gaseous and particulate air pollutants using the seasonal aerosol composition derived by the model despite uncertainties in MISRAOD and aerosol scale height. VISM is underestimated by 11e20% relative to VISO during the monsoon season, which may result from a combination of factors, such as the uncertainty in the model due to large variability in seasonal aerosol vertical distribution and MISR-retrieved AOD (Dey and Di Girolamo, 2010), limited applicability of Koschmieder’s equation (Horvath, 1981), influence of low clouds on surface bext that has not been taken into account in the simulation. Both VISM and VISO are estimated using the same Koschmieder constant (i.e. 3.192), hence any difference in VISM and VISO is due to difference in surface bext. Mean seasonal scale height derived by the parameterized equation (5) was used to convert columnar bext into surface bext. If elevated aerosols layers are present, which are common in the monsoon season in this region (Jaidevi et al., 2011), aerosol scale height should also increase.
Fig. 4. Comparison between mean seasonal visibility as derived by model (VISM) and from observations without the influence of gaseous pollutants (VISO) at five RH regimes during winter (open circle), pre-monsoon (filled square), monsoon (open star) and post-monsoon (filled triangle) seasons. Smallest size of each symbol represents 50% RH followed by 70%, 80%, 90% and 95% RH in the ascending orders of size.
371
Sensitivity study has been carried out to examine the influence of scale height on VISM for the pre-monsoon aerosol composition. We noticed that VISM increases (decreases) by w20% for an increase (decrease) in scale height by 10% for same columnar AOD. This implies that the underestimation of VISM during the monsoon season in the closure study (Fig. 4) may be explained by uncertainty in scale height. VISM shows reasonable agreement with VISO at 50% and 70% RH in the winter season (Fig. 4). However, notable differences (>20%) are observed at RH between 80% and 95% and the difference in VISM and VISO increases with increase in RH (Fig. 4). During winter, Delhi experiences fog and fog formation is facilitated at high RH. Fog droplets contribute to additional attenuation of visible radiation. In this season, the relative influence of aerosols on visibility degradation reduces with an increase in RH above 70% during the winter season and varies from 78% at 80% RH to 40% at 95% RH. Larger difference at higher RH is understandable because more fog droplets will form at higher RH relative to lower RH (Quan et al., 2011). Based on the comparison between model simulated and observed surface bext, excess bext are 0.8, 0.9 and 2.4 km1 respectively for 80%, 90% and 95% RH. At 80e90% RH, 1.6 fog droplets cc1 and at 95% RH, 4.3 fog droplets cc1 can explain this excess bext based on the calculation using the fog microphysical data provided in the OPAC database. A nonlinear relationship exists between Na and number concentration of cloud droplets (Nd) that may nucleate from Na:
Nd ¼ Nab ;
(6)
where, b varies widely in the range 0.06e0.48 depending on numerous factors such as hygroscopicity, primary aerosol size distribution, updraft velocity and usage of bext as proxy of CCN in deriving the relationship (Feingold et al., 2003). Since fog can be considered as clouds at surface, similar relationship is used to test the possibility of nucleation of more than 1.6 fog droplets cc1 from the wintertime Na (Table 1), so that the excess extinction can be justified. It must be noted here, that other values of b are also reported in the literature (e.g. b ¼ 0.7 in Pruppacher and Klett, 1997). The relationship between Na and Nd is not well-defined for polluted place like Delhi and detailed investigation in estimating the value of b is out of scope for this study. Even if we assume the lower most value of b given in Feingold et al. (2003) and put it in equation (6), Nd becomes 2.05 fog droplets cc1 as Na is 1.61 105 particles cc1 in the winter season (Table 1). This is close to Nd ¼ 1.6 fog droplets cc1, which has been calculated to explain the excess bext at 80e90% RH. As b increases with hygroscopicity (Feingold et al., 2003), b ¼ 0.12 results in Nd ¼ 4.3 fog droplets cc1, that can explain the excess bext at 95% RH in Delhi. Similar studies at other places in the region are required to fully understand the applicability of such simple parameterization in estimating the activation of fog droplets during winter season. We further examined the changes of mean seasonal visibility at mean seasonal RH in response to decrease in one particular component in the representative aerosol composition (Fig. 5). The changes of VISM are presented as an average of all seasons. Mean Na for the major aerosol components in the representative aerosol composition (Table 1) fall in regime 2 (as defined in Fig. 3) in all seasons and thus VISO is sensitive to all the components, but with varying degree. We must mention here that the visibility range for regime 2 as depicted in Fig. 3 is larger than the observed visibility and thus may look contradictory. In actuality, the visibility calculated in Fig. 3 represents the ranges as function of individual components, while the visibility range shown in Fig. 4 represents the composite aerosol. What is important to note is that Na of individual components in the seasonally representing aerosol
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VIS (in %)
10 Soot Inso Wsol Dusta Dustc
1
0.1 10
15
20
25
30 35 - M (in %)
40
45
50
Fig. 5. Changes of model-derived visibility (DVIS in %) at mean seasonal RH averaged over four seasons in response to decrease (as indicated by the negative sign) in mass concentrations of each individual components (DM in %). The error bars indicate 1s. Note that the Y-axis is in logarithmic scale for better representation of the variability among different components.
composition lie in the ranges considered as regime 2 in Fig. 3. This implies that each individual aerosol component has potential to contribute to visibility degradation. However, the overall changes depend on the relative abundance of each species. Visibility in Delhi is least sensitive to insoluble particles as shown by small changes (0.33 0.3% to 0.56 0.4%) in visibility due to reduction in mass concentration by 10e50%. It is moderately sensitive to accumulation mode and coarse mode dust particles, because visibility improves in the range 1.1 0.6% to 4.8 2% and 1.2 0.1% to 5.6 1.8% respectively in response to large reduction (10e50%) of mass of accumulation and coarse mode dust particles. Absolute mass of insoluble particles is very low (<2.5 mg m3) and Na is at the junction of regime 1 and 2. Thus its relative influence on attenuated radiation (and hence visibility) is low. Dust particles (both local and transported from Great Indian Desert) have larger (>70%) influence on columnar AOD during the pre-monsoon and monsoon seasons because of their higher relative abundance compared to the other two seasons. On an annual scale, the influence of dust particles on visibility degradation is averaged out. Since coarse mode dust is larger in size, less number of particles has higher relative influence on visibility relative to accumulation mode dust. On the other hand, reduction in mass of soot and watersoluble particles by 10e50% leads to an improvement in visibility by 2.4 0.1% to 11.3 1.6% and 4.9 2% to 29 12% respectively. Water-soluble particles have highest relative influence on VISM during the post-monsoon season, while soot particles have maximum influence in the winter season. 5. Discussion and conclusions In the present work, the role of aerosol composition on seasonal visibility in megacity Delhi has been examined. Modeled visibility matches reasonably well with the observed visibility at different RH in all seasons except winter. The excess attenuation of visible radiation in the winter, which cannot be explained by aerosols and trace gases, is caused by fog droplets. Aerosols contribute w90% to the observed visibility degradation in non-foggy condition in Delhi, while the relative influence of aerosols on VISO decreases at high RH during the winter season in presence of fog. The factors that can influence VISM, derived using seasonally representative aerosol composition, warrant discussion to interpret the results presented here. Firstly, the simulations are done with mean seasonal scale height derived by analyzing CALIPSO data, while day-to-day variations in vertical distribution of aerosols may
result in intra-seasonal variability. CALIPSO, being a spaceborne lidar, has poor temporal coverage and thus a large sampling error. Same methodology can be applied at other places, where groundbased lidar is available, to study the daily (or even hourly) changes in visibility in future. In the present study, on a seasonal time scale, VISM can be explained by the representative aerosol composition. Secondly, each component is assumed to have same vertical distribution. In India, vertical distributions of various aerosol components are not known. Hence, any uncertainty in VISM due to deviation from actual vertical profiles cannot be quantified. Thirdly, the mean seasonal aerosol composition is derived by constraining the model simulations by MISR-AOD and thus the results are biased toward clear sky condition. Relative contribution of low clouds close to surface (particularly in the monsoon season) to surface bext is only possible to quantify if cloud macrophysical (i.e. cloud fraction and cloud top and bottom altitude) and microphysical parameters are available. Fourthly, hygroscopic growth has been considered only for water-soluble particles, while recent works (e.g. Riemer et al., 2010) suggest that soot particles may act as hydrophilic particles with aging. The present study does not consider the influence of conversion of soot particles from hydrophobic to hydrophilic on attenuation of radiation and corresponding effect on visibility. The associated effect of mixing with other components is also not considered in the present study and has scope of full-scale analysis in the future. All these factors are potential sources of uncertainties in estimating bext and hence can influence model simulated visibility. Even if, for the sake of argument, bext is correctly simulated, visibility may not be correctly calculated due to the limited applicability of the Koschmieder constant (K in equation (1)). The threshold of 2% visual contrast to determine K has been argued (e.g. Horvath, 1981; Husar et al., 2000), because all assumptions in the deriving Koschmieder equation are partially fulfilled (Horvath, 1981). If K is assumed to be 1.9 (in accordance with Griffing, 1980), visibility will be reduced by two times relative to K ¼ 3.192 for same bext. This issue needs to be examined in details in future. Our sensitivity study demonstrates that visibility in Delhi can be improved a lot by decreasing mass concentration of soot and watersoluble particles. These components are emitted by anthropogenic activities and hence can be controlled through appropriate policy intervention. It is noteworthy here that decreasing the soot and/or water-soluble particles have co-benefits for three critically important issues. First, large aerosol concentration favors formation of large number of fog drops of small size, which reduces visibility more efficiently (Quan et al., 2011). High aerosol concentration has been reported by in-situ observations in Delhi during fog (Tiwari et al., 2011). This implies that even if fog forms due to strong meteorological influence in Delhi during the winter season, reduction in aerosol mass concentration will lead to formation of less number of fog droplets that may further improve the visibility in addition to the improvement due to aerosols (as shown in Fig. 5). Secondly, reduction in mass concentration of these particles will have health benefits, particularly in the post-monsoon and winter seasons when the aerosols are confined close to the surface (as supported by lower scale height and thickness of aerosol layer, Table 1). Lastly, reduction in soot concentration will reduce large aerosol-induced warming at top-of-the-atmosphere as estimated for Delhi (Srivastava et al., 2012). We have presented a thorough quantitative analysis of the influence of aerosol composition on observed visibility in megacity Delhi. However, this method can also be applied to other sites in the region to examine the spatial variability in visibility degradation. Efforts should be made in future to address the potential sources of uncertainty discussed above. Recent in-situ measurements have only found association of high aerosol concentration in foggy
A. Singh, S. Dey / Atmospheric Environment 62 (2012) 367e373
condition (Mohan and Payra, 2009). There is a critical need to quantify the influence of aerosol composition on fog droplet activation in the winter season in this region. Our results indicate that focus should be on soot and water-soluble particles for this purpose. Acknowledgments This research is supported by financial grant from IIT Delhi under contract IITD/IRD/MI00769 and from DST under Fast Track scheme (SR/FTP/ES-191/2010) operational at IITD (IITD/IRD/ RP02509). Visibility data are distributed by National Climatic Data Centre of USA. MISR and CALIPSO aerosol data are distributed by the NASA Langley Research Atmospheric Science Data Center. We acknowledge the comments by anonymous reviewers which helped us to improve the manuscript. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2012.08.048. References Bäumer, D., Vogel, B., Versick, S., Rinke, R., Mohler, O., Schinaiter, M., 2008. Relationship of visibility, aerosol optical thickness and aerosol size distribution in an ageing air mass over South-West Germany. Atmos. Environ. 42, 989e998. Bodhaine, B.A., Wood, N.B., Dutton, E.G., Slusser, J.R., 1999. On Rayleigh optical depth calculations. J. Atmos. Ocean. Tech. 16, 1854e1861. Dey, S., Di Girolamo, L., 2010. A climatology of aerosol optical and microphysical properties over the Indian subcontinent from 9 years (2000e2008) of Multiangle Imaging SpectroRadiometer (MISR) data. J. Geophys. Res. 115, D15204. http://dx.doi.org/10.1029/2009JD013395. Dey, S., Tripathi, S.N., 2007. Estimation of aerosol optical properties and radiative effects in the Ganga Basin, northern India during the winter time. J. Geophys. Res. 112, D03203. http://dx.doi.org/10.1029/2006JD007267. Dey, S., Tripathi, S.N., 2008. Aerosol radiative effects over Kanpur, Indo-Gangetic basin, northern India: long-term (2001e2005) observations and implications to regional climate. J. Geophys. Res. 113, D04212. http://dx.doi.org/10.1029/ 2007JD009029. De, U.S., Dandekar, M.M., 2001. Natural disasters in urban areas. Dec. Geogrph. 39 (2), 1e2. De, U.S., Dube, R.K., Rao, G.S.P., 2005. Extreme weather events over India in the last 100 years. J. Ind. Geophys. Union. 9 (3), 173e187. Feingold, G., Eberhard, W.L., Veron, D.E., Previdi, M., 2003. First measurements of the Twomey indirect effect using ground-based remote sensors. Geophys. Res. Lett. 30 (6), 1287. http://dx.doi.org/10.1029/2002GL016633. Goyal, P., Sidhartha, 2003. Present scenario of air quality in Delhi: a case study of CNG implementation. Atmos. Environ. 37, 5423e5431. Griffing, G.W., 1980. Relations between the prevailing visibility, nephlometer scattering coefficient and sunphotometer turbidity coefficient. Atmos. Environ. 14, 577e584.
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Groblicki, J.P., Wolff, G.T., Countess, R.J., 1981. Visibility-reducing species in the Denver “ Brown Cloud”dI. Relationship between extinction and chemical composition. Atmos. Environ. 15 (12), 2473e2843. Hayasaka, T., Satake, S., Shimizu, A., Sugimoto, N., Matsui, I., Aoki, K., Muraji, Y., 2007. Vertical distribution and optical properties of aerosols observed over Japan during the atmospheric Brown clouds-East Asia Regional Experiment 2005. J. Geophys. Res. 112, D22S35. http://dx.doi.org/10.1029/2006JD008086. Hess, M., Koepke, P., Schult, I., 1998. Optical properties of aerosols and clouds: the software package OPAC. Bull. Amer. Meteor. Soc. 79 (5), 831e842. Horvath, H., 1995. Estimation of the average visibility in central Europe. Atmos. Environ. 29 (2), 241e246. Horvath, H., 1981. Atmospheric visibility. Atmos. Environ. 15 (10), 1785e1796. Husari, R.B., Husar, J.D., Martin, L., 2000. Distribution of continental surface aerosol extinction based on visual range data. Atmos. Environ. 34, 5067e5078. Jaidevi, J., Tripathi, S.N., gupta, T., Singh, B.N., Gopalakrishnan, V., Dey, S., 2011. Observation-based 3-D view of aerosol radiative properties over Indian Continental Tropical Convergence Zone: implications to regional climate. Tellus B 63, 971e989. Koepke, P., Hess, M., Schult, I., Shettle, E.P., 1997. Global Aerosol Data Set, MPI Meteorologie Hamburg Report No. 243, 44 pp. Koschmieder, H., 1924. Theorie der horizontalen Sichtweite. Beitr. Phys. Freien. Atmos. 12, 33e55. Mohan, M., Payra, S., 2009. Influence of aerosol spectrum and air pollution on fog formation in urban environment of megacity Delhi, India. Environ. Monit. Assess. 151, 265e277. Pandithurai, G., Dipu, S., Dani, K.K., Tiwari, S., Bisht, D.S., Devara, P.C.S., Pinker, R.T., 2008. Aerosol radiative forcing during dust events over New Delhi, India. J. Geophys. Res. 113, D13209. http://dx.doi.org/10.1029/2008JD009804. Pruppacher, H.R., Klett, J.D., 1997. Microphysics of Clouds and Precipitation. Kluwer Acad., Norwell, Mass, 954 pp. Quan, J., Zhang, Q., He, H., Liu, J., Huang, M., Jiu, H., 2011. Analysis of the formation of fog and haze in North China Plain (NCP). Atmos. Chem. Phys. 11, 8205e8214. Riemer, N., West, M., Zaveri, R.A., Easter, R.C., 2010. Estimating black carbon aging time scales with a particle-resolved aerosol model. J. Aerosol Sci. 41, 143e158. Satheesh, S.K., Srinivasan, J., 2005. A method to estimate aerosol radiative forcing from spectral optical depths. J. Atmos. Sci. 63, 1082e1092. Singh, S., Nath, S., Kohli, R., Singh, R., 2005. Aerosol over Delhi during pre-monsoon months: characteristics and effect on surface radiative forcing. Geophys. Res. Lett. 32, L13808. http://dx.doi.org/10.1029/2005GL023062. Srivastava, A., Singh, S., Tiwari, S., Bisht, D.S., 2012. Contribution of anthropogenic aerosols in direct radiative forcing and atmospheric heating rate over Delhi in the Indo-Gangetic basin. Environ. Sci. Pollut. Res. 19, 1144e1158. Tiwari, S., Payra, S., Mohan, M., Verma, S., Bisht, D.S., 2011. Visibility degradation during foggy period due to anthropogenic urban aerosol at Delhi, India. Atmos. Pollut. Res. 2, 116e120. Wang, K., Dickinson, R.E., Liang, S., 2009. Clear sky visibility has decreased over land globally from 1973 to 2007. Science 323 (5920), 1468e1470. Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C.N., Dutton, E.G., Forganm, B., Kallis, A., Russak, V., Tsvetkov, A., 2005. From dimming to brightening: decadal changes in solar radiation at earth’s surface. Science 308, 847e850. WMO, Resolution 40 (Cg-XII), 1996. Exchanging Meteorological Data: Guidelines on Relationships in Commercial Meteorological Activities: WMO Policy and Practice. WMO, Geneva. Yu, H., Chin, M., Winker, D.M., Omar, A.H., Liu, Z., Kittaka, C., Diehl, T., 2010. Global view of aerosol vertical distributions from CALIPSO lidar measurements and GOCART simulations: regional and seasonal variations. J. Geophys. Res. 115, D00H30. http://dx.doi.org/10.1029/2009JD013364.
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Influence of aerosol composition on visibility in megacity Delhi Ajit Singh, Sagnik Dey* Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
h i g h l i g h t s < Role of aerosols on visibility degradation in megacity Delhi is examined. < Sensitivity of visibility to individual aerosol species is examined. < Seasonally representative aerosol composition is derived. < The quantitative estimates can help addressing the visibility problem in this region.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 May 2012 Received in revised form 19 July 2012 Accepted 19 August 2012
Influence of aerosol composition on visibility degradation in megacity Delhi has been quantified using model-derived seasonal mean aerosol composition constrained by satellite-measured columnar aerosol optical depth spectrum. Aerosols contribute w90% to the observed visibility degradation in non-foggy condition, while its relative contribution decreases rapidly at RH 80% due to stronger relative influence of fog droplets on attenuation of radiation, especially during the winter season. Visibility is most sensitive to water-soluble particles and soot in all seasons. Sensitivity study shows that visibility does not respond strongly to reduction of mass concentration of insoluble, accumulation mode and coarse mode dust particles. Reduction of mass concentration of soot and water-soluble particles in the range of 10%e50% will lead to an increase in visibility by 2.4 0.1%e11.3 1.6% and 4.9 2%e29 12% respectively. Reduction of the last two anthropogenic components has co-benefits, as it may reduce fog formation and thus further enhance the visibility along with an improved air quality in terms of associated health and climatic effects. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Aerosol composition Visibility Sensitivity
1. Introduction Visibility is a critically important parameter, because low visibility can disrupt the traffic movement impacting business, public safety and tourism industry. Theoretically, visibility is inversely proportional to extinction coefficient, bext (Koschmieder, 1924):
VIS ¼ K=bext ;
(1)
where K, Koschmieder constant, is equal to 3.912 assuming a 2% contrast threshold to visually detect an object against the horizon sky and bext is the total attenuation of visible radiation due to scattering and absorption by gas molecules, aerosols and other components (e.g. fog and cloud droplets) in the atmosphere. Visibility may vary within a wide range (from few meters to few
* Corresponding author. E-mail address: [email protected] (S. Dey). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.08.048
hundred km) (Horvath, 1995) depending on the concentration of gaseous pollutants and aerosols and their optical and microphysical properties (Bäumer et al., 2008). Hence, visibility may also be utilized as a proxy for concentration of aerosols and trace gases. Many studies exist in the literature that connect visibility with aerosol properties (e.g. Bäumer et al., 2008 and the references therein), but very few studies were carried out in the Indian subcontinent, where the visibility has reduced rapidly over the last 30 years period (Wang et al., 2009) with an increase in number of low visibility days (De and Dandekar, 2001). This rapid decrease in visibility has been attributed to an increase in aerosol concentration due to rise in anthropogenic emission, which also leads to a reduction in surface reaching solar radiation (Wild et al., 2005). The problem is more critical in the winter season, particularly in the Indo-Gangetic Basin (IGB), where favorable meteorological conditions aided by high aerosol concentration (Tiwari et al., 2011) lead to frequent fog formation resulting in a very low visibility (De et al., 2005). However, since attenuation of visible radiation depends strongly on aerosol composition, the changes in visibility in
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response to the changes in various aerosol components and their mixture need to be quantified. In the present study, we focus on the megacity Delhi (28.38 N, 77.12 E), home to w14 million population. Delhi is one of the most polluted cities in the world (Goyal and Sidhartha, 2003), where the major sources of pollutants are vehicular emission and fossil-fuel burning. Besides these two sources, biomass burning in the nearby regions and dust transported from the Great Indian Desert (Singh et al., 2005) and nearby regions also have contributions on the aerosol characteristics in Delhi. The influences of air pollutants (both gaseous and aerosols) on visibility in Delhi have previously been assessed rather qualitatively and empirically (e.g. Goyal and Sidhartha, 2003; Tiwari et al., 2011) and most of the studies focused only on the winter season characterized by frequent fog (Tiwari et al., 2011; Mohan and Payra, 2009). It must be noted that the aerosol and gaseous pollutant concentrations remain high throughout the year in Delhi (Srivastava et al., 2012), yet the role of aerosol composition in visibility degradation has not been fully understood. Here, we examine and present the sensitivity of the visibility to seasonally representative aerosol composition observed in the megacity Delhi and quantify the influences of aerosol composition on visibility degradation as function of relative humidity (RH) using an aerosol microphysical model based on Mie theory constrained by observations. These results may help in formulating appropriate mitigation measures in dealing with the visibility problem in this region. 2. Data analysis Visibility is estimated (in km) by measuring the attenuation of light due to the atmosphere and theoretically represents the farthest distance any human observer can see through. The daily horizontal visibility data used in this study were measured at Safdarjung Airport, Delhi by automatic weather station that has a visibility sensor. Daily mean visibility along with eighteen other meteorological parameters are archived as Global Summary of Day database distributed by National Climatic Data Centre, USA (http:// www7.ncdc.noaa.gov/CDO/cdo) for 30 years (1980e2009) period (Wang et al., 2009). The data archive is maintained as part of data exchange under the World Meteorological Organization World Weather Watch Program according to WMO Resolution (WMO, 1996). Temperature and dew point temperature reported along with visibility were utilized to derive the RH of each day of observation. For the trend analysis, 30 years of data were used. However, the columnar aerosol optical depth (AOD) over Delhi is available for the period of 2000e2009 and hence visibility data during this period were only used to constrain the model simulations to examine the sensitivity of visibility to aerosol composition. Visibility data of first 20 years are only used for trend analysis. The analysis has been carried out for four seasons, winter (Dece Feb), pre-monsoon (MareJun), monsoon (JuleSep) and postmonsoon (OcteNov). As mentioned before, other than aerosols, visibility also depends on Rayleigh scattering and gaseous absorption. Columnar Rayleigh extinction coefficient at 550 nm wavelength has been calculated following Bodhaine et al. (1999). In brief, the scattering coefficient can be expressed as the product of total Rayleigh cross-section per molecule (s) and molecular number density Ns, where s can be written as:
2 24p3 n2s 1 6 þ 3r s¼ 4 : 2 6 7r l Ns2 n2s þ 2
(2)
Here l is the wavelength, r is the depolarization factor and ns is the refraction index for standard air at l, which can be calculated as:
ðn300 1Þ 108 ¼ 8060:51 þ
2480990 2
132:274 l 17455:7 þ : 2 32:32957 l
(3)
n300 in equation (3) is the refraction index at 300 ppm CO2 concentration, which was corrected for present-day CO2 concentration of 390 ppm using:
ðns 1Þ ¼ 1 þ 0:54ðCO2 0:0003Þ: ðn300 1Þ
(4)
Contribution of gaseous absorption on total bext has been estimated following Groblicki et al. (1981), where NO2 concentration over Delhi is taken from the measurements by Central Pollution Control Board (www.cpcb.nic.in). Total bext at surface has been calculated from measured visibility using Koschmieder equation and extinction coefficient due to only aerosols has been derived by subtracting the Rayleigh scattering and absorption coefficient from total bext at surface. Finally, mean observed visibility due to only aerosols (hereafter referred to as VISO) has been calculated for each season from the 10 years of daily data as a function of RH. This is carried out by averaging data of all days with observed RH within 50 2%, 70 2%, 80 2%, 90 2% and 95 2% within each season (the number of days are shown in Table 1). These RH ranges are considered because the model can simulate aerosol optical properties at these specific RH. A 2% RH range is considered to allow sufficient number of days for robust estimation of mean seasonal VISO in assessing model simulated visibility (hereafter denoted as VISM) with minimum error in computed bext due to hygroscopicity of aerosols. Rest of the days have RH other than these ranges and hence are not considered for the closure study. 90% of the observed visibility can be explained by aerosols, while w10% contribution is attributed to gaseous pollutants in non-foggy condition.
Table 1 Mean seasonal Na for individual aerosol components and total (in # cc1), thickness of aerosol layer (DZ) and scale height (H) as considered in OPAC for calculation of visibility. ‘Dusta’ and ‘Dustc’ represent accumulation and coarse mode mineral dust respectively. # of days with RH in the given ranges within each season are considered for closure studies.
Insoluble Water-soluble Soot Dusta Dustc Total Na DZ (in km) H (in km) # Days with RH 50 2% # Days with RH 70 2% # Days with RH 80 2% # Days with RH 90 2% # Days with RH 95 2%
Winter
Pre-monsoon
Monsoon
Post-monsoon
0.8 26,000 135,000 9.4 0.6 1.61 105 2.2 0.79 41
1.1 15,000 86,000 10.3 0.98 1.01 105 4.95 1.7 72
0.7 16,700 80,800 9.4 0.6 0.97 105 4.18 1.9 20
0.2 25,000 115,000 8.5 0.6 1.4 105 3.2 1.4 22
90
60
78
72
43
49
54
30
20
21
7
12
4
5
2
4
A. Singh, S. Dey / Atmospheric Environment 62 (2012) 367e373
3. Model description OPAC (Optical Properties of Aerosol and Clouds, Hess et al., 1998) model has been used for examining the impacts of aerosol composition on visibility. OPAC utilizes the microphysical properties of various aerosol components from Global Aerosol Data set (Koepke et al., 1997) to calculate composite columnar aerosol spectral optical properties following Mie theory assuming an external mixing state. In total, ten individual aerosol components are available and they are ‘insoluble’, ‘water-soluble’, ‘soot’, ‘sea-salt in accumulation mode’, ‘sea-salt in coarse mode’, ‘mineral dust in nuclei mode’, ‘mineral dust in accumulation mode’, ‘mineral dust in coarse mode’, ‘transported mineral dust’ and ‘stratospheric sulfate’. Several standard aerosol compositions have been pre-defined in OPAC (Hess et al., 1998). For example, ‘urban’ composition includes water-soluble scattering aerosols (formed as gas-to-particle conversion from industrial emissions), insoluble and soot (emitted from biomass and bio-fuel emissions), while ‘continental polluted’ composition has same components with a lower concentration of soot relative to ‘urban’ composition. It further allows formulation of a new aerosol composition to be defined by choosing maximum of five individual aerosol components. OPAC model has been used many times to derive aerosol properties in the Indian Subcontinent (e.g. Dey and Tripathi, 2008, 2007), including Delhi (Pandithurai et al., 2008; Srivastava et al., 2012). Here, the aerosol composition is considered to be composed of ‘insoluble’, ‘water-soluble’, ‘soot’, ‘accumulation mode dust’ and ‘coarse mode dust’ (i.e. a combination of ‘urban’ and ‘dust’) based on the previous studies on aerosol characteristics in Delhi (e.g. Singh et al., 2005; Srivastava et al., 2012). Two types of sensitivity studies have been carried out. First column-integrated bext (i.e. AOD) has been calculated as a function of varying number concentrations (Na) of each of these components individually, which has been converted to bext at surface using the mean seasonal aerosol scale height (H). CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization, onboard CALIPSO satellite) version 3.1 level 2 data for the period Jul 2006 to Dec 2010 (CALIOP retrieval started in Jul 2006) have been analyzed for aerosol vertical distribution over Delhi. H is defined as the height above ground level below which 63% (i.e. the height up to which bext reduces by a factor of e) of total columnar extinction (i.e. AOD) is present (Hayasaka et al., 2007):
ZH bext dz ¼
1 e1 AOD ¼ 0:63 AOD:
(5)
0
Although the scale height does not represent the detailed vertical structure, it is a good approximation of the aerosol vertical distribution on regional and global scales, particularly for satellite retrievals, model simulations, and modelemodel and modele satellite inter-comparison (Yu et al., 2010). Since most of the profiles do not show exponential decay of bext with altitude, this parameterized equation has been used to estimate H. Mean seasonal H and thickness of the aerosol layer (DZ) are used to convert columnar bext (computed from the seasonally representative aerosol composition) into surface bext. VISM has then been calculated using Koschmieder equation and sensitivity of visibility to changing Na of these components has been examined. Watersoluble particles are hygroscopic in nature and hence the change in visibility depends on the ambient RH, while for others, hygroscopic growth has been neglected (Srivastava et al., 2012). Since VISO depends on composite aerosol properties, aerosol composition representative of each season has been derived by fixing Na of each individual component through iteration following
369
Dey and Tripathi (2008). Na of each of the five aerosol species are varied until simulated AOD at four wavelengths closest to Multiangle Imaging SpectroRadiometer, MISR channels of 446, 558, 675 and 867 nm wavelengths match with MISR-AOD (Supplementary qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P Fig. S1). c2 (defined as 1=4 4i ¼ 1 ðAODiMISR AODiModel Þ2 ; where AODMISR and AODModel represent MISR-retrieved and modelderived AOD at four wavelengths, i) values are 0.017, 0.023, 0.011 and 0.024 for the winter, pre-monsoon, monsoon and postmonsoon seasons respectively. These values are smaller than the error in MISR-retrieved AOD and thus highlight the robustness of the method (Satheesh and Srinivasan, 2005) to estimate the mean seasonal aerosol composition for visibility closure study. Na of ‘insoluble’ and ‘water-soluble’ components are not allowed to reduce below Na of clean continental composition, while that of ‘soot’ is not allowed to reduce below Na of continental average composition during the iteration. Although, dust is not a common urban aerosol component, it is ubiquitously found in Delhi (e.g. Pandithurai et al., 2008; Srivastava et al., 2012) and their Na are varied from zero with an increment of 0.01 particles cc1. MISR data were considered because of availability of continuous measurements during the study period and the data have been extensively validated in the Indian region (Dey and Di Girolamo, 2010). In accordance with the previous studies (e.g. Singh et al., 2005; Srivastava et al., 2012) coarse mode dust increases during the premonsoon season, while water-soluble and soot concentrations are higher in the post-monsoon and winter seasons, when aerosols are confined below 3 km of the atmosphere with a smaller scale height (0.79 and 1.4 km respectively) relative to the other seasons. Columnar bext are converted to surface bext for each season using the scale height (Table 1) and visibility is calculated from surface bext using Koschmieder equation (1). In the second sensitivity experiment, mass concentration of one component has been reduced at 10% interval keeping the mass concentrations of other components fixed and response of VISM to this change has been examined. It must be noted that the aerosol retrieval (either by satellite or ground-based radiometer) is not possible under ‘cloudy-sky’ condition and hence VISM may be biased toward ‘clear-sky’ condition. 4. Results Visibility in Delhi decreased rapidly at a rate of 0.11 km year1 during 1980e2000 as shown in Fig. 1, but was found to be stabilized after the year 2000. A major air quality policy of transforming the regional transport sector from diesel/petrol to CNG has been implemented during the year 2001 (Goyal and Sidhartha, 2003) and this policy intervention may contribute to this stabilization of rapid decrease in visibility in the recent years. Since aerosol data
Fig. 1. Annual visibility trend over Delhi during last 30 years (1980e2009) period. Major air quality related policy of converting public transport sector from diesel/petrol to CNG has been implemented in the year 2001 (marked by the bold solid line).
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Fig. 2. Variability of mean monthly (1s) observed visibility in Delhi along with RH during the period 2000e2009. Correlation coefficient (R) between visibility and RH, number of samples (N) and the statistical significance following t-test are also provided.
are not available prior to the year 2000, any further attribution of this rapid decrease to pollutant concentrations cannot be quantified. Rather we focused on the last ten years (2000e2009) when visibility shows fluctuating trend. Mean (1 standard deviation, s) monthly observed visibility in Delhi during this period is shown in Fig. 2. As expected, visibility is lowest (1.13 0.4 km) during Dece Jan when fog is prevalent. It increases to 2.29 0.4 km in the month of April followed by a reduction to 2.03 0.35 km during the summer months. Although inter-annual variability exists, in general visibility is lowest in the winter, followed by postmonsoon, monsoon and pre-monsoon seasons. During winter, strong hygroscopic growth of particles at high RH (Dey and Tripathi, 2007) leads to large reduction in visibility due to enhanced scattering of radiation and thus VISO and RH are in opposite phase. The changes of visibility in response to an increase in Na of various individual aerosol components as simulated by OPAC are
shown in Fig. 3. The changes of VISM for hygroscopic water-soluble particles are shown at 50%, 70% and 80% RH, because maximum change in bext occurs in this range of RH (Dey and Tripathi, 2007). The figure clearly shows three regimes. Insignificant sensitivity of VISM in response to an increase in Na at low Na and high Na are defined as regime 1 and 3. In contrast, regime 2 shows a significant change in visibility with an increase in Na. It is noteworthy that the ranges of Na in the three regimes for various individual compositions are different. For example, for mineral dust (accumulation mode) regime 1 extends up to Na < 101 particles cc1, and regime 3 extends beyond Na > 103 particles cc1 (>5.54 103 mg m3 mass). Visibility is most sensitive to coarse mode mineral dust in the range 102 < Na < 101 particles cc1, while for insoluble and soot particles, regime 2 spans in the range 101 < Na < 102 and 101 < Na < 106 particles cc1 respectively. These differences are attributed to the size distributions of the individual components
Fig. 3. Changes of model-derived visibility (in km) with an increase in number concentrations of various aerosol components (in # cc1). Water soluble components are considered as hygroscopic and their influences on visibility depend on RH.
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(Hess et al., 1998). Visibility is affected only if the number concentration is large enough to influence attenuation of visible radiation significantly. Visibility shows significant decrease with an increase in Na in the range 101e105 particles cc1 for water soluble particles with higher rate of decrease at higher RH due to enhanced scattering of radiation by larger effective scattering cross-section area at higher RH (Dey and Tripathi, 2007). This implies that similar mass concentration of water-soluble particles may lead to lower visibility at higher RH. It must also be noted that this figure should be interpreted as the sensitivity of visibility to individual aerosol species and the influence of composite aerosol composition on visibility degradation in reality may be different depending on the relative proportions of Na of various individual components. However, the importance of this sensitivity experiment lies in the different regimes identified for the individual components. For example, if soot particle Na is less than 103 particles cc1, any further change in Na will not influence the visibility significantly. Furthermore, same amount of changes in Na of various individual components will not lead to same amount of changes in visibility due to different mass concentration. To quantify this statement, we derived seasonally representative aerosol composition by constraining the OPAC model with MISR-retrieved AOD spectrum as described earlier. Mean VISM at 50%, 70%, 80%, 90% and 95% RH are compared with VISO (Fig. 4). In general, the model captures the RH-dependence of VISO reasonably well in the pre-monsoon and post-monsoon seasons because all the points lie very close to 1:1 line. This suggests that the observed visibility degradation can be explained by gaseous and particulate air pollutants using the seasonal aerosol composition derived by the model despite uncertainties in MISRAOD and aerosol scale height. VISM is underestimated by 11e20% relative to VISO during the monsoon season, which may result from a combination of factors, such as the uncertainty in the model due to large variability in seasonal aerosol vertical distribution and MISR-retrieved AOD (Dey and Di Girolamo, 2010), limited applicability of Koschmieder’s equation (Horvath, 1981), influence of low clouds on surface bext that has not been taken into account in the simulation. Both VISM and VISO are estimated using the same Koschmieder constant (i.e. 3.192), hence any difference in VISM and VISO is due to difference in surface bext. Mean seasonal scale height derived by the parameterized equation (5) was used to convert columnar bext into surface bext. If elevated aerosols layers are present, which are common in the monsoon season in this region (Jaidevi et al., 2011), aerosol scale height should also increase.
Fig. 4. Comparison between mean seasonal visibility as derived by model (VISM) and from observations without the influence of gaseous pollutants (VISO) at five RH regimes during winter (open circle), pre-monsoon (filled square), monsoon (open star) and post-monsoon (filled triangle) seasons. Smallest size of each symbol represents 50% RH followed by 70%, 80%, 90% and 95% RH in the ascending orders of size.
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Sensitivity study has been carried out to examine the influence of scale height on VISM for the pre-monsoon aerosol composition. We noticed that VISM increases (decreases) by w20% for an increase (decrease) in scale height by 10% for same columnar AOD. This implies that the underestimation of VISM during the monsoon season in the closure study (Fig. 4) may be explained by uncertainty in scale height. VISM shows reasonable agreement with VISO at 50% and 70% RH in the winter season (Fig. 4). However, notable differences (>20%) are observed at RH between 80% and 95% and the difference in VISM and VISO increases with increase in RH (Fig. 4). During winter, Delhi experiences fog and fog formation is facilitated at high RH. Fog droplets contribute to additional attenuation of visible radiation. In this season, the relative influence of aerosols on visibility degradation reduces with an increase in RH above 70% during the winter season and varies from 78% at 80% RH to 40% at 95% RH. Larger difference at higher RH is understandable because more fog droplets will form at higher RH relative to lower RH (Quan et al., 2011). Based on the comparison between model simulated and observed surface bext, excess bext are 0.8, 0.9 and 2.4 km1 respectively for 80%, 90% and 95% RH. At 80e90% RH, 1.6 fog droplets cc1 and at 95% RH, 4.3 fog droplets cc1 can explain this excess bext based on the calculation using the fog microphysical data provided in the OPAC database. A nonlinear relationship exists between Na and number concentration of cloud droplets (Nd) that may nucleate from Na:
Nd ¼ Nab ;
(6)
where, b varies widely in the range 0.06e0.48 depending on numerous factors such as hygroscopicity, primary aerosol size distribution, updraft velocity and usage of bext as proxy of CCN in deriving the relationship (Feingold et al., 2003). Since fog can be considered as clouds at surface, similar relationship is used to test the possibility of nucleation of more than 1.6 fog droplets cc1 from the wintertime Na (Table 1), so that the excess extinction can be justified. It must be noted here, that other values of b are also reported in the literature (e.g. b ¼ 0.7 in Pruppacher and Klett, 1997). The relationship between Na and Nd is not well-defined for polluted place like Delhi and detailed investigation in estimating the value of b is out of scope for this study. Even if we assume the lower most value of b given in Feingold et al. (2003) and put it in equation (6), Nd becomes 2.05 fog droplets cc1 as Na is 1.61 105 particles cc1 in the winter season (Table 1). This is close to Nd ¼ 1.6 fog droplets cc1, which has been calculated to explain the excess bext at 80e90% RH. As b increases with hygroscopicity (Feingold et al., 2003), b ¼ 0.12 results in Nd ¼ 4.3 fog droplets cc1, that can explain the excess bext at 95% RH in Delhi. Similar studies at other places in the region are required to fully understand the applicability of such simple parameterization in estimating the activation of fog droplets during winter season. We further examined the changes of mean seasonal visibility at mean seasonal RH in response to decrease in one particular component in the representative aerosol composition (Fig. 5). The changes of VISM are presented as an average of all seasons. Mean Na for the major aerosol components in the representative aerosol composition (Table 1) fall in regime 2 (as defined in Fig. 3) in all seasons and thus VISO is sensitive to all the components, but with varying degree. We must mention here that the visibility range for regime 2 as depicted in Fig. 3 is larger than the observed visibility and thus may look contradictory. In actuality, the visibility calculated in Fig. 3 represents the ranges as function of individual components, while the visibility range shown in Fig. 4 represents the composite aerosol. What is important to note is that Na of individual components in the seasonally representing aerosol
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VIS (in %)
10 Soot Inso Wsol Dusta Dustc
1
0.1 10
15
20
25
30 35 - M (in %)
40
45
50
Fig. 5. Changes of model-derived visibility (DVIS in %) at mean seasonal RH averaged over four seasons in response to decrease (as indicated by the negative sign) in mass concentrations of each individual components (DM in %). The error bars indicate 1s. Note that the Y-axis is in logarithmic scale for better representation of the variability among different components.
composition lie in the ranges considered as regime 2 in Fig. 3. This implies that each individual aerosol component has potential to contribute to visibility degradation. However, the overall changes depend on the relative abundance of each species. Visibility in Delhi is least sensitive to insoluble particles as shown by small changes (0.33 0.3% to 0.56 0.4%) in visibility due to reduction in mass concentration by 10e50%. It is moderately sensitive to accumulation mode and coarse mode dust particles, because visibility improves in the range 1.1 0.6% to 4.8 2% and 1.2 0.1% to 5.6 1.8% respectively in response to large reduction (10e50%) of mass of accumulation and coarse mode dust particles. Absolute mass of insoluble particles is very low (<2.5 mg m3) and Na is at the junction of regime 1 and 2. Thus its relative influence on attenuated radiation (and hence visibility) is low. Dust particles (both local and transported from Great Indian Desert) have larger (>70%) influence on columnar AOD during the pre-monsoon and monsoon seasons because of their higher relative abundance compared to the other two seasons. On an annual scale, the influence of dust particles on visibility degradation is averaged out. Since coarse mode dust is larger in size, less number of particles has higher relative influence on visibility relative to accumulation mode dust. On the other hand, reduction in mass of soot and watersoluble particles by 10e50% leads to an improvement in visibility by 2.4 0.1% to 11.3 1.6% and 4.9 2% to 29 12% respectively. Water-soluble particles have highest relative influence on VISM during the post-monsoon season, while soot particles have maximum influence in the winter season. 5. Discussion and conclusions In the present work, the role of aerosol composition on seasonal visibility in megacity Delhi has been examined. Modeled visibility matches reasonably well with the observed visibility at different RH in all seasons except winter. The excess attenuation of visible radiation in the winter, which cannot be explained by aerosols and trace gases, is caused by fog droplets. Aerosols contribute w90% to the observed visibility degradation in non-foggy condition in Delhi, while the relative influence of aerosols on VISO decreases at high RH during the winter season in presence of fog. The factors that can influence VISM, derived using seasonally representative aerosol composition, warrant discussion to interpret the results presented here. Firstly, the simulations are done with mean seasonal scale height derived by analyzing CALIPSO data, while day-to-day variations in vertical distribution of aerosols may
result in intra-seasonal variability. CALIPSO, being a spaceborne lidar, has poor temporal coverage and thus a large sampling error. Same methodology can be applied at other places, where groundbased lidar is available, to study the daily (or even hourly) changes in visibility in future. In the present study, on a seasonal time scale, VISM can be explained by the representative aerosol composition. Secondly, each component is assumed to have same vertical distribution. In India, vertical distributions of various aerosol components are not known. Hence, any uncertainty in VISM due to deviation from actual vertical profiles cannot be quantified. Thirdly, the mean seasonal aerosol composition is derived by constraining the model simulations by MISR-AOD and thus the results are biased toward clear sky condition. Relative contribution of low clouds close to surface (particularly in the monsoon season) to surface bext is only possible to quantify if cloud macrophysical (i.e. cloud fraction and cloud top and bottom altitude) and microphysical parameters are available. Fourthly, hygroscopic growth has been considered only for water-soluble particles, while recent works (e.g. Riemer et al., 2010) suggest that soot particles may act as hydrophilic particles with aging. The present study does not consider the influence of conversion of soot particles from hydrophobic to hydrophilic on attenuation of radiation and corresponding effect on visibility. The associated effect of mixing with other components is also not considered in the present study and has scope of full-scale analysis in the future. All these factors are potential sources of uncertainties in estimating bext and hence can influence model simulated visibility. Even if, for the sake of argument, bext is correctly simulated, visibility may not be correctly calculated due to the limited applicability of the Koschmieder constant (K in equation (1)). The threshold of 2% visual contrast to determine K has been argued (e.g. Horvath, 1981; Husar et al., 2000), because all assumptions in the deriving Koschmieder equation are partially fulfilled (Horvath, 1981). If K is assumed to be 1.9 (in accordance with Griffing, 1980), visibility will be reduced by two times relative to K ¼ 3.192 for same bext. This issue needs to be examined in details in future. Our sensitivity study demonstrates that visibility in Delhi can be improved a lot by decreasing mass concentration of soot and watersoluble particles. These components are emitted by anthropogenic activities and hence can be controlled through appropriate policy intervention. It is noteworthy here that decreasing the soot and/or water-soluble particles have co-benefits for three critically important issues. First, large aerosol concentration favors formation of large number of fog drops of small size, which reduces visibility more efficiently (Quan et al., 2011). High aerosol concentration has been reported by in-situ observations in Delhi during fog (Tiwari et al., 2011). This implies that even if fog forms due to strong meteorological influence in Delhi during the winter season, reduction in aerosol mass concentration will lead to formation of less number of fog droplets that may further improve the visibility in addition to the improvement due to aerosols (as shown in Fig. 5). Secondly, reduction in mass concentration of these particles will have health benefits, particularly in the post-monsoon and winter seasons when the aerosols are confined close to the surface (as supported by lower scale height and thickness of aerosol layer, Table 1). Lastly, reduction in soot concentration will reduce large aerosol-induced warming at top-of-the-atmosphere as estimated for Delhi (Srivastava et al., 2012). We have presented a thorough quantitative analysis of the influence of aerosol composition on observed visibility in megacity Delhi. However, this method can also be applied to other sites in the region to examine the spatial variability in visibility degradation. Efforts should be made in future to address the potential sources of uncertainty discussed above. Recent in-situ measurements have only found association of high aerosol concentration in foggy
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condition (Mohan and Payra, 2009). There is a critical need to quantify the influence of aerosol composition on fog droplet activation in the winter season in this region. Our results indicate that focus should be on soot and water-soluble particles for this purpose. Acknowledgments This research is supported by financial grant from IIT Delhi under contract IITD/IRD/MI00769 and from DST under Fast Track scheme (SR/FTP/ES-191/2010) operational at IITD (IITD/IRD/ RP02509). Visibility data are distributed by National Climatic Data Centre of USA. MISR and CALIPSO aerosol data are distributed by the NASA Langley Research Atmospheric Science Data Center. We acknowledge the comments by anonymous reviewers which helped us to improve the manuscript. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2012.08.048. References Bäumer, D., Vogel, B., Versick, S., Rinke, R., Mohler, O., Schinaiter, M., 2008. Relationship of visibility, aerosol optical thickness and aerosol size distribution in an ageing air mass over South-West Germany. Atmos. Environ. 42, 989e998. Bodhaine, B.A., Wood, N.B., Dutton, E.G., Slusser, J.R., 1999. On Rayleigh optical depth calculations. J. Atmos. Ocean. Tech. 16, 1854e1861. Dey, S., Di Girolamo, L., 2010. A climatology of aerosol optical and microphysical properties over the Indian subcontinent from 9 years (2000e2008) of Multiangle Imaging SpectroRadiometer (MISR) data. J. Geophys. Res. 115, D15204. http://dx.doi.org/10.1029/2009JD013395. Dey, S., Tripathi, S.N., 2007. Estimation of aerosol optical properties and radiative effects in the Ganga Basin, northern India during the winter time. J. Geophys. Res. 112, D03203. http://dx.doi.org/10.1029/2006JD007267. Dey, S., Tripathi, S.N., 2008. Aerosol radiative effects over Kanpur, Indo-Gangetic basin, northern India: long-term (2001e2005) observations and implications to regional climate. J. Geophys. Res. 113, D04212. http://dx.doi.org/10.1029/ 2007JD009029. De, U.S., Dandekar, M.M., 2001. Natural disasters in urban areas. Dec. Geogrph. 39 (2), 1e2. De, U.S., Dube, R.K., Rao, G.S.P., 2005. Extreme weather events over India in the last 100 years. J. Ind. Geophys. Union. 9 (3), 173e187. Feingold, G., Eberhard, W.L., Veron, D.E., Previdi, M., 2003. First measurements of the Twomey indirect effect using ground-based remote sensors. Geophys. Res. Lett. 30 (6), 1287. http://dx.doi.org/10.1029/2002GL016633. Goyal, P., Sidhartha, 2003. Present scenario of air quality in Delhi: a case study of CNG implementation. Atmos. Environ. 37, 5423e5431. Griffing, G.W., 1980. Relations between the prevailing visibility, nephlometer scattering coefficient and sunphotometer turbidity coefficient. Atmos. Environ. 14, 577e584.
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