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Correlation of aerosol mass near the ground with aerosol optical depth during two seasons in Munich
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Atmospheric Environment 42 (2008) 4036–4046 www.elsevier.com/locate/atmosenv
Correlation of aerosol mass near the ground with aerosol optical depth during two seasons in Munich Klaus Scha¨fera,, Andreas Harbuscha, Stefan Emeisa, Peter Koepkeb, Matthias Wiegnerb a
Forschungszentrum Karlsruhe, Institute for Meteorology and Climate Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany b Ludwig-Maximilians-Universitaet of Munich, Meteorological Institute (MIM), Theresienstr. 37, 80333 Mu¨nchen, Germany Received 22 August 2007; received in revised form 15 January 2008; accepted 25 January 2008
Abstract Relations of the aerosol optical depth (AOD) with aerosol mass concentration near the ground, particulate matter (PM), have been studied on the basis of measurements. The objective is with respect to possible remote sensing methods to get information on the spatial and temporal variation of aerosols which is important for human health effects. Worldwide the AOD of the atmospheric column is routinely monitored by sun-photometers and accessible from satellite measurements also. It is implied here that the AOD is caused mainly by attenuation processes within the mixing layer because this layer includes nearly all atmospheric aerosols. Thus the mixing layer height (MLH) is required together with the AOD, measured by ground-based sun-photometers (around 560 nm), to get information about aerosols near the ground. MLH is determined here from surface-based remote sensing. Investigations were performed during two measurement campaigns in and near Munich in May and November/December 2003 on the basis of daily mean values. Using AOD and MLH measurements the aerosol extinction coefficient of the mixing layer has been calculated. This quantity was correlated with the measured PM10, PM2.5 and PM1 mass concentrations near the ground by performing a linear regression and thus providing a mass extinction efficiency giving squares of the correlation coefficients (R2) between 0.48 (PM1 during summer campaign) and 0.90 (PM2.5 during winter campaign). These correlations suggest that the derived mass extinction efficiencies represent a statistically significant relation between the aerosol extinction coefficients and the surface-based PM mass concentrations mainly during winter conditions. r 2008 Elsevier Ltd. All rights reserved. Keywords: Optical depth; Air pollution; Particulate matter; Mixing layer height
1. Introduction
Corresponding author. Tel.: +49 8821 183 192;
fax: +49 8821 73573. E-mail address: [email protected] (K. Scha¨fer). URL: http://www.fzk.de (K. Scha¨fer). 1352-2310/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.01.060
During a pollution event, the air is much more opaque than during unpolluted conditions because of the strong extinction of sunlight by a high amount of aerosols. It is the objective of this paper to show that the visibility degradations derived from
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sunlight measurements are significantly correlated with surface-based particulate matter (PM) mass load. The motivation of these investigations is that the haze during high air pollution events which is caused by high emissions, inversion weather episodes and low wind speeds has numerous harmful health effects. European guidelines such as the First Daughter Directive 99/30/EC normally require the monitoring of PM10 (aerodynamic diameters up to a size of 10 mm). Due to the health impacts (fine particles can penetrate deeper into the lungs and thus are much unhealthier than coarse particles), measurements of PM2.5 and PM1 (aerodynamic diameters up to a size of 2.5 and 1 mm, correspondingly) near the ground are necessary also. The study of the correlation of atmospheric aerosol optical depth (AOD) with properties of aerosols near the ground is a very ambitious task which requires corresponding monitoring methods. There are available optical measurements at the ground as well as on board of aircraft and satellites to study the information content of the correlations of these AOD data and derived aerosol extinction coefficients with PM mass concentration. The AOD of atmospheric columns in different spectral regions is routinely monitored at the ground by sunphotometers, e.g., within global networks as the Aerosol Robotic Network (AERONET) (Holben et al., 1998). On the basis of these data (Campanelli et al., 2003) and the data of multifilter rotating shadowband radiometers within the frame of other networks (Alexandrov et al., 2002a, b; Balis et al., 2003; Hand et al., 2004; Engel-Cox et al., 2006), the information content of spectral AOD was investigated already. Engel-Cox et al. (2006) pointed out that further work needs to be conducted to better understand the quantitative relationships between lidar (light detection and ranging), sun-photometer, satellite and ground-monitored PM data. Chiang et al. (2007) used lidar, sun-photometer and visibility observations to characterise optical properties of tropospheric aerosols and to determine sources of aerosols by additional application of satellite data. Adamopoulos et al. (2007) analysed sun-photometer measurements to get particle size distributions and concluded particle hygroscopic properties influencing particle diameters. Further, ground visibility is studied for correlations with particle number density, mass concentrations and composition (McMurry et al., 2004) as well as aerosol formation processes and the hygroscopic growth of hydrophilic aerosols (Kim et al., 2007; Lee and Kim, 2007).
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It is the idea of this paper to use for the interpretation of the optical column measurements the knowledge of the mixing layer height (MLH) which was found to be a key parameter for the characterisation of air pollution near ground (Scha¨fer et al., 2002, 2006; Emeis and Scha¨fer, 2006; Piringer et al., 2007). The mixing layer is that atmospheric layer in which compounds emitted from the ground are released and mixed. The extent of the mixing layer can determine up to 90% of the volume which contributes to the column content of PM. Measurement campaigns were performed in and near Munich in 2003 with sun-photometers for AOD, low-volume samplers near-ground for in situ PM mass concentration as well as sodar (sound detection and ranging) and lidar instruments for MLH data. The aerosol extinction coefficient of the mixing layer has been derived from the measured AOD and MLH data. The measured PM10, PM2.5 and PM1 mass concentrations were correlated with these aerosol extinction coefficients. Performing a linear regression, an aerosol mass extinction efficiency is provided. 2. Theory 2.1. Aerosol microphysics, optical and mass properties Atmospheric aerosols consist of particles from different sources and are modified in their properties due to physical processes and chemical reactions during the time of transport (Go¨tz et al., 1991). The resulting aerosols have variable size distribution and chemical composition. It is often described as a mixture of aerosol components with spectral-dependent complex refractive index (e.g., Dubovik et al., 2002) and with the size distribution of each component given as log-normal distribution (e.g., D’Almeida et al., 1991). Particles of different components, e.g., water-soluble, soot, mineral or sea salt, can be combined to different aerosol types (e.g., Hess et al., 1998; Dubovik et al., 2002). The radiative properties of aerosol particles which are of relevance for this study are the height z dependent extinction coefficient be(z) and the AOD Z 1 AOD ¼ be ðzÞ dz. (1) o
The extinction coefficient (e.g., in km1) for a given aerosol ensemble depends on the geometrical size of
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the particles between the minimum radius rmin and the maximum radius rmax, described by their number distribution dN/dr (N is the number concentration in particles per m3) and their Mie extinction cross-section Qe. The latter depends on the particle size relative to the wavelength of the radiation and on the refractive index of the particles. The extinction cross-section can be calculated from the Mie theory, if the particles are spherical. The extinction coefficient is then Z rmax dN be ðrmin ; rmax Þ ¼ dr. (2) Qe pr2 dr rmin
over Munich in the framework of the European Aerosol Research Lidar Network (EARLINET) (Wiegner et al., 2002). If the aerosol extinction above the mixing layer can be neglected and the aerosol is perfectly mixed within the layer, the AOD is given as the product of the MLH and an altitudeindependent extinction coefficient. For convenience, we can use be(0), the extinction coefficient near the ground, and rewrite Eq. (1) as Z zB AOD ¼ be ð0Þ dz ¼ be ð0ÞzB , (4)
The mass M (in g) of the aerosol particles in a given volume (in m3) of air is determined by their density or concentration r (e.g., in g m3) and their number distribution dN/dr. With the volume of a single particle V ¼ (4/3)pr3, the volume distribution dV/dr instead of dN/dr can be used and it follows: Z rmax dV dr. (3) Mðrmin ; rmax Þ ¼ r dr rmin
with zB as the height of the layer with perfectly mixed aerosols which is equivalent in the context of this paper with MLH. As a consequence, be(0) can be estimated if AOD and MLH are known, and in a second step the relationship with the aerosol mass concentration near the ground can be investigated by performing a linear regression. A mass extinction efficiency (e.g., in g m2) can be introduced which relates be(0) to PM mass concentration.
Again, it is assumed that the particles are spherical. It is obvious from Eqs. (2) and (3) that be is related to r2, while M is related to r3. Furthermore, they depend on the refractive index and on the particle density in a different way. As a consequence, there is no simple and constant relationship between be and M, and any conversion from one quantity to the other must depend on the particle microphysics as well as chemical composition (Hess et al., 1998; Malm and Hand, 2007). 2.2. AOD and extinction coefficient To study the relationship between AOD and aerosol mass M at the ground, we determine the aerosol extinction coefficient at the ground be(0), and then correlate it with measured M. We propose to use the AOD and the MLH for the estimation of be(0). As shown in Eq. (1), the AOD is given as an integral over the height-dependent extinction coefficient. The vertical aerosol distribution in the atmosphere is, however, dominated by the mixing layer, as most of the aerosols are emitted from the ground. In a convective daytime boundary layer, the emitted compounds are assumed to be vertically well mixed and above the mixing layer the aerosol extinction often decreases to o10% of the atmospheric boundary layer average. Elevated layers from long-range transport are relatively rare over the Munich area and do not significantly contribute to the AOD. This was shown by lidar measurements
o
2.3. Sources of uncertainties The relative humidity does not influence relationship (4) but each relation of be(0) with PM mass concentration is valid for a certain particle size distribution and relative humidity only. The size of water-soluble or any hydrophilic aerosol particle increases with growing relative humidity. This changes the mass density of the particles towards the density of water even if the aerosol number density is constant. Due to the chemical difference in the aerosol types, their hygroscopic properties differ as well (Day and Malm, 2001). This effect, caused to the uptake of water, also changes the refractive index of the particles besides the size and therefore their radiative properties (Hess et al., 1998). Relative humidity can play a significant role on the results presented here because the aerosol mass is measured at the surface but the relative humidity within the whole column influences the measured optical depth of aerosols. The focal point of the interpretation of measured data is the relation between the column data measured by remote sensing and the near-surface in situ measurement data. This circumstance requires the continuous determination of MLH. Further, it mainly determines the uncertainties of the study presented here. One difficulty is the possible inhomogeneous vertical distribution of aerosol concentrations within the boundary layer.
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This can be expected in particular from 1 or 2 h after sunrise until noon, when a nocturnal inversion breaks up and a new planetary boundary layer develops and mixes with the residual layer from the previous night. Such situations can easily be monitored by lidars and sodars (see below). A further difficulty is the existence of elevated layers above the mixing layer which contains a certain amount of PM too. 3. Measurements and methods The measurement campaigns took place in two seasons, representing early summer and winter situation in Munich in 2003. The first campaign started on 10 May and lasted until 30 May and was characterised by summer-like temperatures, often strong winds from the West and mostly cloudy sky with some light rain. Two shorter high-pressure periods (around 16 May and 23 May) occurred. The winter campaign took place from 27 November until 15 December and was dominated by two clear sky episodes (30 November to 4 December and 9–11 December) and the passage of some low-pressure systems from the North West. The one on 5 December was followed by rain and stronger winds.
Instrumentation
Measured Parameters
Station Maisach/ Fuerstenfeldbruck
Meteorological Institute University Munich (MIM)
25 km west of city centre
Munich city centre
Gravimetric lowvolume sampler
PM 10 PM 2.5 PM 1
X X X
X X X
Sunphotometer
AOD
X
X
Weather station
Temperature Relative humidity Wind speed and direction
X X X
X X X
Lidar
Vertical light backscatter profile Mixing layer height
Sodar
Vertical acoustic backscatter profile Mixing layer height
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The area around Munich is flat. To the North West, North and North East Munich is surrounded by villages and agriculturally used areas. In the South, larger areas of forest are situated. A ring highway runs outside of Munich, another ring motorway is in the middle and a major ring road is in the city centre. Industry is situated mostly in the North of Munich but no big industrial zones exist. Some larger industrial point sources are in and around the city. A big airport is located 20 km North North East of Munich. The measurement sites were selected to measure background air pollution, air quality within the city and the downwind influence of the emissions of the city (city plume). The sites, instrumentation and measured parameters are presented in Fig. 1 (listed up and shown in the map) and include: Maisach/ Fuerstenfeldbruck and the roof of the Meteorological Institute of the Ludwig-Maximilians-Universitaet of Munich (MIM) in central Munich. The measurement site MIM can be considered as representative for urban background because no strong emitters are close to the site. The measurement site in Maisach which is an AERONET site (Holben et al., 1998) is situated 10 km west of Munich city limits in a distance of 25 km from the
1 2
X X X X
Fig. 1. Stations, instrumentation and measured parameters (available data are marked by X) during the measurement campaigns (table left) and map of the experimental area around Munich (scheme right). Triangles: stations during the measurement campaigns, (1) Maisach (AERONET station also), (2) Meteorological Institute Munich (MIM), black patches: housing area; grey patches: lakes; lines: motorways.
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MIM. It is in rural environment near a small village and a former military airport (Fuerstenfeldbruck) without air traffic. During easterly winds the Munich city plume affects the site. The instrumentation at the sites included in situ or remote sensing systems to measure aerosol mass concentration, AOD and MLH, respectively. In addition, weather stations were running continuously to monitor the key parameters of local weather including relative humidity. For the determination of particle mass concentration in different size fractions, gravimetric lowvolume samplers SEQ47/50 Leckel were operated. Particles were collected at filters which were loaded during 24 h. These filters were weighed before and after loading in ambient air under the same laboratory conditions (relative humidity between 45% and 55%, room temperature) so that the daily mean mass concentrations were determined. Three instruments were installed at each site and by using different specific sampling heads, each instrument measured a different particulate mode so that the particle measurements were divided into PM1, PM2.5 and PM10 for the same 24 h period. AOD was derived from measurements of a Cimel sun-photometer at Maisach and the sun-photometer IFA (interference filter actinometer) at the MIM. The sun-photometers were well calibrated—with Langley method (Shaw, 1976)—and inter-compared prior to the campaign. From the spectral transmission of the direct solar radiation and solar zenith angle the total optical depth of the column was determined (e.g., Quenzel, 1970; Holben et al., 1998) at wavelengths between 340 and 1550 nm. Only cloud-free situations (4 of 20 days during summer and 11 of 20 days during winter campaign) were considered using solar zenith angles up to 801. These data were corrected for the contribution of ozone, water vapour and molecular scattering to get the aerosol effect as described in the literature (Dubovik et al., 2000). As these selected cloud-free days are climatologically representative for Munich summer and winter conditions, the authors characterise this data set as a representative one. The heights of the planetary boundary layer and the MLH were derived from lidar and sodar data. The lidars MULIS (multi-wavelength lidar system, Wiegner et al., 1995) and POLIS (portable lidar system, Heese et al., 2002) are backscatter lidars emitting light pulses at wavelengths of 1064, 532 and 355 nm or only 355 nm, respectively. Being scanning systems, measurements at different eleva-
tion angles are possible. Combination of data from different angles allows the retrieval of aerosol extinction profiles very close to the ground, which is a significant advantage over most standard lidars. The upper limit is in the tropopause region. The lidar data were primarily evaluated with respect to attenuated backscatter (range corrected signals) to monitor the development of the aerosol distribution and the height of the boundary layer with a temporal resolution better than a minute. The typical temporal resolution of aerosol extinction coefficient profiles is in the order of 15 min, the spatial resolution in the order of a few tens of metres. MULIS and POLIS were operated for several hours during all mainly cloud-free days within the campaigns: during the summer campaign, both lidars were operated on 4 days, during the winter campaign, only MULIS was available (4 days). While the lidar retrievals are based on aerosol information, the acoustic backscatter from the sodar is due to temperature fluctuations and gradients in the atmosphere. Temperature fluctuations can be related to the atmospheric turbulence. In this campaign a METEK DSD3x7 mono-static Doppler sodar with three antennas was used. The system is optimised for long-range detection up to 1300 m above the ground under ideal conditions without external noise sources. It provides continuously vertical profiles of the wind vector and turbulence parameters. The vertical variances of acoustic backscatter intensity and of the vertical velocity component are used simultaneously for the determination of the MLH (Emeis and Tu¨rk, 2004). The potential of the above-mentioned instruments to determine the height of the boundary layer or MLH was thoroughly investigated in several papers (Emeis et al., 2004; Emeis and Scha¨fer, 2006; Wiegner et al., 2006). 4. Results and discussion The mean temperature during the summer campaign was 14.5 1C and during the winter campaign 1.6 1C. Note that only data from 11 days during summer (10, 12, 14–17, 19, 21–23 and 29 May 2003) and 4 measurement days during winter at both sites (8–11 December 2003) could be used for the correlation studies because most of the time high cloud amount inhibits AOD retrievals. The relative humidity in percentage during these days was: 80, 81, 80, 80, 51, 65, 70, 74, 61, 67, 67 during
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summer and 94, 93, 91 and 90 during winter, respectively. 4.1. Aerosol measurements The mass concentrations of the three PM modes measured by the aerosol samplers with an averaging time of 24 h were not distinctly spatially different between the sites inside and outside of Munich. Although there is no information about the particle size distribution and the chemical composition of the particles measured during the campaigns, the relationships which can be found are representative of the investigated spatial and temporal as well as relative humidity and particle size distribution conditions. The PM mass concentrations of all modes were much different between summer and winter. The average, the range of values, the standard deviation and the 95% confidence interval during the winter (PM1 21.2/17.8–29.8/4.3/3.8 mg m3, PM2.5 21.8/13.0–37.1/7.3/5.4 mg m3, PM10 33.4/23.5–52.5/ 10.0/7.4 mg m3) and during the summer campaign (PM1 14.0/9.6–18.6/3.2/1.6 mg m3, PM2.5 16.5/ 10.2–24.4/3.9/2.0 mg m3, PM10 20.2/14.0–31.0/5.5/ 2.8 mg m3) show up to 70% (PM10) higher values during winter. The differences between winter and summer campaign are much higher if one considers coarse mode particles only: PM10–PM2.5 is 11.6 and 3.7 mg m3 as well as PM10–PM1 is 12.2 and 6.2 mg m3, respectively. The differences in PM mass concentration between winter and summer are caused at first by the much lower MLH during the winter than during the summer campaign (see Section 4.3) which differs by a factor of three as well as by additional emission sources as house heating (wood burning) and road salt during winter with corresponding differences in particle size distributions (more coarse particles during winter than during summer) and primary PM composition (more soot during winter than during summer). Lower MLH during winter than during summer campaign also causes higher gaseous pollutant concentrations as NOx which are relevant for forming secondary aerosols. There is no correlation between the variations of the PM mass concentration and relative humidity (from 51% up to 81%) during the summer campaign. During the winter campaign, the relative humidity (from 90% up to 94%) was nearly constant.
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4.2. AOD measurements Due to the often cloudy weather conditions during the measuring campaigns, AOD could be obtained only for a few days. AOD was determined for a spectral range close to 560 nm, where both radiometers (Cimel and IFA) provide a spectral channel. An advantage of this wavelength is that the particles of interest scatter at this wavelength very effectively. According to the Mie theory scattering is maximum for particles with diameters close to the observed wavelength. Thus, information from sunphotometers is mainly governed by aerosols in the accumulation mode which are mostly secondary particles generated in the atmosphere as a result of photochemical reactions between primary gases. During winter and summer the average, the range of values, the standard deviation and the 95% confidence interval of the AOD are much different: 0.065/0.045–0.080/0.012/0.009 and 0.176/0.109–0.326/ 0.052/0.026, respectively. 4.3. MLH measurements The daily averaged MLH at Maisach was derived from the sodar measurements. MLH values were averaged from 7 am up to 8 pm, corresponding to the time period where AOD measurements were available during the campaigns, for determination of the extinction coefficient be(0). For Munich, we applied the MLH values of Maisach. This ‘‘extrapolation’’ was possible because simultaneous measurements at both sites had revealed that the spatial variation of the MLH between Munich and its surroundings is quite small. The average, the range of values, the standard deviation and the 95% confidence interval of the MLH are much different between winter (233/176–281/37/36 m) and summer (747/577–879/86/51 m). However, the corresponding values of the derived extinction coefficients be(0) differ not much between winter (0.277/0.191–0.432/ 0.079/0.059 km1) and summer (0.240/0.137–0.401/ 0.079/0.040 km1). It should be noted that the measurements during this campaign quite well agree with the climatologic results, so that they can be considered as representative for Munich. The annual cycle of the MLH over Munich was determined by regular lidar measurements over 3 years in the framework of EARLINET (Bo¨senberg et al., 2002). This climatology showed that the boundary layer—measured 2 h after local noon—has a vertical extension of
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1.5–2.5 km in summer, while in winter aerosols are confined to the lowest 0.5 km or even less (Wiegner et al., 2002), and that the variation in time can be approximated by a sinusoidal curve. Concerning the diurnal cycle, in particular in summer, a pronounced rise of the MLH before early afternoon is observed as well as a residual layer above the layer near the ground during night and the early morning hours. Changes on this temporal scale, however, cannot by considered in our retrieval because the PM measurements are daily averages only. 4.4. Correlations of extinction coefficients and particle mass concentrations The ambient aerosol extinction coefficients be(0) as calculated from Eq. (4) and described in Section 4.3 at Maisach and at the MIM were correlated with the corresponding daily mean particle mass concentrations of PM10, PM2.5 and PM1 as well as coarse particles PM10–PM2.5 measured at the same sites (see Fig. 2 for winter campaign and Fig. 3 for summer campaign). A linear regression was found to fit the data best. The regression was fitted through zero because the AOD data were corrected for the contribution of ozone, water vapour and molecular scattering so that theoretically there is no light attenuation if there are no aerosols. The squares of correlation coefficients certainty (R2) are slightly higher if the fitting through zero is not supposed. The values of R2 are 0.81, 0.86 and 0.90 for PM1, PM10 and PM2.5 as well as 0.64 for coarse particles during winter campaign. The slopes (the reciprocal corresponds to the mass extinction efficiency) are increasing from PM10, PM2.5 up to PM1 and coarse particles (from 0.0082, 0.0125 up to 0.0146 and 0.0222) showing that much less coarse particles or PM1 (about half) than PM10 mass concentration originate the same extinction coefficient. The values of R2 are lower for the summer than for the winter case (values for PM1, PM2.5 and PM10 are 0.48, 0.53 and 0.54; no correlation for coarse particles—R2 is negative—which are not shown in Fig. 3). The slopes increase from PM10, PM2.5 up to PM1 as during the winter campaign and are in the range from 0.0118, 0.0146 up to 0.0171. These values are not as different as during the winter campaign indicating that there are less coarse particles than during winter. This can be concluded from the negative R2 values for coarse particles
during summer also and is in correspondence with the PM mass concentration measurements (see Section 4.1). The regression of aerosol extinction coefficients with the mass concentration of each PM mode during summer and winter campaign using the 95% confidence levels of each quantity gives R2 values of 1.0 so that the linear regression statistics is significant. The slopes in the regressions of be(0) with PM mass concentrations are higher in summer than in winter. This is the consequence of the small differences of measured be(0) values during the summer and winter campaigns (see Figs. 2 and 3) because the measured mass concentrations of all PM types are much higher during the winter campaign than during the summer campaign (see Section 4.1). This means less PM mass concentration during the summer campaign than during the winter campaign originates the same aerosol extinction coefficient. Due to the lack of information about particle size distribution, particle shape, particle chemical composition and hygroscopic properties of particles, a definitive conclusion cannot be drawn. 4.5. Error sources and discussion The main influence upon the correlation of the aerosol extinction coefficient of the column and the PM mass concentrations is the difficulty to define the layer near the ground with perfectly mixed aerosols (see Eq. (4)) so that during certain meteorological conditions there is a lack of reliable information about the MLH. This problem can be demonstrated by the lidar measurements of 16 May as an example (Wiegner et al., 2006). The planetary boundary layer consisted of two internal layers: the top of the upper layer was at about 2.3 km altitude and was slowly descending with time, while the top of the second layer was slowly rising from 0.6 km until at 11:20 UTC both layers began to merge. This is a quite frequently observed phenomenon. So it is obvious that in the early morning the aerosol extinction coefficient in the mixing layer is not at all constant with height, because—due to the beginning convection—a strong vertical layering builds up. As a consequence, the simple approach of calculating the mean extinction coefficient of the mixing layer by dividing the AOD and the MLH as described in Eq. (4) is misleading during morning hours: in this example, the mean aerosol extinction
ARTICLE IN PRESS K. Scha¨fer et al. / Atmospheric Environment 42 (2008) 4036–4046 0.500
Ext = 0.0222 (PM10 - PM2.5) R2 = 0.6386
0.450
Ext = 0.0125 PM2.5
Ext = 0.0146 PM1
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Ext = 0.0082 PM10 R2 = 0.8627
2
R2 = 0.8128
R = 0.8951
0.400
Extinction (km-1)
0.350 0.300 PM1
0.250
PM2.5
0.200
PM10 PM10-PM2.5
0.150
Linear (PM1)
0.100
Linear (PM10) Linear (PM2.5)
0.050
Linear (PM10-PM2.5)
0.000 0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
PM mass concentration
40.0
45.0
50.0
55.0
60.0
(µg/m3)
Fig. 2. Scatter plot of ambient PM mass concentrations (in mg m3) with aerosol extinction coefficients (in km1) during the winter measurement campaign. A linear regression was performed (slope in m2 mg1) through zero and squares of correlation coefficient (R2) are given. Mixing layer height was measured by a sodar in Maisach only. AOD of spectral range close to 560 nm and PM mass concentration were measured in Maisach and at the MIM.
0.500 0.450
Ext = 0.0171 PM1 R2 = 0.4844
0.400
Extinction (km-1)
0.350
Ext = 0.0146 PM2.5 R2 = 0.5323 Ext = 0.0118 PM10 R2 = 0.5404
0.300 0.250 PM1
0.200
PM2.5
0.150
PM10
0.100
Linear (PM1) Linear (PM2.5)
0.050
Linear (PM10) 0.000 0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
60.0
PM mass concentration (µg/m3) Fig. 3. Scatter plot of ambient PM mass concentrations (in mg m3) with aerosol extinction coefficients (in km1) during the summer measurement campaign. A linear regression was performed (slope in m2 mg1) through zero and squares of correlation coefficient (R2) are given. Mixing layer height was measured by a sodar in Maisach only. AOD of spectral range close to 560 nm and PM mass concentration were measured in Maisach and at the MIM.
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coefficient over the two layers and the ground values extrapolated from the lidar data differ by a factor of 2. Near noon, however, vertical mixing typically leads to a distribution that—as a good approximation—can be assumed constant. In our case, the average over the complete aerosol layer, and the aerosol extinction close to the ground differ by 13% only, and Eq. (4) is an acceptable approximation. Another problem exists when aerosol layers contribute to the AOD but are above the mixing layer. Again, the lidar measurements of 16 May can illustrate this issue: two aerosol layers in the free troposphere between 4.5 and 5.5 km and at about 3.2 km altitude were observed (Wiegner et al., 2006). In this example these layers contribute approximately 30% (532 nm) to the total measured AOD. However, such layers are rare and in most cases thin so that they can be considered as a second-order effect with respect to the total AOD. In addition to this temporal variation of the MLH, the traffic and home heating (increasing wood burning mainly) emissions which are a main source of PM show comparable daily variations. They are low during the night and they rise during the day. This correlation is caused by human activities only. The use of mean values during the day of PM mass concentrations, MLH and AOD compensates these accidental simultaneous variations and are advantageous for these studies. A further error source can be the horizontal spatial inhomogeneity of the MLH. However, the spatial variability of MLH is small due to the flat topography of the area. This was demonstrated by POLIS measurements which were taken between 7:00 and 13:00 UTC at different sites in and around Munich during 1 representative day. Consequently, the approximate relationship of Eq. (4) between the AOD and the mean extinction coefficient can be used in the surroundings of Munich as well. The lower R2 values of regressions between the aerosol extinction coefficient and the PM mass concentrations during summer could be caused mainly by the much higher MLH during summer than during winter which is then sometimes out of the range of the sodar. The uncertainty for high mixing layers due to setting MLH at a maximum of 1200 m is in the order of 10% of the presented MLH data. One solution is to use different remote sensing methods such as lidar (see above and Section 3). These remote sensing instruments together gave information that complemented each other due to the higher range of the lidar (Emeis and Scha¨fer, 2006).
Also, an influence upon the accuracy of the retrieval results can be originated by the watersoluble or any hydrophilic aerosol particles which will increase in size and mass with increasing ambient relative humidity. This effect, due to the uptake of water, changes both the size and the refractive index of the particles and, consequently, their radiative properties (Hess et al., 1998). But the sample filters were weighed in the laboratory for measuring the mass concentration during constant relative humidity and temperature conditions which were different (relative humidity was lower) to the conditions during sampling. This measured PM mass concentration is regressed with measurements of the aerosol extinction coefficient in ambient air which is interpreted as a mass extinction efficiency but the real PM mass concentration can be larger than accounted for the weighed relatively dry particles. 5. Conclusions The correlations between the measured aerosol extinction coefficient and the PM mass concentrations measured from the ground monitoring stations are significant. Performing a linear regression, the slopes of the regression correspond to aerosol mass extinction efficiency. Notwithstanding the significant statistical correlations (the R2 values range between 0.48 for PM1 during summer campaign and 0.90 for PM2.5 during winter campaign), the estimation of the aerosol extinction coefficient from optical measurements such as AOD and measurements of MLH has theoretical limitations. These limitations include the amount of particles, which possibly lie above the mixing layer and can form further layers, the possibility of inhomogeneous vertical distribution of particles within the mixing layer under weak mixing conditions and variations of aerosol radiative properties. The humidity influence on the PM size growth can cause the relatively low differences between the aerosol extinction coefficients during winter and summer. Adamopoulos et al. (2007) reported for Athens that particle hygroscopic property can be more efficient during summer than during winter so that the particle diameter is higher during summer than during winter. Correlations of the mass concentrations of all three particulate modes with the aerosol extinction coefficients calculated from the sun-photometer and sodar measurements agree well with the findings of Hand et al. (2004) who report an R2 value of 0.68
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for the dependence of the AOD from the nearground ammoniated sulphate mass concentration measured as PM2.5 in Big Bend National Park, US (a very remote desert in the dry South West of the US). Engel-Cox et al. (2006) describe measurement results in urban environment (Old Town Baltimore) of AOD and PM2.5 mass concentration from 1 July to 31 August 2004 together with corresponding correlations. The highest R2 value of 0.58 was found for daily mean values of AOD and PM2.5 mass concentration as well as AOD values in the boundary layer only. Here, in this work an R2 value of 0.60 for both campaigns was determined which is similar to these reported ones. The relatively lower R2 values between the aerosol extinction coefficients and the PM mass concentrations during summer indicate that the method has shortcomings in the presence of complex layering at the lower atmosphere. Most difficult in the determination of MLH are cases where the lower atmosphere is well mixed by solar heating and high wind speeds prevail, or where the layering of the lower atmosphere is characterised by strong stratification as e.g. during the morning hours. It can be concluded that a refined continuous monitoring of the daily course of MLH is necessary to get higher accurate data of the aerosol extinction coefficient. Conclusions from this work contribute to the comparison of satellite-derived data of AOD with ground-based sun-photometer measurements which is performed routinely today (Holben et al., 1998; Kim et al., 2007). Following the correlations demonstrated in this paper, the spatially high resolved satellite AOD (e.g., channel 2 of the Landsat images) can be correlated with measured PM mass concentrations at the ground. This would require routinely available MLH data from monitoring networks (e.g., lidar at the ground or onboard of satellites) or modelling. Such satellitebased image data studies as started by Sifakis et al. (1998), Sarigiannis et al. (2002), Scha¨fer et al. (2002), Soulakellis et al. (2004) and Beaulant and Wald (2006) would be a step forward to get information about spatially high resolved PM pollution near the surface as required by the EC regulations which cannot be provided by groundbased PM monitoring networks only. Acknowledgements This work was co-financed by the European Commission through the project ‘‘Integrated Compu-
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tational Assessment of Air Quality via Remote Observations Systems Network’’ (ICAROS NET), Contract IST-2000-29264. The authors thank the reviewers for very valuable comments and proposals.
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Middleton, D., Baumann-Stanzer, K., Dandou, A., Karppinen, A., Burzynski, J., 2007. The surface energy balance and the mixing height in urban areas—activities and recommendations of COST-Action 715. Boundary-Layer Meteorology 124, 3–24. Quenzel, H., 1970. Determination of size distribution of atmospheric aerosol particles from spectral solar radiation measurements. Journal of Geophysical Research 75, 2915–2921. Sarigiannis, D.A., Soulakellis, N., Scha¨fer, K., Tombrou, M., Sifakis, N.I., Assimakopoulos, D., Lointier, M., Dantou, A., Saisana, M., 2002. ICAROS: an integrated computational environment for the assimilation of environmental data and models for urban and regional air quality. International Journal on Water, Air, and Soil Pollution: Focus 2, 641–654. Scha¨fer, K., Fo¨mmel, G., Hoffmann, H., Briz, S., Junkermann, W., Emeis, S., Jahn, C., Leipold, S., Sedlmaier, A., Dinev, S., Reishofer, G., Windholz, L., Soulakellis, N., Sifakis, N., Sarigiannis, D., 2002. Three-dimensional ground-based measurements of urban air quality to evaluate satellite derived interpretations for urban air pollution. International Journal on Water, Air, and Soil Pollution: Focus 2, 91–102. Scha¨fer, K., Emeis, S., Hoffmann, H., Jahn, C., 2006. Influence of mixing layer height upon air pollution in urban and suburban area. Meteorologische Zeitschrift 15, 647–658. Shaw, G.E., 1976. Error analysis of multi wavelength sun photometry. Pageoph 114, 1. Sifakis, N., Soulakellis, N., Paronis, D., 1998. Quantitative mapping of air pollution density using Earth observations: a new processing method and application to an urban area. International Journal of Remote Sensing 19, 3289–3300. Soulakellis, N.A., Sifakis, N.I., Tombrou, M., Sarigiannis, D., Scha¨fer, K., 2004. Estimation and mapping of aerosol optical thickness over the city of Brescia—Italy using diachronic and multiangle SPOT 1, SPOT 2 and SPOT 4 imagery. Geocarto International 19, 57–66. Wiegner, M., Quenzel, H., Rabus, D., Vo¨lker, W., Vo¨lger, P., Ackermann, J., Ka¨hler, C., Fergg, F., Wildgruber, G., 1995. The mobile three-wavelength backscatter lidar of the Meteorological Institute of the University Munich. In: Lidar and Atmospheric Sensing. SPIE 2505, Bellingham, WA, USA, pp. 2–10. Wiegner, M., Kumpf, W., Freudenthaler, V., Stachlewska, I., Heese, B., 2002. Surface aerosol layer: annual cycle and parameterization. In: Bissonnette, L., Roy, G., Valle´e, G. (Eds.), Proceedings of the 21th ILRC, Lidar Remote Sensing in Atmospheric and Earth Sciences. Defence R&D CanadaValcartier, Val-Belair, Quebec, Canada, pp. 321–324. Wiegner, M., Emeis, S., Freudenthaler, V., Heese, B., Junkermann, W., Mu¨nkel, C., Scha¨fer, K., Seefeldner, M., Vogt, S., 2006. Mixing layer height over Munich, Germany: variability and comparisons of different methodologies. Journal of Geophysical Research—Atmospheres 111, D13201.
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Atmospheric Environment 42 (2008) 4036–4046 www.elsevier.com/locate/atmosenv
Correlation of aerosol mass near the ground with aerosol optical depth during two seasons in Munich Klaus Scha¨fera,, Andreas Harbuscha, Stefan Emeisa, Peter Koepkeb, Matthias Wiegnerb a
Forschungszentrum Karlsruhe, Institute for Meteorology and Climate Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany b Ludwig-Maximilians-Universitaet of Munich, Meteorological Institute (MIM), Theresienstr. 37, 80333 Mu¨nchen, Germany Received 22 August 2007; received in revised form 15 January 2008; accepted 25 January 2008
Abstract Relations of the aerosol optical depth (AOD) with aerosol mass concentration near the ground, particulate matter (PM), have been studied on the basis of measurements. The objective is with respect to possible remote sensing methods to get information on the spatial and temporal variation of aerosols which is important for human health effects. Worldwide the AOD of the atmospheric column is routinely monitored by sun-photometers and accessible from satellite measurements also. It is implied here that the AOD is caused mainly by attenuation processes within the mixing layer because this layer includes nearly all atmospheric aerosols. Thus the mixing layer height (MLH) is required together with the AOD, measured by ground-based sun-photometers (around 560 nm), to get information about aerosols near the ground. MLH is determined here from surface-based remote sensing. Investigations were performed during two measurement campaigns in and near Munich in May and November/December 2003 on the basis of daily mean values. Using AOD and MLH measurements the aerosol extinction coefficient of the mixing layer has been calculated. This quantity was correlated with the measured PM10, PM2.5 and PM1 mass concentrations near the ground by performing a linear regression and thus providing a mass extinction efficiency giving squares of the correlation coefficients (R2) between 0.48 (PM1 during summer campaign) and 0.90 (PM2.5 during winter campaign). These correlations suggest that the derived mass extinction efficiencies represent a statistically significant relation between the aerosol extinction coefficients and the surface-based PM mass concentrations mainly during winter conditions. r 2008 Elsevier Ltd. All rights reserved. Keywords: Optical depth; Air pollution; Particulate matter; Mixing layer height
1. Introduction
Corresponding author. Tel.: +49 8821 183 192;
fax: +49 8821 73573. E-mail address: [email protected] (K. Scha¨fer). URL: http://www.fzk.de (K. Scha¨fer). 1352-2310/$ - see front matter r 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.01.060
During a pollution event, the air is much more opaque than during unpolluted conditions because of the strong extinction of sunlight by a high amount of aerosols. It is the objective of this paper to show that the visibility degradations derived from
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sunlight measurements are significantly correlated with surface-based particulate matter (PM) mass load. The motivation of these investigations is that the haze during high air pollution events which is caused by high emissions, inversion weather episodes and low wind speeds has numerous harmful health effects. European guidelines such as the First Daughter Directive 99/30/EC normally require the monitoring of PM10 (aerodynamic diameters up to a size of 10 mm). Due to the health impacts (fine particles can penetrate deeper into the lungs and thus are much unhealthier than coarse particles), measurements of PM2.5 and PM1 (aerodynamic diameters up to a size of 2.5 and 1 mm, correspondingly) near the ground are necessary also. The study of the correlation of atmospheric aerosol optical depth (AOD) with properties of aerosols near the ground is a very ambitious task which requires corresponding monitoring methods. There are available optical measurements at the ground as well as on board of aircraft and satellites to study the information content of the correlations of these AOD data and derived aerosol extinction coefficients with PM mass concentration. The AOD of atmospheric columns in different spectral regions is routinely monitored at the ground by sunphotometers, e.g., within global networks as the Aerosol Robotic Network (AERONET) (Holben et al., 1998). On the basis of these data (Campanelli et al., 2003) and the data of multifilter rotating shadowband radiometers within the frame of other networks (Alexandrov et al., 2002a, b; Balis et al., 2003; Hand et al., 2004; Engel-Cox et al., 2006), the information content of spectral AOD was investigated already. Engel-Cox et al. (2006) pointed out that further work needs to be conducted to better understand the quantitative relationships between lidar (light detection and ranging), sun-photometer, satellite and ground-monitored PM data. Chiang et al. (2007) used lidar, sun-photometer and visibility observations to characterise optical properties of tropospheric aerosols and to determine sources of aerosols by additional application of satellite data. Adamopoulos et al. (2007) analysed sun-photometer measurements to get particle size distributions and concluded particle hygroscopic properties influencing particle diameters. Further, ground visibility is studied for correlations with particle number density, mass concentrations and composition (McMurry et al., 2004) as well as aerosol formation processes and the hygroscopic growth of hydrophilic aerosols (Kim et al., 2007; Lee and Kim, 2007).
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It is the idea of this paper to use for the interpretation of the optical column measurements the knowledge of the mixing layer height (MLH) which was found to be a key parameter for the characterisation of air pollution near ground (Scha¨fer et al., 2002, 2006; Emeis and Scha¨fer, 2006; Piringer et al., 2007). The mixing layer is that atmospheric layer in which compounds emitted from the ground are released and mixed. The extent of the mixing layer can determine up to 90% of the volume which contributes to the column content of PM. Measurement campaigns were performed in and near Munich in 2003 with sun-photometers for AOD, low-volume samplers near-ground for in situ PM mass concentration as well as sodar (sound detection and ranging) and lidar instruments for MLH data. The aerosol extinction coefficient of the mixing layer has been derived from the measured AOD and MLH data. The measured PM10, PM2.5 and PM1 mass concentrations were correlated with these aerosol extinction coefficients. Performing a linear regression, an aerosol mass extinction efficiency is provided. 2. Theory 2.1. Aerosol microphysics, optical and mass properties Atmospheric aerosols consist of particles from different sources and are modified in their properties due to physical processes and chemical reactions during the time of transport (Go¨tz et al., 1991). The resulting aerosols have variable size distribution and chemical composition. It is often described as a mixture of aerosol components with spectral-dependent complex refractive index (e.g., Dubovik et al., 2002) and with the size distribution of each component given as log-normal distribution (e.g., D’Almeida et al., 1991). Particles of different components, e.g., water-soluble, soot, mineral or sea salt, can be combined to different aerosol types (e.g., Hess et al., 1998; Dubovik et al., 2002). The radiative properties of aerosol particles which are of relevance for this study are the height z dependent extinction coefficient be(z) and the AOD Z 1 AOD ¼ be ðzÞ dz. (1) o
The extinction coefficient (e.g., in km1) for a given aerosol ensemble depends on the geometrical size of
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the particles between the minimum radius rmin and the maximum radius rmax, described by their number distribution dN/dr (N is the number concentration in particles per m3) and their Mie extinction cross-section Qe. The latter depends on the particle size relative to the wavelength of the radiation and on the refractive index of the particles. The extinction cross-section can be calculated from the Mie theory, if the particles are spherical. The extinction coefficient is then Z rmax dN be ðrmin ; rmax Þ ¼ dr. (2) Qe pr2 dr rmin
over Munich in the framework of the European Aerosol Research Lidar Network (EARLINET) (Wiegner et al., 2002). If the aerosol extinction above the mixing layer can be neglected and the aerosol is perfectly mixed within the layer, the AOD is given as the product of the MLH and an altitudeindependent extinction coefficient. For convenience, we can use be(0), the extinction coefficient near the ground, and rewrite Eq. (1) as Z zB AOD ¼ be ð0Þ dz ¼ be ð0ÞzB , (4)
The mass M (in g) of the aerosol particles in a given volume (in m3) of air is determined by their density or concentration r (e.g., in g m3) and their number distribution dN/dr. With the volume of a single particle V ¼ (4/3)pr3, the volume distribution dV/dr instead of dN/dr can be used and it follows: Z rmax dV dr. (3) Mðrmin ; rmax Þ ¼ r dr rmin
with zB as the height of the layer with perfectly mixed aerosols which is equivalent in the context of this paper with MLH. As a consequence, be(0) can be estimated if AOD and MLH are known, and in a second step the relationship with the aerosol mass concentration near the ground can be investigated by performing a linear regression. A mass extinction efficiency (e.g., in g m2) can be introduced which relates be(0) to PM mass concentration.
Again, it is assumed that the particles are spherical. It is obvious from Eqs. (2) and (3) that be is related to r2, while M is related to r3. Furthermore, they depend on the refractive index and on the particle density in a different way. As a consequence, there is no simple and constant relationship between be and M, and any conversion from one quantity to the other must depend on the particle microphysics as well as chemical composition (Hess et al., 1998; Malm and Hand, 2007). 2.2. AOD and extinction coefficient To study the relationship between AOD and aerosol mass M at the ground, we determine the aerosol extinction coefficient at the ground be(0), and then correlate it with measured M. We propose to use the AOD and the MLH for the estimation of be(0). As shown in Eq. (1), the AOD is given as an integral over the height-dependent extinction coefficient. The vertical aerosol distribution in the atmosphere is, however, dominated by the mixing layer, as most of the aerosols are emitted from the ground. In a convective daytime boundary layer, the emitted compounds are assumed to be vertically well mixed and above the mixing layer the aerosol extinction often decreases to o10% of the atmospheric boundary layer average. Elevated layers from long-range transport are relatively rare over the Munich area and do not significantly contribute to the AOD. This was shown by lidar measurements
o
2.3. Sources of uncertainties The relative humidity does not influence relationship (4) but each relation of be(0) with PM mass concentration is valid for a certain particle size distribution and relative humidity only. The size of water-soluble or any hydrophilic aerosol particle increases with growing relative humidity. This changes the mass density of the particles towards the density of water even if the aerosol number density is constant. Due to the chemical difference in the aerosol types, their hygroscopic properties differ as well (Day and Malm, 2001). This effect, caused to the uptake of water, also changes the refractive index of the particles besides the size and therefore their radiative properties (Hess et al., 1998). Relative humidity can play a significant role on the results presented here because the aerosol mass is measured at the surface but the relative humidity within the whole column influences the measured optical depth of aerosols. The focal point of the interpretation of measured data is the relation between the column data measured by remote sensing and the near-surface in situ measurement data. This circumstance requires the continuous determination of MLH. Further, it mainly determines the uncertainties of the study presented here. One difficulty is the possible inhomogeneous vertical distribution of aerosol concentrations within the boundary layer.
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This can be expected in particular from 1 or 2 h after sunrise until noon, when a nocturnal inversion breaks up and a new planetary boundary layer develops and mixes with the residual layer from the previous night. Such situations can easily be monitored by lidars and sodars (see below). A further difficulty is the existence of elevated layers above the mixing layer which contains a certain amount of PM too. 3. Measurements and methods The measurement campaigns took place in two seasons, representing early summer and winter situation in Munich in 2003. The first campaign started on 10 May and lasted until 30 May and was characterised by summer-like temperatures, often strong winds from the West and mostly cloudy sky with some light rain. Two shorter high-pressure periods (around 16 May and 23 May) occurred. The winter campaign took place from 27 November until 15 December and was dominated by two clear sky episodes (30 November to 4 December and 9–11 December) and the passage of some low-pressure systems from the North West. The one on 5 December was followed by rain and stronger winds.
Instrumentation
Measured Parameters
Station Maisach/ Fuerstenfeldbruck
Meteorological Institute University Munich (MIM)
25 km west of city centre
Munich city centre
Gravimetric lowvolume sampler
PM 10 PM 2.5 PM 1
X X X
X X X
Sunphotometer
AOD
X
X
Weather station
Temperature Relative humidity Wind speed and direction
X X X
X X X
Lidar
Vertical light backscatter profile Mixing layer height
Sodar
Vertical acoustic backscatter profile Mixing layer height
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The area around Munich is flat. To the North West, North and North East Munich is surrounded by villages and agriculturally used areas. In the South, larger areas of forest are situated. A ring highway runs outside of Munich, another ring motorway is in the middle and a major ring road is in the city centre. Industry is situated mostly in the North of Munich but no big industrial zones exist. Some larger industrial point sources are in and around the city. A big airport is located 20 km North North East of Munich. The measurement sites were selected to measure background air pollution, air quality within the city and the downwind influence of the emissions of the city (city plume). The sites, instrumentation and measured parameters are presented in Fig. 1 (listed up and shown in the map) and include: Maisach/ Fuerstenfeldbruck and the roof of the Meteorological Institute of the Ludwig-Maximilians-Universitaet of Munich (MIM) in central Munich. The measurement site MIM can be considered as representative for urban background because no strong emitters are close to the site. The measurement site in Maisach which is an AERONET site (Holben et al., 1998) is situated 10 km west of Munich city limits in a distance of 25 km from the
1 2
X X X X
Fig. 1. Stations, instrumentation and measured parameters (available data are marked by X) during the measurement campaigns (table left) and map of the experimental area around Munich (scheme right). Triangles: stations during the measurement campaigns, (1) Maisach (AERONET station also), (2) Meteorological Institute Munich (MIM), black patches: housing area; grey patches: lakes; lines: motorways.
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MIM. It is in rural environment near a small village and a former military airport (Fuerstenfeldbruck) without air traffic. During easterly winds the Munich city plume affects the site. The instrumentation at the sites included in situ or remote sensing systems to measure aerosol mass concentration, AOD and MLH, respectively. In addition, weather stations were running continuously to monitor the key parameters of local weather including relative humidity. For the determination of particle mass concentration in different size fractions, gravimetric lowvolume samplers SEQ47/50 Leckel were operated. Particles were collected at filters which were loaded during 24 h. These filters were weighed before and after loading in ambient air under the same laboratory conditions (relative humidity between 45% and 55%, room temperature) so that the daily mean mass concentrations were determined. Three instruments were installed at each site and by using different specific sampling heads, each instrument measured a different particulate mode so that the particle measurements were divided into PM1, PM2.5 and PM10 for the same 24 h period. AOD was derived from measurements of a Cimel sun-photometer at Maisach and the sun-photometer IFA (interference filter actinometer) at the MIM. The sun-photometers were well calibrated—with Langley method (Shaw, 1976)—and inter-compared prior to the campaign. From the spectral transmission of the direct solar radiation and solar zenith angle the total optical depth of the column was determined (e.g., Quenzel, 1970; Holben et al., 1998) at wavelengths between 340 and 1550 nm. Only cloud-free situations (4 of 20 days during summer and 11 of 20 days during winter campaign) were considered using solar zenith angles up to 801. These data were corrected for the contribution of ozone, water vapour and molecular scattering to get the aerosol effect as described in the literature (Dubovik et al., 2000). As these selected cloud-free days are climatologically representative for Munich summer and winter conditions, the authors characterise this data set as a representative one. The heights of the planetary boundary layer and the MLH were derived from lidar and sodar data. The lidars MULIS (multi-wavelength lidar system, Wiegner et al., 1995) and POLIS (portable lidar system, Heese et al., 2002) are backscatter lidars emitting light pulses at wavelengths of 1064, 532 and 355 nm or only 355 nm, respectively. Being scanning systems, measurements at different eleva-
tion angles are possible. Combination of data from different angles allows the retrieval of aerosol extinction profiles very close to the ground, which is a significant advantage over most standard lidars. The upper limit is in the tropopause region. The lidar data were primarily evaluated with respect to attenuated backscatter (range corrected signals) to monitor the development of the aerosol distribution and the height of the boundary layer with a temporal resolution better than a minute. The typical temporal resolution of aerosol extinction coefficient profiles is in the order of 15 min, the spatial resolution in the order of a few tens of metres. MULIS and POLIS were operated for several hours during all mainly cloud-free days within the campaigns: during the summer campaign, both lidars were operated on 4 days, during the winter campaign, only MULIS was available (4 days). While the lidar retrievals are based on aerosol information, the acoustic backscatter from the sodar is due to temperature fluctuations and gradients in the atmosphere. Temperature fluctuations can be related to the atmospheric turbulence. In this campaign a METEK DSD3x7 mono-static Doppler sodar with three antennas was used. The system is optimised for long-range detection up to 1300 m above the ground under ideal conditions without external noise sources. It provides continuously vertical profiles of the wind vector and turbulence parameters. The vertical variances of acoustic backscatter intensity and of the vertical velocity component are used simultaneously for the determination of the MLH (Emeis and Tu¨rk, 2004). The potential of the above-mentioned instruments to determine the height of the boundary layer or MLH was thoroughly investigated in several papers (Emeis et al., 2004; Emeis and Scha¨fer, 2006; Wiegner et al., 2006). 4. Results and discussion The mean temperature during the summer campaign was 14.5 1C and during the winter campaign 1.6 1C. Note that only data from 11 days during summer (10, 12, 14–17, 19, 21–23 and 29 May 2003) and 4 measurement days during winter at both sites (8–11 December 2003) could be used for the correlation studies because most of the time high cloud amount inhibits AOD retrievals. The relative humidity in percentage during these days was: 80, 81, 80, 80, 51, 65, 70, 74, 61, 67, 67 during
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summer and 94, 93, 91 and 90 during winter, respectively. 4.1. Aerosol measurements The mass concentrations of the three PM modes measured by the aerosol samplers with an averaging time of 24 h were not distinctly spatially different between the sites inside and outside of Munich. Although there is no information about the particle size distribution and the chemical composition of the particles measured during the campaigns, the relationships which can be found are representative of the investigated spatial and temporal as well as relative humidity and particle size distribution conditions. The PM mass concentrations of all modes were much different between summer and winter. The average, the range of values, the standard deviation and the 95% confidence interval during the winter (PM1 21.2/17.8–29.8/4.3/3.8 mg m3, PM2.5 21.8/13.0–37.1/7.3/5.4 mg m3, PM10 33.4/23.5–52.5/ 10.0/7.4 mg m3) and during the summer campaign (PM1 14.0/9.6–18.6/3.2/1.6 mg m3, PM2.5 16.5/ 10.2–24.4/3.9/2.0 mg m3, PM10 20.2/14.0–31.0/5.5/ 2.8 mg m3) show up to 70% (PM10) higher values during winter. The differences between winter and summer campaign are much higher if one considers coarse mode particles only: PM10–PM2.5 is 11.6 and 3.7 mg m3 as well as PM10–PM1 is 12.2 and 6.2 mg m3, respectively. The differences in PM mass concentration between winter and summer are caused at first by the much lower MLH during the winter than during the summer campaign (see Section 4.3) which differs by a factor of three as well as by additional emission sources as house heating (wood burning) and road salt during winter with corresponding differences in particle size distributions (more coarse particles during winter than during summer) and primary PM composition (more soot during winter than during summer). Lower MLH during winter than during summer campaign also causes higher gaseous pollutant concentrations as NOx which are relevant for forming secondary aerosols. There is no correlation between the variations of the PM mass concentration and relative humidity (from 51% up to 81%) during the summer campaign. During the winter campaign, the relative humidity (from 90% up to 94%) was nearly constant.
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4.2. AOD measurements Due to the often cloudy weather conditions during the measuring campaigns, AOD could be obtained only for a few days. AOD was determined for a spectral range close to 560 nm, where both radiometers (Cimel and IFA) provide a spectral channel. An advantage of this wavelength is that the particles of interest scatter at this wavelength very effectively. According to the Mie theory scattering is maximum for particles with diameters close to the observed wavelength. Thus, information from sunphotometers is mainly governed by aerosols in the accumulation mode which are mostly secondary particles generated in the atmosphere as a result of photochemical reactions between primary gases. During winter and summer the average, the range of values, the standard deviation and the 95% confidence interval of the AOD are much different: 0.065/0.045–0.080/0.012/0.009 and 0.176/0.109–0.326/ 0.052/0.026, respectively. 4.3. MLH measurements The daily averaged MLH at Maisach was derived from the sodar measurements. MLH values were averaged from 7 am up to 8 pm, corresponding to the time period where AOD measurements were available during the campaigns, for determination of the extinction coefficient be(0). For Munich, we applied the MLH values of Maisach. This ‘‘extrapolation’’ was possible because simultaneous measurements at both sites had revealed that the spatial variation of the MLH between Munich and its surroundings is quite small. The average, the range of values, the standard deviation and the 95% confidence interval of the MLH are much different between winter (233/176–281/37/36 m) and summer (747/577–879/86/51 m). However, the corresponding values of the derived extinction coefficients be(0) differ not much between winter (0.277/0.191–0.432/ 0.079/0.059 km1) and summer (0.240/0.137–0.401/ 0.079/0.040 km1). It should be noted that the measurements during this campaign quite well agree with the climatologic results, so that they can be considered as representative for Munich. The annual cycle of the MLH over Munich was determined by regular lidar measurements over 3 years in the framework of EARLINET (Bo¨senberg et al., 2002). This climatology showed that the boundary layer—measured 2 h after local noon—has a vertical extension of
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1.5–2.5 km in summer, while in winter aerosols are confined to the lowest 0.5 km or even less (Wiegner et al., 2002), and that the variation in time can be approximated by a sinusoidal curve. Concerning the diurnal cycle, in particular in summer, a pronounced rise of the MLH before early afternoon is observed as well as a residual layer above the layer near the ground during night and the early morning hours. Changes on this temporal scale, however, cannot by considered in our retrieval because the PM measurements are daily averages only. 4.4. Correlations of extinction coefficients and particle mass concentrations The ambient aerosol extinction coefficients be(0) as calculated from Eq. (4) and described in Section 4.3 at Maisach and at the MIM were correlated with the corresponding daily mean particle mass concentrations of PM10, PM2.5 and PM1 as well as coarse particles PM10–PM2.5 measured at the same sites (see Fig. 2 for winter campaign and Fig. 3 for summer campaign). A linear regression was found to fit the data best. The regression was fitted through zero because the AOD data were corrected for the contribution of ozone, water vapour and molecular scattering so that theoretically there is no light attenuation if there are no aerosols. The squares of correlation coefficients certainty (R2) are slightly higher if the fitting through zero is not supposed. The values of R2 are 0.81, 0.86 and 0.90 for PM1, PM10 and PM2.5 as well as 0.64 for coarse particles during winter campaign. The slopes (the reciprocal corresponds to the mass extinction efficiency) are increasing from PM10, PM2.5 up to PM1 and coarse particles (from 0.0082, 0.0125 up to 0.0146 and 0.0222) showing that much less coarse particles or PM1 (about half) than PM10 mass concentration originate the same extinction coefficient. The values of R2 are lower for the summer than for the winter case (values for PM1, PM2.5 and PM10 are 0.48, 0.53 and 0.54; no correlation for coarse particles—R2 is negative—which are not shown in Fig. 3). The slopes increase from PM10, PM2.5 up to PM1 as during the winter campaign and are in the range from 0.0118, 0.0146 up to 0.0171. These values are not as different as during the winter campaign indicating that there are less coarse particles than during winter. This can be concluded from the negative R2 values for coarse particles
during summer also and is in correspondence with the PM mass concentration measurements (see Section 4.1). The regression of aerosol extinction coefficients with the mass concentration of each PM mode during summer and winter campaign using the 95% confidence levels of each quantity gives R2 values of 1.0 so that the linear regression statistics is significant. The slopes in the regressions of be(0) with PM mass concentrations are higher in summer than in winter. This is the consequence of the small differences of measured be(0) values during the summer and winter campaigns (see Figs. 2 and 3) because the measured mass concentrations of all PM types are much higher during the winter campaign than during the summer campaign (see Section 4.1). This means less PM mass concentration during the summer campaign than during the winter campaign originates the same aerosol extinction coefficient. Due to the lack of information about particle size distribution, particle shape, particle chemical composition and hygroscopic properties of particles, a definitive conclusion cannot be drawn. 4.5. Error sources and discussion The main influence upon the correlation of the aerosol extinction coefficient of the column and the PM mass concentrations is the difficulty to define the layer near the ground with perfectly mixed aerosols (see Eq. (4)) so that during certain meteorological conditions there is a lack of reliable information about the MLH. This problem can be demonstrated by the lidar measurements of 16 May as an example (Wiegner et al., 2006). The planetary boundary layer consisted of two internal layers: the top of the upper layer was at about 2.3 km altitude and was slowly descending with time, while the top of the second layer was slowly rising from 0.6 km until at 11:20 UTC both layers began to merge. This is a quite frequently observed phenomenon. So it is obvious that in the early morning the aerosol extinction coefficient in the mixing layer is not at all constant with height, because—due to the beginning convection—a strong vertical layering builds up. As a consequence, the simple approach of calculating the mean extinction coefficient of the mixing layer by dividing the AOD and the MLH as described in Eq. (4) is misleading during morning hours: in this example, the mean aerosol extinction
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Ext = 0.0222 (PM10 - PM2.5) R2 = 0.6386
0.450
Ext = 0.0125 PM2.5
Ext = 0.0146 PM1
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Ext = 0.0082 PM10 R2 = 0.8627
2
R2 = 0.8128
R = 0.8951
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0.350 0.300 PM1
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0.200
PM10 PM10-PM2.5
0.150
Linear (PM1)
0.100
Linear (PM10) Linear (PM2.5)
0.050
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0.000 0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
PM mass concentration
40.0
45.0
50.0
55.0
60.0
(µg/m3)
Fig. 2. Scatter plot of ambient PM mass concentrations (in mg m3) with aerosol extinction coefficients (in km1) during the winter measurement campaign. A linear regression was performed (slope in m2 mg1) through zero and squares of correlation coefficient (R2) are given. Mixing layer height was measured by a sodar in Maisach only. AOD of spectral range close to 560 nm and PM mass concentration were measured in Maisach and at the MIM.
0.500 0.450
Ext = 0.0171 PM1 R2 = 0.4844
0.400
Extinction (km-1)
0.350
Ext = 0.0146 PM2.5 R2 = 0.5323 Ext = 0.0118 PM10 R2 = 0.5404
0.300 0.250 PM1
0.200
PM2.5
0.150
PM10
0.100
Linear (PM1) Linear (PM2.5)
0.050
Linear (PM10) 0.000 0.0
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15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
55.0
60.0
PM mass concentration (µg/m3) Fig. 3. Scatter plot of ambient PM mass concentrations (in mg m3) with aerosol extinction coefficients (in km1) during the summer measurement campaign. A linear regression was performed (slope in m2 mg1) through zero and squares of correlation coefficient (R2) are given. Mixing layer height was measured by a sodar in Maisach only. AOD of spectral range close to 560 nm and PM mass concentration were measured in Maisach and at the MIM.
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coefficient over the two layers and the ground values extrapolated from the lidar data differ by a factor of 2. Near noon, however, vertical mixing typically leads to a distribution that—as a good approximation—can be assumed constant. In our case, the average over the complete aerosol layer, and the aerosol extinction close to the ground differ by 13% only, and Eq. (4) is an acceptable approximation. Another problem exists when aerosol layers contribute to the AOD but are above the mixing layer. Again, the lidar measurements of 16 May can illustrate this issue: two aerosol layers in the free troposphere between 4.5 and 5.5 km and at about 3.2 km altitude were observed (Wiegner et al., 2006). In this example these layers contribute approximately 30% (532 nm) to the total measured AOD. However, such layers are rare and in most cases thin so that they can be considered as a second-order effect with respect to the total AOD. In addition to this temporal variation of the MLH, the traffic and home heating (increasing wood burning mainly) emissions which are a main source of PM show comparable daily variations. They are low during the night and they rise during the day. This correlation is caused by human activities only. The use of mean values during the day of PM mass concentrations, MLH and AOD compensates these accidental simultaneous variations and are advantageous for these studies. A further error source can be the horizontal spatial inhomogeneity of the MLH. However, the spatial variability of MLH is small due to the flat topography of the area. This was demonstrated by POLIS measurements which were taken between 7:00 and 13:00 UTC at different sites in and around Munich during 1 representative day. Consequently, the approximate relationship of Eq. (4) between the AOD and the mean extinction coefficient can be used in the surroundings of Munich as well. The lower R2 values of regressions between the aerosol extinction coefficient and the PM mass concentrations during summer could be caused mainly by the much higher MLH during summer than during winter which is then sometimes out of the range of the sodar. The uncertainty for high mixing layers due to setting MLH at a maximum of 1200 m is in the order of 10% of the presented MLH data. One solution is to use different remote sensing methods such as lidar (see above and Section 3). These remote sensing instruments together gave information that complemented each other due to the higher range of the lidar (Emeis and Scha¨fer, 2006).
Also, an influence upon the accuracy of the retrieval results can be originated by the watersoluble or any hydrophilic aerosol particles which will increase in size and mass with increasing ambient relative humidity. This effect, due to the uptake of water, changes both the size and the refractive index of the particles and, consequently, their radiative properties (Hess et al., 1998). But the sample filters were weighed in the laboratory for measuring the mass concentration during constant relative humidity and temperature conditions which were different (relative humidity was lower) to the conditions during sampling. This measured PM mass concentration is regressed with measurements of the aerosol extinction coefficient in ambient air which is interpreted as a mass extinction efficiency but the real PM mass concentration can be larger than accounted for the weighed relatively dry particles. 5. Conclusions The correlations between the measured aerosol extinction coefficient and the PM mass concentrations measured from the ground monitoring stations are significant. Performing a linear regression, the slopes of the regression correspond to aerosol mass extinction efficiency. Notwithstanding the significant statistical correlations (the R2 values range between 0.48 for PM1 during summer campaign and 0.90 for PM2.5 during winter campaign), the estimation of the aerosol extinction coefficient from optical measurements such as AOD and measurements of MLH has theoretical limitations. These limitations include the amount of particles, which possibly lie above the mixing layer and can form further layers, the possibility of inhomogeneous vertical distribution of particles within the mixing layer under weak mixing conditions and variations of aerosol radiative properties. The humidity influence on the PM size growth can cause the relatively low differences between the aerosol extinction coefficients during winter and summer. Adamopoulos et al. (2007) reported for Athens that particle hygroscopic property can be more efficient during summer than during winter so that the particle diameter is higher during summer than during winter. Correlations of the mass concentrations of all three particulate modes with the aerosol extinction coefficients calculated from the sun-photometer and sodar measurements agree well with the findings of Hand et al. (2004) who report an R2 value of 0.68
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for the dependence of the AOD from the nearground ammoniated sulphate mass concentration measured as PM2.5 in Big Bend National Park, US (a very remote desert in the dry South West of the US). Engel-Cox et al. (2006) describe measurement results in urban environment (Old Town Baltimore) of AOD and PM2.5 mass concentration from 1 July to 31 August 2004 together with corresponding correlations. The highest R2 value of 0.58 was found for daily mean values of AOD and PM2.5 mass concentration as well as AOD values in the boundary layer only. Here, in this work an R2 value of 0.60 for both campaigns was determined which is similar to these reported ones. The relatively lower R2 values between the aerosol extinction coefficients and the PM mass concentrations during summer indicate that the method has shortcomings in the presence of complex layering at the lower atmosphere. Most difficult in the determination of MLH are cases where the lower atmosphere is well mixed by solar heating and high wind speeds prevail, or where the layering of the lower atmosphere is characterised by strong stratification as e.g. during the morning hours. It can be concluded that a refined continuous monitoring of the daily course of MLH is necessary to get higher accurate data of the aerosol extinction coefficient. Conclusions from this work contribute to the comparison of satellite-derived data of AOD with ground-based sun-photometer measurements which is performed routinely today (Holben et al., 1998; Kim et al., 2007). Following the correlations demonstrated in this paper, the spatially high resolved satellite AOD (e.g., channel 2 of the Landsat images) can be correlated with measured PM mass concentrations at the ground. This would require routinely available MLH data from monitoring networks (e.g., lidar at the ground or onboard of satellites) or modelling. Such satellitebased image data studies as started by Sifakis et al. (1998), Sarigiannis et al. (2002), Scha¨fer et al. (2002), Soulakellis et al. (2004) and Beaulant and Wald (2006) would be a step forward to get information about spatially high resolved PM pollution near the surface as required by the EC regulations which cannot be provided by groundbased PM monitoring networks only. Acknowledgements This work was co-financed by the European Commission through the project ‘‘Integrated Compu-
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tational Assessment of Air Quality via Remote Observations Systems Network’’ (ICAROS NET), Contract IST-2000-29264. The authors thank the reviewers for very valuable comments and proposals.
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