Estimation of Dry Deposition of Inorganics Using Filter Pack Data and

data to estimate deposition fluxes at several sites in. Minnesota over a period of 3 y. Numerous studies have employed filter packs of varying design ...
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Environ. Sci. Technol. 1996, 30, 2168-2177

Estimation of Dry Deposition of Inorganics Using Filter Pack Data and Inferred Deposition Velocity G R E G O R Y C . P R A T T , * ,† E V E L Y N J . O R R , †,‡ D O N A L D C . B O C K , † RICK L. STRASSMAN,† DEAN W. FUNDINE,† CLIFFORD J. TWAROSKI,† J. DAVID THORNTON,† AND TILDEN P. MEYERS§ Air Quality Division, Minnesota Pollution Control Agency, 520 Lafayette Road, St. Paul, Minnesota 55155, and National Oceanic and Atmospheric Administration, Air Resources Laboratory, Atmospheric Turbulence and Diffusion Division, P.O. Box 2456, Oak Ridge, Tennessee 37831

Low-volume filter packs were used at several sites in Minnesota to measure weekly averaged concentrations of sulfur dioxide, nitric acid vapor, and selected inorganic components of the small particles (SO42-, NO3-, NH4+, Ca, Mg, Na, K, and Cl). Hourly dry deposition velocities (Vd) were inferred using a multiple-layer canopy resistance model developed by the NOAA Air Resources Laboratory. Annual mean deposition velocities in centimeters per second (cm/ s) for the years 1991-1993 ranged from 0.83 to 1.46 for HNO3, from 0.28 to 0.42 for SO2, and from 0.09 to 0.15 for small particles. Deposition velocities were distinctly cyclical, both annually and diurnally, with maximum values in the summertime and at midday for all species. Weekly averaged deposition velocities were multiplied with the low-volume filter pack measurements of air concentrations to obtain dry deposition fluxes. Dry deposition of all substances was generally highest in and near the MinneapolisSt. Paul metropolitan area and decreased with increasing distance from the metro area. Dry deposition of total sulfur and total nitrogen, respectively, ranged from 0.98 and 0.53 kg ha-1 yr-1 at a remote site to 9.24 and 2.36 kg ha-1 yr-1 in the metro area. Dry deposition of Ca, K, Mg, and Na constituted a small fraction, typically less than 10%, of total (wet + dry) deposition of these substances; however, the lowvolume filter pack sampling method does not capture the largest of the coarse particles and may miss significant amounts of these elements. Dry deposition of sulfur and nitrogen, summed for all species containing these elements, averaged 22 and 14%, respectively, of the total (wet + dry) deposition of these elements.

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Introduction Accurate estimates of dry deposition fluxes are considerably more difficult to obtain than estimates of wet deposition. While wet deposition flux can be measured directly by collecting and analyzing precipitation samples, there is no comparable method for dry deposition. Instead, many researchers collect and analyze air samples to determine air concentrations of gaseous and particulate constituents. The flux, or deposition rate, is then calculated by assuming or inferring a deposition velocity (Vd). The deposition velocity is only infrequently measured directly due to the requirement for complex and expensive micrometeorological equipment. Several reviews of this subject have been published (1-7). In recent years as a part of the National Acid Precipitation Assessment Program (NAPAP), researchers at the National Oceanic and Atmospheric Administration Air Resources Laboratory (NOAA/ARL) developed modeling approaches for inferring the deposition velocity from more easily obtainable meteorological data (8-11). The earliest of these models treats the plant canopy as a ‘big-leaf’, and deposition velocities are computed as the reciprocal of the sum of the aerodynamic, quasi-laminar, and canopy resistances. Later model versions were extended to include multi-layer treatment of the canopy resistance and to treat the physics of the transfer processes more rigorously. The extensive treatment of canopy processes is a unique and desirable feature of the NOAA/ARL models. The goal of the NOAA/ ARL research was to develop a method for inferring accurate dry deposition velocities from reasonably available meteorological data. Some of the resulting algorithms have been or will be incorporated into dry deposition modules in air deposition models such as those for predicting acidic deposition. The methods are also applicable to filter pack monitoring data to give more accurate estimates of monitored dry deposition fluxes. The NOAA/ARL modeling approach will be used to estimate dry deposition fluxes in the U.S. Environmental Protection Agency’s National Dry Deposition Network (NDDN). The Minnesota Pollution Control Agency (MPCA) began collecting data in 1980 to support its effort to develop rules regulating emissions and deposition of acidifying substances. This work led to the adoption in 1986 of an acidic deposition standard of 11 kg/ha sulfate deposition, designed to protect low-alkalinity lakes found predominantly in the northeastern part of the state. In 1984, recognizing the potential importance of dry deposition, the MPCA began deploying low-volume filter packs to collect data on concentrations of gases and particles relevant to acidification. This data collection continued through the end of 1993. Initial estimates of deposition fluxes were derived by multiplying the measured concentrations by assumed deposition velocities taken from the literature. These estimates were considered preliminary, and improved methods were soughtsthe goal being to more accurately * Corresponding author telephone: 612-296-7664; e-mail address: [email protected]. † Minnesota Pollution Control Agency. ‡ Present address: Minnesota Department of Human Services, 444 Lafayette Road, St. Paul, MN 55155. § National Oceanic and Atmospheric Administration.

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 1996 American Chemical Society

sites at which air concentration data were measured and the principal vegetation type at each site were as follows: Birch Lake (BIR) boreal forest (white spruce and balsam fir) Cedar Creek (CED) mixed deciduous forest (red oak) with meadows Ely (ELY) mixed northern forest (jack pine and aspen) Finland (FIN) mixed northern forest (maple and spruce) Koch (KOC) oak savannah with industrial and suburban development Marcell (MAR) northern hardwood forest (aspen) Sandstone (SAN) mixed deciduous forest (red oak) with cropland

FIGURE 1. Map of Minnesota with the locations of the dry deposition monitoring sites.

quantify dry deposition inputs to Minnesota ecosystems to determine whether the wet deposition standard alone was sufficiently protective of the state’s resources. The present study improves the estimates of dry deposition fluxes by using the NOAA/ARL multi-layer resistance model for determination of deposition velocities. Meteorological data were collected from four sites: one collocated with a filter pack monitoring site and three located at varying distances from filter pack sites. The multilayer resistance model was used to infer dry deposition velocities using the meteorological data from the nearest site and data about the plant species and vegetative canopy at the filter pack site. The deposition velocities were multiplied with the low-volume filter pack air concentration data to estimate deposition fluxes at several sites in Minnesota over a period of 3 y. Numerous studies have employed filter packs of varying design to determine concentrations of gases and particles. However, only a few studies have combined filter pack data, the inferential method for determination of deposition velocity, and include a long-term record of results over a period of years.

Air Monitoring Integrated weekly samples of particulate and gaseous species important to dry deposition were collected at 13 sites in Minnesota for various periods of time beginning in 1984. Seven of the sites with consistent long-term records extending into the 1990s (when collection of suitable meteorological data began) were selected for this analysis (see Figure 1 for site locations). The sites were mainly located in northeastern Minnesota, an area remote from extensive pollutant emissions sources and known to contain systems sensitive to acidic deposition. One urban site (Koch) located near a petroleum refinery in the Minneapolis-St. Paul metropolitan (metro) area was included. This site was originally located in the Pine Bend region along the Mississippi River, but was moved 7.7 mi upriver in 1993 to a new site near another petroleum refinery. The

Samples were collected using a three-stage filter pack consisting of (1) a particulate filter (47-mm Savillex PTFE Teflon filter, 1-2 µm, 2 µm maximum pore size); (2) a 47mm Gelman Nylasorb filter for collection of nitric acid gas (0.2-mm pore size); and (3) a 47-mm Whatman Cellulose No. 41 filter impregnated with 0.5 N KOH for collection of sulfur dioxide. A flow rate of approximately 4 L/min was maintained through the pack, and filters were changed every Tuesday at all sites. Exposed filter packs were sealed in plastic bags and sent to the Minnesota Pollution Control Agency (MPCA) Air Quality Laboratory in St. Paul for analysis. The KOH and nylon filters were extracted overnight in 1.8 mM sodium carbonate/1.8 mM sodium bicarbonate eluent. The particle filters were weighed and divided. Half was extracted in the carbonate/bicarbonate solution for anion analysis. The other half was extracted in 30 mM HCl (later methanesulfonic acid) for cation analysis. Analyses were by ion chromatography. Accuracy of KOH and nylon filter SO42- and NO3measurements at typical ambient concentrations was roughly (10% as determined from spiked filter analyses. Duplicate sample analyses indicated an average analytical method precision of better than 2.5% for these analytes. The more rigorous precision check provided by collocated monitors revealed a total sampling and analysis system median precision of (10-15% with 90% of observations falling within (50% of each other. Duplicate sample analyses indicated an average analytical method precision of better than 6% for particulate sulfate, nitrate, and ammonium, while collocated samples showed that most measurements were within (15% for sulfate and (40% for nitrate and ammonium. Quality assurance data for the remaining particle filter analytes indicated a level of precision that was lower overall than for sulfate, nitrate, and ammonium. Since the nylon filters are known to capture SO2, they were analyzed for sulfate as well as nitrate, and the sulfur dioxide concentration determined from the nylon filter was added to that from the KOH filter to obtain the total sulfur dioxide concentration. It is possible that particulate forms of nitrate originally captured on the particle filter may volatilize during the sampling period and be recaptured on the nylon filter. This phenomenon was not investigated or quantified, but the potential for some overestimation of nitric acid vapor concentrations (and concurrent underestimation of particulate nitrate) was recognized. The importance of this phenomenon in the atmospheric environment is debatable since the volatilization of particulate nitrogen species may occur in the environment. Detailed descriptions of sample collection and analysis procedures and sampler accuracy and precision analyses are available from the MPCA. In addition, a report covering the methods, quality assurance, preliminary statistical

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TABLE 1 3

Annual Mean Ambient Air Concentrations (µg/m ) analyte Ca

Cl

HNO3

K

Mg

Na

NH4+

NO3-

SO2

SO42-

year

BIR

CED

ELY

FIN

KOC

MAR

SAN

91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93

0.11 0.11 0.08 0.05 0.04 0.05 0.45 0.45 0.43 0.06 0.07 0.05 0.04 0.04 0.03 0.09 0.07 0.06 0.52 0.59 0.50 0.23 0.25 0.19 1.41 1.08 1.04 1.52 1.64 1.37

0.40 0.38 0.30 0.13 0.08 0.12 0.81 0.86 0.63 0.09 0.07 0.07 0.11 0.12 0.10 0.14 0.10 0.13 1.29 1.32 1.13 1.72 1.88 1.80 2.59 1.97 1.96 2.39 2.29 1.92

0.11 0.12 0.10 0.05 0.04 0.04 0.49 0.50 0.55 0.08 0.05 0.05 0.04 0.05 0.03 0.08 0.06 0.06 0.52 0.55 0.50 0.30 0.27 0.25 1.33 1.05 1.16 1.52 1.48 1.35

0.16 0.17 0.13 0.05 0.05 0.05 0.64 0.66 0.67 0.08 0.06 0.06 0.05 0.06 0.04 0.09 0.06 0.07 0.68 0.61 0.54 0.45 0.40 0.34 2.18 1.81 2.11 1.77 1.65 1.51

0.92 0.99 1.31 0.48 0.32 0.51 0.83 0.77 0.80 0.09 0.08 0.14 0.33 0.38 0.59 0.31 0.21 0.36 1.69 1.67 1.95 2.57 2.64 2.96 5.39 4.62 16.07 2.86 2.71 3.60

0.17 0.19 0.13 0.05 0.04 0.05 0.51 0.56 0.52 0.11 0.08 0.07 0.05 0.06 0.05 0.10 0.07 0.05 0.66 0.73 0.57 0.51 0.59 0.45 1.58 1.18 1.31 1.74 1.76 1.42

0.27 0.27 0.19 0.07 0.05 0.06 0.70 0.75 0.64 0.08 0.06 0.06 0.07 0.08 0.06 0.09 0.07 0.06 1.13 1.05 0.93 1.15 1.21 1.21 1.94 1.44 1.51 2.28 2.11 1.76

review, analysis of trends and variability, spatial analysis, and comparisons with other networks is available from the MPCA (12). Table 1 gives annual mean concentrations of the monitored substances from the Minnesota network used in this analysis. In general, the concentrations followed a gradient in which the highest concentrations were found in the metro area, and concentrations decreased with distance from the metro area. The concentrations are comparable to measurements made in the early 1980s in Minnesota in a network that used dichotomous samplers and different analytical methods (13). Concentrations of some substances exhibited seasonal patterns. For example, sulfur dioxide concentrations were highest in winter, with the most pronounced seasonality at the remote sites and less pronounced seasonality at the sites nearest the metro area. Particulate nitrate, sulfate, and ammonium concentrations were also highest in winter, but the strongest seasonality was seen nearest the metro area. Nitric acid vapor concentrations were slightly elevated in spring and summer. Calcium and magnesium concentrations were highest in late spring and summer and lower during autumn and winter. Although we report concentrations (and later deposition fluxes) of the base cations (Ca, Mg, K, Na), we recognize the problems in doing so. The low-volume filter pack collection device is of questionable efficiency in collecting large particles. Microscopic examination of the particle filters showed particles as large as 40 µm in diameter; however, a large proportion of the coarse particles consisted of lowdensity biotic materials such as pollen grains and fungal spores. The low-volume filter pack filters did not contain

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the large, dense soil particles that can be seen on total suspended particulate (TSP) filters. Comparisons were made between total mass collected with the low-volume filter packs and TSP and PM10 samples collected at nearby sampling locations (TSP and PM10 sampling locations with identifiable source impacts were excluded). In 31 comparisons of annual averages, the TSP concentration (from every sixth day monitoring) was 21.9 µg/m3 compared to a low-volume filter pack average concentration of 13.7 µg/ m3. At one nearby site with daily PM10 sampling, the annual average PM10 concentration was 30.7 µg/m3 compared with 33.2 µg/m3 for the low-volume filter pack (means significantly different at the 90% confidence level, but not at 95%). These data suggest that the low-volume filter pack appears to be collecting approximately the PM10 fraction, which leads to the conclusion that the base cation concentrations (and deposition fluxes) reported here should be regarded as underestimates or minimum bounds. On the other hand, the concentrations of sulfate and ammonium are probably not subject to the aforementioned bias. Sulfate and ammonium, as has been demonstrated in the past in Minnesota (13), exist predominantly in the fine fraction of the atmospheric aerosol, and we do not expect to significantly underestimate the concentration (or deposition flux) due to the particle measurement technique. Particulate nitrate will also exist in the fine fraction when it occurs as ammonium nitrate; however, there is evidence that nitrate can be found in appreciable amounts in the coarse aerosol fraction in Minnesota (13). The extent to which particulate nitrate concentrations (and deposition) might be biased is uncertain. Lindberg et al. (14) found significant fractions of sulfate and nitrate as well as the base cations in the coarse fraction of the atmospheric aerosol. However, they used surrogate surfaces for coarse particle sampling and a filtration method with a cut point of approximately 2 µm for the fine particle sampling. For a more complete description of the air monitoring results and preliminary statistical analysis, copies of ref 12 are available from the MPCA.

Meteorological Data Hourly meteorological (met) data were obtained from the University of Minnesota, Department of Soil Science, for sites in Cedar Creek, Princeton, and Grand Rapids, MN, for 1991-1993. The MPCA collected hourly meteorological data at the Koch site. The Cedar Creek and Koch sites are described above, the Princeton site is located near a farm field in an area of mixed forest and farming, and the Grand Rapids site is located in an area of predominantly aspen forest. Variables measured included temperature (TA), relative humidity (RH), solar radiation (RG), wind speed (u), wind direction (θ), wind direction standard deviation (σθ), and precipitation. Data capture ranged from 68.7% to 100% in terms of the number of complete records per station per year. The data capture for individual parameters was more variable, ranging from 0% (e.g., Grand Rapids RH completely unusable in 1993) to 100%. In addition, daily precipitation data were obtained for the closest site in the statewide monitoring network to each of the air monitoring locations. The data were processed as follows. The raw data were converted to required units and format. Missing data were replaced using the value from the previous hour. If more than 12 consecutive hours were missing, the value from the preceding 24 h was used. If more than 1 week of data

were missing, data from the nearest available site were used. The exception was RG, which was calculated if missing (15, 16). Solar radiation data were missing entirely from the Koch site and were calculated for that site. (A comparison was made of deposition velocity results obtained by calculation and by substitution of solar radiation data from the Cedar Creek site, and no significant differences were found.) Wind direction standard deviation data were unavailable for Grand Rapids and were substituted with data from Cedar Creek. The Cedar Creek data were used because the site is forested, similar to the Grand Rapids site; whereas, the Princeton site is adjacent to a farm field. When hourly precipitation data were missing, they were taken from the daily precipitation files and partitioned over 24 h. In addition, the partitioned daily precipitation data were used in cases where the location of the hourly data was more than 100 km from the air monitoring site. Leaf wetness (CWET) was calculated from TA, u, RH, and precipitation. If precipitation occurred, leaf wetness was set to 1. If there was no precipitation, CWET was calculated when RH was >90% by:

CWET )

(RH - 90) 1 f(TA,RH) 10 u

where f(TA,RH) ) {if TA (h - 1) < dewpoint (h) ) 1, else ) 0} and if u < 1, u ) 1. We believe that this algorithm is a reasonable approximation but probably an underestimation of dew formation. The algorithm does not account for other ways in which leaves may be wetted such as by guttation, fog, or light precipitation not recorded by monitoring stations. It also does not account for the persistence of leaf wetness beyond 1 h. At sites where air concentration data were collected but meteorological (met) data were not available, met data were taken from the nearest representative site. For example, met data from Princeton were used with the air concentration data from Sandstone (distance approximately 85 km). Similarly, met data from Grand Rapids were used with air concentration data from Marcell (40 km), Ely (165 km), Finland (170 km), and Birch Lake (250 km), and met data from Koch were used with air concentration data from the relocated Koch monitor in 1993 (12 km). The fact that meteorological data were not collected at each site introduces errors but was unavoidable since meteorological data were not routinely collected at the filter pack monitoring sites.

Calculation of Deposition Velocities The DRYDEP2 model developed by the Atmospheric Turbulence and Diffusion Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration was used. The model estimates Vd for SO2, O3, HNO3, and particles with diameters less than 2 µm, although the O3 results were not used in this analysis. The model version used in this study (17) is a more detailed version of the original “big-leaf” model presented by Hicks et al. (9). In the latest version, a multi-layer approach is used to more realistically simulate visible light penetration and the mean wind speed profile, which are used in the parametrization of the stomatal and leaf boundary layer resistances. One of three normalized vertical profiles of leaf area is prescribed for each site simulation. If leaf area index data are available, the normalized leaf area profiles are scaled accordingly. The radiative transfer algorithm is based on a Poisson model

FIGURE 2. Comparison of measured versus modeled SO2 deposition velocities at Huntington Forest over several diurnal cycles.

(18) for estimating the sunlit and shaded fractions at each layer within the canopy. Deciduous forests are known to have clumped foliage, and the use of a Poisson model may underestimate beam penetration. In a study comparing a Poisson model versus a negative binomial model, which is considered to be better suited for clumped foliage, Baldocchi and Hutchison (19) found only a 10% difference in the computed stomatal conductance. As part of an initial validation study for the multi-layer model (20), direct O3 and SO2 flux measurements were made at Huntington Forest, a northern hardwood forest in upstate New York. Comparisons between modeled and measured deposition velocities showed good overall agreement over several diurnal cycles (Figure 2). Dry deposition models have typically been better validated for gases than for particles due to the inherent difficulties in measuring particle fluxes. This model is no exception. Model inputs are listed in Table 2 and include meteorological data, plant species data, and station data. Plant species data were taken from recommendations of the model developers (NOAA/ARL) with small modifications for different species and locations in Minnesota compared with the model development sites. Station data were taken by MPCA staff. Leaf out and leaf fall dates were taken from climatological data. The model was run on hourly meteorological data files, resulting in a deposition velocity value for each hour of the year. A set of these hourly deposition velocities was calculated for each air monitoring site using the plant species and station data from the site and met data from the nearest available met site. The annual mean deposition velocity results are given in Table 3. Annual mean deposition velocities in centimeters per second (cm/s) ranged from 0.83 to 1.46 for HNO3, from 0.28 to 0.42 for SO2, and from 0.09 to 0.15 for small particles. These values are well within the range of values reported in the literature (for reviews see refs 21-23). Some examples of deposition velocities determined by various methods include Vandenberg and Knoerr (24), who used surrogate surfaces and estimated annual sulfate (small particle) dry deposition velocities of 0.03-0.14 cm/s. Meyers et al. (25) estimated HNO3 deposition velocities over a fully leafed deciduous canopy using flux/gradient theory as well as using a detailed canopy turbulence model. Deposition velocities averaged 4.0 cm/s, ranged from 2.2 to 6.0 cm/s, and the flux/gradient calculations agreed well with the canopy turbulence model. Lee et al. (26) measured HNO3 deposition velocities ranging from 0.27 to 4.0 cm/s at Mauna Loa, HI, and found

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TABLE 2

Inputs to the DRYDEP2 Model meteorological data

plant species data

station data

time wind speed wind direction solar radiation relative humidity temperature leaf wetness precipitation wind direction standard deviation

species name stomatal resistance leaf area profile type canopy height stomatal resistance light response optimum leaf temperature maximum temperature for stomatal closure minimum temperature for stomatal closure

site name dominant species names percent canopy in each species maximum leaf area index for each species dormant season leaf area index for each species time period for leaf out time period for leaf fall

TABLE 3

Annual Mean Deposition Velocity (cm/s) analyte HNO3

particles

SO2

year

BIR

CED

ELY

FIN

KOC MAR PRN SAN

91 92 93 91 92 93 91 92 93

1.09 1.19 1.16 0.10 0.10 0.09 0.30 0.31 0.30

1.06 1.06 1.46 0.11 0.10 0.14 0.35 0.35 0.37

1.02 1.02 1.06 0.10 0.09 0.09 0.29 0.28 0.28

1.09 1.09 1.14 0.10 0.09 0.09 0.31 0.31 0.30

1.14 1.16 0.83 0.15 0.15 0.11 0.42 0.42 0.38

1.05 1.06 1.10 0.10 0.10 0.09 0.30 0.29 0.29

0.91 0.97 0.83 0.10 0.10 0.09 0.35 0.36 0.32

0.95 1.01 0.87 0.10 0.10 0.09 0.36 0.37 0.33

reasonable agreement with estimates from a canopy resistance model. Matt et al. (27) compared measured and modeled summertime deposition velocities for SO2. Measured values ranged from 0.69 to 1.92 cm/s while modeled values ranged from 1.39 to 1.78 cm/s. Duan et al. (28) used eddy correlation to measure wintertime particle deposition velocities of 0.034 and 0.021 cm/s for particles in the 0.150.30 and 0.5-1.0 µm size ranges, respectively. Lindberg et al. (14) used a variety of techniques including eddy correlation, profile methods, throughfall, and stemflow to determine growing season deposition velocities of 0.5 cm/s for SO2, 2.0 cm/s for HNO3, and 0.2 cm/s for fine particles. Dormant season values were 0.2 cm/s for SO2, 0.5 cm/s for HNO3, and 0.05 cm/s for fine particles. Figure 3 shows the weekly averaged deposition velocities for SO2, HNO3, and particulate matter for the Birch Lake, Sandstone, and Koch sites. Deposition velocities for all species increased in summer, the most dramatic increase being for HNO3. The dramatic springtime increase and autumn decrease in Vd for HNO3, which coincided with leaf out and leaf fall, respectively, may at first seem to contradict conventional views of the seasonality of HNO3 deposition velocity. However, since the surface uptake resistance for HNO3 is small, the deposition velocity depends mainly on the mean wind speed and the active surface area for uptake. The relative importance of these effects was evaluated by Meyers (29), who showed that doubling the wind speed resulted in a 30% increase in the HNO3 deposition velocity, whereas a 1 unit increase in the leaf area index resulted in a 20% increase in the deposition velocity. Summertime leaf area indices are usually 3-5 units higher than in wintertime periods, so the increased leaf surface area can more than offset the slightly slower winds during the summer months. Another factor that affects wintertime HNO3 deposition is snow cover and accompanying surface temperature. Johansson and Granat (30) have shown that the surface

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FIGURE 3. Deposition velocities (cm/s) for particulate matter, sulfur dioxide, and nitric acid vapor at the Koch, Sandstone, and Birch Lake sites. The plotted values are the weekly averaged deposition velocities. In each graph the top, middle, and bottom lines are the nitric acid vapor, sulfur dioxide, and particulate matter deposition velocities, respectively.

uptake resistance for HNO3 increases considerably when the snow temperature drops below freezing, a common condition at the Minnesota sites in wintertime. However, the HNO3 deposition velocity has been measured over very few natural surface types and not at all over a leafless canopy during winter, so additional measurements over more surface types are needed. Deposition velocities also showed a distinct diurnal pattern, in which values were low during nighttime hours and high during the daytime (Figure 4). In general (depending upon the site), the nitric acid vapor deposition velocity averaged about 0.3 cm/s during the night and peaked at an average of over 2.0 cm/s shortly after midday. Similarly, the sulfur dioxide deposition velocity dropped to an average of 0.1-0.2 cm/s during the night, rising to about

FIGURE 4. Hourly average deposition velocities (cm/s) for particulate matter, sulfur dioxide, and nitric acid vapor at Cedar Creek in 1991. Each value was calculated by averaging all of the deposition velocity estimates for a given hour. The error bars represent the 95% confidence interval on the mean.

0.6 cm/s at midday. Particulate matter deposition velocities averaged about 0.02 cm/s during the night and increased to about 0.3 cm/s at midday. Figure 5 shows the model calculated hourly deposition velocities at the Sandstone site in 1992. This figure illustrates, among other things the variability in the hourly Vd values and the dramatic effect of leaf out and leaf fall on the deposition velocities for sulfur dioxide and nitric acid vapor. Deposition velocities for the three species, sulfur dioxide, nitric acid vapor, and particulate matter were significantly correlated with one another at all sites. For example, at Cedar Creek the Pearson’s correlation coefficients were as follows: Vd(HNO3) vs Vd(SO2) Vd(PM) vs Vd(SO2) Vd(HNO3) vs Vd(PM)

0.87 0.78 0.80

(p < 0.001 in all cases)

The deposition velocity data did not show distinct geographic variability. All sites were located in forested or mainly forested areas, and the meteorological conditions were not dramatically different, so this finding is not a surprise.

Deposition Rate Results Deposition rates were calculated by multiplying weeklyaveraged deposition velocities by the measured weekly air concentrations. The small particle deposition velocities were multiplied with concentrations of SO42-, NH4+, NO3-, Ca, Mg, K, Cl, and Na. This procedure for particles is subject

FIGURE 5. Hourly deposition velocity estimates (cm/s) for the Sandstone site in 1992 for the three species as indicated. Each point represents one hourly model calculated deposition velocity.

to two criticisms. First, the measured air concentrations of the base cations were likely underestimates, as discussed earlier. Second, the filter pack collects particles that can exceed the applicable size range (2 µm) for the modeled deposition velocities. Since sulfate and ammonium typically occur on fine particles, this second criticism is an issue mainly for the base cations. Table 4 gives the annual dry deposition flux in kilograms per hectare (kg/ha). The spatial pattern of deposition was similar to that for air concentrations, showing maximum deposition of all substances at sites nearest the metro area, with decreasing deposition with distance from the metro area. Both sulfur dioxide concentration and deposition were significantly higher at the relocated Koch site in 1993 than at the original site. Other substances were similar or merely slightly different in both concentration and deposition at the relocated Koch site in 1993. Some of the substances exhibited seasonal patterns in dry deposition. For example, calcium, magnesium, nitric acid vapor, sulfate ion, and sulfur dioxide dry deposition were all higher in summer than at other times. The summertime maximum in sulfur dioxide dry deposition occurred despite the wintertime maximum in SO2 concentration owing to the higher deposition velocity in summer. Nitrate ion dry deposition showed no seasonality at the remote sites but showed a wintertime maximum at sites nearest the metro area. Ammonium ion dry deposition tended to be highest in winter near the metro area and highest in summer at remote sites.

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TABLE 4

Annual Dry Deposition (kg/ha) analyte year

BIR

CED

ELY

FIN

Ca

0.04 0.04 0.03 0.01 0.01 0.02 1.53 1.56 1.51 0.02 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.01 0.17 0.17 0.15 0.06 0.06 0.04 1.05 0.87 0.77 0.51 0.46 0.39

0.16 0.14 0.13 0.03 0.02 0.04 3.72 3.62 3.48 0.03 0.02 0.03 0.04 0.04 0.05 0.04 0.02 0.03 0.34 0.32 0.35 0.41 0.40 0.47 2.52 1.67 1.67 0.81 0.66 0.72

0.04 0.05 0.03 0.02 0.01 0.01 1.46 1.77 1.68 0.03 0.02 0.02 0.01 0.02 0.01 0.02 0.02 0.01 0.16 0.17 0.14 0.07 0.07 0.05 0.91 0.82 0.75 0.50 0.46 0.38

0.06 0.47 0.06 0.09 0.09 0.06 0.52 0.07 0.11 0.10 0.04 0.47 0.04 0.05 0.05 0.02 0.13 0.02 0.02 0.02 0.01 0.09 0.01 0.01 0.01 0.02 0.14 0.02 0.02 0.02 2.15 3.82 1.56 2.14 2.22 2.39 3.94 1.99 2.73 2.83 2.14 2.65 1.64 1.64 1.72 0.03 0.04 0.04 0.02 0.02 0.02 0.04 0.02 0.02 0.02 0.02 0.04 0.02 0.02 0.02 0.02 0.17 0.02 0.02 0.02 0.02 0.20 0.02 0.03 0.03 0.01 0.22 0.01 0.02 0.02 0.03 0.09 0.03 0.03 0.02 0.02 0.07 0.02 0.02 0.02 0.02 0.10 0.01 0.01 0.01 0.21 0.59 0.19 0.28 0.27 0.18 0.65 0.20 0.29 0.29 0.14 0.59 0.15 0.21 0.21 0.11 0.86 0.11 0.24 0.24 0.10 0.93 0.13 0.28 0.28 0.07 0.85 0.10 0.23 0.23 1.73 5.77 1.13 1.60 1.63 1.55 5.65 0.88 1.38 1.41 1.70 17.79 0.84 1.00 1.02 0.62 1.27 0.57 0.67 0.66 0.51 1.24 0.52 0.66 0.66 0.42 1.19 0.40 0.45 0.44

Cl

HNO3

K

Mg

Na NH4+ NO3-

SO2

SO42-

91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93

KOC

MAR PRN SAN

FIGURE 7. Air concentration, deposition velocity, and deposition flux of sulfur dioxide over the 3-yr period of record at the Marcell site. Each bar represents one weekly value.

Figures 6-9 show the air concentrations, deposition velocities, and deposition fluxes for nitric acid vapor, sulfur dioxide, sulfate particles, calcium particles, and ammonium particles at the Marcell site. These figures illustrate the seasonality and the range in weekly variability of these parameters as well as the relationships among air concentrations, deposition velocity, and deposition flux. Both the air concentration and the deposition velocity of HNO3 were highest in summer, resulting in spikes of very high summertime (and low wintertime) deposition (Figure 6). In contrast, SO2 concentrations were highest in winter when the deposition velocity was low; however, the resulting deposition rate was more strongly influenced by the deposition velocity so that SO2 deposition peaked in summer (Figure 7).

FIGURE 6. Air concentration, deposition velocity, and deposition flux of nitric acid vapor over the 3-yr period of record at the Marcell site. Each bar represents one weekly value.

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Particle deposition velocities peaked in summer as shown in Figure 8 for the Marcell site. Although SO42concentrations did not appear to follow a clear seasonal trend, the SO42- deposition rate was strongly influenced by the seasonality of the particle deposition velocity, showing similar summertime peaks. Air concentrations of calcium showed some trend toward higher summertime values, and calcium deposition was clearly seasonal, with summertime peaks (Figure 9, the particle deposition velocities from Figure 8 apply to the species shown in Figure 9). Ammonium concentrations, like sulfate, did not appear to follow a clear seasonal trend as shsown for the Marcell site, and the ammonium deposition rate was strongly influenced by the seasonality of the particle deposition velocity with summertime peaks (Figure 9).

FIGURE 8. Air concentration, deposition velocity, and deposition flux of particulate sulfate over the 3-yr period of record at the Marcell site. Each bar represents one weekly value.

Total sulfur (SO2 + SO42-) dry deposition ranged from 0.98 kg ha-1 yr-1 at the Birch Lake site in 1993 to 9.24 kg ha-1 yr-1 at the Koch site in 1993. Total nitrogen (NH4+ + NO3- + HNO3) dry deposition ranged from 0.53 kg ha-1 yr-1 at Birch Lake in 1993 to 2.36 kg ha-1 yr-1 at Koch in 1993. Hubert et al. (31) described some of the difficulties in evaluating deposition fluxes of nitrate species and suggested that the particulate nitrate flux was not proportional to nitrate aerosol concentration, making the inferential approach invalid. However, they found that nitric acid vapor typically dominated the total nitrate flux and suggested that the inferential method would give acceptable results except when aerosol nitrate was a large fraction of the total nitrate concentration. In the present case, particulate nitrate ranged from 34% at the remote Birch Lake site to 77% at the urban Koch site of total nitrate. However, particulate nitrate was only a small fraction (8% at Birch Lake to 28% at Koch) of the total nitrogen. Ammonium was the dominant nitrogen species accounting for 63-74% of the total nitrogen. Thus, while the deposition fluxes for particulate nitrate may be uncertain, the values for total nitrogen are more reliable. The National Atmospheric Deposition Program (NADP) operates wet deposition samplers at the Ely and Marcell sites, and the MPCA operates wet deposition samplers at the Birch Lake, Cedar Creek, Finland, and Sandstone sites. Wet deposition data from these sites was used to calculate total (wet + dry) deposition and to determine the fraction of total deposition that is represented by dry deposition. Table 5 gives the dry deposition percentage of total deposition. The percentage varies by site and by constitu-

FIGURE 9. Air concentration and deposition flux of calcium and ammonium ion over the 3-yr period of record at the Marcell site. Each bar represents one weekly value. The deposition velocities for these two species are the same as shown for particulate sulfate in Figure 7.

ent. Dry deposition of Ca, K, Mg, and Na averaged from 5 to 10% of total deposition of these constituents, and the percentage of dry deposition was highest at the Cedar Creek site (nearest the metro area). As discussed previously, the particle concentrations, deposition velocities, and hence the dry deposition fluxes of the base cations are considered underestimates. Dry deposition of sulfate and ammonium ranged from 3 to 10% of total deposition, and the percentage was similar across all sites. Dry deposition of nitrate was generally less than 5% of total deposition and was usually 1% or less at the most remote sites. Dry deposition of total sulfur (including S from sulfate and sulfur dioxide) ranged from about 15 to 30% of total wet plus dry sulfur deposition (sulfate being the only S species measured in the wet deposition), and averaged about 22%. Total nitrogen dry deposition (including N from NH4+, HNO3, and NO3-) ranged from 9 to 21% of total wet plus dry nitrogen deposition (wet N consisting of N from NH4+ and NO3-) and averaged about 14%. Minnesota lies upwind of the majority of air pollution sources in North America, and as expected, the deposition fluxes of pollutant species are lower in the state than in other areas downwind of pollution sources. For example, surrogate surface techniques used in North Carolina gave estimates of annual sulfate dry deposition of 4.0-21.0 kg ha-1 yr-1 (24). Preliminary estimates from the NDDN of sulfur species (SO2 + SO42-, reported as sulfate) and nitrogen species (HNO3 + NO3-, reported as nitrate) deposition rates using literature-derived deposition velocities ranged from 4.8 and 1.71 kg ha-1 yr-1, respectively, in Maine to 36 and

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TABLE 5

Percentage of Dry to Total (Wet + Dry) Deposition analyte Ca

Cl

K

Mg

Na NH4+

SO42NO3-

total Sa

total Nb

year

BIR

CED

ELY

FIN

MAR

SAN

91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93 91 92 93

4.0 5.1 3.7 2.6 3.4 11.4 9.2 14.3 20.2 4.3 3.1 4.4 11.2 18.2 10.7 9.3 9.6 9.2 7.8 6.5 7.0 1.1 1.1 0.8 25.8 21.0 23.0 16.3 16.5 17.1

6.1 8.9 13.0 3.9 5.1 6.9 8.8 12.7 6.0 5.2 8.5 6.8 7.5 11.1 6.3 6.5 9.1 6.5 6.7 7.1 5.5 3.6 4.8 4.2 28.9 26.8 20.6 15.9 21.2 15.5

3.2 5.3 3.3 3.3 3.0 2.1 20.0 15.5 13.6 4.8 12.3 5.3 5.0 7.4 2.0 7.6 9.2 8.2 7.7 7.2 6.2 0.9 1.3 0.8 23.8 22.1 20.7 12.1 17.6 15.5

4.0 5.4 0.8 3.5 2.3 4.2 11.0 8.9 2.3 3.7 4.8 0.6 10.3 9.9 9.0 5.5 4.8 3.4 3.7 3.2 3.7 1.1 1.0 0.6 16.6 15.7 21.5 11.7 12.1 9.4

2.9 4.3 3.8 2.3 1.9 3.7 9.0 8.7 10.8 5.5 6.8 5.0 4.2 4.4 2.2 6.6 6.8 6.2 6.0 6.3 5.6 1.1 1.6 1.2 20.1 19.1 19.7 10.8 13.9 12.5

3.5 5.1 1.3 3.0 2.7 5.8 6.6 11.5 13.1 3.4 4.9 3.8 9.6 9.5 5.7 5.7 7.8 5.8 5.6 6.4 6.2 2.2 3.1 2.6 21.8 22.4 22.9 11.4 16.4 11.5

a Total S ) SO + SO 2- (only SO 2- measured in precipitation). b Total 2 4 4 N ) HNO3 + NO3- + NH4+ (only NO3- and NH4+ measured in precipitation).

9 kg ha-1 yr-1, respectively, in Pennsylvania (32). At Huntington Forest in New York, Shepard et al. (33) used the big-leaf model (9) to estimate annual average deposition of NH4+ at 0.16 kg ha-1 yr-1, Ca at 1.18 kg ha-1 yr-1, NO3at 7.13 kg ha-1 yr-1, and SO42- at 6.19 kg ha-1 yr-1. Dry deposition constituted 12, 58, 55, and 26%, respectively, of total deposition of those four species. Likens et al., working at Hubbard Brook, NH, over a period of 23 yr, used watershed mass balance techniques to estimate dry deposition of sulfur species (reported as sulfate) at about 20 kg ha-1 yr-1, constituting about 37% of the total (wet + dry) sulfur deposition (34). Lindberg and Lovett (35) determined annual average fluxes of sulfate using the big-leaf model (9) as well as other techniques as part of the Integrated Forest Study. They reported values of about 3.4 kg ha-1 yr-1 for the northwestern United States, about 6.9 kg ha-1 yr-1 for the southeastern United States, 3.31 kg ha-1 yr-1 for the northeastern United States, and 4.7 kg ha-1 yr-1 for a site in Norway. At Walker Branch, TN, Lindberg et al. reported sulfate deposition at 3.36 kg ha-1 yr-1, total sulfur species at 42.2 kg ha-1 yr-1 (reported as sulfate, dry deposition 55% of total), nitrate at 0.03 kg ha-1 yr-1, nitrogen species at 10.7 kg ha-1 yr-1 (reported as nitrate, dry deposition 64% of total), ammonium at 0.51 kg ha-1 yr-1 (dry deposition 32% of total), and calcium at 6.2 kg ha-1 yr-1 (dry deposition 72% of total). They included methods for measuring coarse particles and found that the coarse particles contributed the vast majority of the calcium deposition (30 times grater than fine particles). In addition, coarse particles contributed the majority of the nitrate (83

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times greater than fine particles) and sulfate (2.7 times greater) deposition. The particles sampling methods used by Lindberg et al. were different from the present study as discussed previously.

Problems Several problems were encountered in this work, most notably that complete meteorological data with all of the required measurements were not routinely collected at the filter pack monitoring sites. Specific difficulties and the solutions employed where possible included the following: (1) The low-volume filter pack sampling method systematically underestimated base cation concentrations by preferentially collecting small particles. In addition, the deposition velocity for the base cations may have been underestimated since they were presumed to apply to particles less than 2 µm. In sum, the base cation concentrations and deposition fluxes are considered underestimates or lower bounds. (2) Solar radiation data were not available on an hourly basis at all sites; however, the lacking values were calculated with reasonable accuracy since the model results are not sensitive to small variations in solar radiation. (3) Leaf wetness was unavailable but could also be calculated. Model results are quite sensitive to leaf wetness, so the method of estimation should be considered with care. In this work, leaf wetness was estimated conservatively, which may have introduced some underestimation of the deposition velocity. (4) Hourly precipitation data were also difficult to obtain but were substituted by partitioning daily precipitation over the 24-h period. This remedy, while not appropriate if accurate estimation of Vd is required for a specific hour, does not appear to greatly bias the results over the weekly and annual periods considered here. (A comparison was made of the deposition velocity results obtained using hourly precipitation data and substituting partitioned daily data at one site for 1 yr. No significant differences were seen in the weekly averaged deposition velocities, and the annual deposition velocity was nearly identical.) (5) One of the most difficult meteorological parameters to obtain was the standard deviation of the wind direction. This parameter cannot be calculated or estimated from other measurements, and the model results are sensitive to variations in it. We recommend that routine meteorological data collection stations record this parameter. (6) The MPCA filter pack monitoring network did not include on-site collection of meteorological data. As a result, we were forced to extrapolate meteorological data in some cases in the present work. We compared the deposition velocities obtained at the Koch site using the on-site meteorological data with data extrapolated from Cedar Creek. We found that the differences in predicted deposition velocities were small (usually statistically insignificant, but sometimes there were statistically significant differences). Despite the fact that it is possible to extrapolate off-site meteorological data and obtain useable results, we recommend that filter pack monitoring sites include routine monitoring of the meteorological data necessary to calculate deposition velocities. (7) The air concentration measurements consisted of weekly averages. The weekly time scale is not adequate to capture the short-term variations in the deposition velocity. The use of weekly average concentrations and deposition

velocities to estimate weekly average fluxes is valid only if the correlation between the deposition velocity and the concentration is low. Matt and Meyers (36) found a nonnegligible correlation between hourly SO2 concentrations and modeled deposition velocities. The net result was that summertime fluxes estimated by weekly averages were biased low by 40% with annual fluxes underestimated by 20%. Those results may imply that the SO2 deposition fluxes calculated here could also be biased low. Further studies are needed to investigate this issue.

Acknowledgments We thank the site operators, who faithfully changed the filter packs; the laboratory staff, who participated in making the measurements; and other MPCA/AQD staff, who assisted in numerous ways. In addition, we thank Mark Seeley and Brian Day of the University of Minnesota, Soil Science Department, for providing extensive meteorological data, and Gretchen Rohweder for providing summaries of wet deposition data.

Literature Cited (1) Wesely, M. L.; Hicks, B. B. J. Air Pollut. Control Assoc. 1977, 27, 1110. (2) McMahon, T. A.; Denilson, P. J. Atmos. Environ. 1979, 13, 571. (3) Sehmel, G. A. Atmos. Environ. 1980, 14, 983. (4) Hosker, R. P., Jr.; Lindberg, S. E. Atmos. Environ. 1982, 16, 889. (5) Hicks, B. B. In The Acidic Deposition Phenomenon and its Effects; Altshuller, A. P., Linhurst, R. A., Eds.; U.S. EPA 600/8-83-018A; U.S. EPA: Washington, DC, 1984; Chapter A-7, 772 pp. (6) Hosker, R. P., Jr. In Air Pollutants and Their Effects on the Terrestrial Ecosystem; Legge, A. H., Krupa, S. V., Eds.; John Wiley: New York, 1986; pp 505-567. (7) Voldner, E. C.; Barrie, L. A.; Sirois, A. Atmos. Environ. 1986, 20, 2101. (8) Baldocchi, D. Atmos. Environ. 1988, 22, 869. (9) Hicks, B. B.; Baldocchi, D. D.; Meyers, T. P.; Hosker, R. P., Jr.; Matt, D. R. Water Air Soil Pollut. 1987, 36, 311. (10) Meyers, T. P.; Baldocchi, D. D. Tellus 1988, 40B, 270. (11) Baldocchi, D. D.; Hicks, B. B.; Camara, P. Atmos. Environ. 1987, 21, 91. (12) Stoeckenius, T. E.; Shepard, S. B.; Iwamiya, R. K. Analysis of Dry Deposition Monitoring Data in Minnesota and Surrounding Areas. Report to the Minnesota Pollution Control Agency by Systems

Applications International, SYSAPP95-94/087, 1995. (13) Pratt, G. C.; Krupa, S. V. Atmos. Environ. 1985, 19, 961. (14) Lindberg, S. E.; Lovett, G. M.; Richter, D. D.; Johnson, D. W. Science 1986, 231, 141. (15) Walraven, B. Sol. Energy 1978, 20, 393. (16) Weiss, A.; Norman, J. M. Agric. Forest Meteorol. 1985, 34, 205. (17) Meyers, T. P.; Finklestein, P.; Clarke, J.; Ellestad, T. Atmos. Environ. Submitted for publication. (18) Norman, J. In Modification of the aerial environment of plants; Barfield, B. J., Gerber, J. F., Eds.; ASAE Monograph 2; American Society of Agricultural Engineers Monograph, St. Joseph, MI, 1979; pp 249-280. (19) Baldocchi, D. D.; Hutchison, B. A. Tree Physiol. 1986, 2, 155. (20) Meyers, T. P. Dry Deposition Measurements at Huntington Forest, Report to U.S. EPA for IAG No. DW13933238-01-0, 1991. (21) Voldner, E. C.; Barrie, L. A.; Sirois, A. Atmos. Environ. 1986, 20, 2101. (22) Sehmel, G. A. Atmos. Environ. 1980, 14, 983. (23) Davidson, C. I.; Wu, Y.-L. In Control and Fate of Atmospheric Trace Metals; Pacyna, J. M., Ottar, B., Eds.; Kluwer Academic Publishers: Boston, 1989; pp 147-202. (24) Vandenberg, J. J.; Knoerr, K. R. Atmos. Environ. 1985, 19, 627. (25) Meyers, T. P.; Huebert, B. J.; Hicks, B. B. Boundary-Layer Meteorol. 1989, 49, 395. (26) Lee, G.; Zhuang, L.; Huebert, B. J.; Meyers; T. P. J. Geophys. Res. 1993, 98 (D7), 12,661. (27) Matt, D. R.; McMillen, R. T.; Womack, J. D.; Hicks, B. B. Water Air Soil Pollut. 1987, 36, 331. (28) Duan, B.; Fairall, C. W.; Thomson, D. W. J. Appl. Meteorol. 1988, 27, 642. (29) Meyers, T. P. Water Air Soil Pollut. 1987, 35, 261. (30) Johansson, C.; Granat, L. Atmos. Environ. 1986, 20, 1165. (31) Hubert, B. J.; Luke, W. T.; Delany, A. C.; Brost, R. A. J. Geophys. Res. 1988, 93, 7127. (32) Edgerton, E. S.; Lavery, T. F.; Boksleitner, R. P. Environ. Pollut. 1992, 75, 145. (33) Shepard, J. P.; Mitchell, M. J.; Scott, T. J.; Zhang, Y. M.; Raynal, D. J. Water Air Soil Pollut. 1989, 48, 225. (34) Likens, G. E.; Bormann, F. H.; Hedin, L. O.; Driscoll, C. T.; Eaton, J. S. Tellus 1990, 42B, 319. (35) Lindberg, S. E.; Lovett, G. M. Atmos. Environ. 1992, 26A, 1477. (36) Matt, D. R.; Meyers, T. P. Atmos. Environ. 1993, 27, 493.

Received for review July 26, 1995. Revised manuscript received March 12, 1996. Accepted March 13, 1996.X ES9505558 X

Abstract published in Advance ACS Abstracts, May 1, 1996.

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