The aerosol at Barrow, Alaska: long-term trends and source locations

Atmospheric Environment 33 (1999) 2441}2458

The aerosol at Barrow, Alaska: long-term trends and source locations A.V. Polissar 
, P.K. Hopke *, P. Paatero, Y.J. Kaufmann, D.K. Hall, B.A. Bodhaine, E.G. Dutton, J.M. Harris Department of Chemistry, Clarkson University, Potsdam, NY 13699-5810, USA
On leave from the Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, 109017, Russia Department of Physics, University of Helsinki, P.O. Box 9, FIN-00014 Helsinki, Finland NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA Climate Monitoring and Diagnostics Laboratory R/E/CGI, NOAA, 325 Broadway, Boulder, CO 80303, USA Received 2 September 1998; accepted 16 November 1998

Abstract Aerosol data consisting of condensation nuclei (CN) counts, black carbon (BC) mass, aerosol light scattering (SC), and aerosol optical depth (AOD) measured at Barrow, Alaska from 1977 to 1994 have been analyzed by three-way positive matrix factorization (PMF3) by pooling all of the di!erent data into one large three-way array. The PMF3 analysis identi"ed four factors that indicate four di!erent combinations of aerosol sources active throughout the year in Alaska. Two of the factors (F1, F2) represent Arctic haze. The "rst Arctic haze have factor F1 is dominant in January}February while the second factor F2 is dominant in March}April. They appear to be material that is generally ascribed to long-range transported anthropogenic particles. A lower ratio of condensation nuclei to scattering coe$cient loadings is obtained for F2 indicating larger particles. Factor F3 is related to condensation nuclei. It has an annual cycle with two maxima, March and July}August indicating some involvement of marine biogenic sources. The fourth factor F4 represents the contribution to the stratospheric aerosol from the eruptions of El Chichon and Mt. Pinatubo. No signi"cant long-term trend for F1 was detected while F2 shows a negative trend over the period from 1982 to 1994 but not over the whole measurement period. A positive trend of F3 over the whole period has been observed. This trend may be related to increased biogenic sulfur production caused by reductions in the sea-ice cover in the Arctic and/or an air temperature increase in the vicinity of Barrow. Potential source contribution function (PSCF) analysis showed that in winter and spring during 1989 to 1993 regions in Eurasia and North America are the sources of particles measured at barrow. In contrast to this, large areas in the North Paci"c Ocean and the Arctic Ocean was contributed to observed high concentrations of CN in the summer season. Three-way positive matrix factorization was an e!ective method to extract time-series information contained in the measured quantities. PSCF was useful for the identi"cation possible source areas and the potential pathways for the Barrow aerosol. The e!ects of long-distance transport, photochemical aerosol production, emissions from biogenic activities in the ocean, volcanic eruptions on the aerosol measurements made at Barrow were extracted using this combined methodology.  1999 Elsevier Science Ltd. All rights reserved. Keywords: Atmospheric aerosol; Arctic haze; Factor analysis; Trends; Potential source contribution function

*Corresponding author. 1352-2310/99/$ - see front matter  1999 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 9 8 ) 0 0 4 2 3 - 3

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1. Introduction Particulate species attributed to emissions from midlatitudinal industrial sources (Arctic haze) have been observed in the Arctic for many years (Mitchell, 1957; Rahn, 1981). A number of reviews of the Arctic haze phenomenon have been published (e.g., Barrie, 1986; Shaw and Khalil, 1989; Sokolik, 1992; Vinogradova, 1993; Shaw, 1995). The highest concentrations of the particulate species have been measured during the winter and spring seasons and the lowest values were measured during the summer (e.g., Rahn, 1985; Barrie, 1986). It has been shown that such seasonal variations of the aerosol concentration in the Arctic are the result of a combination of a seasonal variability in the long-range transport of air (Miller, 1981; Raatz and Shaw, 1984; Raatz, 1989), in the atmospheric blocking phenomenon (Iversen and Joranger, 1985), in pollutant removal processes (Barrie et al., 1981, 1989), in the oxidation rate of SO (Barrie and  Ho!, 1984; Barrie and Barrie, 1990), and in the thickness of surface temperature inversions (Sakunov et al., 1990). Surface aerosol data including condensation nuclei (CN) counts, black carbon mass (BC), particle light scattering (SC), and aerosol optical depth (AOD) measurements have been reported from Barrow, Alaska (Bodhaine, 1989, 1995; Bodhaine and Dutton, 1993; Ferek et al., 1995). These parameters are measured in real time at this site. The aerosol scattering and absorption coe$cients also showed strong annual cycles with the Arctic haze maximum in the winter and spring, and a minimum in the summer and fall (Bodhaine, 1989, 1995). On the other hand, the annual cycles for condensation nucleus (CN) data show maxima in March and August (Bodhaine, 1989). A decreasing long-term trend in the tropospheric aerosol optical depth (AOD) and the surface aerosol scattering coe$cient in the March}April period from 1982 to 1991 at Barrow was reported by Bodhaine and Dutton (1993). A similar decrease of the integral atmospheric optical thickness in the Russian Arctic was reported by Radionov et al. (1995). The decrease in the Arctic haze was explained by possible reduction in anthropogenic pollution emissions in Eurasia (Bodhaine and Dutton, 1993, 1995; Radionov et al., 1995) and reduced transport of anthropogenic aerosol to Barrow (Ja!e et al., 1995). The presence of anthropogenic aerosol in the Arctic region may result in changes in the solar radiation balance and visibility. Therefore, the investigation of the seasonal variations and long-term trends in these aerosol parameters as well as the identi"cation of possible source areas are important. The results could aide in understanding of the mechanisms of atmospheric aerosol transport and transformation. They also could improve the knowledge of the climatology and optical properties of aerosol over Alaska that can be used to assess the radiative forcing of climate in this region and to obtain

atmospheric corrections of satellite data. This paper presents the results of a three-way factor analysis and the potential source contribution function analysis for aerosol data from Barrow, Alaska.

2. Instrumentation and data The station at Barrow (71.323N, 156.613E) is a part of a global network of baseline monitoring stations operated by the Climate Monitoring and Diagnostics Laboratory (CMDL) of the National Oceanic and Atmospheric Administration (NOAA). The description of the monitoring site at Barrow, instrumentation, and the screened aerosol data have previously been presented by Bodhaine (1989, 1995) and Dutton and Christy (1992). Aerosol scattering coe$cient was measured continuously with a Meteorology Research Inc. four-wavelength nephelometer similar to the design of Ahlquist and Charlson (1969) (Bodhaine, 1982). A rotating "lter wheel in front of the photomultiplier allows continuous measurements at 450, 550, 700, and 850 nm wavelengths. The nephelometer automatically switches between ambient air and "ltered air and does a real time subtraction to eliminate the instrument background and Rayleigh scattering of air. The nephelometer can measure aerosol scattering as low as about 10\ m\ with the accuracy of about $20%. A "lter-wheel normal incidence pyrheliometer was used for measurements of direct solar light at Barrow. Aerosol optical depth data for the 0.3}0.69 lm wavelength band are derived from pyrheliometric measurements using the spectral one-layer radiative transfer model of Bird and Riordan (1986). The accuracy for the computed aerosol optical depth is about $0.04. The resulting aerosol optical depth values may be biased by up to 0.05. This error is not likely to a!ect the results of the type of analysis presented below because only the relative variations of the aerosol parameters have been studied. Scattering coe$cients were measured without any preconditioning of the air. Since the air is warmed when it passes through the sampling stack and enters the building, the aerosol may be considered to be dried, especially during the winter. The air was not humidity controlled. It was assumed that dry scattering coe$cients would not change the results of the current study because the study depends mainly on annual or semiannual cycles and long-term trends, which might change slightly in magnitude but not in timing. Condensation nucleus concentrations were originally measured at Barrow using General Electric automatic counter. A TSI Inc. continuous condensation nucleus counter was installed at Barrow in March 1990. General Electric CN counter is generally considered to have a lower limit of about 0.015 lm diameter particle while

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the TSI Inc. counter has a threshold diameter about 0.01 lm. Black carbon mass concentration was measured with an aethalometer (Hansen et al., 1982) beginning in 1988. The stability of aethalometer optics allows detection of about 1.5 ng m\ for a 1 h collection period. Hourly average data sets were used for the calculation of monthly averages. The Barrow data were manually edited to remove spikes from local contamination. If local pollution was suspected, that hour data point was

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excluded. Wind data for that hour were checked, and the data point was excluded if wind direction was not from the clean-air sector (03}1303) or if wind speed was less than 0.5 m s\. The details of the algorithm are provided by Bodhaine (1995). Monthly geometric means of condensation nucleus (CN) number concentration, black carbon (BC) mass concentration, aerosol scattering coe$cients at the wavelengths 450 (SC1), 550 (SC2), 700 (SC3), 850 (SC4) nm, and monthly arithmetic means of the aerosol optical

Fig. 1. Barrow monthly geometric means of condensation nucleus number concentration (A), black carbon mass concentration (B), aerosol scattering coe$cient at the wavelengths 450 (SC1) (C), 550 (SC2) (D), 700 (SC3) (E), 850 (SC4) (F) nm and monthly arithmetic means of aerosol optical depth anomaly (G). The dotted line at the year-mark represents the January datum.

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depth (AOD) anomaly relative to a base of the nonvolcanic years are shown in Fig. 1. Black carbon (Fig. 1B), scattering coe$cients (Fig. 1C}F), and aerosol optical depth (Fig. 1G) time series show an annual cycle with the typical Arctic haze maxima in winter}spring and minima in the summer. It has been hypothesized that the Arctic haze maxima are caused by long-range transport from industrial regions in the winter and spring (e.g., Barrie, 1986). The scattering coe$cient time series usually have broad peaks or a combination of two major peaks when the winter maximum is connected with long-range aerosol transport and the spring peak associated with the long-range transport plus photochemically enhanced sulfate production from SO (Barrie and Barrie, 1990; Hop per et al., 1994; Polissar et al., 1998a). The condensation nucleus plot shows maxima in March and August and minima in May and November (Fig. 1A). Unusually high condensation nucleus concentrations were measured in

1984, 1986, 1988, and 1989 (Fig. 1A). The highest aerosol optical depth values were measured in 1982, 1983, and 1992 (Fig. 1G). These values are attributed to volcanic eruptions of Nyamuragira (1981), El Chicon (1982) and Pinatubo (1991) (Dutton and Christy, 1992; Bodhaine and Dutton, 1993). Scatter plots for the monthly means of the measured aerosol parameters are shown in Fig. 2. Two di!erent symbols represent the data for the high-pollution Arctic haze season from October to June and for the lowpollution season from July to September, respectively. It can be seen from Fig. 2E and F that the black carbon concentration and the scattering coe$cients are well correlated and probably connected with the aerosol from the similar sources. The squared correlation coe$cient (r) between black carbon concentration and the scattering coe$cient at the 450 nm (0.79) was higher than for the scattering coe$cient at the 850 nm (0.49) (Fig. 2E

Fig. 2. Monthly means of BC versus CN (A), SC1 versus CN (B), AOD anomaly versus CN (C), AOD anomaly versus SC1 (D), BC versus SC1 (E), BC versus SC (F), AOD anomaly versus BC (G), SC4 versus SC1 (H), and SC2 versus SC1 (I) scatter plots for Barrow. 

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and F). This result suggests that black carbon particles are mostly connected with smaller particle size fractions in agreement with prior observations in the Eastern Arctic reported by Polissar (1993). Scattering coe$cients in di!erent wavelengths were well correlated with the highest SC2/SC1 (Fig. 2I) and the lowest SC4/SC1 (Fig. 2H) squared correlation coe$cients equal to 0.99 and 0.84, respectively. BC/CN and SC1/CN (Fig. 2A and B) scatter plots show that there are at least two separate groups of data points in each scatter plot. These two groups are related to the periods from October to June and from July to September, respectively, and most likely represent particles from di!erent sources. The group with the higher BC to CN ratio in Fig. 2A represents the Arctic haze aerosol (the winter/spring season when both black carbon and condensation nucleus concentrations are high). The second group with the lower BC-to-CN ratio represents the summertime marine production of condensation nucleus (high condensation nucleus but low black carbon concentrations) (Fig. 2A). The SC1/CN plot shown in Fig. 2B is similar to the BC/CN plot (Fig. 2A). The AOD/CN, AOD/SC1, and AOD/BC scatter plots (Fig. 2C, D and G, respectively) also have at least two subsets of data points but the separation of these two subsets is not as clear as for the BC/CN and SC1/CN plots (Fig. 2A and B). The groups with the lower AOD/CN and AOD/BC ratios in Fig. 2C and G, respectively, probably represent Arctic haze aerosol while the groups with the higher AOD/CN and AOD/BC ratios represent the stratospheric part of aerosol optical depth connected with aerosol from volcanic eruptions (high aerosol optical depth but low condensation nucleus and black carbon concentrations). It can be seen from this preliminary analysis of the time series and the scatter plots that there are probably at least four major types of particles at Barrow: Arctic haze from distant sources, particles produced by photochemical conversion of SO to SO\, particles resulting   from the emission of biogenic sulfur from the ocean, and stratospheric aerosol associated with large volcanic eruptions.

3. The PMF3 solution of the trilinear model In order to investigate the seasonal variations and the long-term trends associated with the sources of these di!erent types of background aerosol at Barlow, the data were treated as a three-way array similarly to the treatment of Arctic aerosol data at Alert (Xie et al., 1999a, b). A new approach to applying the three-way trilinear (PARAFAC) model was applied (Harshman, 1970). The trilinear model is given by X"A ' B ' C#E,

(1)

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where X is an array of observed data, A, B, and C are the unknown factor matrices and E is the matrix of residuals. The multiplication symbol denotes the matrix outer product. The di!erence between the model and the data is E"X!A ' B ' C.

(2)

The least-squares problem can then be posed as min Q(X, $, A, B, C),

(3)

A, B, C

where




e  Q" GHI , (4) m HI G H I L e "x ! a ' b ' c (5) GHI GHI GF HF IF F and $ is the known array of estimated uncertainties (inverse weights) for X. For solving the model, a new approach, positive matrix factorization of three-way arrays (PMF3), was used. In contrast to the original PARAFAC program, the new variant has the following useful features: error estimates of individual points can be utilized, factor loadings can be constrained to be nonnegative, and the algorithm converges much faster (Paatero, 1997). Recently, Ross and Leurgans (1995) have also described the individually weighted, nonnegative solution of the PARAFAC model, but their algorithm is not quite so e$cient for di$cult problems (Hopke et al., 1998). The program PMF3 is not based on the alternating least-squares algorithm. Instead, PMF3 solves for all variables simultaneously. The solution is forced to be nonnegative by the use of a penalty function. The details of the algorithm are provided by Paatero (1997). Accompanying the factors, individual error estimates were also computed for each of the factor elements in all the three ways. The error estimates are calculated in a manner similar to that described by Roscoe and Hopke (1981) in which the errors in one factor matrix are estimated based on the errors in the ambient concentration values and assuming that the other matrix or matrices are error free. Each matrix is treated similarly in turn such that each matrix element has an uncertainty associated with it. The corresponding two-way method (PMF2) (individually weighted, non-negatively constrained factor analysis) was applied to data sets of major ion compositions of daily precipitation samples collected over a number of sites in Finland (Anttila et al., 1995), and considerable information on the sources of these ions was obtained. The method also was used for analysis of aerosol chemical composition data from seven National Park Service sampling sites in Alaska, and the identi"cation of possible sources of aerosol species at these remote

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locations was performed (Polissar et al., 1996; Polissar et al., 1998b). The identi"cation of source nature by positive matrix factorization for aerosol chemical composition data from Alert, Canada, was accomplished by Xie et al. (1999a). Because of the high correlation of the scattering coe$cients at di!erent wavelengths (Fig. 2H and I) only the values of the scattering coe$cient at the 450 nm were used in the factor analysis. To obtain the same order of magnitude for input values of di!erent aerosol parameters, scaling factors equal to the mean values of the corresponding parameters for the whole period of measurements have been used. This scaling interpretation of the results was obtained. All of the data sets had large numbers of missing values for 1976 so that only the data for the period from 1977 to 1994 were analyzed. A three-dimensional array of scaled monthly means of condensation nucleus number concentration, black carbon mass concentration, scattering coe$cient at the wavelength of 450 nm, and aerosol optical depth has been constructed. Then the corresponding three-dimensional array of scaled error estimates (weights) for these parameters has been created. These two arrays had 4;18;12"864 elements. Each of the four planes of the three-dimensional array corresponded to a particular aerosol parameter (CN, BC, SC1 or AOD) and had 18 rows equal to the number of years in the period of measurements from 1977 to 1994 and 12 columns corresponding to the number of months in each year. Thus, the three output modes of three-way factor analysis have represented long-term variations (mode A), seasonal variations (mode B), and relative contributions (loadings) of the measured aerosol parameters (mode C) for the each particular factor. The corresponding weight array is the same size as the data array. The scaled minimum monthly mean values were used as a part of the total error in the error estimates of measured values for each of the four aerosol parameters. An important advantage of positive matrix factorization is its ability to handle missing data by adjusting the corresponding error estimates of these data points. This capability was essential for the aerosol optical depth and black carbon data because they had large numbers of missing values. Missing data were replaced by geometric mean values obtained over the entire measurement period and large error estimates (low weights) equal to 1000 times of the corresponding mean for such values were used. It was assumed that data values are lognormally distributed so that in addition to the constant error estimates (minimum monthly mean values), a lognormal error estimate was added representing the e!ective geometric standard deviation (GSD) of the lognormal distribution of the data. The numerical value for the log(GSD) was chosen so as to make the obtained value of Q close to

the theoretical value (number of degrees of freedom). The initial guess was re"ned during the "rst runs of the program, while observing the values of Q that were obtained. Final values of Q and log(GSD) were equal to 483 and 0.28, respectively.

4. Back trajectories and potential source contribution function 4.1. Trajectory data The isentropic transport model (Harris and Kahl, 1994) developed at the Climate Monitoring and Diagnostics Laboratory (CMDL) was used in the current study of the potential source contribution function for Barrow, Alaska. Ten-day backward trajectories arriving twice daily at 00 and 12 UT at the 500 m elevation above sea level were calculated. The movement of an air parcel is described by segment endpoints of coordinates in terms of latitude, longitude, and height of each point. Isentropic trajectories account for adiabatic vertical motions that air parcels may experience on route to their destinations. In the near-surface layer an air parcel cannot always be traced isentropically because the isentropic surface on which it is travelling may either intersect the ground or be ill de"ned in an unstable boundary layer. This transport model, therefore, calculates trajectories on isentropic surfaces until the speci"ed surface descends to within 100 m of the ground. At this point, the model switches to a layer-averaged mode, where an air parcel is advected by winds averaged through the layer 100}600 m above the surface topography. These heights were chosen to diminish the e!ects of surface friction and to represent winds in the lower boundary layer. Input to the trajectory model is in the form of 2.53 latitude}longitude gridded meteorological parameters and topography furnished by the European Centre for Medium Range Weather Forecasts or the US National Centers for Environmental Prediction. Most of the techniques employed in this model, such as the transformation from isobaric to isentropic coordinates, horizontal interpolation procedures, and the predictor-correction method for advection, derive from earlier isobaric (Harris, 1982) and isentropic (Harris and Bodhaine, 1983) trajectory models. The trajectory model is subject to uncertainty arising from interpolation of sparse meteorological data, assumptions regarding vertical transport, observational errors, sub-grid-scale phenomenon, turbulence, convection, evaporation, and condensation. Five studies estimated average horizontal trajectory errors to be 140}290 km in 24 h (Kuo et al., 1985; Kahl and Samson, 1986, 1988; Haagenson et al., 1987; Draxler, 1987). These uncertainties are also detailed in Merrill et al. (1985) and Harris

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(1992) and references therein. Any given trajectory produced by this model should be reasonably representative of the large-scale circulation, and as such, may be used to suggest potential source regions. However, this does not imply that a particular air parcel sampled at the trajectory destination exactly followed this path.

m ()n ) may result in PSCF with high uncertainty in GH GH GH the apparent high value. PSCF would equal unity if GH n and m are identical. For large values of n, there is more statistical stability in the calculated value. Thus, to reduce the e!ect very small values of n , an arbitrary GH weight function =(n ) is multiplied into the PSCF value GH to better re#ect the uncertainty in the values for these cells:

4.2. Potential source contribution function

1.00 n *4 GH 0.85 n "3 GH . (9) =(n )" GH 0.65 n "2 GH 0.50 n "1 GH The grid size chosen for the potential source contribution function computations should be su$ciently large to assimilate the uncertainty of a trajectory endpoint. Kahl et al. (1989) estimated median horizontal uncertainty of a "ve-day back trajectory is the 800 to 1000 km range. The uncertainty is higher for a 10 day backward trajectory. Thus, for the analysis of the potential source contribution function, a 5 by 53 grid size corresponding to a distance of about 500 km in the latitudinal direction, and about 48}355 km in the longitudinal direction was chosen. The potential source contribution function analysis identi"es the potential source areas or possible pathways of transport into the Arctic from regions within the domain covered by 10 day backtrajectories that result in measured above average concentrations. However, the analysis does not estimate the spatial distribution of all the emission sources. Only those emissions that have been transported to the sampling site can be identi"ed. A high potential source contribution function region should coincide with a known emissions region within the domain. However, a region with a low value of the potential source contribution function does not necessarily indicate low emissions from the region. The potential source contribution function analysis was used by Cheng et al. (1993) and Hopke et al. (1995) for identi"cation possible sources and preferred pathways for biogenic, non-sea-salt sulfur, and other aerosol species to Alert, Canada. To perform analysis of the potential source contribution function 10 day backward trajectories arriving twice daily at 00 and 12 UT at 500 m above sea level over Barrow for each day of the "ve year period from 1989 to 1993 were calculated. Arithmetic means of the three consecutive values of each parameter (CN, BC, and SC1) measured at 23, 00, and 01 UT, as well as at 11, 12, and 13 UT corresponding to each trajectory for a particular date have been calculated. Then measurements which were higher than the "ve-year averages were identi"ed and used to determine the trajectory segment end point associated with these high pollution level measurements.

The construct of the potential source contribution function can be described as follows: if a trajectory endpoint lies at a cell of address (i, j ), the trajectory is assumed to collect material emitted in the cell. Once aerosol is incorporated into the air parcel, it can be transported along the trajectory to the receptor site. The objective is to develop a probability "eld suggesting likely source locations of the material that results in high measured values at the receptor site. If the total number of endpoints that fall in the cell is n , then the cumulative probability of these endpoints, GH P[A ], can be given by GH n (6) P[A ]" GH , GH N where N is the total number of endpoints summarized over all cells in the modeling region. The probability P[A ] represents the potential for transport of material GH from a grid cell to the receptor site. Among the n counts, GH there will be m points for which the measured aerosol GH parameter exceeds a criterion value selected for this parameter. In this work the criterion values for each parameter is the arithmetic mean for the whole period from 1989 to 1993. Note that the criterion value is obtained through o!-line statistical analyses. The probability that cell (i, j ) is related to the observed high concentrations, B , can be de"ned as GH m P[B ]" GH , (7) GH N The conditional probability, the potential source contribution function (PSCF), can then be de"ned as m (8) PSCF "P[B "A ]" GH . GH GH GH n GH Thus, the potential source contribution function can be interpreted as a conditional probability describing the spatial distribution of probable geographical source locations inferred by using trajectories arriving at the sampling site. Cells related to the high values of potential source contribution function are the potential source areas. Since the potential source contribution function is computed as a ratio of the counts of selected events (m ) GH to the counts of all events (n ), it is likely that a small GH

 

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5. Results and discussion 5.1. Positive matrix factorization results A four-factor solution for the three-way positive matrix factorization model was the most reasonable representation of the Barrow aerosol data behavior. This result agrees with the results of the time series and scatter plot analyses discussed earlier. This four-factor solution is presented in Figs. 3 and 4. Two factors, F1 and F2, represent the Arctic haze aerosol. The "rst Arctic haze factor F1 has seasonal variations with a maximum in January (B1, Fig. 4) while the F2 factor peaked in March (B2, Fig. 4). The species modes, the C-modes, have approximately the same loadings of black carbon concentration (C1 and C2, Fig. 4). However, the winter Arctic haze factor F1 has a higher values for the scattering coe$cient (C2, Fig. 4). The winter factor, F1, is connected primarily with "ner particle sizes (high values of condensation nucleus counts) while the spring Arctic haze factor F2 is related to larger particles (high values of scattering coe$cient) (C1 and C2, Fig. 4). This size increase may be due to the higher rate of photochemical conversion of SO to SO\   such that the additional particle mass condenses onto existing particle surfaces. These particles are larger, and therefore the spring factor F2 shows a lower condensa-

Fig. 3. B and C modes of the factors F1}F4 obtained by the three-way positive matrix factorization for the aerosol data set from Barrow. Thin solid lines show standard errors obtained by the three-way positive matrix factorization calculation.

tion nucleus concentration and higher scattering coe$cient and aerosol optical depth (C2, Fig. 4). This result is consistent with observations reported by Barrie and Barrie (1990), Hopper et al. (1994), and Polissar et al. (1998a). Thus, two major in#uences on the aerosol concentration are hypothesized: long-range transport of anthropogenic aerosol giving rise to the winter peak and the long-range transport plus photochemical oxidation of SO in the spring.  Two factors, F3 and F4, with high values for condensation nucleus concentrations (C3, Fig. 4) and aerosol optical depth (C4, Fig. 4), respectively, were identi"ed. The F3 factor with high loadings of condensation nucleus concentration has maxima in March and in July}August (C3, Fig. 4). The spring maximum for the condensation nucleus factor could be mostly connected with longrange transported aerosol and/or photochemical oxidation of SO while the summer maximum could be related  to the formation of the biogenic sulfur particles (Charlson et al., 1987). Part of the dimethyl sul"de (DMS) that leads to particle formation may be from the Arctic Ocean (Ferek et al., 1995), and some is transported from more southerly latitudes similar to what was observed at Alert, Northwest Territories, Canada (Hopke et al., 1995). The CN source areas in summer were identi"ed by the potential source contribution function method, and the results will be presented below. The F4 factor has the highest aerosol optical depth value (C4, Fig. 4) and the low condensation nuclei counts. The long-term mode had peaks in 1983 and 1992 (A4, Fig. 3). These peaks appear to be related to high concentrations of stratospheric particles after the eruptions of Nyamuragira (20 December 1981), Alaid (30 April 1981), El Chichon (4 April 1982) and Mt. Pinatubo (14 June 1991). Smaller 1980 and 1986 maxima may be associated with the eruptions of St. Helens (18 May 1980) (McCormick and Trepte, 1987) and Nevada del Ruiz (13 November 1985), respectively. Decreases in integral aerosol optical thickness in the Arctic starting in March 1983 and in March 1992 after the eruptions of El Chichon and Mt. Pinatubo, respectively, have been reported by Radionov and Marshunova (1994). Maximum in aerosol turbidity was measured in the Arctic in April 1983 and in March 1992 (Radionov and Marshunova, 1992, 1994; Radionov et al., 1995). Since no measurements of aerosol optical depth are made during the polar night, an increase in aerosol optical depth was not observed in the Arctic until a year after an eruption. It has been shown by Thompson and Lockwood (1996) that extinction from the Mt. Pinatubo cloud (1991}1994) rose and fell more steeply than that from El Chichon (1982}1984). A complicated behavior of stratospheric volcanic aerosol comprising an approximately 200 day decay period and a superimposed longer decay period of about 500 days of a volcanic cloud has been reported by JaK ger et al. (1995), Post et al. (1996), and

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Fig. 4. A modes of the factors F1}F4 obtained by the three-way positive matrix factorization for the aerosol data set from Barrow. Dashed lines represent linear regression "ts for A2 and A3.

Thompson and Lockwood (1996). It can be seen that behavior of the time series for the aerosol optical depth factor F4 for Barrow (A4, Fig. 3) agrees well with these earlier reports of the volcanic stratospheric aerosol behavior. Thus, factor F4 could be primarily attributed to the stratospheric part of aerosol optical depth. This factor also has a small loading of condensation nucleus concentration (C4, Fig. 4). It was found that correlation coe$cient between A4 and the sea surface temperature near the west coast of North and South America are in the range of 0.60 to 0.75 for the months of March to August. This correlation could be a result of the ocean cooling related to the aerosol optical depth increase caused by major volcanic eruptions. It has been shown that one of the major sources of condensation nuclei is the emission from biological activities in the ocean (Ferek et al., 1995). Therefore, it is possible that condensation nucleus con-

centration in factor F4 (C4, Fig. 4) could be connected with the emission rate changes caused by these volcanic related sea surface temperature variations. The B-mode of factor F4 shows a maximum in March}May followed by a gradual decrease through October (B4, Fig. 4). High error values for the period from November to February are related to a large number of missing values of aerosol optical depth during the polar night. A high degree of variability for the long-term A-mode of both Arctic haze factors F1 and F2 was observed (A1 and A2, Fig. 3). This variability can be explained by the variable e!ectiveness of long-range transport from year by year in the Arctic. No signi"cant long-term trend for the winter Arctic haze factor F1 was observed (A1, Fig. 3). F1 has maxima in 1981, 1984 and 1991 (A1, Fig. 3). For factor F2 (A2, Fig. 3) a negative trend is observed during the period from 1982 to 1994 but not for the entire measurement period. A positive trend was found for the

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condensation nucleus factor F3 over the whole period of measurements (A3, Fig. 3). 5.2. Trend analysis Linear regression in conjunction with a jackknife approach (Mosteller and Tukey, 1977) has been used to calculate the trends in the factors' time series after a logarithmic transformation. The jackknife method was used to obtain a better estimate of uncertainty in the slope. The calculation was made for the complete long-term A-mode factor scores. The values were grouped into 18 subsets. Each of the 18 subsets contained values for 17 out of 18 years of measurements. Let s be the calculated G least-squares slope for the group that omits the values for the ith year. Let s be the corresponding result for the entire set and n is the number of the groups. Pseudovalues sJ are de"ned by G sJ "ns !(n!1)s . (10) G G The jackknifed value sJ and an estimate of its variance vJ  are given by 1 L sJ " sJ , G n G L sJ !(1/n) ( L sJ ) G G , v" G G n!1 v vJ " . n

(11) 5.3. Dependence of A3 on meteorological parameters (12) (13)

The corresponding value of the trend ¹I and its estimated standard deviation qJ are then equal to ¹I "exp(sJ )!1,

(14)

" sJ vJ . (15) L\   The values ¹I and qJ corresponding to the jackknifed values sJ and vJ for factors F2 and F3 were calculated. No signi"cant long-term trend for F1 was detected while F2 shows a negative trend over the period from 1982 to 1994 but not over the whole measurement period. The trend in the A-mode of the spring Arctic haze factor (F2) is equal to !4.5$2.8% yr\ over the period from 1982 to 1994. On the other hand, the trend for this factor over the whole period of measurements is !2.5$3.3% yr\ and is not statistically signi"cant. The results of these trend calculations agree with the values for the aerosol scattering coe$cient and tropospheric aerosol optical depth decreases calculated for the spring months by Bodhaine and Dutton (1993). This trend value of !4.5$2.8% yr\ is equivalent to a 34$17% decrease of the spring Arctic haze factor over the 10 yr period. This value is higher than the 21% decrease in SO emissions in Euro pe reported over the period from 1980 to 1990 (Iversen et al., 1991; Ja!e et al., 1995). However, the observed decrease in the spring Arctic haze factor could be more

qJ ""t

closely related to decreases in SO emissions in Eastern  Europe and the Former Soviet Union which have not been reported. The calculated trend for the condensation nucleus factor F3 over the measurement period is 8.4$4.6% yr\. This positive trend of the condensation nucleus factor could be explained by a higher rate of the biogenic sulfur production caused by a changing air temperature in the vicinity of Barrow and reductions in the sea-ice cover in the Arctic over the time interval of these measurements (Chapman and Walsh, 1993; Johannessen et al., 1995; Maslanik et al., 1996; McPhee et al., 1998; Levi, 1998). In addition, it was shown that the decline in the extent of the sea ice is generally consistent with rising air temperatures and with changes in atmospheric circulation in the Arctic (Chapman and Walsh, 1993). A number of studies related to these changes were published recently (Walsh et al., 1996; Proshutinsky and Johnson, 1997; Steele and Boyd, 1998). However, currently there is no way to validate these interpretations of the observed trends. Further study is needed to determine the origin of the observed positive trend of the condensation nucleus factor and the negative trend of the spring Arctic haze factor.

To study the dependence of the long-term variations of the condensation nucleus factor (F3) upon meteorological parameters, regression analyses were performed of the factor values against meteorological parameters. The results of these analyses are shown in Fig. 5. It can be seen that the highest positive correlation (r"0.73) was obtained between A3 and the Barrow July mean air temperature (Fig. 5A). On the other hand, the highest negative correlation (r"!0.75) was obtained between A3 and the snow area in the Northern Hemisphere in August (Fig. 5D). The correlation coe$cient between A3 and the mean level of precipitations at Barrow in August was 0.59 (Fig. 5B). It was found that the mean temperature and the level of precipitations at Barrow are the local meteorological parameters that correlate best with the A-mode of the condensation nucleus factor F3. A comparison of Figs. 5A and B shows that groups of points which do not "t into the linear regression relationship shown in Fig. 5A (e.g., the group representing 1978, 1979, and 1981, as well as the group representing 1984, 1988, and 1994) "t well into the linear regression shown in Fig. 5B. On the other hand, the outlying groups of points in Fig. 5B (the groups representing 1980, and 1983, as well as 1986, 1989, 1990, and 1993) "t well into the linear regression in Fig. 5A. Only the 1977 value is an outlying point for all of the plots. These results suggest that several local meteorological parameters rather than only one of them are related to the long-term changes of the condensation nucleus concentration in summer.

A.V. Polissar et al. / Atmospheric Environment 33 (1999) 2441}2458

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Fig. 5. A3 versus mean air temperature at Barrow in July (A), A3 versus mean level of precipitation in Barrow in August (B), A3 versus mean temperature anomaly in the Northern Hemisphere in May (C) (Jones et al., 1998), and A3 versus mean snow area in the North Hemisphere in August (Northern Hemisphere Snow Cover Data, 1998) (D). The year numbers have been used as the scatter plot symbols. Dotted lines represent 95% con"dence intervals.

Finally, the correlation coe$cient between A3 and the mean northern hemisphere temperature anomaly in May was 0.48. This result could be related to a time delay between increases in the northern hemispheric temperature and a corresponding response of the mechanisms that control the condensation nuclei emission. The lower correlation coe$cient for the northern hemispheric temperature (Fig. 5C) than for the Barrow temperature (Fig. 5A) means that regional or local rather than distant sources make high contributions to observed concentrations of condensation nuclei in the summer. Thus, higher mean summer temperatures and precipitation amounts at Barrow in summer and the smaller Northern Hemisphere snow area led to higher condensation nucleus factor (F3) values. In addition, high values of F3 are observed when the mean northern hemispheric temperature anomaly in spring is high. This result agrees with the assumption that the major sources of condensation nuclei in the summer are emissions from biogenic activities in the ocean. During the transport of cold air from the north in summer, the condensation nucleus

concentration is low while transport of warm and humid (high levels of precipitation) air from the North Paci"c Ocean leads to high condensation nuclei concentration such that F3 correlates with local meteorological parameters. In addition, reductions in the sea-ice cover in the Arctic increases the open water area and therefore the particle precursor emission rate yielding a negative correlation between F3 and the snow area. To validate these assumptions, as well as to identify possible aerosol source areas and to study the nature of aerosol long-term variability at Barrow, potential source contribution function analysis has been used. 5.4. Potential source contribution function results Because of the periodic behavior of the time series shown in Figs. 1A}F and 3B1}B3, the data for the 5 yr period from 1989 to 1993 were divided into three sets representing di!erent seasons. Potential source contribution function maps were calculated for the periods from October to February (&&winter''), from March to May

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(spring), and from June to September (&&summer''), respectively, corresponding to the B-mode maxima obtained by the positive matrix factorization (B1}B3, Fig. 3). The potential source contribution function plots for these three seasons of the "ve-year interval from 1989 to 1993 for CN, BC, and SC1 are presented in Fig. 6. The Eurasian part of the trajectory domain in spring is smaller than in winter, while the trajectory

domain does not reach any large industrial regions in Eurasia and in North America in the summer (Fig. 6). Thus, long-range transport of anthropogenic aerosol is more e!ective in winter and spring than in the summer. This conclusion agrees with the seasonal variations shown in Fig. 1, the PMF results shown in Fig. 4 (B1, B2), and earlier published results (e.g. Barrie, 1986).

Fig. 6. The potential source contribution function plots for condensation nuclei (a, d, g), black carbon (b, e, h), and particles related to light scattering at 450 nm (c, f, i) during the months of October}February (a}c), of March to May (d}f ), and of June to September (g}i) for the period from 1989 to 1993.

A.V. Polissar et al. / Atmospheric Environment 33 (1999) 2441}2458

Major sources of black carbon and particles related to light scattering during the &&winter'' are located in the Former Soviet Union (Fig. 6b,c). These source regions are Kola Peninsula and Arkhangel'sk region, Ural industrial zone, Kuznetsk, Krasnoyarsk, and Irkutsk Regions, as well as industrial regions in Middle Asia. These results agree with sulfur emissions results for the former Soviet Union territory (Ryaboshapko et al., 1998) and with the global gridded inventories of anthropogenic emissions (Benkovitz et al., 1998). There are also sources of particles connected with light scattering but not of black carbon aerosol in the Western United States. Source areas in Eastern Canada and the Eastern United States in spring (Fig. 6e and f ) coincide with high SO emission regions in  eastern North America (Benkovitz et al., 1998). An additional PSCF analysis was performed for each year of the 1989}1993 period. It showed that the contributions from source areas in the Eastern North America were observed only in 1989 (Fig. 7e,f ). Relatively high PSCF values observed within the Arctic basin during winter and spring (Fig. 6b,c,e, and f ) could be connected with the preferred transport pathways and the aerosol formation caused by the SO to  SO\ oxidation during the transport rather than with  aerosol emissions in the Arctic. Condensation nuclei source regions in Eurasia during winter and spring (Fig. 6a,d) coincide with source regions of black carbon (Fig. 6b,e) and particles related to light scattering (Fig. 6c,f ). However, the high PSCF areas are much smaller for condensation nuclei (Fig. 6a,d) than for black carbon (Fig. 6b,e) and scattering (Fig. 6c,f ). This result could be related to the shorter residence time for condensation nuclei in air so that fewer of the small particles are transported to the Arctic. In addition, the major source of condensation nuclei in the Arctic during winter and spring could be not a long-range transport itself but SO  to SO\ conversion during the transport.  In summer no major high potential source contribution function areas for black carbon and particles connected with light scattering can be observed (Fig. 6h, i). In contrast to this, the summer potential source contribution function map for condensation nuclei shows major source areas in the North Paci"c Ocean and in the Arctic Ocean (Fig. 6g). These areas represent sources of particles in the ocean connected with the oxidation of dimethyl sul"de (Charlson et al., 1987; Ferek et al., 1995; Hopke et al., 1995). Thus, the assumption described above that the summer maximum of the condensation nucleus factor F3 (B3, Fig. 4) is related to the emissions from biological activities in the ocean is con"rmed. Figs. 7 and 8 represent the potential source contribution function plots for 1989 and 1991. These two years have been chosen as an example in order to understand the large observed di!erences between the 1989 and 1991 factor values (A1}A3, Fig. 3). The winter Arctic haze factor score (A1, Fig. 3) was much higher in 1991 than in

2453

1989 while the spring Arctic haze factor and the summer condensation nuclei factor scores were both lower in 1991 than in 1989 (A2, A3, Fig. 3). The comparison the potential source contribution function plots for 1989 (Fig. 7) and 1991 (Fig. 8) shows that the winter trajectory domain in Eurasia in 1989 (Fig. 7a}c) was much smaller than in 1991 (Fig. 8a}c). In contrast to the situation in winter 1991 (Fig. 8a}c), the 1989 winter high values of the PSCF covered the Ural industrial zone but did not reach the major industrial sources in Kuznetsk, Krasnoyarsk, and Irkutsk Regions, as well as industrial zones in Middle Asia (Fig. 7a}c). In addition, the contributions from the source areas in western North America were much higher in 1991 (Fig. 8a}c) than in 1989 (Fig. 7a}c) suggesting the origin of the low value of F1 observed in 1989 and the high value of F1 observed in 1991 (A1, Fig. 3). A di!erent behavior for spring 1989 and 1991 is observed. The spring PSCF plot has larger trajectory domains in Eurasia and in the eastern North America in 1989 (Fig. 7d}f ) than in 1991 (Fig. 8d}f ). In contrast to the winter situation, long-range transport from Eurasia, as well as from the eastern and the western parts of North America was more e!ective in spring 1989 than in spring 1991. Therefore, the spring Arctic haze factor has the higher value in 1989 than in 1991 (A2, Fig. 3). Finally, during the summer, the trajectory domain in the North Paci"c Ocean was much larger in 1989 (Fig. 7g}i) than in 1991 (Fig. 8g}i). In addition, a much larger area of high values of the PSCF for condensation nuclei in was observed 1989 (Fig. 7g) than in 1991 (Fig. 8g). Therefore, the transport of condensation nuclei from large oceanic areas was more e$cient in 1989 than in 1991 so that and the A-mode of F3 has a high value in 1989 and a low value in 1991 (A3, Fig. 3). A di!erent CN behavior for summer 1989 (Fig. 7g}i) and 1991 (Fig. 8g}i) could be a result of a CN emission rate reduction and/or changes in transport from the North Paci"c Ocean caused by the ocean cooling after the eruption of Mt. Pinatubo in June 1991. From these examples, it can be seen that the long-term variations in concentration of di!erent aerosol species at Barrow (Fig. 3, A1}A3) are strongly related to the longterm synoptical variability. It has been demonstrated that this variability can be e!ectively studied by using PSCF analysis.

6. Summary and conclusions Aerosol data from Barrow, Alaska, for the period from 1977 to 1994 have been analysed by three-way positive matrix factorization. A four-factor positive matrix factorization solution for the data set has been obtained. Two Arctic haze factors, F1 and F2, and two factors related to

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Fig. 7. The potential source contribution function plots for condensation nuclei (a, d, g), black carbon (b, e, h), and particles related to light scattering at 450 nm (c, f, i) during the months of January, February, and October}December (a}c), March}May (d}f ), and June}September (g}i) for 1989.

high condensation nucleus (F3) and aerosol optical depth values (F4), respectively, have been identi"ed. Factor F1 has seasonal variations with its maximum in January. The long-term mode of F1 has maxima in 1981, 1984, and 1991. No signi"cant long-term trend for this factor was detected. The second Arctic haze factor has an annual cycle with maximum in March. The long-term mode for the spring Arctic haze factor F2 shows a nega-

tive trend of over the period from 1982 to 1994 but not over the whole period of measurements. The winter Arctic haze factor F1 has high loadings of condensation nucleus concentration, while the spring Arctic haze factor F2 has high loadings of aerosol scattering. F1 is associated with long-range aerosol transport while F2 with long-range aerosol transport plus more e!ective photochemical transformation of SO to SO\ in spring. The  

A.V. Polissar et al. / Atmospheric Environment 33 (1999) 2441}2458

2455

Fig. 8. The potential source contribution function plots for condensation nuclei (a, d, g), black carbon (b, e, h), and particles related to light scattering at 450 nm (c, f, i) during the months of January, February, and October}December (a}c), March}May (d}f ), and June}September (g}i) for 1991.

seasonal variations and compositions of the F1 and F2 factors, particularly high black carbon loadings, suggest anthropogenic origins of the aerosol. The factor with the highest loadings of condensation nucleus condensation F3 has maxima in March and July}August. The spring maximum for F3 could be connected with the long-range transported aerosol while the summer maximum could be related to biogenic sulfur

precursor compounds emitted from the Arctic Ocean and the North Paci"c Ocean. A positive trend of the F3 factor over the measurement period has been observed. This trend may be the result of increases in dimethyl sul"de emission from the ocean caused by reductions in the sea-ice cover in the Arctic and/or in air temperature increases in the vicinity of Barrow over this time interval. Factor F4 with the highest aerosol optical depth loading

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has peaks in 1983 and 1992 connected with eruptions of El Chichon and Mt. Pinatubo. It is related to the stratospheric part of aerosol optical depth. Potential source contribution function (PSCF) analysis has been used for identi"cation of possible source areas and/or the possible pathways that give rise to the observed high aerosol concentrations. The PSCF maps show that in winter and spring industrial regions in Eurasia and North America are the major sources of aerosol measured at Barrow. However, in summer large areas of the North Paci"c Ocean and the Arctic Ocean contribute to observed high condensation nucleus concentrations. The long-term variability in F1}F3 is largely related to long-term synoptical variability observed in the PSCF plots. Positive matrix factorization was e!ective in analyzing the aerosol data to extract time information contained in di!erent measured parameters. PSCF analysis was useful in identifying possible source areas and the potential pathways of aerosol measured at Barrow and in investigation of the long-term variations of aerosol concentration. It is shown that the long distance transport from industrial regions, photochemical aerosol production, emissions from biogenic activities in the ocean, and volcanic eruptions are major sources of the aerosol properties measured at Barrow.

Acknowledgements The work at Clarkson University was supported by the National Science Foundation under Grant ATM 9523731. We thank the Barrow observatory sta! and NOAA/CMDL base funding. Financial support to Pentti Paatero from the Vilho, YrjoK , and Kalle VaK isaK laK Foundation is gratefully acknowledged. We thank John Ogren for his helpful comments, and Yu-Long Xie and Meng-Dawn Cheng for their help with potential source contribution function analysis.

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