Atmos. Chem. Phys., 8, 2729–2739 , 2008

www.atmos-chem-phys.net/8/2729/2008/ © Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License. 

## Atmospheric Chemistry and Physics



# Weekly patterns of aerosol in the United States

D. M. Murphy 1 , S. L. Capps 1,2,* , J. S. Daniel 1 , G. J. Frost 1,3 , and W. H. White4

1 Earth System Research Laboratory, Chemical Sciences Division, National Oceanic and Atmospheric Administration Boulder, CO 80305, USA

2 Vanderbilt University, Nashville, TN 37240, USA
3 Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, CO 80309, USA

4 Crocker Nuclear Laboratory, University of California, Davis Davis, CA 95616, USA

* now at: School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Received: 4 December 2007 – Published in Atmos. Chem. Phys. Discuss.: 10 January 2008 Revised: 13 March 2008 – Accepted: 3 April 2008 – Published: 22 May 2008

Abstract. Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network of aerosol samplers and NOAA monitoring sites are examined forweekly cycles. At remote and rural sites, fine particle elemental carbon, crustal elements, and coarse particle mass had pronounced (up to 20%) weekly cycles with minima on Sunday or Monday. Fine particle organic carbon and mass had smaller amplitude cycles, also with Sunday or Mondayminima. There was no statistically significant weekly cycle in fine particle sulfate despite a 5 to 15% weekly cycle in power plant SO 2 emissions. Although results for nitrate may be more susceptible to sampling artifacts, nitrate also showed a pronounced weekly cycle with an amplitude similar to elemental carbon. The only species found with a weekend maximum was Pb, probably from general aviationon weekends. Aerosol optical properties at NOAA monitoring sites were consistent with the IMPROVE chemical data, with significant weekly cycles in aerosol light absorption but not light scattering. These results support a large role of
diesel emissions in elemental carbon aerosol over the entire United States and suggest that a large fraction of the airborne soil dust is anthropogenic. They also suggest that studies of weekly cycles in temperature, cloudiness, precipitation, or other meteorological variables should look for causes more in light-absorbing particles and possible ice nucleation by
dust rather than sulfate or total aerosol. There are also implications for personal exposure and epidemiological studies
of aerosol health effects.

Correspondence to: D. Murphy (daniel.m.murphy@noaa.gov) 1 Introduction

Observations of the weekly cycle are a powerful tool for distinguishing anthropogenic from natural causes. Only anthropogenic activities are likely to influence concentrations, temperatures, or other atmospheric variables on a seven-day cycle. Weekly trends have been found for diurnal temperature range over the United States (Forster and Solomon, 2003)and China (Gong et al., 2006) as well as cloudiness and precipitation in Germany (B  ̈ aumer and Vogel, 2007). Whiteet al. (1990, 1991) studied transport of urban pollutants using weekly cycles. Blanchard and Tanenbaum (2003) found significantly less NO 2 on weekends in Southern California. Beirle et al. (2003) found weekly cycles in column NO 2 measured from satellite, with different amplitudes and phases on
various continents that could be related to various societies. A number of studies have examined weekly cycles of ozone concentrations and photochemistry (e.g. Elkus and Wilson, 1977; Marr and Harley, 2002; Murphy et al., 2007). Several studies have specifically studied weekly cycles of aerosol. Almeida et al. (2006) found that chemical elements such as Ca, Si, and Fe associated with soil were significantly higher on weekdays at a site in a suburb of Lisbon. Motallebi et al. (2003) found weekly cycles at selected urban sites in California. Lough et al. (2006) and Harley et al. (2005) found significant weekly trends in aerosol components in the Los Angeles area. In particular, both found much lower ratios of elemental carbon to organic carbon on weekends. Jin et al. (2005) used satellite data to discern a midweek maximum in aerosol optical thickness near New York City. B  ̈ aumer et al. (2008) examined aerosol optical thickness over Europe.Delene and Ogren (2002) found slightly higher single scattering albedos on Sundays and Mondays at Bondville, Illinois

Published by Copernicus Publications on behalf of the European Geosciences Union.2730 D. M. Murphy et al.: Weekly patterns of aerosol

Fig. 1. Clusters generated from the IMPROVE site locations. Eachcluster is shown by a circle with lines to the positions of the individual sites in that cluster. Next to each cluster is the number of elemental carbon samples for the analysis in this paper; other species have similar numbers of samples. Also shown as squares are theBondville and Southern Great Plains NOAA monitoring sites discussed in this paper.

and Southern Great Plains, Oklahoma than on other days of
the week. With the exception of Delene and Ogren (2002), studies on weekly aerosol properties have concentrated on urban sites. In this paper we use data from the IMPROVE network and NOAA aerosol monitoring sites to examine weekly cycles in aerosol across the United States. An important feature of theIMPROVE aerosol data set is the chemical analysis, allowing much better weekly analysis than a simple mass measurement.

2 Data

2.1 IMPROVE network

IMPROVE data in this paper are from 1 September 2000 through 31 August 2006. This is an integral number of weeks and years, which helps keep annual cycles from being aliased into weekly cycles. We use data after summer 2000 becausethe network shifted then from a Wednesday–Saturday sampling schedule to a once every third day schedule that rotates through the days of the week, an important feature for this
work. There were also few sites in the eastern United States before 2000. Except as noted for coarse mass, all data in the paper are for fine mode aerosol smaller than 2.5 μ m. The IMPROVE monitoring program is described by Malm et al. (2004). Routine monitoring began in 1988 with the objectives of establishing current background haze levels, identifying the chemical composition and emissions sources of regional aerosols, and documenting long-term trends in aerosol concentrations. Most of the samplers are in locationsrepresentative of the 156 National Parks and Wilderness Areas where the Clean Air Act provides special protections forvisibility. The network now includes about 170 rural or remote sites in the United States. Data, standard operating procedures and site descriptions
are all available in detail at http://vista.cira.colostate.edu/ improve/. Every third day (all sites sample on the same days),
a set of three PM 2

. 5 samples and one PM 10 sample is collected by four independent filter sampling trains. The PM 10 filter is analyzed only for mass. The PM 2

. 5 modules sample behind AIHL cyclones (John and Reischl, 1980) at nominal
flow rates of 22.8 l min

− 1 . One module collects PM 2

. 5 on a 37 mm Nylon filter behind an annular denuder coated with
Na2 CO 3 and glycerol. This filter is analyzed by ion chromatography for the anions chloride, nitrate, and sulfate. A
second module collects undenuded PM 2

. 5 on a 25 mm Teflon filter for weighing and energy-dispersive x-ray fluorescence analysis (XRF). The third module collects undenuded PM 2

. 5on a 25 mm quartz-fiber filter for thermal fractionation of organic and elemental carbon with an optical correction for pyrolysis. The methods used for elemental and carbon analysis have undergone some minor changes since August 2000. BeforeDecember 2001, the elements lighter than iron were determined by proton-induced x-ray emission, and since January 2005 the XRF analysis for elements lighter than nickel has been done under vacuum. Carbon analyses since January
2005 have been conducted with new instrumentation according to a more precise protocol (Chow et al., 2007). These
one-time method transitions should have no effect on multiyear weekly cycles.There is a weekly pattern of sampler operations: the local operator checks the system’s operation each Tuesday, and replaces the cartridge of exposed filter cassettes in eachmodule with one of unexposed filter cassettes. This schedule imposes extra handling for Tuesday samples, which arebriefly interrupted while the operator transfers the active filter cassette from the old cartridge to the new one. It also imposes sample-day-dependent delays between exposure and unloading, when losses might potentially occur within thesampler. (Any potential for contamination would presumably exist both before and after exposure, and so be independent of sample day.) Such hypothetical effects have notbeen detected in previous testing. With the exception of winter nitrate, they would not explain the calendar-week cycles presented here, whose extremes usually occur away from the Tuesday sampler maintenance.
The sites in the IMPROVE database were combined into25 groups using hierarchical cluster analysis (Fig. 1). Fifteen urban sites were eliminated (ATLA1, BALT1, CHIC1, DETR1, FRES1, HOUS1, NEYO1, OLTO1, OMAH1, PHOE1, PITT1, PUSO1, RUBI1, SAGU1, and WASH1), as were some sites that were closed before 2000. Then great circle distances were calculated between all site pairs and the closest pair of sites or clusters combined with each other. The process was repeated until the closest clusters were more

Atmos. Chem. Phys., 8, 2729–2739 , 2008 www.atmos-chem-phys.net/8/2729/2008/D. M. Murphy et al.: Weekly patterns of aerosol 2731

than 390 km apart. This threshold was chosen to keep separate clusters in California. Single sites that had not ended upin a cluster were paired together (in Louisiana and the Carolinas) or combined with the nearest cluster (Everglades and Big Bend). Of sites outside of the continental United States, only Hawaii was kept because there were fewer samples in Alaska or the Virgin Islands. Use of clusters is essential to this work. Weekly cycles atsingle sites are often not statistically significant when samples are taken every third day. Several sampling sites must beaveraged to distinguish weekly cycles from random variability. Yet one cannot average the entire United States without first looking at regional patterns. It does not make sense to average together mineral dust concentrations from Arizona
and Maine or sulfate concentrations in California and Ohio.The exact technique used to generate the clusters is less important than having some sort of regional averaging.To start with a consistent set of data, all samples were required to have valid values for mass, elemental carbon, and sulfate. About 0.1% of the data were eliminated as high outliers. These are probably valid data influenced by localevents such as fires or nearby dust generation. For example, fine mode mass was required to be less than 50 μ g m3

. The results are not sensitive to these cutoffs but they do help keep a single sample from skewing one day of the week for an entire cluster. This left 92744 samples in the IMPROVE data during the time period after eliminating the urban sites. The actual number of samples for a given species may be slightly smaller, since not all analyses were performed on every sample. Data below detection limit were retained as zeros but species with many values below detection limit arenot discussed in the paper. Notably, our mineral dust analysis does not consider aluminum because of a poor detection limit compared to silicon and iron. Most of the subsequent results are presented in terms of the average weekly cycle in a cluster. For each day of the week, this is calculated as the average of all observations on that day of the week during the September 2000 to August 2006 period, at all sites in the cluster. For visual presentation, and for network averages, each cluster’s cycle is normalized to that cluster’s overall September 2000 to August 2006 mean. There is an option whether or not to normalize data from individual sites before calculating the weekly cycles for each cluster. Normalizing prevents a few sites with high absoluteconcentrations from dominating the averages, but has the disadvantage of amplifying noise from sites with low concentrations that may be near detection limits. Normalizing can also overemphasize sites with incomplete records. We have chosen to directly average all data within a cluster. We have done the calculations both ways and the calculated weeklycycles are not sensitive to whether or not the sites are normalized before averaging. In part, this is because the exclusion
of urban sites leaves sites within each individual cluster thatusually have similar absolute concentrations. For a particular species, the standard deviations of the concentrations of the sites in individual clusters are usually about half as large
as the standard deviation of the clusters from the continental

average. Judging the statistical significance of the weekly cycles isdifficult. The most important problem is the number of independent measurements. The standard errors in subsequentfigures are calculated as usual by dividing the standard deviation of individual measurements by the square root of the number of measurements. The samples are not completelyindependent of each other, however, as this calculation implicitly assumes. Concentration variations are driven largely by meteorological variables, which are correlated in both time and space. The IMPROVE protocol of samples everythird day should reduce but not completely eliminate the autocorrelation between successive samples (Anderson et al., 2003). Similarly, multiple sites within a cluster will often lie in the same synoptic airmass.Dilution can cause statistical significance to be underestimated. Consider the simple case of two sites downwind of a consistent weekly source with rapid transport. Both would show the same weekly cycles, but the site further downwind
would have lower absolute values. If the sites with different absolute concentrations are averaged together, the standard deviations for each day would be large even though the sites were sampling the same source.To assess the statistical significance, we compared the amplitude of the observed weekly cycle with the amplitude of the weekly cycle for synthetic time series generated fromrandomized versions of the same data. To preserve shortterm correlations, data within each month were kept as a block and each month was shifted by a random number ofdays of the week. The resulting statistics for 500 randomized samples of the data are shown in Table 1. The weekly cycle inferred from random data varies by species because
of different variance and skew in the concentrations of each species. Since we are considering the absolute value of the weeklycycle in the randomized data, the distributions are not centered on zero. This is why the 90th and 99th percentiles may appear closer than expected. Also, these percentilesof the absolute values of weekly cycles represent the amplitude required for confidence that a cycle of any phase is real. They are not the same as the 90 percent confidence limits on the amplitude of the derived cycles (these are not computedhere). The reason the two quantities are different is that random noise will always generate a positive weekly cycle ofsome phase whereas it can generate either a positive or negative error in a cycle whose phase is already determined. We also checked for cycles of 5, 8, and 10 days using the same software as the 7-day cycle (6 and 9 day cycles do not work well with samples taken every third day). For the species with cycles that are statistically significant compared to the randomized data, the 7-day cycle was much larger than the other periods. For example, by one measure (maximum two consecutive days minus minimum two consecutive days)

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Table 1. Statistics of weekly cycles for selected species averaged over the continental United States.

Species Overall continental Weekly range Weekly two-day Randomized data weekly two-day range US average (%) range ∗ (%) (%)

( μ g m − 3 ) 90th percentile 99th percentile
Silicon 0.149 22.4 15.3 9.1 11.4
Iron 0.040 26.5 18.1 10.2 14.2
Calcium 0.046 24.3 17.4 9.6 12.8
Coarse mass 4.91 23.1 18.4 9.8 12.8
Elemental carbon 0.230 14.2 11.4 6.3 8.1 Organic carbon 1.159 7.9 5.6 4.9 6.2
Fine mass – EC 5.90 7.9 6.2 5.1 6.4 Fine mass – (EC+soil) 5.17 5.5 4.8 5.4 7.2
Sulfate 1.835 4.5 2.3 5.4 7.2
Nitrate 0.592 13.4 11.0 8.0 9.7
Pb 0.0012 9.1 6.7 5.0 6.6 Pb/(Pb + Zn) 0.26 (ratio) 8.4 7.0 3.6 5.2
Zn 0.0041 8.3 6.6 4.3 5.3
V 0.0008 4.5 3.2 6.6 8.0
Se 0.0005 3.3 1.8 4.4 5.9

∗ maximum two consecutive days – minimum two consecutive days. Bold: exceeds 99th percentile of randomized data; italics: less than 90th percentile.

the weekly cycle for silicon was 15.3%. The 5, 8, and 10 day cycles were 2.6%, 2.7%, and 5.6% respectively. For sulfate, which was not statistically significant, the weekly cycle was 2.3% and the 8-day cycle was 2.5%. Finally, all of the weekly patterns shown here are robust with respect to changing the time period to various subsets of 2000 to 2006.

2.2 NOAA aerosol optical properties

The National Oceanic and Atmospheric Administration (NOAA) continuously measures aerosol optical properties ata number of baseline and regional monitoring stations (Delene and Ogren, 2002). Bondville, Illinois, and SouthernGreat Plains, Oklahoma, are relevant to this work. Their locations are shown on Fig. 1. Daily averaged data were used to construct the weekly cycles. Aerosol light absorption was measured at 565 nm usingParticle Soot/Absorption Photometers (PSAP, Radiance Research). The data were corrected for scattering using Bondet al. (1999). Aerosol light scattering was measured using TSI 3563 nephelometers. Only the 550 nm data are shown here. The sample stream was heated, if necessary, to maintain the relative humidity at about 50%. Further descriptions are available at http://www.esrl.noaa.gov/gmd/
aero/instrumentation/instrum.html

.The PSAP filters were changed less frequently on weekends at the Southern Great Plains sit and therefore had lower average total filter transmission on Sundays and Mondays (J. Ogren, personal communication, 2007). The Bondville site
was less affected. More recent work on PSAP correctionsby Virkkula et al. (2005) suggests that the Bond et al. corrections have an accurate dependence on filter transmission except for highly absorbing aerosols (albedo less than about 0.7) that are not likely to be important for these sites.

2.3 Emissions inventories

Weekly cycles in SO 2 and NO x emissions from power plants were computed for comparison with observed weekly cycles in aerosols. US power plant emission data were obtainedfrom the Environmental Protection Agency’s Clean Air Markets Division, http://camddataandmaps.epa.gov/gdm/index.
cfm?fuseaction=emissions.wizard . 1999 and 2005 hourly emissions recorded by continuous emission monitors at each power plant were aggregated over large geographic regions containing 50 to 300 individual plants. The hourly emissions for each region were averaged for each day of week over the entire year, and the day-of-week averages were normalized using the average emissions in each region. Weekly cycles in 1999 were very similar to the 2005 data shown in Fig. 6.

3 Results

3.1 Crustal elements

The largest percentage weekly cycles in aerosol concentrations were observed for the crustal elements Si, Ca, Ti, and Fe (Fig. 2). All had weekly cycles with 20% or greater range. Minima were on Sunday for most locations and maxima were usually on Thursday. Aluminum also had similar patternsbut more values below the detection limit. The weekly cyAtmos. Chem. Phys., 8, 2729–2739 , 2008 www.atmos-chem-phys.net/8/2729/2008/D. M. Murphy et al.: Weekly patterns of aerosol 2733

Fig. 2. Weekly cycles of crustal elements in the fine mode (below
2.5 μ m). The top panel shows weekly cycles for silicon at each cluster of IMPROVE sites. For each cluster of sites, Saturday through Friday averages are shown in red or orange as departures from the weekly mean, with standard errors. Red curves indicate statisticalsignificance is more likely than for orange. Weekly cycles in medians are shown in blue. Rectangles are proportional in size to theannual mean for each cluster. The lower left panel shows the normalized weekly cycles for silicon for each cluster in the continental United States. The heavy line is the average over all clusters. The lower right panel shows the average weekly cycle for the continental United States for various crustal elements and coarse mass, defined as the difference between fine and total (PM 10 ) mass.

cles were consistent throughout the country except for a few
locations where the absolute values were small. One of the largest weekly cycles was observed for the cluster in Arizona that had the largest annual averages for these elements of any
cluster in the continental United States. That cluster had low values on both Saturday and Sunday, perhaps because thesoil particles were generated locally so the atmospheric concentrations could respond more quickly to low emissions on the weekend. A curious feature for many locations is that the weekly maximum was on Thursday rather than Friday, as might be expected if weekday emissions built up in theatmosphere throughout the week. It is not clear to what extent this is a statistical fluctuation or a consequence of lower emissions on Friday caused by four-day work weeks.On Fig. 2 and subsequent figures, the average for the continental United States was generated from the normalized averages of the clusters. This means that individual sites in areas with a dense network of sites contribute less to the averagethan sites where the network is sparse. Averaging the normalized weekly cycles means that regions with low absolute Fig. 3. Weekly cycles for fine mode elemental carbon and the balance of fine mass. As in Fig. 2, red lines are average percentage daily departures from the mean for that cluster and blue lines aredepartures of the medians. Rectangles are proportional to the annual mean for each cluster of sites.

concentrations contribute as much to the continental average as regions with high concentrations. As a guide to the eye, normalized weekly cycles for individual clusters are plotted in orange or red if the maximum of two consecutive days is
less or more than four standard errors from the minimum of two consecutive days.

3.2 Elemental carbon, organic carbon, and aerosol mass

Weekly cycles for elemental carbon, organic carbon, and several measures of aerosol mass are shown in Figs. 3 and
4. Elemental carbon is subtracted from fine mode mass because for climate forcing calculations it is sensible to separate absorbing and non-absorbing aerosols. This also facilitates comparison with the optical measurements discussed later. The weekly cycles for fine mass are not statistically significant for individual clusters. Although Fig. 3 shows one highly significant cluster, testing with the randomized series shows that by chance one out of 25 clusters can exceed the four standard error threshold for the color code on Fig. 3. However, the continental US average weekly cycle for fine mass is statistically significant (Table 1) at about the 95%
level.

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Fig. 4. A comparison of the weekly cycles of elemental and organic carbon as well as various measures of the fine and coarse mode mass. Each line is the weekly cycle for one cluster of sites, colored by the annual mean. The heavy black lines are the averages for all clusters in
the continental United States.

Elemental carbon has a much larger weekly cycle than organic carbon or the fine mode mass. Figure 4d comparessome of the weekly cycles. In view of the large weekly cycle for crustal elements, a curve is shown for fine mass after removing the contribution from soil calculated using an equation modified slightly from Malm and Hand (2007):

Soil = (2

. 42 + 1

. 5 )

· Fe + 1

. 63

· Ca+ 2

. 2

· Al + 2

. 49

· Si + 1

. 94

· Ti (1)

The factors account for oxides, and in the case of Fe, crustal elements such as K that cannot easily be separated
from other sources. The additional factor of 1.5 ∗ Fe is a very rough estimate of a contribution from total carbon in the soil estimated from examining some strong dust events. We infer from this curve that the weekly cycle for fine mass is only partially due to the strong cycle in soil dust. It is also due to organic carbon and nitrate.Figure 5 shows histograms of elemental carbon concentrations for one cluster. The lower averages on Sunday and Monday were caused by a shift of the entire distribution rather than changes in a few extreme values. This is the case for other clusters as well. The histograms also show the formof the distributions: on a log scale such as Fig. 5 the distributions are skewed left. On a linear scale they are skewed to the right, so the distributions are between log-normal and
normal.

The IMPROVE network uses a thermo-optical methodto separate organic and elemental carbon. For all thermooptical methods, there are questions about the absolute accuracy of how organic carbon that chars during heating is separated from elemental carbon (Novakov et al., 2005; Chow et al., 2007). Despite these questions, weekly cycles depend more on consistency of the carbon analysis than the absolutecut point between elemental and organic carbon. In addition, the different weekly cycles for elemental and organic carbon show that the operational definition of elemental and organic carbon in the IMPROVE protocol does represent auseful separation for species with differing sources and behavior in the atmosphere.

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Fig. 5. Histograms of elemental carbon concentrations for one cluster of IMPROVE sites centered around Pennsylvania and West Virginia. The histograms compare Sunday and Monday with Wednesday and Thursday.

3.3 Sulfate and nitrate

Sulfate has a very different pattern than elemental carbon. There is no statistically significant weekly cycle in sulfate, despite a weekly cycle in SO 2 emissions (Fig. 6). Thisis probably caused both by the emissions and the chemistry of SO 2 . Emissions of SO 2 from power plants decline by 5 to 15% on weekends compared to weekdays, a much smaller change than the approximately 75% reduction in diesel emissions on weekends in California (Chinkin etal., 2003; Motallebi et al., 2003). Electric power generation accounted for about 70% of US SO 2 emissions during the period 2000–2006 (National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data, http://www.epa.gov/
ttn/chief/trends/index.html). Unlike crustal material or elemental carbon, the SO 2 emissions must be chemically transformed to produce sulfate. This will delay and spread out any weekly pattern. Also, oxidation of SO 2 to sulfate takes place by both gas phase reactions and liquid phase chemistry in clouds. These pathways proceed at different rates, further spreading out any weekly pattern. The variable presence of clouds may add to the daily scatter in sulfate and make cycles
harder to discern. We compared the weekly cycles for winter and summer sulfate concentrations. Sulfate concentrations are generally higher in summer than in winter, when photochemistry isslower and liquid phase clouds are less common in the northern United States. Distinguishing seasons did not reveal any statistically significant cycles. Fig. 6. Weekly cycles of sulfate in the IMPROVE network and
emissions of SO 2 from power plants. Symbols and lines are the same as Fig. 2.

There are two main types of aerosol nitrate: ammonium nitrate and nitrate chemically bound to crustal cations suchas calcium (Murphy et al., 2006). With few exceptions, ammonium nitrate is only stable when the sulfate has been completely neutralized. Otherwise it dissociates to gas phase ammonia and nitric acid (Bassett and Seinfeld, 1984). Because of the widespread oxidation of SO 2 to sulfuric acid, ammonium nitrate is more often limited by ammonium than the availability of gas phase nitric acid. The absolute amounts of nitrate vary greatly, with much larger concentrations in
southern California and the Midwest than the mountain west.
California and the Midwest are locations with substantial sources of ammonia and, especially for California, small
emissions of SO 2 . The reaction of nitric acid with calcium in
mineral dust is much less sensitive to sulfate.

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Fig. 7. Weekly cycles of nitrate in the IMPROVE network. The lower right panel shows the average for the continental US separated by season. Symbols and lines are the same as Fig. 2.

In contrast to sulfate, nitrate shows a weekly cycle (Fig. 7). Some of the cycle in nitrate is driven by the strong weekly cycle in crustal elements, but examination of the absolutevalues shows that calcium nitrate and similar species cannot account for all of the observed cycle in nitrate. The weekly nitrate minimum is Sunday through Tuesday, later than other species. The presence of a weekly cycle in nitrateis a little surprising because of the possible limitations by ammonia. For example, emissions of ammonia from farm animals should be fairly constant. NO x emissions from power plants have a similar cycle to that shown for sulfate in Fig. 6.
NO x emissions from vehicles have a significant component from heavy vehicles, so vehicular emissions will have a much stronger weekly cycle than NOx from power plants (Chinkin et al., 2003). The fraction of US NO x emitted by electric power generation decreased from 24% in 2000 to 20% in 2006. During the same period, on-road mobile sources produced about 37% while the contribution of off-highwayvehicles has increased from 18% to 22% (National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data, http://www.epa.gov/ttn/chief/trends/index.html). A weekly cycle for nitrate with a Sunday–Monday minimum has also been found in some urban areas (Millstein et al., 2007). Although the data are noisy when separated by season,there does appear to be a shift in the timing from a Monday minimum in summer to a Tuesday minimum in winter (Fig. 7). There are two possible explanations for this shift. First, conversion of NOx to HNO 3 , followed by rapid equilibration with aerosol nitrate, will be much slower in winter Fig. 8. Weekly cycles for Pb and the ratio of Pb to Pb+Zn. As for other figures, the red lines are the departures of the daily averages from each cluster mean, blue departures for medians, and the boxes are proportional to the annual mean for each cluster.

than in summer. That could shift the phase of the weekly cycle. Second, there could be an unrecognized artifact associated with the extra handling Tuesday samples receive fromthe site operator. Low temperatures and low sulfate concentrations favor the stability of ammonium nitrate particles, so nitrate concentrations in most of the country peak during the winter. The Nylon filters used for IMPROVE’s nitratemeasurement retain any nitric acid gas released after collection by thermal dissociation of ammonium nitrate. Warming of winter-Tuesday samples during the brief handling period, when any volatilized nitric acid would not be drawn backinto the filter, must for the moment be considered as a possible explanation for the nitrate minimum on winter Tuesdays.

3.4 Lead and other metals

Lead is the only species studied with a weekend maximum(Fig. 8). The largest single source of airborne Pb, accounting for about 25% of US emissions, is leaded gasoline in small piston aircraft (Murphy et al., 2007). Many of these small planes are recreational and emissions probably peak onweekends. The next largest sources of airborne Pb are coalfired power plants and heavy steel industry. Those emissions probably go down slightly on weekends as shown earlier for
SO 2

.Zinc and Pb are highly correlated in atmospheric samples, probably because of similar electric utility and industrial sources and similar condensation in combustion exhaust

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(Murphy et al., 2007). Their ratio is less variable than theirabsolute concentrations with a very consistent Sunday maximum (Fig. 8). Most locations in the United States have Pb/(Pb+Zn) in a narrow range between 0.3 and 0.33. The ratio Pb/(Pb+Zn) is computed instead of Pb/Zn because the latter diverges when Zn concentrations are small.By itself, Zn had a small weekly cycle with a Sunday minimum. Neither vanadium nor selenium had a statistically significant weekly cycle. The data for copper are noisy.

3.5 Optical properties of aerosols

Changes in the optical properties of aerosols at two sites in the central United States are consistent with the weekly cycles from the IMPROVE network. These NOAA sites use completely different instrumentation than the IMPROVE sites and operate continuously rather than sampling for 24 h every third day. At both NOAA sites, there was a weeklyminimum in aerosol light absorption on Monday (Fig. 9). Although the national minimum for IMPROVE elemental carbon was on Sunday, the clusters of sites close to Bondville and Southern Great Plains had minima on Monday (Fig. 3). With samples every third day, there were not enough data to do the direct comparison of just the Bondville IMPROVE site with the NOAA Bondville data. There were no statistically significant weekly cycles in light scattering at either NOAAsite. This is consistent with the lack of a statistically significant weekly cycle in sulfate and the small cycle in organic
carbon.

4 Implications

The weekly cycles shown here are unlikely to be from local sources at the sampling sites for two reasons. First, the data show regional rather than local patterns. Second, with the urban sites excluded many of the remaining IMPROVEsites are at national parks and monuments. One would expect weekend maxima rather than minima if local traffic were generating the weekly cycles. Large-scale weekly cycles in aerosol have implications for emissions, health effects, and climate studies. The weekly cycles in emissions must be much stronger than the up to 20% cycles observed in aerosol species. At any site and time, some sources will be very close, some one day upwind, and others further upwind. Multiple sources and variations inwinds will both tend to smear out the weekly cycle in emissions. Weekly cycles of traffic emissions have been studied in California. There, passenger vehicle travel is similar on weekdays and weekends but heavy truck emissions decrease by 70 to 80% on weekends (Marr et al., 2002; Chinkin et al.,2003; Motallebi et al., 2003; Harley et al., 2005). For passenger vehicles, there are diurnal differences between weekday and weekend. The peak emissions are at rush hours during Fig. 9. Aerosol light absorption and scattering from the NOAA monitoring sites at Bondville, Illinois, and Southern Great Plains,
Oklahoma.

the week but around midday on weekends. The dominant effect for elemental carbon emissions is the decrease of diesel
emissions on weekends. Elemental carbon concentrations onall days should decrease as new controls on heavy truck emissions penetrate the fleet.
The weekly cycle in crustal elements implies a large anthropogenic source in the United States besides wind-blown dust. The crustal elements have an even larger weekly cycle than elemental carbon, which is affected by the 70% decrease in heavy truck emissions on weekends. That means that the anthropogenic source for crustal elements must be both large and have decrease very significantly on weekends. The larger weekly cycle for crustal elements and PM10 compared to
PM 2

. 5 is consistent with the analysis by Bell et al. (2008). Light vehicle traffic on dirt or dusty roads probably does not have a sufficiently large weekly cycle. We do not know what fractions of plowing and other agricultural operations are conducted on weekends, and these may vary with region and season. Construction and heavy truck traffic (Venkatram et al., 1999; Muleski et al., 2005) are possible sources of dustwith large weekly cycles. For Pb, the IMPROVE data support a significant source from leaded aviation fuel.

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Weekly cycles may have implications for epidemiological studies of the health effects of aerosols. First, personal exposure to certain species will be affected if people spendmore time outdoors on Sundays when mineral dust and elemental carbon concentrations are lower. Second, there might
be some effects on the statistics in time series studies. Such studies often include day-of-week in the statistical analysis (Pope and Dockery, 2006). This should take out the biggest
effects but it is hard to exclude subtle interactions in the time series. On the other hand, the consistency of weekly cycles throughout the United States means that studies comparingdifferent cities over the same time period should compensate very well for any weekly cycles. Third, health care can be better on weekdays than weekends, resulting in different mortality rates for the same conditions (Bell and Redelmeier,2001). This could conceivably cause aerosol-health correlations to be underestimated if aerosol health effects maximizeon weekdays when treatment is better. Finally, it is possible that the differing weekly cycles of elemental carbon and
sulfate could be used in a statistical sense to examine the difficult question of what aerosol components are most responsible for the health effects. These results are important for studies of weekly and other
short-term climate effects of aerosols. The direct climateeffects of elemental carbon and non-absorbing fine particle mass have opposite signs. It will not be possible to relate weekly cycles in temperature or precipitation to aerosols without considering elemental carbon separately from other species. The differing weekly cycles of various aerosol species could provide a powerful check on aerosol transport models. The species with the biggest weekly cycles, crustalmaterial and elemental carbon, are insoluble. The most common soluble species is sulfate, which shows no significantweekly cycle. Therefore, weekly cycles in cloud condensation nuclei are likely to be small unless there are systematicchanges in the size distribution. Finally, mineral dust particles can be potent ice nuclei so the strong weekly cycle in crustal elements could affect ice clouds and precipitation.

Acknowledgements. S. Capps was supported by the National Oceanic and Atmospheric Administration’s Ernest F. Hollings Undergraduate Scholarship Program administered by the Oak Ridge Institute for Science and Education.

Edited by: A. Laaksonen

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