Synoptic evaluation of regional PM2.5 concentrations

Atmospheric Environment 43 (2009) 594–603

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Synoptic evaluation of regional PM2.5 concentrations Kenneth J. Walsh a, *, Matthew Milligan a, John Sherwell b a b

Science Applications International Corporation, 615 Oberlin Road Suite 100, Raleigh, NC 27605, USA Maryland Department of Natural Resources, Power Plant Research Program, 580 Taylor Avenue, Tawes State Office Building, B-3, Annapolis, MD 21401, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 June 2008 Received in revised form 22 September 2008 Accepted 6 October 2008

By comparing short-term fluctuations in PM2.5 species concentrations among nearby air quality monitors and among species, it becomes possible to understand the regional and local events leading to higher concentrations. This approach was applied at thirteen sites in the Maryland area for the 2001– 2006 timeframe in order to identify and explain the behavior of eighteen different analytes as well as the daily Air Quality Index. Findings included identification of local upwind events such as fireworks displays, construction and demolition, the spatial extent of sulfate, nitrate, and ammonium correlations between ground-level monitors, correlations between some crustal species to indicate similar emissions sources in urban areas, and indicators of particle adsorption as a rate-limiting step for certain species. For example, the bromine behavior suggests that bromine concentrations on particulate matter may be limited by the particle adsorption rate and thus show a dependence on the Air Quality Index measurements. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Particulate matter Speciation Air Quality Index Spatial trends Synoptic perturbation Bromine

1. Introduction The National Ambient Air Quality Standards (NAAQS) for particulate matter less than 2.5 mm in aerodynamic diameter (PM2.5) are set at 15.0 mg m3 for the annual average and 35 mg m3 for the highest 24-h measurements. Because some Maryland monitors do not meet the annual average standards, parts of Maryland have been designated as nonattainment areas, and the state must develop a state implementation plan in order to attain in future years. The United States Environmental Protection Agency (EPA) has proposed nonattainment designations for the highest 24h measurements based on 2005–2007 data, including Baltimore, Maryland (EPA, 2008). To target the appropriate reductions, it will be necessary to understand the emissions sources and the atmospheric behavior of pollutants. Fortunately thirteen monitor sites within 56 km of Maryland have been collecting speciated PM2.5 measurements over the timeframe from 2001 to 2006 and reporting results to EPA’s Speciation Trends Network (STN). A previous study in the same region examined speciated data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network (Walsh and Gilliland, 2001). The IMPROVE network has been collecting speciated PM2.5 measurements in the MidAtlantic area since the early 1990s in Washington, DC, Dolly

* Corresponding author. Tel.: þ1 919 836 7579; fax: þ1 919 832 7243. E-mail address: [email protected] (K.J. Walsh). 1352-2310/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.10.019

Sods National Wilderness Area, WV, Shenandoah National Park, VA, and Brigantine Wilderness Area in the Edwin B. Forsythe National Wildlife Refuge, NJ. The last three monitor sites were considered rural, but sulfate particulate matter concentrations at Washington, DC correlated well with the rural sites in the raw data, for the seasonal averages, and with respect to synoptic perturbations. Synoptic perturbations were defined as the fractional deviation of the 24-h measurements from monthly averages. However, nitrate PM2.5 did not correlate well at these distances, and the carbon fractions (organic and elemental carbon) showed only fair correlations between Washington, DC and the rural sites. The previous study suggested the geographic extent that regional concentrations of different PM2.5 species affect local monitors, but the three rural monitors at that time were spaced up to 240 km from the central urban site. However, speciated particulate data has been collected more recently from many more sites within this range as the STN has developed in more urban settings. The data collection and analysis procedures are slightly different between the STN and IMPROVE networks, so the sparser network of IMPROVE monitor sites was not used in this study. The STN sites include thirteen monitor sites within 190 km of one another and should be able to identify good correlations of species with spatial extents smaller than that of sulfates. Receptor models, such as UNMIX, have been effective for linking chemically speciated measurements to particular types of sources (Chen et al., 2002). Chen et al. used the Ft. Meade data from 1999– 2001 to attribute data to six different factors to explain the variance of the species. They concluded that six factors were sufficient and

K.J. Walsh et al. / Atmospheric Environment 43 (2009) 594–603

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Fig. 1. Map of meteorological and Speciation Trends Network sites for study.

necessary for including major PM2.5 contributors (except sea salt and crustal matter) in an analysis of the Baltimore–Washington corridor. The incremental probability that a significant fraction of a particular factor came from a particular location was calculated based on back trajectories from the HYSPLIT model. However, the authors acknowledge that complex atmospheric processes could weaken the source signatures, making it difficult to resolve factors to specific real source emissions. Because the study described in this paper focused on daily measurements and compliance with standards that are averaged over three-year periods, the hourly measurements from the Baltimore PM Supersite were not considered directly comparable to the STN measurements. However, the measurements at these sites could provide valuable insight into the causes for the findings noted in this study’s analysis. For example, one paper (Tolocka et al., 2005) describes 99% of the fine and ultrafine particles by ten major composition types, seven attributed to local sources. Sulfate, nitrate, and other particulate aerosol species show strong seasonal patterns in the MidAtlantic states; for example, the Washington, DC monitor measured a seasonal average sulfate concentration from 2001 to 2006 of 8.1 mg m3 in summer months but only 3.1 mg m3 in winter months. Previous work (Walsh and Gilliland, 2001) showed that these seasonal variations apply across the entire geographic area. The seasonal pattern for sulfates is expected because the oxidation rate of precursor SO2 emissions is limited by the reaction of SO2 with hydroxyl radicals, and high concentrations of hydroxyl radicals are associated with high temperatures and solar radiation (Kulmala et al., 1995). To examine how individual air parcels affect ambient air quality, a study must remove the strong influence associated with such seasonal effects to avoid misinterpreting seasonal effects with regional air parcels. This study relies on statistical comparisons of measured values as well as the synoptic perturbations where the perturbations represent fractional concentration changes relative to the monthly average values. By defining synoptic perturbations as the fractional deviations from monthly averages, the normal seasonal variations in pollutant concentrations are not expected to have as great an influence on the correlations between sites. High correlations between the synoptic perturbations indicated similar event influences between the sites with a greater degree of confidence than correlations of raw data. The methodology may be applied in other areas and for other time periods in order to better understand the geographic extent that various emissions sources have on ambient air quality data.

2. Data preparation PM2.5 speciation data was downloaded from EPA’s Air Quality System (AQS) website (EPA, 2007a) for the period from January 2001 to August 2006. A map is shown in Fig. 1, and the location settings range from rural woodlands (Arendtsville) to urban industrial districts (e.g., Wilmington). Most sites began operation in the first half of 2001, but the Lancaster, York, Philadelphia, Harrisburg, and Chester sites did not begin reporting until April 2002. In 2004 the monitor at Ft. Meade was moved to Beltsville. Essex and Washington had collection schedules of once-every-three days; all others collected samples every six days. The PM2.5 speciation analytes/indices for statistical analyses were chosen based on the number of non-zero values in the Washington data set. After excluding analytes with numerous zero or blank values, the following analytes were included in the data sets: bromine, calcium, copper, iron, lead, selenium, titanium, vanadium, silicon, zinc, sulfur, potassium, ammonium ion, sodium ion, organic carbon, elemental carbon, total nitrate, and sulfate; the Air Quality Index (AQI) for pollutant code 88502 was also included. Level II validated meteorology data were used from the Beltsville, MD Clean Air Status and Trends Network (CASTNET) site shown in Fig. 1 (EPA, 2007b). This data set included hourly temperature ( C), solar radiation (W m2), relative humidity (%), precipitation (mm), wind speed (m s1), wind direction (degrees), and sq (the standard deviation of the wind direction measurements during the hour). Upper air data were downloaded from the Integrated Global Radiosonde Archive (NOAA, 2007) for Washington Dulles International Airport (IAD). The fractional synoptic perturbation values for each analyte (i) at each site (j) were calculated by the following equation:

synoptic perturbationi;j ¼

Ci;j  Ci;j;

month

Ci;j; month

where Ci,j represents the measured analyte concentration and Ci; j;month the monthly average value based on the entire operational period (all years). The linear correlation coefficients were calculated for the raw data, comparing the same analytes at different sites, different analytes at the same site, and analytes against meteorological parameters. The same calculations were done using the synoptic perturbation data in order to exclude any seasonal influences from the comparisons. This paper does not discuss the correlations noted

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Fig. 2. Average concentrations of organic carbon, sulfate, ammonium and nitrate.

with meteorological parameters because strong correlations were not found between pollutant species and meteorological parameters. Interested readers may refer to the full report (Milligan and Walsh, 2008). 3. Results This section is organized to present findings from each analytical method sequentially, and the discussion sections that follow focus on the interpretation of the results on an analyte-by-analyte basis. Figs. 2 and 3 show the average concentrations of some analytes at the sites. The average iron, copper, and vanadium concentrations at Arendtsville (westernmost and most rural site of the study area) were less than half the mean values for all thirteen sites. Similarly Dover copper and zinc average concentrations were less than half the mean values for all sites. Sites where the average values of some analytes were 50% greater than the mean concentrations included Wilmington (5 analytes), Chester (4), Harrisburg (2), Philadelphia (2), York (2), and Lancaster (2). Individual analyte concentrations measured at each pair of sites were compared based on the data collected between 2001 and 2006. Fig. 4a shows concurrent total nitrate concentrations for Essex and Philadelphia and a high degree of correlation. Fig. 4b shows the synoptic perturbations of the same total nitrate concentrations at the Essex and Philadelphia monitors. These perturbations reflect the deviations on each date from the monthly averages and are yet well correlated with one another. High linear correlation coefficients (greater than 0.70) for both raw data and

synoptic perturbations for site pairs indicated compelling evidence that the air quality at both sites is influenced by similar emissions or meteorological events. The cutoff of 0.70 was chosen based on the bimodal distribution shown by all of the correlation coefficients for the synoptic perturbations. Fig. 5a shows which sites had a 0.70 or greater correlation coefficient for synoptic perturbations with the Washington monitor for the different analytes. Note that the sites closest to Washington (Beltsville and Ft. Meade) correlated for more analytes than those further away. Fig. 5b–d shows monitor locations at which analytes/ indices had a 0.70 or greater correlation coefficient for synoptic perturbations with the Arendtsville, Chester, and Dover monitors. Correlations for the remaining sites were similar to these, most closely matching their nearest sites. Recognizing that analytes share emission sources and atmospheric formation mechanisms (e.g., ammonia replaces hydrogen in sulfuric acid to form particulate ammonium sulfate), it is also important to understand correlations among analytes at individual sites. Fig. 6 shows the reasonably high raw data correlations between calcium and iron concentrations for the Essex site, as well as the synoptic perturbations. Both correlation coefficients in Fig. 6 are identical, indicating that any seasonal influences affect both calcium and iron in similar ways. Linear correlation coefficients of both raw data and synoptic perturbations above 0.70 for analytes at the same site were also considered to be compelling evidence that these species are influenced by similar emissions or meteorological conditions. These pairings of analytes helped indicate which showed similar behavior and could be grouped for the discussions in the following section.

Fig. 3. Average concentrations of bromine, copper, lead, selenium, titanium, vanadium, and zinc.

K.J. Walsh et al. / Atmospheric Environment 43 (2009) 594–603

10

Essex Total Nitrate (µg/m3)

a

r = 0.84 8 6 4 2 0 0

2

4

6

8

Philadelphia Total Nitrate

Essex Synoptic Perturbation-Total Nitrate

b

10

12

(µg/m3)

2 r = 0.70 1

0 -1

0

1

2

-1

Philadelphia Synoptic Perturbation-Total Nitrate Fig. 4. Correlation between Philadelphia and Essex sites for total nitrate: (a) raw concentrations and (b) synoptic correlations. Error bars reflect the reported uncertainties for the individual measurements.

To produce Table 1 (showing pairs of analytes with correlation coefficients above 0.70), the correlation coefficients between analytes at the same site were calculated based on the synoptic perturbations instead of the raw data. Fig. 7 shows which analytes correlated well with the AQI at each of the thirteen sites. Nitrate, organic carbon, potassium, and bromine had some linear correlation coefficients greater than 0.70 with the AQI, but only sulfate, ammonium, and sulfur correlated at twelve of the thirteen sites. Recognizing that meteorological conditions might influence the correlations of analyte concentrations more than local or regional emissions patterns, this study also calculated the correlation coefficients between meteorological parameters and the analyte concentrations. Meteorological parameters were compared based on both raw data and synoptic perturbations. Correlation coefficients with meteorological parameters were considerably lower than those with other analyte measurements (no absolute values of correlation coefficients for raw data or synoptic perturbations exceeded 0.70). 4. Discussion At a few sites, metallic analyte concentrations were sometimes much higher around the July 4th holidays. Table 2 presents some of the obvious outliers. These incidents suggest that the monitor site may have been downwind of fireworks displays (certain fireworks contain each of the elements listed in Table 2 as well as other analytes) that contributed to increases in the fine particulate matter. Several studies (Kulshrestha et al., 2004; Drewnick et al., 2006; Dutcher et al., 1999; Perry, 1999) suggest that studies of fireworks displays should include not only the analytes discussed in this report but also others that are emitted primarily from fireworks (e.g., strontium). The role of fireworks displays is discussed in more detail in Section 4.6 with respect to specific analytes. Recall that Fig. 5a–d showed more good correlations at sites that were in close proximity to the monitor of interest. Correlation

597

coefficients were also compared to the distance between pairs of sites. A typical chart is shown in Fig. 8 for the synoptic perturbations of organic carbon and of elemental carbon. As distance increases, correlation coefficients for organic carbon drop on average at a rate of 0.026 for every 10 km, but the comparison of correlation coefficients with distance shows only an r2 of 0.165 for organic carbon. The intercept for this comparison is just 0.89 but would ideally be 1.0 for analytes that are well dispersed across the study area. The parameters for the best-fit lines (slope, intercept, and correlation coefficient) of the plots showing distance versus correlation coefficients are presented in Figs. 9 and 10 for all of the analytes. High correlation coefficients and intercepts near 1.0 in Fig. 9 indicate that the analytes could be described as regional in nature among all of the sites because they correlate with distance and go to nearly perfect correlations at short distances. For example, the correlation coefficients and intercepts for the well-established regional pollutant sulfate (Eldred, 1997; Malm et al., 1994) are all above 0.80, with the synoptic perturbation correlations indicating that sulfate correlations regionally are caused by more than seasonal trends with temperature and solar radiation. The slopes of the correlation coefficients versus distance (presented in Fig. 10) indicate how rapidly site correlations are lost as distance increases, likely indicating that other processes (e.g., additional emission sources) affect monitor readings when the slopes are steep. For example, the slopes for nitrate (both raw data and synoptic correlations) are much more negative than those for sulfate, possibly because atmospheric nitrate formation is a rapid process (minutes) compared to atmospheric sulfate production from sulfur dioxide (a multi-day process). 4.1. Sulfates and sulfur The complete formation of atmospheric ammonium sulfate is a multi-day process involving the oxidation of emitted sulfur dioxide, conversion from sulfuric acid to a salt, and either aerosol nucleation or adsorption onto existing particles. Because the process requires days in a well-mixed atmosphere, particulate sulfate concentrations are generally homogeneous over larger spatial areas than other analytes. In this way, sulfate concentrations exemplify good regional behavior (Eldred, 1997; Malm et al., 1994; Walsh and Gilliland, 2001). Fig. 9 clearly shows that sulfate’s regional behavior is stronger than those of the other analytes because both raw and synoptic perturbation correlations with distance have correlation coefficients exceeding 0.70 with intercepts of 0.99 and 0.95. The slopes are nearly 0.001 km1 for raw and synoptic slopes for both sulfates and sulfur (Fig. 10), indicating that air parcels with mostly uniform sulfate/sulfur concentrations are often large and affect many sites within the study area. In this context, an air parcel is considered 1) to have similar meteorological properties (e.g., temperature, relative humidity, and wind direction); 2) to have similar air quality concentrations across its extent for the pollutant of interest; and 3) to be affected by the same emissions sources for that pollutant. The ratios of mean sulfate concentration to mean sulfur concentration at each of the sites average 3.05 (with a standard deviation of 0.06 among sites), indicating that the sulfur exists predominantly as sulfate ion (the ratio of the molecular weights for sulfate to sulfur is 3.0). Average sulfate concentrations are highest at the four Pennsylvania monitors furthest west, located in more rural and suburban areas, and lowest at the Dover site. This lower annual sulfate average at Dover appears to result from behavior on low-sulfate days; days with sulfate concentrations below 5 mg m3 have, on average, 1 mg m3 less sulfate at Dover than at Lancaster.

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Fig. 5. Correlation coefficients above 0.70 for synoptic perturbations between MidAtlantic monitors and (a) Washington; (b) Arendtsville; (c) Chester; and (d) Dover.

At individual sites, sulfate, sulfur, and ammonium concentrations correlated well at every site (r > 0.70) for both raw data and synoptic perturbations (Table 1). Both raw sulfur and sulfate data also correlated well with the AQI for seven sites: Arendtsville, Beltsville, Dover, Essex, Ft. Meade, Lancaster, and Washington. In addition, raw sulfur data and synoptic sulfate perturbations from three sites in the Delaware Valley (New Garden, Philadelphia, and Wilmington) correlated well with the AQI. The synoptic perturbations for sulfur showed good agreement with the AQI for all thirteen sites, as expected for this major particulate species. 4.2. Nitrates and ammonium The formation of nitrate particulate is complicated by the competition with sulfate for cations and is thus dependent on the presence of ammonium and the absence of sulfate. Nitrate concentrations in MidAtlantic states are typically higher during the winter when the sulfate concentrations are lowest. Nitrate captured on filters may also be lost if the filter is not handled in a manner to prevent volatilization. Despite the complicated chemistry, Fig. 9 shows that the nitrate correlations with distance between sites are strong, with correlation coefficients greater than 0.80 and intercepts of 0.97 and 0.92 for both raw and synoptic perturbation data. Nitrates had the greatest slope when plotted versus distance, and the steep slope indicates that nitrates are well correlated locally but not as well across the entire region that spans 190 km. Ammonium ions on particles exist predominantly bound to either sulfates or nitrates. Therefore, it was not surprising to find

that the correlations for ammonium ions were similar to those for sulfates and nitrates (Fig. 9). Sulfates showed a strong regional effect and nitrates a weaker effect, and the regional effect for ammonium ions fell between these two anions. In Fig. 5a–d, nitrate perturbations correlate well only with monitors in close proximity. However, Fig. 4 shows how the nitrate perturbations in Essex also correlate well with the nitrate perturbations in Philadelphia and Washington, indicating that nitrate particulate concentrations may be influenced by nearby urban emissions (e.g., from mobile sources in similar proximity to the I-95 corridor). Good correlations of synoptic perturbations of ammonium ion concentrations extend further than those for nitrates in Fig. 5a–d to include nearly all of the sites, indicating strong regional behavior by ammonium. The average nitrate concentration (Fig. 2) is highest at the eastern Pennsylvania monitors (with Lancaster’s concentration 1 mg m3 higher than other sites), and lowest in the Washington area (Washington, Ft. Meade, and Beltsville). The same spatial trends are seen in the average ammonium ion concentrations. These findings may be related to the ammonia emissions in Lancaster County, Pennsylvania. The 2002 National Emissions Inventory (EPA, 2007c) estimated that nearly 16,000 tons of ammonia were released in Lancaster County from agricultural sources in that year (the largest sources were attributed to livestock operations). The average total ammonia emissions from the other counties in the study area were only 1800 tons ammonia per year. The supposition is that increased availability of ammonia in the Lancaster area leads to more local ammonium nitrate particulate formation.

K.J. Walsh et al. / Atmospheric Environment 43 (2009) 594–603

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Calcium Concentration (µg/m3)

a

0.3 r = 0.71

0.2

0.1

0 0

0.2

0.4

0.6

0.8

Iron Concentration (µg/m3) Calcium Synoptic Perturbation

b

Fig. 7. Analytes with synoptic perturbation correlation coefficients with local AQI above 0.70.

4 r = 0.71

ammonium at suburban monitors found in Beltsville and Ft. Meade neutralized only about 70 percent of the sulfate and nitrate. The findings in Fig. 11 do not necessarily correspond well to the geospatial findings of Suh et al. (1995) that ammonia neutralization was highest near the center of Philadelphia rather than the suburbs, but that study examined only two summers of data before Phases I and II of the EPA’s Acid Rain Program were implemented. That program significantly reduced ambient sulfate levels in the area by reducing sulfur dioxide emissions in the upwind Ohio River Valley (Walsh and Gilliland, 2001).

3 2 1 0 -1

0

1

2

3

4

5

6

7

-1

Iron Synoptic Perturbation

Fig. 6. Correlations of iron and calcium at Essex: (a) raw concentrations and (b) synoptic correlations. Error bars reflect the reported uncertainties for the individual measurements.

Fig. 11 shows the average fractions of the nitrate and sulfate species that are neutralized by the ammonium ion measured in the samples. The value was calculated on a daily basis, and the daily fractions averaged to produce the figure. The areas near Lancaster showed the neutralizations nearest 100 percent while the

Table 1 Sites with correlation coefficients above 0.70 between analytes for synoptic perturbations. Monitor site

Analyte pairs

Arendtsville, PA

NH4–S

S–SO4

Beltsville, MD

Ca–Fe S–SO4 NH4–S

Ca–Si NH4–SO4 S–SO4 NH4– SO4 Fe–Si NH4–S

Fe–Zn

NH4–S

NH4– NO3 S–SO4

NH4–SO4

Cu–K Ca–Ti Fe–K NH4–S

NH4–S Ca–Si NH4–S S–SO4

S–SO4 Ca–K S–SO4 NH4–SO4

NH4–S

S–SO4

New Garden, PA NH4–S

S–SO4

Philadelphia, PA

NH4–S

S–SO4

Washington, DC

Elem. Carbon–Fe Elem. Carbon–Org. Carbon Ca–Fe NH4–SO4

Ca–Fe S–SO4

NH4– SO4 NH4– SO4 Ca–Si NH4– SO4 Fe–Si

Chester, PA Dover, DE

Harrisburg, PA

Ca–Fe NH4–NO3 Ca–Fe Ca–Fe Fe–Zn Ca–Fe

Lancaster, PA

Ca–Fe

Essex, MD Fort Meade, MD

Wilmington, DE

York, PA

NH4–S

Ca–Si NH4– NO3 S–SO4

NH4– SO4 Cu–K

NH4– SO4

NH4–SO4 Fe–Ti NH4–SO4 NH4– NO3 NH4–SO4 NH4– NO3 NH4– NO3 NH4– NO3 Cu–K NH4–S

NH4–S

NH4– NO3

S–SO4

4.3. Organic and elemental carbon A previous unpublished study by the authors indicated that organic carbon and elemental carbon fractions were not expected to show as good a spatial correlation as sulfate concentrations over long distances. That study compared the urban Washington, DC IMPROVE monitor with the rural IMPROVE sites at distances from 130 to 240 km. The best correlation for organic carbon showed synoptic perturbation correlations as high as 0.60. At shorter distances the correlations for raw and synoptic perturbation data are better. Fig. 8 shows that the correlation coefficients range roughly from 0.70 to 0.90 for organic carbon across the study area. Fig. 9 shows that the intercepts for organic carbon’s best-fit lines versus distance are still nearly 1.0, indicating that the analyte shows some regional influence. The correlation coefficients for organic carbon with distance only show a correlation coefficient of 0.41, indicating that regional influences cannot fully explain the concentrations measured at the sites and that local influences are also likely involved. Fig. 5a–d shows that synoptic perturbations for organic carbon concentrations were geospatially well correlated (r > 0.70) for monitors other than Arendtsville. In contrast, the synoptic perturbations for elemental carbon correlated well only with the nearest sites. The raw and synoptic perturbation correlations for elemental carbon did not show as strong a fit to distance as organic carbon

Table 2 Largest analyte excursions near July 4th holidays. Analyte

Site

Date

Number of standard deviations above mean

Calcium Iron Titanium

Wilmington Wilmington Washington

Titanium

Essex

7/4/2006 7/4/2006 7/4/2002 7/5/2003 7/3/2002 7/4/2002 7/5/2003

15 6 5 16 5 5 7

600

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4.4. Bromine and selenium

1.1 OC CorrCoef = -0.0010(Dist) + 0.89 r = -0.41

Correlation Coefficient

1.0 0.9 0.8 0.7 0.6 0.5

EC CorrCoef = -0.0006(Dist) + 0.66 r = -0.32

0.4 0.3

0

20

40

60

80

100

120

140

160

180

200

Distance between sites (kilometers) Fig. 8. Correlations for synoptic perturbation data among sites with distance (organic carbon and elemental carbon). Best-fit lines are also illustrated.

did; Fig. 8 illustrates that the correlation is not strongly associated with distance between monitors. Parameters other than geography seem to influence how well elemental carbon data correlate between sites.

Bromine correlation coefficients were reasonably high among the sites for both raw data and synoptic perturbations (Figs. 9 and 10). Correlation coefficients for bromine remained relatively constant as distance increased. This finding and the fact that the correlation coefficients do not approach 1.0 as distance approaches zero indicate that bromine behavior is not necessarily linked to uniformly mixed air parcels passing through the area. The comparisons of selenium correlation coefficients to distance yielded similar results to that for bromine but with considerably smaller correlation coefficients. The mean bromine concentrations were almost identical at all thirteen sites (Fig. 3), but the Chester and Essex sites recorded maximum bromine concentrations at least double the maxima at other sites. The maximum bromine concentration at Essex was measured on January 1, 2005, so it may be connected to the fireworks displays that occurred the previous evening. Similarly the mean selenium concentrations were nearly identical at all thirteen sites, and Washington was the only monitor recording a maximum selenium concentration much different than the maxima at other sites (about four times higher on 7/20/2004). Fig. 5a–d geographically displays which monitors show linear correlations over 0.70. Bromine only appears once in these figures

Fig. 9. Best linear fit correlations of various analytes with distance between sites for raw and synoptic perturbation data.

K.J. Walsh et al. / Atmospheric Environment 43 (2009) 594–603

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Table 3 Selected analytes with correlation coefficients for synoptic perturbations greater than 0.70 between sites.a

Ammonium Bromine

Site Calcium

Beltsville Dover Essex Ft. Meade Washington Wilmington

Copper

Raw Slope Synoptic Slope

Elemental C Iron

Beltsville – Cu Cu, Si –

Dover

Essex

–

Cu Naþ

Naþ Naþ – Naþ

Si, Naþ – Naþ

Ft. Meade

Washington

Wilmington

Naþ Si, Naþ

Cu, Si – – Ca, Fe, Si

– Naþ Naþ Naþ –

Ca, Fe, Si Naþ

–

a

All listed analytes also correlated well for raw data among the pairs of sites listed.

Lead Nitrate Organic C Potassium Selenium Silicon Sodium Ion Sulfate Sulfur Titanium Vanadium Zinc

-0.0030

-0.0025

-0.0020

-0.0015

-0.0010

-0.0005

0.0000

0.0005

(kilometers-1) Fig. 10. Slopes for best linear fits of various analytes with distance between sites for raw and synoptic perturbation data.

at a nearby monitor, and selenium not at all. At individual sites, the raw data and synoptic perturbations for bromine never correlated well (r > 0.70) with other analytes for more than a single site. However, the synoptic perturbations for bromine correlated well with the AQI at six sites: Lancaster, Wilmington, Essex, Ft. Meade, Beltsville, and Washington. Six other sites had correlation coefficients for this relationship of at least 0.58 (all except Chester with a correlation coefficient of only 0.34). A relationship clearly exists between the bromine and AQI behavior. The synoptic perturbations

for selenium did not correlate well with the synoptic correlations of any of the other analytes or AQI. Neither bromine nor selenium raw data or synoptic perturbations correlated well (jrj > 0.40) with any of the meteorological parameters at more than two sites. Chen et al. (2002) associates higher bromine concentrations at the Ft. Meade site with a vegetative burning/wood smoke factor. Sander et al. (2003) report significant atmospheric bromine sources to be sea salt, mineral aerosol (e.g., Saharan dust), biomass burning, fossil fuel combustion, degradation of methyl bromide, and volcanic emissions. The behavior by bromine discussed above shows no indication that it acts as a pollutant from a particular set of emissions sources or occurring under particular weather conditions. However, synoptic perturbations for bromine show reasonably good correlations between sites and with the local AQI measurement. A further study into bromine might examine whether adsorption to existing particulate matter is the rate-limiting factor for measured bromine particulate concentrations. Moyers et al. (1972) report complex interactions between gaseous and aerosol bromine. Sander et al. (2003) reported that gaseous bromine depletion depends on aerosol acidity and discussed the reaction scheme for marine bromine chemistry that involves at least seven gaseous and three aerosol species. Because complicated atmospheric processes lead to bromine in aerosols (Spatola and Gentry, 1980), the observed good correlation between the synoptic perturbations of bromine and AQI at several sites may be best explained if the ratelimiting step for most bromine incorporation into aerosols is adsorption to existing particles. This adsorption may depend on the concentration of existing particles, and this would imply that bromine should not have been used as a tracer in source attribution methods under such conditions (Tolocka et al., 2005). More than half the selenium is also naturally emitted to the atmosphere, much of it from the ocean (Mosher and Duce, 1987). Mosher and Duce estimate that 65 percent of the global selenium emissions result from coal combustion and the marine biosphere, and both sources emit selenium predominantly in the vapor phase. Selenium exists atmospherically predominantly in the aerosol phase, indicating that the gas-to-particle conversion processes are rapid compared to aerosol residence times. The chemistry associated with the atmospheric aerosol conversions may explain why selenium showed some geospatial correlation patterns similar to those for bromine. 4.5. Calcium, copper, iron, silicon, and sodium ion

Fig. 11. Average daily fraction of sulfate/nitrate neutralization by ammonium ion (based on average of daily neutralizations).

Fig. 9 shows that calcium, copper, iron, silicon, and sodium ion concentrations are not well correlated with distance. These analytes did not show strong correlations among most of the site pairs. However, they did show some strong correlations when comparing only the sites in Maryland, Delaware, and Washington, DC. Table 3 shows the sites with correlation coefficients greater than 0.70 for the synoptic perturbations; these same analytes at the site pairs also showed good correlation in the raw data. The good correlations

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Table 4 Comparison of average concentrations for selected analytes. Analyte

Lowest

Highest

Calcium Copper

Arendtsville Arendtsville, Ft. Meade, and Dover Arendtsville and Dover Arendtsville and Harrisburg Beltsville

York and Wilmington Wilmington and Philadelphia York and Wilmington Wilmington and Chester Chester

Iron Silicon Sodium ion

for calcium, copper, iron, and silicon were found only for sites within 45 km of one another, but the good correlations were maintained for sodium ion up to 123 km distant (Wilmington to Ft. Meade). Table 4 shows the sites with the highest and lowest average concentrations for calcium, copper, iron, silicon, and sodium ion. No specific geospatial gradient was evident other than high average concentrations were measured in Wilmington and low ones in Arendtsville. At individual sites, correlation coefficients between analytes that were greater than 0.70 most often were those between calcium and iron. The raw data correlated well for these analytes at Harrisburg, Lancaster, Essex, Ft. Meade, and Washington. Correlations for synoptic perturbations at these five sites were also good, as well as at the Wilmington, Beltsville, and Dover sites. Calcium also correlated well (r > 0.70) with silicon at Beltsville, Ft. Meade, and Wilmington for both raw data and synoptic perturbations. Strong correlations between monitors and between analytes can lead investigators to sites of interest. Calcium, iron, and silicon are often associated with particulate matter with crustal origins, but the analytes behave differently on a temporal basis at the different sites. For example, at the Washington site, calcium, iron, and silicon concentrations are typically higher in the spring months (Fig. 12). However, at the Wilmington site, high concentrations of these analytes were not measured until after November 2004 (Fig. 12), suggesting that new activities might be affecting the monitor’s readings in later years. Sequences of aerial and satellite imagery from Globe Explorer ImageAtlas (GlobeXplorer, 2007) revealed that several industrial buildings were demolished in the blocks just south of the Wilmington site between August 2004 and September 2005 (the demolished buildings are shown in Fig. 13). Between September 2005 and July 2006, two new buildings were constructed in place of the old buildings. These activities likely explain why higher calcium

0.7 Washington Wilmington

0.5

concentrations were measured in later years, as well as explaining higher levels for the other crustal components. However, the monitor data were not flagged to indicate nearby demolition and construction activities. In conjunction with correlations against meteorological parameters, the above results provide no strong evidence of any particular emissions sources for calcium, copper, iron, silicon, or sodium ion. The monitor readings in the Maryland–Delaware– Washington area do seem to be affected in similar manners, but further investigation would be necessary to draw conclusions about the reasons for the correlations between monitors. 4.6. Lead, potassium, titanium, vanadium, and zinc The remaining analytes (lead, potassium, titanium, vanadium, and zinc) did not correlate well (r > 0.70) between any of the pairs of sites, thereby offering no indication of regional behavior. When comparing pollutants, these analytes showed no synoptic correlations over 0.70 for more than a single site except in one case. Copper and potassium correlated well both in the raw data and synoptically for the Essex, Beltsville, and Washington sites (Essex shown in Fig. 14), suggesting that copper and potassium may have similar emissions sources or atmospheric formation mechanisms in the Baltimore–Washington corridor. However, the correlation coefficients drop significantly if the highest outlying points are

Nov. 5, 2004

0.4 0.3 0.2 0.1 0.0 0

365

730

1095

1460

1825

2190

Julian Day (since 1/1/2001)

Synoptic Perturbation (K)

Raw Ca (µg/m3)

0.6

Fig. 13. Satellite imagery showing buildings demolished between August 2004 and September 2005. The circle indicates the approximate location of the Wilmington monitor and the X’s the demolished buildings.

20 18 16 14 12 10 8 6 4 2 0 -1 -2 0

1/1/2005

7/5/2003 7/4/2006 7/3/2005 12/31/2002

1

2

3

4

5

6

7

8

9

10

11

12

13

Synoptic Perturbation (Cu) Fig. 12. Raw calcium concentrations at Washington and Wilmington sites. Note that 2.39 mg m3 value on 7/4/2006 (Julian day 2011) for Wilmington site is not shown in the figure.

Fig. 14. Relationship between copper and potassium synoptic perturbations at Essex (best-fit line also shown).

K.J. Walsh et al. / Atmospheric Environment 43 (2009) 594–603

dropped (7/5/2003, 1/1/2005, and 7/4/2006 at Essex, 7/4/2006 at Beltsville, and 7/4/2006 at Washington). These outliers all occurred near holidays, and thus the good correlation between copper and potassium may result largely due to emissions from nearby fireworks displays. A signature pollutant for fireworks displays is strontium (Dutcher et al., 1999; Perry, 1999; Vecchi et al., 2008), and the five highlighted dates in Fig. 14 represent five of the seven highest strontium concentrations measured at the Essex site. Future analyses that aim to develop source attributions should include pollutant signatures produced primarily from fireworks displays (e.g., strontium) in order to properly attribute the PM2.5 for these dates. 5. Summary of findings This study revealed a number of insights into the behavior of PM2.5 constituent species in and around Maryland, as well as the AQI, between 2001 and 2006. Data were not available for all analytes, but eighteen analytes and AQI with mostly complete records were examined at thirteen sites. Correlations of the raw data indicated how the analytes behaved on seasonal bases and with dependence on meteorological parameters. Examination of the fractional synoptic perturbations in the concentrations indicated how closely correlated the sites were to one another, the different analytes were to one another at the sites, and the analytes were to the meteorological parameters. Some findings were expected and confirmed that the data time series was sufficiently long to detect strong trends. For example, sulfate concentrations showed strong signs of regional behavior on both seasonal and synoptic scales. Fig. 7 shows the analytes that correlated well synoptically with the AQI. Fluctuations in those analyte concentrations at those sites will likely be accompanied by a concurrent change in the AQI. The highest bromine concentrations are less than 0.1 mg m3, suggesting that bromine concentrations do not influence the AQI. This study found evidence that bromine concentrations at those sites on particulate matter may be limited by the adsorption rate onto particles and thus show a dependence on the AQI. Selected findings from this study are summarized below: 1. Signatures of fireworks display emissions were determined based on high concentrations of particular metallic analytes near major holidays. Future incorporation of fireworks signature profiles into source attribution studies would help explain behavior on certain days. 2. Sulfate concentration correlation coefficients between sites correlated extremely well (r > 0.90 and intercept >0.90) with the distance between sites at eleven of the thirteen monitors and reasonably well at the remaining two. This suggests a wellmixed air parcel for sulfates, a pollutant formed over several days. 3. Synoptic perturbations in the nitrate concentrations correlated well between Essex and other nearby sites as well as with the Philadelphia site. This suggested that homogeneous nitrate air parcels do not span the distances that sulfate does. 4. For twelve sites, correlation coefficients of ammonium ion synoptic perturbations versus distance lie between those for sulfates and nitrates, suggesting that particulate ammonium ion concentrations are driven by the sulfate and nitrate concentrations. The high degree of sulfate and nitrate neutralization (ammoniation) in Lancaster is likely driven by the high ammonia emissions in that county. 5. Calcium, copper, iron, silicon, and sodium ions showed different pairings of good correlations at the Washington, Maryland, and Delaware sites, suggesting that the species may

603

be generated from the same local emissions sources in this area. 6. Because analytes correlated very well at Wilmington, this site was investigated in more detail. The crustal material concentrations at the Wilmington site showed marked increases in November 2004, and a time series of satellite imagery identified that these increases were likely caused by demolition and construction activities in the same block as the monitor. The collected data points had not been flagged to indicate nearby demolition or construction, but future investigators should look for high correlations as indicators of nearby infrequent events. 7. Bromine (and possibly selenium) analyte concentrations on PM2.5 depend on atmospheric processes and are not necessarily directly related to the emissions rates. The rate-limiting step on their aerosol formation may be adsorption to existing particles.

References Chen, L.W.A., Doddridge, B.G., Dickerson, R.R., Chow, J.C., Henry, R.C., 2002. Origins of fine aerosol mass in the Baltimore–Washington corridor: implications from observation, factor analysis, and ensemble air parcel back trajectories. Atmos. Environ. 36, 4541–4554. Drewnick, F., Hings, S.S., Curtius, J., Eerdekens, G., Williams, J., 2006. Measurement of fine particulate and gas-phase species during the New Year’s fireworks 2005 in Mainz, Germany. Atmos. Environ. 40, 4316–4327. Dutcher, D.D., Perry, K.D., Cahill, T.A., Copeland, S.A., 1999. Effects of indoor pyrotechnic displays on the air quality in the Houston Astrodome. J. Air Waste Manag. Assoc. 49, 156–160. Eldred, R.A., 1997. Comparison of selenium and sulfur at remote sites throughout the United States. J. Air Waste Manag. Assoc. 47, 204–211. EPA, 2007a. TTN AQS Download Data Files. (last accessed April 2007). EPA, 2007b. Clean Air Markets – Data and Maps. (last accessed April 2007). EPA, 2007c. 2002 National Emission Inventory Data & Documentation. (last accessed April 2007). EPA, 2008. Fine Particle (PM2.5) Designations. (last accessed September 2008). GlobeXplorer, 2007. GlobeXplorer Aerial Photos Satellite Images and Maps. (last accessed April 2007). Kulmala, M., Kerminen, V.-M., Laaksonen, A., 1995. Simulation on the effect of sulphuric acid formation on atmospheric aerosol concentrations. Atmos. Environ. 29, 377–382. Kulshrestha, U.C., Rao, T.N., Azhaguvel, S., Kulshrestha, M.J., 2004. Emissions and accumulation of metals in the atmosphere due to crackers and sparkles during Diwali festival in India. Atmos. Environ. 38, 4421–4425. Malm, W.C., Sisler, J.F., Huffmann, D., Eldred, R.A., Cahill, T.A., 1994. Spatial and seasonal trends in particle concentration and optical extinction in the United States. J. Geophys. Res. 99 (D1), 1347–1370. Milligan, M., Walsh, K., 2008. Synoptic Evaluation of Regional PM2.5 Concentrations. Maryland Department of Natural Resources Power Plant Research Program. Report Number DNR-123112008–289 PPRP 144. Mosher, B.W., Duce, R.A., 1987. A global atmospheric selenium budget. J. Geophys. Res. 92 (D11), 13,289–13,298. Moyers, J.L., Zoller, W.H., Duce, R.A., Hoffman, G.L., 1972. Gaseous bromine and particulate lead, vanadium, and bromine in a polluted atmosphere. Environ. Sci. Technol. 6, 68–71. NOAA, 2007. Integrated Global Radiosonde Archive. (last accessed March 2007). Perry, K.D., 1999. Effects of outdoor pyrotechnic displays on the regional air quality of Western Washington State. J. Air Waste Manag. Assoc. 49, 146–155. Sander, R., Keene, W.C., Pszenny, A.A.P., Arimoto, R., Ayers, G.P., Baboukas, E., Cainey, J.M., Crutzen, P.J., Duce, R.A., Ho¨nninger, G., Huebert, B.J., Maenhaut, W., Mihalopoulos, N., Turekian, V.C., Van Dingenen, R., 2003. Inorganic bromine in the marine boundary layer: a critical review. Atmos. Chem. Phys. 3, 1301–1336. Spatola, J.A., Gentry, J.W., 1980. The interaction of bromine with micron and submicron aerosols. Am. Ind. Hyg. Assoc. J. 41, 784–795. Suh, H.H., Allen, G.A., Koutrakis, P., Burton, R., 1995. Spatial variations in acidic sulfate and ammonia concentrations within metropolitan Philadelphia. J. Air Waste Manag. Assoc. 45, 442–452. Tolocka, M.P., Lake, D.A., Johnston, M.V., Wexler, A.S., 2005. Size-resolved fine and ultrafine particle composition in Baltimore, Maryland. J. Geophys. Res. 110. Vecchi, R., Bernardoni, V., Cricchio, D., D’Allesandro, A., Fermo, P., Lucarelli, F., Nava, S., Piazzalunga, A., Valli, G., 2008. The impact of fireworks on airborne particles. Atmos. Environ. 42, 1121–1132. Walsh, K.J., Gilliland, A.B., 2001. Regional variations in sulfate and nitrate on annual, seasonal, and synoptic time scales. J. Air Waste Manag. Assoc. 51, 1339–1345.