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Field performance of dichotomous sequential PM air samplers
Atmospheric Environment 36 (2002) 3289–3298
Field performance of dichotomous sequential PM air samplers N. Poora,*, T. Clarkb, L. Nyec, T. Tamaninic, K. Tateb, R. Stevensd, T. Atkesonb a
College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd., Tampa, FL 33612, USA b Florida Department of Environmental Protection, 2600 Blair Stone Road, Tallahassee, FL 32399, USA c Environmental Protection Commission of Hillsborough County, 1410 N. 21st St., Tampa, FL 33605, USA d FDEP Mercury Program, c/o USEPA, NERL, MD-47, Research Triangle Park, NC 27711, USA Received 12 November 2001; accepted 17 April 2002
Abstract For over one year, the Environmental Protection Commission of Hillsborough County (EPCHC) in Tampa, Florida, operated two dichotomous sequential particulate matter air samplers collocated with a manual Federal Reference Method (FRM) air sampler at a waterfront site on Tampa Bay. The FRM was alternately configured as a PM2.5, then as a PM10 sampler. For the dichotomous sampler measurements, daily 24-h integrated PM2.5 and PM10–2.5 ambient air samples were collected at a total flow rate of 16.7 l min1. A virtual impactor split the air into flow rates of 1.67 and 15.0 l min1 onto PM10–2.5 and PM2.5 47-mm diameter PTFEs filters, respectively. Between the two dichotomous air samplers, the average concentration, relative bias and relative precision were 13.3 mg m3, 0.02% and 5.2% for PM2.5 concentrations (n ¼ 282), and 12.3 mg m3, 3.9% and 7.7% for PM10–2.5 concentrations (n ¼ 282). FRM measurements were alternate day 24-h integrated PM2.5 or PM10 ambient air samples collected onto 47-mm diameter PTFEs filters at a flow rate of 16.7 l min1. Between a dichotomous and a PM2.5 FRM air sampler, the average concentration, relative bias and relative precision were 12.4 mg m3, 5.6% and 8.2% (n=43); and between a dichotomous and a PM10 FRM air sampler, the average concentration, relative bias and relative precision were 25.7 mg m3, 4.0% and 5.8% (n ¼ 102). The PM2.5 concentration measurement standard errors were 0.95, 0.79 and 1.02 mg m3; for PM10 the standard errors were 1.06, 1.59, and 1.70 mg m3 for two dichotomous and one FRM samplers, respectively, which indicate the dichotomous samplers have superior technical merit. These results reveal the potential for the dichotomous sequential air sampler to replace the combination of the PM2.5 and PM10 FRM air samplers, offering the capability of making simultaneous, self-consistent determinations of these particulate matter fractions in a routine ambient monitoring mode. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Relative standard deviation; Relative precision; PM2.5; PM10; Technical merit
1. Introduction In 1997, the USEPA made final new National Ambient Air Quality Standards (NAAQS) for both PM10 and PM2.5, and within two years had launched a national PM2.5 monitoring trends network that now has 1700 PM2.5 monitors at 1100 sites and augments the existing 1400 PM10 monitors at 930 sites (USEPA, 2000). The USEPA requires that at urban centers with a population of more than 1 million people daily *Corresponding author. Fax: +1-813-974-4986. E-mail address: [email protected] (N. Poor).
or continuous PM2.5 measurements must be made at a minimum of two core sites (USEPA, 1998). Collocated FRM PM2.5 and PM10 samplers are now requisite for NAAQS compliance determination, even though PM10 includes PM2.5. Long-term and episodic increases in ambient air concentrations of both PM10 and PM2.5 are significantly associated with higher rates of mortality and morbidity; however, the PM2.5 contribution is thought to be the more toxic constituent of PM10 (Dockery et al., 1993; Samet et al., 2000). The new NAAQS were challenged on several counts; on one specific technical point germane to this analysis,
1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 2 ) 0 0 3 0 4 - 7
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the US District Court of Appeals found that ‘‘yretention of the PM10 indicator simultaneously with the establishment of the new fine particle indicator is unsupported by the evidence and arbitrary and capricious’’. Thus, in order for the agency to support policy for fine particulate control a technically sound, selfconsistent method of determining both fine and coarse particulate matter is needed. (US Court of Appeals, 1997) The Rupprecht and Patashnick (R&P) Partisols-Plus Dichotomous Model 2025 Sequential Air Sampler automates a week of daily monitoring for the separate PM2.5 and PM10–2.5 components of PM10. In principle, one dichotomous sampler could replace two separate particulate matter (PM) monitors for NAAQS compliance determination. For speciation analysis and epidemiological studies of toxicity, this has the advantages of segregating the predominantly anthropogenic particles, PM2.5, from particles mostly of geologic or marine origin, PM10–2.5 (Wilson and Suh, 1997). Moreover, separating these size fractions minimizes artifacts associated with mixing on the filter substrate acidic fine particles with basic coarse particles.
2. Experimental Two R&P Partisols Model 2025 dichotomous sequential PM air samplers and one R&P Partisols Model 2000 Federal Reference Method (FRM) PM air sampler were collocated at the eastern end of the Gandy Bridge in Tampa, Florida, as part of an air toxics and nutrient deposition study. This project was sponsored by the
Unit 2
Florida Department of Environmental Protection (FDEP), Tampa Bay Estuary Program and USEPA. Fig. 1 shows the arrangement of the dichotomous air samplers and the FRM at this site. The 1.2 m 1.2 m platforms were spaced B1.5 m apart, the inlet heights were 2.0 m, and the FRM was on the same platform as one of the dichotomous air samplers. The first of two dichotomous samplers, Unit 1, began operation in January 2000, as did the PM2.5 FRM air sampler. The second dichotomous sampler, Unit 2, began operation in late February 2000. In June 2000, the PM2.5 FRM was converted to a PM10 FRM. Dichotomous sampler measurements were daily 24-h integrated PM2.5 and PM10–2.5 ambient air samples collected at a total flow rate of 16.7 l min1. A virtual impactor split the airflow into flow rates of 1.67 and 15.0 l min1 onto PM10–2.5 and PM2.5 47-mm diameter PTFEs filters, respectively. The PM2.5 and PM10–2.5 concentrations were calculated as shown in Eqs. (1) and (2) (Dzubay et al., 1978). Eq. (2) shows the correction made for PM2.5 particles collected on the PM10–2.5 filter. FRM measurements were alternate day 24-h integrated PM2.5 or PM10 ambient air samples collected onto 47-mm diameter Whatman PTFEs filters at a flow rate of 16.7 l min1. The dichotomous air sampler flow rate of 15.0 l min1 to the PM2.5 filter is less than the 16.7 l min1 flow rate specified in the FRM for PM2.5 in 40CFR50, Appendix L. Mass2:5 PM2:5 ¼ ; ð1Þ Volume2:5 PM102:5 ¼
Unit 1
Mass1022:5 Volume1022:5 PM2:5 ; Volumetotal Volumetotal
ð2Þ
FRM
Fig. 1. Arrangement of two dichotomous sequential PM air samplers (Units 1 and 2) and one FRM PM air sampler (FRM) at the Gandy Bridge site in Tampa, Florida.
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
where PM2.5 is the concentration in mg m3, PM10–2.5 the concentration in mg m3, Mass2.5 the mass collected on the PM2.5 filter in mg, Mass102.5 the mass collected on the PM10–2.5 filter in mg, Volume2.5 the volume of air through the PM2.5 filter in m3 (B21.6 m3), Volume102.5 the volume of air through the PM10–2.5 filter in m3 (B2.4 m3) and Volumetotal is the total volume of air through the sampler in m3 (B24 m3). The Environmental Protection Commission of Hillsborough County (EPCHC) operates and maintains the Gandy Bridge air pollution monitoring site. For the period of this study, an EPCHC technician visited the site daily. The temperature and pressure sensors of the PM samplers were calibrated annually with an NIST-traceable transfer standard. For each channel, flow rates were tested against an NIST-traceable flow meter during filter magazine (for the dichotomous samplers) or filter cassette (for the FRM) installation. The flow rate coefficient of variation (CV) for the air samplers was typically o0.5%. The FRM virtual impactor was cleaned each month (after 14 daily samples), and on this cleaning schedule, no significant particle build-up on the impactor filter was observed. The PM10 inlet was exchanged with a clean inlet at least once every 2 months. Five magazines of pre-weighed filters were shipped weekly from the FDEP by courier to the EPCHC in a small cooler containing a chain-of-custody form and blue ice for the return trip. The chain-of-custody form listed the filter identification numbers and filter cassette screen identification numbers, in sequence, by magazine.
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Dichotomous sampler magazines were stored in the EPCHC air-monitoring laboratory until installation at the Gandy Bridge site. Dual magazines were installed, unopened, in the dichotomous sampler and the sampler was programmed for 1 week of uninterrupted operation. For the FRM, a single magazine held the filter cassettes, which were unpacked and transferred to the FRM as needed. FRM filters within the magazine were stored in a temperature-controlled trailer at the Gandy Bridge site before use. After the air samples were collected, EPCHC technicians electronically downloaded the run data onto a laptop computer for direct input into a spreadsheet, and manually transcribed data from the air sampler computer screen to field data sheets. The dichotomous sampler magazines were removed, unopened, and frozen until shipped; the FRM filter cassettes were repacked in sequence into an awaiting magazine, which once filled, was frozen until shipped. This protocol did not conform to 40CFR50, Appendix L, which requires PM2.5 filters to be removed from the sampler within 4 days of exposure, and for filters removed from the sampler to be immediately chilled. A small cooler was packed with frozen blue ice, and the chain-of-custody form and a copy of the EPCHC filter log sheet showing the filter receipt and run dates were returned with the magazines by courier to FDEP. The more detailed data on the EPC field data sheet were checked against the electronic spreadsheet database. At the USEPA-approved FDEP weighing laboratory, a technician unpacked the cooler and removed the filter
Average PM10 Concentration (µg m-3)
35
30
25
20
15
10
5
Ju ly A ug Se ust pt em be r O ct ob er N ov em be r D ec em be r
Ju ne
M ay
A pr il
Ja nu ar Fe y br ua ry M ar ch
0
Average PM2.5 Average PM10-2.5
Fig. 2. Average monthly PM10 concentrations at the Gandy Bridge site in Tampa, Florida. The contributions from PM2.5 and PM10–2.5 are shown.
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cassettes from the magazines. The filters were placed in plastic petri dishes and conditioned before post-weighing. Gravimetric analyses were done in accordance with 40CFR50, Appendix L, 8.3. The methods’ lower detection level was 2 mg m3, higher concentration limit was 200 mg m3, and precision and bias were 70.6 and 70.1 mg m3, respectively. Filter pre- and post-weights and the dates of each weight measurement are tracked in a data management system (LIMS), along with qualifiers or comments for each filter number. The LIMS automatically calculated the change in mass on the filter. The FDEP laboratory manager verified and validated the LIMS database, and forwarded to the EPCHC a LIMS hard copy and electronic report with these data fields. At the EPCHC a data quality assurance technician not involved in the field sampling activity merged the LIMS data set with the field data to calculate the ambient air concentration. The technician reviewed the data integrity and validated the field data at this time. The data set, complete with field data, gravimetric analysis data, comments and qualifiers, was forwarded to the University of South Florida College of Public Health (COPH) for analysis and assimilation with other air and rainwater pollutant concentration data sets. For this investigation of equipment performance, only the data that were flagged as void either by FDEP or EPCHC were eliminated from the data set for the analyses. This analysis consists of four parts: a summary of the PM concentration data, a comparison of the two collocated dichotomous samplers, a comparison of the dichotomous samplers with the FRM, and an analysis of the measurement errors and estimates of technical merit. The field performance of the dichotomous samplers were judged on (1) their ability to meet the USEPA requirements for PM2.5 and PM10 measurements in general accordance with 40CFR50, and (2) technical merit equal or better than that of the FRM. A statistical summary of the PM concentration data revealed the underlying distributions, which were important in the choice of analytical assumptions and tools; gave the concentration ranges over which the conclusions will be valid, and provided information on the air quality in Tampa, Florida. Subject to interpretation, regression statistics indicate the agreement of measurements between air samplers. Histograms, simple statistics, and simple linear regressions were done with the automated features of SigmaPlots 2001, which uses standard techniques. To test that the simple linear regression slope was no different than 1.00 and that the intercept was no different than 0.00, we used a 95% confidence level. The Wilcoxon signed rank tests as described in Ott (1993), however, were calculated in an electronic spreadsheet. For the Wilcoxon signed rank tests (a non-parametric equivalent to the paired t-tests), statistical significance was based on a two-tailed test at a
95% confidence level, and the null hypothesis was that the collocated measurements were identical. If the jzj > 1:96 (or for np50; T > Tcritical ), then the collocated measurements were different. The relative bias (RB) and relative precision (RP) were calculated for the entire period of record according to Eqs. (3)–(5), respectively (USEPA, 1998). RBi ¼
2ðyi xi Þ ; ðyi þ xi Þ
ð3Þ
RB ¼
1X RBi ; n i
ð4Þ
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u u1 X RB 2 i pffiffiffi : RP ¼ t i n 2
ð5Þ
In the above equations, yi and xi refer to the ith PM concentrations of two collocated units, n is the number of observations and RBi is the relative bias for the ith observation. This approach assumes that neither collocated sampler is the reference or true value. For PM2.5, a relative bias of 10% and relative precision of 10%; and for PM10, a relative bias of 10% and precision of 5 mg m3, are consistent with the 40CFR50 requirements for compliance monitoring. The integrity of the measurement, here defined as the mass concentration on a field blank assuming the nominal flow rate, must be below 5 mg m3. Although the robustness of the dichotomous sequential air samplers, in this case, cannot be entirely segregated from the complexity of the filter handling, transport and storage process, data completeness gives another perspective of the equipment performance. For this analysis, data completeness is defined as the ratio of final (accepted) records to planned records, and a completeness of 75% or better meets the conditions of 40CFR50. Another approach to compare the two dichotomous samplers and one FRM sampler is through a statistic known as ‘‘technical merit’’. This statistic compares the precision between two instruments based on their calibration functions (Mandel, 1964) and is re-interpreted in Eq. (6) for the problem at hand, where sFRMPM =sUnit1PM is the ratio of the measurement standard error for the FRM and the Unit 1 dichotomous PM concentration data. In a similar manner, the technical merit was calculated for the FRM versus Unit 2, and Unit 1 versus Unit 2. A smaller measurement standard error and a lower technical merit statistic indicate better performance. sFRMPM ¼ technical merit: ð6Þ sUnit1PM To estimate sFRMPM ; sUnit1PM ; and sUnit2PM we began with data subsets of collocated FRM, Unit 1 and Unit 2 PM2.5 and PM10 measurements. Simple linear regressions of FRM versus Unit 1, FRM versus Unit 2, and
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
and a dichotomous air sampler:
180
Number of Observations
160 140 120 100 80
Average PM2.5 Concentration (µg m-3)
l¼ N = 412 Minimum = 4.0 Maximum = 47.1 Median = 11.7 Average = 13.0 Standard Deviation = 6.0
s# FRMPM ¼
s# Unit1PM ¼
40 20 0
140 120 100 80
ð7Þ P
di2 2 # n 2 1 þ lb l
i
P
di2 1 þ lb# 2 n 2 1
i
1=2 ;
ð8Þ
;
ð9Þ
1=2
# i: di ¼ yi a# bx
180
Number of Observations
s2FRMPM ; s2Unit1PM
60
160
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PM10-2.5 Concentration (µg m-3) N = 412 Minimum = 2.0 Maximum = 50.9 Median = 11.2 Average = 11.9 Standard Deviation = 5.3
60
ð10Þ
As can be seen from Eqs. (8) and (9), unless l equals either 1.0 (equal variances) or zero (e.g., no measurement error on the independent variable), a single regression of FRM versus Unit 1 concentration data does not provide sufficient information to finish the calculations. With three regressions, however, the above non-linear equations for each regression can be solved simultaneously to derive the desired error estimates.
40 20
3. Results and discussion
0
180
Number of Observations
160 140 120 100 80 60
PM10 Concentration (µg m-3) N = 412 Minimum = 8.8 Maximum = 74.0 Median = 23.6 Average = 24.9 Standard Deviation = 8.8
40 20 0
2 6 10 14 18 22 26 30 34 38 42 46 50 54 58 62 66 70 74 78
Fig. 3. Distribution of average daily PM2.5, PM10–2.5 and PM10 concentrations at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001.
Unit1 versus Unit 2 concentration data yielded the statistics shown in Table 4. For each regression, the measurement errors for both instruments must be considered. Applying the technique described by Mandel (1964), we estimated sFRMPM and sUnit1PM as shown in Eqs. (7)–(10), where yi and xi are the ith observed Unit 1 and FRM PM concentrations, respectively; a# and b# are the intercept and slope from the least squares regression line; and n is the number of observations. According to Eq. (7), l is the ratio of the measurement variances of two instruments, in this example, an FRM
3.1. Summary of the PM data Summary particulate matter concentration data obtained from dichotomous sequential air sampler measurements at the Gandy Bridge site from January 2000 through March 2001 are presented in Figs. 2 and 3. Fig. 2 is a time series plot of the average monthly PM concentrations. For this period, the average PM10 concentration was 24.9 mg m3 and the highest average PM10 concentration was 74.0 mg m3. The PM2.5 concentration was on the average 50% of the PM10 concentration. No strong seasonal trends in PM concentrations were evident. The distributions of the PM2.5, PM10–2.5 and PM10 concentration data are graphed in Fig. 3. Distributions for these data sets were decidedly skewed, which is reflected in higher average than median concentrations. The 11.7 mg m3 median and 13.0 mg m3 average concentrations for PM2.5 were higher than the 11.2 mg m3 median and 11.8 mg m3 average concentrations for PM102.5, and greater variability was seen in the PM2.5 compared with the PM10–2.5 concentration data. The PM10 ambient air concentrations met the National Ambient Air Quality Standards (NAAQS) of 150 and 50 mg m3 for the 2nd highest 24-h and annually averaged 24-h concentrations, respectively. The PM2.5 NAAQS are based on 3-yr averaging techniques; PM2.5 ambient air concentrations were below the 24-h and annually averaged concentrations of 65 and 15 mg m3, respectively.
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Table 1 Summary statistics for daily PM2.5, PM102.5 and PM10 concentrations measured by collocated dichotomous air samplers at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001 Statistic N Average ( mg m3) Standard deviation ( mg m3) Relative bias (%) Relative precision (%) Wilcoxon signed rank test
Unit 1 PM2.5
Unit 2 PM2.5
Unit 1 PM102.5
13.3 6.3
12.0 5.4
282 13.3 6.2
Unit 2 PM102.5
Unit 1 PM10
12.6 5.8
25.3 9.1
282
0.02 5.2 z=1.58
3.2. Comparison of collocated dichotomous samplers The collocated performance of two dichotomous sequential samplers is presented in Table 1. Daily PM10 concentrations were calculated as the sum of daily PM2.5 and PM10–2.5 concentrations for each dichotomous sampler. The relative bias and relative precision were 0.02% and 5.2% for PM2.5 concentrations and 3.9% and 7.7% for PM10–2.5 concentrations. For PM10 concentrations, the relative bias was 2.1%, and the relative precision was 4.2%, which yields a standard error of 3.4 mg m3 at an 80 mg m3 concentration. These performance statistics are within the alreadydescribed USEPA requirements. Unit 2 measurements for PM10 and PM10–2.5 were consistently higher than Unit 1 measurements, as revealed by the Wilcoxon signed rank tests. Simple linear regressions of Unit 2 versus Unit 1 concentration data confirm these results, as shown in Fig. 4. For PM2.5 concentrations, the slope was not different from 1.00; for PM10–2.5 concentrations, however, the slope was 1.05 with Unit 2 concentration data significantly higher than were those of Unit 1. The intercepts for these regression analyses were not different from zero. Better than 95% of the variability in Unit 1 concentration data was explained by Unit 2. Of two dichotomous sequential air samplers, the data completeness was 81% (368/451) for Unit 1 and 82% (326/401) for Unit 2; with both units considered together, the data completeness from January 2000 to March 2001 was 91%. These levels of completeness were achieved through daily inspection of the air samplers. 3.3. Comparison of collocated dichotomous and FRM air samplers The collocated performance of dichotomous and FRM air samplers are presented in Tables 2 and 3. When comparing the performance of Unit 1 dichotomous with the FRM air sampler, the relative bias and relative precision were 5.6% and 8.2% for PM2.5 concentrations, and 4.0% and 5.8% for PM10 concentrations. The Wilcoxon signed rank tests indicate
3.9 7.7 z=7.9
Unit 2 PM10 282 25.9 9.4 2.1 4.2 Z=7.3
that the collocated PM10 but not the PM2.5 concentration measurements were statistically different. A simple linear regression of FRM versus Unit 1 concentration data, Fig. 5, show that for PM2.5 concentrations, the slope was not different from 1.00; for PM10 concentrations, however, the slope was 0.95 with Unit 1 concentration data lower than those of the FRM. The intercepts for both regression analyses were not different from zero. Better than 94% of the variability in FRM concentration data was explained by Unit 1. When comparing the performance of Unit 2 dichotomous with the FRM air sampler, the relative bias and relative precision were 7.5% and 8.4% for PM2.5 concentrations, and 1.8% and 6.2% for PM10 concentrations. The Wilcoxon signed rank tests indicate that the collocated PM10 but not the PM2.5 concentration measurements were statistically different. A simple linear regression of the dichotomous versus FRM concentration data contradicts these results, as shown in Fig. 6. For both PM2.5 and PM10 concentrations, the slope was not different from 1.00 and the intercepts were not different from zero. Better than 91% and 96% of the variability in FRM concentration data were explained by Unit 2 PM2.5 and PM10 concentration data, respectively. The fewer number of observations for the FRM PM2.5 concentrations weakened the comparative statistics. In a quality assurance summary report for the USEPA PM2.5 monitoring network, relative precision and bias estimates were available for collocated Rupprecht and Patashnick PM2.5 FRM (USEPA, 2001) measurements. For the national network, with outliers included, the relative bias and relative precision were 4.1% and 6.1%; 5.3% and 7.6% for the Florida network. These values are higher than the collocated dichotomous PM2.5 relative bias and relative precision of 0.02% and 5.2% (Table 1). 3.4. Analysis of error and estimation of technical merit Linear regression statistics of FRM versus Unit 1, FRM versus Unit 2, and Unit 1 versus Unit 2 PM10
Unit 2 PM2.5 Concentration (µg m-3)
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298 60 50 40 30
N = 282 Slope = 0.995 Standard Error (Slope) = 0.010 Intercept = 0.092 Standard Error (Intercept) = 0.147 R2 = 0.97
20 10 0 0
10
20
30
40
50
60
Unit 2 PM10-2.5 Concentration (µg m-3)
Unit 1 PM2.5 Concentration (µg m-3) 60 50 40 30
N = 282 Slope = 1.053 Standard Error (Slope) = 0.015 Intercept = -0.123 Standard Error (Intercept) = 0.191 R2 = 0.95
20 10 0
0
10
20
30
40
Unit 2 PM10 Concentration (µg m-3)
Unit 1 PM10-2.5 Concentration
50 -3 (µg m )
60
80
60
40
N = 282 Slope = 1.019 Standard Error (Slope) = 0.01 Intercept = 0.07 Standard Error (Intercept) = 0.265 R2 = 0.97
20
0 0
10
20
30
40
50
60
70
80
Unit 1 PM10 Concentration (µg m-3)
Fig. 4. Simple linear regression of PM2.5, PM10–2.5, PM10 concentration data for two collocated dichotomous air samplers.
concentration data were needed to first estimate the measurement error for each instrument, and second to compute the technical merit. The results of six regressions are presented in Table 4, the estimates of the standard error on PM2.5 and PM10 concentration measurements for each instrument are listed in Table 5, and the technical merit statistics are found in Table 6. The standard errors on the PM2.5 concentration
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measurements were 1.02, 0.95 and 0.79 mg m3 for the FRM, Unit 1 dichotomous and Unit 2 dichotomous air samplers, respectively. For PM10 concentrations, these standard errors were 1.70, 1.06 and 1.59 mg m3 for the three samplers, given in the same order (Table 5). In order of technical merit, for PM2.5 concentration measurements the field performance of Unit 2 was superior to that of Unit 1 dichotomous sequential air sampler, and the performance of Unit 1 sampler was superior to that of the FRM air sampler. For PM10 concentration measurements, the field performance of the Unit 1 was superior to that of the Unit 2 dichotomous sequential air sampler, and the performance of Unit 2 was superior to that of the FRM air sampler. With respect to PM10 concentration measurements, the performance of Unit 2 was closer to that of the FRM than to Unit 1. In collocating this equipment, we have assumed that the air quality near the air samplers is homogeneous. If, for the purpose of discussion, we accept that systematic error manifests itself as bias, and random error as diminished precision, we can perhaps identify and classify sources of error that contribute to differences between the PM air samplers. Air leaks, temperature or pressure sensor deterioration, pump (brush) wear, buildup on the inertial impactor (FRM) could lead to flow rate changes and thus bias in the observed PM concentrations between two units, and these contributions were likely minimized by routine inspection and maintenance. A statistically significant bias of 0.6 mg m3 and lower precision were evident, however, between the PM10–2.5 and thus the PM10 concentrations of the Unit 2 compared to the Unit 1 sequential dichotomous air sampler. The physical differences between the dichotomous and FRM sampler inlets (as seen in Fig. 1) and inertial separators could well explain the overall lower PM2.5 and PM10 concentrations obtained with dichotomous samplers as compared with the FRM (Tables 2 and 3). A lower air flow rate and thus filter face velocity through the dichotomous sampler PM2.5 filter may reduce the amount of particle volatilization, and thus bias the PM2.5 measurement high as compared with an PM2.5 FRM sampler. Although not seen in this investigation, we suspect that this bias would be more severe in an urban setting with relatively high ambient air ammonium nitrate and semi-volatile organic compound concentrations. Sources of random error are likely linked to filter handling, storage and transport. Filters experience, for example, ambient temperatures of over 281C to freezer temperatures below 41C, and the loss or gain of mass on the filters from volatilizing species or condensing water vapor could reduce measurement precision. Field blanks help to quantify this error. For the period of
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Table 2 Summary statistics for daily PM2.5 and PM10 concentrations measured by collocated dichotomous and FRM air samplers at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001 Statistic
Dichot Unit 1 PM2.5 FRM PM2.5
N Average Standard deviation Relative bias (%) Relative precision (%) Wilcoxon signed rank test
Dichot Unit 1 PM10 Unit 1 PM2.5
FRM PM10
12.1 4.9
26.2 10.9
43 12.8 4.9
Unit 1 PM10 102
5.6 8.2 ToTcritical (198o310) np50
25.2 10.5 4.0 5.8 z ¼ 4:8
Table 3 Summary statistics for daily PM2.5 and PM10 concentrations measured by collocated dichotomous and FRM air samplers at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001 Statistic
Dichot Unit 2 PM2.5 FRM PM2.5
N Average Standard deviation Relative bias (%) Relative precision (%) Wilcoxon signed rank test
Dichot Unit 2 PM10 Unit 2 PM2.5
FRM PM10
11.3 4.0
27.2 11.6
29 12.1 4.1 7.5 8.4 ToTcritical (56o126) np50
record, 132 paired field blanks were cycled through the dichotomous air samplers. Blank PM2.5 and PM10–2.5 filters had on the average 11.2 and 9.4 mg of particle mass, respectively. The average blank values were 0.47 and 0.39 mg m3 if reported as PM2.5 and PM10–2.5 concentrations, in that order. This suggests that filter handling, storage and transport could account for 50– 90% of the measurement error. Another source of random error is the loss of volatile air pollutants from the filters that remained at ambient air temperatures longer, for example, 7 days versus 1 day. To reduce the impact of this error for the instrument performance evaluations, filters for all three samplers were shipped, loaded, retrieved and stored on a comparable schedule.
4. Conclusions We evaluated the collocated field performance of two dichotomous sequential PM air samplers with the traditional USEPA statistical approach and criteria. Observed at a monitoring site adjacent to Tampa Bay were PM2.5 and PM10 concentrations between 4 and
Unit 2 PM10 83 26.9 11.8 1.8 6.2 Z=2.41
50 mg m3, and 8 and 80 mg m3, respectively. In this 1-yr study, the dichotomous samplers demonstrated their capability to measure PM2.5 and PM10–2.5 within a 10% relative bias and a 10% relative precision, and PM10 within a 10% relative bias and a 5 mg m3 relative precision. The integrity of the PM2.5 and PM102.5 measurements were o1 mg m3, well below the 5 mg m3 performance criterion. Data completeness for each dichotomous sampler was 81% and 82%, above the criterion of 75%. The relative bias and relative precision were also within 10% between the dichotomous air samplers and either a PM2.5 or a PM10 FRM. An observed bias between the FRM and dichotomous air samplers was best explained as a shift in the particle penetration curve, most likely due to physical differences between the inlets and inertial impactors. Filter handling, transport and storage accounted for B50–90% of the error in the observed PM concentrations. A statistical technique that apportions the measurement error between collocated instruments, using data from three collocated instruments, was successfully applied to estimate the measurement standard error for each instrument. With the standard error known, the performance of each instrument is easy to evaluate,
30
Dichot Unit 2 PM2.5 Concentration (µg m-3)
Dichot Unit 1 PM2.5 Concentration (µg m-3)
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
25
20
15
N = 43 Slope = 0.957 Standard Error (Slope) = 0.039 Intercept = -0.134 Standard Error (Intercept) = 0.527 R2 = 0.94
10
5
0 0
5
10
15
20
25
24 22 20 18 16 14
N = 29 Slope = 0.918 Standard Error (Slope) = 0.055 Intercept = -0.140 Standard Error (Intercept) = 0.710 R2 = 0.91
12 10 8 6 4
30
4
-3
80 70 60 50 40
N = 102 Slope = 0.945 Standard Error (Slope) = 0.017 Intercept = 0.424 Standard Error (Intercept) = 0.478 R2 = 0.97
20 10 0 0
20
40
60
6
8
10
12
14
16
18
20
22
24
FRM PM2.5 Concentration (µg m-3) Dichot Unit 2 PM10 Concentration (µg m-3)
Dichot Unit 1 PM10 Concentration (µg m-3)
FRM PM2.5 Concentration (µg m )
30
3297
80
60
40
N = 83 Slope = 0.993 Standard Error (Slope) = 0.022 Intercept = -0.134 Standard Error (Intercept) = 0.650 R2 = 0.96
20
0
80
0
FRM PM10 Concentration (µg m-3)
20
40
60
80
FRM PM10 Concentration (µg m-3)
Fig. 5. Simple linear regression of PM2.5 and PM10 concentration data for a collocated dichotomous and FRM air sampler.
Fig. 6. Simple linear regression of PM2.5 and PM10 concentration data for a collocated dichotomous and FRM air sampler.
Table 4 Simple linear regression statistics for data subsets used in the measurement error analyses (concentration units of mg m3) X Concentration
Y Concentration
Intercept a#
Slope b#
N
P
FRM PM2.5 FRM PM2.5 Unit 1 PM2.5 FRM PM10 FRM PM10 Unit 1 PM10
Unit Unit Unit Unit Unit Unit
0.504 0.142 0.083 0.474 0.132 0.385
0.882 0.918 1.013 0.947 0.993 1.039
29 29 29 83 83 83
46.5 40.4 41.8 300 435 303
1 2 2 1 2 2
PM2.5 PM2.5 PM2.5 PM10 PM10 PM10
i
di2
Table 5 PM2.5 and PM10 measurement standard error for each air sampler FRM Unit 1 dichotomous Unit 2 dichotomous
sFRMPM2:5 sUnit1PM2:5 sUnit2PM2:5
either by a direct comparison of the standard errors or by a statistic known as technical merit (Mandel, 1964). With this technique, we demonstrated that the
1.02 mg m3 0.95 mg m3 0.79 mg m3
sFRMPM10 sUnit1PM10 sUnit2PM10
1.70 mg m3 1.06 mg m3 1.59 mg m3
dichotomous sequential air sampler PM2.5 and PM10 concentrations were more precise than those of the FRM.
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
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Table 6 Technical merit statistic for each pair of air samplers sFRMPM2:5 =sUnit1PM2:5 sFRMPM2:5 =sUnit2PM2:5 sUnit1PM2:5 =sUnit2PM2:5
1.07 1.29 1.20
sFRMPM10 =sUnit1PM10 sFRMPM10 =sUnit2PM10 sUnit1PM10 =sUnit2PM10
1.60 1.07 0.67
Acknowledgements This research was funded in part by the Tampa Bay Estuary Program, the Florida Department of Environmental Protection (contract AQ-156), and the USEPA, and would not have been possible without the contributions from the Environmental Protection Commission of Hillsborough County. The findings and opinions expressed here are those of the authors and not necessarily those of the supporting organizations.
References Dockery, D.W., Pope III., C.A., Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris Jr., B.G., Speizer,, F.E., 1993. An association between air pollution and mortality in six us cities. New England Journal of Medicine 329, 1753–1759. Dzubay, T.G., Stevens, R.K., Hines, L.A., Russwurm, G.M., Rickel, D.G., 1978. Manual dichotomous and series filter
respirable samplers. In: Intercomparison of Samplers Used in the Determination of Aerosol Composition. USEPA, ESRL, RTP, NC. EPA 600/7-78-118, July 1978. USEPA, Office of Research and Development, Cincinnati, OH, pp. 76–80. Mandel, J., 1964. The Statistical Analysis of Experimental Data. Interscience, New York; republished in 1984 by Dover Publications, New York, p. 410. Ott, L.R., 1993. An Introduction to Statistical Methods and Data Analysis, 4th Edition. Duxbury Press, Belmont, CA. Samet, J.M., Dominici, F., Curriero, F., Coursac, I., Zeger, S.L., 2000. Fine particulate air pollution and mortality in 20 US cities, 1987–1994. New England Journal of Medicine 343, 1742–1749. US Court of Appeals, 1997. American Trucking Associations, Inc., et al, Petitioners v. United States Environmental Protection Agency, Respondent. No. 97-1441, Section IV. USEPA, 1998. Quality Assurance Handbook for Air Pollution Measurement Systems, Vol. II, Part I. EPA-454/R-98-004, OAQPS, August 1998. USEPA, 2000. National Air Quality and Emissions Trends Report, 1998. EPA 454/R-00-003, OAQPS, RTP, NC, March 2000. USEPA, 2001. Quality Assurance Report, Calendar Year 2000, The PM2.5 Ambient Air Monitoring Program. DRAFT, EPA-454, RTP, NC, October 2001. Wilson, W.E., Suh, H.H., 1997. Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. Journal of the Air and Waste Management Association 47, 1238–1249.
Field performance of dichotomous sequential PM air samplers N. Poora,*, T. Clarkb, L. Nyec, T. Tamaninic, K. Tateb, R. Stevensd, T. Atkesonb a
College of Public Health, University of South Florida, 13201 Bruce B. Downs Blvd., Tampa, FL 33612, USA b Florida Department of Environmental Protection, 2600 Blair Stone Road, Tallahassee, FL 32399, USA c Environmental Protection Commission of Hillsborough County, 1410 N. 21st St., Tampa, FL 33605, USA d FDEP Mercury Program, c/o USEPA, NERL, MD-47, Research Triangle Park, NC 27711, USA Received 12 November 2001; accepted 17 April 2002
Abstract For over one year, the Environmental Protection Commission of Hillsborough County (EPCHC) in Tampa, Florida, operated two dichotomous sequential particulate matter air samplers collocated with a manual Federal Reference Method (FRM) air sampler at a waterfront site on Tampa Bay. The FRM was alternately configured as a PM2.5, then as a PM10 sampler. For the dichotomous sampler measurements, daily 24-h integrated PM2.5 and PM10–2.5 ambient air samples were collected at a total flow rate of 16.7 l min1. A virtual impactor split the air into flow rates of 1.67 and 15.0 l min1 onto PM10–2.5 and PM2.5 47-mm diameter PTFEs filters, respectively. Between the two dichotomous air samplers, the average concentration, relative bias and relative precision were 13.3 mg m3, 0.02% and 5.2% for PM2.5 concentrations (n ¼ 282), and 12.3 mg m3, 3.9% and 7.7% for PM10–2.5 concentrations (n ¼ 282). FRM measurements were alternate day 24-h integrated PM2.5 or PM10 ambient air samples collected onto 47-mm diameter PTFEs filters at a flow rate of 16.7 l min1. Between a dichotomous and a PM2.5 FRM air sampler, the average concentration, relative bias and relative precision were 12.4 mg m3, 5.6% and 8.2% (n=43); and between a dichotomous and a PM10 FRM air sampler, the average concentration, relative bias and relative precision were 25.7 mg m3, 4.0% and 5.8% (n ¼ 102). The PM2.5 concentration measurement standard errors were 0.95, 0.79 and 1.02 mg m3; for PM10 the standard errors were 1.06, 1.59, and 1.70 mg m3 for two dichotomous and one FRM samplers, respectively, which indicate the dichotomous samplers have superior technical merit. These results reveal the potential for the dichotomous sequential air sampler to replace the combination of the PM2.5 and PM10 FRM air samplers, offering the capability of making simultaneous, self-consistent determinations of these particulate matter fractions in a routine ambient monitoring mode. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Relative standard deviation; Relative precision; PM2.5; PM10; Technical merit
1. Introduction In 1997, the USEPA made final new National Ambient Air Quality Standards (NAAQS) for both PM10 and PM2.5, and within two years had launched a national PM2.5 monitoring trends network that now has 1700 PM2.5 monitors at 1100 sites and augments the existing 1400 PM10 monitors at 930 sites (USEPA, 2000). The USEPA requires that at urban centers with a population of more than 1 million people daily *Corresponding author. Fax: +1-813-974-4986. E-mail address: [email protected] (N. Poor).
or continuous PM2.5 measurements must be made at a minimum of two core sites (USEPA, 1998). Collocated FRM PM2.5 and PM10 samplers are now requisite for NAAQS compliance determination, even though PM10 includes PM2.5. Long-term and episodic increases in ambient air concentrations of both PM10 and PM2.5 are significantly associated with higher rates of mortality and morbidity; however, the PM2.5 contribution is thought to be the more toxic constituent of PM10 (Dockery et al., 1993; Samet et al., 2000). The new NAAQS were challenged on several counts; on one specific technical point germane to this analysis,
1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 2 ) 0 0 3 0 4 - 7
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the US District Court of Appeals found that ‘‘yretention of the PM10 indicator simultaneously with the establishment of the new fine particle indicator is unsupported by the evidence and arbitrary and capricious’’. Thus, in order for the agency to support policy for fine particulate control a technically sound, selfconsistent method of determining both fine and coarse particulate matter is needed. (US Court of Appeals, 1997) The Rupprecht and Patashnick (R&P) Partisols-Plus Dichotomous Model 2025 Sequential Air Sampler automates a week of daily monitoring for the separate PM2.5 and PM10–2.5 components of PM10. In principle, one dichotomous sampler could replace two separate particulate matter (PM) monitors for NAAQS compliance determination. For speciation analysis and epidemiological studies of toxicity, this has the advantages of segregating the predominantly anthropogenic particles, PM2.5, from particles mostly of geologic or marine origin, PM10–2.5 (Wilson and Suh, 1997). Moreover, separating these size fractions minimizes artifacts associated with mixing on the filter substrate acidic fine particles with basic coarse particles.
2. Experimental Two R&P Partisols Model 2025 dichotomous sequential PM air samplers and one R&P Partisols Model 2000 Federal Reference Method (FRM) PM air sampler were collocated at the eastern end of the Gandy Bridge in Tampa, Florida, as part of an air toxics and nutrient deposition study. This project was sponsored by the
Unit 2
Florida Department of Environmental Protection (FDEP), Tampa Bay Estuary Program and USEPA. Fig. 1 shows the arrangement of the dichotomous air samplers and the FRM at this site. The 1.2 m 1.2 m platforms were spaced B1.5 m apart, the inlet heights were 2.0 m, and the FRM was on the same platform as one of the dichotomous air samplers. The first of two dichotomous samplers, Unit 1, began operation in January 2000, as did the PM2.5 FRM air sampler. The second dichotomous sampler, Unit 2, began operation in late February 2000. In June 2000, the PM2.5 FRM was converted to a PM10 FRM. Dichotomous sampler measurements were daily 24-h integrated PM2.5 and PM10–2.5 ambient air samples collected at a total flow rate of 16.7 l min1. A virtual impactor split the airflow into flow rates of 1.67 and 15.0 l min1 onto PM10–2.5 and PM2.5 47-mm diameter PTFEs filters, respectively. The PM2.5 and PM10–2.5 concentrations were calculated as shown in Eqs. (1) and (2) (Dzubay et al., 1978). Eq. (2) shows the correction made for PM2.5 particles collected on the PM10–2.5 filter. FRM measurements were alternate day 24-h integrated PM2.5 or PM10 ambient air samples collected onto 47-mm diameter Whatman PTFEs filters at a flow rate of 16.7 l min1. The dichotomous air sampler flow rate of 15.0 l min1 to the PM2.5 filter is less than the 16.7 l min1 flow rate specified in the FRM for PM2.5 in 40CFR50, Appendix L. Mass2:5 PM2:5 ¼ ; ð1Þ Volume2:5 PM102:5 ¼
Unit 1
Mass1022:5 Volume1022:5 PM2:5 ; Volumetotal Volumetotal
ð2Þ
FRM
Fig. 1. Arrangement of two dichotomous sequential PM air samplers (Units 1 and 2) and one FRM PM air sampler (FRM) at the Gandy Bridge site in Tampa, Florida.
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
where PM2.5 is the concentration in mg m3, PM10–2.5 the concentration in mg m3, Mass2.5 the mass collected on the PM2.5 filter in mg, Mass102.5 the mass collected on the PM10–2.5 filter in mg, Volume2.5 the volume of air through the PM2.5 filter in m3 (B21.6 m3), Volume102.5 the volume of air through the PM10–2.5 filter in m3 (B2.4 m3) and Volumetotal is the total volume of air through the sampler in m3 (B24 m3). The Environmental Protection Commission of Hillsborough County (EPCHC) operates and maintains the Gandy Bridge air pollution monitoring site. For the period of this study, an EPCHC technician visited the site daily. The temperature and pressure sensors of the PM samplers were calibrated annually with an NIST-traceable transfer standard. For each channel, flow rates were tested against an NIST-traceable flow meter during filter magazine (for the dichotomous samplers) or filter cassette (for the FRM) installation. The flow rate coefficient of variation (CV) for the air samplers was typically o0.5%. The FRM virtual impactor was cleaned each month (after 14 daily samples), and on this cleaning schedule, no significant particle build-up on the impactor filter was observed. The PM10 inlet was exchanged with a clean inlet at least once every 2 months. Five magazines of pre-weighed filters were shipped weekly from the FDEP by courier to the EPCHC in a small cooler containing a chain-of-custody form and blue ice for the return trip. The chain-of-custody form listed the filter identification numbers and filter cassette screen identification numbers, in sequence, by magazine.
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Dichotomous sampler magazines were stored in the EPCHC air-monitoring laboratory until installation at the Gandy Bridge site. Dual magazines were installed, unopened, in the dichotomous sampler and the sampler was programmed for 1 week of uninterrupted operation. For the FRM, a single magazine held the filter cassettes, which were unpacked and transferred to the FRM as needed. FRM filters within the magazine were stored in a temperature-controlled trailer at the Gandy Bridge site before use. After the air samples were collected, EPCHC technicians electronically downloaded the run data onto a laptop computer for direct input into a spreadsheet, and manually transcribed data from the air sampler computer screen to field data sheets. The dichotomous sampler magazines were removed, unopened, and frozen until shipped; the FRM filter cassettes were repacked in sequence into an awaiting magazine, which once filled, was frozen until shipped. This protocol did not conform to 40CFR50, Appendix L, which requires PM2.5 filters to be removed from the sampler within 4 days of exposure, and for filters removed from the sampler to be immediately chilled. A small cooler was packed with frozen blue ice, and the chain-of-custody form and a copy of the EPCHC filter log sheet showing the filter receipt and run dates were returned with the magazines by courier to FDEP. The more detailed data on the EPC field data sheet were checked against the electronic spreadsheet database. At the USEPA-approved FDEP weighing laboratory, a technician unpacked the cooler and removed the filter
Average PM10 Concentration (µg m-3)
35
30
25
20
15
10
5
Ju ly A ug Se ust pt em be r O ct ob er N ov em be r D ec em be r
Ju ne
M ay
A pr il
Ja nu ar Fe y br ua ry M ar ch
0
Average PM2.5 Average PM10-2.5
Fig. 2. Average monthly PM10 concentrations at the Gandy Bridge site in Tampa, Florida. The contributions from PM2.5 and PM10–2.5 are shown.
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cassettes from the magazines. The filters were placed in plastic petri dishes and conditioned before post-weighing. Gravimetric analyses were done in accordance with 40CFR50, Appendix L, 8.3. The methods’ lower detection level was 2 mg m3, higher concentration limit was 200 mg m3, and precision and bias were 70.6 and 70.1 mg m3, respectively. Filter pre- and post-weights and the dates of each weight measurement are tracked in a data management system (LIMS), along with qualifiers or comments for each filter number. The LIMS automatically calculated the change in mass on the filter. The FDEP laboratory manager verified and validated the LIMS database, and forwarded to the EPCHC a LIMS hard copy and electronic report with these data fields. At the EPCHC a data quality assurance technician not involved in the field sampling activity merged the LIMS data set with the field data to calculate the ambient air concentration. The technician reviewed the data integrity and validated the field data at this time. The data set, complete with field data, gravimetric analysis data, comments and qualifiers, was forwarded to the University of South Florida College of Public Health (COPH) for analysis and assimilation with other air and rainwater pollutant concentration data sets. For this investigation of equipment performance, only the data that were flagged as void either by FDEP or EPCHC were eliminated from the data set for the analyses. This analysis consists of four parts: a summary of the PM concentration data, a comparison of the two collocated dichotomous samplers, a comparison of the dichotomous samplers with the FRM, and an analysis of the measurement errors and estimates of technical merit. The field performance of the dichotomous samplers were judged on (1) their ability to meet the USEPA requirements for PM2.5 and PM10 measurements in general accordance with 40CFR50, and (2) technical merit equal or better than that of the FRM. A statistical summary of the PM concentration data revealed the underlying distributions, which were important in the choice of analytical assumptions and tools; gave the concentration ranges over which the conclusions will be valid, and provided information on the air quality in Tampa, Florida. Subject to interpretation, regression statistics indicate the agreement of measurements between air samplers. Histograms, simple statistics, and simple linear regressions were done with the automated features of SigmaPlots 2001, which uses standard techniques. To test that the simple linear regression slope was no different than 1.00 and that the intercept was no different than 0.00, we used a 95% confidence level. The Wilcoxon signed rank tests as described in Ott (1993), however, were calculated in an electronic spreadsheet. For the Wilcoxon signed rank tests (a non-parametric equivalent to the paired t-tests), statistical significance was based on a two-tailed test at a
95% confidence level, and the null hypothesis was that the collocated measurements were identical. If the jzj > 1:96 (or for np50; T > Tcritical ), then the collocated measurements were different. The relative bias (RB) and relative precision (RP) were calculated for the entire period of record according to Eqs. (3)–(5), respectively (USEPA, 1998). RBi ¼
2ðyi xi Þ ; ðyi þ xi Þ
ð3Þ
RB ¼
1X RBi ; n i
ð4Þ
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u u1 X RB 2 i pffiffiffi : RP ¼ t i n 2
ð5Þ
In the above equations, yi and xi refer to the ith PM concentrations of two collocated units, n is the number of observations and RBi is the relative bias for the ith observation. This approach assumes that neither collocated sampler is the reference or true value. For PM2.5, a relative bias of 10% and relative precision of 10%; and for PM10, a relative bias of 10% and precision of 5 mg m3, are consistent with the 40CFR50 requirements for compliance monitoring. The integrity of the measurement, here defined as the mass concentration on a field blank assuming the nominal flow rate, must be below 5 mg m3. Although the robustness of the dichotomous sequential air samplers, in this case, cannot be entirely segregated from the complexity of the filter handling, transport and storage process, data completeness gives another perspective of the equipment performance. For this analysis, data completeness is defined as the ratio of final (accepted) records to planned records, and a completeness of 75% or better meets the conditions of 40CFR50. Another approach to compare the two dichotomous samplers and one FRM sampler is through a statistic known as ‘‘technical merit’’. This statistic compares the precision between two instruments based on their calibration functions (Mandel, 1964) and is re-interpreted in Eq. (6) for the problem at hand, where sFRMPM =sUnit1PM is the ratio of the measurement standard error for the FRM and the Unit 1 dichotomous PM concentration data. In a similar manner, the technical merit was calculated for the FRM versus Unit 2, and Unit 1 versus Unit 2. A smaller measurement standard error and a lower technical merit statistic indicate better performance. sFRMPM ¼ technical merit: ð6Þ sUnit1PM To estimate sFRMPM ; sUnit1PM ; and sUnit2PM we began with data subsets of collocated FRM, Unit 1 and Unit 2 PM2.5 and PM10 measurements. Simple linear regressions of FRM versus Unit 1, FRM versus Unit 2, and
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
and a dichotomous air sampler:
180
Number of Observations
160 140 120 100 80
Average PM2.5 Concentration (µg m-3)
l¼ N = 412 Minimum = 4.0 Maximum = 47.1 Median = 11.7 Average = 13.0 Standard Deviation = 6.0
s# FRMPM ¼
s# Unit1PM ¼
40 20 0
140 120 100 80
ð7Þ P
di2 2 # n 2 1 þ lb l
i
P
di2 1 þ lb# 2 n 2 1
i
1=2 ;
ð8Þ
;
ð9Þ
1=2
# i: di ¼ yi a# bx
180
Number of Observations
s2FRMPM ; s2Unit1PM
60
160
3293
PM10-2.5 Concentration (µg m-3) N = 412 Minimum = 2.0 Maximum = 50.9 Median = 11.2 Average = 11.9 Standard Deviation = 5.3
60
ð10Þ
As can be seen from Eqs. (8) and (9), unless l equals either 1.0 (equal variances) or zero (e.g., no measurement error on the independent variable), a single regression of FRM versus Unit 1 concentration data does not provide sufficient information to finish the calculations. With three regressions, however, the above non-linear equations for each regression can be solved simultaneously to derive the desired error estimates.
40 20
3. Results and discussion
0
180
Number of Observations
160 140 120 100 80 60
PM10 Concentration (µg m-3) N = 412 Minimum = 8.8 Maximum = 74.0 Median = 23.6 Average = 24.9 Standard Deviation = 8.8
40 20 0
2 6 10 14 18 22 26 30 34 38 42 46 50 54 58 62 66 70 74 78
Fig. 3. Distribution of average daily PM2.5, PM10–2.5 and PM10 concentrations at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001.
Unit1 versus Unit 2 concentration data yielded the statistics shown in Table 4. For each regression, the measurement errors for both instruments must be considered. Applying the technique described by Mandel (1964), we estimated sFRMPM and sUnit1PM as shown in Eqs. (7)–(10), where yi and xi are the ith observed Unit 1 and FRM PM concentrations, respectively; a# and b# are the intercept and slope from the least squares regression line; and n is the number of observations. According to Eq. (7), l is the ratio of the measurement variances of two instruments, in this example, an FRM
3.1. Summary of the PM data Summary particulate matter concentration data obtained from dichotomous sequential air sampler measurements at the Gandy Bridge site from January 2000 through March 2001 are presented in Figs. 2 and 3. Fig. 2 is a time series plot of the average monthly PM concentrations. For this period, the average PM10 concentration was 24.9 mg m3 and the highest average PM10 concentration was 74.0 mg m3. The PM2.5 concentration was on the average 50% of the PM10 concentration. No strong seasonal trends in PM concentrations were evident. The distributions of the PM2.5, PM10–2.5 and PM10 concentration data are graphed in Fig. 3. Distributions for these data sets were decidedly skewed, which is reflected in higher average than median concentrations. The 11.7 mg m3 median and 13.0 mg m3 average concentrations for PM2.5 were higher than the 11.2 mg m3 median and 11.8 mg m3 average concentrations for PM102.5, and greater variability was seen in the PM2.5 compared with the PM10–2.5 concentration data. The PM10 ambient air concentrations met the National Ambient Air Quality Standards (NAAQS) of 150 and 50 mg m3 for the 2nd highest 24-h and annually averaged 24-h concentrations, respectively. The PM2.5 NAAQS are based on 3-yr averaging techniques; PM2.5 ambient air concentrations were below the 24-h and annually averaged concentrations of 65 and 15 mg m3, respectively.
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Table 1 Summary statistics for daily PM2.5, PM102.5 and PM10 concentrations measured by collocated dichotomous air samplers at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001 Statistic N Average ( mg m3) Standard deviation ( mg m3) Relative bias (%) Relative precision (%) Wilcoxon signed rank test
Unit 1 PM2.5
Unit 2 PM2.5
Unit 1 PM102.5
13.3 6.3
12.0 5.4
282 13.3 6.2
Unit 2 PM102.5
Unit 1 PM10
12.6 5.8
25.3 9.1
282
0.02 5.2 z=1.58
3.2. Comparison of collocated dichotomous samplers The collocated performance of two dichotomous sequential samplers is presented in Table 1. Daily PM10 concentrations were calculated as the sum of daily PM2.5 and PM10–2.5 concentrations for each dichotomous sampler. The relative bias and relative precision were 0.02% and 5.2% for PM2.5 concentrations and 3.9% and 7.7% for PM10–2.5 concentrations. For PM10 concentrations, the relative bias was 2.1%, and the relative precision was 4.2%, which yields a standard error of 3.4 mg m3 at an 80 mg m3 concentration. These performance statistics are within the alreadydescribed USEPA requirements. Unit 2 measurements for PM10 and PM10–2.5 were consistently higher than Unit 1 measurements, as revealed by the Wilcoxon signed rank tests. Simple linear regressions of Unit 2 versus Unit 1 concentration data confirm these results, as shown in Fig. 4. For PM2.5 concentrations, the slope was not different from 1.00; for PM10–2.5 concentrations, however, the slope was 1.05 with Unit 2 concentration data significantly higher than were those of Unit 1. The intercepts for these regression analyses were not different from zero. Better than 95% of the variability in Unit 1 concentration data was explained by Unit 2. Of two dichotomous sequential air samplers, the data completeness was 81% (368/451) for Unit 1 and 82% (326/401) for Unit 2; with both units considered together, the data completeness from January 2000 to March 2001 was 91%. These levels of completeness were achieved through daily inspection of the air samplers. 3.3. Comparison of collocated dichotomous and FRM air samplers The collocated performance of dichotomous and FRM air samplers are presented in Tables 2 and 3. When comparing the performance of Unit 1 dichotomous with the FRM air sampler, the relative bias and relative precision were 5.6% and 8.2% for PM2.5 concentrations, and 4.0% and 5.8% for PM10 concentrations. The Wilcoxon signed rank tests indicate
3.9 7.7 z=7.9
Unit 2 PM10 282 25.9 9.4 2.1 4.2 Z=7.3
that the collocated PM10 but not the PM2.5 concentration measurements were statistically different. A simple linear regression of FRM versus Unit 1 concentration data, Fig. 5, show that for PM2.5 concentrations, the slope was not different from 1.00; for PM10 concentrations, however, the slope was 0.95 with Unit 1 concentration data lower than those of the FRM. The intercepts for both regression analyses were not different from zero. Better than 94% of the variability in FRM concentration data was explained by Unit 1. When comparing the performance of Unit 2 dichotomous with the FRM air sampler, the relative bias and relative precision were 7.5% and 8.4% for PM2.5 concentrations, and 1.8% and 6.2% for PM10 concentrations. The Wilcoxon signed rank tests indicate that the collocated PM10 but not the PM2.5 concentration measurements were statistically different. A simple linear regression of the dichotomous versus FRM concentration data contradicts these results, as shown in Fig. 6. For both PM2.5 and PM10 concentrations, the slope was not different from 1.00 and the intercepts were not different from zero. Better than 91% and 96% of the variability in FRM concentration data were explained by Unit 2 PM2.5 and PM10 concentration data, respectively. The fewer number of observations for the FRM PM2.5 concentrations weakened the comparative statistics. In a quality assurance summary report for the USEPA PM2.5 monitoring network, relative precision and bias estimates were available for collocated Rupprecht and Patashnick PM2.5 FRM (USEPA, 2001) measurements. For the national network, with outliers included, the relative bias and relative precision were 4.1% and 6.1%; 5.3% and 7.6% for the Florida network. These values are higher than the collocated dichotomous PM2.5 relative bias and relative precision of 0.02% and 5.2% (Table 1). 3.4. Analysis of error and estimation of technical merit Linear regression statistics of FRM versus Unit 1, FRM versus Unit 2, and Unit 1 versus Unit 2 PM10
Unit 2 PM2.5 Concentration (µg m-3)
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298 60 50 40 30
N = 282 Slope = 0.995 Standard Error (Slope) = 0.010 Intercept = 0.092 Standard Error (Intercept) = 0.147 R2 = 0.97
20 10 0 0
10
20
30
40
50
60
Unit 2 PM10-2.5 Concentration (µg m-3)
Unit 1 PM2.5 Concentration (µg m-3) 60 50 40 30
N = 282 Slope = 1.053 Standard Error (Slope) = 0.015 Intercept = -0.123 Standard Error (Intercept) = 0.191 R2 = 0.95
20 10 0
0
10
20
30
40
Unit 2 PM10 Concentration (µg m-3)
Unit 1 PM10-2.5 Concentration
50 -3 (µg m )
60
80
60
40
N = 282 Slope = 1.019 Standard Error (Slope) = 0.01 Intercept = 0.07 Standard Error (Intercept) = 0.265 R2 = 0.97
20
0 0
10
20
30
40
50
60
70
80
Unit 1 PM10 Concentration (µg m-3)
Fig. 4. Simple linear regression of PM2.5, PM10–2.5, PM10 concentration data for two collocated dichotomous air samplers.
concentration data were needed to first estimate the measurement error for each instrument, and second to compute the technical merit. The results of six regressions are presented in Table 4, the estimates of the standard error on PM2.5 and PM10 concentration measurements for each instrument are listed in Table 5, and the technical merit statistics are found in Table 6. The standard errors on the PM2.5 concentration
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measurements were 1.02, 0.95 and 0.79 mg m3 for the FRM, Unit 1 dichotomous and Unit 2 dichotomous air samplers, respectively. For PM10 concentrations, these standard errors were 1.70, 1.06 and 1.59 mg m3 for the three samplers, given in the same order (Table 5). In order of technical merit, for PM2.5 concentration measurements the field performance of Unit 2 was superior to that of Unit 1 dichotomous sequential air sampler, and the performance of Unit 1 sampler was superior to that of the FRM air sampler. For PM10 concentration measurements, the field performance of the Unit 1 was superior to that of the Unit 2 dichotomous sequential air sampler, and the performance of Unit 2 was superior to that of the FRM air sampler. With respect to PM10 concentration measurements, the performance of Unit 2 was closer to that of the FRM than to Unit 1. In collocating this equipment, we have assumed that the air quality near the air samplers is homogeneous. If, for the purpose of discussion, we accept that systematic error manifests itself as bias, and random error as diminished precision, we can perhaps identify and classify sources of error that contribute to differences between the PM air samplers. Air leaks, temperature or pressure sensor deterioration, pump (brush) wear, buildup on the inertial impactor (FRM) could lead to flow rate changes and thus bias in the observed PM concentrations between two units, and these contributions were likely minimized by routine inspection and maintenance. A statistically significant bias of 0.6 mg m3 and lower precision were evident, however, between the PM10–2.5 and thus the PM10 concentrations of the Unit 2 compared to the Unit 1 sequential dichotomous air sampler. The physical differences between the dichotomous and FRM sampler inlets (as seen in Fig. 1) and inertial separators could well explain the overall lower PM2.5 and PM10 concentrations obtained with dichotomous samplers as compared with the FRM (Tables 2 and 3). A lower air flow rate and thus filter face velocity through the dichotomous sampler PM2.5 filter may reduce the amount of particle volatilization, and thus bias the PM2.5 measurement high as compared with an PM2.5 FRM sampler. Although not seen in this investigation, we suspect that this bias would be more severe in an urban setting with relatively high ambient air ammonium nitrate and semi-volatile organic compound concentrations. Sources of random error are likely linked to filter handling, storage and transport. Filters experience, for example, ambient temperatures of over 281C to freezer temperatures below 41C, and the loss or gain of mass on the filters from volatilizing species or condensing water vapor could reduce measurement precision. Field blanks help to quantify this error. For the period of
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
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Table 2 Summary statistics for daily PM2.5 and PM10 concentrations measured by collocated dichotomous and FRM air samplers at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001 Statistic
Dichot Unit 1 PM2.5 FRM PM2.5
N Average Standard deviation Relative bias (%) Relative precision (%) Wilcoxon signed rank test
Dichot Unit 1 PM10 Unit 1 PM2.5
FRM PM10
12.1 4.9
26.2 10.9
43 12.8 4.9
Unit 1 PM10 102
5.6 8.2 ToTcritical (198o310) np50
25.2 10.5 4.0 5.8 z ¼ 4:8
Table 3 Summary statistics for daily PM2.5 and PM10 concentrations measured by collocated dichotomous and FRM air samplers at the Gandy Bridge site in Tampa, Florida, from January 2000 to March 2001 Statistic
Dichot Unit 2 PM2.5 FRM PM2.5
N Average Standard deviation Relative bias (%) Relative precision (%) Wilcoxon signed rank test
Dichot Unit 2 PM10 Unit 2 PM2.5
FRM PM10
11.3 4.0
27.2 11.6
29 12.1 4.1 7.5 8.4 ToTcritical (56o126) np50
record, 132 paired field blanks were cycled through the dichotomous air samplers. Blank PM2.5 and PM10–2.5 filters had on the average 11.2 and 9.4 mg of particle mass, respectively. The average blank values were 0.47 and 0.39 mg m3 if reported as PM2.5 and PM10–2.5 concentrations, in that order. This suggests that filter handling, storage and transport could account for 50– 90% of the measurement error. Another source of random error is the loss of volatile air pollutants from the filters that remained at ambient air temperatures longer, for example, 7 days versus 1 day. To reduce the impact of this error for the instrument performance evaluations, filters for all three samplers were shipped, loaded, retrieved and stored on a comparable schedule.
4. Conclusions We evaluated the collocated field performance of two dichotomous sequential PM air samplers with the traditional USEPA statistical approach and criteria. Observed at a monitoring site adjacent to Tampa Bay were PM2.5 and PM10 concentrations between 4 and
Unit 2 PM10 83 26.9 11.8 1.8 6.2 Z=2.41
50 mg m3, and 8 and 80 mg m3, respectively. In this 1-yr study, the dichotomous samplers demonstrated their capability to measure PM2.5 and PM10–2.5 within a 10% relative bias and a 10% relative precision, and PM10 within a 10% relative bias and a 5 mg m3 relative precision. The integrity of the PM2.5 and PM102.5 measurements were o1 mg m3, well below the 5 mg m3 performance criterion. Data completeness for each dichotomous sampler was 81% and 82%, above the criterion of 75%. The relative bias and relative precision were also within 10% between the dichotomous air samplers and either a PM2.5 or a PM10 FRM. An observed bias between the FRM and dichotomous air samplers was best explained as a shift in the particle penetration curve, most likely due to physical differences between the inlets and inertial impactors. Filter handling, transport and storage accounted for B50–90% of the error in the observed PM concentrations. A statistical technique that apportions the measurement error between collocated instruments, using data from three collocated instruments, was successfully applied to estimate the measurement standard error for each instrument. With the standard error known, the performance of each instrument is easy to evaluate,
30
Dichot Unit 2 PM2.5 Concentration (µg m-3)
Dichot Unit 1 PM2.5 Concentration (µg m-3)
N. Poor et al. / Atmospheric Environment 36 (2002) 3289–3298
25
20
15
N = 43 Slope = 0.957 Standard Error (Slope) = 0.039 Intercept = -0.134 Standard Error (Intercept) = 0.527 R2 = 0.94
10
5
0 0
5
10
15
20
25
24 22 20 18 16 14
N = 29 Slope = 0.918 Standard Error (Slope) = 0.055 Intercept = -0.140 Standard Error (Intercept) = 0.710 R2 = 0.91
12 10 8 6 4
30
4
-3
80 70 60 50 40
N = 102 Slope = 0.945 Standard Error (Slope) = 0.017 Intercept = 0.424 Standard Error (Intercept) = 0.478 R2 = 0.97
20 10 0 0
20
40
60
6
8
10
12
14
16
18
20
22
24
FRM PM2.5 Concentration (µg m-3) Dichot Unit 2 PM10 Concentration (µg m-3)
Dichot Unit 1 PM10 Concentration (µg m-3)
FRM PM2.5 Concentration (µg m )
30
3297
80
60
40
N = 83 Slope = 0.993 Standard Error (Slope) = 0.022 Intercept = -0.134 Standard Error (Intercept) = 0.650 R2 = 0.96
20
0
80
0
FRM PM10 Concentration (µg m-3)
20
40
60
80
FRM PM10 Concentration (µg m-3)
Fig. 5. Simple linear regression of PM2.5 and PM10 concentration data for a collocated dichotomous and FRM air sampler.
Fig. 6. Simple linear regression of PM2.5 and PM10 concentration data for a collocated dichotomous and FRM air sampler.
Table 4 Simple linear regression statistics for data subsets used in the measurement error analyses (concentration units of mg m3) X Concentration
Y Concentration
Intercept a#
Slope b#
N
P
FRM PM2.5 FRM PM2.5 Unit 1 PM2.5 FRM PM10 FRM PM10 Unit 1 PM10
Unit Unit Unit Unit Unit Unit
0.504 0.142 0.083 0.474 0.132 0.385
0.882 0.918 1.013 0.947 0.993 1.039
29 29 29 83 83 83
46.5 40.4 41.8 300 435 303
1 2 2 1 2 2
PM2.5 PM2.5 PM2.5 PM10 PM10 PM10
i
di2
Table 5 PM2.5 and PM10 measurement standard error for each air sampler FRM Unit 1 dichotomous Unit 2 dichotomous
sFRMPM2:5 sUnit1PM2:5 sUnit2PM2:5
either by a direct comparison of the standard errors or by a statistic known as technical merit (Mandel, 1964). With this technique, we demonstrated that the
1.02 mg m3 0.95 mg m3 0.79 mg m3
sFRMPM10 sUnit1PM10 sUnit2PM10
1.70 mg m3 1.06 mg m3 1.59 mg m3
dichotomous sequential air sampler PM2.5 and PM10 concentrations were more precise than those of the FRM.
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Table 6 Technical merit statistic for each pair of air samplers sFRMPM2:5 =sUnit1PM2:5 sFRMPM2:5 =sUnit2PM2:5 sUnit1PM2:5 =sUnit2PM2:5
1.07 1.29 1.20
sFRMPM10 =sUnit1PM10 sFRMPM10 =sUnit2PM10 sUnit1PM10 =sUnit2PM10
1.60 1.07 0.67
Acknowledgements This research was funded in part by the Tampa Bay Estuary Program, the Florida Department of Environmental Protection (contract AQ-156), and the USEPA, and would not have been possible without the contributions from the Environmental Protection Commission of Hillsborough County. The findings and opinions expressed here are those of the authors and not necessarily those of the supporting organizations.
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