Letter Cite This: Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX
pubs.acs.org/journal/estlcu
Sources of Variability in Real-Time Monitoring Data for Fine Particulate Matter: Comparability of Three Wearable Monitors in an Urban Setting Jared A. Fisher,*,† Melissa C. Friesen,† Sungduk Kim,‡ Sarah J. Locke,† Yonathan Kefelegn,§ Jason Y. Y. Wong,† Paul S. Albert,‡ and Rena R. Jones†
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†
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20850, United States ‡ Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20850, United States § Department of Public Health Sciences, Division of Environmental and Occupational Health, University of California, Davis, California 95616, United States S Supporting Information *
ABSTRACT: The increasing availability of portable air pollution monitoring devices has greatly enhanced the ability to measure personal exposure in real time. However, the cost and specifications of these devices vary considerably, and questions about their reliability and practicality for use in epidemiological investigations remain. In this field study, three personal PM2.5 exposure monitors (two nephelometers and one optical particle counter) were compared in an urban setting to assess their feasibility for use in future studies. In total, 3963 1-min measurements were collected over 12 days from locations of several types (e.g., above- and below-ground subway stations, sidewalks next to urban traffic, outdoor construction sites, etc.) in the Washington, DC, metropolitan area. Overall, we observed moderate to high levels of agreement in pairwise comparisons of PM2.5 concentrations between devices (R2 range of 0.37−0.75). Bland−Altman plots showed that differences in device agreement varied over the range of mean concentrations. In linear mixed models adjusting for temperature and relative humidity, we saw significant interaction between the device and location (p < 0.05), suggesting that the relationship between devices was not constant in all locations. Our finding of heterogeneity in instrument comparability by location may have important implications for epidemiologic studies incorporating personal PM2.5 measurements.
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INTRODUCTION Particulate matter (PM) is a complex aerosol mixture arising from both anthropogenic and natural sources and is a major cause of morbidity and mortality worldwide.1,2 Fine PM [≤2.5 μm in aerodynamic diameter (PM2.5)] is one of six criteria air pollutants regulated by the U.S. Environmental Protection Agency (EPA) with daily (35 μg/m3) and annual (12 μg/m3) standards.3 Studies that have used outdoor concentrations of ambient fine PM as a proxy for personal exposure have consistently found associations with cardiovascular and respiratory morbidity and mortality and with lung cancer.4−6 The gold standard for the characterization of personal PM exposure has traditionally been a gravimetric measurement collected and summarized for a relevant time period of interest using a filter-based sampler. However, instruments based on light scattering technologies, such as nephelometers, which measure light scattered from multiple angles on a group of particles, or optical particle counters, which count individual © XXXX American Chemical Society
particles by measuring scattered light produced as each particle passes by a photodetector, can provide real-time personal exposure monitoring without the extra time needed for gravimetric analysis. Nephelometers, such as the MicroPEM v3.2A (MicroPEM; RTI International) and the MIE pDR1500 (pDR-1500; Thermo Scientific), have been used previously for personal PM exposure monitoring of households, occupational settings, and public transportation systems.7−11 The pDR-1500, specifically, has also been used as a reference device for other personal sensors and has compared well to gravimetric methods.12 Technological advances have enabled many inexpensive and smaller personal PM monitoring devices (generally, optical Received: February 21, 2019 Revised: March 8, 2019 Accepted: March 11, 2019
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DOI: 10.1021/acs.estlett.9b00115 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX
Letter
Environmental Science & Technology Letters Table 1. Specifications of the Clarity P1, MicroPEM, and pDR1500 Devices instrument
detection method
Clarity P1 MicroPEM
optical particle counter light scattering nephelometer light scattering nephelometer
pDR-1500
sampling frequency interval (s)
PM2.5 air flow rate; method
PM size range; size selective inlet type
2.5 30
NA; fan 0.5 L/min; pump
30
1.52 L/min; pump
0.35−2.5 μm; nonea 0.1−2.5 μm; two-stage impactor inlet 0.1−2.5 μm; cyclone inlet
flow rate calibration method
approximate cost (USD)
factory-calibrated user-calibrated
50−100 2000
user-calibrated
5000
a
The Clarity device uses an algorithm to convert the particle count to mass concentration.
particle counters) to come to market in recent years,13−15 making the addition of real-time personal monitoring a feasible component of ambient exposure assessments.16 Increasingly, studies have been evaluating the performance of these devices in comparison to that of well-validated reference devices, both under controlled laboratory conditions12,17,18 and at ambient field sites.13,19,20 However, because certain sensors may be sensitive to changes in environmental conditions, such as temperature and relative humidity,19,21 evaluations of personal monitors as they move through ambient microenvironments are needed to gauge their real-world performance. Additionally, studies in settings that mirror those of a personal exposure assessment (e.g., wearable device and mobile measurements) offer the benefit of evaluating device performance under realistic operating conditions. This also provides the opportunity to examine the practicality and feasibility of sensor operation before deployment in larger investigations. In this field study, we evaluated three real-time PM2.5 personal monitoring devices: an inexpensive Bluetooth-enabled prototype optical particle counter available to the research community and two light scattering nephelometers used in previous real-time exposure assessments, the MicroPEM and pDR-1500. Measurements were collected along public transit routes in an urban area. The objectives of this study were to examine the overall comparability in concentrations measured between devices and to evaluate sources of variability.
analysis: (1) Metro train above ground, (2) Metro train underground, (3) in a Metro station, (4) near an active construction site, (5) proximal to urban traffic, and (6) other outside locations. Temperature, relative humidity, and atmospheric pressure were collected in real time by the pDR-1500. Concentrations and meteorological measurements were temporally aligned and averaged to 1-min arithmetic means and log-transformed. The 1-min observations were removed if they were missing concentrations (pDR, 3.9%; MicroPEM, 9.3%; Clarity, 18.4%), temperature (0.7%), or relative humidity (0.6%). Data were visualized through a combination of timeseries plots, scatter plots, and Bland−Altman plots with three pairwise comparisons of concentrations (Clarity vs pDR, Clarity vs MicroPEM, and pDR vs MicroPEM). Bland− Altman plots were constructed with log-scale mean differences (MD) and limits of agreement (MD ± 1.96 SD of differences) to visualize differences between device measurements over the range of PM concentrations. The relationship between devices was further evaluated by fitting three separate linear mixed models with the PM2.5 concentration of one instrument as the dependent variable and the corresponding measurement from another instrument as the independent variable (treated as a fixed effect). In addition, fixed effect terms for temperature and relative humidity and a multiplicative interaction term between location category and the PM2.5 concentration of the comparison instrument were included. The linear mixed model included a random effect term corresponding to day and a continuous-time exponential correlation structure to account for serial correlation in the measurements. For ease of interpretation, rather than present the parameter estimates for the exponential correlation structure, we show the estimated correlation for measurements 1 min and 1 day apart. Additional models incorporating atmospheric pressure as a covariate and including relative humidity− or temperature− concentration interaction terms as fixed effects were examined in sensitivity analyses.
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MATERIALS AND METHODS We simultaneously measured PM2.5 concentrations using three real-time personal particle monitoring devices, a prototype sensor from Clarity Movement Co. (Clarity P1), the MicroPEM v3.2A, and the MIE pDR-1500 personal particulate monitor. Technical specifications of the devices are listed in Table 1, and additional information about the devices can be found in the Supporting Information. The field measurements were collected on 12 weekdays from June 30 to August 18, 2016. Air sampling took place along common commuting modes in Washington, DC. All sampling instruments were housed side by side in a customdesigned personal backpack. Instrument inlets were attached to the pack shoulder straps and situated within the technician’s breathing zone. To avoid systematic bias from relative positioning, the position of instrument intakes was rotated each sampling day. The sampled locations involved combinations of walking along suburban and urban pedestrian ways and near idling vehicles and construction sites and during activities associated with Metro train use, including standing at stations and riding in passenger compartments of Metro trains, both above and below ground. Detailed location information (e.g., active construction site) and possible ancillary determinants of exposure (e.g., visible dust present) were noted by a technician at the time of sampling. After sampling was complete, sampling locations were categorized into one of six location types for
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RESULTS AND DISCUSSION In total, 3963 1-min averages were collected over 12 days. The geometric mean of PM2.5 concentrations during the sampling period was 31.6 μg/m3 (interquartile range of 10.1−69.2 μg/ m3) as estimated by the pDR-1500. Overall, temperatures and relative humidity ranged from 19.1 to 44.2 °C and from 24.1% to 77.0%, respectively, but varied by location (Table S1). There was strong agreement on the linear scale between the two nephelometers (R2min = 0.75). However, the Clarity prototype showed a lower level of agreement with both the MicroPEM (R2min = 0.37) and the pDR-1500 (R2min = 0.39). A time-series plot of 1-min averages showed periods of both agreement and discord between device concentrations (Figure S1). B
DOI: 10.1021/acs.estlett.9b00115 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX
Letter
Environmental Science & Technology Letters
Figure 1. Bland−Altman plots for pairwise comparisons of log-transformed PM2.5 concentrations between personal monitoring devices for all collocated 1-min averages. (A) Differences calculated as Clarity minus MicroPEM, (B) differences calculated as MicroPEM minus pDR, and (C) differences calculated as Clarity minus pDR. In each panel, the x axis reflects the mean of the two log-transformed pairwise measurements and the y axes reflect the difference of these two log-transformed values.
concentrations for the Clarity−pDR comparison: as mean concentrations increased, the magnitude of the pairwise differences also increased. Pairwise scatter plots suggested differences in device agreement by location (Figure S2). These differences were statistically significant in all three pairwise linear models that adjusted for meteorological covariates and included interaction terms between the comparison device concentration and location (Table 2). In models predicting pDR concentrations from Clarity measurements, the slope of the linear agreement in concentrations, as calculated by the addition of the main effect and location-specific interaction terms, ranged from 0.094 (urban traffic; 0.483−0.389) to 0.623 (Metro underground; 0.483 + 0.140). For models predicting MicroPEM concentrations from Clarity measurements, slopes were highest for Metro station and urban traffic locations (0.844 and 0.849,
Pairwise Bland−Altman plots showed the differences between the two device’s PM2.5 concentrations by mean concentration for all collocated 1-min averages (Figure 1). PM2.5 concentrations (log micrograms per cubic meter) from the Clarity device were lower on average than those from both the MicroPEM (MD, −0.22; 95% CI, −0.24, −0.20) and pDR (MD, −0.59; 95% CI, −0.57, −0.54). MicroPEM concentrations were also lower in comparison to pDR concentrations (MD, −0.34; 95% CI, −0.36, −0.33); however, the standard deviation of differences was lower for this pairwise comparison (0.48) than for either the Clarity−MicroPem (0.56) or Clarity−pDR comparisons (0.56). In both Clarity comparisons, more pairwise differences fell outside the upper agreement boundary, indicating the Clarity device generally had higher concentrations when agreement was poor. The level of agreement also appeared to change over the range of mean C
DOI: 10.1021/acs.estlett.9b00115 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX
Letter
Environmental Science & Technology Letters Table 2. Pairwise Linear Mixed Modelsa with Interactions by Locationb Clarity (device 1) and pDR (device 2)
Clarity (device 1) and MicroPEM (device 2)
pDR (device 1) and MicroPEM (device 2)
model term
Est (SD)
p value
Est (SD)
p value
Est (SD)
p value
intercept term device 1 temperature relative humidity location 1 (Metro train above ground) location 2 (Metro underground) location 3 (Metro station) location 4 (construction site) location 5 (traffic) location 6 (other) device1:location 1 device1:location 2 device1:location 3 device1:location 4 device1:location 5 device1:location 6 StdDayc StdResc CorrelationDayc CorrelationMinc
1.215 (0.403) 0.483 (0.082) 0.017 (0.009) 0.002 (0.003) referent 0.140 (0.202) 0.148 (0.362) 0.290 (0.288) 1.497 (0.216) −0.213 (0.212) referent 0.140 (0.087) 0.139 (0.113) −0.163 (0.101) −0.389 (0.086) 0.020 (0.098) 0.159 0.482 0.002 0.749
0.003
