Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas
Abstract
:1. Introduction
2. Materials and Methods
2.1. Equipment
2.2. Sampling Locations
- Clinton Drive, Houston, Texas (major road with a higher percentage of traffic emissions from heavy-duty diesel vehicles (HDDV)). Clinton Drive is located in the eastern part of the Houston metropolis in Texas (Figure 1b). In this study, the traffic on this road was made up of a higher number of HDDV (28%), emitting diesel particles. All samplers were deployed near the fence line of the Clinton CAMS, about twenty meters from Clinton Drive.
- US-59 Highway, Houston, Texas (major highway with traffic emissions mainly from gasoline vehicles (GV)). The US-59 highway in Houston, Texas runs from southwest to northeast in Houston (Figure 1c). The proportion of HDDVs among total traffic counts was 3% in this study. All samplers were deployed about 50 m from the road on a side street (Eastside Street).
- Residential home (residential location with no major sources of PM), which was located in a suburban area of Houston, Texas (Figure 1d). There were no significant sources of PM near the sample location. The closest major roadway from the residence was about 6400 m away, and there were no factories or industrial facilities close to the residence. All samplers were deployed in the backyard of the residence.
2.3. Study Design
2.4. Data Analysis
- Y = natural log of the 3 h PM2.5 mass concentration measured by the PEM or Grimm;
- X1 = natural log of the 3 h PM0.5–2.5 particle number concentration measured by the Dylos;
- X2 = binary dummy variable coded as 1 for US-59 and zero (0) for the other two locations (Clinton Drive and the residence); and
- X3 = binary dummy variable coded as 1 for the residence and zero (0) for all other locations (Clinton Drive and US-59).
- General equation (GE) method: A single fitted regression line equation from the linear regression of the total combined data was obtained and used to convert the Dylos PM2.5 measurements from all three locations.
- Sampling location equation (SLE) method: A different regression line was constructed, stratified by each sampling location. Three fitted regression equations, one for each sampling location (Clinton, US-59, and the residence), were used to convert the Dylos PM2.5 measurements.
- Dylos PM2.5 = converted PM2.5 mass concentrations from the 3 h mean Dylos count measurements collected over a single sample duration; and
- PEM or Grimm PM2.5 = 3 h integrated PM2.5 mass concentration collected by the PEM or Grimm over a single sample duration.
3. Results
3.1. Statistical Summary
3.2. Effect of Road Traffic as a Proxy of PM2.5 Emission Source on the Linear Relationship between Dylos and Research Grade Instruments (PEM and Grimm 11R)
3.3. Effect of Temperature and Truck Ratio (HDDV%) on the Linear Relationship between Dylos and Research Grade Instruments (PEM and Grimm 11R)
3.4. Agreement between the Dylos and the Reference Grade Instruments (PEM and Grimm 11R)
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Location | Instrument | Measurement | Sampling Days | Mean ± SD a | Median | Min, 25% b, 75% c, Max |
---|---|---|---|---|---|---|
Clinton Drive | PEM | PM mass (µg/m3) | 18 | 39.9 ± 36.8 | 21.9 | 7.4, 12.5, 52.5, 137.8 |
Grimm 11R | PM mass (µg/m3) | 18 | 19.0 ± 14.7 | 12.5 | 2.6, 8.0, 30.2, 47.6 | |
Dylos 1700 | PM number (particles/0.01 ft3) | 18 | 1737 ± 1178 | 137.6 | 246, 920, 2680, 4394 | |
HOBO | Temp (°C) | 18 | 27.3 ± 5.2 | 28.0 | 13.4, 24.3, 30.6, 37.0 | |
US-59 | PEM | PM mass (µg/m3) | 17 | 18.9 ± 9.9 | 21.3 | 5.1, 10.0, 27.0, 40.1 |
Grimm 11R | PM mass (µg/m3) | 17 | 10.4 ± 5.2 | 8.2 | 3.2, 6.9, 14.3, 21.5 | |
Dylos 1700 | PM number (particles/0.01 ft3) | 17 | 1235 ± 854 | 95.7 | 289, 957, 1529, 3844 | |
HOBO | Temp (°C) | 17 | 21.3 ± 5.9 | 22.1 | 10.9,17.3, 25.5, 32.6 | |
Residence | PEM | PM mass (µg/m3) | 18 | 15.2 ± 5.6 | 15.7 | 7.2, 10.2, 19.5, 28.8 |
Grimm 11R | PM mass (µg/m3) | 18 | 11.6 ± 7.8 | 9.4 | 1.9, 7.4, 13.7, 36.2 | |
Dylos 1700 | PM number (particles/0.01 ft3) | 18 | 1332 ± 1082 | 95.4 | 158, 723, 1560, 4144 | |
HOBO | Temp (°C) | 17 * | 26.3 ± 7.1 | 26.6 | 12.7, 26.6, 30.9, 37.3 |
PEM | Model 1 a | Model 2 b | |
Slope | Total | 0.70 | 0.68 |
Clinton | 0.98 | 0.93 | |
US-59 | 0.63 | 0.82 | |
Residence | 0.29 | 0.28 | |
Slope difference | Clinton vs. US-59 | −0.35 (p = 0.10) | −0.12 (p = 0.54) |
Clinton vs. Residence | −0.69 (p < 0.01) | −0.59 (p < 0.01) | |
US-59 vs. Residence | 0.33 (p = 0.13) | 0.47 (p = 0.03) | |
R2 | 0.68 | 0.74 | |
GRIMM | Model 1 | Model 2 | |
Slope | Total | 0.91 | 0.89 |
Clinton | 1.10 | 1.03 | |
US-59 | 0.73 | 0.84 | |
Residence | 0.76 | 0.77 | |
Slope difference | Clinton vs. US-59 | −0.37 (p = 0.03) | −0.20 (p = 0.05) |
Clinton vs. Residence | −0.34 (p = 0.02) | −0.25 (p = 0.01) | |
US-59 vs. Residence | −0.03 (p = 0.80) | −0.05 (p = 0.62) | |
R2 | 0.90 | 0.94 |
Location | Dylos vs. PEM (Mean (%) ± SD) | Dylos vs. Grimm (Mean (%) ± SD) | PEM vs. Grimm (Mean (%) ± SD) | |||
---|---|---|---|---|---|---|
a GE | b SLE | a GE | b SLE | a GE | b SLE | |
Clinton (n = 18) | 38 ± 22 | 37 ± 33 | 19 ± 13 | 14 ± 13 | 36 ± 23 | 35 ± 36 |
US-59 (n = 17) | 38 ± 45 | 37 ± 43 | 24 ± 17 | 19 ± 13 | 32 ±35 | 31 ± 33 |
Residence (n = 18) | 51 ± 35 | 27 ± 21 | 22 ± 19 | 19 ± 16 | 42 ± 39 | 25 ± 21 |
c Combined (n = 53) | 42 ± 35 | 34 ± 33 | 22 ± 16 | 17 ± 14 | 37 ± 33 | 30 ± 30 |
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Oluwadairo, T.; Whitehead, L.; Symanski, E.; Bauer, C.; Carson, A.; Han, I. Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas. Int. J. Environ. Res. Public Health 2022, 19, 1086. https://doi.org/10.3390/ijerph19031086
Oluwadairo T, Whitehead L, Symanski E, Bauer C, Carson A, Han I. Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas. International Journal of Environmental Research and Public Health. 2022; 19(3):1086. https://doi.org/10.3390/ijerph19031086
Chicago/Turabian StyleOluwadairo, Temitope, Lawrence Whitehead, Elaine Symanski, Cici Bauer, Arch Carson, and Inkyu Han. 2022. "Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas" International Journal of Environmental Research and Public Health 19, no. 3: 1086. https://doi.org/10.3390/ijerph19031086