Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Environmental Data
2.3. Hardware
2.3.1. COTS Sensors
2.3.2. Kit Development
2.4. PM Measurements
2.4.1. Measurements under Laboratory Conditions
2.4.2. Preliminary Outdoor Testing
2.4.3. Comparison with TEOM Data
2.4.4. Mobile Measurement Campaign by Bicycle
2.4.5. Satellite Based PM Models
2.4.6. Data Structuring and Interpolation
- Linear regression of COTS sensor PM as a function of TEOM PM
- Linear regression of COTS sensor PM as a function of TEOM PM and four meteorological variables (temperature, relative humidity, wind speed and wind direction)
- A random forests model of COTS sensor PM as a function of TEOM PM and the abovementioned four meteorological variables. The random forest framework was used given that its highly equipped to deal with non-linear relationships, since decision tree models in general, and random forest specifically, are able to choose by which features to split the data, with no limitation on the amount of splits. It can create a decision boundary, which is complex and non-linear.
3. Results
3.1. Lab Tests
3.2. Outdoor Tests
3.3. Comparison with TEOM
3.4. Mobile Measurement Campaign
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Variable | Model | Slope | Intercept | R2 |
---|---|---|---|---|
Honeywell-PM2.5 | Linear model—TEOM | 30.6388 | −0.011 | −0.022722584 |
Honeywell-PM2.5 | Linear model: TEOM + Meteorological factors | 27.9802 | 0.0552 | −0.001513723 |
Honeywell-PM2.5 | Random Forests: TEOM + Meteorological factors | 28.46 | 0.0968 | 0.161438272 |
Honeywell-PM10 | Linear model: TEOM | 32.4745 | −0.0534 | 0.019061521 |
Honeywell-PM10 | Linear model: TEOM + Meteorological factors | 29.133 | 0.0436 | −0.028409946 |
Honeywell-PM10 | Random Forests: TEOM + Meteorological factors | 32.4729 | 0.0417 | −0.008819204 |
OPC-N3-PM2.5 | Linear model: TEOM | 6.7076 | 0.3038 | 0.483495144 |
OPC-N3-PM2.5 | Linear model: TEOM + Meteorological factors | 2.6469 | 0.7821 | 0.57609834 |
OPC-N3-PM2.5 | Random Forests: TEOM + Meteorological factors | 6.1345 | 0.3937 | 0.554083995 |
OPC-N3-PM10 | Linear model: TEOM | 24.7203 | 0.4026 | 0.581336121 |
OPC-N3-PM10 | Linear model: TEOM + Meteorological factors | 25.6637 | 0.3808 | 0.589789448 |
OPC-N3-PM10 | Random Forests: TEOM + Meteorological factors | 34.1448 | 0.194 | 0.362786724 |
Sharp-PM2.5 | Linear model: TEOM | 54.9663 | 0.0014 | −0.048504135 |
Sharp-PM2.5 | Linear model: TEOM + Meteorological factors | 64.0294 | 0.3702 | 0.124500294 |
Sharp-PM2.5 | Random Forests: TEOM + Meteorological factors | 50.3221 | 0.276 | 0.325243306 |
Sharp-PM10 | Linear model: TEOM | 126.2785 | 0.055 | 0.191369713 |
Sharp-PM10 | Linear model: TEOM + Meteorological factors | 159.4208 | 0.189 | 0.104123008 |
Sharp-PM10 | Random Forests: TEOM + Meteorological factors | 131.6534 | 0.1987 | 0.422302067 |
Dylos-PM2.5 | Linear model: TEOM | 52.8526 | 0.2487 | 0.140869256 |
Dylos-PM2.5 | Linear model: TEOM + Meteorological factors | 12.6777 | 0.9736 | 0.858485926 |
Dylos-PM2.5 | Random Forests: TEOM + Meteorological factors | 43.0559 | 0.2858 | 0.384341244 |
Dylos-PM10 | Linear model: TEOM | 97.8045 | 0.4519 | 0.390104326 |
Dylos-PM10 | Linear model: TEOM + Meteorological factors | 5.205 | 1.2015 | 0.828236601 |
Dylos-PM10 | Random Forests: TEOM + Meteorological factors | 103.1206 | 0.2891 | 0.661054684 |
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COTS Sensor (Manufacturer) | Sharp-GP2Y1030AU0F | Alphanese-OPC-N3 | Honeywell-HPMA115S0-XXX | Dylos-DC1700 |
---|---|---|---|---|
Method | An infrared emitting diode and a phototransistor are diagonally arranged into this device, to allow it to detect the reflected light of dust in air. | OPCs provide digital outputs of PM1, PM2.5 and PM10 every second, along with histograms of the particle count for each size. Device’s flow correction improves stable readings, even in high dust environments. | utilizes a laser-based light scattering particle sensing method to detect particulates from 0.3 μm to 5 μm | Counts individual particles, gives immediate response to change in environment and provides three different history modes; minute, hour and day, up to 30 days of stored history data. |
Range | 0–500 μg/m3 | 0–2000 μg/m3 | 0–1000 μg/m3 | Unspecified |
Price | $12 | $250 | $25 | $450 |
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Lesser, D.; Katra, I.; Dorman, M.; Harari, H.; Kloog, I. Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment. Sensors 2021, 21, 63. https://doi.org/10.3390/s21010063
Lesser D, Katra I, Dorman M, Harari H, Kloog I. Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment. Sensors. 2021; 21(1):63. https://doi.org/10.3390/s21010063
Chicago/Turabian StyleLesser, Dan, Itzhak Katra, Michael Dorman, Homero Harari, and Itai Kloog. 2021. "Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment" Sensors 21, no. 1: 63. https://doi.org/10.3390/s21010063
APA StyleLesser, D., Katra, I., Dorman, M., Harari, H., & Kloog, I. (2021). Validating and Comparing Highly Resolved Commercial “Off the Shelf” PM Monitoring Sensors with Satellite Based Hybrid Models, for Improved Environmental Exposure Assessment. Sensors, 21(1), 63. https://doi.org/10.3390/s21010063