Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sensor Configuration and Field Deployment
2.2. Data Processing
2.3. Evaluation Parameters
2.4. Calibration Methods
2.4.1. Simple Linear Regression and Multiple Linear Regressions
2.4.2. Random Forest Regressor
2.4.3. Long Short-Term Memory Networks
3. Results and Discussion
3.1. Performances of Sensors with Linear Regressions (SLR/MLR)
3.2. Calibration by Machine Learning (RFR)
3.3. Calibration by Neural Network (LSTMs)
3.4. Sensor Biases under Different Pollution Conditions
3.5. Impacts of Different Environmental Factors on the Calibration Results
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Configuration | Value |
---|---|
Number of hidden layers | 10 |
Number of neurons in the hidden layer | 150 |
Input variable | Time, MEE, Alpha, Vw, Va, Temp, RH |
Number of the output variable | 1 |
Training data percentage | 50% |
Validation data percentage | 50% |
Data normalization | Minmax |
Training algorithm | Long short-term memory networks |
Pollutant | Statistic | Autumn (October–November 2019) | Winter (December 2019–February 2020) | Spring (March–May 2020) | Summer (June–August 2020) | Autumn (September–October 2020) |
---|---|---|---|---|---|---|
CO | number | 633 | 1513 | 1559 | 957 | 665 |
R2 | 0.85 | 0.88 | 0.79 | 0.64 | 0.67 | |
RMSE | 232 | 239 | 156 | 168 | 145 | |
slope | 1.41 | 1.71 | 1.32 | 1.03 | 1.11 | |
intercept | 701.73 | 767.28 | 598.10 | 579.68 | 567.64 | |
O3 | number | 884 | 627 | 2140 | 1705 | 0 |
R2 | 0.74 | 0.79 | 0.45 | 0.35 | -- | |
RMSE | 11 | 12 | 34 | 43 | -- | |
slope | 1.34 | 1.59 | 1.31 | 0.89 | -- | |
intercept | 60.45 | 74.93 | 80.35 | 95.97 | -- | |
NO2 | number | 866 | 2096 | 1890 | 1118 | 754 |
R2 | 0.76 | 0.77 | 0.28 | 0.12 | 0.32 | |
RMSE | 13 | 12 | 18 | 17 | 17 | |
slope | 0.93 | 1.22 | 0.32 | 0.12 | 0.32 | |
intercept | −51.2 | −94.05 | −2.11 | 15.82 | 1.33 | |
SO2 | number | 866 | 2096 | 1890 | 1118 | 752 |
R2 | 0.01 | 0.05 | 0.00 | 0.00 | 0.00 | |
RMSE | 5 | 5 | 3 | 1 | 1 | |
slope | −0.03 | 0.11 | 0 | 0 | −0.01 | |
intercept | 4.04 | 12.44 | 3.77 | 2.64 | 2.2 |
Linear Regression Calibration Model | Train Data | Test Data | ||
---|---|---|---|---|
before Correction | after Correction | before Correction | after Correction | |
C_correction = a*C_raw + b | R2 = 0.83 | R2 = 0.85 | ||
RMSE = 227.85 | RMSE = 242.33 | |||
C_correction = a*C_raw + b*Temp + c | R2 = 0.83 | R2 = 0.86 | ||
R2 = 0.83 | RMSE = 217.19 | R2 = 0.85 | RMSE = 221.72 | |
C_correction = a*C_raw + b*RH + c | RMSE = 734.13 | R2 = 0. 83 | RMSE = 788.68 | R2 = 0.85 |
RMSE = 227.85 | RMSE = 242.33 | |||
C_correction = a*C_raw + b*Temp + c*RH + d | R2 = 0.79 | R2 = 0.81 | ||
RMSE = 295.10 | RMSE = 325.25 |
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Han, P.; Mei, H.; Liu, D.; Zeng, N.; Tang, X.; Wang, Y.; Pan, Y. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2. Sensors 2021, 21, 256. https://doi.org/10.3390/s21010256
Han P, Mei H, Liu D, Zeng N, Tang X, Wang Y, Pan Y. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2. Sensors. 2021; 21(1):256. https://doi.org/10.3390/s21010256
Chicago/Turabian StyleHan, Pengfei, Han Mei, Di Liu, Ning Zeng, Xiao Tang, Yinghong Wang, and Yuepeng Pan. 2021. "Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2" Sensors 21, no. 1: 256. https://doi.org/10.3390/s21010256
APA StyleHan, P., Mei, H., Liu, D., Zeng, N., Tang, X., Wang, Y., & Pan, Y. (2021). Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2. Sensors, 21(1), 256. https://doi.org/10.3390/s21010256