Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods
Abstract
:1. Introduction
2. Materials and Methods
2.1. Particulate Matter Instruments
2.1.1. Sensor Monitors
2.1.2. Reference Instruments
2.2. Evaluation of Sensor Monitors in Outdoor and Indoor Environments
2.2.1. Evaluation of Sensor Monitors in Outdoor Environment
2.2.2. Evaluation of Sensor Monitors in Indoor Environment
2.3. Statistical Analysis
2.3.1. Intra-Consistency within Sensor Monitors
2.3.2. Inter-Consistency between Sensor Monitors and Reference Instruments
2.3.3. Validation and Optimization of Sensor Monitors by Machine Learning Methods
2.3.4. Marginal Effects of Explanatory Features on the Validation Model
3. Results
3.1. Applicability of TSI as a Reference Instrument for Sensor Monitors
3.2. Parallel Measurements between Sensor Monitors and TEOM in Outdoor Environment
3.3. Parallel Measurements between Sensor Monitors and TSI Instrument in Indoor Environment
3.4. Validation of Sensor Monitor Data Optimized by Machine Learning Models
3.5. Marginal Effects of Explanatory Features on the Validation Model
3.6. Evaluation of Sensor Performance after Optimal Validation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Measurement Days (Sensors/TOEM) | PM2.5 (μg/m3) | PM10 (μg/m3) | |||||
---|---|---|---|---|---|---|---|
Sensors | TEOM | R2 | Sensors | TEOM | R2 | ||
All year | 374/372 | 38.3 ± 25.6 * | 33.3 ± 23.6 | 0.79 | 45.4 ± 29.5 * | 54.7 ± 38.4 | 0.26 |
(0.0~180.6) | (1~211.0) | (0.5~210.3) | (0.0~493.0) | ||||
Spring (1 March–30 May) | 92/91 | 43.4 ± 22.5 * | 35.0 ± 20.5 | 0.69 | 52.3 ± 25.9 * | 60.6 ± 45.7 | 0.04 |
(3.5~172.8) | (1.0~142.0) | (4.3~191.6) | (0.0~493.0) | ||||
Summer (1 June–31 August) | 89/90 | 25.3 ± 15.7 * | 21.1 ± 12.6 | 0.72 | 30.1 ± 19.4 * | 38.8 ± 18.2 | 0.22 |
(0.7~87.9) | (1.0~95.0) | (1.7~104.7) | (0.00~111.0) | ||||
Autumn (1 September–30 November) | 91/89 | 29.4 ± 20.2 | 29.0 ± 18.4 | 0.73 | 34.8 ± 23.6 * | 56.3 ± 31.4 | 0.41 |
(0.0~113.2) | (1.0~146.0) | (0.0~124.6) | (0.0~323.0) | ||||
Winter (1 December–28 February) | 102/101 | 52.8 ± 30.1 * | 46.2 ± 30.1 | 0.80 | 62.1 ± 33.6 | 60.9 ± 37.8 | 0.42 |
(2.5~180.6) | (2.0~211.0) | (3.3~210.3) | (0.0~363.0) |
PM2.5 (μg/m3) | PM10 (μg/m3) | |||||||
---|---|---|---|---|---|---|---|---|
Indoor Sources | Measurement Time | Sensors | TSI | R2 | Sensors | TSI | R2 | |
Office | No obvious emission sources | 60 days | 17.6 ± 13.3 * | 19.9 ± 14.3 | 0.66 | 20.4 ± 16.0 * | 27.1 ± 18.8 | 0.64 |
(0.3~58.9) | (0.8~91.5) | (0.4~71.8) | (1.9~236.6) | |||||
Residence | Mosquito-repellent | 130 min | 67.4 ± 55.9 * | 83.5 ± 82.9 | 0.98 | 73.6 ± 56.2 * | 107.5 ± 105.0 | 0.98 |
(25.5~222.7) | (25.1~305.3) | (28.3~229.8) | (33.0~388.1) | |||||
Worship incense | 110 min | 34.1 ± 42.7 * | 58.7 ± 76.9 | 0.98 | 37.3 ± 44.7 * | 77.2 ± 98.6 | 0.97 | |
(2.7~139.2) | (8.5~254.7) | (3.8~145.0) | (11.2~328.2) | |||||
Candle | 115 min | 23.8 ± 12.0 * | 27.5 ± 14.5 | 0.96 | 28.6 ± 16.0 * | 38.5 ± 18.7 | 0.97 | |
(10.5~73.3) | (16.5~87.2) | (12.7~88.5) | (22.8~113.7) | |||||
Cooking | 150 min | 27.7 ± 28.5 * | 47.1 ± 60.0 | 0.98 | 36.3 ± 40.0 * | 66.1 ± 84.0 | 0.98 | |
(8.7~150.7) | (14.2~327.5) | (10.3~212.2) | (19.7~465.2) | |||||
Smoking | 151 min | 58.3 ± 63.0 * | 62.4 ± 66.9 | 0.99 | 82.8 ± 84.6 * | 65.0 ± 66.4 | 0.97 | |
(10.0~308.2) | (19.5~370.7) | (27.7~471.6) | (11.2~326.3) |
References | Particles | NM | ML | SVM | MLP | DT | KNN | RF | |
---|---|---|---|---|---|---|---|---|---|
Outdoor (hourly averages) | TEOM | PM2.5 | 0.79 | 0.82 | 0.85 | 0.84 | 0.84 | 0.83 | 0.90 |
TEOM | PM10 | 0.26 | 0.33 | 0.23 | 0.41 | 0.59 | 0.76 | 0.80 | |
Indoor (minutely averages) | TSI | PM2.5 | 0.66 | 0.84 | 0.85 | 0.90 | 0.95 | 0.93 | 0.97 |
TSI | PM10 | 0.64 | 0.77 | 0.77 | 0.80 | 0.85 | 0.84 | 0.91 |
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Tang, H.; Cai, Y.; Gao, S.; Sun, J.; Ning, Z.; Yu, Z.; Pan, J.; Zhao, Z. Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods. Sensors 2024, 24, 3448. https://doi.org/10.3390/s24113448
Tang H, Cai Y, Gao S, Sun J, Ning Z, Yu Z, Pan J, Zhao Z. Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods. Sensors. 2024; 24(11):3448. https://doi.org/10.3390/s24113448
Chicago/Turabian StyleTang, Hao, Yunfei Cai, Song Gao, Jin Sun, Zhukai Ning, Zhenghao Yu, Jun Pan, and Zhuohui Zhao. 2024. "Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods" Sensors 24, no. 11: 3448. https://doi.org/10.3390/s24113448
APA StyleTang, H., Cai, Y., Gao, S., Sun, J., Ning, Z., Yu, Z., Pan, J., & Zhao, Z. (2024). Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods. Sensors, 24(11), 3448. https://doi.org/10.3390/s24113448