An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution
Abstract
:1. Introduction
2. Materials and Methods
2.1. PM2.5 Data and Area of Study
2.2. Satellite Data
2.3. Data Analysis
2.3.1. Multiple Linear Regression (MLR)
2.3.2. Random Forest (RF)
2.3.3. eXtreme Gradient Boosting (XGBoost)
2.3.4. Support Vector Machines (SVM)
2.3.5. Model Assessment
3. Results
3.1. Data Descriptive Statistics
3.2. Modeling Results
3.3. Estimation of Daily PM2.5
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Types | PCD (n = 34,748) | BAQ (n = 7339) | |
---|---|---|---|---|
Training (n = 27,798) | Validation (n = 6950) | Testing | ||
Stations | Nominal | 68 stations | 68 stations | 49 stations |
Date | Date | 2778 days | 1865 days | 734 days |
Month | Nominal | 12 months | 12 months | 12 months |
Year | Discrete | 10 years | 10 years | 6 years |
WOY | Nominal | 53 weeks | 53 weeks | 53 weeks |
PM2.5 (μg/m3) | Continuous | µ: 32.2, s: 23.7, IQR: 26 | µ: 32.4, s: 23.8, IQR: 26 | µ: 30.1, s: 16.2, IQR: 21 |
AOD | Continuous | µ: 0.5, s: 0.3, IQR: 0.4 | µ: 0.5, s: 0.3, IQR: 0.4 | µ: 0.5, s: 0.3, IQR: 0.4 |
LST (°C) | Continuous | µ: 33.3, s: 4.5, IQR: 6 | µ: 33.4, s: 4.5, IQR: 6 | µ: 36.1, s: 3.8, IQR: 4.3 |
NDVI | Continuous | µ: 0.1, s: 0.2, IQR: 0.3 | µ: 0.1, s: 0.2, IQR: 0.3 | µ: −0.1, s: 0.1, IQR: 0.2 |
EV (m) | Continuous | µ: 144.6, s: 198.9, IQR: 265.3 | µ: 142.4, s: 197.3, IQR: 265.3 | µ: 6.8, s: 1.6, IQR: 2.9 |
Models | R2 (RMSE (μg/m3)) | ||
---|---|---|---|
Training | Validation | Testing | |
MLR | |||
AOD | 0.18 (21.48) | 0.19 (21.26) | 0.04 (16.79) |
AOD + LST | 0.21 (21.25) | 0.22 (21.04) | 0.01 (17.15) |
AOD + LST + NDVI | 0.22 (21.26) | 0.22 (21.19) | 0.01 (17.27) |
AOD + LST + NDVI + EV | 0.25 (20.49) | 0.25 (20.38) | 0.01 (17.35) |
AOD + LST + NDVI + EV + WOY | 0.51 (18.42) | 0.51 (17.94) | 0.35 (14.07) |
AOD + LST + NDVI + EV + WOY + Year | 0.51 (18.28) | 0.52 (17.83) | 0.35 (13.78) |
RF | |||
AOD | 0.79 (11.39) | 0.16 (23.08) | 0.02 (20.52) |
AOD + LST | 0.86 (10.12) | 0.25 (20.88) | 0.04 (18.59) |
AOD + LST + NDVI | 0.90 (8.82) | 0.44 (17.87) | 0.10 (16.03) |
AOD + LST + NDVI + EV | 0.89 (8.82) | 0.60 (15.17) | 0.15 (15.05) |
AOD + LST + NDVI + EV + WOY | 0.92 (7.23) | 0.74 (12.35) | 0.60 (10.47) |
AOD + LST + NDVI + EV + WOY +Year | 0.95 (5.58) | 0.78 (11.18) | 0.71 (8.79) |
XGBoost | |||
AOD | 0.31 (19.77) | 0.27 (20.27) | 0.04 (17.45) |
AOD + LST | 0.34 (19.34) | 0.30 (19.85) | 0.05 (17.63) |
AOD + LST + NDVI | 0.40 (18.39) | 0.38 (18.71) | 0.08 (15.90) |
AOD + LST + NDVI + EV | 0.49 (16.94) | 0.47 (17.34) | 0.12 (15.23) |
AOD + LST + NDVI + EV + WOY | 0.61 (14.93) | 0.60 (15.14) | 0.43 (12.40) |
AOD + LST + NDVI + EV + WOY + Year | 0.62 (14.74) | 0.60 (15.00) | 0.45 (12.12) |
SVM | |||
AOD | 0.28 (20.59) | 0.28 (20.66) | 0.04 (17.15) |
AOD + LST | 0.31 (20.08) | 0.31 (20.16) | 0.05 (16.91) |
AOD + LST + NDVI | 0.39 (18.83) | 0.38 (18.93) | 0.09 (15.68) |
AOD + LST + NDVI + EV | 0.47 (17.60) | 0.46 (17.79) | 0.14 (15.65) |
AOD + LST + NDVI + EV + WOY | 0.59 (15.64) | 0.60 (15.44) | 0.51 (11.51) |
AOD + LST + NDVI + EV + WOY + Year | 0.61 (15.32) | 0.62 (15.17) | 0.52 (11.63) |
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Buya, S.; Usanavasin, S.; Gokon, H.; Karnjana, J. An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution. Sustainability 2023, 15, 10024. https://doi.org/10.3390/su151310024
Buya S, Usanavasin S, Gokon H, Karnjana J. An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution. Sustainability. 2023; 15(13):10024. https://doi.org/10.3390/su151310024
Chicago/Turabian StyleBuya, Suhaimee, Sasiporn Usanavasin, Hideomi Gokon, and Jessada Karnjana. 2023. "An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution" Sustainability 15, no. 13: 10024. https://doi.org/10.3390/su151310024