An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.2.1. PM2.5
2.2.2. AOD
2.2.3. Meteorological Factors
2.3. Methods
2.3.1. Data Preprocessing
2.3.2. Establishment of BPNN
2.3.3. Model Comparison
2.3.4. Correlation Evaluation Indexes
3. Results and Discussion
3.1. Temporal Distributions of AOD and PM2.5
3.2. Spatial Distributions of AOD and PM2.5
3.3. BPNN
3.4. Comparison of BPNN with Regression Analysis and SVR
3.5. Discussion
3.5.1. Research Findings
3.5.2. Limitations
- (1)
- Here, the limited number of ground measurement points impeded the analysis of the spatiotemporal correlations between AOD and PM2.5.
- (2)
- Seasonal differences in AOD and PM2.5 were not incorporated into the establishment of BPNN.
- (3)
- The BPNN model can be used to estimate the trend of interannual PM2.5 and needs to be improved for estimating the daily extreme value of PM2.5 in the future.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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R/p-Values | PM2.5 | AOD | TEMP | RH | WS | PRE |
---|---|---|---|---|---|---|
PM2.5 | - | <0.001 | <0.001 | <0.001 | <0.001 | 0.300 |
AOD | 0.800 | - | <0.001 | <0.001 | <0.001 | 0.420 |
TEMP | 0.244 | 0.351 | - | <0.001 | <0.001 | 0.009 |
RH | 0.385 | 0.463 | 0.384 | - | <0.001 | <0.001 |
WS | −0.186 | −0.176 | −0.310 | −0.233 | - | 0.973 |
PRE | −0.040 | 0.031 | 0.101 | 0.214 | −0.001 | - |
Variable | Min | Max | Avg | SD |
---|---|---|---|---|
PM2.5 (μg/m3) | 10.750 | 76.493 | 26.808 | 10.862 |
AOD | 0.025 | 1.776 | 0.289 | 0.249 |
TEMP (°C) | −11.500 | 32.300 | 10.235 | 10.533 |
RH (%) | 93.000 | 16.000 | 50.739 | 15.248 |
WS (m/s) | 3.038 | 8.600 | 3.038 | 1.269 |
PRE (mm) | 0.000 | 22.600 | 0.291 | 1.766 |
Data Set | Training Set (Year) | Test Set (Year) |
---|---|---|
1 | a (2016, 2017, 2018, 2019, 2020) | 2015 |
2 | b (2015, 2017, 2018, 2019, 2020) | 2016 |
3 | c (2015, 2016, 2018, 2019, 2020) | 2017 |
4 | d (2015, 2016, 2017, 2019, 2020) | 2018 |
5 | e (2015, 2016, 2017, 2018, 2020) | 2019 |
6 | f (2015, 2016, 2017, 2018, 2019) | 2020 |
Hidden Layer Activation Function | Output Layer Activation Function | Training Function | Target Error | Number of Iterations | Learning Rate |
---|---|---|---|---|---|
tansig | purelin | trainlm | 10−5 | 3000 | 0.1 |
Hidden Layer Neurons | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
RMSEa | 10.64 | 6.41 | 6.42 | 11.66 | 11.18 | 58.40 | 13.59 | 33.73 | 12.03 | 10.34 | 13.71 |
RMSEb | 6.44 | 6.45 | 6.48 | 7.50 | 6.76 | 6.83 | 6.80 | 6.96 | 7.18 | 7.23 | 7.97 |
RMSEc | 6.48 | 6.77 | 6.82 | 7.32 | 10.07 | 8.25 | 7.43 | 10.87 | 14.66 | 13.42 | 12.48 |
RMSEd | 6.47 | 15.74 | 10.48 | 44.45 | 44.40 | 11.42 | 10.77 | 26.51 | 10.57 | 21.53 | 18.61 |
RMSEe | 6.39 | 6.37 | 6.50 | 10.06 | 6.80 | 6.46 | 6.47 | 6.83 | 8.08 | 8.58 | 6.82 |
RMSEf | 6.49 | 6.95 | 6.67 | 61.71 | 52.15 | 36.39 | 38.52 | 53.71 | 32.86 | 32.26 | 18.18 |
Input Variable | Test Set (Year) | ||||||||
---|---|---|---|---|---|---|---|---|---|
2015 | 2016 | 2017 | |||||||
R2 | RMSE | Acc | R2 | RMSE | Acc | R2 | RMSE | Acc | |
AOD | 0.640 | 6.66 | 80.7% | 0.656 | 6.56 | 81.4% | 0.723 | 6.27 | 82.9% |
AOD + TEMP | 0.661 | 6.47 | 82.0% | 0.672 | 6.48 | 82.0% | 0.731 | 6.23 | 83.2% |
AOD + RH | 0.656 | 6.58 | 81.9% | 0.661 | 6.50 | 81.2% | 0.729 | 6.23 | 83.1% |
AOD + PRE | 0.648 | 6.60 | 81.8% | 0.658 | 6.55 | 81.9% | 0.719 | 6.25 | 83.0% |
AOD + WS | 0.645 | 6.65 | 81.7% | 0.658 | 6.62 | 81.8% | 0.711 | 6.26 | 82.9% |
AOD + All Features | 0.676 | 6.45 | 82.2% | 0.691 | 6.34 | 82.7% | 0.752 | 6.23 | 83.4% |
Input Variable | Test Set (Year) | ||||||||
2018 | 2019 | 2020 | |||||||
R2 | RMSE | Acc | R2 | RMSE | Acc | R2 | RMSE | Acc | |
AOD | 0.656 | 6.33 | 82.0% | 0.640 | 6.81 | 79.9% | 0.640 | 6.34 | 81.9% |
AOD + TEMP | 0.679 | 6.29 | 82.7% | 0.671 | 6.74 | 80.4% | 0.661 | 6.20 | 82.6% |
AOD + RH | 0.672 | 6.31 | 82.5% | 0.654 | 6.77 | 80.1% | 0.651 | 6.22 | 82.6% |
AOD + PRE | 0.671 | 6.33 | 82.2% | 0.643 | 6.78 | 80.0% | 0.653 | 6.31 | 82.3% |
AOD + WS | 0.667 | 6.33 | 82.2% | 0.642 | 6.80 | 79.9% | 0.650 | 6.34 | 82.0% |
AOD + All Features | 0.677 | 6.30 | 82.8% | 0.686 | 6.54 | 82.0% | 0.663 | 6.32 | 82.4% |
Model | Model Parameter | Model Expression | |||||||
---|---|---|---|---|---|---|---|---|---|
Hidden Neurons | C | g | R2 | RMSE/μg/m3 | RMSE SD/μg/m3 | Acc | Time | ||
BPNN | 2 | - | - | 0.723 | 6.35 | 0.26 | 82.4% | 2″00 | - |
SVR | - | 4 | 0.06 | 0.672 | 6.37 | 0.27 | 82.2% | 13″12 | - |
LR | - | - | - | 0.656 | 6.42 | 0.22 | 82.0% | - | PM2.5 = 34.28AOD + 17.00 |
NLR | - | - | - | 0.672 | 6.37 | 0.23 | 82.2% | - | PM2.5 = 14.87 + 47.09AOD − 14.16AOD2 + 3.15AOD3 |
MLR | - | - | - | 0.689 | 6.20 | 0.26 | 83.4% | - | PM2.5 = 0.80AOD + 0.07TEMP + 0.04RH − 0.05WS − 0.06PRE |
Meteorological factors–SVR | - | 2 | 1 | 0.689 | 6.25 | 0.28 | 83.3% | 11″29 | - |
Meteorological factors–BPNN | 2 | - | - | 0.757 | 6.11 | 0.26 | 84.4% | 2″00 | - |
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Gu, J.; Wang, Y.; Ma, J.; Lu, Y.; Wang, S.; Li, X. An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors. Remote Sens. 2022, 14, 1617. https://doi.org/10.3390/rs14071617
Gu J, Wang Y, Ma J, Lu Y, Wang S, Li X. An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors. Remote Sensing. 2022; 14(7):1617. https://doi.org/10.3390/rs14071617
Chicago/Turabian StyleGu, Jilin, Yiwei Wang, Ji Ma, Yaoqi Lu, Shaohua Wang, and Xueming Li. 2022. "An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors" Remote Sensing 14, no. 7: 1617. https://doi.org/10.3390/rs14071617