Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period
Abstract
:1. Introduction
2. Study Area
3. Data and Methods
3.1. Data
3.1.1. MODIS AOD Data
3.1.2. AERONET AOD Data
3.1.3. Auxiliary Data
3.2. Methodology
3.2.1. MODIS AOD Merging
3.2.2. Merged AOD Evaluation
3.2.3. Data Integration
3.2.4. Influencing Factors Identification
4. Results
4.1. Evaluation of the Merged AOD
4.1.1. Validation of the Merged AOD
4.1.2. Assessment of the Spatiotemporal Coverage of the Merged AOD
4.2. Spatiotemporal Characteristics of AOD
4.2.1. Spatial Variations of AOD
4.2.2. Temporal Characteristics of AOD
4.3. Contribution of Each Factor to AOD Distribution
5. Discussion
5.1. AOD Gap-Filling
5.2. The Impacts of Factors on the Spatial Variations of AOD
5.3. The Effect of Environmental Policy on the Temporal Variability of AOD
5.4. Limitations
6. Conclusions
- The merged AOD are better than the original Terra/Aqua DT AOD, with the average spatial coverage increased by 94% and 132% respectively.
- The AOD over the PYRD were high in the northeast and low in the southwest and decreased from 2014 to 2017. Seasonal average AOD were relatively higher in spring and summer than in autumn and winter.
- Topographical factors contributed most to AOD, followed by precipitation and population density, while NDVI showed a relatively week impact on AOD.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
Appendix A
Validation of the Resampled 3-km DB AOD Data
AOD | Nearest Neighbor | Bilinear Interpolation | Cubic Convolution | |||
---|---|---|---|---|---|---|
Terra DB AOD | 0.78 | 0.16 | 0.77 | 0.17 | 0.77 | 0.17 |
Aqua DB AOD | 0.82 | 0.17 | 0.81 | 0.18 | 0.78 | 0.20 |
The Calibration of AOD
Seasons | Terra DT AOD | Aqua DT AOD | Terra DB AOD | Aqua DB AOD | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Spring | 0.01 | 0.75 | 0.82 | 0.03 | 0.77 | 0.78 | 0.19 | 0.85 | 0.83 | 0.17 | 0.90 | 0.77 |
Summer | −0.12 | 1.0 | 0.89 | −0.08 | 0.93 | 0.80 | 0.17 | 0.95 | 0.89 | 0.14 | 0.95 | 0.86 |
Autumn | 0.03 | 0.86 | 0.84 | 0.12 | 0.71 | 0.84 | 0.17 | 0.76 | 0.84 | 0.21 | 0.63 | 0.76 |
Winter | 0.05 | 0.86 | 0.76 | 0.11 | 0.76 | 0.82 | 0.18 | 0.67 | 0.87 | 0.16 | 0.77 | 0.81 |
Year | N | R |
---|---|---|
2014 | 115 | 0.8462 |
2015 | 129 | 0.8734 |
2016 | 116 | 0.8324 |
2017 | 132 | 0.8267 |
2014–2017 | 492 | 0.8477 |
Model | Year | ||||
---|---|---|---|---|---|
Predict Terra DT AOD with Aqua DT AOD | 2014 | 0.14 | 28.8 | 0.83 | 0.0054 |
2015 | 0.12 | 27.5 | 0.85 | 0.0017 | |
2016 | 0.12 | 29.6 | 0.83 | 0.0051 | |
2017 | 0.10 | 28.7 | 0.82 | 0.0064 | |
2014−2017 | 0.12 | 28.8 | 0.83 | 0.0110 | |
Predict Aqua DT AOD with Terra DT AOD | 2014 | 0.13 | 26.3 | 0.82 | 0.0028 |
2015 | 0.11 | 24.8 | 0.85 | 0.0019 | |
2016 | 0.11 | 27.3 | 0.83 | 0.0032 | |
2017 | 0.10 | 24.7 | 0.83 | 0.0050 | |
2014−2017 | 0.11 | 25.9 | 0.83 | 0.0061 | |
Predict Terra DB AOD with Aqua DB AOD | 2014 | 0.11 | 23.5 | 0.87 | 0.0024 |
2015 | 0.10 | 21.0 | 0.85 | 0.0015 | |
2016 | 0.10 | 21.2 | 0.86 | 0.0018 | |
2017 | 0.09 | 21.6 | 0.83 | 0.0013 | |
2014−2017 | 0.10 | 22.1 | 0.86 | 0.0028 | |
Predict Aqua DB AOD with Terra DB AOD | 2014 | 0.12 | 23.6 | 0.88 | 0.0010 |
2015 | 0.10 | 20.8 | 0.85 | 0.0003 | |
2016 | 0.10 | 21.5 | 0.85 | 0.0034 | |
2017 | 0.09 | 21.2 | 0.84 | 0.0002 | |
2014−2017 | 0.10 | 22.0 | 0.86 | 0.0011 | |
Predict Terra DT AOD with Terra DB AOD | 2014 | 0.12 | 23.7 | 0.86 | 0.0021 |
2015 | 0.12 | 23.0 | 0.85 | 0.0014 | |
2016 | 0.11 | 24.8 | 0.85 | 0.0030 | |
2017 | 0.10 | 25.4 | 0.84 | 0.0022 | |
2014−2017 | 0.12 | 24.2 | 0.86 | 0.0045 | |
Predict Aqua DT AOD with Aqua DB AOD | 2014 | 0.11 | 21.9 | 0.86 | 0.0044 |
2015 | 0.11 | 21.2 | 0.85 | 0.0032 | |
2016 | 0.11 | 23.9 | 0.84 | 0.0023 | |
2017 | 0.10 | 22.5 | 0.83 | 0.0012 | |
2014−2017 | 0.11 | 22.4 | 0.85 | 0.0037 |
Multiple Linear Regression Models
Model | Regression Function | Max VIF (Variable) | ||
---|---|---|---|---|
Annual | AOD = 2.492 × 10−15 0.566 × DEM − 0.307 × PREC + 0.098 × AWS − 0.025 × PBLH − 0.076 × NDVI + 0.210 × POP | 0.792 | 0.792 | 2.365 (DEM) |
Spring | AOD = −9.663 × 10−16 − 0.500 × DEM − 0.265 × PREC + 0.103 × AWS − 0.127 × PBLH − 0.118 × NDVI + 0.164 × POP | 0.806 | 0.806 | 2.817 (DEM) |
Summer | AOD = 3.121 × 10−15 − 0.616 × DEM − 0.202 × PREC + 0.173 × AWS + 0.099 × ARH - 0.032 × PBLH − 0.102 × NDVI + 0.247 × POP | 0.677 | 0.677 | 2.061 (ARH) |
Autumn | AOD = 1.445 × 10−15 − 0.639 × DEM − 0.348 × PREC + 0.188 × AWS + 0.081 × ARH − 0.068 × PBLH − 0.072 × NDVI + 0.324 × POP | 0.824 | 0.823 | 2.411 (PREC) |
Winter | AOD = 4.822 × 10−16 − 0.523 × DEM − 0.250 × PREC + 0.110 × AWS − 0.090 × PBLH − 0.234 × NDVI + 0.228 × POP | 0.833 | 0.833 | 2.832 (DEM) |
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AOD Data Products Types | Scientific Data Set (SDS) | Contents | Temporal Range | Use |
---|---|---|---|---|
Terra/Aqua 3-km DT AOD | Optical_Depth_Land_And_Ocean | DT AOD (QA = 3) | 2005.1.1–2013.12.31 | Calibration |
2014.1.1–2017.12.31 | Spatiotemporal characteristics and influencing factors analysis | |||
Terra/Aqua 10-km DB AOD | Deep_Blue_Aerosol_Optical_Depth_550_Land_Best_Estimate | DB AOD (QA ≥ 2) | 2005.1.1–2013.12.31 | Calibration |
2014.1.1–2017.12.31 | Spatiotemporal characteristics and influencing factors analysis |
Number | Site Name | Longitude (°N) | Latitude (°E) | Elevation (m) | Period of Available Data |
---|---|---|---|---|---|
1 | XuZhou-CUMT | 117.1417 | 34.2167 | 59.7 | 2013–2017 |
2 | Shouxian | 116.7820 | 32.5584 | 22.7 | 2008 |
3 | Hefei | 117.1622 | 31.9047 | 36 | 2005–2008, 2016 |
4 | NUIST | 118.7172 | 32.2065 | 62 | 2007–2010 |
5 | SONET_Nanjing | 118.9570 | 32.1150 | 52 | 2016 |
6 | Taihu | 120.2153 | 31.4210 | 20 | 2005–2017 |
7 | SONET_Shanghai | 121.4810 | 31.2840 | 24 | 2016 |
8 | Shanghi_Minhang | 121.3973 | 31.1305 | 49 | 2008–2009 |
9 | Shanghi_Met | 121.5485 | 31.2214 | 5 | 2007 |
10 | Hangzhou_City | 120.1569 | 30.2896 | 30 | 2008–2009 |
11 | Hangzhou-ZFU | 119.7274 | 30.2572 | 42 | 2007–2009 |
12 | LA-TM | 119.4400 | 30.3240 | 439 | 2007–2009 |
13 | Qiandaohu | 119.0526 | 29.5557 | 133 | 2007–2008 |
14 | Ningbo | 121.5469 | 29.8599 | 37 | 2007–2008 |
15 | SONET_Zhoushan | 122.1880 | 29.9940 | 29 | 2016 |
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Cheng, L.; Li, L.; Chen, L.; Hu, S.; Yuan, L.; Liu, Y.; Cui, Y.; Zhang, T. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period. Int. J. Environ. Res. Public Health 2019, 16, 3522. https://doi.org/10.3390/ijerph16193522
Cheng L, Li L, Chen L, Hu S, Yuan L, Liu Y, Cui Y, Zhang T. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period. International Journal of Environmental Research and Public Health. 2019; 16(19):3522. https://doi.org/10.3390/ijerph16193522
Chicago/Turabian StyleCheng, Liang, Long Li, Longqian Chen, Sai Hu, Lina Yuan, Yunqiang Liu, Yifan Cui, and Ting Zhang. 2019. "Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014–2017 Period" International Journal of Environmental Research and Public Health 16, no. 19: 3522. https://doi.org/10.3390/ijerph16193522