GIS-Based Urban Afforestation Spatial Patterns and a Strategy for PM2.5 Removal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Leaf Sample Collection
2.3. Analysis between PM2.5 Concentration and Adsorption by Leaves
2.4. Tree Planting Range
2.5. Tree Planting Plans
2.6. Effect of Planting Plan on PM2.5 Concentration
2.7. Plan Evaluation of Significance
2.8. Spatial Strategy of Afforestation
3. Results
3.1. Regularity between PM2.5 Concentration and Adsorption by Leaves
3.2. Plans of Planting
3.3. Effects of Plans on PM2.5 Concentration
3.4. Planting Spatial Pattern
4. Discussion
4.1. Afforestation Strategy and Policy Implications
4.2. Evaluation of the Methods in This Paper
4.2.1. Sample Selection
4.2.2. Measuring Method
4.2.3. GIS-Based Forest Spatial Distribution
4.3. Limitations and Prospects
5. Conclusions
Author Contributions
Funding
Acknowledgements
Conflicts of Interest
References
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PM2.5 Concentration (μg m−3) | Cinnamomum Camphor | Magnolia Grandiflora | Overall Sample | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Time | 3 day | 2 day | 1 day | hour | W-PM2.5 | M-PM2.5 | W-PM2.5 | M-PM2.5 | W-PM2.5 | M-PM2.5 |
11 May | 42 | 48 | 45 | 31 | 0.0023 | 0.0029 | 0.0032 | 0.0060 | 0.0028 | 0.0045 |
13 May | 55.6 | 61 | 58 | 33 | 0.0018 | 0.0023 | 0.0040 | 0.0075 | 0.0029 | 0.0048 |
14 May | 64.3 | 64.5 | 71 | 68 | 0.0030 | 0.0037 | 0.0071 | 0.0134 | 0.0051 | 0.0086 |
16 May | 46 | 33.5 | 41 | 44 | 0.0011 | 0.0014 | 0.0023 | 0.0043 | 0.0017 | 0.0028 |
17 May | 36.3 | 41.5 | 42 | 45 | 0.0010 | 0.0012 | 0.0055 | 0.0104 | 0.0033 | 0.0058 |
18 May | 40.6 | 40.5 | 39 | 25 | 0.0009 | 0.0011 | 0.0031 | 0.0058 | 0.0020 | 0.0034 |
19 May | 35.3 | 32 | 25 | 20 | 0.0007 | 0.0008 | 0.0034 | 0.0064 | 0.0020 | 0.0036 |
20 May | 28.3 | 23.5 | 22 | 16 | 0.0006 | 0.0007 | 0.0023 | 0.0044 | 0.0015 | 0.0026 |
21 May | 29.6 | 32 | 42 | 60 | 0.0033 | 0.0041 | 0.0040 | 0.0075 | 0.0036 | 0.0058 |
22 May | 40 | 49 | 56 | 96 | 0.0012 | 0.0015 | 0.0034 | 0.0063 | 0.0023 | 0.0039 |
23 May | 48 | 51 | 46 | 46 | 0.0007 | 0.0009 | 0.0023 | 0.0044 | 0.0015 | 0.0026 |
24 May | 50.6 | 48 | 50 | 49 | 0.0008 | 0.0010 | 0.0045 | 0.0083 | 0.0026 | 0.0047 |
27 May | 19.6 | 16 | 20 | 16 | 0.0005 | 0.0007 | 0.0007 | 0.0013 | 0.0006 | 0.0010 |
28 May | 17 | 19.5 | 19 | 29 | 0.0004 | 0.0005 | 0.0013 | 0.0023 | 0.0008 | 0.0014 |
29 May | 20.3 | 20.5 | 22 | 36 | 0.0014 | 0.0018 | 0.0016 | 0.0029 | 0.0015 | 0.0023 |
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Zhou, Y.; Liu, H.; Zhou, J.; Xia, M. GIS-Based Urban Afforestation Spatial Patterns and a Strategy for PM2.5 Removal. Forests 2019, 10, 875. https://doi.org/10.3390/f10100875
Zhou Y, Liu H, Zhou J, Xia M. GIS-Based Urban Afforestation Spatial Patterns and a Strategy for PM2.5 Removal. Forests. 2019; 10(10):875. https://doi.org/10.3390/f10100875
Chicago/Turabian StyleZhou, Yejing, Helin Liu, Jingxuan Zhou, and Meng Xia. 2019. "GIS-Based Urban Afforestation Spatial Patterns and a Strategy for PM2.5 Removal" Forests 10, no. 10: 875. https://doi.org/10.3390/f10100875