Name
Title | ||
|---|---|---|
Investigating Impacts of Ambient Air Pollution on the Terrestrial Gross Primary Productivity (GPP) From Remote Sensing |
Abstract | ||
|---|---|---|
In contrast to the threats to urban human health, impacts of air pollutants on the ecosystem photosynthesis seem to be less concerned. The existence of aerosols could promote photosynthesis by increasing the ratio of diffuse to direct solar radiation; on the contrary, ozone (O-3) could inhibit photosynthesis, as it is detrimental to leaf stomata. However, it is unknown whether these two opposite impacts worldwide cancel each other out. In the current mainstream methods, earth system models may show conflicts with in situ experimental results due to their relatively coarse resolution. In virtue of satellite remote sensing and a global eddy covariance (EC) network, we studied ten years of data to explore the impacts of aerosol and O-3 on photosynthesis by fitting an explainable machine learning model. The impacts of aerosol on gross primary productivity (GPP) were positive in many cases, yet very weak. By means of the nitrogen dioxide (NO2) to formaldehyde (HCHO) ratio, O-3 was seen with positive impacts on photosynthesis under the NOx-sensitive regime, but the apparent positive impacts correlated with the plant phenology. Under the volatile organic compound (VOC)-sensitive regime, the impacts of O-3 on GPP were not obvious, which was likely due to the prioritized depletion of O-3 by NO2 and VOCs. The impacts of air pollutants depended on many factors and results varied case by case, but the overall net impacts were negative. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2022.3163775 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Deep forest, machine learning, mountainous areas, ozone (O-3) pollution, Sentinel-5p, TROPOspheric Monitoring Instrument (TROPOMI) | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songyan Zhu | 1 | 0 | 2.37 |
| Jian Xu | 2 | 26 | 5.23 |
| Hao Zhu | 3 | 0 | 0.34 |
| Jingya Zeng | 4 | 0 | 0.34 |
| Yapeng Wang | 5 | 0 | 0.68 |
| Qiaolin Zeng | 6 | 0 | 2.70 |
| Dejun Zhang | 7 | 0 | 0.34 |
| Xiaoran Liu | 8 | 0 | 0.34 |
| Shiqi Yang | 9 | 0 | 0.34 |