Related Experiment Video
Updated: Jul 27, 2026

Assessing the Particulate Matter Removal Abilities of Tree Leaves
Published on: October 7, 2018
[Estimation of PM2.5 Concentration over the Yangtze Delta Using Remote Sensing: Analysis of Spatial and Temporal
Abstract:
Satellite remote sensing retrieved aerosol optical thickness is widely used to monitor surface PM2.5 concentration. In order to monitor PM2.5 by remote sensing in the Yangtze delta, estimate model of PM2.5 concentration was constructed based on MODIS AOT, PM2.5 concentration data of the 36 ground air quality observation sites and meteorological data in 2013. Afterwards, the model estimated PM2.5 was validated by PM2.5 concentration data from the 17 ground air quality observation sites, and the results showed that the model estimation was higher. The correlation coefficient value of R2 between model estimation of PM2.5 concentration and the value of the ground monitoring of spring, summer, autumn and winter were 0. 45, 0. 50, 0. 58 and 0. 52, respectively. The variation characteristics of temporal and spatial was analyzed based on the long time PM2.5 data together with model estimated, and an increase trend of PM2.5 concentration was observed from 2000 to 2013, with the maximum concentration of PM2.5 (66. 2 µg.m-3 ± 19. 3 µg.m-3) in February and minimum in December (22.6 µg.m-3 ± 5. 9 µg.m-3). In addition, it was found that the distribution of PM2.5 concentration was of obvious features, displaying high value in south and low in north. Mass concentration of PM2.5 was peaked in the zone of urban agglomeration which was grouped to a delta-shaped region by Shanghai, Hangzhou and Nanjing, while the low value areas were in the forest away from city. The result suggested that MODIS AOT and meteorological data can be used to monitor regional PM2.5 by the established multi-linear regression model.
More Related Videos
10:28Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
Published on: June 13, 2020
07:13Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021