[Dataset Title/Name]: Annual and monthly mean PM2.5 [ug/m3] at 0.01° × 0.01° 1998 - 2022

[Favioleiva]

Contact Details

[email protected]

Dataset description

This dataset provides annual and monthly mean PM2.5 [ug/m3] at 0.01° × 0.01°: since 1998 to 2022 combining satellite information with ground sensors where available. The images are provided in .nc format. Quoting them:

"We estimate annual and monthly ground-level fine particulate matter (PM2.5) for 2000-2019 by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, SeaWIFS, and VIIRS with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a residual Convolutional Neural Network (CNN), as detailed in the below reference for V6.GL.01. V6.GL.02.02 follows the methodology of V6.GL.01 but updates the ground-based observations used to calibrate the geophysical PM2.5 estimates for the entire time series, extends temporal coverage through 1998 – 2022, and includes retrievals from the SNPP VIIRS instrument."

Data can be downloaded here: https://wustl.app.box.com/s/iwvi2avusnz3fpabl6v5ouyobavbt70a/folder/273478247951?page=1&sortColumn=date&sortDirection=ASC

Citation: Shen, S. Li, C. van Donkelaar, A. Jacobs, N. Wang, C. Martin, R. V.: Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. (2024) ACS ES&T Air. DOI: 10.1021/acsestair.3c00054 [Link]

Earth Engine Snippet if dataset already in GEE

for example

var asset_name = ee.ImageCollection("path to your collection")

Sample Code: Add a sample code maybe just adding your datasets in the code editor

Enter license information

Satellite-derived PM2.5 data V6.GL.02.02 are licensed under CC BY 4.0

Keywords

PM2.5

Code of Conduct

• I agree to follow this project's Code of Conduct