V6.GL.02.04
This version is recommended for users interested in our most state of the science global algorithm, and is available for 1998-2023
We estimate annual and monthly ground-level fine particulate matter (PM₂.₅) for 1998-2023 by combining Aerosol Optical Depth (AOD) retrievals (Dark Target, Deep Blue, MAIAC) that make use of observations from numerous satellite-based NASA instruments (MODIS/Terra, MODIS/Aqua, MISR/Terra, SeaWiFS/SeaStar, VIIRS/SNPP, and VIIRS/NOAA20) 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.02.04 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 – 2023, and includes retrievals from the SNPP VIIRS instrument. Also, previous versions were reported to contain abnormally low values in certain, rare circumstances. This limitation has been addressed in V6.GL.02.04 using a modified padding strategy and stronger geophysical constraints.
Reference:
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]
Scientific Datasets:
Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection.
Note that these estimates are primarily intended to aid in large-scale studies. Annual and coarse-resolution averages correspond to a simple mean of within-grid values. Gridded datasets are provided to allow users to agglomerate data as best meets their particular needs. High-resolution (0.01° × 0.01°) datasets are gridded at the finest resolution of the information sources that were incorporated but are unlikely to fully resolve PM₂.₅ gradients at the gridded resolution due to influence by information sources at coarser resolution.
Annual and monthly mean PM₂.₅ [µg/m3] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25]
Annual and monthly mean PM₂.₅ [µg/m3] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25c0p10]
This SatPM₂.₅ data is alternatively available via the Registry of Open Data on AWS:
[https://registry.opendata.aws/surface-pm2-5-v6gl/]
How to Access:
To comply with WashU's Cloud Security policy, this dataset is configured with specific access protocols. If you wish to access the dataset on AWS S3, please see the commands below for browsing and downloading data.
1. Web Access (Recommended). Browse and download specific files directly through the secure web portal URL: https://d15downnhi4nn0.cloudfront.net/
When prompted for the S3 bucket name, enter the bucket name v6.gl.02.04
2. Command Line Browsing using AWS CLI (Listing Only). No credentials are required for listing.
3. Command Line Downloading. To download a specific file, use wget or curl pointing to the secure endpoint:
Note that a VPN may be necessary to access the AWS/Box data repositories, depending on local web traffic restrictions.
Processed Datasets:
These summary files are processed from the Scientific Datasets above for ease of accessibility. Population-weighted estimates and total population describe only those people covered by the V6.GL.02.04 dataset and are provided by GPWv4. Country borders are defined following GAD3.6.
Annual Global country-level mean PM₂.₅
Annual Canada provincial-level mean PM₂.₅
Annual China regional-level mean PM₂.₅
Annual India regional-level mean PM₂.₅
Annual United States state-level mean PM₂.₅
SatPM₂.₅ V6.GL.02.04 are licensed under CC BY 4.0