Version: 0.2.0 | Published: 3 Jul 2026 | Updated: 0 days ago
PM2.5 concentration per MSOA in 2018
Dataset
Summary
Description:
This dataset provides the average annual and monthly PM2.5 concentrations in µg/m³ aggregated to Middle Super Output Area (MSOA) level for the period 01-01-2018:31-12-2018 for the United Kingdom. The underlying source data is the global PM2.5 product (V6.GL.02.04) at 0.01° x 0.01° (approximately 1km x 1km) spatial resolution, published by Shen et al.(2024). The aggregation was performed using the MSOA-equivalent boundaries (2021) by calculating the weighted mean of all grid cells falling within each MSOA geography.
Contact Point:
Documentation
Documentation:
The dataset contains 15 variables for each MSOA: the annual mean PM2.5 concentration in µg/m³ (pm2.5_mean), 12 monthly mean PM2.5 concentrations in µg/m³ (pm2.5_mean_01 to pm2.5_mean_12), the MSOA-equivalent regional code (geo_code), and a MSOA-equivalent regional name (geo_label). This data is provided in two distinct formats: a CSV file, which contains the tabular data; and a GPKG file, a geospatial format that combines the tabular data with the MSOA boundary geometries.
Coverage
Spatial
Spatial Coverage:
United Kingdom
Geographical Levels:
MSOA
Temporal
Start Date:
01-01-2018:31-12-2018
Frequency:
annual and monthly
Date of Latest Release:
03 July 2026
Date of First Release:
03 July 2026
Provenance
Origin
Purpose:
The underlying global PM2.5 estimates (WUSTL V6.GL.02.04) are estimated
globally, not specifically for the UK. Further details on the methodology and
calculation are available in the source publication. It is important to note
that the underlying input data has a spatial resolution of 1kmx1km, which should
be considered when interpreting results for administrative areas finer than this
scale.
Source:
The underlying methods and source information used to construct the pre-processed dataset are documented in the following paper: 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. PM2.5 concentration estimates combine satellite-derived Aerosol Optical Depth (AOD) from multiple instruments like MODIS/VIIRS, GEOS-Chem chemical transport model simulations, and ground-based monitor observations. The AOD and model outputs are fused using relative uncertainties calibrated by AERONET ground-based sun photometer data. The aggregation was performed by the Imago Team.
Collection Status:
0.2.0
Author 1
Name Organisation:
Imago: Data Service for Imagery
Family Name Person:
Martina Pardy
Access and Governance
Usage
Data Use Requirements:
None
Access
Access Rights:
CC-BY-4.0
Licence:
CC-BY-4.0
Format and Standards
Estimated Dataset Size:
2.4 MB (CSV), 89.3 MB (GPKG)
Vocabulary Encoding Scheme:
EPSG:27700, OSGB36/British National Grid