Version: 0.1.0 | Published: 1 Jul 2026 | Updated: 1 day ago
PM2.5 concentration per LAD in 2015
Dataset
Summary
Description:
This dataset provides the average annual and monthly PM2.5 concentrations in µg/m³ aggregated to Local Authority District (LAD) level for the period 01-01-2015:31-12-2015 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 LAD boundaries (2021) by calculating the weighted mean of all grid cells falling within each LAD geography.
Contact Point:
Documentation
Documentation:
The dataset contains 15 variables for each LAD: 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 LAD regional code (geo_code), and a LAD 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 LAD boundary geometries.
Coverage
Spatial
Spatial Coverage:
United Kingdom
Geographical Levels:
LAD
Temporal
Start Date:
01-01-2015:31-12-2015
Frequency:
annual and monthly
Date of Latest Release:
01 July 2026
Date of First Release:
30 June 2026
Provenance
Origin
Purpose:
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.
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:
0.1.0
Author 1
Name Organisation:
Imago: Data Service for Imagery
Family Name Person:
Martina Pardy
Access and Governance
Usage
Data Use Requirements:
None
Format and Standards
Estimated Dataset Size:
93.9 KB (CSV), 75.2 MB (GPKG)
Vocabulary Encoding Scheme:
EPSG:27700, OSGB36/British National Grid