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Dataset

 

Machine Learning for Hourly Air Pollution Prediction in England (ML-HAPPE)

Update Frequency: Not Planned
Latest Data Update: 2025-05-02
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2025-04-30
DOI Publication Date: 2025-05-01
Download Stats: last 12 months
Dataset Size: 20.16K Files | 1TB
Abstract

This dataset contains estimates of air pollution levels across England for every hour of the year 2018. It covers seven major air pollutants that can affect human health and the environment. The data cover major air pollutants, including Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each air pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios.

The spatial coverage of the dataset includes the entire area of England, structured as an evenly spaced grid, with each grid square covering an area of 1 square kilometre (1 km x 1 km). Data points correspond to the centre of these grid squares. The complete air pollution England dataset includes hourly estimates for the entire year 2018, covering all 365 days and 24 hours of each day. There is also training data used for the model from real-world ambient air pollution monitoring stations, which encompasses the temporal period of 2014-2018, alongside the models used to make the predictions.

These pollution estimates were produced using a supervised machine learning method, which is a computational approach where algorithms are trained to identify patterns in historical data and apply these learned patterns to predict new data points. The predictions incorporated various environmental factors, including weather conditions (e.g., temperature, wind, precipitation), human activities (traffic patterns), satellite measurements, land-use types (urban, rural, industrial areas), and emission inventories (datasets detailing pollutants released into the atmosphere). Additionally, the dataset provides uncertainty intervals through percentile-based estimates, giving users insights into the reliability of the predictions.

This dataset was created to provide access to detailed, high-resolution estimates of actual air pollution concentrations across England. Unlike simpler models or general air quality scenarios, this dataset offers hour-by-hour predictions of air pollution levels at a fine spatial scale (1 km x 1 km), delivering a realistic and actionable understanding of air quality patterns at a resolution not previously available. By providing detailed, hourly estimates based on real-world environmental conditions and emissions data, the dataset makes it possible to support evidence-based decision-making and address essential challenges in regions where direct pollution measurements may not be available.

The dataset was created by Liam J. Berrisford at the University of Exeter during his PhD studies, supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Environmental Intelligence. Full methodological details and data validation information are available in the associated open-access scientific publication. For more information about the data, see the README.md archived alongside this dataset.

This dataset provides hourly predictions of air pollution concentrations for England throughout the year 2018. These values are not direct measurements from monitoring stations but rather model-based estimates generated using a supervised machine-learning approach. The model was trained using real observations from the UK's national monitoring network, but it is capable of making predictions even in areas without any nearby monitoring stations. This means that the dataset offers complete spatial and temporal coverage, filling in gaps where no sensor data exists. This dataset focuses exclusively on England for the year 2018 and does not include data for other years or regions of the UK.

Citable as:  Berrisford, L. (2025): Machine Learning for Hourly Air Pollution Prediction in England (ML-HAPPE). NERC EDS Centre for Environmental Data Analysis, 01 May 2025. doi:10.5285/fc735f9878ed43e293b85f85e40df24d. https://dx.doi.org/10.5285/fc735f9878ed43e293b85f85e40df24d
Cite this dataset

Abbreviation: Not defined
Keywords: Ambient Air Quality, England, Nitrogen Dioxide, Nitrogen Oxides, Ozone, Particulate Matter, Sulphur Dioxide, Machine Learning, AI

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence(s):
http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

This dataset was produced through a research initiative at the University of Exeter. It was created using a supervised machine learning model, a type of artificial intelligence that learns patterns from existing data and applies those patterns to predict unknown values. Historical data from ambient air pollution monitoring stations across England were used to train the machine learning model. Input data included meteorological factors (such as wind speed and temperature), traffic activity levels, land-use information, satellite-based measurements, and emissions data. Predictions were validated for accuracy and reliability, with uncertainty intervals (5th, 50th, and 95th percentiles) calculated to indicate confidence in the estimates.
Data Quality:
The quality of this dataset was ensured through rigorous validation and uncertainty quantification practices. The data were generated using supervised machine learning methods, which involved comprehensive testing and validation against real-world measurements from official ambient air quality monitoring stations across England. Quality assessments included comparisons between predicted concentrations and actual measured values, statistical analyses of prediction errors, and evaluations of the reliability of the estimates provided. Predictions were produced alongside uncertainty intervals (expressed as the 5th, 50th, and 95th percentiles), enabling transparent communication about the confidence associated with each pollution estimate. All datasets were formatted according to established scientific data standards (NetCDF format), ensuring consistency, interoperability, and ease of use for stakeholders.
File Format:
NetCDF and JSON

Related Documents

 Berrisford LJ, Neal LS, Buttery HJ, Evans BR, Menezes R. A framework for scalable ambient air pollution concentration estimation. Environmental Data Science. 2025;4:e17. doi:10.1017/eds.2025.9

Process overview

This dataset was generated by the computation detailed below.
Title

Machine-Learning-Based Prediction of Air Pollution Estimates in England
Abstract

The dataset was created using a supervised machine-learning pipeline. The pipeline generates air pollution concentration predictions across a 1 km^2^ grid over England.
Input Description

None
Output Description

None
Software Reference

None
  • units: µg/m³
  • var_id: no_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled NO
  • units: µg/m³
  • var_id: no2_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled NO₂
  • units: µg/m³
  • var_id: nox_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled NOₓ
  • units: µg/m³
  • var_id: o3_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled O₃
  • units: µg/m³
  • var_id: pm10_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled PM₁₀
  • units: µg/m³
  • var_id: pm2p5_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled PM₂.₅
  • units: µg/m³
  • var_id: so2_Prediction_0p05_Quantile
  • long_name: 5th percentile of modeled SO₂
  • units: µg/m³
  • var_id: no_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled NO
  • units: µg/m³
  • var_id: no2_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled NO₂
  • units: µg/m³
  • var_id: nox_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled NOₓ
  • units: µg/m³
  • var_id: o3_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled O₃
  • units: µg/m³
  • var_id: pm10_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled PM₁₀
  • units: µg/m³
  • var_id: pm2p5_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled PM₂.₅
  • units: µg/m³
  • var_id: so2_Prediction_0p95_Quantile
  • long_name: 95th percentile of modeled SO₂
  • var_id: Sentinel_5P_AAI
  • long_name: Absorbing Aerosol Index
  • units: K
  • var_id: Temperature_2m
  • long_name: Air temperature at 2 m
  • units: m
  • var_id: Boundary_Layer_Height
  • long_name: Atmospheric boundary layer height
  • units: kilotonne
  • var_id: NAEI_SNAP_1_CO
  • long_name: CO (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_CO
  • long_name: CO (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_CO
  • long_name: CO (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_CO
  • long_name: CO (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_CO
  • long_name: CO (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_CO
  • long_name: CO (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_CO
  • long_name: CO (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_CO
  • long_name: CO (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_CO
  • long_name: CO (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_CO
  • long_name: CO (SNAP 8, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_9_CO
  • long_name: CO (SNAP 9, NAEI)
  • units: K
  • var_id: Dewpoint_Temperature_2m
  • long_name: Dewpoint temperature at 2 m
  • units: m
  • var_id: Road_Infrastructure_Distance_Cycleway
  • long_name: Distance to nearest Cycleway (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Footway
  • long_name: Distance to nearest Footway (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Living_Street
  • long_name: Distance to nearest Living Street (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Motorway
  • long_name: Distance to nearest Motorway (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Path
  • long_name: Distance to nearest Path (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Pedestrian
  • long_name: Distance to nearest Pedestrian (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Primary
  • long_name: Distance to nearest Primary (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Residential
  • long_name: Distance to nearest Residential (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Secondary
  • long_name: Distance to nearest Secondary (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Service
  • long_name: Distance to nearest Service (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Tertiary
  • long_name: Distance to nearest Tertiary (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Track
  • long_name: Distance to nearest Track (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Trunk
  • long_name: Distance to nearest Trunk (OpenStreetMaps)
  • units: m
  • var_id: Road_Infrastructure_Distance_Unclassified
  • long_name: Distance to nearest Unclassified (OpenStreetMaps)
  • var_id: Downward_UV_Radiation_at_the_Surface
  • long_name: Downward UV Radiation at surface
  • units: W/m²
  • units: m
  • var_id: Easting
  • long_name: Easting (EPSG:3395)
  • units: m/s
  • var_id: U_Component_of_Wind_10m
  • long_name: East–west wind at 10 m
  • units: m/s
  • var_id: U_Component_of_Wind_100m
  • long_name: East–west wind at 100 m
  • var_id: Hour_Number
  • long_name: Hour of day (0–23)
  • var_id: HGV_Score
  • long_name: Hourly traffic-flow score for HGVs
  • var_id: LGV_Score
  • long_name: Hourly traffic-flow score for LGVs
  • var_id: Bicycle_Score
  • long_name: Hourly traffic-flow score for bicycles
  • var_id: Bus_and_Coach_Score
  • long_name: Hourly traffic-flow score for buses/coaches
  • var_id: Car_and_Taxi_Score
  • long_name: Hourly traffic-flow score for cars/taxis
  • var_id: Week_Number
  • long_name: ISO week number (1–53)
  • var_id: Acid_grassland
  • long_name: Land Cover Fraction: Acid grassland
  • var_id: Arable_and_Horticulture
  • long_name: Land Cover Fraction: Arable and Horticulture
  • var_id: Bog
  • long_name: Land Cover Fraction: Bog
  • var_id: Broadleaved_woodland
  • long_name: Land Cover Fraction: Broadleaved woodland
  • var_id: Calcareous_Grassland
  • long_name: Land Cover Fraction: Calcareous Grassland
  • var_id: Coniferous_Woodland
  • long_name: Land Cover Fraction: Coniferous Woodland
  • var_id: Fen_Marsh_and_Swamp
  • long_name: Land Cover Fraction: Fen, Marsh, Swamp
  • var_id: Freshwater
  • long_name: Land Cover Fraction: Freshwater
  • var_id: Heather
  • long_name: Land Cover Fraction: Heather
  • var_id: Heather_grassland
  • long_name: Land Cover Fraction: Heather grassland
  • var_id: Improved_Grassland
  • long_name: Land Cover Fraction: Improved Grassland
  • var_id: Inland_Rock
  • long_name: Land Cover Fraction: Inland Rock
  • var_id: Littoral_Rock
  • long_name: Land Cover Fraction: Littoral Rock
  • var_id: Littoral_sediment
  • long_name: Land Cover Fraction: Littoral sediment
  • var_id: Neutral_Grassland
  • long_name: Land Cover Fraction: Neutral Grassland
  • var_id: No_Land
  • long_name: Land Cover Fraction: No Land
  • var_id: Saltmarsh
  • long_name: Land Cover Fraction: Saltmarsh
  • var_id: Saltwater
  • long_name: Land Cover Fraction: Saltwater
  • var_id: Suburban
  • long_name: Land Cover Fraction: Suburban
  • var_id: Supralittoral_Rock
  • long_name: Land Cover Fraction: Supra-littoral Rock
  • var_id: Supralittoral_Sediment
  • long_name: Land Cover Fraction: Supra-littoral Sediment
  • var_id: Urban
  • long_name: Land Cover Fraction: Urban
  • units: µg/m³
  • var_id: no_Prediction_Mean
  • long_name: Mean of modeled NO
  • units: µg/m³
  • var_id: no2_Prediction_Mean
  • long_name: Mean of modeled NO₂
  • units: µg/m³
  • var_id: nox_Prediction_Mean
  • long_name: Mean of modeled NOₓ
  • units: µg/m³
  • var_id: o3_Prediction_Mean
  • long_name: Mean of modeled O₃
  • units: µg/m³
  • var_id: pm10_Prediction_Mean
  • long_name: Mean of modeled PM₁₀
  • units: µg/m³
  • var_id: pm2p5_Prediction_Mean
  • long_name: Mean of modeled PM₂.₅
  • units: µg/m³
  • var_id: so2_Prediction_Mean
  • long_name: Mean of modeled SO₂
  • units: µg/m³
  • var_id: pm10_Measurement
  • long_name: Measured PM₁₀ (particulate matter <10 µm) concentration
  • units: µg/m³
  • var_id: nox_Measurement
  • long_name: Measured nitrogen oxides (NOₓ) concentration
  • units: µg/m³
  • var_id: no_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled NO
  • units: µg/m³
  • var_id: no2_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled NO₂
  • units: µg/m³
  • var_id: nox_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled NOₓ
  • units: µg/m³
  • var_id: o3_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled O₃
  • units: µg/m³
  • var_id: pm10_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled PM₁₀
  • units: µg/m³
  • var_id: pm2p5_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled PM₂.₅
  • units: µg/m³
  • var_id: so2_Prediction_0p5_Quantile
  • long_name: Median (50th pct) of modeled SO₂
  • var_id: Month_Number
  • long_name: Month (1=January–12=December)
  • units: kilotonne
  • var_id: NAEI_SNAP_1_NH3
  • long_name: NH3 (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_NH3
  • long_name: NH3 (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_NH3
  • long_name: NH3 (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_NH3
  • long_name: NH3 (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_NH3
  • long_name: NH3 (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_NH3
  • long_name: NH3 (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_NH3
  • long_name: NH3 (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_NH3
  • long_name: NH3 (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_NH3
  • long_name: NH3 (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_NH3
  • long_name: NH3 (SNAP 8, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_9_NH3
  • long_name: NH3 (SNAP 9, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_1_NMVOC
  • long_name: NMVOC (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_NMVOC
  • long_name: NMVOC (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_NMVOC
  • long_name: NMVOC (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_NMVOC
  • long_name: NMVOC (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_NMVOC
  • long_name: NMVOC (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_NMVOC
  • long_name: NMVOC (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_NMVOC
  • long_name: NMVOC (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_NMVOC
  • long_name: NMVOC (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_NMVOC
  • long_name: NMVOC (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_NMVOC
  • long_name: NMVOC (SNAP 8, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_9_NMVOC
  • long_name: NMVOC (SNAP 9, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_1_NOx
  • long_name: NOx (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_NOx
  • long_name: NOx (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_NOx
  • long_name: NOx (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_NOx
  • long_name: NOx (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_NOx
  • long_name: NOx (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_NOx
  • long_name: NOx (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_NOx
  • long_name: NOx (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_NOx
  • long_name: NOx (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_NOx
  • long_name: NOx (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_NOx
  • long_name: NOx (SNAP 8, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_9_NOx
  • long_name: NOx (SNAP 9, NAEI)
  • units: m
  • var_id: Northing
  • long_name: Northing (EPSG:3395)
  • units: m/s
  • var_id: V_Component_of_Wind_10m
  • long_name: North–south wind at 10 m
  • units: m/s
  • var_id: V_Component_of_Wind_100m
  • long_name: North–south wind at 100 m
  • units: kilotonne
  • var_id: NAEI_SNAP_1_PM10
  • long_name: PM10 (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_PM10
  • long_name: PM10 (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_PM10
  • long_name: PM10 (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_PM10
  • long_name: PM10 (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_PM10
  • long_name: PM10 (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_PM10
  • long_name: PM10 (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_PM10
  • long_name: PM10 (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_PM10
  • long_name: PM10 (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_PM10
  • long_name: PM10 (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_PM10
  • long_name: PM10 (SNAP 8, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_9_PM10
  • long_name: PM10 (SNAP 9, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_1_PM25
  • long_name: PM25 (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_PM25
  • long_name: PM25 (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_PM25
  • long_name: PM25 (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_PM25
  • long_name: PM25 (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_PM25
  • long_name: PM25 (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_PM25
  • long_name: PM25 (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_PM25
  • long_name: PM25 (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_PM25
  • long_name: PM25 (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_PM25
  • long_name: PM25 (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_PM25
  • long_name: PM25 (SNAP 8, NAEI)
  • var_id: NAEI_SNAP_9_PM25
  • long_name: PM25 (SNAP 9, NAEI)
  • units: kilotonne
  • units: m/s
  • var_id: Instantaneous_10m_Wind_Gust
  • long_name: Peak wind gust at 10 m
  • units: kilotonne
  • var_id: NAEI_SNAP_1_SOx
  • long_name: SOx (SNAP 1, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_10_SOx
  • long_name: SOx (SNAP 10, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_11_SOx
  • long_name: SOx (SNAP 11, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_2_SOx
  • long_name: SOx (SNAP 2, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_3_SOx
  • long_name: SOx (SNAP 3, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_4_SOx
  • long_name: SOx (SNAP 4, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_5_SOx
  • long_name: SOx (SNAP 5, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_6_SOx
  • long_name: SOx (SNAP 6, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_7_SOx
  • long_name: SOx (SNAP 7, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_8_SOx
  • long_name: SOx (SNAP 8, NAEI)
  • units: kilotonne
  • var_id: NAEI_SNAP_9_SOx
  • long_name: SOx (SNAP 9, NAEI)
  • units: m
  • var_id: Total_Length_Cycleway
  • long_name: Summed length of Cycleway in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Footway
  • long_name: Summed length of Footway in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Living_Street
  • long_name: Summed length of Living Street in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Motorway
  • long_name: Summed length of Motorway in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Path
  • long_name: Summed length of Path in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Pedestrian
  • long_name: Summed length of Pedestrian in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Primary
  • long_name: Summed length of Primary in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Residential
  • long_name: Summed length of Residential in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Secondary
  • long_name: Summed length of Secondary in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Service
  • long_name: Summed length of Service in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Tertiary
  • long_name: Summed length of Tertiary in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Track
  • long_name: Summed length of Track in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Trunk
  • long_name: Summed length of Trunk in 1 km² (OpenStreetMaps)
  • units: m
  • var_id: Total_Length_Unclassified
  • long_name: Summed length of Unclassified in 1 km² (OpenStreetMaps)
  • units: hPa
  • var_id: Surface_Pressure
  • long_name: Surface atmospheric pressure
  • units: mol/m²
  • var_id: Sentinel_5P_CO
  • long_name: Total column CO
  • units: mol/m²
  • var_id: Sentinel_5P_HCHO
  • long_name: Total column HCHO
  • var_id: Sentinel_5P_O3
  • long_name: Total column O
  • units: mol/m²
  • units: mol/m²
  • var_id: Sentinel_5P_NO2
  • long_name: Tropospheric NO₂
  • var_id: UK_Model_Grid_ID
  • long_name: Unique identifier for each 1 km² grid cell
  • var_id: Total_Column_Rain_Water
  • long_name: Vertically integrated rain water
  • units: kg/m²
  • var_id: Day_of_Week_Number
  • long_name: Weekday integer (0=Mon ... 6=Sun)

Co-ordinate Variables

  • standard_name: time
  • units: hours
  • var_id: Timestamp
  • long_name: Time (hourly)
Coverage
Temporal Range
Start time:
2014-01-01T00:00:00
End time:
2018-12-31T00:00:00
Geographic Extent

 
55.8100°
  
-5.7200°
  
1.7600°
 
49.9600°
  
Related parties
Authors (1)
Publishers (1)