DOI: 10.5285/4cbd9c53ab07497ba42de5043d1f414b
title: Synthetic Hourly Air Pollution Prediction Averages for England (SynthHAPPE)

description: This dataset contains synthetic estimates of ambient air pollution concentrations across England, provided as hourly averages representing typical conditions. The data cover major 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 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^2). Data points correspond to the centre of these grid squares. Temporally, the dataset does not represent actual hourly measurements from specific dates; instead, it provides aggregated "typical day" profiles constructed by averaging observations collected from multiple years (2014-2018) for each month, weekday, and hour. This method offers representative insights into typical air pollution patterns, avoiding the complexity of handling large-scale raw datasets.

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.

The dataset was developed to facilitate easier access to high-quality air pollution information for diverse stakeholders, such as researchers, policymakers, urban planners, and health professionals. By providing clear, simplified air quality scenarios, it helps users make informed decisions in urban planning, public health, environmental management, and policy development, as well as to assess potential impacts and interventions related to air pollution.

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.

In terms of completeness, this dataset intentionally provides representative hourly pollution estimates rather than exact historical measurements or specific pollution events. While it extensively covers typical pollution scenarios across England, direct measurements from specific air quality monitoring stations are not included. Users requiring detailed historical observations or data about specific events should refer to original monitoring station datasets.