A Framework for Scalable Ambient Air Pollution Concentration Estimation
Liam J Berrisford Lucy S Neal Helen J Buttery Benjamin R Evans Ronaldo Menezes
Ambient air pollution remains a critical issue in the United Kingdom, where
data on air pollution concentrations form the foundation for interventions
aimed at improving air quality. However, the current air pollution monitoring
station network in the UK is characterized by spatial sparsity, heterogeneous
placement, and frequent temporal data gaps, often due to issues such as power
outages. We introduce a scalable data-driven supervised machine learning model
framework designed to address temporal and spatial data gaps by filling missing
measurements. This approach provides a comprehensive dataset for England
throughout 2018 at a 1kmx1km hourly resolution. Leveraging machine learning
techniques and real-world data from the sparsely distributed monitoring
stations, we generate 355,827 synthetic monitoring stations across the study
area, yielding data valued at approximately \pounds70 billion. Validation was
conducted to assess the model's performance in forecasting, estimating missing
locations, and capturing peak concentrations. The resulting dataset is of
particular interest to a diverse range of stakeholders engaged in downstream
assessments supported by outdoor air pollution concentration data for NO2, O3,
PM10, PM2.5, and SO2. This resource empowers stakeholders to conduct studies at
a higher resolution than was previously possible.