A Framework for Scalable Ambient Air Pollution Concentration Estimation

TL;DR

This paper presents a scalable machine learning framework that estimates high-resolution ambient air pollution concentrations across England, filling data gaps and generating a comprehensive dataset valuable for policy and health assessments.

Contribution

The study introduces a novel supervised machine learning approach to address spatial and temporal data gaps in air quality monitoring, creating a detailed 1kmx1km hourly pollution dataset for England.

Findings

Generated 355,827 synthetic monitoring stations.

Achieved high accuracy in forecasting and gap filling.

Produced a dataset valued at approximately a370 billion.

Abstract

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.