arXiv subjects

Liam J Berrisford

Publications and source records attributed to Liam J Berrisford.

4 recordsLinked to original sources

arXiv2024

Environmental Insights: Democratizing Access to Ambient Air Pollution Data and Predictive Analytics with an Open-Source Python Package

Ambient air pollution is a pervasive issue with wide-ranging effects on human health, ecosystem vitality, and economic structures. Utilizing data on ambient air pollution concentrations, researchers can perform comprehensive analyses to uncover the multifaceted impacts of air pollution across society. To this end, we introduce Environmental Insights, an open-source Python package designed to democratize access to air pollution concentration data. This tool enables users to easily retrieve historical air pollution data and employ a Machine Learning model for forecasting potential future conditions. Moreover, Environmental Insights includes a suite of tools aimed at facilitating the dissemination of analytical findings and enhancing user engagement through dynamic visualizations. This comprehensive approach ensures that the package caters to the diverse needs of individuals looking to explore and understand air pollution trends and their implications.

physics.soc-ph↗

arXiv2024

A Data-Driven Supervised Machine Learning Approach to Estimating Global Ambient Air Pollution Concentrations With Associated Prediction Intervals

Global ambient air pollution, a transboundary challenge, is typically addressed through interventions relying on data from spatially sparse and heterogeneously placed monitoring stations. These stations often encounter temporal data gaps due to issues such as power outages. In response, we have developed a scalable, data-driven, supervised machine learning framework. This model is designed to impute missing temporal and spatial measurements, thereby generating a comprehensive dataset for pollutants including NO$_2$, O$_3$, PM$_{10}$, PM$_{2.5}$, and SO$_2$. The dataset, with a fine granularity of 0.25$^{\circ}$ at hourly intervals and accompanied by prediction intervals for each estimate, caters to a wide range of stakeholders relying on outdoor air pollution data for downstream assessments. This enables more detailed studies. Additionally, the model's performance across various geographical locations is examined, providing insights and recommendations for strategic placement of future monitoring stations to further enhance the model's accuracy.

cs.LG↗

arXiv2024

A Framework for Scalable Ambient Air Pollution Concentration Estimation

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.

stat.AP↗

arXiv2022

Estimating Ambient Air Pollution Using Structural Properties of Road Networks

In recent years, the world has become increasingly concerned with air pollution. Particularly in the global north, countries are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are essential but they have at least three shortcomings: (1) they are costly and are difficult to implement expediently; (2) they focus on urban areas, which is where most people live, but this choice is prone to inequalities; and (3) the process of estimating air pollution lacks transparency. In this paper, we demonstrate that we can estimate air pollution using open-source information about the structural properties of roads; we focus on England and Wales in the United Kingdom (UK) in this paper although the methods here described are not dependent on specific datasets. Our approach makes it possible to implement an inexpensive method of estimating air pollution concentrations to an accuracy level that can underpin policymakers' decisions while providing an estimate in all districts, not just urban areas, and in a process that is transparent and explainable. Impact Statement. We show that a linear regression model using a single structural property -- length of the track and unclassified road network within 0.36% of districts within England and Wales (in the UK) -- can accurately estimate which districts are the most polluted. The model presents a transparent and low-cost, yet effective, alternative to more expensive models such as the one currently used by DEFRA in the UK. The model has apparent practical uses for policymakers who want to pursue clean-air initiatives but lack the capital to invest in comprehensive monitoring networks. Its low implementation cost, accessible model design, and worldwide coverage of the dataset provide a basis for implementing systems to estimate air pollution concentrations in low-income countries.

physics.soc-ph↗