Environmental Insights: Democratizing Access to Ambient Air Pollution Data and Predictive Analytics with an Open-Source Python Package
This work addresses the need for accessible air pollution data and analytics for researchers and individuals, though it is incremental as it builds on existing data and ML methods.
The authors tackled the problem of limited access to ambient air pollution data by developing Environmental Insights, an open-source Python package that enables users to retrieve historical data and forecast future conditions using a Machine Learning model, resulting in a tool that democratizes data access and includes visualization tools for analysis dissemination.
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.