Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

A comprehensive guide to accessing and working with air quality data from multiple open repositories including OpenAQ, EPA AQS, NASA Earthdata, CAMS, and others. Covers practical Python code snippets for data retrieval, explains common geospatial file formats (NetCDF, GeoJSON, Parquet, COG, GRIB, Zarr), and provides format selection guidance for different use cases. Part of a series building toward an open-source air quality modeling workflow.

Air for Tomorrow: Mapping the Digital Air-Quality Landscape, from Repositories and Data Types to Starter Code

Repository quick-starts (with minimal Python)

Data types you will meet (and how to read them right)

Conclusion — from “where” the data lives to “how” you use it