Building a Reliable Environmental Data Accumulation Pipeline with Python Integrating US EPA Data for Pollution Assessment Category: Scientific Data Engineering Tags: Python, ETL, US EPA, environmental data, chemical properties, pollution analysis

High-quality environmental assessments depend on credible reference data. For chemical pollution analysis, this includes physical, chemical, and environmental properties sourced from trusted institutions. At Brussels Environment, I developed a Python program for environmental data accumulation, designed to support robust EQS evaluations. The Challenge Environmental datasets often: • Come from multiple external sources • Use different formats and parameter definitions • Require scientific validation before use Manual data collection is time-consuming and error-prone, especially when dealing with regulatory assessments. The Solution I created a Python-based data accumulation system that: • Automatically retrieves reference data from authoritative sources such as the US Environmental Protection Agency (US EPA) • Collects physical, chemical, and environmental parameters • Structures the data into analysis-ready formats • Preserves traceability and source credibility This program functions as a scientific ETL pipeline, optimized for environmental research and regulatory use. Impact The system: • Strengthened the scientific credibility of pollution analyses • Enabled deeper interpretation of chemical behavior in soil, water, and air • Reduced manual effort and improved reproducibility • Supported evidence-based environmental decision-making Reliable data accumulation is essential for turning environmental monitoring into actionable science.