Building a Reliable Environmental Data Accumulation Pipeline with Python

#python #environment #etl #chem

Category: Scientific Data Engineering
Tags: Python, ETL, US EPA, environmental data, chemical properties, pollution analysis

Integrating Reference Data for Pollution Assessment

Environmental assessments rely on trusted reference data describing the physical, chemical, and environmental properties of substances.

Manual data collection from external sources is time-consuming and error-prone — particularly in regulatory contexts.

At Brussels Environment, I developed a Python-based data accumulation pipeline to support scientifically robust EQS evaluations.

The Challenge

Environmental reference data:

Comes from multiple authoritative sources
Uses heterogeneous formats and parameters
Must remain traceable and scientifically defensible

Without automation, maintaining credibility and consistency becomes difficult.

The Solution

I implemented a Python ETL-style pipeline that:

Retrieves reference data from sources such as the US Environmental Protection Agency (US EPA)
Accumulates physical, chemical, and environmental properties
Structures the data for direct analytical use
Preserves source attribution and traceability

Code Example: Fetching Reference Data


python
import requests

def fetch_reference_data(cas_number):
    url = f"https://example-epa-api.org/chemical/{cas_number}"
    response = requests.get(url)
    if response.ok:
        return response.json()
    return None