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Retrieve real-time and historical air quality measurements including PM2.5, PM10, ozone, NO2, and CO from monitoring stations worldwide. Used in environmental data engineering pipelines for pollution trend analysis, public health analytics, geospatial mapping of air quality, and time-series ingestion in Python.
Engineers use the `openaq` Python client or `requests` against the OpenAQ v3 API to query measurements by location, date range, and pollutant. Time-series data is stored in TimescaleDB or InfluxDB for long-term trend analysis and anomaly detection.
OpenAQ's global air quality data enables AI systems that predict pollution spikes, optimize outdoor activity recommendations, and generate health alerts. RAG pipelines retrieve real-time pollution readings so LLMs can answer 'Is air quality safe for running in Beijing today?' with current data.
# pip install requests pandas
import requests, pandas as pd
resp = requests.get("https://api.openaq.org/v3/locations",
params={"country_id": "DE", "limit": 10},
headers={"X-API-Key": "YOUR_API_KEY"})
df = pd.DataFrame(resp.json()["results"])[["name", "city", "country"]]
print(df)Official dataset source
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