Airnow Python API Docs | dltHub

AirNow is a public API that provides current and forecasted air quality index (AQI) data and related observations. The REST API base URL is https://www.airnowapi.org and All requests require an API key passed as the ‘api_key’ query parameter..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Airnow data in under 10 minutes.

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What data can I load from Airnow?

Here are some of the endpoints you can load from Airnow:

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How do I authenticate with the Airnow API?

Include the API key as a query parameter api_key on every request; no special headers are required.

1. Get your credentials

1. Visit https://docs.airnowapi.org/ and click the link to register for an API account.
2. Fill out the registration form with your contact information.
3. After approval, you will receive an email containing your unique API key.
4. Log in to the AirNow developer portal and copy the API key from the dashboard for use in requests.

2. Add them to .dlt/secrets.toml

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[sources.airnow_source]
api_key = "your_airnow_api_key"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Airnow API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python airnow_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline airnow_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset airnow_data
The duckdb destination used duckdb:/airnow.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline airnow_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads forecast_zip and observation_zip from the Airnow API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def airnow_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.airnowapi.org",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "forecast_zip", "endpoint": {"path": "aq/forecast/zipCode", "data_selector": "Data"}},
            {"name": "observation_zip", "endpoint": {"path": "aq/observation/zipCode", "data_selector": "Data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="airnow_pipeline",
        destination="duckdb",
        dataset_name="airnow_data",
    )
    load_info = pipeline.run(airnow_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("airnow_pipeline").dataset()
sessions_df = data.forecast_zip.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM airnow_data.forecast_zip LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("airnow_pipeline").dataset()
data.forecast_zip.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Airnow data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication Errors

• 401 Unauthorized / 403 Forbidden – Returned when the api_key is missing, invalid, or revoked. Verify that the correct API key is included as a query parameter.

Rate Limiting

• 429 Too Many Requests – AirNow enforces request limits per minute/hour. If encountered, back‑off for at least 60 seconds before retrying.

Parameter Errors

• 400 Bad Request – Occurs when required parameters (e.g., zipCode, date) are omitted or malformed. Check the API documentation for the exact parameter names and formats.

Pagination / Data Volume

• The AirNow API does not provide built‑in pagination; large result sets must be filtered by date ranges or geographic bounds to stay within response size limits.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack