air-quality-openaq

name	air-quality-openaq
description	Use this Skill to access OpenAQ air quality data (PM2.5, O3, NO2), compute spatial interpolation, health exposure indices, and city comparisons.
tags	["earth-science","air-quality","openaq","environmental-health","spatial-interpolation"]
version	1.0.0
authors	[{"name":"Rosetta Skills Contributors","github":"@xjtulyc"}]
license	MIT
platforms	["claude-code","codex","gemini-cli","cursor"]
dependencies	{"python":["openaq>=2.0","pandas>=2.0","plotly>=5.15","pykrige>=1.7","matplotlib>=3.7","numpy>=1.24","geopandas>=0.14","requests>=2.31"]}
last_updated	2026-03-17
status	stable

Air Quality Analysis with OpenAQ

One-line summary: Download and analyze PM2.5/PM10/O3/NO2 air quality time series from OpenAQ, compute kriging interpolation, health exposure indices, and city comparisons.

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When to Use This Skill

• When accessing ground-level air quality measurements globally
• When comparing air quality between cities or countries
• When computing AQI (Air Quality Index) from raw pollutant data
• When interpolating sparse station data to spatial grids (kriging)
• When estimating population exposure to PM2.5 exceedances
• When analyzing seasonal patterns and pollution episodes

Trigger keywords: OpenAQ, PM2.5, PM10, NO2, O3, air quality index, AQI, kriging interpolation, pollution, health exposure

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Background & Key Concepts

OpenAQ Platform

OpenAQ aggregates real-time and historical air quality data from ~30,000 monitoring stations in 100+ countries. Data includes PM2.5, PM10, NO2, O3, SO2, CO, and BC.

WHO Air Quality Guidelines (2021)

Pollutant	Annual mean	24h mean
PM2.5	5 µg/m³	15 µg/m³
PM10	15 µg/m³	45 µg/m³
NO2	10 µg/m³	25 µg/m³
O3	—	100 µg/m³ (8h)

Air Quality Index (US EPA)

$$ \text{AQI} = \frac{I_{hi} - I_{lo}}{C_{hi} - C_{lo}} \times (C - C_{lo}) + I_{lo} $$

AQI categories: Good (0-50), Moderate (51-100), Unhealthy for Sensitive Groups (101-150), Unhealthy (151-200), Very Unhealthy (201-300), Hazardous (301+).

Ordinary Kriging

Spatial interpolation assuming stationary covariance:

$$ Z(x_0) = \sum_{i=1}^n \lambda_i Z(x_i), \quad \sum_i \lambda_i = 1 $$

Variogram model: $\gamma(h) = C_0 + C_1 (1 - e^{-h/a})$ (exponential).

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Environment Setup

Install Dependencies

pip install openaq>=2.0 pandas>=2.0 plotly>=5.15 pykrige>=1.7 \
            matplotlib>=3.7 numpy>=1.24 geopandas>=0.14 requests>=2.31

API Key Setup (OpenAQ v3)