Air Quality and Pollution Assessment with Environmental and Demographic Factors

5000 samples of air quality data integrate environmental metrics from WHO monitoring and demographic density from World Bank sources. The dataset captures critical factors like temperature, humidity, and multiple pollutant concentrations to predict categorical air quality levels. It was created by Mujtaba Mateen and shared via Kaggle and OpenML under an Apache 2.0 license.

Use Cases

• Predicting categorical air quality levels based on pollutant concentrations like PM2.5 and NO2.
• Analyzing the relationship between proximity to industrial areas and pollution metrics.
• Modeling the influence of demographic factors such as population density on regional air quality.
• Building multi-pollutant risk assessment tools using combined SO2, CO, and PM10 data.

Strengths

• Contains 5000 samples, providing a substantial base for model training.
• Includes 9 specific environmental and demographic features, such as temperature, humidity, and population density.
• Target variable is a clear 4-level categorical classification (Good to Hazardous) for prediction tasks.
• Derived from authoritative real-world sources like the World Health Organization and World Bank.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Geographic and temporal coverage are not specified, which may limit generalizability.

Provenance

Source

World Health Organization (WHO), World Bank Data, Kaggle

Collection Method

Aggregated from several real-world sources monitoring air quality and environmental factors.

Time Range

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Freshness

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Geography

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