Air Quality Dataset

Air pollution is a pressing global health issue, and advanced datasets that combine sensor readings with reference measurements are invaluable for research. One standout resource is the UCI Air Quality Dataset, hosted in the UCI Machine Learning Repository. This dataset offers rich, real-world data that’s ideal for machine learning, environmental modeling, and sensor calibration studies. UCI Machine Learning Repository

What is the UCI Air Quality Dataset?

• It consists of 9,358 hourly averaged observations, collected between March 2004 and February 2005, from metal oxide gas sensors in an Italian city. UCI Machine Learning Repository

• These sensor outputs are paired with “ground truth” measurements from certified analyzers for gases like CO, Benzene (C6H6), NOx, and NO2. UCI Machine Learning Repository

• The dataset has 15 features including environmental variables (temperature, humidity, absolute humidity) and sensor readings. Missing values are encoded with –200. UCI Machine Learning Repository

• Licensed under CC BY 4.0, meaning you can use, share, and adapt it (with credit). UCI Machine Learning Repository

 Why use this dataset

• Sensor drift & cross-sensitivity issues are naturally present—making it perfect for testing robust calibration and ML models. UCI Machine Learning Repository

• Because it's real field data, it helps bridge the gap between lab settings and real-world deployment.

• It’s widely cited in environmental ML and signal processing communities.

Use cases & research ideas

1. Supervised regression models — Predict pollutant concentrations based on sensor readings + environment data.

2. Sensor drift correction — Build models to adjust for drift over time or environmental influence.

3. Multivariate forecasting — Predict future pollutant levels (e.g., NO2) using time-series and ML.

4. Feature selection & interpretability — Identify which sensors or environmental metrics most influence predictions.

5. Anomaly detection — Detect when readings deviate due to sensor failure or extreme events.

How to get started

• Download the dataset as .zip here,.csv or .xlsx or from UCI. UCI Machine Learning Repository

• Load into pandas (Python), R, or your tool of choice.

• Preprocess: replace –200 with NaN, impute missing data, normalize features.

• Try baseline models: linear regression, random forest, gradient boosting. Then move to neural networks or hybrid models.

• Validate using cross-validation (temporal folds help).

• Interpret model behavior and test model generalization in more polluted vs. less polluted subsets.