SoyVitou/Air-Quality-Italian-City

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Air quality monitoring  |  Time-series forecasting  |  Smart city research  |  Gas sensor analysis

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1. Project Introduction

This repository provides a Hugging Face friendly version of the Air Quality Italian City Dataset from the UCI Machine Learning Repository.

The dataset contains hourly air-quality measurements from a gas multisensor device deployed in a polluted urban area in an Italian city. It includes chemical sensor responses, reference analyzer measurements, temperature, relative humidity, and absolute humidity.

This dataset is useful for building projects related to:

• air quality prediction
• smart city monitoring
• pollution forecasting
• sensor drift analysis
• environmental digital twins
• LLM-assisted environmental data explanation

2. Dataset Summary

Item	Description
Dataset type	multivariate time-series
Task	regression
Records	9,358 hourly samples
Features	15 variables
Time period	March 2004 to February 2005
Missing values	tagged as -200 in the original file
Source	UCI Machine Learning Repository

3. Repository Structure

Air-Quality-Italian-City/
├── README.md
├── data/
│   └── AirQualityUCI.xlsx
└── processed/
    └── air_quality.parquet

• data/ contains the original Excel file.
• processed/ contains the cleaned Parquet file for the Hugging Face Dataset Viewer.
• missing values marked as -200 are converted to null values in the processed file.

4. Main Use Cases

Use case	Description
Air quality forecasting	predict future pollution levels using historical sensor data
Regression modeling	estimate pollutant concentrations such as CO, NOx, NO2, or benzene
Sensor analysis	study chemical sensor behavior, drift, and cross-sensitivity
Smart city research	build pollution monitoring and environmental intelligence systems
Digital twin project	create a city air-quality digital twin for monitoring and explanation

5. Column Dictionary

Column Description Date date of measurement Time time of measurement CO_GT true CO concentration PT08_S1_CO CO-targeted sensor response NMHC_GT true non-methanic hydrocarbons concentration C6H6_GT true benzene concentration PT08_S2_NMHC NMHC-targeted sensor response NOx_GT true NOx concentration

Column Description PT08_S3_NOx NOx-targeted sensor response NO2_GT true NO2 concentration PT08_S4_NO2 NO2-targeted sensor response PT08_S5_O3 O3-targeted sensor response T temperature RH relative humidity AH absolute humidity datetime combined date and time column

6. Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("SoyVitou/Air-Quality-Italian-City")

print(dataset)
print(dataset["train"][0])

7. Example Project: Air Quality Digital Twin

This dataset can be used to create a smart city air-quality digital twin.

sensor data
    ↓
pollution forecasting model
    ↓
city air-quality digital twin
    ↓
LLM explanation assistant
    ↓
environmental recommendation

Example question:

why did NO2 increase during this time period?

Example answer:

NO2 increased because the reference analyzer value rose together with related sensor responses. Temperature and humidity may also affect sensor behavior, so these variables should be considered during analysis.

8. Suggested Machine Learning Tasks

Task	Target
pollution regression	predict CO, NOx, NO2, or C6H6
time-series forecasting	forecast future air-quality values
anomaly detection	detect unusual pollution or sensor readings
missing value handling	improve data quality and imputation
digital twin monitoring	track city air-quality state over time

9. Original Source

This dataset is derived from the UCI Machine Learning Repository:

• Dataset: Air Quality
• Creator: Saverio Vito
• DOI: https://doi.org/10.24432/C59K5F
• Source: https://archive.ics.uci.edu/dataset/360/air+quality

10. Citation

If you use this dataset, please cite the original UCI dataset:

Vito, S. (2008). Air Quality [Dataset].
UCI Machine Learning Repository.
https://doi.org/10.24432/C59K5F

11. Note

This repository keeps the original .xlsx file and provides a processed .parquet version to make the dataset easier to preview and use in Hugging Face workflows.