Dataset: Daily Air Quality Index and Pollutant Concentrations for Pune, India
Description
This dataset contains daily air quality measurements and Air Quality Index values for Pune, India, compiled from multiple Maharashtra Pollution Control Board monitoring stations across the city. The data spans from June 2019 to January 2026 and reflects long term air quality conditions across residential, educational, traffic influenced, and peri urban areas of Pune. The dataset includes station level daily records from five monitoring locations: Revenue Colony Shivajinagar, Savitribai Phule Pune University, Karve Road, Katraj Dairy, and Park Street Wakad Pimpri Chinchwad. Each station captures local air pollution characteristics influenced by land use, traffic density, and surrounding activities, allowing spatial comparison within the city. Hourly air quality measurements were obtained from the OpenAQ platform and aggregated to daily values. PM2.5, PM10, NO2, and SO2 concentrations are calculated as daily means from hourly observations. CO and O3 concentrations are calculated as the daily maximum of rolling 8 hour averages, following the Indian National Air Quality Index methodology defined by the Central Pollution Control Board. Daily AQI values are computed using CPCB pollutant breakpoint tables, where the final AQI for each day is defined as the maximum sub index among available pollutants, and the dominant pollutant is reported. In addition to station specific datasets, the collection includes a combined stations file that aggregates all station level daily observations into a single table for cross station analysis. A city averaged dataset is also provided, representing the daily mean pollutant concentrations and AQI across all available stations in Pune for each date. The dataset preserves missing values to reflect periods when a pollutant or station did not report data. Temporal coverage varies by station due to differences in installation dates, maintenance interruptions, and data availability. No gap filling or interpolation has been applied. This dataset is suitable for air quality trend analysis, spatial variability studies, public health exposure assessment, policy evaluation, and machine learning applications related to air pollution forecasting and urban environmental analysis in Pune.