Dataset: Daily Air Quality Index and Pollutant Concentrations for Mumbai, India
Description
This dataset provides daily air quality measurements and Air Quality Index values for Mumbai, India, derived from multiple Maharashtra Pollution Control Board monitoring stations. The data spans from June 2019 to January 2026 and represents long term urban air quality conditions across different regions of the city. The dataset is organized into station level files and aggregated outputs. Individual station files contain daily pollutant concentrations and AQI values computed independently for each monitoring location, including Kurla, Bandra, Bandra Kurla Complex, Sion, and Chhatrapati Shivaji International Airport. These stations capture spatial variability in air pollution across residential, commercial, industrial, and traffic influenced zones of Mumbai. Hourly air quality measurements were sourced from the OpenAQ platform and aggregated to daily values. PM2.5, PM10, NO2, and SO2 concentrations are calculated as daily means from hourly data. CO and O3 concentrations are calculated as the daily maximum of rolling 8 hour averages, following the Central Pollution Control Board methodology. Daily AQI is computed using CPCB breakpoint tables, where the final AQI for each day is defined as the maximum sub index among available pollutants, and the dominant pollutant is recorded. The files included are: - Station specific daily AQI datasets for five MPCB monitoring locations in Mumbai - A combined stations dataset with all station observations stacked by date and station - A city averaged daily dataset representing mean air quality across Mumbai Missing values indicate days or pollutants not reported by a station. The temporal coverage varies by station due to differences in commissioning dates, maintenance outages, and data availability. The dataset preserves these gaps to maintain transparency and support rigorous data quality handling. This dataset is suitable for long term air quality trend analysis, spatial variability studies, exposure assessment, policy evaluation, and machine learning applications related to air pollution forecasting in Mumbai.