AQCLIM_GRINS Dataset - The Italian Daily Dataset on Air Quality

--> Make sure you are downloading the latest version: v.1.0.1 <-- GRINS AQCLIM dataset The GRINS AQCLIM dataset record contains datasets about the measurements of air pollutant concentrations, recorded by the Italian ground-based monitoring network, along with related climate variables, uncertainities and stations-related information. There are mainly three datasets: GRINS AQCLIM dataset, Station registry information, GRINS AQCLIM imputation uncertainty. This dataset provides daily summary statistics for numerous air pollutants, logged at 744 locations over an eleven-year period, from 2013 to 2023. The pollutants considered include: nitrogen monoxide and dioxide (NO and NO2), particulate matter (PM10 and PM2.5), ozone (O3), ammonia (NH3), carbon monoxide (CO), and sulfur dioxide (SO2). Further details are available on the dedicated GitHub page: https://github.com/GRINS-Spoke0-WP2/AQ-EEA. The dataset are organized into two primary categories: Air Quality (AQ) and Climate (CL). This division is reflected in the column naming convention, where each column name includes a corresponding prefix (e.g., "AQ_" or "CL_"). The dataset provides a unique space-time identification from: "AirQualityStation": The ID code of the monitoring station "time": Represents the recording day. For the Air Quality (AQ) dimension, there are 48 columns formed by combining the "AQ_" prefix with various summary statistics (minimum, first quartile, mean, median, third quartile, and maximum, denoted as "min", "q1", "mean", "med", "q3", "max") for each considered pollutant. As an illustration, the column name "AQ_q1_NO2" signifies the first quartile of the daily nitrogen dioxide distribution recorded by that particular station. Regarding the Climate (CL) dimension, the variables included are detailed in Table 2. Comprehensive information about these climate variables is accessible on the Copernicus Climate Data Store website: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview. The dataset in available in rda and CSV format. To facilitate optimal management and accommodate the considerable overall data volume, the dataset in CSV format has been segmented into biennial periods. The specific naming helps the identification of the period covered. GRINS AQCLIM Station registry information The dataset contains useful information on the monitoring stations that should be coupled to the overall dataset. We kept these information in a separate file because they are not changing over time, in order to optimize storage. The Station_registry_information dataset contains: "AirQualityStation": The ID code of the monitoring station "Longitude" and "Latitude": Provide the geographical coordinates of the station, using the WGS-84 reference system. "AirQualityStation": The ID code of the monitoring station "Altitude": Indicates the station's altitude. "AirQualityStationType": Describes the predominant type of emission sources in the station's vicinity. "AirQualityStationArea": Specifies the type of area surrounding the station. GRINS AQCLIM imputation uncertainty Hourly data were converted to daily resolution. In order to avoid bias we have imputed some missing hourly data using a local level model and the Kamlan smoother. We notice that the days with more than five consecutive missing hourly data are set as missing in the daily dataset. In order to take into account for the uncertainty of the missing-data imputation strategy, we provided the GRINS AQCLIM imputation uncertainty dataset. This estimates are calculated considering both variances and covariances through all the daily summary statistics. In particular, if the minimum or maximum daily values is imputed, the root square of the Kalman smoother conditional variance is reported. For quartiles and median, only conditional variances and covariances of the two selected ordered statistics are considered. For the daily average, all the conditional variances and covariances intra-day are considered. We notice that zero values refer to daily averages without missing values at the hourly level, NaN values refer to missing daily averages, and positive uncertainties are related to daily averages with one or more hourly imputed values. In the GRINS AQCLIM imputation uncertainty dataset are reported only the days when at least one imputation is done, to optimize storage. Columns are names the same as the GRINS AQCLIM dataset with a "sd_" in front of each of them. Table 1: Air Quality variables present in the GRINS AQCLIM Dataset along with summary statistics. Unit of measure: micrograms by meter cube. Air pollutant # stations Min. Mean Max. NA's (%) CO 268 0 0.5 58.2 34 NH3 1 0 5.71 17.26 57 NO2 710 0 21.71 362.21 27 NO 336 0 11.44 583.46 65 O3 402 0 56.45 756.33 29 PM10 648 0 24.08 2,575 30 PM2.5 351 0 15.51 907 35 SO2 274 0 3.11 868.42 36 Table 2: Climate variables in the GRINS AQCLIM Dataset Variable Name Description Unit CL_blh Daily mean of the height of the atmosphere boundary layer m CL_lai_hv Daily fixed value of high vegetation leaf area index m2/m2 CL_lai_lv Daily fixed value of low vegetation leaf area index m2/m2 CL_rh Daily mean of relative humidity % CL_ssr Daily maximum of surface solar radiation J/m2 CL_t2m Daily mean of temperature at 2 meters ∘C CL_tp Daily cumulative total precipitation m CL_winddir Daily mode of wind direction (1=N, 2=E, 3=S, 4=W) - CL_windspeed Daily mean of wind speed m/s