DOI: 10.5281/zenodo.17479212
title: GRINS AQCLIM: The GRINS datasets on Air Quality and CLimate for Italy
title: GRINS AQCLIM: I Dataset GRINS Italiani sulla Qualità dell'Aria e sul Clima

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The GRINS_AQCLIM dataset suite

This dataset suite is made up of a number of datasets covering daily air quality (AQ) and climate (CL) around Italy from 2013 to 2023.

In particular, it provides harmonised pollutants' concentrations at 744 Italian monitoring stations matched with ERA5Land climate information. It also contains NO2 concentrations and CL data at the municipal level.

Future releases will also cover airborne emissions and concentrations of the other major pollutants at the municipal level and a more recent time span.

In the following subsections, details of the various files containing data and metadata are provided.

A paper will be available soon, providing more details on dataset construction.

 

1 GRINS_AQCLIM_points_Italy

The GRINS_AQCLIM_points_Italy dataset contains data about the measurements of air pollutant concentrations, recorded by the Italian ground-based monitoring network, along with related climate variables, uncertainties and station-related information.

There are mainly three file types: AQ and CLIM data, the Station registry information, and the GRINS AQCLIM imputation uncertainty.

1.1 GRINS_AQCLIM_points_Italy files

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 is organised 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") and pollutant name. 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 is available in RDA and CSV formats. To facilitate optimal management and accommodate the considerable overall data volume, the dataset in CSV format has been segmented into biennial periods. The specific file naming helps the identification of the period covered.

1.2 GRINS_AQCLIM_Station_registry_information file

This CSV/RDA file contains information on the monitoring stations that should be coupled to the overall dataset. In order to save storage, we stored this information in a separate file because it does not change over time. 

 The Station_registry_information file 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.


1.3 GRINS_AQCLIM_imputation_uncertainty

Hourly data were converted to the daily resolution. In order to avoid bias, we have imputed some missing hourly data using a local level model and the Kalman 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 the uncertainty of the missing-data imputation strategy, we provided the GRINS AQCLIM imputation uncertainty.csv file. The related estimates are calculated considering both variances and covariances through all the daily summary statistics. In particular, if the minimum or maximum daily values are imputed, the square root 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 files, only the days when at least one imputation is done are reported to save storage space. Columns are named the same as the GRINS AQCLIM dataset, with a "sd_" in front of each of them.

2. GRINS_AQ_NO2_LAU

The dataset contains daily concentrations of nitrogen dioxide for Italian municipalities. Predictions are obtained from the implementation of the Fixed Rank Kriging on air quality data from the EEA monitoring stations, along with several external variables, such as weather and emissions. Output grid (0.05°x0.05°) is then aggregated to the municipal level. Period covered is 2013-2023. PRO_COM codes refer to the 2025 administrative Italian municipalities' borders.

The GRINS_AQ_NO2_LAUs dataset contains:



"PRO_COM": The ID code of the municipality

 "mean_NO2_FRK": The aggregated prediction obtained from the FRK model

"sd_NO2_FRK": The aggregated uncertainty obtained from the FRK model

"time": The day considered


3. GRINS_CL_LAUs dataset

The dataset contains daily climate variables for Italian municipalities. Data are obtained from ERA5-Land and ERA5 datasets. Due to a better resolution, ERA5-Land is preferred to ERA5 when available. Grids (0.1°x0.1°) are aggregated to the municipal level. Period covered is 2013-2023. PRO_COM codes refer to the 2025 administrative Italian municipalities' borders. Informative spatial and temporal variables are:



"PRO_COM": The ID code of the municipality

"time": The day considered


Then, the climate variables are the average in the municipality of the variables listed in Table 2 (i.e. "mean_variable") along with the intra-municipal variability (i.e. "sd_variable").

Supplementary LAUs information 

Along with the municipal codes, further information and geometries are available in the file named "metadata_GRINS_LAUs". Note that the R format file also contains the geometries managed using the sf package. To retrieve geometries and merge with the LAUs datasets, an example with climate variables is available in the script "example_LAUs_merge_and_visualization".

Tables


Table 1: Air Quality variables present in the GRINS AQCLIM points dataset along with summary statistics. Unit of measure: micrograms per cubic meter.





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_points and in the GRINS_CL_LAUs datasets




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