{"notebook_content":"\n\n\n\n\nNotebook\n\n\n\n\n\n\n\n\n\n\n\n\n
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\n\"No\n

Daily, monthly and annual mean concentrations of NO2\u00b6

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Example script to plot the timeseries of daily and monthly mean of NO2 mass concentration data for 2022 at ACTRIS stations. Furher, the annual means are plotted on a European map.

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Import libraries\u00b6

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In\u00a0[1]:
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import xarray as xr\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport pandas as pd\nimport numpy as np\nimport json\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport cartopy.io.img_tiles as cimgt\nfrom matplotlib.colors import BoundaryNorm\nfrom matplotlib import colormaps as cm\nimport os\n
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Load data\u00b6

In this example the data is selected by searching in the ACTRIS Data Portal. A basket (.json) file from choosing variable Nitrogen dioxide mass concentration and framework ACTRIS is stored in static. A more thorugh example of this process is shown in file \"Search and access/Search with the ACTRIS Data Portal.ipynb\".

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In\u00a0[2]:
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os.chdir(os.getcwd())\n#named the basket file downloaded from ACTRIS Data Portal \"basket_no2_2022.json\"\nwith open('static/basket_no2_2022.json') as f:\n  basket = json.load(f)\n\n# get the list of the urls in the NO2 files in netCDF format\ndf = pd.DataFrame(basket).T\nfile_urls = df['opendapUrl'].tolist()\nprint(file_urls)\n
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['https://thredds.nilu.no/thredds/dodsC/ebas_doi/JE/CK/XJ/JECK-XJ9C.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/XC/VP/A5/XCVP-A5BH.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/3M/9Z/64/3M9Z-648F.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/9K/6S/D6/9K6S-D6A9.nc', 'https://thredds.nilu.no/thredds/dodsC/ebas_doi/DQ/83/EQ/DQ83-EQBE.nc']\n
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Depending on the type of data, the documentation for each file can be found here: https://data.actris.eu/docs

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Documentation on the In-Situ file formats (NetCDF) is available here: https://ebas.pages.nilu.no/ebas-io/fileformat_netcdf/index.html

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Define the year and variable of interest\u00b6

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In\u00a0[3]:
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# define the variable of interest and the year of interest\nvariable_interested_standard_name = 'mass_concentration_of_nitrogen_dioxide_expressed_as_nitrogen_in_air'\nyear_interested = 2022\n
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Calculate daily, monthly and annual mean concentrations\u00b6

Inspect the data coverage by printing out how many days of data are available

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In\u00a0[4]:
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# define dataframes with the data of interest\ndaily_means_all_sites = []\nmonthly_means_all_sites = []\nannual_means_all_sites = []\nsite_name_list = []\n\nfor i in range(len(file_urls)):\n    # Open the netCDF file\n    data = xr.open_dataset(file_urls[i])\n    print(i)\n\n    # Check if the dataset's time range overlaps with the wanted period\n    dataset_start_time = data.time.min().values\n    dataset_end_time = data.time.max().values\n\n\n    # check time period of the data\n    if (data.time.dt.year == year_interested).any():\n\n        # check if the dataset contains the variable of interest\n        for var in data.data_vars:\n            if 'standard_name' in data[var].attrs and data[var].attrs['standard_name'] == variable_interested_standard_name:\n                print(' File %d, %s, variable: %s %s Period: %s - %s' % (i, data.attrs['ebas_station_code'], \"{:<35s}\".format(var), \"{:<40s}\".format('('+data.attrs['ebas_station_name']+')'), data.time.min().values, data.time.max().values))\n\n                # get the variable values for the year of interest\n                variable_values = data[var].sel(time = data.time.dt.year == year_interested)\n\n                # Calculate daily, monthly and annual means\n                daily_means = variable_values.resample(time='1D').mean('time')\n                print('      ..has data for '+str(len(daily_means))+' days..')\n                monthly_means = variable_values.resample(time='ME').mean('time')\n                annual_means = variable_values.resample(time='YE').mean('time')\n\n                #add the means to the file that collects all the sites\n                daily_means_all_sites.append(daily_means)\n                monthly_means_all_sites.append(monthly_means)\n                annual_means_all_sites.append(annual_means)\n                site_name_list.append(data.attrs['ebas_station_name'])\n                break              \n\n        else:\n            print(f\"File {i} does not contain data in the desired time range.\")\n    data.close()\n
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0\n File 0, FI0050R, variable: nitrogen_dioxide_ug_N_per_m3_amean  (Hyyti\u00e4l\u00e4)                               Period: 2018-01-01T00:30:00.000000000 - 2022-12-31T23:30:00.000000000\n      ..has data for 365 days..\n1\n File 1, IT0009R, variable: nitrogen_dioxide_ug_N_per_m3_amean  (Monte Cimone)                           Period: 2021-01-01T00:30:00.000000000 - 2022-12-31T23:30:00.000000000\n      ..has data for 365 days..\n2\n File 2, CZ0003R, variable: nitrogen_dioxide_ug_N_per_m3        (Kosetice (NAOK))                        Period: 2022-01-01T00:30:00.000000000 - 2022-12-31T23:30:00.000000000\n      ..has data for 365 days..\n3\n File 3, FR0020R, variable: nitrogen_dioxide_ug_N_per_m3        (SIRTA Atmospheric Research Observatory) Period: 2022-01-01T00:30:00.000000000 - 2022-12-31T23:30:00.000000000\n      ..has data for 365 days..\n4\n File 4, CH0001G, variable: nitrogen_dioxide_ug_N_per_m3        (Jungfraujoch)                           Period: 2021-01-01T00:30:00.000000000 - 2022-12-31T23:30:00.000000000\n      ..has data for 365 days..\n
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Plot daily and monthly with one panel for each site\u00b6

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In\u00a0[5]:
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#First, prepare some variables to use as x values in the plotting\nndays=len(daily_means.time)\nnmonths=len(monthly_means.time)\ndays=np.linspace(1,ndays,ndays)\nmonthsdaily=np.linspace(1,nmonths,ndays)    #x values for the daily mean dots in the plot\nmonths=np.linspace(1,nmonths,nmonths)       #x values for the monthly mean lines in the plot\nnstations=len(site_name_list)\n\n\n# Prepare the figure. panelheight can be varied.\npanelheight=3\nfig, axes = plt.subplots(nrows=nstations,ncols=1, figsize=(10,int(panelheight*len(site_name_list))))\n\nfor col in range(0,nstations):\n    ax=axes[col]\n    ax.scatter(monthsdaily,daily_means_all_sites[col],label='Daily means, '+str(year_interested),alpha=0.3) #scatterplot of daily mean values\n    ax.plot(months,monthly_means_all_sites[col],label='Monthly means, '+str(year_interested),linewidth=2) #line plot of monthly mean values\n    ax.set_ylabel('NO$_2$ conc. (\u00b5gN/m$^3$)')\n    ax.set_title(site_name_list[col])\n    ax.legend(loc='upper left')\n    ax.set_xticks(np.arange(1, 13), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n\n\nplt.tight_layout()\nplt.show()\n
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Plot the monthly mean data from all the sites in the same panel\u00b6

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In\u00a0[6]:
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plt.figure(figsize=(10, 6))\nfor i in range(len(monthly_means_all_sites)):\n    plt.plot(monthly_means_all_sites[i], label=site_name_list[i], marker='*')\n\nplt.legend()\nplt.ylabel('\u00b5gN/m$^3$')\nplt.title('Monthly mean concentrations of NO$_2$  for year %d' % year_interested)\nplt.xticks(np.arange(0, 12), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\nplt.show()\n
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Make a table with the aggregated data\u00b6

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In\u00a0[7]:
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#Define a file to included in the table\ndata = []\n\nMonths = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n\nfor site_monthly, site_annual in zip(monthly_means_all_sites, annual_means_all_sites):\n    site_name = site_monthly.attrs['ebas_station_name']\n    lat = round(float(site_monthly.attrs['ebas_station_latitude']), 2)\n    lon = round(float(site_monthly.attrs['ebas_station_longitude']),2)\n    monthly_means = [round(mean, 2) for mean in site_monthly.values.tolist()] # Extract and round monthly means\n    annual_mean = [round(mean, 2) for mean in site_annual.values.tolist()] # Extract and round annual mean\n     # Append data to the list\n    data.append([site_name, lat, lon] + monthly_means + annual_mean) \n\n# Define column names\ncolumns = ['Site Name', 'Latitude', 'Longitude'] + Months + ['Annual Mean']\n\n# Create DataFrame\ndf_no2_2022 = pd.DataFrame(data, columns=columns)\ndf_no2_2022\n
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Site NameLatitudeLongitudeJanFebMarAprMayJunJulAugSepOctNovDecAnnual Mean
0Hyyti\u00e4l\u00e461.8524.280.600.790.480.210.190.280.240.340.310.450.750.950.47
1Monte Cimone44.1910.700.190.280.230.200.120.120.140.200.150.080.190.160.17
2Kosetice (NAOK)49.5715.084.222.834.343.523.012.852.853.343.173.383.936.233.62
3SIRTA Atmospheric Research Observatory48.712.153.921.434.172.111.491.821.812.012.402.302.124.492.53
4Jungfraujoch46.557.990.040.060.060.110.070.060.060.070.060.050.05NaN0.06
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Make a map plot with the annual means colored by its average value\u00b6

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In\u00a0[8]:
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#Extract data from the df_no2_2022 DataFrame created above\nsite_name_list = df_no2_2022['Site Name'].tolist()\nlongitudes = df_no2_2022['Longitude'].tolist()\nlatitudes = df_no2_2022['Latitude'].tolist()\nmean_values = df_no2_2022['Annual Mean'].tolist()\n\n#Choose max and min - this will depend on your data\nminval=0\nmaxval=5 \n\n# Create the map plot\ntiler = cimgt.OSM()\nfig = plt.figure(figsize=(9, 9))\nax = plt.axes(projection=tiler.crs)\n\n# Add map features (coastlines, borders, etc.)\nzoom = 4\nax.add_image(tiler, zoom)\n\n# Define discrete color levels and the colormap\ncmap = cm.get_cmap('viridis').reversed()  # Choose your colormap, e.g., 'viridis', 'plasma', etc.\nncolors = 15  # 15 color levels\nlevels = np.linspace(minval, maxval, ncolors)  # Discrete values for the colorbar (e.g., from 0 to 1)\nnorm = BoundaryNorm(boundaries=levels, ncolors=cmap.N)\n\n# Plot the stations as colored circles\nsc = ax.scatter(longitudes, latitudes, c=mean_values, cmap=cmap, norm=norm, s=70, edgecolor='black', transform=ccrs.PlateCarree())\n\n# Numerate the stations on the map\nfor i, (lon, lat) in enumerate(zip(longitudes, latitudes), start=1):\n    ax.text(lon + 0.8, lat + 0.8, str(i), fontsize=16, ha='left', va='center', \n            transform=ccrs.PlateCarree())  # , fontweight='bold')\n\n# Add a colorbar with discrete intervals\ncbar = plt.colorbar(sc, ax=ax, orientation='vertical', pad=0.05)\ncbar.set_label('\u00b5gN/m$^3$')\ncbar.set_ticks(levels)  # Set colorbar ticks to discrete levels\ncbar.ax.set_yticklabels([f'{level:.1f}' for level in levels])  # Customize tick labels if needed\n\n# Set titles, labels, and other map properties\nax.set_title('NO$_2$ annual mean concentrations in ' + str(year_interested))\n\nax.set_extent([min(longitudes) - 10, max(longitudes) + 10, min(latitudes) - 10, max(latitudes) + 10], crs=ccrs.PlateCarree())  # Set the extent of the map\n\n# Add the legend as text, positioned just below the map\nlegend_text = \", \".join([f\"{i}. {name}\" for i, name in enumerate(site_name_list, start=1)])\nplt.figtext(0.1, 0.01, legend_text, ha=\"left\", fontsize=9, wrap=True)\n\n# Adjust the layout to avoid cutting off the legend\nplt.tight_layout(rect=[0.01, 0.01, 1, 1])\nplt.show()\n
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To further analyse NO2 data by looking at trends and climatology, have a look at notebook example \"Trends and clomatology NO2.ipynb\".

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