6. Equivalent black carbon (eBC).ipynb

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   "source": [
    "<img src='https://www.actris.eu/sites/default/files/inline-images/Actris%20logo.png' width=200 align=right>\n",
    "\n",
    "# ACTRIS DC \n",
    "## Equivalent black carbon (eBC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81fa8c9f",
   "metadata": {},
   "source": [
    "Equivalent black carbon (eBC) is used to describe carbon mass derived from the aerosol absorption coefficient. The aerosol absorption coefficient is measured using filter absorption photometers, such as AE33, MAAP, CLAP etc. In this example eBC is calculated using the aerosol absorption coefficient at 637 nm. If no such wavelength exists, an extrapolation using the two closest wavelengths and the Ångström exponent (AAE) is performed. The aerosol absorption coefficient is then devided by a mass absorption coefficient (MAC) to aqcuire equivalent black carbon mass concentration (µg/m$^3$)\n",
    "\n",
    "A mass absorption coefficient (MAC) value of 10.0 m$^2$ g$^{-1}$ (geometric standard deviation = 1.33) at a wavelength of 637 nm can be considered to be representative of the mixed boundary layer at European background sites, where BC is expected to be internally mixed to a large extent. [1] \n",
    "\n",
    "##### References: \n",
    "\n",
    "[1] M. Zanatta, M. Gysel, N. Bukowiecki, T. Müller, E. Weingartner, H. Areskoug, M. Fiebig, K.E. Yttri, N. Mihalopoulos, G. Kouvarakis, D. Beddows, R.M. Harrison, F. Cavalli, J.P. Putaud, G. Spindler, A. Wiedensohler, A. Alastuey, M. Pandolfi, K. Sellegri, E. Swietlicki, J.L. Jaffrezo, U. Baltensperger, P. Laj, A European aerosol phenomenology-5: Climatology of black carbon optical properties at 9 regional background sites across Europe,\n",
    "Atmospheric Environment, Volume 145, 2016, Pages 346-364, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2016.09.035."
   ]
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  {
   "cell_type": "code",
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   "id": "4643680b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr \n",
    "import netCDF4 as nc \n",
    "import threddsclient\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fadd4c5d",
   "metadata": {},
   "source": [
    "### Access NRT filter absorption photometer data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "4114eeb0",
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     "text": [
      "All NRT filter_absorption_photometer datasets with opendap protocol: \n",
      " ['https://thredds.nilu.no/thredds/dodsC/actris_nrt/RO0010R.20240212100000.20240426070409.filter_absorption_photometer...74d.1h.RO03L_Magee_AE33_INO_NRT.RO03L_AE33.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/IT0025U.20231221110000.20240321090604.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_MIL_NRT.IT06L_AE33.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/IT0022C.20240126050000.20240426070606.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_BO_NRT.IT06L_AE33.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/IT0009R.20240117110000.20240417070409.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_CMN_NRT.IT06L_AE33.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/GR0100B.20240126050000.20240426070604.filter_absorption_photometer...3mo.1h.GR05L_Magee_AE33_DEM_dry_NRT.GR05L_abs_coef_AE33_v1.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/FR0020R.20240126050000.20240426070602.filter_absorption_photometer...3mo.1h.FR01L_Magee_AE33_SIR_pm10_NRT.FR01L_Magee_AE33_SIR.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/ES0019U.20240126050000.20240426050605.filter_absorption_photometer...3mo.1h.ES05L_Magee_AE33_sn143_NRT.ES05L_AE33.lev1.5.nc', 'https://thredds.nilu.no/thredds/dodsC/actris_nrt/BG0001R.20231209230000.20240309203803.filter_absorption_photometer..aerosol.3mo.1h.BG02L_GMD_CLAP-3W_BEO_NRT.BG02L_abs_coef.lev1.5.nc']\n"
     ]
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   ],
   "source": [
    "# Get the ACTRIS NRT thredds catalog\n",
    "all_opendap_urls = threddsclient.opendap_urls('https://thredds.nilu.no/thredds/catalog/actris_nrt/catalog.xml')\n",
    "\n",
    "# Get all filter absorption photometer opendap urls and then choose level NRT (1.5) files. \n",
    "# This code also works with level 2 files. \n",
    "fap_opendap_urls = [x for x in all_opendap_urls if 'filter_absorption_photometer' in x]\n",
    "fap_opendap_urls = [x for x in fap_opendap_urls if 'lev1.5' in x]\n",
    "print('All NRT filter_absorption_photometer datasets with opendap protocol: \\n',fap_opendap_urls)"
   ]
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      "text/plain": [
       "'https://thredds.nilu.no/thredds/dodsC/actris_nrt/IT0022C.20240126050000.20240426070606.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_BO_NRT.IT06L_AE33.lev1.5.nc'"
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    "# For this example only one opendap_url is used. \n",
    "# If you wish to use another dataset, change the number in the brackets.\n",
    "opendap_url = fap_opendap_urls[2]\n",
    "opendap_url"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                               (time: 1991,\n",
       "                                                           tbnds: 2,\n",
       "                                                           metadata_time: 1,\n",
       "                                                           Location: 1,\n",
       "                                                           pressure_qc_flags: 2,\n",
       "                                                           temperature_qc_flags: 2,\n",
       "                                                           Wavelength: 7,\n",
       "                                                           aerosol_absorption_coefficient_amean_qc_flags: 2,\n",
       "                                                           aerosol_absorption_coefficient_prec1587_qc_flags: 2,\n",
       "                                                           aerosol_absorption_coefficient_perc8413_qc_flags: 2)\n",
       "Coordinates:\n",
       "  * time                                                  (time) datetime64[ns] ...\n",
       "  * metadata_time                                         (metadata_time) datetime64[ns] ...\n",
       "  * Location                                              (Location) |S64 b&#x27;i...\n",
       "  * Wavelength                                            (Wavelength) float64 ...\n",
       "Dimensions without coordinates: tbnds, pressure_qc_flags, temperature_qc_flags,\n",
       "                                aerosol_absorption_coefficient_amean_qc_flags,\n",
       "                                aerosol_absorption_coefficient_prec1587_qc_flags,\n",
       "                                aerosol_absorption_coefficient_perc8413_qc_flags\n",
       "Data variables: (12/17)\n",
       "    time_bnds                                             (time, tbnds) datetime64[ns] ...\n",
       "    metadata_time_bnds                                    (metadata_time, tbnds) datetime64[ns] ...\n",
       "    pressure_qc                                           (Location, pressure_qc_flags, time) float64 ...\n",
       "    pressure_ebasmetadata                                 (Location, metadata_time) |S64 ...\n",
       "    temperature_qc                                        (Location, temperature_qc_flags, time) float64 ...\n",
       "    temperature_ebasmetadata                              (Location, metadata_time) |S64 ...\n",
       "    ...                                                    ...\n",
       "    aerosol_absorption_coefficient_perc8413_ebasmetadata  (Wavelength, metadata_time) |S64 ...\n",
       "    pressure                                              (Location, time) float64 ...\n",
       "    temperature                                           (Location, time) float64 ...\n",
       "    aerosol_absorption_coefficient_amean                  (Wavelength, time) float64 ...\n",
       "    aerosol_absorption_coefficient_prec1587               (Wavelength, time) float64 ...\n",
       "    aerosol_absorption_coefficient_perc8413               (Wavelength, time) float64 ...\n",
       "Attributes: (12/104)\n",
       "    Conventions:                                   CF-1.8, ACDD-1.3\n",
       "    featureType:                                   timeSeries\n",
       "    title:                                         Aerosol_absorption_coeffic...\n",
       "    keywords:                                      aerosol_absorption_coeffic...\n",
       "    id:                                            IT0022C.20240126050000.202...\n",
       "    naming_authority:                              EBAS\n",
       "    ...                                            ...\n",
       "    geospatial_lat_units:                          degrees_north\n",
       "    geospatial_lon_units:                          degrees_east\n",
       "    comment:                                       {\\n    &quot;Data definition&quot;: ...\n",
       "    standard_name_vocabulary:                      CF-1.7, ACDD-1.3\n",
       "    history:                                       None\n",
       "    creator_url:                                   ebas.nilu.no</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7efe2d2a-dc46-4762-9c8b-904ccc69ea8f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7efe2d2a-dc46-4762-9c8b-904ccc69ea8f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1991</li><li><span>tbnds</span>: 2</li><li><span class='xr-has-index'>metadata_time</span>: 1</li><li><span class='xr-has-index'>Location</span>: 1</li><li><span>pressure_qc_flags</span>: 2</li><li><span>temperature_qc_flags</span>: 2</li><li><span class='xr-has-index'>Wavelength</span>: 7</li><li><span>aerosol_absorption_coefficient_amean_qc_flags</span>: 2</li><li><span>aerosol_absorption_coefficient_prec1587_qc_flags</span>: 2</li><li><span>aerosol_absorption_coefficient_perc8413_qc_flags</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74d49cda-c434-43e5-b4af-3c99e751366e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74d49cda-c434-43e5-b4af-3c99e751366e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-26T05:30:00 ... 2024-04-...</div><input id='attrs-6f81f20b-d4b3-47ee-9ad5-f7c76a2f2045' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f81f20b-d4b3-47ee-9ad5-f7c76a2f2045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9739c1e6-6c9e-45e8-904d-8d4a169b83ed' class='xr-var-data-in' type='checkbox'><label for='data-9739c1e6-6c9e-45e8-904d-8d4a169b83ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time of measurement</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-26T05:30:00.000000000&#x27;, &#x27;2024-01-26T06:30:00.000000000&#x27;,\n",
       "       &#x27;2024-01-26T07:30:00.000000000&#x27;, ..., &#x27;2024-04-26T02:30:00.000000000&#x27;,\n",
       "       &#x27;2024-04-26T03:30:00.000000000&#x27;, &#x27;2024-04-26T04:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>metadata_time</span></div><div class='xr-var-dims'>(metadata_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-03-11T17:00:00</div><input id='attrs-7bbd6c2d-3071-4758-b79b-8f10f5cab510' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bbd6c2d-3071-4758-b79b-8f10f5cab510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eacdb6d5-7107-48d2-8400-d720fe7152be' class='xr-var-data-in' type='checkbox'><label for='data-eacdb6d5-7107-48d2-8400-d720fe7152be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time of ebas metadata intervals</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>metadata_time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-03-11T17:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Location</span></div><div class='xr-var-dims'>(Location)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>b&#x27;instrument internal&#x27;</div><input id='attrs-721de77e-1ce7-47c5-bbd6-e34d40085acd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-721de77e-1ce7-47c5-bbd6-e34d40085acd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5417a4d3-2566-4b92-a156-f8f293f4c5a5' class='xr-var-data-in' type='checkbox'><label for='data-5417a4d3-2566-4b92-a156-f8f293f4c5a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([b&#x27;instrument internal&#x27;], dtype=&#x27;|S64&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Wavelength</span></div><div class='xr-var-dims'>(Wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>370.0 470.0 525.0 ... 880.0 950.0</div><input id='attrs-d8e26360-69db-4925-8179-e797d4c8e3e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8e26360-69db-4925-8179-e797d4c8e3e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b91b4199-7adc-4038-85a9-555c6dad052e' class='xr-var-data-in' type='checkbox'><label for='data-b91b4199-7adc-4038-85a9-555c6dad052e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([370., 470., 525., 590., 660., 880., 950.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23411853-8d3e-41bf-b5c2-a22f836c2214' class='xr-section-summary-in' type='checkbox'  ><label for='section-23411853-8d3e-41bf-b5c2-a22f836c2214' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, tbnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e40b1998-5856-400e-a1cf-61ab33f17eae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e40b1998-5856-400e-a1cf-61ab33f17eae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5341fb2-3d82-48fb-a3a5-85b9302b1d19' class='xr-var-data-in' type='checkbox'><label for='data-d5341fb2-3d82-48fb-a3a5-85b9302b1d19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time bounds for measurement</dd></dl></div><div class='xr-var-data'><pre>[3982 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>metadata_time_bnds</span></div><div class='xr-var-dims'>(metadata_time, tbnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2005ae33-baaf-4057-b60c-06eb1c03090a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2005ae33-baaf-4057-b60c-06eb1c03090a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8724d8ec-5842-4b94-9cfa-4bdd52d41001' class='xr-var-data-in' type='checkbox'><label for='data-8724d8ec-5842-4b94-9cfa-4bdd52d41001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time bounds for ebas metadata intervals</dd></dl></div><div class='xr-var-data'><pre>[2 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_qc</span></div><div class='xr-var-dims'>(Location, pressure_qc_flags, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19fc9090-ec82-45f6-af8b-1df536ce5578' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19fc9090-ec82-45f6-af8b-1df536ce5578' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bf4fd9e-5fc3-4a7a-a5f4-04c08c6022d5' class='xr-var-data-in' type='checkbox'><label for='data-0bf4fd9e-5fc3-4a7a-a5f4-04c08c6022d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[3982 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_ebasmetadata</span></div><div class='xr-var-dims'>(Location, metadata_time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95460117-b749-4f28-9528-3d30afc7a15d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95460117-b749-4f28-9528-3d30afc7a15d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c321efb0-8898-4d63-a29b-4c69cd921bb8' class='xr-var-data-in' type='checkbox'><label for='data-c321efb0-8898-4d63-a29b-4c69cd921bb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ebas metadata for different time intervals; json encoded</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_qc</span></div><div class='xr-var-dims'>(Location, temperature_qc_flags, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6539d49-1048-4c6d-ba8f-29dd02555b00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6539d49-1048-4c6d-ba8f-29dd02555b00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-643e2c1c-26ab-459f-a4be-890c82ea7341' class='xr-var-data-in' type='checkbox'><label for='data-643e2c1c-26ab-459f-a4be-890c82ea7341' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[3982 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature_ebasmetadata</span></div><div class='xr-var-dims'>(Location, metadata_time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-81352072-7534-44bc-830d-79be0beb3b3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81352072-7534-44bc-830d-79be0beb3b3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c56d7c0a-d006-4f19-9d0e-4eb0e3795147' class='xr-var-data-in' type='checkbox'><label for='data-c56d7c0a-d006-4f19-9d0e-4eb0e3795147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ebas metadata for different time intervals; json encoded</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_amean_qc</span></div><div class='xr-var-dims'>(Wavelength, aerosol_absorption_coefficient_amean_qc_flags, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1128c143-40bf-4f27-9c7b-cb6ef078c32e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1128c143-40bf-4f27-9c7b-cb6ef078c32e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3b257f9-9359-4b0c-bfd3-180e9871f9e2' class='xr-var-data-in' type='checkbox'><label for='data-c3b257f9-9359-4b0c-bfd3-180e9871f9e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[27874 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_amean_ebasmetadata</span></div><div class='xr-var-dims'>(Wavelength, metadata_time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bf365ca-a367-426d-93ea-ac9a0d480f02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bf365ca-a367-426d-93ea-ac9a0d480f02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1ccf0fc-da2d-4be8-8eb9-2cbf141eb676' class='xr-var-data-in' type='checkbox'><label for='data-f1ccf0fc-da2d-4be8-8eb9-2cbf141eb676' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ebas metadata for different time intervals; json encoded</dd></dl></div><div class='xr-var-data'><pre>[7 values with dtype=|S64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_prec1587_qc</span></div><div class='xr-var-dims'>(Wavelength, aerosol_absorption_coefficient_prec1587_qc_flags, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-281b834e-e374-4ee3-a58e-f0fb42b07093' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-281b834e-e374-4ee3-a58e-f0fb42b07093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df877a88-fff7-4f0a-a072-e04863d43e47' class='xr-var-data-in' type='checkbox'><label for='data-df877a88-fff7-4f0a-a072-e04863d43e47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[27874 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_prec1587_ebasmetadata</span></div><div class='xr-var-dims'>(Wavelength, metadata_time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3261d054-eb20-4d60-a68c-a8a984c45938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3261d054-eb20-4d60-a68c-a8a984c45938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a37e2f7f-61ed-4f29-a33b-2c880f936c8a' class='xr-var-data-in' type='checkbox'><label for='data-a37e2f7f-61ed-4f29-a33b-2c880f936c8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ebas metadata for different time intervals; json encoded</dd></dl></div><div class='xr-var-data'><pre>[7 values with dtype=|S64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_perc8413_qc</span></div><div class='xr-var-dims'>(Wavelength, aerosol_absorption_coefficient_perc8413_qc_flags, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb918993-9c30-405a-a182-b7a852af752d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb918993-9c30-405a-a182-b7a852af752d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d76a7987-25ce-44ef-81fd-397add6f2b06' class='xr-var-data-in' type='checkbox'><label for='data-d76a7987-25ce-44ef-81fd-397add6f2b06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[27874 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_perc8413_ebasmetadata</span></div><div class='xr-var-dims'>(Wavelength, metadata_time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-023595ce-0532-4cc9-9102-106e98dc2df6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-023595ce-0532-4cc9-9102-106e98dc2df6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e89e98e4-1c34-44b3-b937-4ce2a42cac45' class='xr-var-data-in' type='checkbox'><label for='data-e89e98e4-1c34-44b3-b937-4ce2a42cac45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>ebas metadata for different time intervals; json encoded</dd></dl></div><div class='xr-var-data'><pre>[7 values with dtype=|S64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(Location, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8722863-f7a7-47c0-a80f-6d97d8d3ea23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8722863-f7a7-47c0-a80f-6d97d8d3ea23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c387bfb1-70ba-4ae1-a351-d8f1c289032d' class='xr-var-data-in' type='checkbox'><label for='data-c387bfb1-70ba-4ae1-a351-d8f1c289032d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hPa</dd><dt><span>ancillary_variables :</span></dt><dd>pressure_qc pressure_ebasmetadata</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_statistics :</span></dt><dd>arithmetic mean</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_component :</span></dt><dd>pressure</dd><dt><span>ebas_unit :</span></dt><dd>hPa</dd><dt><span>ebas_matrix :</span></dt><dd>instrument</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd></dl></div><div class='xr-var-data'><pre>[1991 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(Location, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4faf40a4-875a-4033-ba1f-0e4e8fff0add' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4faf40a4-875a-4033-ba1f-0e4e8fff0add' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e728cf1b-df93-4618-8e37-c145f0ec3ed2' class='xr-var-data-in' type='checkbox'><label for='data-e728cf1b-df93-4618-8e37-c145f0ec3ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>ancillary_variables :</span></dt><dd>temperature_qc temperature_ebasmetadata</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_statistics :</span></dt><dd>arithmetic mean</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_component :</span></dt><dd>temperature</dd><dt><span>ebas_unit :</span></dt><dd>K</dd><dt><span>ebas_matrix :</span></dt><dd>instrument</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd></dl></div><div class='xr-var-data'><pre>[1991 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_amean</span></div><div class='xr-var-dims'>(Wavelength, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f692774-b1fa-415d-94fd-022484aeb26d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f692774-b1fa-415d-94fd-022484aeb26d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c6f50c8-cedf-407b-9645-31a746d6cfe5' class='xr-var-data-in' type='checkbox'><label for='data-1c6f50c8-cedf-407b-9645-31a746d6cfe5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1/Mm</dd><dt><span>ancillary_variables :</span></dt><dd>aerosol_absorption_coefficient_amean_qc aerosol_absorption_coefficient_amean_ebasmetadata</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_statistics :</span></dt><dd>arithmetic mean</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_component :</span></dt><dd>aerosol_absorption_coefficient</dd><dt><span>ebas_unit :</span></dt><dd>1/Mm</dd><dt><span>ebas_matrix :</span></dt><dd>pm10</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd></dl></div><div class='xr-var-data'><pre>[13937 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_prec1587</span></div><div class='xr-var-dims'>(Wavelength, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-caf361e0-eed9-472a-9229-3a35ffe96e2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-caf361e0-eed9-472a-9229-3a35ffe96e2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca20ce4a-4c9e-4cdc-a10d-8f21c09b91ff' class='xr-var-data-in' type='checkbox'><label for='data-ca20ce4a-4c9e-4cdc-a10d-8f21c09b91ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1/Mm</dd><dt><span>ancillary_variables :</span></dt><dd>aerosol_absorption_coefficient_prec1587_qc aerosol_absorption_coefficient_prec1587_ebasmetadata</dd><dt><span>cell_methods :</span></dt><dd>time: percentile:15.87</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_statistics :</span></dt><dd>percentile:15.87</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_component :</span></dt><dd>aerosol_absorption_coefficient</dd><dt><span>ebas_unit :</span></dt><dd>1/Mm</dd><dt><span>ebas_matrix :</span></dt><dd>pm10</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd></dl></div><div class='xr-var-data'><pre>[13937 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aerosol_absorption_coefficient_perc8413</span></div><div class='xr-var-dims'>(Wavelength, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f9aad59-0788-42d3-9c49-66da08b376ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f9aad59-0788-42d3-9c49-66da08b376ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80549547-d29d-4840-9444-c97a91edb9f7' class='xr-var-data-in' type='checkbox'><label for='data-80549547-d29d-4840-9444-c97a91edb9f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1/Mm</dd><dt><span>ancillary_variables :</span></dt><dd>aerosol_absorption_coefficient_perc8413_qc aerosol_absorption_coefficient_perc8413_ebasmetadata</dd><dt><span>cell_methods :</span></dt><dd>time: percentile:84.13</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_statistics :</span></dt><dd>percentile:84.13</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_component :</span></dt><dd>aerosol_absorption_coefficient</dd><dt><span>ebas_unit :</span></dt><dd>1/Mm</dd><dt><span>ebas_matrix :</span></dt><dd>pm10</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd></dl></div><div class='xr-var-data'><pre>[13937 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7676c506-d7c5-40c3-839c-19616a723353' class='xr-section-summary-in' type='checkbox'  ><label for='section-7676c506-d7c5-40c3-839c-19616a723353' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39cc3b29-a8d3-493b-8968-24d3763d0268' class='xr-index-data-in' type='checkbox'/><label for='index-39cc3b29-a8d3-493b-8968-24d3763d0268' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-01-26 05:30:00&#x27;, &#x27;2024-01-26 06:30:00&#x27;,\n",
       "               &#x27;2024-01-26 07:30:00&#x27;, &#x27;2024-01-26 08:30:00&#x27;,\n",
       "               &#x27;2024-01-26 09:30:00&#x27;, &#x27;2024-01-26 10:30:00&#x27;,\n",
       "               &#x27;2024-01-26 11:30:00&#x27;, &#x27;2024-01-26 12:30:00&#x27;,\n",
       "               &#x27;2024-01-26 13:30:00&#x27;, &#x27;2024-01-26 14:30:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-04-25 19:30:00&#x27;, &#x27;2024-04-25 20:30:00&#x27;,\n",
       "               &#x27;2024-04-25 21:30:00&#x27;, &#x27;2024-04-25 22:30:00&#x27;,\n",
       "               &#x27;2024-04-25 23:30:00&#x27;, &#x27;2024-04-26 00:30:00&#x27;,\n",
       "               &#x27;2024-04-26 01:30:00&#x27;, &#x27;2024-04-26 02:30:00&#x27;,\n",
       "               &#x27;2024-04-26 03:30:00&#x27;, &#x27;2024-04-26 04:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1991, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>metadata_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-20a5fec5-4a00-4534-9311-a466e23d272f' class='xr-index-data-in' type='checkbox'/><label for='index-20a5fec5-4a00-4534-9311-a466e23d272f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-03-11 17:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;metadata_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Location</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-48869d7f-55a7-42cf-a55f-006e7c03b01d' class='xr-index-data-in' type='checkbox'/><label for='index-48869d7f-55a7-42cf-a55f-006e7c03b01d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([b&#x27;instrument internal&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Location&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-129f9e77-1aa1-4142-918f-1565420738b6' class='xr-index-data-in' type='checkbox'/><label for='index-129f9e77-1aa1-4142-918f-1565420738b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([370.0, 470.0, 525.0, 590.0, 660.0, 880.0, 950.0], dtype=&#x27;float64&#x27;, name=&#x27;Wavelength&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e55653cc-2869-4807-a204-49fc8f80887b' class='xr-section-summary-in' type='checkbox'  ><label for='section-e55653cc-2869-4807-a204-49fc8f80887b' class='xr-section-summary' >Attributes: <span>(104)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>featureType :</span></dt><dd>timeSeries</dd><dt><span>title :</span></dt><dd>Aerosol_absorption_coefficient at ISAC Bologna II</dd><dt><span>keywords :</span></dt><dd>aerosol_absorption_coefficient, GAW-WDCA_NRT, IT0022C, pm10, ACTRIS_NRT, RI-URBANS_NRT, ISAC Bologna II</dd><dt><span>id :</span></dt><dd>IT0022C.20240126050000.20240426070606.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_BO_NRT.IT06L_AE33.lev1.5.nc</dd><dt><span>naming_authority :</span></dt><dd>EBAS</dd><dt><span>project :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>citation :</span></dt><dd>Marinoni, A., Orsini, D., Roccato, F., Calzolari, F., ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT, 2024, Aerosol_absorption_coefficient at ISAC Bologna II, data hosted by EBAS at NILU</dd><dt><span>summary :</span></dt><dd>Aerosol_absorption_coefficient at ISAC Bologna II. These measurements are gathered as a part of the following projects ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: aerosol_absorption_coefficient in pm10, aerosol_absorption_coefficient in pm10, aerosol_absorption_coefficient in pm10</dd><dt><span>source :</span></dt><dd>surface observation</dd><dt><span>institution :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, 40129, Bologna, Italy</dd><dt><span>processing_level :</span></dt><dd>processing_level_test</dd><dt><span>date_created :</span></dt><dd>2024-04-26T07:06:06 UTC</dd><dt><span>date_metadata_modified :</span></dt><dd>2024-04-26T07:06:06 UTC</dd><dt><span>creator_name :</span></dt><dd>Angela Marinoni, Douglas Orsini, Fabrizio Roccato, Francescopiero Calzolari</dd><dt><span>creator_type :</span></dt><dd>person</dd><dt><span>creator_email :</span></dt><dd>a.marinoni@isac.cnr.it, d.orsini@isac.cnr.it, f.roccato@isac.cnr.it, f.calzolari@isac.cnr.it</dd><dt><span>creator_institution :</span></dt><dd>&quot;Institute of Atmospheric Sciences and Climate, ISAC&quot;, &quot;Institute of Atmospheric Sciences and Climate, ISAC&quot;, &quot;Institute of Atmospheric Sciences and Climate, ISAC&quot;, &quot;Institute of Atmospheric Sciences and Climate, Institute for Atmospheric and Climate Science, ISAC&quot;</dd><dt><span>contributor_name :</span></dt><dd>Angela Marinoni</dd><dt><span>contributor_role :</span></dt><dd>data submitter</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_name :</span></dt><dd>NILU - Norwegian Institute for Air Research, ATMOS, EBAS</dd><dt><span>publisher_institution :</span></dt><dd>NILU - Norwegian Institute for Air Research, ATMOS, EBAS</dd><dt><span>publisher_email :</span></dt><dd>ebas@nilu.no</dd><dt><span>publisher_url :</span></dt><dd>https://www.nilu.no/</dd><dt><span>geospatial_bounds :</span></dt><dd>POINT Z (44.523586 11.338474 54.0)</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4979</dd><dt><span>geospatial_lat_min :</span></dt><dd>44.523586</dd><dt><span>geospatial_lat_max :</span></dt><dd>44.523586</dd><dt><span>geospatial_lon_min :</span></dt><dd>11.338474</dd><dt><span>geospatial_lon_max :</span></dt><dd>11.338474</dd><dt><span>geospatial_vertical_min :</span></dt><dd>54.0</dd><dt><span>geospatial_vertical_max :</span></dt><dd>54.0</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>time_coverage_start :</span></dt><dd>2024-01-26T05:00:00 UTC</dd><dt><span>time_coverage_end :</span></dt><dd>2024-04-26T05:00:00 UTC</dd><dt><span>time_coverage_duration :</span></dt><dd>P0000-03-00T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P0000-00-00T01:00:00</dd><dt><span>timezone :</span></dt><dd>UTC</dd><dt><span>ebas_data_definition :</span></dt><dd>EBAS_1.1</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_citation :</span></dt><dd>Marinoni, A., Orsini, D., Roccato, F., Calzolari, F., ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT, 2024, Aerosol_absorption_coefficient at ISAC Bologna II, data hosted by EBAS at NILU</dd><dt><span>ebas_set_type_code :</span></dt><dd>TI</dd><dt><span>ebas_timezone :</span></dt><dd>UTC</dd><dt><span>ebas_file_name :</span></dt><dd>IT0022C.20240126050000.20240426070606.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_BO_NRT.IT06L_AE33.lev1.5.nc</dd><dt><span>ebas_file_creation :</span></dt><dd>2024-04-26T08:59:25.779253 UTC</dd><dt><span>ebas_export_state :</span></dt><dd>2024-04-26T05:25:03.084170 UTC</dd><dt><span>ebas_export_filter :</span></dt><dd>exclude-900,exclude-invalid</dd><dt><span>ebas_startdate :</span></dt><dd>20240126050000</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_period_code :</span></dt><dd>3mo</dd><dt><span>ebas_resolution_code :</span></dt><dd>1h</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>{\n",
       "    &quot;Data definition&quot;: &quot;EBAS_1.1&quot;,\n",
       "    &quot;Data license&quot;: &quot;https://creativecommons.org/licenses/by/4.0/&quot;,\n",
       "    &quot;Citation&quot;: &quot;Marinoni, A., Orsini, D., Roccato, F., Calzolari, F., ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT, 2024, Aerosol_absorption_coefficient at ISAC Bologna II, data hosted by EBAS at NILU&quot;,\n",
       "    &quot;Set type code&quot;: &quot;TI&quot;,\n",
       "    &quot;Timezone&quot;: &quot;UTC&quot;,\n",
       "    &quot;File name&quot;: &quot;IT0022C.20240126050000.20240426070606.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_BO_NRT.IT06L_AE33.lev1.5.nc&quot;,\n",
       "    &quot;File creation&quot;: &quot;2024-04-26T08:59:25.779253 UTC&quot;,\n",
       "    &quot;Export state&quot;: &quot;2024-04-26T05:25:03.084170 UTC&quot;,\n",
       "    &quot;Export filter&quot;: &quot;exclude-900,exclude-invalid&quot;,\n",
       "    &quot;Startdate&quot;: &quot;20240126050000&quot;,\n",
       "    &quot;Revision date&quot;: &quot;20240426070606&quot;,\n",
       "    &quot;Version&quot;: &quot;1&quot;,\n",
       "    &quot;Version description&quot;: &quot;initial revision, TROPOS_AE33&quot;,\n",
       "    &quot;Data level&quot;: &quot;1.5&quot;,\n",
       "    &quot;Period code&quot;: &quot;3mo&quot;,\n",
       "    &quot;Resolution code&quot;: &quot;1h&quot;,\n",
       "    &quot;Sample duration&quot;: &quot;1h&quot;,\n",
       "    &quot;Orig. time res.&quot;: &quot;1mn&quot;,\n",
       "    &quot;Station code&quot;: &quot;IT0022C&quot;,\n",
       "    &quot;Platform code&quot;: &quot;IT0022S&quot;,\n",
       "    &quot;Station name&quot;: &quot;ISAC Bologna II&quot;,\n",
       "    &quot;Station land use&quot;: &quot;Residential&quot;,\n",
       "    &quot;Station setting&quot;: &quot;Suburban&quot;,\n",
       "    &quot;Station latitude&quot;: &quot;44.523586&quot;,\n",
       "    &quot;Station longitude&quot;: &quot;11.338474&quot;,\n",
       "    &quot;Station altitude&quot;: &quot;54.0 m&quot;,\n",
       "    &quot;Measurement height&quot;: &quot;25.0 m&quot;,\n",
       "    &quot;Regime&quot;: &quot;IMG&quot;,\n",
       "    &quot;Laboratory code&quot;: &quot;IT06L&quot;,\n",
       "    &quot;Instrument type&quot;: &quot;filter_absorption_photometer&quot;,\n",
       "    &quot;Instrument name&quot;: &quot;Magee_AE33_BO_NRT&quot;,\n",
       "    &quot;Instrument manufacturer&quot;: &quot;Magee&quot;,\n",
       "    &quot;Instrument model&quot;: &quot;AE33&quot;,\n",
       "    &quot;Instrument serial number&quot;: &quot;AE33-S05-00538&quot;,\n",
       "    &quot;Method ref&quot;: &quot;IT06L_AE33&quot;,\n",
       "    &quot;Standard method&quot;: &quot;Single-angle_Correction=Drinovec2015&quot;,\n",
       "    &quot;Inlet type&quot;: &quot;Impactor--direct&quot;,\n",
       "    &quot;Inlet description&quot;: &quot;PM10, flow 150 l/min&quot;,\n",
       "    &quot;Flow rate&quot;: &quot;4.0 l/min&quot;,\n",
       "    &quot;Humidity/temperature control&quot;: &quot;Nafion dryer&quot;,\n",
       "    &quot;Humidity/temperature control description&quot;: &quot;sample dried to below 40% RH with Magee nafion dryer&quot;,\n",
       "    &quot;Volume std. temperature&quot;: &quot;273.15 K&quot;,\n",
       "    &quot;Volume std. pressure&quot;: &quot;1013.25 hPa&quot;,\n",
       "    &quot;Measurement uncertainty expl.&quot;: &quot;typical value of unit-to-unit variability&quot;,\n",
       "    &quot;Zero/negative values code&quot;: &quot;Zero/negative possible&quot;,\n",
       "    &quot;Zero/negative values&quot;: &quot;Zero and neg. values may appear due to statistical variations at very low concentrations&quot;,\n",
       "    &quot;Organization&quot;: &quot;IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy&quot;,\n",
       "    &quot;Framework acronym&quot;: &quot;ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT&quot;,\n",
       "    &quot;Framework name&quot;: &quot;Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS&quot;,\n",
       "    &quot;Framework description&quot;: &quot;ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., &quot;,\n",
       "    &quot;Framework contact name&quot;: &quot;Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig&quot;,\n",
       "    &quot;Framework contact email&quot;: &quot;clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no&quot;,\n",
       "    &quot;Originator&quot;: &quot;Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626&quot;,\n",
       "    &quot;Submitter&quot;: &quot;Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126&quot;,\n",
       "    &quot;Acknowledgement&quot;: &quot;Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator&quot;,\n",
       "    &quot;Comment&quot;: &quot;automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags&quot;\n",
       "}</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF-1.7, ACDD-1.3</dd><dt><span>history :</span></dt><dd>None</dd><dt><span>creator_url :</span></dt><dd>ebas.nilu.no</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                               (time: 1991,\n",
       "                                                           tbnds: 2,\n",
       "                                                           metadata_time: 1,\n",
       "                                                           Location: 1,\n",
       "                                                           pressure_qc_flags: 2,\n",
       "                                                           temperature_qc_flags: 2,\n",
       "                                                           Wavelength: 7,\n",
       "                                                           aerosol_absorption_coefficient_amean_qc_flags: 2,\n",
       "                                                           aerosol_absorption_coefficient_prec1587_qc_flags: 2,\n",
       "                                                           aerosol_absorption_coefficient_perc8413_qc_flags: 2)\n",
       "Coordinates:\n",
       "  * time                                                  (time) datetime64[ns] ...\n",
       "  * metadata_time                                         (metadata_time) datetime64[ns] ...\n",
       "  * Location                                              (Location) |S64 b'i...\n",
       "  * Wavelength                                            (Wavelength) float64 ...\n",
       "Dimensions without coordinates: tbnds, pressure_qc_flags, temperature_qc_flags,\n",
       "                                aerosol_absorption_coefficient_amean_qc_flags,\n",
       "                                aerosol_absorption_coefficient_prec1587_qc_flags,\n",
       "                                aerosol_absorption_coefficient_perc8413_qc_flags\n",
       "Data variables: (12/17)\n",
       "    time_bnds                                             (time, tbnds) datetime64[ns] ...\n",
       "    metadata_time_bnds                                    (metadata_time, tbnds) datetime64[ns] ...\n",
       "    pressure_qc                                           (Location, pressure_qc_flags, time) float64 ...\n",
       "    pressure_ebasmetadata                                 (Location, metadata_time) |S64 ...\n",
       "    temperature_qc                                        (Location, temperature_qc_flags, time) float64 ...\n",
       "    temperature_ebasmetadata                              (Location, metadata_time) |S64 ...\n",
       "    ...                                                    ...\n",
       "    aerosol_absorption_coefficient_perc8413_ebasmetadata  (Wavelength, metadata_time) |S64 ...\n",
       "    pressure                                              (Location, time) float64 ...\n",
       "    temperature                                           (Location, time) float64 ...\n",
       "    aerosol_absorption_coefficient_amean                  (Wavelength, time) float64 ...\n",
       "    aerosol_absorption_coefficient_prec1587               (Wavelength, time) float64 ...\n",
       "    aerosol_absorption_coefficient_perc8413               (Wavelength, time) float64 ...\n",
       "Attributes: (12/104)\n",
       "    Conventions:                                   CF-1.8, ACDD-1.3\n",
       "    featureType:                                   timeSeries\n",
       "    title:                                         Aerosol_absorption_coeffic...\n",
       "    keywords:                                      aerosol_absorption_coeffic...\n",
       "    id:                                            IT0022C.20240126050000.202...\n",
       "    naming_authority:                              EBAS\n",
       "    ...                                            ...\n",
       "    geospatial_lat_units:                          degrees_north\n",
       "    geospatial_lon_units:                          degrees_east\n",
       "    comment:                                       {\\n    \"Data definition\": ...\n",
       "    standard_name_vocabulary:                      CF-1.7, ACDD-1.3\n",
       "    history:                                       None\n",
       "    creator_url:                                   ebas.nilu.no"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open and show dataset with xarray \n",
    "ds_xr = xr.open_dataset(opendap_url)\n",
    "ds_xr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d49eeea",
   "metadata": {},
   "source": [
    "### Calculate eBC from aerosol absorption coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ed52ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "660.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the closest wavelength to 637.0 nm. This is the wavelength used to calculate eBC.\n",
    "given_wavelength = 637. \n",
    "all_wavelengths = ds_xr.Wavelength.values\n",
    "\n",
    "closest_wavelength = min(all_wavelengths, key=lambda x:abs(x-given_wavelength))\n",
    "closest_wavelength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3999b9a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "660.0 590.0\n"
     ]
    }
   ],
   "source": [
    "# Find the index of the second closest wavelength \n",
    "cw_index = np.where(all_wavelengths==closest_wavelength)[0][0]\n",
    "if closest_wavelength > given_wavelength: \n",
    "    scw_index = cw_index-1\n",
    "    print(all_wavelengths[cw_index], all_wavelengths[scw_index])\n",
    "elif closest_wavelength < given_wavelength: \n",
    "    scw_index = cw_index+1\n",
    "    print(all_wavelengths[cw_index], all_wavelengths[scw_index])\n",
    "else: \n",
    "    # No need for second closest wavelength as the closest wavelength is 637.0 nm\n",
    "    scw_index = False\n",
    "    print(all_wavelengths[cw_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ee118f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;aerosol_absorption_coefficient_amean&#x27; (time: 1991)&gt;\n",
       "[1991 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2024-01-26T05:30:00 ... 2024-04-26T04:3...\n",
       "    Wavelength  float64 660.0\n",
       "Attributes: (12/52)\n",
       "    units:                                         1/Mm\n",
       "    ancillary_variables:                           aerosol_absorption_coeffic...\n",
       "    cell_methods:                                  time: mean\n",
       "    ebas_data_license:                             https://creativecommons.or...\n",
       "    ebas_revision_date:                            20240426070606\n",
       "    ebas_version:                                  1\n",
       "    ...                                            ...\n",
       "    ebas_framework_contact_name:                   Cathrine Lund Myhre, Marku...\n",
       "    ebas_framework_contact_email:                  clm@nilu.no, Markus.Fiebig...\n",
       "    ebas_originator:                               Calzolari, Francescopiero,...\n",
       "    ebas_submitter:                                Marinoni, Angela, a.marino...\n",
       "    ebas_acknowledgement:                          Data used in this &lt;study/r...\n",
       "    ebas_comment:                                  automatic data processing ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'aerosol_absorption_coefficient_amean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8b84a865-3cda-404e-b289-749206ca01b4' class='xr-array-in' type='checkbox' checked><label for='section-8b84a865-3cda-404e-b289-749206ca01b4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1991 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-3e1cbad7-c188-4fb3-8cf8-b923a5fc34a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e1cbad7-c188-4fb3-8cf8-b923a5fc34a1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-26T05:30:00 ... 2024-04-...</div><input id='attrs-d4626757-7de6-46da-8eb4-86c9d361ded0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4626757-7de6-46da-8eb4-86c9d361ded0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c682658-2d15-4c6e-acdd-05a7fafd1d9e' class='xr-var-data-in' type='checkbox'><label for='data-1c682658-2d15-4c6e-acdd-05a7fafd1d9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time of measurement</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-26T05:30:00.000000000&#x27;, &#x27;2024-01-26T06:30:00.000000000&#x27;,\n",
       "       &#x27;2024-01-26T07:30:00.000000000&#x27;, ..., &#x27;2024-04-26T02:30:00.000000000&#x27;,\n",
       "       &#x27;2024-04-26T03:30:00.000000000&#x27;, &#x27;2024-04-26T04:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>660.0</div><input id='attrs-847e4133-79f5-4fc2-b276-8930f37f4153' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-847e4133-79f5-4fc2-b276-8930f37f4153' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4c6cff0-4a50-4e9b-9732-6488ab0e8c31' class='xr-var-data-in' type='checkbox'><label for='data-e4c6cff0-4a50-4e9b-9732-6488ab0e8c31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(660.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e416366f-eea4-4d72-a02f-dc8adeb621a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-e416366f-eea4-4d72-a02f-dc8adeb621a1' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41e5d805-d2b8-48b9-a75e-0c135ab4bbfc' class='xr-index-data-in' type='checkbox'/><label for='index-41e5d805-d2b8-48b9-a75e-0c135ab4bbfc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-01-26 05:30:00&#x27;, &#x27;2024-01-26 06:30:00&#x27;,\n",
       "               &#x27;2024-01-26 07:30:00&#x27;, &#x27;2024-01-26 08:30:00&#x27;,\n",
       "               &#x27;2024-01-26 09:30:00&#x27;, &#x27;2024-01-26 10:30:00&#x27;,\n",
       "               &#x27;2024-01-26 11:30:00&#x27;, &#x27;2024-01-26 12:30:00&#x27;,\n",
       "               &#x27;2024-01-26 13:30:00&#x27;, &#x27;2024-01-26 14:30:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-04-25 19:30:00&#x27;, &#x27;2024-04-25 20:30:00&#x27;,\n",
       "               &#x27;2024-04-25 21:30:00&#x27;, &#x27;2024-04-25 22:30:00&#x27;,\n",
       "               &#x27;2024-04-25 23:30:00&#x27;, &#x27;2024-04-26 00:30:00&#x27;,\n",
       "               &#x27;2024-04-26 01:30:00&#x27;, &#x27;2024-04-26 02:30:00&#x27;,\n",
       "               &#x27;2024-04-26 03:30:00&#x27;, &#x27;2024-04-26 04:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1991, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74ae4d3e-1c42-4d36-a5fa-078cc3f927cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-74ae4d3e-1c42-4d36-a5fa-078cc3f927cd' class='xr-section-summary' >Attributes: <span>(52)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1/Mm</dd><dt><span>ancillary_variables :</span></dt><dd>aerosol_absorption_coefficient_amean_qc aerosol_absorption_coefficient_amean_ebasmetadata</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>ebas_data_license :</span></dt><dd>https://creativecommons.org/licenses/by/4.0/</dd><dt><span>ebas_revision_date :</span></dt><dd>20240426070606</dd><dt><span>ebas_version :</span></dt><dd>1</dd><dt><span>ebas_version_description :</span></dt><dd>initial revision, TROPOS_AE33</dd><dt><span>ebas_statistics :</span></dt><dd>arithmetic mean</dd><dt><span>ebas_data_level :</span></dt><dd>1.5</dd><dt><span>ebas_sample_duration :</span></dt><dd>1h</dd><dt><span>ebas_orig_time_res :</span></dt><dd>1mn</dd><dt><span>ebas_station_code :</span></dt><dd>IT0022C</dd><dt><span>ebas_platform_code :</span></dt><dd>IT0022S</dd><dt><span>ebas_station_name :</span></dt><dd>ISAC Bologna II</dd><dt><span>ebas_station_land_use :</span></dt><dd>Residential</dd><dt><span>ebas_station_setting :</span></dt><dd>Suburban</dd><dt><span>ebas_station_latitude :</span></dt><dd>44.523586</dd><dt><span>ebas_station_longitude :</span></dt><dd>11.338474</dd><dt><span>ebas_station_altitude :</span></dt><dd>54.0 m</dd><dt><span>ebas_measurement_height :</span></dt><dd>25.0 m</dd><dt><span>ebas_regime :</span></dt><dd>IMG</dd><dt><span>ebas_component :</span></dt><dd>aerosol_absorption_coefficient</dd><dt><span>ebas_unit :</span></dt><dd>1/Mm</dd><dt><span>ebas_matrix :</span></dt><dd>pm10</dd><dt><span>ebas_laboratory_code :</span></dt><dd>IT06L</dd><dt><span>ebas_instrument_type :</span></dt><dd>filter_absorption_photometer</dd><dt><span>ebas_instrument_name :</span></dt><dd>Magee_AE33_BO_NRT</dd><dt><span>ebas_instrument_manufacturer :</span></dt><dd>Magee</dd><dt><span>ebas_instrument_model :</span></dt><dd>AE33</dd><dt><span>ebas_instrument_serial_number :</span></dt><dd>AE33-S05-00538</dd><dt><span>ebas_method_ref :</span></dt><dd>IT06L_AE33</dd><dt><span>ebas_standard_method :</span></dt><dd>Single-angle_Correction=Drinovec2015</dd><dt><span>ebas_inlet_type :</span></dt><dd>Impactor--direct</dd><dt><span>ebas_inlet_description :</span></dt><dd>PM10, flow 150 l/min</dd><dt><span>ebas_flow_rate :</span></dt><dd>4.0 l/min</dd><dt><span>ebas_humidity_temperaure_control :</span></dt><dd>Nafion dryer</dd><dt><span>ebas_humidity_temperaure_control_description :</span></dt><dd>sample dried to below 40% RH with Magee nafion dryer</dd><dt><span>ebas_volume_std_temperature :</span></dt><dd>273.15 K</dd><dt><span>ebas_volume_std_pressure :</span></dt><dd>1013.25 hPa</dd><dt><span>ebas_measurement_uncertainty_expl :</span></dt><dd>typical value of unit-to-unit variability</dd><dt><span>ebas_zero_negative_values_code :</span></dt><dd>Zero/negative possible</dd><dt><span>ebas_zero_negative_values :</span></dt><dd>Zero and neg. values may appear due to statistical variations at very low concentrations</dd><dt><span>ebas_organization :</span></dt><dd>IT06L, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Section of Bologna, Via Gobetti 101, , 40129, Bologna, Italy</dd><dt><span>ebas_framework_acronym :</span></dt><dd>ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT</dd><dt><span>ebas_framework_name :</span></dt><dd>Near Realtime Data for European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases, Near Realtime Data for World Data Centre for Aerosols, RI-URBANS</dd><dt><span>ebas_framework_description :</span></dt><dd>ACTRIS is the European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases. ACTRIS is composed of observing stations, exploratory platforms, instruments calibration centres, and a data centre., The World Data Centre for Aerosols is the data repository and archive for microphysical, optical, and chemical properties of atmospheric aerosol of the World Meteorological Organisation&#x27;s (WMO) Global Atmosphere Watch (GAW) programme., </dd><dt><span>ebas_framework_contact_name :</span></dt><dd>Cathrine Lund Myhre, Markus Fiebig, Markus Fiebig</dd><dt><span>ebas_framework_contact_email :</span></dt><dd>clm@nilu.no, Markus.Fiebig@nilu.no, Markus.Fiebig@nilu.no</dd><dt><span>ebas_originator :</span></dt><dd>Calzolari, Francescopiero, f.calzolari@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, Institute for Atmospheric and Climate Science, Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0003-4535-9626</dd><dt><span>ebas_submitter :</span></dt><dd>Marinoni, Angela, a.marinoni@isac.cnr.it, Institute of Atmospheric Sciences and Climate, ISAC, , Via P. Gobetti 101, , I-40129, Bologna, Italy, ORCID=0000-0002-6580-7126</dd><dt><span>ebas_acknowledgement :</span></dt><dd>Data used in this &lt;study/report/figure/etc.&gt; were accessed from EBAS (https://ebas.nilu.no) hosted by NILU. Specifically, the use included data affiliated with the frameworks: ACTRIS_NRT, GAW-WDCA_NRT, RI-URBANS_NRT. Request acknowledgement details from data originator</dd><dt><span>ebas_comment :</span></dt><dd>automatic data processing using &#x27;ebasproc&#x27; R package. Performed processing: aggregation, setting of error flags</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'aerosol_absorption_coefficient_amean' (time: 1991)>\n",
       "[1991 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2024-01-26T05:30:00 ... 2024-04-26T04:3...\n",
       "    Wavelength  float64 660.0\n",
       "Attributes: (12/52)\n",
       "    units:                                         1/Mm\n",
       "    ancillary_variables:                           aerosol_absorption_coeffic...\n",
       "    cell_methods:                                  time: mean\n",
       "    ebas_data_license:                             https://creativecommons.or...\n",
       "    ebas_revision_date:                            20240426070606\n",
       "    ebas_version:                                  1\n",
       "    ...                                            ...\n",
       "    ebas_framework_contact_name:                   Cathrine Lund Myhre, Marku...\n",
       "    ebas_framework_contact_email:                  clm@nilu.no, Markus.Fiebig...\n",
       "    ebas_originator:                               Calzolari, Francescopiero,...\n",
       "    ebas_submitter:                                Marinoni, Angela, a.marino...\n",
       "    ebas_acknowledgement:                          Data used in this <study/r...\n",
       "    ebas_comment:                                  automatic data processing ..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create datasets with the aerosol absorption coefficient for the wavelength(s)\n",
    "\n",
    "ab = ds_xr[\"aerosol_absorption_coefficient_amean\"] \n",
    "ab_cw = ab.sel(Wavelength = all_wavelengths[cw_index]) \n",
    "if scw_index != False: \n",
    "    ab_scw = ab.sel(Wavelength = all_wavelengths[scw_index])\n",
    "ab_cw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2be0b515",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Calculate Ångström coefficient from the two closest wavelengths and aerosol absorption coefficients to 637.0nm\n",
    "\n",
    "Parameters:\n",
    "    sigma1      aerosol absorption coefficient for lambda1\n",
    "    sigma2      aerosol absorption coefficient for lambda2\n",
    "    lambda1     wavelength nr. 1\n",
    "    lambda2     wavelength nr. 2\n",
    "\n",
    "Returns:\n",
    "    Ångström coefficient (angstrom_lambda1_lambda2)\n",
    "\"\"\"\n",
    "\n",
    "def angstrom(sigma1,sigma2,lambda1, lambda2):\n",
    "    # For a smooth operation, check that datasets are even in size.\n",
    "    if len(sigma1) == len(sigma2):\n",
    "        A = len(sigma1)\n",
    "    else:\n",
    "        return print(\"Match length of datasets.\")\n",
    "    \n",
    "    alpha = np.zeros(A) # empty array with length of dataset\n",
    "    \n",
    "    for i in range(A):\n",
    "        # checking for NaNs and negative numbers\n",
    "        if np.isnan(sigma1[i]) or np.isnan(sigma2[i]):\n",
    "            alpha[i] = np.nan\n",
    "        if sigma1[i] < 0 or sigma2[i] < 0:\n",
    "            alpha[i] = np.nan\n",
    "        else:\n",
    "            alpha[i] = (np.log(sigma2[i]/sigma1[i]))/(np.log(lambda1/lambda2))\n",
    "             # checking for Ångström coefficient values outside of the typical range\n",
    "            if alpha[i] < 0.7: \n",
    "                alpha[i] = 0.7\n",
    "            if alpha[i] > 3.0: \n",
    "                alpha[i] = 3.0\n",
    "                \n",
    "        time = sigma1.index\n",
    "    return time, alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f7fbdb4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if scw_index != False: \n",
    "    #Create dataframes for plotting\n",
    "    ab_cw_df = ab_cw.to_dataframe()\n",
    "    ab_scw_df = ab_scw.to_dataframe()\n",
    "    lambda_cw = all_wavelengths[cw_index]\n",
    "    lambda_scw = all_wavelengths[scw_index]\n",
    "\n",
    "    time, alpha = angstrom(ab_cw_df[\"aerosol_absorption_coefficient_amean\"], ab_scw_df[\"aerosol_absorption_coefficient_amean\"],lambda_cw, lambda_scw)\n",
    "\n",
    "    # Plot the Ångström exponent \n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.plot(time, alpha, color='g')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.title(\"Ångström exponent\")\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.ylabel(\"α\")\n",
    "    plt.gca().set_facecolor('white')\n",
    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
    "    text_box_props = dict(boxstyle='round', facecolor='white', alpha=0.5)\n",
    "    plt.text(0.02, 0.87, 'Particle size. PM10\\nWavelengths: 522nm, 660nm', transform=plt.gca().transAxes, bbox=text_box_props)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aec28952",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Calculate aerosol absorption coefficients at 637.0nm\n",
    "\n",
    "Parameters:\n",
    "    alpha       Ångström exponent\n",
    "    sigma       Known Aerosol Absorption Coefficient, depending on what you calculated the exponent for.\n",
    "    lambda_s    Wavelength for sigma\n",
    "    lambda_a    Wavelength you want to find the coefficient for, here 637.0 nm\n",
    "    precision   precision (digits)\n",
    "\n",
    "Returns:\n",
    "    Aerosol Absorption Coefficients at 637.0nm (abs_coeff)\n",
    "\"\"\"\n",
    "def calculate_abs_coeff(alpha, sigma, lambda_s, lambda_a):\n",
    "    abs_coeff = np.zeros(len(alpha))\n",
    "    for i in range(len(alpha)):\n",
    "        abs_coeff[i] = ((lambda_a/lambda_s)**(-alpha[i]))*sigma[i]\n",
    "    time = sigma.index\n",
    "    return time, abs_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfb9b0b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     estimated_aerosol_absorption_coefficient\n",
      "time                                                         \n",
      "2024-01-26 05:30:00                                       NaN\n",
      "2024-01-26 06:30:00                                       NaN\n",
      "2024-01-26 07:30:00                                       NaN\n",
      "2024-01-26 08:30:00                                       NaN\n",
      "2024-01-26 09:30:00                                       NaN\n",
      "...                                                       ...\n",
      "2024-04-26 00:30:00                                  3.174784\n",
      "2024-04-26 01:30:00                                  3.798279\n",
      "2024-04-26 02:30:00                                  4.608202\n",
      "2024-04-26 03:30:00                                  5.574542\n",
      "2024-04-26 04:30:00                                  4.419260\n",
      "\n",
      "[1991 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "if scw_index != False: \n",
    "    # Executing the function for deriving absoprtion coefficient at wanted wavelength\n",
    "    time, abs_coeff637 = calculate_abs_coeff(alpha, ab_cw_df[\"aerosol_absorption_coefficient_amean\"], given_wavelength, lambda_cw)\n",
    "\n",
    "    df_ab637 = pd.DataFrame(abs_coeff637, time) # making into dataframe\n",
    "    df_ab637.columns = [\"estimated_aerosol_absorption_coefficient\"]\n",
    "\n",
    "    print(df_ab637)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9bcd204f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if scw_index != False:\n",
    "    # Plot of extrapolated aerosol absorption coefficient at 637.0 nm compared to aerosol absorption coefficient at closest wavelength\n",
    "    # This is a check that the extrapolation is correct\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    ab_cw_df[\"aerosol_absorption_coefficient_amean\"].plot(ax=ax, label=\"Absorption coefficient, {} nm\".format(lambda_cw))\n",
    "    #ab_scw_df[\"aerosol_absorption_coefficient_amean\"].plot(ax=ax, label=\"Estimated absorption coefficient, {}nm\".format(lambda_scw))\n",
    "    if scw_index != False: \n",
    "        df_ab637[\"estimated_aerosol_absorption_coefficient\"].plot(ax=ax, label=\"Estimated absorption coefficient, 637 nm\")\n",
    "\n",
    "    plt.title(\"Estimated absorption coefficient\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"[1/Mm]\")\n",
    "\n",
    "    plt.gca().set_facecolor('white')\n",
    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58c38550",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;estimated_aerosol_absorption_coefficient&#x27; (time: 1991)&gt;\n",
       "array([       nan,        nan,        nan, ..., 4.60820203, 5.57454194,\n",
       "       4.41926048])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2024-01-26T05:30:00 ... 2024-04-26T04:3...\n",
       "    Wavelength  float64 637.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'estimated_aerosol_absorption_coefficient'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2da237df-a16c-48ce-b49e-896f9f36aaff' class='xr-array-in' type='checkbox' checked><label for='section-2da237df-a16c-48ce-b49e-896f9f36aaff' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 2.623 3.175 3.798 4.608 5.575 4.419</span></div><div class='xr-array-data'><pre>array([       nan,        nan,        nan, ..., 4.60820203, 5.57454194,\n",
       "       4.41926048])</pre></div></div></li><li class='xr-section-item'><input id='section-93092bc0-de97-472b-a207-95dfc1c5b278' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93092bc0-de97-472b-a207-95dfc1c5b278' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-26T05:30:00 ... 2024-04-...</div><input id='attrs-f200403c-8f32-4a5a-a5cc-30d3cd5b6200' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f200403c-8f32-4a5a-a5cc-30d3cd5b6200' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bdd875d-db79-4da6-ad32-3e1e5b04288a' class='xr-var-data-in' type='checkbox'><label for='data-1bdd875d-db79-4da6-ad32-3e1e5b04288a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-26T05:30:00.000000000&#x27;, &#x27;2024-01-26T06:30:00.000000000&#x27;,\n",
       "       &#x27;2024-01-26T07:30:00.000000000&#x27;, ..., &#x27;2024-04-26T02:30:00.000000000&#x27;,\n",
       "       &#x27;2024-04-26T03:30:00.000000000&#x27;, &#x27;2024-04-26T04:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>637.0</div><input id='attrs-4885a6d3-4766-4c1d-98b4-e635084b7b90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4885a6d3-4766-4c1d-98b4-e635084b7b90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-578fa9f9-fbb3-4e5d-8303-1ee471a28940' class='xr-var-data-in' type='checkbox'><label for='data-578fa9f9-fbb3-4e5d-8303-1ee471a28940' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(637.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04bfc7c6-dd0e-49a0-b724-ee79c3d92825' class='xr-section-summary-in' type='checkbox'  ><label for='section-04bfc7c6-dd0e-49a0-b724-ee79c3d92825' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-79c2a45a-d581-4e00-9dcb-4e750a684c39' class='xr-index-data-in' type='checkbox'/><label for='index-79c2a45a-d581-4e00-9dcb-4e750a684c39' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-01-26 05:30:00&#x27;, &#x27;2024-01-26 06:30:00&#x27;,\n",
       "               &#x27;2024-01-26 07:30:00&#x27;, &#x27;2024-01-26 08:30:00&#x27;,\n",
       "               &#x27;2024-01-26 09:30:00&#x27;, &#x27;2024-01-26 10:30:00&#x27;,\n",
       "               &#x27;2024-01-26 11:30:00&#x27;, &#x27;2024-01-26 12:30:00&#x27;,\n",
       "               &#x27;2024-01-26 13:30:00&#x27;, &#x27;2024-01-26 14:30:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-04-25 19:30:00&#x27;, &#x27;2024-04-25 20:30:00&#x27;,\n",
       "               &#x27;2024-04-25 21:30:00&#x27;, &#x27;2024-04-25 22:30:00&#x27;,\n",
       "               &#x27;2024-04-25 23:30:00&#x27;, &#x27;2024-04-26 00:30:00&#x27;,\n",
       "               &#x27;2024-04-26 01:30:00&#x27;, &#x27;2024-04-26 02:30:00&#x27;,\n",
       "               &#x27;2024-04-26 03:30:00&#x27;, &#x27;2024-04-26 04:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1991, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9fabd72c-9627-4d4c-9b74-5a6426529fef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9fabd72c-9627-4d4c-9b74-5a6426529fef' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'estimated_aerosol_absorption_coefficient' (time: 1991)>\n",
       "array([       nan,        nan,        nan, ..., 4.60820203, 5.57454194,\n",
       "       4.41926048])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2024-01-26T05:30:00 ... 2024-04-26T04:3...\n",
       "    Wavelength  float64 637.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if scw_index != False: \n",
    "    # Convert from dataframe to Data Array with correct wavelength\n",
    "    da_ab637 = df_ab637.to_xarray().assign_coords({\"Wavelength\": (637.0)}).estimated_aerosol_absorption_coefficient\n",
    "elif scw_index == False:\n",
    "    da_ab637 = ab_cw_df[\"aerosol_absorption_coefficient_amean\"].to_xarray().assign_coords({\"Wavelength\": (637.0)})\n",
    "da_ab637"
   ]
  },
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;eBC&#x27; (time: 1991)&gt;\n",
       "array([       nan,        nan,        nan, ..., 0.4608202 , 0.55745419,\n",
       "       0.44192605])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2024-01-26T05:30:00 ... 2024-04-26T04:3...\n",
       "    Wavelength  float64 637.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'eBC'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1991</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-46212ec7-110e-4367-916a-7f1d185d58bd' class='xr-array-in' type='checkbox' checked><label for='section-46212ec7-110e-4367-916a-7f1d185d58bd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan ... 0.2623 0.3175 0.3798 0.4608 0.5575 0.4419</span></div><div class='xr-array-data'><pre>array([       nan,        nan,        nan, ..., 0.4608202 , 0.55745419,\n",
       "       0.44192605])</pre></div></div></li><li class='xr-section-item'><input id='section-cca91c0a-f7f8-464c-9eab-97436703ddd9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cca91c0a-f7f8-464c-9eab-97436703ddd9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-26T05:30:00 ... 2024-04-...</div><input id='attrs-cca33ae6-7ea9-42b1-808e-5ef79810fa9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cca33ae6-7ea9-42b1-808e-5ef79810fa9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed852660-a56d-4027-97e9-506ffc31c50f' class='xr-var-data-in' type='checkbox'><label for='data-ed852660-a56d-4027-97e9-506ffc31c50f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-26T05:30:00.000000000&#x27;, &#x27;2024-01-26T06:30:00.000000000&#x27;,\n",
       "       &#x27;2024-01-26T07:30:00.000000000&#x27;, ..., &#x27;2024-04-26T02:30:00.000000000&#x27;,\n",
       "       &#x27;2024-04-26T03:30:00.000000000&#x27;, &#x27;2024-04-26T04:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Wavelength</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>637.0</div><input id='attrs-ad867359-3626-46b6-821c-557ad54202fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad867359-3626-46b6-821c-557ad54202fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e420b75c-d17f-48f6-a396-350e3221fb98' class='xr-var-data-in' type='checkbox'><label for='data-e420b75c-d17f-48f6-a396-350e3221fb98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(637.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dea5045d-e673-411f-a825-ffdab3e487f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-dea5045d-e673-411f-a825-ffdab3e487f7' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3df59cf-8853-4f43-9a50-f0d4ad75d110' class='xr-index-data-in' type='checkbox'/><label for='index-b3df59cf-8853-4f43-9a50-f0d4ad75d110' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-01-26 05:30:00&#x27;, &#x27;2024-01-26 06:30:00&#x27;,\n",
       "               &#x27;2024-01-26 07:30:00&#x27;, &#x27;2024-01-26 08:30:00&#x27;,\n",
       "               &#x27;2024-01-26 09:30:00&#x27;, &#x27;2024-01-26 10:30:00&#x27;,\n",
       "               &#x27;2024-01-26 11:30:00&#x27;, &#x27;2024-01-26 12:30:00&#x27;,\n",
       "               &#x27;2024-01-26 13:30:00&#x27;, &#x27;2024-01-26 14:30:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-04-25 19:30:00&#x27;, &#x27;2024-04-25 20:30:00&#x27;,\n",
       "               &#x27;2024-04-25 21:30:00&#x27;, &#x27;2024-04-25 22:30:00&#x27;,\n",
       "               &#x27;2024-04-25 23:30:00&#x27;, &#x27;2024-04-26 00:30:00&#x27;,\n",
       "               &#x27;2024-04-26 01:30:00&#x27;, &#x27;2024-04-26 02:30:00&#x27;,\n",
       "               &#x27;2024-04-26 03:30:00&#x27;, &#x27;2024-04-26 04:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1991, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3687a12d-8c18-46a3-b825-bf9eaa229a5a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3687a12d-8c18-46a3-b825-bf9eaa229a5a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'eBC' (time: 1991)>\n",
       "array([       nan,        nan,        nan, ..., 0.4608202 , 0.55745419,\n",
       "       0.44192605])\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2024-01-26T05:30:00 ... 2024-04-26T04:3...\n",
       "    Wavelength  float64 637.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate equivalent black carbon from aerosol absorption coefficient. \n",
    "MAC = 10.0 #m2 g-1\n",
    "\n",
    "da_eBC = (da_ab637/MAC).rename('eBC')\n",
    "\n",
    "da_eBC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f19efc",
   "metadata": {},
   "source": [
    "### Plot of equivalent black carbon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4be483fc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=eBC<br>time=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "eBC",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "eBC",
         "showlegend": true,
         "type": "scattergl",
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          "2024-01-26T09:30:00",
          "2024-01-26T10:30:00",
          "2024-01-26T11:30:00",
          "2024-01-26T12:30:00",
          "2024-01-26T13:30:00",
          "2024-01-26T14:30:00",
          "2024-01-26T15:30:00",
          "2024-01-26T16:30:00",
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          "2024-01-26T18:30:00",
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           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Aerosol_absorption_coefficient at ISAC Bologna II"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Time"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "µg/m3 (equivalent black carbon)"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert xarray's DataArray to pandas DataFrame and plot data.\n",
    "df_eBC = da_eBC.to_dataframe()\n",
    "\n",
    "fig = px.line(df_eBC.eBC)\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"{0}\".format(ds_xr.title),\n",
    "    xaxis_title=\"Time\",\n",
    "    yaxis_title=\"µg/m3 (equivalent black carbon)\",\n",
    "    font=dict(\n",
    "        family=\"Courier New, monospace\",\n",
    "        size=11,\n",
    "        color=\"RebeccaPurple\"\n",
    "    ),\n",
    "    legend=dict(\n",
    "        title='Calculated at {0} nm:'.format(da_eBC.Wavelength.values)\n",
    "    )\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90cae53a",
   "metadata": {},
   "source": [
    "### Daily mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c16a95d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample to daily and monthly mean values\n",
    "df_day = df_eBC.eBC.resample('D').mean()\n",
    "df_month = df_eBC.eBC.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed317c64",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily mean for 2024-04-26 00:00:00 , eBC = 0.431501 µg/m3\n"
     ]
    }
   ],
   "source": [
    "daily_mean = df_day.iloc[-1:].to_dict()\n",
    "\n",
    "# create variables for daily mean\n",
    "sample_date_daily_mean = list(daily_mean.keys())[0]\n",
    "sample_value_daily_mean = round(list(daily_mean.values())[0],6)\n",
    "print('Daily mean for',sample_date_daily_mean,', eBC =', sample_value_daily_mean, 'µg/m3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0e4165",
   "metadata": {},
   "source": [
    "### Monthly mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c3a518f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monthly mean for March , eBC = 0.275313 µg/m3\n"
     ]
    }
   ],
   "source": [
    "monthly_mean = df_month.iloc[-2:-1].to_dict()\n",
    "\n",
    "# create variables for monthly mean\n",
    "sample_date_monthly_mean = list(monthly_mean.keys())[0]\n",
    "sample_value_monthly_mean = round(list(monthly_mean.values())[0],6)\n",
    "print('Monthly mean for',sample_date_monthly_mean.month_name(),', eBC =', sample_value_monthly_mean, 'µg/m3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d705cf6",
   "metadata": {},
   "source": [
    "### Last 3 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "066bbb60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last 3 months ( January - March ), eBC = 0.792017 µg/m3\n"
     ]
    }
   ],
   "source": [
    "last3months = statistics.mean(list(df_month.to_dict().values())[0:3])\n",
    "\n",
    "# create variables for daily mean\n",
    "sample_date_last3months_mean = list(df_month.to_dict().keys())\n",
    "sample_value_last3months_mean = round(last3months,6)\n",
    "\n",
    "sample_date_last3months_mean = \"{0} - {1}\".format(sample_date_last3months_mean[0].month_name(),sample_date_last3months_mean[2].month_name())\n",
    "\n",
    "print('Last 3 months (',sample_date_last3months_mean,'), eBC =', sample_value_last3months_mean, 'µg/m3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "471f87eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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