9. PM1 from wood burning and traffic.ipynb

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   "metadata": {},
   "source": [
    "<img src='https://www.actris.eu/sites/default/files/inline-images/Actris%20logo.png' width=200 align=right>\n",
    "\n",
    "# ACTRIS DC \n",
    "## PM1 from wood burning and traffic\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aerosol Light Absorption Measurements can be used for quantitative determination of wood burning and traffic emission contributions to Carbonaceous Matter (CM). Based on the method described by Sandradewi et al., 2008, calculations of PM1 from fossil fuel (traffic) and wood burning (wb) can be derived from light absorption at 470 nm and 950 nm with light absorption exponents of 1.1 and 1.85 for traffic and wood burning, respectively. This method is mainly valid for Alpine regions in Europe, especially in winter.\n",
    "\n",
    "The aerosol light absorption coefficients are measureded from filter absorption photometers, such as AE33 and CLAP. In the following examples it is demonstrated how Near Real Time (NRT) data from the ACTRIS thredds server are used to calculate and plot PM1 from wood burning and traffic. \n",
    "\n",
    "__Reference__:<br>\n",
    "Sandradewi, J., Prevot, A. S. H., Szidat, S., Perron, N., Alfarra, M. R., Lanz, V. A., Weingartner, E., and Baltensperger, U.: Using aerosol light absorption measurements for the quantitative determination of wood burning and traffic emission contributions to particulate matter, Environ. Sci. Technol., 42, 3316–3323, https://doi.org/10.1021/es702253m, 2008"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import threddsclient\n",
    "#from processing import calc_daily_mean, calc_monthly_mean, calc_last3months_mean\n",
    "import plotly.express as px\n",
    "import re"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The libraries \"calc_daily_mean\", \"calc_monthly_mean\" and \"calc_last3months_mean\" can be imported from a separate python program \"processing.py\", or they can be part the the main program. These libraries are used when we want to select the last row(s) of the array and write dates and values on a specific format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "def calc_daily_mean(df_day):\n",
    "\n",
    "    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],4)\n",
    "\n",
    "    return [sample_date_daily_mean, sample_value_daily_mean]\n",
    "\n",
    "def calc_monthly_mean(df_month):\n",
    "\n",
    "    monthly_mean = df_month.iloc[-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],4)\n",
    "\n",
    "    return [sample_date_monthly_mean, sample_value_monthly_mean]\n",
    "\n",
    "def calc_last3months_mean(df_month):\n",
    "\n",
    "    # create date for last 3 months\n",
    "    months = list(df_month.to_dict().keys())\n",
    "    sample_date_last3months_mean = \"{0} - {1}\".format(months[-3].month_name(),months[-1].month_name())\n",
    "\n",
    "    # create value (mean) for last 3 months\n",
    "    last3months = statistics.mean(df_month.iloc[-3:].to_dict().values()) #include current month\n",
    "    sample_value_last3months_mean = round(last3months,4)\n",
    "    #last3months = statistics.mean(list(df_month.to_dict().values())[-3:-1]) #Do not include current month\n",
    "    \n",
    "    return [sample_date_last3months_mean, sample_value_last3months_mean]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read NRT data and plot time series\n",
    "The program below will loop through all ACTRIS NRT 'filter_absorption_photometer' data files, extract the appropriate wavelengths, calculate PM1 from wood burning and traffic (plus total carbonaceous material), create html plots and print average PM1 values for the stations."
   ]
  },
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   "source": [
    "#### Equations and constants:\n",
    "\n",
    "wavelength_traffic = 950.<br>\n",
    "wavelength_wb = 470. <br>\n",
    "$\\alpha_{traffic}$=1.1 <br>\n",
    "$\\alpha_{wb}$=1.86 <br>\n",
    "\n",
    "Trom Table 2:<br>\n",
    "Traffic: $c1(1.1)=258831 ~(=0.258831*10^6)$ <br>\n",
    "Wood: $c2(1.8)=601131,~ c2(1.9)=652516$ <br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&rarr; $c2(1.86)=631962.4 ~(=0.6319624*10^6)$ <br>\n",
    "\n",
    "Equations:<br>\n",
    "\n",
    "(1) &nbsp;&nbsp;&nbsp;&nbsp;  $\\frac{b_{abs}(470nm)_{traffic}}{b_{abs}(950nm)_{traffic}}=(\\frac{470}{950})^{-\\alpha_{traffic}} = ktr= kff$  \n",
    "\n",
    "(2) &nbsp;&nbsp;&nbsp;&nbsp;  $\\frac{b_{abs}(470nm)_{wb}}{b_{abs}(950nm)_{wb}}=(\\frac{470}{950})^{-\\alpha_{wb}} = kwb$  \n",
    "\n",
    "(3a) &nbsp;&nbsp;&nbsp;  $b_{abs}(470nm)=b_{abs}(470nm)_{wb} + b_{abs}(470nm)_{traffic}$  \n",
    "(3b) &nbsp;&nbsp;&nbsp;  $b_{abs}(950nm)=b_{abs}(950nm)_{wb} + b_{abs}(950nm)_{traffic}$  \n",
    "\n",
    "Combination of Eq 1-3:\n",
    "\n",
    " $b_{abs}(950nm)_{wb}=(b_{abs}(470nm) - ktr*b_{abs}(950nm))/(kwb - ktr)$  \n",
    " $b_{abs}(470nm)_{wb}= kwb*b_{abs}(950nm)_{wb}$  \n",
    " $b_{abs}(950nm)_{traffic}= b_{abs}(950nm)-b_{abs}(950nm)_{wb}$  \n",
    "\n",
    "Calcultate PM1:\n",
    "\n",
    "(4) &nbsp;&nbsp;&nbsp;&nbsp;  $CM(PM1)=PM1_{traffic}+PM1_{wb}=c1*b_{abs}(950nm)_{traffic} + c2*b_{abs}(470nm)_{wb}$ \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define constants from Sandradewi et al., 2008:\n",
    "given_wavelength_ff = 950. \n",
    "given_wavelength_wb = 470. \n",
    "alpha_ff=1.1\n",
    "alpha_wb=1.86\n",
    "\n",
    "#Table 2: c1=258831 (=0.258831E+6); c2(1.8)=601131, c2(1.9)=652516 --> c2(1.86)=631962.4\n",
    "c1_ff=0.258331\n",
    "c2_wb=0.631962\n",
    "\n",
    "kff=(given_wavelength_wb/given_wavelength_ff)**(-alpha_ff)\n",
    "kwb=(given_wavelength_wb/given_wavelength_ff)**(-alpha_wb)\n",
    "\n",
    "#print(kwb)\n",
    "#print(kff)"
   ]
  },
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   "execution_count": 4,
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     "text": [
      "https://thredds.nilu.no/thredds/dodsC/actris_nrt/IT0009R.20240117110000.20240417070818.filter_absorption_photometer...3mo.1h.IT06L_Magee_AE33_CMN_NRT.IT06L_eBC_v1.lev3b.nc\n",
      "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\n"
     ]
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           "showlakes": true,
           "showland": true,
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           "align": "left"
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          "hovermode": "closest",
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        },
        "title": {
         "text": "Aerosol_absorption_coefficient at Monte Cimone"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Time"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "µg/m3"
         }
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     },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM1 monthly mean from Monte in April\n",
      "Wood burning: 0.4633 ug/m3\n",
      "Traffic: 0.0896 ug/m3\n",
      "\n"
     ]
    }
   ],
   "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\n",
    "#nrt_files = [f for f in all_opendap_urls if 'filter_absorption_photometer' in f]\n",
    "\n",
    "# To select Italian stations (IT0009R) with filter absorption photometers\n",
    "nrt_files = [f for f in all_opendap_urls if 'filter_absorption_photometer' in f and 'IT0009R' in f]\n",
    "print('\\n'.join(nrt_files))\n",
    "\n",
    "for f in nrt_files:\n",
    "    nrt_data = xr.open_dataset(f)\n",
    "        \n",
    "    #Read wavelengts in NRT file and select the two ones closest to 470n mand 950nm\n",
    "    all_wavelengths = nrt_data.Wavelength.values \n",
    "    closest_wavelength_wb = min(all_wavelengths, key=lambda x:abs(x-given_wavelength_wb))\n",
    "    closest_wavelength_ff = min(all_wavelengths, key=lambda x:abs(x-given_wavelength_ff))\n",
    "    \n",
    "    #Move to next NRT-file if wavelengts don't match\n",
    "    if (np.abs(closest_wavelength_wb-given_wavelength_wb)>50) or (np.abs(closest_wavelength_ff-given_wavelength_ff)>50): continue   \n",
    "   \n",
    "    #Create data arrays with hourly aerosol absorption coefficients (arithmetic mean) for the two wavelengts (1/Mm)   \n",
    "    ab470 = nrt_data.sel(Wavelength=closest_wavelength_wb).aerosol_absorption_coefficient_amean\n",
    "    ab950 = nrt_data.sel(Wavelength=closest_wavelength_ff).aerosol_absorption_coefficient_amean\n",
    "\n",
    "    #Calculate ab(950nm) for wood burning, based on Eq. 1-3 in Sandradewi. Neagative values are set to zero\n",
    "    ab950wb = (ab470 - ab950*kff)/(kwb - kff)\n",
    "    ab950wb[ab950wb < 0] = 0\n",
    "\n",
    "    #Calculate PM1 from Eq. 1-4 in Sandradewi\n",
    "    da_PM1_wb = (ab950wb * kwb * c2_wb).rename('PM1_wb')\n",
    "    da_PM1_ff = ((ab950 - ab950wb) * c1_ff).rename('PM1_ff')\n",
    "\n",
    "    # Convert DataArray (xarray) to pandas DataFrame, neagative values are set to zero\n",
    "    df_wb = da_PM1_wb.to_dataframe()\n",
    "    df_ff = da_PM1_ff.to_dataframe()\n",
    "    df_wb[df_wb < 0] = 0\n",
    "    df_ff[df_ff < 0] = 0\n",
    "\n",
    "    #Create station name: a)selct the first part of the name, b)remove non-alphanumeric characters \n",
    "    station_string = nrt_data.ebas_station_name.split(\" \",1)[0]\n",
    "    station_name = re.sub('\\W+','', station_string)\n",
    "\n",
    "    # Create html plots of hourly data\n",
    "    x=df_wb.index[:]\n",
    "    df_hour_wb = df_wb.PM1_wb[:].rename('PM1_wood')\n",
    "    df_hour_ff = df_ff.PM1_ff[:]\n",
    "    df_hour_tot = df_hour_wb + df_hour_ff\n",
    "\n",
    "    fig=px.line(df_hour_wb)\n",
    "    fig.add_scatter(x=x,y=df_hour_ff, name = \"PM1_traffic\")\n",
    "    fig.add_scatter(x=x,y=df_hour_tot, name=\"Carbonaceous mat.(PM1)\")\n",
    "    fig.update_layout(\n",
    "        title=\"{0}\".format(nrt_data.title),\n",
    "        xaxis_title=\"Time\",\n",
    "        yaxis_title=\"µg/m3\",\n",
    "        font=dict(\n",
    "            family=\"Courier New, monospace\",\n",
    "            size=18,\n",
    "            color=\"RebeccaPurple\"\n",
    "        ),\n",
    "    )\n",
    "    #fig.write_html(\"timeseries_{0}.html\".format(station_name.lower()))\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "\n",
    "    #Calculate daily and monthly means (Pandas resample function)\n",
    "    df_day_wb = df_wb.PM1_wb.resample('D').mean()\n",
    "    df_day_ff = df_ff.PM1_ff.resample('D').mean()\n",
    "    df_month_wb = df_wb.PM1_wb.resample('M').mean()\n",
    "    df_month_ff = df_ff.PM1_ff.resample('M').mean()\n",
    "   \n",
    "    #Daily and monthly mean of total carbonaceous matter (PM1)\n",
    "    df_day_tot = df_day_wb + df_day_ff\n",
    "    df_month_tot = df_month_wb + df_month_ff\n",
    "\n",
    "\n",
    "    # run mean functions and print values\n",
    "    sample_date_daily_mean = calc_daily_mean(df_day_wb)[0]\n",
    "    sample_value_daily_mean_wb = calc_daily_mean(df_day_wb)[1]\n",
    "    sample_value_daily_mean_ff = calc_daily_mean(df_day_ff)[1]\n",
    "    sample_value_daily_mean_tot = calc_daily_mean(df_day_tot)[1]\n",
    "\n",
    "    sample_date_monthly_mean = calc_monthly_mean(df_month_wb)[0].month_name()\n",
    "    sample_value_monthly_mean_wb = calc_monthly_mean(df_month_wb)[1]\n",
    "    sample_value_monthly_mean_ff = calc_monthly_mean(df_month_ff)[1]\n",
    "    sample_value_monthly_mean_tot = calc_monthly_mean(df_month_tot)[1]\n",
    "\n",
    "    sample_date_last3months_mean = calc_last3months_mean(df_month_wb)[0]\n",
    "    sample_value_last3months_mean_wb = calc_last3months_mean(df_month_wb)[1]\n",
    "    sample_value_last3months_mean_ff = calc_last3months_mean(df_month_ff)[1]\n",
    "    sample_value_last3months_mean_tot = calc_last3months_mean(df_month_tot)[1]\n",
    " \n",
    "\n",
    "    print('PM1 monthly mean from',station_name,'in',sample_date_monthly_mean)\n",
    "    print('Wood burning:',sample_value_monthly_mean_wb, 'ug/m3')\n",
    "    print('Traffic:',sample_value_monthly_mean_ff, 'ug/m3')\n",
    "    \n",
    "print()\n",
    "#print(\"Eosc-future example: https://folk.nilu.no/~toves/eosc/pm1_linux/station_monte.html\")\n"
   ]
  }
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