{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aerosol specification\n", "\n", "Contributed by Abigail Williams and Lynn Russell\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "The aerosol specification is a parameterized particle size distribution (PSD) made up of three lognormal modes:\n", "\n", "| |Mode 1 | Mode 2 | Mode 3 |\n", "|-----|-------|--------|--------|\n", "|**Na (mg-1)** | 45.7 | 105.5 | 1.6 |\n", "|**Dg ($\\mu$m)** | 0.04 | 0.14 | 0.50 |\n", "|**$\\sigma$g** | 1.7 | 1.6 | 1.7 |\n", "|**Kappa** | 0.3 | 0.5 | 0.9 |\n", "\n", "The above values are modal fits to the median aerosol PSD measured at Zeppelin Observatory in Svalbard (~1000 km upwind of COMBLE) during March-May 2020. We refer to this size distribution as PSDZSM where ZSM stands for Zeppelin-Observatory Springtime Median. Kappa values are informed by the analysis of filter measurements of aerosol composition at Zeppelin Observatory and HTDMA measurements during COMBLE.\n", "\n", "Let's plot PSDZSM below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read data from Google Sheets\n", "The PSDZSM parameters are stored in a google sheet here. Let's read in that data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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ModeN_aD_gsigma_gkappa
0Aitken580.041.70.3
1Accumulation1340.141.60.5
2Sea Spray20.501.70.9
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" ], "text/plain": [ " Mode N_a D_g sigma_g kappa\n", "0 Aitken 58 0.04 1.7 0.3\n", "1 Accumulation 134 0.14 1.6 0.5\n", "2 Sea Spray 2 0.50 1.7 0.9" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PSD_zsm = pd.read_excel('https://docs.google.com/spreadsheets/d/' +\n", " '1q4YkJc5dF-ZOIx-xWU_IkmTYCUPoM0kODj5ro_7Vg_c' +\n", " '/export?gid=0&format=xlsx',\n", " sheet_name='PSD')\n", "PSD_zsm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the size distribution\n", "The lognormal distribution described by the above modal parameters is defined by the equation below (following *Seinfeld and Pandis 2016*):\n", "\n", "$$\n", "n(D) = \\frac{dN}{dD} = \\frac{N}{(2\\pi)^{0.5}\\log_{10}\\sigma_g}exp(-\\frac{(\\log_{10}D - \\log_{10}D_g)^{0.5}}{2\\log_{10}^2\\sigma_g})\n", "$$\n", "\n", "Let's calculate the size distribution corresponding to the PSDZSM parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [], "source": [ "#create a diameter array\n", "D = np.logspace(-2, 1, num=100)\n", "\n", "#calculate lognormal mode number size distribution\n", "PSD_mode1 = np.multiply(np.divide(PSD_zsm.at[0, 'N_a'], (np.sqrt(2 * np.pi) * np.log10(PSD_zsm.at[0, 'sigma_g']))), \\\n", " np.exp(-(np.square(np.subtract(np.log10(D), np.log10(PSD_zsm.at[0, 'D_g'])))) \\\n", " / (2 * np.square(np.log10(PSD_zsm.at[0, 'sigma_g'])))))\n", "PSD_mode2 = np.multiply(np.divide(PSD_zsm.at[1, 'N_a'], (np.sqrt(2 * np.pi) * np.log10(PSD_zsm.at[1, 'sigma_g']))), \\\n", " np.exp(-(np.square(np.subtract(np.log10(D), np.log10(PSD_zsm.at[1, 'D_g'])))) / \\\n", " (2 * np.square(np.log10(PSD_zsm.at[1, 'sigma_g'])))))\n", "PSD_mode3 = np.multiply(np.divide(PSD_zsm.at[2, 'N_a'], (np.sqrt(2 * np.pi) * np.log10(PSD_zsm.at[2, 'sigma_g']))), \\\n", " np.exp(-(np.square(np.subtract(np.log10(D), np.log10(PSD_zsm.at[2, 'D_g'])))) / \\\n", " (2 * np.square(np.log10(PSD_zsm.at[2, 'sigma_g'])))))\n", "\n", "#sum three individual lognormal modes together to make a single tri-modal size distribution\n", "PSD_sum = PSD_mode1 + PSD_mode2 + PSD_mode3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the PSD" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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2rOmavn7+Ro0acfv2bc6dO5fo1126dGly585t+t3FxYVChQqZqr3iU7FiRR4/fkz79u1ZuXIlDx48iHffN9+HVatWxdfX1/TajDJlykSdOnUSHHvjxo2xt7c3/W7ssGKM/dy5c9y5c4cPPvggxvNy585NtWrVEnye+GTLlo1y5cqZfvf09MTb25vSpUuTPXt20/aiRYvGiCssLIzNmzfTokUL3NzcYl3LsLCwOKvk3+Tv74+7u3us8wQEBMR4L715/j179vDw4UO6dOkS49zR0dE0bNiQgwcPEhoaSmhoKAcPHqRly5a4uLiYjufh4UHTpk0T9DdasmQJ1apVI126dDg4OODo6Mj06dPj/N/i7+8fo5Omj48P3t7e73wvJvQ8q1evxmAw0LFjxxivO2vWrPj5+cX6/Mb1ftyyZQvFihWL1e+ka9euKKXYsmULkLDPx5YtW6hbt26s/zVdu3bl+fPn8XZMfZevvvoKR0dHXFxcKFeuHEFBQUyePDlGJ9bvvvuOoKAgZsyYwccff0y6dOmYNGkS5cqVi9G0mhBKqUTtbxOJ2WAw0K1bN+bNm8ekSZMoVKgQNWrUiHPf4OBgsmbNGqut0NvbGwcHB1PbQXBwMA4ODnh5ecXYL2vWrLGOOXDgQL777juaN2/OqlWr2L9/PwcPHjT11LMWb2sXyZ49u+nxpBo0aBCbN29m5MiRsTpEGN29e5fHjx/j5OSEo6NjjJ87d+7E+vDF9fc2bnsz3vj2Ne539+5dAL788stY5+7Tpw/AW5NjfN58jwA4Ozu/89p36tSJGTNmcO3aNVq1aoW3tzeVKlVi06ZNcb6Ot702o8T2en4zdmdnZwBT7Mbjx9UpJaEdVd7G09Mz1jYnJ6dY252cnAAtIRvjioyMZNy4cbGupfGfZ0KuZXznedf5je+l1q1bxzr/yJEjUUrx8OFDHj16RHR09Fvfx2+zbNkyPvjgA3LkyMG8efPYu3evqeBhjOV1SX0vJvQ8d+/eRSmFj49PrNe9b9++WH/zuN6PwcHB8f4PMj4OCft8JPRYifXZZ59x8OBBDh8+zKVLl7h9+3asvjKgfQa6devGpEmTOH78ONu3b8fJyYnPPvsswecKDQ0lODg4xhfRd7H6XtlGXbt25fvvv2fSpEn89NNP8e7n5eXF/v37UUrFSM737t0jMjKSzJkzm/aLjIwkODg4xpv9zp07sY45b948OnfuzM8//xxj+4MHD8iYMWMyX5n5GF/H7du3Yz1269Yt02tPigULFjB69Gjatm3LF198Ee9+mTNnxsvLK86OPECsIVlx/b2N2978JxTfvsb9jK9vyJAhtGzZMs7zFy5cON7YLaFbt25069aN0NBQduzYwdChQ2nSpAnnz5/H19fXtF98r61AgQIxtpl7nLXxb2dMRG+eXy+ZMmXC3t6eTp060bdv3zj3yZs3r8XOb3wvjRs3Lt5RCT4+PkRERGAwGN76Pn6befPmkTdvXhYtWhTj2r7ZUTW5EnqezJkzYzAY2Llzp+lL3Ove3BbX+9HLyyve/0HGcxi96/ORmGMlRs6cOWP0oE6omjVrUr9+fVasWMG9e/fw9vZ+53PWrFlDVFRUouYZsIkSM0COHDn4v//7P5o2bUqXLl3i3a9u3bo8e/aMFStWxNg+Z84c0+OAqcT3559/xthv/vz5sY5pMBhivSHXrFnDzZs3E/06EuvNEs7bVKlSBVdXV+bNmxdj+40bN0xVQklx/PhxevbsSYkSJZg+ffpb923SpAnBwcFERUVRvnz5WD9vJsZTp05x7NixGNvmz5+Ph4cHZcuWjbF9wYIFMaqErl27xp49e0xv+MKFC1OwYEGOHTsW57nLly+v27h3d3d3AgIC+OabbwgPDzcNVzN68324Z88erl27ZvFJQwoXLkzWrFlj9VoPCgpiz549MbYl5r2YXG5ubvj7+3P06FFKlSoV57WMq/RoLtWqVSNjxoycPn063veSk5MT7u7uVKxYkWXLlsUoeT59+pRVq1a98zwGg8E0wY/RnTt34uwtnRwJPU+TJk1QSnHz5s04X3PJkiXfea66dety+vTpWE1Rc+bMwWAwxFnbFt/no27dumzZssWUiF8/lpubm8WGl969ezfOIVFRUVFcuHABNze3BBXKgoKC+PLLL8mQIQMff/xxgs9vMyVmgBEjRrxzn86dOzN+/Hi6dOnC1atXKVmyJLt27eLnn3+mUaNGpkHe9evXp2bNmgwaNIjQ0FDKly/P7t27mTt3bqxjNmnShFmzZlGkSBFKlSrF4cOH+eWXX1JkzKvxgzBy5EgCAgKwt7enVKlSpqq312XMmJHvvvuOr7/+ms6dO9O+fXuCg4P54YcfcHFxYejQoYk+/6NHj2jevDkvX77kq6++ineIWJYsWcifPz/t2rXjzz//pFGjRnz22WdUrFgRR0dHbty4wdatW3n//fdp0aKF6XnZs2enWbNmDBs2jGzZsjFv3jw2bdrEyJEjY42HvHfvHi1atKBXr148efKEoUOH4uLiwpAhQ0z7TJ48mYCAABo0aEDXrl3JkSMHDx8+5MyZMxw5coQlS5Yk+m+QVL169cLV1ZVq1aqRLVs27ty5w/Dhw8mQIQMVKlSIse+hQ4fo2bMnbdq04fr163zzzTfkyJHDVAVvKXZ2dvzwww98/PHHtG7dmu7du/P48WN++OEHsmXLFmNYiIeHB76+vqxcuZK6devi6elJ5syZTUOqzO3333+nevXq1KhRg08++YQ8efLw9OlTLl68yKpVq0xtlZaQLl06xo0bR5cuXXj48CGtW7fG29ub+/fvc+zYMe7fv8/EiRMB+PHHH2nYsCH16tXjiy++ICoqipEjR+Lu7s7Dhw/fep4mTZqwbNky+vTpQ+vWrbl+/To//vgj2bJl48KFC2Z7PQk9T7Vq1fjoo4/o1q0bhw4dombNmri7u3P79m127dpFyZIl+eSTT956rgEDBjBnzhwaN27Mf/7zH3x9fVmzZg0TJkzgk08+oVChQkDCPh9Dhw5l9erV+Pv78/333+Pp6cmff/7JmjVrGDVqFBkyZDDb3+h1c+fOZfLkyXTo0IEKFSqQIUMGbty4wbRp0zh16hTff/99rP/BJ0+eNLXJ37t3j507dzJz5kzs7e1Zvnx54sb5J7ibWApLaK/hN3tlK6X1/Ozdu7fKli2bcnBwUL6+vmrIkCGxhvY8fvxYde/eXWXMmFG5ubmpevXqqbNnz8bqefro0SPVo0cP5e3trdzc3FT16tXVzp074+0Ja85e2S9fvlQ9e/ZUWbJkUQaDIVbP17hMmzZNlSpVSjk5OakMGTKo999/X506dSrGPgn9+xpjetfP6z1NIyIi1K+//qr8/PyUi4uLSpcunSpSpIj6+OOP1YULF0z7+fr6qsaNG6ulS5eq4sWLKycnJ5UnTx41evToOGOYO3eu6t+/v8qSJYtydnZWNWrUiNHj1+jYsWPqgw8+UN7e3srR0VFlzZpV1alTR02aNOmtf+v4emU3btw41jnevPZxmT17tvL391c+Pj7KyclJZc+eXX3wwQemnr9KvboOGzduVJ06dVIZM2Y09ax//W9lPGfx4sXjPFd878U3h4UopeLsWT1lyhRVoEAB5eTkpAoVKqRmzJih3n///Vg9g//55x9VpkwZ5ezsHOO6x9crO6544/ubxvWZuHLliurevbvKkSOHcnR0VFmyZFFVq1aN0Vs9PvEdL66/i/H9sGTJkhjbt2/frho3bqw8PT2Vo6OjypEjh2rcuHGs/f7++2/TZy537txqxIgR8b6f3uyVPWLECJUnTx7l7OysihYtqqZOnRrnc+N6PfEdMy4JPY9SSs2YMUNVqlRJubu7K1dXV5U/f37VuXPnGJ+3t70fr127pjp06KC8vLyUo6OjKly4sPrll19MoyKUStjnQymlTpw4oZo2baoyZMignJyclJ+fX6z/sckdLvWm06dPqy+++EKVL19eZcmSRTk4OKhMmTKpWrVqqblz58bY1/jeN/44OTkpb29vVatWLfXzzz+re/fuvfVccTEolcjuYkKYSZ48eShRosQ7J0jYtm0b/v7+LFmyJFZve1s3a9YsunXrxsGDB5PU5mUpjx8/plChQjRv3pwpU6boHY4QaYpNVWULIczvzp07/PTTT/j7++Pl5cW1a9cYM2YMT58+TVTvUyGEeaTqxPz06VPq1KlDREQEUVFR9O/fn169eukdlhBWxdnZmatXr9KnTx8ePnxo6lQzadIkihcvrnd4QqQ5qboqOyoqipcvX+Lm5sbz588pUaIEBw8etGhvTiGEECI5bGa4VFLY29ubevaGhYURFRWV6BlYhBBCiJRk1Yl5x44dNG3alOzZs2MwGGKNTQZtHdq8efOaplbbuXNnjMcfP36Mn58fOXPmZNCgQcmaZEMIIYSwNKtOzKGhofj5+fG///0vzscXLVrE559/zjfffMPRo0epUaMGAQEBBAUFmfbJmDEjx44d48qVK8yfPz/OGY6EEEIIa2EzbcwGg4Hly5fTvHlz07ZKlSpRtmxZ00B/0Cajb968OcOHD491jE8++YQ6derQpk2bOM/x8uXLGFPURUdH8/DhQ7y8vMw+FaIQQgjLU0rx9OlTsmfPnuB1lPVms72yw8PDOXz4MIMHD46xvX79+qapBO/evYurqyvp06cnJCSEHTt2vHXWmuHDh/PDDz9YNG4hhBAp7/r16ykyW6M52GxifvDgAVFRUbFWwPHx8TFNHn/jxg169OiB0tadpl+/fqZl7+IyZMgQBg4caPr9yZMn5M6dm/Pnz8e5So5IPSIiIti6dSv+/v44OjrqHY6wMLneacfDhw8pVKiQbvPkJ4XNJmajN6uY1WurSpUrV47AwMAEH8vZ2TnOFVU8PT1liFUqFxERgZubG15eXvKPOg2Q65322FJzpG1UuMchc+bM2Nvbx1pa7d69e2ZZR1YIIYTQg80mZicnJ8qVKxdr0flNmzZRtWpVnaISQgghkseqq7KfPXvGxYsXTb9fuXKFwMBAPD09yZ07NwMHDqRTp06UL1+eKlWqMGXKFIKCgujdu3eyzjt+/HjGjx9PVFRUcl+CEEIIkShWnZgPHToUY1FtY8esLl26MGvWLNq2bUtwcDD/+c9/uH37NiVKlGDt2rX4+vom67x9+/alb9++hISEWGy9TyGEECIuVp2Ya9eu/c4pNPv06WPxxeSFEEKIlGKzbcxCCCFEaiSJWQghhLAikpiFEEIIKyKJOQ7jx4+nWLFiVKhQQe9QhBBCpDGSmOPQt29fTp8+zcGDB/UORQghRBojiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiTkOMo5ZCCGEXiQxx0HGMQshhNCLJGYhhBDCikhiFkIIIayIJGYhhBDCikhiFkIIIayIJGYhhBDCikhiFkIIIayIJOY4yDhmIYQQepHEHAcZxyyEEEIvkpiFEEIIKyKJWQghhLAikpiFEEIIKyKJWQghhLAikpiFEEIIKyKJWQghhLAikpiFEEIIKyKJWQghhLAikpjjIDN/CSGE0Isk5jjIzF9CCCH0IolZCCGEsCKSmIUQQggrIolZCCGEsCKSmIUQQggrIolZCCGEsCKSmIUQQggrIolZCCGEsCKSmIUQQggrIolZCCGEsCKSmIUQQggrIok5DjJXthBCCL1IYo6DzJUthBBCL5KYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiVkIIYSwIpKYhRBCCCsiiTkOsuyjEEIIvUhijoMs+yiEEEIvkpiFEEIIKyKJWQghhLAiDnoHIIStU0rx+PFjAOzt7bGzs8PNzQ07O/neK4RIPEnMQiTBzZs3+eeff9i8eTObN2/m1q1bMR7PkCED9evXp3HjxgQEBODt7a1TpEIIWyOJWYhEOHv2LEOHDmXx4sVv3e/JkycsWbKEJUuWYDAYaNasGb/++isFChRIoUiFELZK6tqESIArV67QtWtXihcvzuLFizEYDFSsWJGOHYcA/wDPgHAgDAgF9gLfkiVLGZRSrFy5kuLFizNkyBCePXum4ysRQlg7KTEL8RZKKaZMmcJnn33Gy5cvAWjcuDnDh/+HkiVLEhUFBw5AkSLaT/bs2k9ISGX2769My5Y/4ut7hs8//5yNGzcyYsQIpk2bzYIFc3jvvfd0fnVCCGskiVmIeDx9+pSPPvqIhQsXAlC7dh0yZRpBYGAFcufW9rG3hxMnwMkp9vN79DDeK8r69eupXHkVBw4M4MGDyzRoEMD06dPp2rVzirwWIYTtkKpsIeJw/PhxypUrx8KFC7G3t+eLL37h/v1/WL68Alevwvr1r/aNKym/yWAwMH9+M9q1OwW0Jzo6km7duvD99/9FKWWplyGEsEGSmIV4w/bt26lWrRoXLlwgV65c/PHHDiZO/JJTpwz4+MDGjdC2beKPmz8/LFjgwpw587Cz+wqAH3/8jo4dPyYqKsrMr0IIYaskMQvxmg0bNtCwYUOePXuGv78/06cfZfDgqjx/DnXqwLFjkNym4U6d7Ni5cwTp0o0H7Jg/fyo9e35plviFELZPErMQ/9q3bx8tW7YkLCyMxo0bM3LkGtq29eLpU/D3h9WrwcfHPOeqWhWOH+9Djhx/AjBr1lgmTZpknoMLIWyaJGYhgCVLljBq1CgiIiJo3bo1y5YtI1s2V7y9oUoV+PtvcHU17znz5oXAwHb88MN/AejXrx+bNm0y70mEEDZHErNI87Zs2ULXrl2Jjo6mY8eOLFiwACcnJ3LmhB07YO1aSJfOMufOnBm+++5rOnXqRFRUFC1atOH06TOWOZkQwiZIYhZp2rFjx2jevDkRERFUrVqVqVOncf78q1GE3t6QMaNlYzAYDEydOpUcOaoTGvqE6tWb8vTpU8ueVAhhtSQxizTr2rVrBAQE8PTpU2rWrMnnn3/OzJn2lCwJw4enbCzOzs58+eVywJdHjy7x4YeDUjYAIYTVkMQs0qSHDx/SsGFDbt++TYkSJVi6dCm3b3sxcKA90dHg6JjyMX3+eWaaNJkJwKpVk1ixQtqbhUiLJDGLNCcyMpK2bdty9uxZcubMybp163Byyshvv5UnLMxAgwYwcKA+sc2f74+HRz8AOnbswZMnT/QJRAihG0nMIs356quv+Oeff3B3d2fNmjXkzJmTb7+1IygoPVmzKubMAb2WUvbwgMWLRwD5CA29zgcf6PQNQQihG0nMIk2ZM2cOo0ePBmD27NmUKlWKY8dgwgTtozBtWhR6L53csKE7rVvPAgxs3DiD5cvX6huQECJFSWKOw/jx4ylWrBgVKlTQOxRhRgcPHuSjjz4C4LvvvqNVq1YoBf36QXS0gapVb1K/vnXMWz17dg1y5vwcgAED+hAWFqZvQEKIFCOJOQ59+/bl9OnTHDx4UO9QhJncvXuXFi1a8PLlS5o1a8awYcMAiI6GNm0ge3ZFt24n9Q3yNW5ucO7cf8mRIwfXrl3jjz/+0DskIUQKkcQsUr3IyEjatWvHzZs3KVq0KHPnzsXu30Zke3vo3x/On48kSxbrKpW6ubnx008/AfDTTz9x7959nSMSQqQEScwi1fv222/Ztm0b6dKlY9myZaRPnx7QSstGCVm6UQ+dOnUiV64yhISE0K3bD3qHI4RIAZKYRaq2YsUKRo4cCcDMmTMpUqQIAGfOQIkSsG6dntG9m52dHdWr/wbA2rWTOHHirM4RCSEsTRKzSLUuXLhAly5dABg4cCCtW7c2PTZ0qJacbWFBp0mT/HFyagpEyYxgQqQBkphFqhQaGkrLli0JCQmhRo0ajBgxwvTYyZOwZIl2/98mXKuWPj0MHvwL4MCJE6tYtWqr3iEJISxIErNIdZRS9OrVi5MnT5I1a1YWLVqE42tzbP7nP9ptmzZadbYt+O67wmTM+DEA/foN0zcYIYRFSWIWqc4ff/zBggULcHBwYMmSJWTLls302Oul5e+/1ynAJHBwgJEjBwOOBAXtYMOGXXqHJISwEEnMIlXZuXMnX375JQC//fYb1atXj/G4LZaWjXr2zEmGDF0B+Prrn/UNRghhMZKYRapx69Yt2rRpQ2RkJB06dODTTz+N8fi5c7ZZWjays4OJEwdhZ2fHkSPrOHr0qN4hCSEsQBKzSBVevHhBixYtuHv3LiVLlmTKlCkYDIYY+xQqBKtXw3ff2V5p2ah9+wK0a9cOgJ9/llKzEKmRJGZh85RSdO/enQMHDuDp6cmyZctwd3ePtZ/BAI0bv6rOtlVDhgwB4K+//uLEiTM6RyOEMDdJzMLm/ec//2HhwoU4ODjw119/UaBAgVj7vD7Ll60rUaIE+fK9j1KKvn1H6h2OEMLMJDELm7Zo0SLTghSTJk2idu3asfZ5/BgKFtTalV++TNHwLKZ+/a8B2LlzHleuXNM5GiGEOUliFjZr9+7ddO3aFdBm9urRo0ec+82YAZcvw/Ll1jsndmINH14Re/s6QBRffWUD05cJIRJMErOwSYcPH6ZRo0aEhYXRpEkTRo0aFed+UVEwbpx2/7PPtHbm1CBjRmjYsB8AK1ZMlfWahUhFJDELm3Pq1CkaNGhASEgINWvWZNGiRdjb28e57+rVcPUqeHpChw4pG6eljRzZFMhFREQwEycu1jscIYSZSGIWNuXixYu89957BAcHU6FCBVatWoWbm1u8+//+u3b70Ufwlt1sUvHiDuTL1xuAX38dr3M0QghzkcQsbMapU6eoU6cOd+7coWTJkqxfv960tnJcjh+HrVvB3h769EnBQFPQN9/0BJy4desABw4c1DscIYQZSGIWNmHLli1Uq1aN69evU7hwYTZt2oSnp+dbn2NsW27ZEnLlSoEgddCpkzcVK34AwIQJUmoWIjWQxCys3uzZs2nQoAFPnjyhevXq7N69Gx8fn3c+r08f6NcPBg5MgSB14ugIv//eF4CFCxfy4MEDnSMSQiSXJGZhtV6+fMngwYPp2rUrkZGRtG3blk2bNuHl5ZWg55cpo5WaK1e2cKA6q1SpEmXLluXly5dMmTJd73CEEMkkiVlYpcDAQCpUqMDIkdrMVoMHD2b+/Pm4uLjoHJn1MRgM1K2rDZ0aMWIS0alpmjMh0iBJzMKqhIeH8+OPP1KhQgVOnDhB5syZWbp0KcOHD8fOLmFv1/PnoWtX2LnTsrFak9q12wEZePr0KuvXb9M7HCFEMkhiFlYhPDycKVOmULBgQb7//nsiIyNp2bIlp06dolWrVok61syZMHs2jExD00gHBLiSLl1bAEaMmK1zNEKI5JDELHT19OlTJk+eTKFChfj4448JCgoia9aszJs3j6VLl+Lt7Z2o40VGakkZoHt3CwRspQwGaNOmKwB79izl6dOn+gYkhEgyScwixUVFRbFp0yY6duyIj48PvXv35tq1a2TNmpWxY8dy+fJlPvzww1jrKSfExo1w+zZkzgxNmlggeCs2eHBloCBRUc+ZMeMvvcMRQiSRg94BiLTh6dOnbNq0idWrV7NmzRru3btneqxQoUL06dOHjz76CFdX12SdZ8YM7bZjx9SzYEVCFSpkIGfOrty48Q3jxs3is8+66h2SECIJJDELi1BKce7cOdatW8fatWvZsWMH4eHhpsczZcpE+/bt6dy5MxUrVkxS6fhNDx7A339r97t1S/bhbNLHH3fiu+++5dKl7Vy5coW8efPqHZIQIpEkMQuzUUpx+PBhFi9ezF9//cXly5djPF6gQAGaNm1KkyZNqF69Ok5mLtLOnw8REVC2LJQqZdZD24xPPsnFH3/U5f79f5g9ew7Dhg3VOyQhRCKl6sR8/fp1OnXqxL1793BwcOC7776jTZs2eoeV6ty4cYNJkyaxYMGCGMnYycmJWrVq0ahRIwICAihcuLBF43BzgwIFtKFSaZWXF4we3YVOnf5hzpzZfP/9dwkeZiaEsA6pOjE7ODgwduxYSpcuzb179yhbtiyNGjXC3d1d79BShQMHDjB27FiWLFlCZGQkAG5ubjRp0oQPPviABg0akC5duhSLp2dP6NFDW4M5LWvRogUeHh5cuXKFXbt2UbNmTb1DEkIkQqr+Kp0tWzZKly4NgLe3N56enjx8+FDfoFKBwMBA6tSpQ6VKlViwYAGRkZHUqlWLBQsWcO/ePRYtWkSrVq1SNCkbGQzgkKq/br6bu7s7DRtqNUO//CJjmoWwNVadmHfs2EHTpk3Jnj07BoOBFStWxNpnwoQJ5M2bFxcXF8qVK8fOeKZ7OnToENHR0eRKrcsMpYD79+/Tu3dvypUrx9atW3FycqJLly4cOXKEbdu20a5dO91qI7ZsgZcvdTm1VQoL6wTAxo3LYnS6E0JYP6tOzKGhofj5+fG///0vzscXLVrE559/zjfffMPRo0epUaMGAQEBBAUFxdgvODiYzp07M2XKlJQIO1WaNWsWhQoVYvLkyURHR9OiRVvmzz9Ps2az2LevDMOGwZgxsG4dPHqUsrFdvAh160KOHPDiRcqe21oNHFgDyEp4+GPWrt2kdzhCiESw6kq/gIAAAgIC4n189OjR9OjRg549ewIwduxYNmzYwMSJExk+fDigrVDUokULhgwZQtWqVd96vpcvX/LytWJXSEgIABEREURERCT35dik0NBQ+vfvz9y5cwHw8/OjYsWxzJpVi+XL4x7itH59JHXqKEArxTo5aVXMljJ/vh1gT9my0Tg4RJGUS2W8vqnlOleuDG5urXn+/H/88ccCGjeur3dIViW1XW8RP1u8xladmN8mPDycw4cPM3jw4Bjb69evz549ewBt+E7Xrl2pU6cOnTp1eucxhw8fzg8//BBr+9atW3FzczNP4DYkKCiIUaNGcePGDQwGOzp0aE/Lli3Zt8+diAgDWbI8x9MzjIwZX5Ihw0uePXPkxg0Pbt7cy9q1YQAsWlSI7dtz0bDhFRo2vIqTk/lXPpoxozaQgcKFj7F2bdC7dn+rTZtST+myZMka7N//P3bs+JsVK1aYfXhaapCarreI2/Pnz/UOIdEMSimldxAJYTAYWL58Oc2bNwfg1q1b5MiRg927d8coCf/888/Mnj2bc+fOmXqklnptUOvcuXMpWbJknOeIq8ScK1cubt++neA1gFOLNWvW0KFDB168eIHBkI2uXf9k8uTqADx/DlevQrFibz+GUlC6tANnzmjF5UKFFFOnRlGlivnecmfOgJ+fI46Oihs3IsmUKWnHiYiIYNOmTdSrVw9HR0ezxaenzZsVAQH5gJssXvwXzZs31Tskq5Ear7eIW3BwMNmyZePJkyekT59e73ASxGZLzEZvzhillDJtq169eqLWpnV2dsbZ2TnWdkdHxzT14V24cCGdOnX6dwjUeyj1JydPemNvD3Z2kCED+Pkl7Fj792sTfwwbBufPG6hd24FPP4WffwZz9BNbtky7rV/fgLd38q9RarrW770HLi5tCAsby6RJf9GmTUu9Q7I6qel6i7jZ4vW16s5fb5M5c2bs7e25c+dOjO337t3Dx8dHp6hs37Rp0+jQocO/Sbkj6dOvY+xYb3bv1pJyYnl4wMcfw+nT2jSZSsEff2iJ/cKF5MWqFCxerN1v2zZ5x0qNHBygfv0PANi9+29eSM84IWyCzSZmJycnypUrF6uNaNOmTe/s5CXiNmbMGHr16oXWutGbKlVmc+6cA599Bsn90pkpk7bAxIYNkDs33LwJr61jkSRnz2pV2c7O8P77yTtWajV1amVy5crNy5fPWLdund7hCCESwKoT87NnzwgMDCQwMBCAK1euEBgYaBoONXDgQKZNm8aMGTM4c+YMAwYMICgoiN69eyfrvOPHj6dYsWJUqFAhuS/BZsyaNYuBAwf++9v/0anTBLZutSNrVvOep359OHgQVq+GatWSd6wiReDUKZg7F2yk6SjFeXsb+OADbbKRxcbqBSGEdVNmEB4eroKCgtTZs2dVcHCwOQ6plFJq69atCoj106VLF9M+48ePV76+vsrJyUmVLVtWbd++3Wznf/LkiQLUgwcPzHZMa7R582bl4OCgANWkyVdq+PBoFR2dcucPDFRqwYKUO19cwsPD1YoVK1R4eLi+gVjA/v37FaBcXd1UaGio3uFYhdR8vUVMDx48UIB68uSJ3qEkWJI7fz179ow///yTBQsWcODAgRi9mXPmzEn9+vX56KOPklXqrF279r/VqvHr06cPffr0SfI50rrTp0/TsmVLIiMjadeuHX/++TN2dhYcdPyGu3chIECr1vb01ErUwrwyZqyAo2MeXry4yqpVa2jbVhZyEcKaJakqe8yYMeTJk4epU6dSp04dli1bRmBgIOfOnWPv3r0MHTqUyMhI6tWrR8OGDbmQ3F4+wiLu3r1LzZqNefLkCeXKVWPmzJkpvhJRlizarF1RUdC6NRw7lrDnzZqldfiSYajv5utrwGBoDcD06Sv0DUYI8U5JKjHv2bOHrVu3xjseuGLFinTv3p1JkyYxffp0tm/fTsGCBZMVqDCviIgIatZsSXDwVaAATZqswMXFJcXjsLOD6dPhxg3Ytg0aN4Z9+yBnzrc/b8EC2LgRypWDevVSJFSb5ewMtWo1Z9OmX9mxYw0RERE2OYREiLQiScWjJUuWxJuUX+fs7EyfPn1MU2YK69G37w+cP78HyMCHH65l6NDMusXi5KSNRy5aVOut3bgx/DsbapxCQmDrVu2+9MZOmG7dKgNZePnyCdu3b9c7HCHEWyQ6MT969Mi0dOL9+/f566+/OHnypNkDE5azZs1Wpk79GYDSpacyZ05Bi85lnRCZMmkLYGTNCsePQ/fu2jjluKxfDxERUKgQFC6csnHaqoAAewwGbeavOXNW6hyNEOJtEpWYp02bRvny5SlXrhwTJ06kRYsWbN68mXbt2qWqlZtS83Cp+/cf0KZNR0Dh5taTTZvaJGniEEvw9YWVK7WJMa5ejb/U/Pff2q2UlhMuY0YoXlz7g61atfKdnSqFEPpJVBvzuHHjOHXqFM+fPyd37txcuXKFLFmyEBISQs2aNfnoo48sFWeK6tu3L3379iUkJIQMGTLoHY7ZKKWoX78HL17cAoqwcuVYMutXgx2nihW1Dl1Vq2pV3G+KiIA1a7T7zZqlbGy27sMP6zFkiBuPH18nMDCQMmXK6B2SECIOiSor2dvb4+LigqenJwUKFCBLliwApE+fPtac1cL6TJ48mcDAvzEYnPjsswW8954ZJqu2gNq1407KALt2wePHWm/uKlVSMirb16qVK3nyaOPRVq6U6mwhrFWiErODgwNhYdpyfq93IHn69Kl5oxJmd+vWLb766isAfvllBKNHl9Y3oASIiIBvvoEhQ2JuK18emjYFe3v9YrNFBQvC0KFadbYkZiGsV6Kqsrds2WJafen1Kt4XL14wffp080YmzOqTTwYSEhJChQoV+Pzz/lbTrvw227drq1AZDFpP7erVtQlI6teHyEi9o7NNTZo0wc7OjsDAQK5evUqePHn0DkkI8YZE/XtOly5djCpr48pO3t7elC1b1ryRCbOZPXsDf/+9CIPBjt9/n4y9jRQ133sPevTQemd/8olWWjZysPkFS/Xh6ZmZUqW0dbVXrvxb52iEEHFJVrmpvsyfaPWeP3/BJ59oU5bmyNGfypVtq8PPyJHg5QUnT8LQoSCtJskTGQlnzmjV2fPmSXW2ENYoWYk5tQ65SE3DpTp0+C8vXlwGcvL33//RfbxyYnl5wahR2v1Ro7T5tBcu1DcmW+bkBP7+WmI+fHg7jx490jkiIcSbkpWYU2tP7L59+3L69GkOHjyodyjJsm/fWVau/AWADh3+oEwZD50jSpquXaFSJW0+7chISMCkc+It2rXLDxRDqSg2btyodzhCiDfYQBcgkVQdOw4BIkiXrjEzZjTXO5wks7ODLl20+wYDuFvnKC+bERAA0AiAv/5ap2ssQojYJDGnUosX7+bSpRWAHWPH/oKzs23Xbpw/r91+8AFIR+Lk8faGwoW1xLxhwzqio6N1jkgI8bpkJWan+GaBELpSSvGf/2hjlnPm7EGPHkV1jij5NmzQblu31jeO1KJ582pAOkJC7nH06FG9wxFCvCZZifnQoUPmikOY0d9//82pU7txdXVl+fJheoeTbNeuwZkz2oQi772nbTt0CEJD9Y3LlgUEOAHaeplr167VNxghRAxSlZ3KREZGMuTfqbIGDBhA+fLZdY4o+Yyl5cqVtcUYBg6EChXgjz90DcumVakCX3+tVWevWyftzEJYk2RP0/D48WOmT5/OnTt3yJs3L6VLl8bPzw936aGji0GDZnHmzBm8vLwYNGiQ3uGYRaNGMH48pgU3ypXTbkeNgt69tSUjReI4OcEnnzTk559h3759BAcH4+XlpXdYQgjMUGJu2bIlI0aM4NSpU0yYMIHatWuTIUMGChUqxAcffGCOGFOcrY5jDg0NY9y4oQDUqPFtqlkZK2dO6NNH6/gF0L69NmTq8eNXY5xF4uXMmZNSpUqhlJJhU0JYkWQn5v3797Nu3TrWrl3LyZMnefr0KQcOHGDIkCFkz26b1ai2Oo75009nEBl5Czu73Eya9Ine4ViMnZ02hzbA77/D7dv6xmOrnj8HCABg1SppZxbCWiS7KrtEiRLYvbYigrOzM2XLlpW5s1PYy5cRzJunFR8bNhyEj4+zzhGZx4IFEBKirSb1+ve8xo21NZv37IH//ler6haJ4+oKt241Akaydu16oqOjY3yWhRD6SPancOTIkXz33Xem5SCFPgYNmk9ExDUMBh+mTu2udzhmM2aM1o68aVPM7QbDq1LztGlSak4KgwEaNaoCpOfJkwcyykIIK5HsxJw3b16ePn1K0aJF+frrr1m5ciVBQUHmiE0kUGRkFFOmDAegVq2BZM/uqnNE5vH4MRw+rN03DpN6Xc2aWqnZ0/PVBCQicRo0cAS0xWhk2JQQ1iHZiblVq1Zcv34df39/Dhw4QI8ePcibNy9eXl7UqVPHHDGKd/jhh+WEhZ0DMjJtWm+9wzGbHTsgOhoKFYIcOWI/bjDA/Plw5QrUqpXy8aUG2hcebdjU33/LsCkhrEGy25hPnz7Nvn37KFWqlGlbUFAQR48eJTAwMLmHF++glGLxYq1Ot2LF/uTPn17niMxnyxbt9m3f73x9UyaW1MrbG4oXb8CpUxAYeFCGTQlhBZJdYq5QoQLPnj2LsS137ty8//77DB06NLmHF++wfv16zp8/iru7O8uX99c7HLNKSGI2ioqCZctAujokXqNG2YHiKKXYYvyjCyF0k+zE/PnnnzNs2DBZ11UnI0aMAKB3795kz556Sjr378OJE9r92rXfvX+DBtCqFcyZY9GwUqUGDcDTU5uec9ObveyEECnOLG3M//zzDwULFqR79+5MmTKFgwcP8vLlS3PEJ95ixYqj7NixAwcHBwYMGKB3OGZ19Kg2XrlkSciS5d37N22q3f7yi1Z6FglXty7MnfsqMSuldI5IiLQt2Yn5ypUrLF++nP79+/P48WNGjhxJ5cqV8fDwiNHubEtsZeavIUPGAZA1a2tyxNU7yobVrw8PH2rjmBOiZ0+td/bFi1qVtkicWrVq4ejoyNWrV7l06ZLe4QiRpiW785evry++vr68//77pm1Pnz4lMDCQ48ePJ/fwuujbty99+/YlJCTEaqe1vHz5AWfPzgdg0KDU1bZslCGD9pMQ7u7Qty/8+KO2uEWbNpaNLbVxd3encuWq7Ny5nU2bNlGgQAG9QxIizUp2iXnXrl0cO3YsxjYPDw9q1KhB3759k3t4EY/+/acCL3FxKUffvpX1Dscq9O4NDg6waxfY6HdC3Rw9Cnv3SjuzENYg2Yn5008/5bBxFojXnD9/nidPniT38CIOYWERrF8/AYB27fpjZ2fQOSLzWrhQmzhkypTEPS97dmjRQrsvU3QmTuHCYFyfedOmzURGRuoajxBpWbIT87lz56gVx+wOW7dupX379sk9vIjD0KEriIq6gcHgzW+/tdU7HLPbuBH27oWkNHUaK2nOnAHpw5Rwbm5QuXI5IBPPnoXY3AIuQqQmyU7M6dOn5+HDh7G216hRgwMHDiT38CIOU6b8AUDlyh/j6Zk6FqswUgo2b9buJ2XiuJo14dAh2L5dmxlMJJy/vz1QF5DqbCH0lOzE3KxZM3799dfYB7azIzw8PLmHF284ePAojx/vAhwYOzb1TL9pdOUKBAVpbcXVqyf++QYDlCsnSTkptPHi0s4shN6SnZh//vlndu7cyXvvvWfqhR0WFsbIkSNtdriUNZs6dSIArVq1pmJF21zv+m2ME09Vrqz1tE6OJ0/g6tVkh5RmVK4Mjo5aYt63bx9Pnz7VOSIh0qZkJ+bMmTOzd+9enJycKF26NK6urnh4eLBq1Sp++eUXc8Qo/vXs2TMW/Duw99NPU19pGbSFKyBhs329zZIl2sIX/VPnSDKL0NqZ8wL5iYyMZNu2bXqHJESalOxxzKCNZV67di3Xr1/n6NGjODo6UqlSJTw9Pc1xePGvUaOW8OzZMwoUKEDNmjX1Dscidu7UbmvUSN5xSpWC0FBYvRquXZPFLhLqo48gPLwe+/dfYtOmTTQ1TqkmhEgxZknMRrly5SJXrlzmPKR4ze+/TwegZMkeGFJhI2pYGJQpAy9fQpUqyTtW4cJa57EtW2DWLJD1VBKmY0dwdX2P1q0nyYIWQugkSVXZQUFBidr/5s2bSTmNeM369WcICdkN2PP11130DsciXFy06TRv3gQPj+Qfr3t37XbWLG1dZ5Ewtf9tRzh16hR3797VNxgh0qAkJeYKFSrQq1evtw6HevLkCVOnTqVEiRIss7HJi61xruxhw2YA4O3dmPLls+kcjWWZqzKgRQtIn17rACbNpQkXEeGFr29pQJuPQAiRspKUmM+cOUOGDBlo2LAhPj4+NG7cmF69evHpp5/SsWNHypYti7e3N7NmzeKXX37h008/NXfcFtW3b19Onz5tNZMsPH8ezsGDswHo1q2HztFYzvXr5p0UxM0NjHPczJxpvuOmdhMmwLVr2iByqc4WIuUlKTF7enry66+/cuvWLSZOnEihQoV48OABFy5cAODDDz/k8OHD7N69m4CAALMGnBb9+ONqoqPvY2eXle+/b6R3OBbx8CHkzg25csGzZ+Y7brdu2u3y5fDihfmOm5pp48clMQuhl2R1/nJxcaFly5a0bNnSXPGIOMyYMQ2A8uW74uZm1v56VmP3bu3W3R3SpTPfcStWhHHj4P33wdXVfMdNzSpXBoOhBkrZc+nSJa5du4avdGsXIsUkexyzsKwLF25w794GAIYN665zNJaza5d2m9xhUm8yGKBfP60kLhImfXrw80sPaH0spJ1ZiJQlidnKLV8+H4imZMkaBAQU1DscizHX+OV3kYUtEqZaNQB/QBKzEClNErOV+/PPPwHo16+jzpFYzosX2sITYLnEvGsXBATAt99a5vipjZaYX7UzK/lGI0SKkcRsxQ4fPsnx48dxdHSkdevWeodjMfv3Q0SEtp5y3ryWOcf9+7B+vdY7OyrKMudITbTEXBVw4saNG1y8eFHniIRIOyQxW7HPPpsPQKlSjVL19KavV2NbakKzxo3B0xNu39aWhBRvlzs3LF/uRpV/p2CT3tlCpJxkJebo6GhmzJhBkyZNKFGiBCVLlqRZs2bMmTNHqr6SKSoqmv37tcRcrVoHnaOxrDp1YMAAsGSlgJMTtGql3f93HRDxDs2bQ8OGMmxKiJSW5MSslKJZs2b07NmTmzdvUrJkSYoXL861a9fo2rUrLVq0MGecac60aXuIjLwGePDdd6l7IYFq1WD0aMsmZng12chff4EsFZ4wdepoiXnr1q1Ey7ymQqSIJA+KnTVrFjt27GDz5s34+/vHeGzLli00b96cOXPm0Llz52QHmRb9739aaTlfvpZkziwDcM2hZk3Ilk2rzt6wAWThpLcLDYXNmytib+/G/fv3OXXqFCVLltQ7LCFSvSSXmBcsWMDXX38dKymD9i178ODBph7FInGeP4/g1KnFAHTvnrqrsQ8d0laAevrU8ueyt4cPPtDuS3X2uzk7w6+/OhEVVR2Q6mwhUkqSE/Px48dp2LBhvI8HBARw7NixpB4+TRs1agNKBWMw+PDFF3X0Dseixo6FunVhzJiUOV+HDlons3r1UuZ8tszBwbj8pvble7v0mhMiRSS5Kvvhw4f4+PjE+7iPjw+PHj1K6uHTtFmztJqGUqXa4eKSOqfgNNq7V7tN7vrLCVWxIuzYkTLnSg2qVYNNm2oDsGPHDqKjo7Gzk8EcQlhSkj9hUVFRODjEnzTs7e2JjIxM6uF1peeyj6Ghody9+zcAAwak7mrse/fg8mVtiFTFinpHI+KiLWhRDoPBneDgYE6dOqV3SEKkekkujiml6Nq1K87OznE+/vLlyyQHpbe+ffvSt29fQkJCyJAhQ4qee/369YSFPSdv3rx07mw960Fbwr592m2xYpDCf2bu34elS7We2hkzpuy5bUmlSmBv70hUVDVgI9u2bZMOYEJYWJJLzF26dMHb25sMGTLE+ePt7S09spNg6dKlALRq1QqDpWbbsBIpXY39uvr1oU8fbTlIEb906cDPD6A2ANu2bdMxGiHShiSXmGfKyvNm9+RJGH/9tRqAli1T7xScRsYSc+XKKX/u1q0hMBAWLny1ZrOIW5UqcOpUbV6+1DqASTuzEJaV7J5FAwcOjHO7wWDAxcWFAgUK8P7776fqKSXN5ddfNxIR8Qw7u5y6tG+npMhIOHBAu69HiblNG21Biy1b4PFjbR1oEbeffoJffilP5sxupnZmqc4WwnKSnZiPHj3KkSNHiIqKonDhwiiluHDhAvb29hQpUoQJEybwxRdfsGvXLooVK2aOmFOtBQv+AqBEiZY4OKTuEomdnTZH9v79UKRIyp+/UCEoXhxOnYLVq6Ft25SPwVZo7f+OVK9enY0bpZ1ZCEtL9n//999/n/fee49bt25x+PBhjhw5ws2bN6lXrx7t27fn5s2b1KxZkwEDBpgj3lQrNDScy5dXAtCzZ+qvxrazg7Jl4ZNPtPt6aNlSu122TJ/z25ratWsD0s4shKUl+1/iL7/8wo8//kj69OlN29KnT8+wYcMYNWoUbm5ufP/99xw+fDi5p0rVxo7dglJPsLPz4eOPq+odTppgTMzr18Pz5/rGYu3GjIFJk2oBr8YzCyEsI9mJ+cmTJ9y7dy/W9vv37xMSEgJAxowZCZdVA95q7lytN3aRIi1xcrLXORrL++ormDo1ZabijI+fn7b+c3g4HD6cunvAJ9eTJxAUVB57ezcePHjA6dOn9Q5JiFTLLFXZ3bt3Z/ny5dy4cYObN2+yfPlyevToQfPmzQE4cOAAhQoVSu6pUq2wsEjOn18BQJcurfQNJgU8eACjRsFHH2mdwPRiMMCiRXD3LtSoIcuUvk2lSgBOODtXA6Q6WwhLSnZinjx5MnXr1qVdu3b4+vqSO3du2rVrR926dZk0aRIARYoUYdq0ackONrVasmTHv3Nje/Hpp7X0DsfijMOkihSBTJn0jaVCBfDy0jcGW2Ccme3589qAJGYhLCnZvbLTpUvH1KlTGTNmDJcvX0YpRf78+UmXLp1pn9KlSyf3NKna3r1aNXabNs1xdU3dc2PDq8SsxzCpt5Fm0/h5eUHBgnDhQm1AxjMLYUlm+1SlS5eOUqVK4efnFyMpi7dTSvH339rc2F26tNQ5mpRhnPFLj4lF4vLPP1C7tj1TppTSOxSrplVnl8fRUdqZhbAksxTPHj9+zPTp0zlz5gwGg4GiRYvSo0ePFJ9n2hbt2XOUmzdv4u7uTp06qXuJR4CoqFcTi1hLYo6Ohj177MiYMRtRUeDoqHdE1qlSJZg3z4n06asQHLyZ7du3U6JECb3DEiLVSXaJ+dChQ+TPn58xY8bw8OFDHjx4wJgxY8ifPz9HjhwxR4yp2nffrQIgf/56uLi46ByN5Z09C8+eaTNtFS+udzSa2rUhY0bF48cu7N0rvbPjU6WKtuBIoUJaPwhZn1kIy0h2Yh4wYADNmjXj6tWrLFu2jOXLl3PlyhWaNGnC559/boYQU7f9+7Vq7LJlm+kcScow1n6WLQv2VjIqzMkJGjXSemWvXi2JOT7lymkzpY0Y8Wo8s1LSm10IczNLifmrr76KsTazg4MDgwYN4tChQ8k9fKp2+PBNnj8/Ahj44ovGeoeTItq0gYcPYfp0vSOJqXFjrefXmjXSmeldKlasiLOzM3fv3uX8+fN6hyNEqpPs/0Lp06cnKCgo1vbr16/j4eGR3MOnamPGaCtJpUtXiRIlvHWOJuVkyqT18LUm9eop7OyiOXfOwKVLekdj3RwcXChbVusgINXZQphfshNz27Zt6dGjB4sWLeL69evcuHGDhQsX0rNnT9q3b2+OGFOtTZu09uXKlZvqHInImBGKFQsGYM0afWOxZps3a4taXLsm7cxCWEqye2X/+uuvGAwGOnfuTOS/0zg5OjryySefMGLEiGQHmFrdvx/KvXubAejdO220Lx87BgMGQN268M03ekcTW9Wqt8iXz4tChaQ6Oz7582vzioeFvUrMSikMBmmbF8Jckv0fyMnJid9//51Hjx4RGBjI0aNHefjwIWPGjMHZ2dkcMaZKY8f+A4Rhb5+HFi2spHuyhe3dC1u3grUWsho1usrSpVE0bKh3JNbL1xeyZIHo6Mo4ODhy8+ZNLl++rHdYQqQqZisauLm5UbJkSUqVKoWbm5u5DptqnT+vVWNXqtQUO7u0Udo4eFC7rVBB3zhE0hkMUL48gBu5c2sXUqqzhTCvJFVlDxw4MMH7jh49Oimn0NX48eMZP348UVFRFjl+dHQ0O3dqHb+GDUs77cu2kpgvXYIrV+C99/SOxDqVLw/r1kG6dLWAPWzfvp3u3bvrHZYQqUaSEvPRo0cTtJ+ttjv17duXvn37EhISYpHZyw4dOsTdu3fx8PCgVq3Uv2gFQGioNgYWrDsx795twN8ffHzg1i2QqaBj00rM8PRpLWC4lJiFMLMkJeatW7eaO440ZexYrdvve+81wMnJSedoUsbRo9rUl9myQY4cekcTvwoVFB4e2lKQhw9b95cIvZQrp91eu1YVe3t7rl27xrVr1/D19dU3MCFSCSkP6ODvv9cDkDFjI50jSTnGuWasPdE5OUH9+tp9GTYVt+zZoXNn+OEHD0qX1rL0jh07dI5KiNRD2phT2LlzDwgN1Rpb+/ZtoHM0KeflS/D0tP7EDNCkCfz1F6xeDcOG6R2N9TEYYPZs7X5ISC0OHz7A9u3b6dSpk76BCZFKmKWN+fDhw0RFRVG4cGEAzp8/j729PeWMdV7C5H//2wQoXFxKUa5cdr3DSTFffQWDBkF4uN6RvFtAgHZ7+LDWzpw97VymRKtVqxa//PIL27Zt0zsUIVKNZLcxjx49Gg8PD2bPnk2mTJkAePToEd26daNGjRrmiTIVWbdOq8YuVSrtDZY1GMAWhrb7+GgdnA4dgk2boEsXvSOyPkrB9esQHl4dOzs7Ll26xM2bN8lhzR0IhLARyW5j/u233xg+fLgpKQNkypSJ//73v/z222/JPXyqEhkZzZUrGwD44IO0k5ijo/WOIPEa/NvKsHGjvnFYqzt3tMlGWrfOgJ9fGUDGMwthLslOzCEhIdy9ezfW9nv37vH06dPkHj5VWbr0GNHRdwF3Pvqomt7hpJiRIyF3brCl7gadO8PKlTBxot6RWKds2bQq/ujoV+szS3W2EOaR7MTcokULunXrxtKlS7lx4wY3btxg6dKl9OjRg5YtW5ojxlTjzz+1amwfnzp4eKSNYVKgTSxy/bptlZwLFYJmzSB9er0jsV7GLiQeHrUBKTELYS7JTsyTJk2icePGdOzYEV9fX3x9ffnwww8JCAhgwoQJ5ogx1QgJ0RJz9+5ppxob4MgR7dY4MYVIHYzXMySkBgaDgfPnz3P79m19gxIiFUhyYv766685cOAAbm5uTJgwgeDgYI4ePcqRI0d4+PAhEyZMwN3d3Zyx2rSQkBD27NkDQM+eaScxP3wI165p90uX1jWURLt5E779Fvr00TsS62RMzCdOZKT0vxdXSs1CJF+SE/Pt27dp0qQJ2bJl46OPPmL79u0ULlwYPz8/Schx2LJlC5GRkRQsWJB8+fLpHU6KMY6sy59fW/PYloSFwU8/wdSpIN0lYjNWZZ89C1WqyPrMQphLkhPzzJkzuXv3LosXLyZjxowMHDiQzJkz07JlS2bNmsWDBw/MGafNGzZMq8b280s7pWV4lZjLlNE3jqTIn1/7iYzUlqsUMfn4QM6c2tCpHDlqA9IBTAhzSFYbs8FgoEaNGowaNYqzZ89y4MABKleuzNSpU8mRIwc1a9bk119/5ebNm+aK1yZFRytOntQSc/HiaSsxG9uXy5bVN46kMk7PKcOm4vaf/8D8+dCmjdbOfPbs2ThHaQghEs6sc2UXLVqUQYMGsXv3bm7cuEGXLl3YuXMnCxYsMOdpbM6GDeeJiroGONOnT9pYTcqoWDGoVAkqVtQ7kqQxjmfesEHfOKxVt27Qvj0ULOhJqVKlAKnOFiK5LLaIRZYsWejRowcrV67kyy+/tNRpbMLMmf8AkDFjNby901b7+7ffwr59ULeu3pEkjb8/ODjAxYtw+bLe0Vg34xKmkpiFSB5ZXSoF7NqlJeYKFWw0O6Vh6dNDlSrafanOjk0p2LwZfvsNKleuDUg7sxDJleTEbGdnh729/Vt/HBySNBV3qhIeHsWdO9sAaNs2bSXme/fgxQu9o0i+Bg20lbGeP9c7EutjMECnTvDll5AhgzY3/unTp7l//77OkQlhu5KcOZcvXx7vY3v27GHcuHEopZJ6+FRjwYIjKPUYyMCHH6at1ba++grmzoUxY+DTT/WOJukGDIDBg8HeXu9IrFOZMnD7Nly9mpmSJUty4sQJduzYQatWrfQOTQiblOTE/P7778fadvbsWYYMGcKqVav48MMP+fHHH5MVXGqwZ89mALJlq42LS9qqQTh6FKKiIFcuvSNJHjc3vSOwbqVLw9q12vWuVasWJ06cYOvWrZKYhUgis7Qx37p1i169elGqVCkiIyMJDAxk9uzZ5M6d2xyHt2mXL2uJefDgtFWN/fIlnDql3bfVoVJvUgpCQvSOwvoYx6gfPQr+/v5AzKVhhRCJk6zE/OTJE7766isKFCjAqVOn2Lx5M6tWraJEiRLmis+mhYWFsWvXLgDeey9tJeaTJ7WJOby8bL/EDNq6zLlzQ+vWekdifYyJ+eRJqFq1FgaDgdOnT8t4ZiGSKMmJedSoUeTLl4/Vq1ezYMEC9uzZQ40aNcwZm81bt24PYWFhZMuWjaJFi+odTooyTixSpozWQcjWZc8ON27Arl1abYB4JW9e8PDQ/i7373vh5+cHSKlZiKRKcqPn4MGDcXV1pUCBAsyePZvZs2fHud+yZcuSHJyt+/VXrRo7S5a6GFJDdkoE41ScqaUau1gx8PbWeprv3w81a+odkfWws9PamXfuhMBArTo7MDCQrVu30q5dO73DE8LmJDkxd+7cOc0lm8Q6flxLzGlx/LKtT8X5JoMB6tSBhQthyxZJzG8aMQIcHaFkSciUqQ5jxoxhy5YteoclhE1KcmKeNWuWGcNIfa5de8yzZwcB6NUr7SXmTp2gQAHbnYozLq8n5mHD9I7GulSt+up+jRo1sLOz4+LFi1y/fp1cqaGTgRApKEmJeeDAgQned/To0Uk5hc2bMmU7EI2jYyEqVUp7/5j69tV+UpM6dbTbffsgNBRkddO4ZciQgfLly3PgwAG2bt1K586d9Q5JCJuSpMR81NiA+K/Dhw8TFRVF4cKFATh//jz29vaUK5e2JtR43Zo1WjV2wYJpr7ScWuXLp/XMDgqC3btfrTwlNDNmwMGD2vzo/v7+kpiFSKIkJebXe1uOHj0aDw8PZs+eTaZMmQB49OgR3bp1S9O9tM+e1RJzQEDaS8yBgeDsDAULagtApBYGA/ToAY8fQ44cekdjfcaN0659gwZQp04dRo4cyZYtW1BKSX8UIRIh2ROM/PbbbwwfPtyUlAEyZcrEf//7X3777bfkHt4mnTp1j5cvTwPw0Ue19Q1GB19+qfVijqejvk37/nsYPRqKF9c7EutTurR2e/QoVKtWDUdHR4KCgrhy5YqucQlha5KdmENCQuKcSODevXs8ffo0uYe3SYcO7QAgc+aSFCrkpXM0KUspOHZMu//vcFaRRhgnGgkMBHd3dypVqgQgvbOFSKRkJ+YWLVrQrVs3li5dyo0bN7hx4wZLly6lR48etGzZ0hwxJju+TJky0ToFp2w6dEhbj7Zdu1opdk5rcecOPHigjW1NraXKFy+0pQ5Pn9Y7EuvyeokZZHpOIZIq2Yl50qRJNG7cmI4dO+Lr64uvry8ffvghAQEBTJgwwRwxJkv//v2ZM2dOip7TuFC8ceH4tMRYWi5cGFxd9Y3FUr78Et57DyZP1jsS62JMzNevQ3Cw1s4MmNqZhRAJk+zE7ObmxoQJEwgODubo0aMcOXKEhw8fMmHCBNytYDyJv78/Hh4eKXa+S5eCOXHiBAA10+AsFMbEXKqUvnFYkvH71o4d+sZhbdKnh/z5tfuBgVC5cmWcnZ25c+cOZ8+e1TU2IWyJWVaXAq1NqVSpUvj5+ZktIe/YsYOmTZuSPXt2DAYDK1asiLXPhAkTyJs3Ly4uLpQrV46dO3ea5dxJNXWq9t/ayakY3t7eusaih7TQvmwcbHDsmNZDW7xiLDWfPw8uLi5Uq1YNgM2bN+sXlBA2xqoHs4SGhuLn50e3bt3iXNt10aJFfP7550yYMIFq1aoxefJkAgICOH36dJKWnHz58iUvX1uhIOTfNf4iIiKIiIhI0DE2bNgGQIECNRP8nNQkMNABMFC8eCQREbZTfWm8Vgm5ZpkzQ4ECDly8aGD79kgaNbKd12lpI0fC+PHg6QkREVp19pYtW1i/fj0ff/yx3uGZJOZ6C9tmi9fYqhNzQEAAAQEB8T4+evRoevToQc+ePQEYO3YsGzZsYOLEiQwfPjzR5xs+fDg//PBDrO1bt27Fzc0tQcc4fVrr6JI3by7Wrl2b6BhsXYsWPvj5ZeTRo6usXWt7yzBt2rQpQfvlyVOaixd9mT37CiC9wOLj+m9Hgy1btvD333/jYGUD2xN6vYXtev78ud4hJJp1fUoSITw8nMOHDzN48OAY2+vXr8+ePXuSdMwhQ4bEmG40JCSEXLly4e/vj5fXu4c9Xb36iPDwkwAMG9YJP7+sSYrDljVqZLyXX88wEi0iIoJNmzZRr149HB0d37l/cLCBf/6BW7fy06hRHssHaKOio6MZPnw4Dx48wMvLy1S1rbfEXm9hu4KDg/UOIdFsNjE/ePCAqKgofHx8Ymz38fHhzp07pt8bNGjAkSNHCA0NJWfOnCxfvpwKFSrEeUxnZ2ecnZ1jbXd0dEzQh3fOnP2AwtGxEOXLp735sVODhF7ruv9O6Hb4sB3h4XYyb/Zrhg2DPXvgjz+gSBGoW7cuixYtYsuWLdSuXVvv8GJI6PUWtssWr6/ZOn/p5c2p/t6c/m/Dhg3cv3+f58+fc+PGjXiTsjmsW6cNk8qfP+0NkwLYuBFWrdLGMad2vr4waRIcOAAuLnpHY13++Qc2bXq19Gf9fycVl2pjIRLGZhNz5syZsbe3j1E6Bm3GsTdL0Snl7FktMfv7p83EPHw4NGsGq1frHUnK+PhjbbYre3u9I7EuxqFyx49rt/Xq1QPgwIEDPJZu7EK8k80mZicnJ8qVKxfrW/imTZuo+vrisCkkJCSEFy+0IkLv3mkvMctUnMLozcScK1cuihQpQnR0tEzPKUQCWHVifvbsGYGBgQQGBgJw5coVAgMDCQoKArR1oadNm8aMGTM4c+YMAwYMICgoiN69eyfrvOPHj6dYsWKJqvbevXs30dHR5MuXj1Klcibr/Lboxg149EgrPRYrpnc0KSMyEmbO1FacCgvTOxrrUbKkdvvvPDvAq1KzVGcL8W5WnZgPHTpEmTJlKPPv7PgDBw6kTJkyfP/99wC0bduWsWPH8p///IfSpUuzY8cO1q5di6+vb7LO27dvX06fPs3BgwcT/BzjNJzW1rklpRhLR0WKaEs+pgX29jB48Kt1iIWmRAnt9sYNePhQu29sZ964caNOUQlhO6w6MdeuXRulVKyfWbNmmfbp06cPV69e5eXLlxw+fFi3aTBnz9ZmHCtRIu1NwwlpsxrbYADj203nCeesSoYMkCePdt9Yaq5VqxYODg5cvnyZy5cv6xabELbAqhOzrXj8OIw7dw4B4OdXXedo9JEWEzO8Sswyb3ZMpUpBpkyveuh7eHhQpUoVQKqzhXgXScxmMH/+ISAcOzsfatfOp3c4ujBWZafVxLx7t9bmLDTz52srTL0+k65UZwuRMJKYzWDVqt0AZMtWHTs7wzv2Tp2WLoV586BiRb0jSVklSkDGjPDsmbaiktC4u2tV/a8zdgDbsmULkfItRoh4SWKOQ2J7ZR85sguA8uWtY7pBPRQvDh9+qFVfpiX29lD939YLaWd+u/Lly+Pp6cnjx4/Zu3ev3uEIYbUkMcchMb2yIyOjuX9fm5u7RYu02b6c1hmnf75yRd84rE2HDpAvH1y4oP1ub29vWpRmzZo1OkYmhHWTxJxMa9eeRamHgBtt2pTWOxxdLF4MY8bAmTN6R6KPjz7SOjn98YfekViXCxe0LyvG/gcAjRs3BiQxC/E2kpiTafNmrX05Y8ZKuLnZ3mTp5jB9OgwcCLt26R2JPjw9IQGLj6U5xolGXk/MDRo0wM7OjpMnT3Lt2jV9AhPCykliTqbHj7Vs1KNH2m1fPqmtdGmaWEIIiD01J4Cnp6dp6UcpNQsRN0nMybR7t1ZirlcvbbYvP3wIt25p94sX1zcWPa1bB/7+8OWXekdiPYyJ+fWpOUGqs4V4F0nMyXDr1h0uXbqEwWCgcuXKeoejC2Np2dcX0qfXNxY9PX8O27Zpyx0KjbEq+9IlbTiZUZMmTQBt2NTz5891iEwI6yaJOQ4JHS41ZoxWWs6SpRQZMmRIidCsjlRja4wLmp04ASEh+sZiLbJkgWzZtPuvl5qLFSuGr68vYWFhstqUEHGQxByHhA6X2rxZa1/29pb25bSemLNlg7x5teUv9+3TOxrrUaWKNulMePirbQaDwVSdvTqtLN4tRCJIYk6G8+e1EnOtWmmzfRkkMb/OOJ55zx5947Amf/0F+/dDrTeWKDdWZ69ZswallA6RCWG9JDEn0b17oYSGHgHgww/Tbol57VrtH2/DhnpHoj9jYv63P6B4i9q1a+Pq6sqNGzc48WbvMCHSOEnMSTRv3gEgCnv7XFSpklvvcHSTLp1WVZk5s96R6M/YzrxvH0RF6RuLtXm9KhvA1dWV9957D5DqbCHeJIk5idat0+ors2evqnMkwloUL66tQ1yzJjx6pHc01iE8XPu7uLtrQ+teZ2xnXrVqlQ6RCWG9JDEn0fHjWg+f8uWr6ByJfhYvhr59QVbx09jbw+XLsGaN1CAYOTlBaKi2JOapUzEfa9q0KQaDgX379nHjxg19AhTCCkliTgKlFM+eaYm5adO0OX4ZtEk1JkyQXsive3OpQ/Fq4hljR0Gj7NmzU/Xf+v/ly5encFRCWC9JzHF41zjmS5cu8fz5A5ydnfnwwzIpHJ31MP6jNU4kIV65fVvvCKyHscf+myVmgFatWgHw119/pWBEQlg3ScxxeNc45n3/FhHLli2Lk5NTSoZmNaKiXv2jlaFSr0REQP78kD073LypdzTWIb4SM0DLli0B2LlzJ/fu3UvBqISwXpKYk2DjRi0xp9VpOEFbzu/FC3Bx0dbcFRpHR62nOmjDyETMxPzmkGVfX1/Kly9PdHQ0K1asSPHYhLBGkpiT4K+/tMTs6pp2E7Ox9FOsmNbpSbxi/L4miVlTtKjW9h4cDHEViqU6W4iYJDEn0oMHz3n+/BgATZum3R7Z0r4cP2Nilk5xGjc3aNwY2reHsLDYjxsT85YtW3j45pgqIdIgScyJtHDhYSASO7vsVKyYU+9wdBMUpN1K+3JslSppt4cOacOEBKxaBfPna6uQvalgwYKUKlWKyMhI/v7775QPTggrI4k5kdat04pBWbNWxs4u7Y6NmTIFHjyAHj30jsT6FCmiLYH5/HncHZ5EbFKdLcQrkpgTKTBQS8ylS6fd9mUjLy/IlEnvKKyPnZ02TSlIO/ProqIgvnlEjIl548aNhMi6mSKNk8ScCNHRijt39gLQqFHabV8W79amDfTrJ23wRufPa73VS5aM3TMbtDWaCxcuTHh4uMydLdI8ScyJcODAdaKjbwMOtG1bVu9wdLN+vbaa1B9/6B2J9froIxg37tXCFmld7tzaGO/Hj+OefMVgMNCmTRsAFixYkLLBCWFlJDHHIb6Zv06e1KqxfXz8yJzZTY/QrMKBA7BhAwQG6h2JsBUuLlCggHY/vnb3Dh06ALB+/Xru37+fQpEJYX0kMcchvpm/Tp/WEnPr1mm7ffn0ae22WDF947B2z5/Drl3aZCzi1UQjcU3NCVC0aFHKlStHZGQkixcvTrnAhLAykpgTwTgVZ1qe8QskMSdUr15QowbMm6d3JNbBOLTubT3VO3XqBMA8+aOJNEwScwI9ffqSgwePAFC5ctrt+BUZCefOafeLFtU3FmsnPbNjeleJGaBdu3bY29uzb98+Ll68mDKBCWFlJDEn0LJlx4mMfInB4EW+NDw59OXLEB4Orq5xTxYhXnl9as64eiKnNa+vMhUdHfc+Pj4+1KtXD5BSs0i7JDEn0Jo1BwDInLlimp5Y5MwZ7bZoUW28rohf6dLg5KRNxHL5st7R6K9gQWjdGj7/XPtyF5+OHTsCWmJW8o1GpEHyrzWBjhzROoIVLRr3Gs1pxePHkDGjtC8nhLOzlpxBqrNBW3lryRL48Uetl3Z8mjdvjru7O5cuXWK//OFEGiSJOYGuX9cSc+3aaTsxd+kCDx9qU3KKd5OVphLP3d2dFi1aAFKdLdImScwJcPv2U8LDtTrcNm3SdmIGbQk/V1e9o7ANxgUtZKUpjVLaAijGnv3xMVZnL1y4kIiIiBSITAjrIYk5AVauPA4o7O1zU6KEj97hCBtSuzaMHQu//653JNZhwQKt0+DHH799v7p165I1a1aCg4NZs2ZNygQnhJWQxJwA27YdBSBr1rRdWr5yRevA88EHekdiO7Jnh88+e1WlndYZh9gZOxHGx8HBgc6dOwMwdepUC0clhHWRxJwAdnaHAWjatKLOkejr9Gm4ePHVOGYhEqtwYa0pJDgY3jXrZs+ePQFtis7r16+nQHRCWAdJzHF4c67sM2cCAfjgg7RdYpYZv5Lm9m2YPRvmz9c7Ev25ub0a//6uUnPBggWpXbs20dHRzJgxw/LBCWElJDHH4c25sq9fv47BYKBcuXI6R6YvScxJs38/dO0Ko0bpHYl1SGh1NkCvXr0AmD59OlFRURaMSgjrIYk5gXLnLkL69On1DkNXkpiTpnx57fbkSW1hi7TO+P5JSGJu2bIlmTJl4vr162zcuNGygQlhJSQxJ5C9fdquxlZKEnNS5cgBWbNCVBQcO6Z3NPpLTInZxcXFtLCFdAITaYUk5gQqVy5td/y6cQOePQMHh1fr6oqEMRhelZrfWEk0TapaFb766t1DpoyM1dmrVq3izp07FoxMCOsgiTmBAgLSdon5yROoUAHKltWmVhSJ828/Qg4d0jcOa1C0KIwYAS1bJmz/EiVKULlyZSIjI5k1a5ZFYxPCGkhiThAHWrXy0zsIXZUoAQcOyNSSSZXWSsyRkdrCHRs3wqJFsGOHNuNXUhlLzdOmTSM6vqWphEglJDEngKtrSdKnd9Y7DGHDjCXmc+fg6VN9Y7GUkBD4+msoUkSbsjV/fmjQANq1g1q14N/5QgBtDPPy5XD2bMKO3bZtW9KnT8+lS5ekE5hI9SQxJ0Du3GX0DkF3UkhJnixZYMMGuHULPDz0jsYynJ21xU3OndNKzC4uULw41Kih9Ut4vW/C119rVdl16sDChe9er9rd3Z2uXbsC2jwDQqRmkpgToFy50nqHoLtcubTe2Feu6B2J7apfX+udnVqcPg1ffvkqqTo7w8iR2tKO169DaKg2RGzHDrhwAaZNe/Vc47KPt29D+/ZQt+67F7bo06cPAGvWrOGKvBFFKiaJOQE+/DBtl5gfPNBKemfOgLe33tEIvb18CcOGaWtN//YbvN4fq0cPaN0acuYEu7f8d2neXLv19NSS9Nat4OcHgwbFP9a7cOHC1KtXD6UUEydONM+LEcIKSWJOgPLlC+kdgq6Mc2Pnzg3u7vrGYsuCg+H777U1rW3VjRvaUpY//AAREdCkiVbaTSzjWObHjyEwEJo106q/f/lFa4+Orx2+X79+gDYT2IsXL5L0GoSwdpKYE8De3l7vEHRl7KBTpIi+cdg6e3v48UeYM0erhbA1J05oq2QdO6a1mS9aBH//rX1hS6xs2SB9eq3vQkQErFypHcvLSys5p0sX9/MaN26Mr68vDx8+ZOHChcl7QUJYKUnM4p0kMZtHxoxQ6N/KF1sbz7xtG1SvDjdvaqXdgwe15T8NhqQdz2CIPTVn06Zw+DBMnPjquG92CrO3t+eTTz4BtE5g6l29xoSwQZKYxTtJYjYf47ApWxvPbGcHYWFaD+vdu1+tEJUccU3N6ev7agKbyEitLXrFipjP69GjB87Ozhw+fJgDBw4kPxAhrIwk5ji8uexjWieJ2XyME40cOaJvHIlVsyZs3qxNGJIpk3mO2amTNryqTZu4H//zT616u107rWe3UebMmWnXrh0A48aNM08wQlgRScxxeHPZx7RMKa2zT+nSkpjNoWxZ7dYWEvPJk9rsXUbVq78a5mQO/v7Qq9erkvObPvwQ3n9f6wXerBkcP/7qMWMnsMWLF8v82SLVkcQs3spggHnz4OhRrcOOSJ7SpbXboCDr7gB24wY0bAhVqmidvvTg4AALFmjV50+eaLOIGYcvly9fnipVqhAREcHkyZP1CVAIC5HELEQKSp8eChbUeh2/Xhq1Jk+eQECA1tHLy0tbttJS9u6F6dO1oWRxcXXVqrNLloQ7d7QvC8ahVP379wdg4sSJvHz50nJBCpHCJDGLt3r6VKbjNLcdO7TkV9EKVxJVCnr21Kqxs2aFdeu0SUAspXt37Xxvq9rPmBHWr9cmLTl/Hv7tlE2rVq3Inj07d+/eZcmSJZYLUogUJolZvFXfvtqkIlOm6B1J6pE169tnxdLT9OmwdKlWjbxypXl6X79N4cLa7bsWs8ieXZtTO2tWre0ZwNHR0TRN5++//y5Dp0SqYaX/HoS1OHtWGyaTObPekQhLO3MG/q0d5uefU6ZEb+xQaJxd7m2qVdOq/wMCXm376KOPcHZ25tChQ+zbt88yQQqRwiQxi3gpJUOlLCEyEjp00EqLT57oHc0rQ4fCixdQrx588UXKnNNYYk5IYgatzdno2jVwc8tChw4dAPjjjz/MHJ0Q+pDELOJ1547Wxmxvr62tK8zDwQH27NHaS48e1TuaV2bOhAEDYPbslKtqT2xiNlq9Wuvh/sUX8OmnnwKwdOlSbt68ad4AhdCBJGYRL2NpOV8+bUk/YT7WOJ7Z3R1Gj07ZYXHGxGxcJjKh3Ny0BTAmT4bQ0DLUqFGDyMhIWXVKpAqSmEW8jInZ+M9TmI+1JOaXL7Vx6nr1vPfyetV/4fz5hD+vTh1tiUmAjz+GPn20xvEpU6YQFhZm5iiFSFmSmEW8pH3ZcqwlMf/6qzY1ZsuW+sUwebI23WfBgol73qhR2ipXp0/D+fPNyZkzJ/fv32fRokWWCVSIFCKJWcSrbFlo1QqqVtU7ktTHmJjPnk1cFa45Xb4M//2vdj+++apTQsuWWgk4vqUe4+PpCWPGaPd//tmBtm21oVPjxo2ToVPCpkliFvHq0kUb09qihd6RpD5Zs2ptuUpp6xunNKWgXz9tKFydOlovcVvUoYPWi/zlSzhwoJdp1am9e/fqHZoQSSaJWQidVKoEfn7w/HnKn3vZMm1WLycnmDAh6esqm8PDh1pP8KQsFGUwaOs3u7uDn19m2rbVvmHIqlPClkliFnF6+lSbK1lqBC1n2TIIDIT33kvZ8754oQ2LAvjqK/07992/D127wpAhSXu/5c+vVcuPGweff/5q6NStW7fMG6gQKUQSs4jTmjXa3MT16ukdSeqlVyl1wgRteFKuXFoy1Fu+fNrY7tBQ7ctgUnh7a7dlyrwaOjVp0iTzBSlECpLELOJkHLpi6bmSBUREQFRUyp2vcmVtOccffog5k5ZeHB215AyJn2jkTadOgVJaqXnSpEmy6pSwSZKYRZyM/yALFdI3jtQuIEDrjZySw6aqVYPdu7XOfdYiqTOAvU4paNcOdu1qjodHDu7fv8/SpUvNE6AQKUgSs4iTscQsidmyoqIgPFxra05JBoN1rXBlHCv/rlWm3sZgMA7/cuTFi48BmDBhQrJjEyKlWdFHU1gLpSQxp5TSpbXblEjM//d/8M031rVwhpE5SswAzZppNQKRkT0xGBzYs2cPx/QYjyZEMkhiFrHcuwchIVoJRBavsKyUSswXL8LYsdpyjildOk8IcyVmg0GbEQyyoZQ2AF/mzxa2RhJzHMaPH0+xYsWoUKGC3qHo4vWOXy4u+saS2hkT8/Hjlp2v+j//0ZabDAiAWrUsd56kKl0a1q+HbduSf6yqVaFJEwBtJrB58+YREhKS/AMLkUIkMcehb9++nD59moMHD+odii68vbVqz65d9Y4k9StUSFu569kzbSyuJVy5AvPna/d//NEy50iudOmgQQPIk8c8x/vmG4BaQFFCQ0OZO3eueQ4sRAqQxCxiKVxYqw4cOlTvSFI/BwcoWVK7b6kq5t9+0zqZ1a8P5cpZ5hzWpnJl+PBDA82bfwJoncBk/mxhKyQxC6Gzhg21xUKMyx+a0927MH26dn/wYPMf35z27oXvv9dmRDOHefNg1qzOuLm5cfr0aXbs2GGeAwthYZKYRSwHD2r/0KWAkTJ+/FFbLKR2bfMf+48/tIUqKlWyzPHNaetW7W+xYoX5jpkhQwY6duwIyNApYTsc9A5AWJeoKKheXRtbe/ky5M2rd0QiObp3h+BgaNxY34UqEsI4NM/Y+dAcoqMhX75PgCksX76c+/fvkyVLFvOdQAgLkBKziCEoSEvKzs6QO7fe0aQdSsHVq9oCE+aUPz9MmgRNm5r3uJZQsKB2e+GCeY/755+lgQpEREQwZ84c8x5cCAuQxCxiMJZWChQAe3t9Y0lLypXTaif27NE7Ev0UKKDdPnyolfLNwc7OuFBHTwCmTp0mncCE1ZPELGKQGb/0YWwyMFfP7BkzoE0bsKVJr9zdIUcO7b45S81t2kDOnO0AN86dO8uetPztR9gEScwiBknM+jBONGKORKqU1ulr6VL455/kHy8lGd935kzMDg7Qr196oC0A06ZNM9/BhbAAScwiBknM+jDn1Jy7d2sJ3tVV6/xlS4ztzObsAAbQsyc4OWnV2QsXLuaJNU4YLsS/JDGLGCQx68PPT7s9c0Yb3pQc48Zptx07QqZMyTtWShsyRJvX29yT23h5QceOVYCihIU9Z/HixeY9gRBmJIlZxPD11zBwIBQtqnckaUuuXFoSjYyE06eTfpybN19N0NG3r3liS0l58mg9yR0sMJDzs88M5M+vlZpnzJhh/hMIYSaSmEUMvXppUzh6eekdSdpiMJinnXnKFC2516jxqhQuNKVKwb59nXF0dOTw4cNcttTk5EIkkyRmIaxE+/bw1VdaAkmK8HCYPFm736+f+eJKaT/9pFXD379v/mNnzpyZFi205SD/sbWecSLNkMQsTE6c0MbRPn6sdyRpU69eMGJE0heaiIrSVgWrXh3+zT02ado0+PNPOHvWMsdv0ULrEbd1627Cw8MtcxIhkkESszAZOxaqVdOG2gjb4+oKX3wBO3eCo6Pe0SSdJabmfN2lS3WBbLx48YT169db5iRCJIMkZmEiPbL1d/8+bN4Mjx7pHYl+LDU1p1GPHg4YDB0AmDhxnmVOIkQySGIWJpKY9Ve3Lrz3HuzalbjnTZ6sLXP4/Lll4kpJlphk5HVZs0LNmp0A2Lp1DQ8fPrTMiYRIIknMAoAnT+DePe2+scQiUp6x49eJEwl/Tng4fPstdOqkLZ1o6yw1ycjrPvusOOBHdHQE8+fLmGZhXSQxC+BV6SRrVvDw0DeWtMyYmI8fT/hzVq+GBw8gWzZo0MAycaUkY2K+eFFbttESGjZUuLq2B2DcOFlxSlgXScwC0P4JgpSW9VaypHabmBKzca6MLl0sMzFHSsuTR3sdERFw545lzuHgAHXqVAfsOH9+LxcsVW8uRBJIYhbAqxKzcek9oQ9jifncuYRNzXnrFqxbp93v1s1ycaUkBwetGvv5c8ie3XLnadz4Bfb2WhXDnDlzLXciIRJJErMA4P334X//0ya5EPrJnh08PbUxyWfOvHv/uXO16t7q1VNXp728ecHJybLnyJ49lClTtN7Z8+bNJdpS9eZCJJIkZgFoJbW+faFePb0jSdsMhoRXZyv1qho7tZSWU1rr1s1Inz49V69eZffu3XqHIwQgiVkIq/PppzB1KtSs+fb9Hj7UOnylSwdt2qRMbCnlyBGtzXzQIMuex9XVlVatWgEwYcICy55MiASSxCwIDdWmQNy/X+9IBECrVtr6wXnyvH0/Ly/Ytg2uXk19PekfPYI5c2DlSsufKzKyHQArViwlMjLS8icU4h0kMQvOntUWDXj/fb0jEUmRGlcCM7aXX76srZZlSV271gEyExZ2nw0bUsFAcGHzJDEL01Ap6ZFtPfbt02bzim9qzkuXLLP6krXIkQNcXLSkfPWqZc9Vu7YD7u6tARgzZpFlTyZEAkhiFpKYrVDHjtC7Nxw9GvfjgwZp7cvTp6dsXCnFzu7V+9HSQ4zt7KBJk7YA7Njxl6w4JXQniVnI5CJW6G0zgD15AmvWaEOqKlRI2bhSkjExX7pk+XMNGlQDyEpExGOWL99o+RMK8RaSmIVMLmKF3paYly+Hly+hWLFXQ6tSI+P70fjF0ZLKlLHH0/MDAH7/Xaqzhb5SfWJevXo1hQsXpmDBgkybNk3vcKySVGVbn7eNZV7w76ie9u21cc+pVYEC2ut78sTy5zIYoHVrrTr74MEVvHjxwvInFSIeqToxR0ZGMnDgQLZs2cKRI0cYOXKkLPH2hqdP4e5d7b4kZuthLDGfPKlVWRvdvaut1wzQrl3Kx5WSOnWCFy9g5syUOd+331YmS5bcREY+Y51xnlMhdJCqE/OBAwcoXrw4OXLkwMPDg0aNGrFhwwa9w7IqTk6wdi1MnAgZMugdjTDKlw9cXbX5sl9vY12yREvUFSum/i9Sbm7g7Jxy58uVy44uXbTq7IULF6bciYV4g1Un5h07dtC0aVOyZ8+OwWBgxYoVsfaZMGECefPmxcXFhXLlyrFz507TY7du3SJHjhym33PmzMnNmzdTInSb4ewMAQFaD2BhPeztoUQJ7f7r7cyL/106WOY0t4y2bbXq7NWrV/Ps2TOdoxFplVUvEhcaGoqfnx/dunUzTZv3ukWLFvH5558zYcIEqlWrxuTJkwkICOD06dPkzp0bpVSs5xje0ij38uVLXr58afr9yb+NW1L9nfpFRETw/PlzgoODcXR01DscAL76yoCDA/j5KYKDtW3TpsHff9tRr160aVtq9tNPduzda+C776KpVCn25zmp4rvevr55yJAhD0+eXGXChCX06NHMbOcU+jD+/44rH1gtZSMAtXz58hjbKlasqHr37h1jW5EiRdTgwYOVUkrt3r1bNW/e3PRY//791Z9//hnvOYYOHaoA+ZEf+ZEf+UllP5cuXTJfQrIwg1K28TXCYDCwfPlymjdvDkB4eDhubm4sWbKEFi1amPb77LPPCAwMZPv27URGRlK0aFG2bdtG+vTpKVu2LPv27cMrnjkM3ywxP378GF9fX4KCgshgRQ2wFSpU4ODBg1Z13MQ+N6H7v2u/tz2emMdCQkLIlSsX169fJ3369O+MK6VY47VOyvMTsn9y94nvsbi2p6Xrndav9ZMnT8idOzePHj0iY8aMbw/eSlh1VfbbPHjwgKioKHx8fGJs9/Hx4c6dOwA4ODjw22+/4e/vT3R0NIMGDYo3KQM4OzvjHEdvkwwZMljVh9fe3t4i8STnuIl9bkL3f9d+b3s8KY+lT59errUFnp+Q/ZO7T3yPve05aeF6y7XW2NlZdZeqGGw2MRu92WaslIqxrVmzZjRrlrraifr27Wt1x03scxO6/7v2e9vjSX3MmljjtU7K8xOyf3L3ie8xW7nWYJlY5VrbnlRdlZ1cISEhZMiQgSdPnljVt2phfnKt0xa53mmHLV5r2ynbv8HJyYly5cqxadOmGNs3bdpE1apVzXIOZ2dnhg4dGmf1tkhd5FqnLXK90w5bvNZWXWJ+9uwZF/+dL7JMmTKMHj0af39/PD09yZ07N4sWLaJTp05MmjSJKlWqMGXKFKZOncqpU6fw9fXVOXohhBAi8aw6MW/btg1/f/9Y27t06cKsWbMAbYKRUaNGcfv2bUqUKMGYMWOoWbNmCkcqhBBCmIdVJ2YhhBAirbHZNmYhhBAiNZLELIQQQlgRScxCCCGEFZHEbCbXr1+ndu3aFCtWjFKlSrFkyRK9QxIW1KJFCzJlykTr1q31DkWY2erVqylcuDAFCxZk2rRpeocjLMhaP8fS+ctMbt++zd27dyldujT37t2jbNmynDt3Dnd3d71DExawdetWnj17xuzZs1m6dKne4QgziYyMpFixYmzdutU0v/7+/fvx9PTUOzRhAdb6OZYSs5lky5aN0qVLA+Dt7Y2np6csF5mK+fv74+HhoXcYwswOHDhA8eLFyZEjBx4eHjRq1IgNGzboHZawEGv9HKeZxLxjxw6aNm1K9uzZMRgMrFixItY+EyZMIG/evLi4uFCuXDl27tyZpHMdOnSI6OhocuXKlcyoRVKk5LUW1iW51/7WrVvkyJHD9HvOnDm5efNmSoQuEik1f87TTGIODQ3Fz8+P//3vf3E+vmjRIj7//HO++eYbjh49So0aNQgICCAoKMi0T7ly5ShRokSsn1u3bpn2CQ4OpnPnzkyZMsXir0nELaWutbA+yb32cbXsvblQjrAO5vicWy09FoHWG6CWL18eY1vFihVV7969Y2wrUqSIGjx4cIKPGxYWpmrUqKHmzJljjjCFGVjqWiul1NatW1WrVq2SG6KwkKRc+927d6vmzZubHuvfv7/6888/LR6rSJ7kfM6t8XOcZkrMbxMeHs7hw4epX79+jO3169dnz549CTqGUoquXbtSp04dOnXqZIkwhRmY41oL25SQa1+xYkVOnjzJzZs3efr0KWvXrqVBgwZ6hCuSwdY/5za/HrM5PHjwgKioKHx8fGJs9/Hx4c6dOwk6xu7du1m0aBGlSpUytXXMnTuXkiVLmjtckQzmuNYADRo04MiRI4SGhpIzZ06WL19OhQoVzB2uMKOEXHsHBwd+++03/P39iY6OZtCgQXh5eekRrkiGhH7OrfVzLIn5NW+2JSmlEty+VL16daKjoy0RlrCA5FxrQHrq2rB3XftmzZrRrFmzlA5LWMC7rrW1fo6lKhvInDkz9vb2sUpM9+7di/WNS9g2udZpl1z7tMPWr7UkZsDJyYly5cqxadOmGNs3bdpE1apVdYpKWIJc67RLrn3aYevXOs1UZT979oyLFy+afr9y5QqBgYF4enqSO3duBg4cSKdOnShfvjxVqlRhypQpBAUF0bt3bx2jFkkh1zrtkmufdqTqa61rn/AUtHXrVgXE+unSpYtpn/HjxytfX1/l5OSkypYtq7Zv365fwCLJ5FqnXXLt047UfK1lrmwhhBDCikgbsxBCCGFFJDELIYQQVkQSsxBCCGFFJDELIYQQVkQSsxBCCGFFJDELIYQQVkQSsxBCCGFFJDELIYQQVkQSsxBCCGFFJDELIYQQVkQSsxBCCGFFJDELYWG1a9fm888/1zuMFBMcHIy3tzdXr161+Llat27N6NGjLX4eIVKSLGIhRBJ17dqV2bNnA+Dg4ICnpyelSpWiffv2dO3aFTs77Xvvw4cPcXR0xMPDQ89wAe1LQunSpRk7dqzFzvHll1/y6NEjpk+fbrFzGB0/fhx/f3+uXLlC+vTpLX4+IVKClJiFSIaGDRty+/Ztrl69yrp16/D39+ezzz6jSZMmREZGAuDp6WkVSdmcwsPD49z+4sULpk+fTs+ePVMkjlKlSpEnTx7+/PPPFDmfEClBErMQyeDs7EzWrFnJkSMHZcuW5euvv2blypWsW7eOWbNmAbGrstevX0/16tXJmDEjXl5eNGnShEuXLsU4bu3atfn000/5/PPPyZQpEz4+PkyZMoXQ0FC6deuGh4cH+fPnZ926dabnKKUYNWoU+fLlw9XVFT8/P5YuXWp6vGvXrmzfvp3ff/8dg8GAwWDg6tWr73yeMZ5+/foxcOBAMmfOTL169eL8e6xbtw4HBweqVKli2rZr1y4cHR15+fKladuVK1cwGAxcu3YNgKtXr2IwGFi2bBk1a9bE1dWVcuXKcfXqVbZt20bFihVxc3PD39+fhw8fxjhns2bNWLBgQQKulhC2QRKzEGZWp04d/Pz8WLZsWZyPh4aGMnDgQA4ePMjmzZuxs7OjRYsWREdHx9hv9uzZZM6cmQMHDvDpp5/yySef0KZNG6pWrcqRI0do0KABnTp14vnz5wB8++23zJw5k4kTJ3Lq1CkGDBhAx44d2b59OwC///47VapUoVevXty+fZvbt2+TK1eudz7v9XgcHBzYvXs3kydPjvO17dixg/Lly8fYFhgYSNGiRXF2do6xLWPGjPj6+pp+B5gwYQI///wze/fuJTg4mE6dOjFy5EjGjx/Ptm3bOHHiRKwq8ooVK3LgwIEYiV8IW+agdwBCpEZFihTh+PHjcT7WqlWrGL9Pnz4db29vTp8+TYkSJUzb/fz8+PbbbwEYMmQII0aMIHPmzPTq1QuA77//nokTJ3L8+HFKlizJ6NGj2bJli6m0mi9fPnbt2sXkyZOpVasWGTJkwMnJCTc3N7JmzQpoXxLe9TyjAgUKMGrUqLe+7qtXr5I9e/YY244dO0aZMmVibAsMDMTPzy/GPpkyZWLhwoVkzpwZAH9/f7Zs2cLp06dxd3cHoEKFCty5cyfGsXLkyMHLly+5c+eOKdELYcskMQthAUopDAZDnI9dunSJ7777jn379vHgwQNTSTkoKChGYi5VqpTpvr29PV5eXpQsWdK0zcfHB4B79+5x+vRpwsLCYlUxh4eHx0qKr0vM894sCcflxYsXuLi4xNgWGBhIhw4dYmw7evRojMQcGBhIs2bNTEkZtL9H+/btTUnZuK1x48YxjuXq6gpgqjkQwtZJYhbCAs6cOUPevHnjfKxp06bkypWLqVOnkj17dqKjoylRokSsDlWOjo4xfjcYDDG2GRN/dHS0KbmvWbOGHDlyxHje61XIb0rM815PkPHJnDkzjx49Mv0eFRXFqVOnYiX5I0eO0KJFC9Pvx44dY9CgQTH2CQwMpF+/fqbfw8LCOH/+PKVLl46xn7HNOUuWLO+MTwhbIIlZCDPbsmULJ06cYMCAAbEeCw4O5syZM0yePJkaNWoAWueo5CpWrBjOzs4EBQXFqH5+k5OTE1FRUYl+XkKVKVOGefPmmX4/d+4cL168iFG9vXfvXm7evGkqMYeEhHD16tUYyfvatWs8fPgwxrZTp04RFRUVo6QNcPLkSXLmzBmjtC2ELZPELEQyGNs2o6KiuHv3LuvXr2f48OE0adKEzp07x9o/U6ZMeHl5MWXKFLJly0ZQUBCDBw9OdhweHh58+eWXDBgwgOjoaKpXr05ISAh79uwhXbp0dOnSBYA8efKwf/9+rl69Srp06fD09EzQ8xKqQYMGDBkyhEePHpE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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(D, PSD_mode1, '--b')\n", "plt.plot(D, PSD_mode2, '--b')\n", "plt.plot(D, PSD_mode3, '--b')\n", "plt.plot(D, PSD_sum, 'k')\n", "plt.axis('square')\n", "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.xlim([1e-2, 1e1])\n", "plt.ylim([1e0, 1e3])\n", "plt.grid()\n", "plt.xlabel('Diameter $(\\mu m)$')\n", "plt.ylabel('dN/dlogD $(cm^{-3})$')\n", "plt.title('Modal fit of Zeppelin springtime median aerosol PSD')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "- We thank Radovan Krejci, Paul Zieger, and Peter Tunved for supplying the dataset of aerosol measurements taken at Zeppelin Observatory.\n", "\n", "- A manuscript detailing aerosol characteristics during cold-air outbreaks around the Norwegian Sea is currently in preparation (Williams et al., *in prep*). This manuscript further describes the observations and methods used to derive the above parameters for the modal fit to the springtime median aerosol PSD at Zeppelin Observatory. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" }, "nbdime-conflicts": { "local_diff": [ { "diff": [ { "diff": [ { "key": 0, "op": "addrange", "valuelist": [ "Python 3" ] }, { "key": 0, "length": 1, "op": "removerange" } ], "key": "display_name", "op": "patch" } ], "key": "kernelspec", "op": "patch" } ], "remote_diff": [ { "diff": [ { "diff": [ { "key": 0, "op": "addrange", "valuelist": [ "Python3" ] }, { "key": 0, "length": 1, "op": "removerange" } ], "key": "display_name", "op": "patch" } ], "key": "kernelspec", "op": "patch" } ] }, "toc-autonumbering": false }, "nbformat": 4, "nbformat_minor": 4 }