{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(sec:theory_measurementError)=\n",
    "# Example: Measuring Air Quality\n",
    "\n",
    "Across the US, [sensors to measure air pollution are widely used](https://web-strategist.com/blog/2020/09/20/how-the-crowd-provides-air-quality-data/) by individuals, community groups, and state and local air monitoring agencies. For example, on two days in September 2020, approximately 600,000 Californians and 500,000 Oregonians viewed PurpleAir’s map as fire spread through their states and evacuations were planned. ([PurpleAir](https://www2.purpleair.com) creates air quality maps from crowdsourced data that streams in from its sensors.) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sensors measure the amount of particulate matter in the air that has a diameter smaller than 2.5 micrometers (the unit of measurement is micrograms per cubic meter: μg/m<sup>3</sup>). The measurements recorded are the average concentrations over two minutes. While the level of particulate matter changes over the course of a day as, for example, people commute to and from work, there are certain times of the day, like at midnight, when we expect the two minute averages to change little in a half hour. If we examine the measurements taken during these times of the day, we can get a sense of the combined variability in the instrument recordings and the mixing of particles in the air. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anyone can access sensor measurements from PurpleAir's site. The site provides a download tool, and data are available for any sensor that appears on PurpleAir's map. We downloaded data from one sensor over a 24-hour period and selected three half-hour time intervals spread  throughout the day where the readings were roughly constant over the 30-minute period. This gave us three sets of 15 two-minute averages, for a total of 45 measurements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aq2.5</th>\n",
       "      <th>time</th>\n",
       "      <th>hour</th>\n",
       "      <th>meds</th>\n",
       "      <th>diff30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.14</td>\n",
       "      <td>2022-04-01 00:01:10 UTC</td>\n",
       "      <td>0</td>\n",
       "      <td>5.38</td>\n",
       "      <td>0.59</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>5.29</td>\n",
       "      <td>2022-04-01 00:05:10 UTC</td>\n",
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       "      <td>5.38</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>7.55</td>\n",
       "      <td>2022-04-01 19:27:20 UTC</td>\n",
       "      <td>19</td>\n",
       "      <td>8.55</td>\n",
       "      <td>-1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>9.47</td>\n",
       "      <td>2022-04-01 19:29:20 UTC</td>\n",
       "      <td>19</td>\n",
       "      <td>8.55</td>\n",
       "      <td>0.63</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>8.55</td>\n",
       "      <td>2022-04-01 19:31:20 UTC</td>\n",
       "      <td>19</td>\n",
       "      <td>8.55</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 5 columns</p>\n",
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       "    aq2.5                     time  hour  meds  diff30\n",
       "0    6.14  2022-04-01 00:01:10 UTC     0  5.38    0.59\n",
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       "2    5.29  2022-04-01 00:05:10 UTC     0  5.38   -0.26\n",
       "..    ...                      ...   ...   ...     ...\n",
       "42   7.55  2022-04-01 19:27:20 UTC    19  8.55   -1.29\n",
       "43   9.47  2022-04-01 19:29:20 UTC    19  8.55    0.63\n",
       "44   8.55  2022-04-01 19:31:20 UTC    19  8.55   -0.29\n",
       "\n",
       "[45 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm = pd.read_csv('data/pm30.csv')\n",
    "pm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line plots can give us a sense of variation in the measurements. In one 30-minute period, we expect the measurements to be roughly the same, with the exception of minor variations from the particles moving in the air and the measurement error of the instrument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(pm, x=\"time\", y=\"aq2.5\", color=\"hour\",\n",
    "             labels={\n",
    "        \"time\": \"2-minute intervals in a half hour\",\n",
    "        \"aq2.5\": \"Particulate matter\",\n",
    "    }\n",
    "      )\n",
    "\n",
    "fig.add_annotation(x=12, y=5, text=\"Midnight\", showarrow=False)\n",
    "fig.add_annotation(x=24, y=6, text=\"11 a.m.\", showarrow=False)\n",
    "fig.add_annotation(x=36, y=8, text=\"7 p.m.\", showarrow=False)\n",
    "\n",
    "fig.update_xaxes(showticklabels=False)\n",
    "fig.update_layout(width=500, height=250, showlegend=False)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot shows us how the air quality worsens throughout the day, but in each of these half-hour intervals, the air quality is roughly constant at 5.4, 6.6, and 8.6 μg/m<sup>3</sup> at midnight, 11 a.m., and 7 p.m., respectively.  We can think of the data scope as follows: at this particular location in a specific half-hour time interval, there is an average particle concentration in the air surrounding the sensor. This concentration is our target, and our instrument, the sensor, takes many measurements that form a sample from the access frame. (See {numref}`Chapter %s <ch:data_scope>` for the dart board analogy of this process.) If the instrument is working properly, the measurements are centered on the target: the 30-minute average.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a better sense of the variation in a half-hour interval, we can examine the differences of the measurements from the median for the corresponding half hour. The distribution of these \"errors\" is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "remove-input"
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   "outputs": [
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   ],
   "source": [
    "fig = px.histogram(pm, x='diff30', nbins=20,\n",
    "                   labels={'diff30':'Deviation from 30-minute median'})\n",
    "\n",
    "fig.update_xaxes(range=[-1.7, 1.8])\n",
    "fig.update_layout(width=350, height=250)                     "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows us that the typical fluctuations in measurements are often less than 0.5 μg/m<sup>3</sup> and rarely greater than 1 μg/m<sup>3</sup>. With instruments, we often consider their _relative standard error_, which is the standard deviation as a percentage of the mean. The standard deviation of these 45 deviations is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6870817156282193"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(pm['diff30'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that the hourly measurements range from 5 to 9 μg/m<sup>3</sup>, the relative error is 8% to 12%, which is reasonably accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the urn model to simulate the variability in this measurement process. We place in the urn the deviations of the measurements from their 30-minute medians for all 45 readings, and we simulate a 30-minute air quality sequence of measurements by drawing 15 times *with replacement* from the urn and adding the deviations drawn to a hypothetical 30-minute average: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "urn = pm[\"diff30\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(221212)\n",
    "sample_err = np.random.choice(urn, size=15, replace=True)\n",
    "aq_imitate = 11 + sample_err"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add a line plot for this artificial set of measurements to our earlier line plots, and compare it to the three real ones:"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ytick ticks crisp\" d=\"M56,0h-5\" transform=\"translate(0,119.2)\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ytick ticks crisp\" d=\"M56,0h-5\" transform=\"translate(0,75.4)\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; stroke-width: 1px;\"/><path class=\"ytick ticks crisp\" d=\"M56,0h-5\" transform=\"translate(0,31.61)\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; stroke-width: 1px;\"/><g class=\"ytick\"><text text-anchor=\"end\" x=\"48.6\" y=\"4.199999999999999\" transform=\"translate(0,206.79)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\">4</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"48.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,162.99)\">6</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"48.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,119.2)\">8</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"48.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,75.4)\">10</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"48.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,31.61)\">12</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"smithlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-34c0bb\"><g class=\"clips\"/></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"273.5\" y=\"239.8\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">2-minute intervals in a half hour</text></g><g class=\"g-ytitle\" transform=\"translate(4.9248046875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.075000000000003,110)\" x=\"10.075000000000003\" y=\"110\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Particulate matter</text></g><g class=\"annotation\" data-index=\"0\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,145.07,184.89)\"><g class=\"cursor-pointer\" transform=\"translate(118,174)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"53\" height=\"20\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"26.90625\" y=\"15\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">Midnight</text></g></g></g><g class=\"annotation\" data-index=\"1\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,233.14,162.99)\"><g class=\"cursor-pointer\" transform=\"translate(211,152)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"44\" height=\"20\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); stroke-opacity: 0; fill: rgb(0, 0, 0); fill-opacity: 0;\"/><text class=\"annotation-text\" text-anchor=\"middle\" x=\"22.53125\" y=\"15\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">11 a.m.</text></g></g></g><g class=\"annotation\" data-index=\"2\" style=\"opacity: 1;\"><g class=\"annotation-text-g\" transform=\"rotate(0,321.2,119.2)\"><g class=\"cursor-pointer\" transform=\"translate(302,109)\"><rect class=\"bg\" x=\"0.5\" y=\"0.5\" width=\"38\" height=\"20\" style=\"stroke-width: 1px; stroke: rgb(0, 0, 0); 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = np.arange(1, 16)\n",
    "\n",
    "fig = px.line(\n",
    "    pm,\n",
    "    x=\"time\",\n",
    "    y=\"aq2.5\",\n",
    "    color=\"hour\",\n",
    "    labels={\n",
    "        \"time\": \"2-minute intervals in a half hour\",\n",
    "        \"aq2.5\": \"Particulate matter\",\n",
    "    },\n",
    ")\n",
    "\n",
    "fig.add_trace(go.Scatter(x=times, y=aq_imitate, mode=\"lines\"))\n",
    "\n",
    "fig.add_annotation(x=12, y=5, text=\"Midnight\", showarrow=False)\n",
    "fig.add_annotation(x=24, y=6, text=\"11 a.m.\", showarrow=False)\n",
    "fig.add_annotation(x=36, y=8, text=\"7 p.m.\", showarrow=False)\n",
    "fig.add_annotation(x=52, y=9.7, text=\"Simulated\", showarrow=False)\n",
    "fig.update_xaxes(showticklabels=False)\n",
    "fig.update_layout(width=500, height=250, showlegend=False)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of the line plot from the simulated data is similar to the others, which indicates that our model for the measurement process is reasonable.  Unfortunately, what we don't know is whether the measurements are close to the true air quality. To detect bias in the instrument, we need to make comparisons against a more accurate instrument or take measurements in a protected environment where the air has a known quantity of particulate matter. In fact, [researchers](https://thebolditalic.com/understanding-purpleair-vs-airnow-gov-measurements-of-wood-smoke-pollution-562923a55226) have found that low humidity can distort the readings so that they are too high. In {numref}`Chapter %s <ch:pa>`, we carry out a more comprehensive analysis of the PurpleAir sensor data and calibrate the instruments to improve their accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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