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      <img src="" class="logo" alt=""><h1>Properties of the predefined scenarios from the EFSA guidance from 2017</h1>
      <small class="dont-index">Source: <a href="https://github.com/jranke/pfm/blob/HEAD/R/soil_scenario_data_EFSA_2017.R" class="external-link"><code>R/soil_scenario_data_EFSA_2017.R</code></a></small>
      <div class="d-none name"><code>soil_scenario_data_EFSA_2017.Rd</code></div>
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    <div class="ref-description section level2">
    <p>Properties of the predefined scenarios used at Tier 1, Tier 2A and Tier 3A for the
concentration in soil as given in the EFSA guidance (2017, p. 14/15). Also, the
scenario and model adjustment factors from p. 16 and p. 18 are included.</p>
    </div>


    <div class="section level2">
    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
    <p>A data frame with one row for each scenario. Row names are the scenario codes,
e.g. CTN for the Northern scenario for the total concentration in soil. Columns are
mostly self-explanatory. <code>rho</code> is the dry bulk density of the top soil.</p>
    </div>
    <div class="section level2">
    <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a></h2>
    <p>EFSA (European Food Safety Authority) (2017)
EFSA guidance document for predicting environmental concentrations
of active substances of plant protection products and transformation products of these
active substances in soil. <em>EFSA Journal</em> <b>15</b>(10) 4982
doi:10.2903/j.efsa.2017.4982</p>
    </div>

    <div class="section level2">
    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">soil_scenario_data_EFSA_2017</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>        Zone Country T_arit T_arr Texture  f_om theta_fc   rho f_sce f_mod</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CTN   North Estonia    5.7   7.6  Coarse 0.220    0.244 0.707   1.4     3</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CTC Central  Poland    7.4   9.3  Coarse 0.122    0.244 0.934   1.4     3</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CTS   South  France   10.2  11.7  Medium 0.070    0.349 1.117   1.4     3</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CLN   North Denmark    8.0   9.2  Medium 0.025    0.349 1.371   1.6     4</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CLC Central Austria    9.3  11.3  Medium 0.018    0.349 1.432   1.6     4</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CLS   South   Spain   15.4  16.7  Medium 0.010    0.349 1.521   1.6     4</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>     FOCUS_zone prec</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CTN    Hamburg  639</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CTC    Hamburg  617</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CTS    Hamburg  667</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CLN    Hamburg  602</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CLC Châteaudun  589</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> CLS    Sevilla  526</span>
</code></pre></div>
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