Updates to the documentation

Signed-off-by: bvandekerkhof <[email protected]>

README.md

@@ -52,10 +52,23 @@ Use cases where the pyELQ code has been applied are described in the following p
 * Weidmann, D., Hirst, B. et al. "Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach." ACS Earth and Space Chemistry 2022 6 (9), 2190-2198  (https://doi.org/10.1021/acsearthspacechem.2c00093)
 
 ## Deployment design
-The pyELQ code needs high-quality methane concentration and wind data to be able to provide reliable estimates of methane emission location and quantification. This requires that methane sensors of sufficiently high precision are used in a layout that allows the detection of relevant methane emissions. The optimal sensor layout typically depends on the prevailing meteorological conditions at the site of interest: sensors need to be placed in locations where plumes from methane emission sources are likely to occur. 
+The pyELQ code needs high-quality methane concentration and wind data to be able to provide reliable output on location 
+and quantification of methane emission sources. This requires methane concentration sensors of sufficiently high 
+precision in a layout that allows the detection of relevant methane emission sources, in combination with wind 
+measurements of high enough frequency and accuracy. The optimal sensor layout typically depends on the prevailing 
+meteorological conditions at the site of interest and requires multiple concentration sensors to cover the site under 
+different wind directions. 
 
 ## pyELQ data interpretation
-The estimates from pyELQ come with uncertainty ranges that are representative of probability density functions sampled by a Markov Chain Monte Carlo method. One should take these uncertainty ranges into account when interpreting the pyELQ output data. Remember that absence of evidence for methane emissions does not always imply evidence for absence of methane emissions; for instance, when meteorological conditions are such that there is no sensor downwind of a methane source, then it will be impossible to detect this particular source. Also, there are limitations to the forward dispersion model which is used in the analysis. For example, the performance of the Gaussian plume dispersion model will degrade at lower windspeeds. Therefore, careful interpretation of the data is always required. 
+The results from pyELQ come with uncertainty ranges that are representative of probability density functions sampled 
+by a Markov Chain Monte Carlo method. One should take these uncertainty ranges into account when interpreting the pyELQ 
+output data. Remember that absence of evidence for methane emissions does not always imply evidence for absence of 
+methane emissions; for instance, when meteorological conditions are such that there is no sensor downwind of a methane 
+source during the selected monitoring period, then it will be impossible to detect, localize and quantify 
+this particular source. 
+Also, there are limitations to the forward dispersion model which is used in the analysis. 
+For example, the performance of the Gaussian plume dispersion model will degrade at lower wind speeds. 
+Therefore, careful interpretation of the data is always required. 
 
 ***
 # Installing pyELQ as a package

docs/index.md

@@ -35,11 +35,23 @@ Use cases where the pyELQ code has been applied are described in the following p
 * Weidmann, D., Hirst, B. et al. "Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach." ACS Earth and Space Chemistry 2022 6 (9), 2190-2198  (https://doi.org/10.1021/acsearthspacechem.2c00093)
 
 ## Deployment design
-The pyELQ code needs high-quality methane concentration and wind data to be able to provide reliable estimates of methane emission location and quantification. This requires that methane sensors of sufficiently high precision are used in a layout that allows the detection of relevant methane emissions. The optimal sensor layout typically depends on the prevailing meteorological conditions at the site of interest: sensors need to be placed in locations where plumes from methane emission sources are likely to occur. 
+The pyELQ code needs high-quality methane concentration and wind data to be able to provide reliable output on location 
+and quantification of methane emission sources. This requires methane concentration sensors of sufficiently high 
+precision in a layout that allows the detection of relevant methane emission sources, in combination with wind 
+measurements of high enough frequency and accuracy. The optimal sensor layout typically depends on the prevailing 
+meteorological conditions at the site of interest and requires multiple concentration sensors to cover the site under 
+different wind directions. 
 
 ## pyELQ data interpretation
-The estimates from pyELQ come with uncertainty ranges that are representative of probability density functions sampled by a Markov Chain Monte Carlo method. One should take these uncertainty ranges into account when interpreting the pyELQ output data. Remember that absence of evidence for methane emissions does not always imply evidence for absence of methane emissions; for instance, when meteorological conditions are such that there is no sensor downwind of a methane source, then it will be impossible to detect this particular source. Also, there are limitations to the forward dispersion model which is used in the analysis. For example, the performance of the Gaussian plume dispersion model will degrade at lower windspeeds. Therefore, careful interpretation of the data is always required. 
-
+The results from pyELQ come with uncertainty ranges that are representative of probability density functions sampled 
+by a Markov Chain Monte Carlo method. One should take these uncertainty ranges into account when interpreting the pyELQ 
+output data. Remember that absence of evidence for methane emissions does not always imply evidence for absence of 
+methane emissions; for instance, when meteorological conditions are such that there is no sensor downwind of a methane 
+source during the selected monitoring period, then it will be impossible to detect, localize and quantify 
+this particular source. 
+Also, there are limitations to the forward dispersion model which is used in the analysis. 
+For example, the performance of the Gaussian plume dispersion model will degrade at lower wind speeds. 
+Therefore, careful interpretation of the data is always required. 
 ***
 # Installing pyELQ as a package
 Suppose you want to use this pyELQ package in a different project.