## 3 Results
### Precision  
The precision of the CF_1 and pm2.5 alt algorithms is calculated by comparing the A and B sensors within a PA-II or Flex monitor. We have calculated precision by abs(A - B)/(A + B), although some prefer to use the coefficient of variation (CV) or relative standard deviation (RSD). The CV and RSD are equal to sqrt(2) times the precision as defined above. A reasonable choice for an upper limit on precision would be, say, 20% using the definition above, which corresponds to a CV or RSD of 28%. For each of the nine monitors during the 6-month 2023 period in both the Santa Rosa and Redwood City locations, the number of measurements meeting the precision standard of 20% is provided for both the CF_1 and pm2.5 alt algorithms (Table 12). For the CF_1 algorithm, the fraction of observations meeting the standard ranges from 0.22 to 0.74; for the pm2.5 alt algorithm, the fraction ranges from 0.85 to 0.99. The major loss of observations for the CF_1 algorithm shown in the above table is due in part to the many values of zero that occur. However, the zero values are not the only reason for the poor performance. Even considering only those observations with a precision meeting the standard, the mean and median precision estimates for the CF_1 algorithm are consistently worse than for the pm2.5 alt algorithm. Comparing the precision of the six Santa Rosa monitors in 2023 for the two algorithms CF_1 and pm2.5 alt, the upper limit of 0.2 is easily met by the pm2.5 alt algorithm, whereas no mean value for the CF_1 algorithm was able to meet the 0.2 upper limit (Table 13). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Sensor**}} & \multicolumn{3}{c}{**Individual Models**} & \multicolumn{3}{c}{**General Model**} \\ \hline  & **Intercept** & **Slope** & **R\({}^{2}\)** & **Intercept** & **Slope** & **R\({}^{2}\)** \\ \hline
7a & 0.0114 & 0.9949 & 0.9949 & \(-\)0.1413 & 0.9932 & 0.9995 \\ \hline
7b & 0.0133 & 0.995 & 0.9953 & \(-\)0.1942 & 0.9502 & 0.9993 \\ \hline
8a & 0.0266 & 0.9905 & 0.9923 & \(-\)0.04 & 0.994 & 0.9856 \\ \hline
8b & 0.1107 & 0.962 & 0.9611 & 0.0513 & 0.9892 & 0.9907 \\ \hline
9a OUT & 0.0568 & 0.991 & 0.991 & 0.7474 & 1.0646 & 0.9967 \\ \hline
9b OUT & 0.0202 & 0.9972 & 0.9972 & \(-\)0.3219 & 1.0242 & 0.9979 \\ \hline  & & & & & & \\ \hline mean & 0.039833 & 0.988433 & 0.988633 & 0.016883 & 1.002567 & 0.99495 \\ \hline SD & 0.038427 & 0.013201 & 0.013669 & 0.380112 & 0.038466 & 0.005601 \\ \hline SE & 0.015688 & 0.005389 & 0.00558 & 0.15518 & 0.015704 & 0.002287 \\ \hline RSD & 0.965 & 0.013 & 0.014 & 22.514 & 0.038 & 0.006 \\ \hline RSE & 0.394 & 0.005 & 0.006 & 9.191 & 0.016 & 0.002 \\ \hline RSE (\%) & 39.0\% & 0.5\% & 0.6\% & 919\% & 1.6\% & 0.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Regression parameters (intercept, slope, r2) for the 2023 data from Redwood city. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Valid N** & **Mean** & **Median** & \
\begin{tabular}{c} **Upper** \\ **Quartile** \\ \end{tabular} &
\begin{tabular}{c} **90 th** \\ **Perceentile** \\ \end{tabular} \\ \hline
1 precision & 39,012 & 0.050 & 0.039 & 0.067 & 0.11 \\
2 p2.5 alt & 39,011 & 0.059 & 0.043 & 0.081 & 0.13 \\ \hline
4 precision & 39,012 & 0.062 & 0.047 & 0.084 & 0.13 \\ \hline
3 OUT & & & & & \\ precision & 38,993 & 0.048 & 0.038 & 0.067 & 0.10 \\ p2.5 alt & & & & & \\ \hline
5 precision & 39,002 & 0.053 & 0.040 & 0.071 & 0.11 \\ \hline
6 precision & 39,002 & 0.053 & 0.037 & 0.068 & 0.11 \\
2 p2.5 alt & 32,650 & 0.53 & 0.51 & 0.94 & 1.00 \\ \hline
2 precision & 29,103 & 0.26 & 0.11 & 0.32 & 1.00 \\ \hline
4 precision & 30,282 & 0.26 & 0.11 & 0.32 & 1.00 \\ CF\_1 & & & & & \\ \hline
3 OUT & & & & & \\ precision & 36,584 & 0.21 & 0.13 & 0.23 & 0.45 \\ CF\_1 & & & & & \\ \hline
5 precision & 29,618 & 0.24 & 0.09 & 0.29 & 1.00 \\ CF\_1 & & & & & \\ \hline
6 precision & 30,060 & 0.23 & 0.08 & 0.27 & 1.00 \\ \hline \hline \end{tabular}
\end{table}