The capability to do so requires monitoring correlations for an extraordinarily large set of signal/attribute pairs, and some associations are so unlikely to be causal that the system would be well served to treat them as accidental.
The results indicated that there was a relatively good correlation between both types of monitors (correlation coefficient 0.608; p < 0.001).
We estimated the within-county monitor-to-monitor correlation for each of the seven PM2.5 components to obtain a measure of the spatial homogeneity of each component.
One method considered is to monitor correlation of the signalled group with that of the previous day, as well as to monitor which variables are being signalled and in what way. Figure 12 presents an example of a way to visualise such monitoring.
In the remaining areas, between monitor correlations generally were > 0.70, with a few exceptions.
More than 40 PM2.5 chemical species were available, but many of them had high percentages of observations below their detection limits or poor monitor-to-monitor correlations.
Table 1 summarizes median, interquartile range (IQR), within-county monitor-to-monitor correlations, and percent contribution for each of the seven components of the PM2.5 total mass.
A previous study showed moderate to low monitor-to-monitor correlations between daily concentrations of several species (arsenic, EC, and nickel) in the New York City area, which suggest high spatial variability in some speciation concentrations (Ito et al. 2004).
Based on co-culture conditions, we introduced genetic material into BCECs at different states of barrier maturity, which enabled us to monitor correlations between cell division, barrier maturity and transfection efficiency.
