AI Summary & Key Takeaways
AI InsightThe removal of the monitor with the highest index value would have a significant effect on thenetwork compared to the impact of removing any of the other seven monitors.
📌 3 Key Takeaways
Özet
In this paper, we develop a criticality index for air pollution monitoring networks. The index quantifiesthe effect on the system of removing an air pollution monitor, which is calculated as a weighted sum ofthe differences between baseline values interpolated with historical data using the entire monitoring setand values interpolated with each monitor independently removed from the set. The interpolationprocedure tests for linear dependencies in the north-south and east-west direction and then universalkriging is selected when linear dependencies exist, otherwise ordinary kriging is applied. The index's costfunction is evaluated on a regular grid. The cost function includes a multiplication factor when falsepositivesor false-negatives above an air quality standard are introduced because of the removal of amonitor. The sensitivity analysis indicated the final index ranks were not sensitive to variations in themultiplication factors. The index captures the effects of wind direction in the study area. We apply theindex to an industrial air pollution monitoring network in Hamilton, Ontario, Canada, which consists ofeight air pollution monitoring units measuring PM10. We find a large difference in the cost with aminimum of 91,087 to a maximum cost of 1,733,835 or 0.052 and 1.00, respectively, for the proposedindex. The removal of the monitor with the highest index value would have a significant effect on thenetwork compared to the impact of removing any of the other seven monitors.