# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 3 Results and Discussion
### Field Deployment and Samples of Preliminary Data  
The factory under study contained a number of manufacturing processes, including arc welding, milling, and painting. The work is generally divided into regions with similar manufacturing processes. The sensors were deployed in the factory in several stages, starting with five monitors and ultimately leading to 40 monitors. Significant effort was invested in making the wireless connectivity of the monitors robust and compatible with the network of the factory in which the devices were deployed. Periodic failures from unique circumstances also led us to develop several layers of fail-safes to restart devices should they fail to consistently record data, ranging from simply retrying the wireless connection to shutting down and restarting the wireless subsystem. These fail-safes themselves occasionally caused bugs but were ultimately helpful in producing a system that could run reliably for months on end, despite the vagaries of the shop environment, including power or internet connection outages, and the constant onslaught of dust, vibration, and oil. A 30-day laboratory test yielded no communication or hardware failures. Over a period of 235 days in the factory, defining a failure as one hour without report, the devices ran with a 10.5 day mean time to failure. Failures were typically repaired by cycling the power. While running, monitors provided an average of 275.8 readings per day,  
Figure 8: Average noise sensor measurements relative to the reference measurements. The \(x\)-axis and \(y\)-axis error bars represent one standard deviation. dropping less than 5% of all reports. Dropped reports were stored to the monitor's flash drive. Because of our limited access to the facility and the facility's network, it was not possible to further diagnose the source of the system failures. Somewhat to our surprise, the dust itself became a challenge for the monitors. Figure 9 shows what the interior of the particle sensor looked like after just 19 weeks of deployment in the factory. The readings from this sensor trended steadily upward during deployment, suggesting that the sensor itself was obstructed. We hypothesized that a passive sensor, e.g., one without a fan to pull the air through the sensor, would avoid this heavy contamination. New monitors with passive sensors were constructed and hung in close proximity to the active sensors for several weeks to compare performance. Figure 10 suggests that the passive sensors were less affected by the dust, but a more thorough analysis is underway. Figure 10: The accumulation of dust on the active Sharp sensors caused the readings to drift upward over time. (**a**) One example pair of collocated active and passive sensors; and (**b**) a second pair of collocated active and passive sensors in which the active sensor signal eventually flatlines. The passive version of the sensors seemed to suffer much less drift. Gaps in the graph represent periods when data were not recorded from the devices. Figure 9: Dust fouled the active Sharp sensor after just 19 weeks of deployment in the factory. Figures 11 and 12 present samples of the data collected by the network. Figure 11 is an example of four hazard maps constructed from the readings of all of the sensors, averaged over a period of one hour and interpolated between the various sensor locations. Figure 11 displays the variations for the particle sensors, Figure 11 the carbon monoxide sensors, Figure 11 the oxidizing gas sensors, and Figure 11 the noise sensors. This figure makes it clear that the central region, where many welding stations are located, generates the most particles, while the lower left region, where torch cutting equipment is located, was the greatest source of carbon monoxide. Figure 12 illustrates the temporal variations among the four sensor types in three monitors hung at different locations in the factory. Here, the weekly variation and the diurnal variation is unmistakable.