# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 4 Conclusions  
The particle sensor, carbon monoxide sensor, oxidizing gas sensor, and noise sensor all performed with accuracy and precision levels consistent with monitoring for multiple hazards in an active factory environment. These were integrated into a plastic case, along with a temperature and humidity sensor, which allowed them to be safely deployed for sustained periods in a sensor network. The data from these sensors were consistently collected and saved to a database.  
The network of low-cost monitors demonstrated here increases the data available upon which to base effective risk mitigation strategies. Conventional sampling provides a single measurement of one hazard, typically integrated over a work shift, and only becomes available weeks after sampling. In contrast, the approach presented here provides continuous measurements of multiple hazards throughout the workplace, and these measurements are immediately available for interpretation and action. Such measurements can be used to identify sources of hazards, formulate strategies for improved control, and evaluate the effectiveness of controls over time. Combined with worker location, these measurements can be used to estimate personal exposures for an entire workforce rather than a single person. This departure from the status quo of conventional sampling will enable a shift to comprehensive exposure assessment with substantially greater certainty that workers are adequately protected from workplace hazards.  
The data from the system will form the basis of several future studies, including the development of a real-time mapping system to understand how the levels of various hazards change throughout the day and over longer periods, periods in which the air handling requirements change as weather and environmental loads change alongside factory demand. The system will also be used to understand the individual exposure of a single worker and, once integrated with a position tracking system, can be modeled as the worker moves through the factory. The sensor network itself could be improved by optimizing energy efficiency, congestion control, network connectivity and network coverage (e.g., [33; 34; 35; 36]). Finally, we are interested in how data from the network may be used to change individual exposure to workers, and how such data might be utilized to implement control strategies to reduce harmful concentrations and intensities.  
[PERSON] designed the initial hardware and software. [PERSON] completed the aerosol experiments. [PERSON] designed and participated in testing of the noise sensors and led the construction of the devices. [PERSON] built the database software. [PERSON] participated in the testing of the sensors and provided text and figures for the manuscript. [PERSON] helped design the software. [PERSON] and [PERSON] led the device selection and the scientific direction.  
This research was funded by U.S. National Institute for Occupational Safety and Health, grant number R01 OH010533.  
The authors would like to acknowledge [PERSON], [PERSON] and [PERSON], then engineering undergraduates at the University of Iowa, and [PERSON], a computer science undergraduate at the University of Iowa, for their help in building and testing the devices. We would also like to acknowledge graduate students [PERSON] and [PERSON] from the Department of Occupational and Environmental Health at the University of Iowa, particularly for their help in testing and validating the sensor readings of the gas sensors and sound sensors, respectively. Thanks also to [PERSON] for his invaluable editing. Finally, we acknowledge the work of [PERSON], a postdoctoral fellow in Environmental Health & Engineering at Johns Hopkins University, for his work finding the calibration parameters of the carbon monoxide and oxidizing gas sensors.  
The authors declare no conflict of interest.