# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 3 Results and Discussion
### Field Deployment and Samples of Preliminary Data
The variation among locations can also be appreciated by comparing the three lines within each subfigure. Figure 11: Hazard maps showing the average of (**a**) PM, (**b**) CO, (**c**) oxidizing gases, and (**d**) noise between 8:00 a.m. and 8:55 a.m. on a typical production day. Circles represent the location of the monitors in the factory. An inverse distance weighting scheme was used to estimate hazard levels between monitor locations. The lines indicate work areas in the factory that are generally divided by aisles. The gray-shaded area indicates the office area which is isolated from the manufacturing floor and was not part of the study area. The hue indicates the intensity of the hazard level. The study area is approximately 290 m \(\times\) 250 m (75,000 m\({}^{2}\)). Other researchers have recognized the advantages of inexpensive sensors that might be used in distributed networks. In 2012, [PERSON], [PERSON] and [PERSON] [26] described how a network of dust sensors would be useful for mapping hazards in urban areas and for monitoring long-term personal exposure. In 2013, [PERSON] et al. [27] went further by developing a prototype monitor based on an inexpensive dust sensor produced by Sharp in 2006 (Sharp, Model GP2Y1010 AU0F, Osaka, Japan) [28]. They connected the sensor to a microcontroller and transmitted the signals through an RF transceiver. In 2014, [PERSON] et al. [29] also connected the Sharp sensor to a microprocessor, but used the wireless Zigbee communication protocol rather than the ST-TR1100 wireless transceiver used by [PERSON] et al. [PERSON] and [PERSON] [30] pursued a similar approach, but connected two different dust sensors: the same Sharp sensor used by [PERSON] et al., and one manufactured by the Shinyei corporation (Shinyei Corporation, Model PPD4 NS, New York, NY, USA), connecting them through an Arduino microcontroller and a wireless transceiver. They measured the responses of both receivers against a third inexpensive commercial device, the Dylos DC1100 (Dylos, Model DC1100, Riverside, CA, USA). Also in 2014, [PERSON] and [PERSON] [31] went still further, integrating a camera, a temperature and humidity sensor, the familiar Sharp sensor, and an air odor sensor (Shenzhen Dovelet Sensors Technology Co., Model TPM-300E, Shenzhen, China). Their prototype-integrated system also communicated wirelessly. A similar prototype developed by [PERSON] et al. in 2015 [32] was based on the Yun microprocessor system and integrated a sensor for volatile organic compounds (Winsen Electronics, Model MQ138, Zhengzhou, China), the aforementioned Sharp dust sensor, and a temperature and humidity sensor (Aosong Electronics, Model AMT2001, Guangzhou, China). The Yun microprocessor acted as a web server, which could then be accessed by another device, such as a cell phone. The monitors described in this work include a suite of instruments specifically selected to monitor aerosol, gas, and noise hazards in a manufacturing environment. The monitors incorporate several new sensors that have not been reported in previous work. Also, this report describes how the system was calibrated in the lab and deployed in the field, which has not been described in previous work. Figure 12: Timeseries plots of (**a**) PM, (**b**) CO, (**c**) oxidizing gases, and (**d**) noise recorded by three monitors at different locations over several days. The two larger patterns indicate work weeks, with hazards being lowest during the weekends. Small peaks within the work week indicate diurnal variation. Differences between the colored lines in a single plot indicate differences caused by the location of the monitors within the factory.