Portable, Ambient PM_2.5 Sensor for Human and/or Animal Exposure Studies

Abstract

A field portable device for logging PM2.5 mass concentration data has been developed. The device combines the Arduino microprocessor with an SD card, an Sharp DN7C3CA006 optical dust monitor, and 10 000 mAh battery. The PM2.5 dust sensor uses a virtual impactor to size select particles prior to illuminating them with an LED. The LED is triggered by a circuit controlled with the Arduino. Nephelometric detection at 120 referenced to incidence is employed. The voltage signal reported by the dust sensor is converted to PM2.5 mass thru calibration onboard the Arduino. Data points can be saved to the SD card as rapidly as 0.3 s, although averaging signals over 60 s produced more optimal detection limits. For a 60 s average, the PM2.5 mass limit of detection was 9 g/m 3 indicating the sensor will be useful for monitoring human exposure to fine particles. Portable exposure monitoring has been demonstrated with the Downloaded by [University of Lethbridge] at 15:20 25 June 20162 sensing platform as several individuals carried the device with them during daily activities in Lubbock, TX and Atlanta, GA. For this group of test subjects, values of PM2.5 exposure varied from 0 to 1000 g/m 3 during the sampling periods. It was observed that, by far, the highest levels of PM2.5 occur during periods of cooking, or being near cooking operations. Other periods of high PM2.5 occurred during ground transportation, use of personal care products, vacuuming, and visiting restrooms. When hourly personal exposure data was correlated with hourly average PM2.5 for outdoor air for the Atlanta dataset, a very weak correlation was found (R 2 = 0.026). For only two of eight sampling periods did the personal monitoring estimate of exposure agree with that predicted by outdoor monitoring to within 15%. Personal exposure was often affected by circumstantial, short-term, high exposure events that are difficult to model or predict effectively.The short-term exposure events generally cause true exposure to be higher than that predicted by using outdoor ambient PM2.5 to generate estimates. This finding complicates interpretation of epidemiological studies that find links between ambient outdoor PM2.5 levels and human health, while it buttresses the case for employing personal ambient monitors.

Assessment and mitigation of personal exposure to particulate air pollution in cities: An exploratory study

“…A realistic methodology to assess the integrated 8 PE of individuals to PM in both outdoor and indoor MEs is needed. Previously reported 24-hour PE studies focused on the characterization of PM2.5 (Assimakopoulos et al, 2018;Cao and Thompson, 2017;Gao et al, 2018), as well as BC (Dons et al, 2011;Dons et al, 2019), UFPs (Bekö et al, 2015), and a combination of these air pollutants by Koehler et al (2019). These studies did not cover a wide range of outdoor and indoor MEs with varying levels of human activities.…”

Calibration of Portable Particulate Matter–Monitoring Device using Web Query and Machine Learning

“…As PM 2.5 in the air enters the light scattering unit consisting of an infrared light source and a photodiode, it scatters infrared light rays. Intensity of scattered light is measured by the photodiode placed at an angle of 120 degrees from the light source, resulting in voltage proportional to PM 2.5 concentration via nephelometric detection [19].…”

Personal Exposure Estimates via Portable and Wireless Sensing and Reporting of Particulate Pollution

“…The current study aims to build upon previous work from our laboratory in which a field-portable device for logging PM 2.5 mass concentration data was developed [39]. The previous device also used the Arduino, and a Sharp sensor with nephelometric detection, but logged all collected data to an SD card.…”