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

#### **Harsshit Agrawaal, Courtney Jones and J.E. Thompson \***

Department of Chemistry & Biochemistry, Texas Tech University, Box 41061, Lubbock, TX 79409-1061, USA; Harsshit.Agrawaal@ttu.edu (H.A.); Courtney.K.Jones@ttu.edu (C.J.)

**\*** Correspondence: jon.thompson@ttu.edu

Received: 28 November 2019; Accepted: 27 January 2020; Published: 29 January 2020

**Abstract:** Low-cost, portable particle sensors (n = 3) were designed, constructed, and used to monitor human exposure to particle pollution at various locations and times in Lubbock, TX. The air sensors consisted of a Sharp GP2Y1010AU0F dust sensor interfaced to an Arduino Uno R3, and a FONA808 3G communications module. The Arduino Uno was used to receive the signal from calibrated dust sensors to provide a concentration (μg/m3) of suspended particulate matter and coordinate wireless transmission of data via the 3G cellular network. Prior to use for monitoring, dust sensors were calibrated against a reference aerosol monitor (RAM-1) operating independently. Sodium chloride particles were generated inside of a 3.6 m<sup>3</sup> mixing chamber while the RAM-1 and each dust sensor recorded signals and calibration was achieved for each dust sensor independently of others by direct comparison with the RAM-1 reading. In an e ffort to improve the quality of the data stream, the e ffect of averaging replicate individual pulses of the Sharp sensor when analyzing zero air has been studied. Averaging data points exponentially reduces standard deviation for all sensors with n < 2000 averages but averaging produced diminishing returns after approx. 2000 averages. The sensors exhibited standard deviations for replicate measurements of 3–6 μg/m<sup>3</sup> and corresponding 3σ detection limits of 9–18 μg/m<sup>3</sup> when 2000 pulses of the dust sensor LED were averaged over an approx. 2 min data collection/transmission cycle. To demonstrate portable monitoring, concentration values from the dust sensors were sent wirelessly in real time to a *ThingSpeak* channel, while tracking the sensor's latitude and longitude using an on-board Global Positioning System (GPS) sensor. Outdoor and indoor air quality measurements were made at di fferent places and times while human volunteers carried sensors. The measurements indicated walking by restaurants and cooking at home increased the exposure to particulate matter. The construction of the dust sensors and data collected from this research enhance the current research by describing an open-source concept and providing initial measurements. In principle, sensors can be massively multiplexed and used to generate real-time maps of particulate matter around a given location.

**Keywords:** air quality; crowd-sensing; crowd-sourced sensing; environmental analysis; pollution; particulate matter; dust sensor; human exposure; Arduino; wireless networks; IoT
