Utilizing Low-cost Mobile Monitoring to Estimate the PM2.5 Inhaled Dose in Urban Environment

Abstract

This study has developed a compact, low-cost, and real-time mobile monitoring (MM) device for estimating the PM2.5 inhaled dose. The MM device consists of a low-cost PM2.5 sensor, temperature and humidity sensor, Wi-Fi module, and microcontroller unit. The MM system (carried on vehicle) has been used to measure PM2.5 concentration, geolocation, and meteorological factors during rush hour. To examine repeatability, a new method was proposed to calculate the coefficient of variance of the PM2.5 sensor reading. We used several vehicle speeds to evaluate its dependency on the PM2.5 sensor reading. A sensor cover was also introduced to prevent the airspeed effect during carried on the vehicle. In this study, mobile monitoring was performing in several areas. The measured PM2.5 concentration then used for estimating PM2.5 inhaled dose. The Monte Carlo technique was used to introduce the probabilistic of body weight and PM2.5 concentration. The result shows that the coefficient of variation of the PM2.5 sensor reading was 2% on average in 2 minutes. We found that vehicle speed and sensor cover affects the standard deviation of PM2.5 sensor reading. Statistical analysis shows that the on-road area (53 µg m–3) has higher PM2.5 concentration than residential area (41 µg m–3). The area around the toll gate where many trucks pass has a higher concentration of PM2.5. In addition, low variability on the meteorological factors caused weak relationship with the PM2.5 concentration. We found that children were estimated to receive a higher inhaled dose of PM2.5 than adults. Therefore, variations in the microenvironment and local pollution sources such as truck and food stalls are dominant factors that affect spatial variation of PM2.5. Real-time mobile monitoring can help the government make policy and give warnings to people traveling around polluted areas.