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 PM_2.5 inhaled dose. The MM device consists of a low-cost PM_2.5 sensor, temperature and humidity sensor, Wi-Fi module, and microcontroller unit. The MM system (carried on vehicle) has been used to measure PM_2.5 concentration, geolocation, and meteorological factors during rush hour. To examine repeatability, a new method was proposed to calculate the coefficient of variance of the PM_2.5 sensor reading. We used several vehicle speeds to evaluate its dependency on the PM_2.5 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 PM_2.5 concentration then used for estimating PM_2.5 inhaled dose. The Monte Carlo technique was used to introduce the probabilistic of body weight and PM_2.5 concentration. The result shows that the coefficient of variation of the PM_2.5 sensor reading was 2% on average in 2 minutes. We found that vehicle speed and sensor cover affects the standard deviation of PM_2.5 sensor reading. Statistical analysis shows that the on-road area (53 µg m^–3) has higher PM_2.5 concentration than residential area (41 µg m^–3). The area around the toll gate where many trucks pass has a higher concentration of PM_2.5. In addition, low variability on the meteorological factors caused weak relationship with the PM_2.5 concentration. We found that children were estimated to receive a higher inhaled dose of PM_2.5 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 PM_2.5. Real-time mobile monitoring can help the government make policy and give warnings to people traveling around polluted areas.