The purposes of this study are to develop a healthy commute map indicating cleanest route in Taipei metropolitan area for any given journey and to evaluate the pollutant doses exposed in different commuting modes. In Taiwan, there are more than 13.6 million motorcycles and 7.7 million vehicles among the 23 million people. Exposure to traffic-related air pollutants can thus cause adverse health effects. Moreover, increasing the level of physical activity during commuting and longer distances will result in inhalation of more polluted air. In this study, we utilized air pollution monitoring data (CO, SO2, NO2, PM10, and PM2.5) from Taiwan EPA's air quality monitoring stations in Taipei metropolitan area to estimate each pollutant exposure while commuting by different modes (motorcycling, bicycling, and walking). Spatial interpolation methods such as inverse distance weighting (IDW) were used to estimate each pollutant's distribution in Taipei metropolitan area. Three routes were selected to represent the variety of different daily commuting pathways. The cleanest route choice was based upon Dijkstra's algorithm to find the lowest cumulative pollutant exposure. The IDW interpolated values of CO, SO2, NO2, PM10, and PM2.5 ranged from 0.42-2.2 (ppm), 2.6-4.8 (ppb), 17.8-42.9 (ppb), 32.4-65.6 (μg/m3), and 14.2-38.9 (μg/m3), respectively. To compare with the IDW results, concentration of particulate matter (PM10, PM2.5, and PM1) along the motorcycle route was measured in real time. In conclusion, the results showed that the shortest commuting route for motorcyclists resulted in a much higher cumulative dose (PM2.5 3340.8 μg/m3) than the cleanest route (PM2.5 912.5 μg/m3). The mobile personal monitoring indicated that the motorcyclists inhaled significant high pollutants during commuting as a result of high-concentration exposure and short-duration peaks. The study could effectively present less polluted commuting routes for citizen health benefits.
Keywords: Air pollution; Dijkstra’s algorithm; Exposure measurement; Geographic information system; Inverse distance weighting; PM2.5.