Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities.

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM2.5 ranged from 13.51 to 15.40 µg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R2 of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R2 of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

The mediating role of self-compassion between proactive coping and perceived stress among students.

Proactive coping has been documented as a significant predictor of perceived stress. When facing stressful events, the more individuals use proactive coping strategies, the lower their stress level will be. However, there is still little research of possible latent factors participating in this relationship to explain how proactive coping can reduce of perceived stress, directly and indirectly. This study aimed to examine whether self-compassion can mediate the relationship between proactive coping and perceived stress among students. In a cross-sectional study carried out in 2019, we invited 384 undergraduate students in Hanoi (Vietnam) to voluntarily complete a self-report questionnaire that measured proactive coping, self-compassion and perceived stress scale. Results showed that proactive coping was positively related to level of self-compassion, and both proactive coping and self-compassion were negatively related to stress scores. The effect of proactive coping on stress was eliminated when self-compassion was controlled, showing the mediating role of self-compassion in the relationship between proactive coping and perceived stress score. Furthermore, among six factors contributing to the concept of self-compassion, data documented mediating role of "self-kindness,""self-judgment," and "mindfulness" while no mediating role of "common humanity,""isolation," and "over-identification" was observed. Among three mediating factors, mindfulness appeared to be the most important factor explaining the relationship between proactive coping and perceived stress. These results consolidate existing literature of the protective role of self-compassion on psychological health, and hence provide more support for the application of self-compassion, especially of mindfulness, in working with people with stress.