Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.
A novel portable wireless volatile organic compound (VOC) monitoring device with disposable sensors is presented. The device is miniaturized, light, easy-to-use, and cost-effective. Different field tests have been carried out to identify the operational, analytical, and functional performance of the device and its sensors. The device was compared to a commercial photo-ionization detector, gas chromatography-mass spectrometry, and carbon monoxide detector. In addition, environmental operational conditions, such as barometric change, temperature change and wind conditions were also tested to evaluate the device performance. The multiple comparisons and tests indicate that the proposed VOC device is adequate to characterize personal exposure in many real-world scenarios and is applicable for personal daily use.
Methods: This study was a randomized, wait-list, control trial with assessments at baseline and post-intervention (week 4). Participants were asked to meditate using Calm for 10 minutes per day. A p value ≤0.05 was considered statistically significant.
Results: The majority of participants (n=19) stated using Calm helped them cope with the stress of PA school. The intervention group participated in meditation for an average of 76 minutes/week. There were significant differences in all outcomes for the intervention group (all p ≤0.06). There was a significant interaction between group and time factors in emotional exhaustion (p=.016) and depersonalization (p=.025).
Conclusions: Calm is a feasible way to reduce burnout in PA students. Our findings provide information that can be applied to the design of future studies.