Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. This project aims to retrieve useful information from the PM2.5 emission dataset in order to gauge the air pollution problem.

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as wearables. These type of devices are part of the “quantified self” movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior. This project aims to illustrate the activity patterns according to the measurements on wearables.

With dataset adapted from Anguita et al., this study summarizes the means and standard deviations (std) of each variables according to each activity and each subject prior to identify the most significant variable for predicting human activity.

This project reflects the most devastating climate events affecting population health and nation’s economy in general. The NOAA storm database used will tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.