Temporal Patterns in Fine Particulate Matter Time Series in Beijing: A Calendar View

Extremely high fine particulate matter (PM2.5) concentration has become synonymous to Beijing, the capital of China, posing critical challenges to its sustainable development and leading to major public health concerns. In order to formulate mitigation measures and policies, knowledge on PM2.5 variation patterns should be obtained. While previous studies are limited either because of availability of data, or because of problematic a priori assumptions that PM2.5 concentration follows subjective seasonal, monthly, or weekly patterns, our study aims to reveal the data on a daily basis through visualization rather than imposing subjective periodic patterns upon the data. To achieve this, we conduct two time-series cluster analyses on full-year PM2.5 data in Beijing in 2014, and provide an innovative calendar visualization of PM2.5 measurements throughout the year. Insights from the analysis on temporal variation of PM2.5 concentration show that there are three diurnal patterns and no weekly patterns; seasonal patterns exist but they do not follow a strict temporal division. These findings advance current understanding on temporal patterns in PM2.5 data and offer a different perspective which can help with policy formulation on PM2.5 mitigation.