Statistics and Its Interface

Volume 12 (2019)

Number 3

Photographic diary: a new estimation approach to PM2.5 monitoring

Pages: 387 – 395

DOI: https://dx.doi.org/10.4310/18-SII553

Authors

Ke Xu (School of Statistics, University of International Business and Economics, Beijing, China)

Jianqiao Wang (Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Penn., U.S.A.)

Rui Pan (School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China)

Hansheng Wang (Guanghua School of Management, Peking University, Beijing, China)

Abstract

Air pollution is a global environmental problem that has been particularly severe in China over the past few years. Among all air pollutants, PM2.5 is one of the most hazardous to human health; therefore, monitoring and reducing PM2.5 pollution has become an issue of fundamental importance. Despite the comprehensive air quality monitoring system established by Chinese government, it is still a problem in China. Developing an effective and more economical way to monitor PM2.5 has become a pressing challenge. In this study, we explore a promising solution: the possibility of recovering PM2.5 values using a new haze indicator known as a photographic diary. Based on the related literature, our method is a cost-effective way to monitor PM2.5 at any location and at any point in time with an acceptable accuracy. The government could use our method to conduct data quality monitoring and detect outliers. We also constructed features that the general public could use and interpret directly. Our method allows them to monitor air quality and protect the environment using their cellphones.

Keywords

$\textrm{PM}_{2.5}$ pollution, haze indicator, photographic diary

Ke Xu and Hansheng Wang are supported by National Natural Science Foundation of China (Grant No. 11525101, No. 71332006, No. 71532001), and by China’s National Key Research Special Program (No. 2016YFC0207704).

Rui Pan is supported by National Natural Science Foundation of China (NSFC, No. 11601539, No. 11631003), and by Fundamental Research Funds for the Central Universities (No. QL18010).

Received 19 June 2018

Published 4 June 2019