Title: Spatial-temporal variability of PM_2.5 concentration in Xuzhou based on satellite remote sensing and meteorological data

Authors: Xi Kan; Linglong Zhu; Yonghong Zhang; Yuan Yuan

Addresses: School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China ' Department of Computer Science and Engineering, Michigan State University, East Lansing, 48913, USA

Abstract: Accurate estimation of the spatiotemporally continuous distribution of PM_2.5 concentration is of great significance for the research on atmospheric pollution. The effect of aerosol characteristics such as aerosol types was seldom considered in PM_2.5 estimation in previous studies. In this manuscript, authors applied an aerosol classification-based method to generate ground-level PM_2.5 concentration datasets in Xuzhou from 2014 to 2017. The coefficient of determination (R2) of aerosol classification-based model increases from 0.57 to 0.61 verified by ground station measurements, comparing to the empirical model. The results of spatiotemporal analysis show that the PM_2.5 concentration has a slowly decreased trend in last three years, despite has an extreme high value in the winter of 2016 due to the heavy haze pollution occurred in Xuzhou. With regard to the spatial distribution of estimated PM_2.5 over Xuzhou, there is a high-PM_2.5 area anchoring over the urban district, while low concentration occurs in county town.

Keywords: PM_2.5; AOD; aerosol optical depth; satellite remote sensing; aerosol classification; spatial-temporal variation.

DOI: 10.1504/IJSNET.2019.098285

International Journal of Sensor Networks, 2019 Vol.29 No.3, pp.181 - 191

Received: 28 Jul 2018
Accepted: 29 Jul 2018
Published online: 11 Mar 2019 *

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