Skip to main navigation Skip to search Skip to main content

Spatial-temporal variability of PM2.5 concentration in Xuzhou based on satellite remote sensing and meteorological data

  • Xi Kan
  • , Linglong Zhu
  • , Yonghong Zhang*
  • , Yuan Yuan
  • *Corresponding author for this work
  • Nanjing University of Information Science & Technology
  • Michigan State University

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate estimation of the spatiotemporally continuous distribution of PM2.5 concentration is of great significance for the research on atmospheric pollution. The effect of aerosol characteristics such as aerosol types was seldom considered in PM2.5 estimation in previous studies. In this manuscript, authors applied an aerosol classification-based method to generate ground-level PM2.5 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 PM2.5 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 PM2.5 over Xuzhou, there is a high-PM2.5 area anchoring over the urban district, while low concentration occurs in county town.

Original languageEnglish
Pages (from-to)181-191
Number of pages11
JournalInternational Journal of Sensor Networks
Volume29
Issue number3
DOIs
StatePublished - 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • AOD
  • Aerosol classification
  • Aerosol optical depth
  • PM
  • Satellite remote sensing
  • Spatial-temporal variation

Fingerprint

Dive into the research topics of 'Spatial-temporal variability of PM2.5 concentration in Xuzhou based on satellite remote sensing and meteorological data'. Together they form a unique fingerprint.

Cite this