Next Article in Journal
Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map
Previous Article in Journal
A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 262; doi:10.3390/rs8030262

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

1
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
College of Sciences, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 28 December 2015 / Revised: 13 March 2016 / Accepted: 16 March 2016 / Published: 22 March 2016
View Full-Text   |   Download PDF [13199 KB, uploaded 22 March 2016]   |  

Abstract

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE). View Full-Text
Keywords: hourly ground-level PM2.5 concentration; 500 m resolution; GTWR model; SARA AOD; AERONET; MODIS hourly ground-level PM2.5 concentration; 500 m resolution; GTWR model; SARA AOD; AERONET; MODIS
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bai, Y.; Wu, L.; Qin, K.; Zhang, Y.; Shen, Y.; Zhou, Y. A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD. Remote Sens. 2016, 8, 262.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top