Next Article in Journal
Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers
Next Article in Special Issue
Attributing Accelerated Summertime Warming in the Southeast United States to Recent Reductions in Aerosol Burden: Indications from Vertically-Resolved Observations
Previous Article in Journal
PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain
Previous Article in Special Issue
Estimation of Surface NO2 Volume Mixing Ratio in Four Metropolitan Cities in Korea Using Multiple Regression Models with OMI and AIRS Data
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(7), 668; doi:10.3390/rs9070668

Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China

School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
NOAA-CREST, City College of the City University of New York, New York, NY 10031, USA
Author to whom correspondence should be addressed.
Received: 29 April 2017 / Revised: 15 June 2017 / Accepted: 23 June 2017 / Published: 28 June 2017
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
View Full-Text   |   Download PDF [5532 KB, uploaded 29 June 2017]   |  


The Planetary Boundary Layer Height (PBLH) plays an important role in the formation and development of air pollution events. Particulate Matter is one of major pollutants in China. Here, we present the characteristics of PBLH through three-methods of Lidar data inversion and show the correlation between the PBLH and the PM2.5 (PM2.5 with the diameter <2.5 μm) in the period of December 2015 through November 2016, over Nanjing, in southeast China. We applied gradient method (GRA), standard deviation method (STD) and wavelet covariance transform method (WCT) to calculate the PBLH. The results show that WCT is the most stable method which is less sensitive to the signal noise. We find that the PBLH shows typical seasonal variation trend with maximum in summer and minimum in winter, respectively. The yearly averaged PBLH in the diurnal cycle show the minimum of 570 m at 08:00 and the maximum of 1089 m at 15:00 Beijing time. Furthermore, we investigate the relationship of the PBLH and PM2.5 concentration under different particulate pollution conditions. The correlation coefficient is about −0.70, which is negative correlation. The average PBLH are 718 m and 1210 m when the PM2.5 > 75 μg/m3 and the PM2.5 < 35 μg/m3 in daytime, respectively. The low PBLH often occurs with condition of the low wind speed and high relative humidity, which will lead to high PM2.5 concentration and the low visibility. On the other hand, the stability of PBL is enhanced by high PM concentration and low visibility. View Full-Text
Keywords: planetary boundary layer; PM2.5; air pollution; Lidar planetary boundary layer; PM2.5; air pollution; Lidar

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).

Supplementary material

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

Qu, Y.; Han, Y.; Wu, Y.; Gao, P.; Wang, T. Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China. Remote Sens. 2017, 9, 668.

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



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