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The relationships between surface-column aerosol concentrations and meteorological factors observed at major cities in the Yangtze River Delta, China

  • Han Ding
  • Kanike Raghavendra KumarEmail author
  • Richard Boiyo
  • Tianliang ZhaoEmail author
Research Article
  • 89 Downloads

Abstract

Monitoring of particulate matter (PM) is important in air quality, public health, and epidemiological studies and in decision-making for policy implementation. In the present study, the temporal variability of surface-measured PM concentrations ([PM]) and their relationship with meteorological variables and aerosol optical depth (AOD), with the aid from source apportionment studies, are investigated at four urban cities in the Chinese Yangtze River Delta (YRD) region during January 2014 to December 2017. The annual mean concentrations of [PM2.5] ([PM10]) observed at Shanghai (SH), Nanjing (NJ), Hangzhou (HZ), and Hefei (HF) were 46.98 ± 12.21, 54.84 ± 46.14, 52.82 ± 16.98, and 64.03 ± 20.57 μg m−3 (68.07 ± 14.33, 96.48 ± 26.86, 83.08 ± 22.38, and 97.61 ± 20.19 μg m−3), respectively. However, the [PM] exceeded the Chinese National Air Quality Standards of GB3095-2012, being higher (lower) during winter (summer). The [PM] was found higher in the morning (08:00–10:00 LT) and evening (18:00–20:00 LT) and lower in early morning (04:00 LT) and afternoon (14:00 LT) attributed to the dynamics of boundary layer height and varied emission sources. With an annual mean of 0.6–0.7, the PM ratio (PMr = PM2.5/PM10) was observed to have a single peak distribution in all seasons indicating the dominance of fine particles (PM2.5). Further, the [PM10] and [PM2.5] were highly correlated (r ≥ 0.90) in all cities, with slope > 0.70 representing the abundance of fine particles, except for NJ (< 0.70). A low correlation (< 0.5) was noticed between [PM10] and AOD550 suggesting that the aerosol particles had a large influence on AOD, contributing less to PM10. Finally, the concentration bivariate probability function (CBPF) and trajectory statistical models like potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) suggested that local and regional sources contributed a lot for the high [PM2.5] observed at the four cities in the YRD, China.

Keywords

Particulate matter concentration Temporal and diurnal changes Meteorological variables Statistical correlations PSCF and CWT models Yangtze River Delta 

Notes

Acknowledgments

We acknowledge the Ministry of Environmental Protection of China (http://113.108.142.147:20,035/emcpublish/) and China air quality online monitoring and analysis platform (https://www.aqistudy.cn/) for providing PM2.5 and PM10 data and NASA (https://modis-atmos.gsfc.nasa.gov/) for providing the MODIS AOD data. The boundary layer height and meteorological data derived from the ECMWF and MICAPS can be downloaded at https://www.ecmwf.int/ and http://www.cru.uea.ac.uk/, respectively. One of the authors Dr. KRK would like to thank the Department of Science and Technology (DST), Govt. of India for the award of DST-FIST Level-1 (SR/FST/PS-1/2018/35) scheme to Dept of Physics, KLEF.The authors would like to acknowledge Dr. Constantini Samara, the editor of the journal, and the two anonymous reviewers for their constructive comments and valuable suggestions toward the improvement of an earlier version of the manuscript.

Funding information

This research has been financially supported by the National Natural Science Foundation of China (Grant nos. 41830965, 91644223, 91744209, 91644224) and the National Key Research and Development Program Pilot Projects of China (Grant no. 2016YFC0203304).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11356_2019_6730_MOESM1_ESM.docx (5.8 mb)
ESM 1 (DOCX 5987 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), International Joint Laboratory on Climate and Environment Change (ILCEC), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric PhysicsNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Department of Physics, School of Sciences and HumanitiesKoneru Lakshmaiah Education Foundation, K. L. UniversityGunturIndia
  3. 3.Department of Physical SciencesMeru University of Science and TechnologyMeruKenya
  4. 4.Department of Environment, Energy and ResourcesCounty Government of VihigaKenya

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