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Volume 18, issue 17
Atmos. Chem. Phys., 18, 12933-12952, 2018
https://doi.org/10.5194/acp-18-12933-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Regional transport and transformation of air pollution in...

Atmos. Chem. Phys., 18, 12933-12952, 2018
https://doi.org/10.5194/acp-18-12933-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Sep 2018

Research article | 07 Sep 2018

Spatiotemporal variability of NO2 and PM2.5 over Eastern China: observational and model analyses with a novel statistical method

Mengyao Liu1, Jintai Lin1, Yuchen Wang1,2, Yang Sun3, Bo Zheng4, Jingyuan Shao1, Lulu Chen1, Yixuan Zheng5, Jinxuan Chen1,6, Tzung-May Fu1, Yingying Yan1, Qiang Zhang4, and Zhaohua Wu7,8 Mengyao Liu et al.
  • 1Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
  • 2Earthquake Research Institute, The University of Tokyo, Tokyo 113-0032, Japan
  • 3Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 4Center for Earth System Science, Tsinghua University, Beijing 100084, China
  • 5Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 6Max Planck Institute for Biogeochemistry, Hans-Knöll-Str.10, 07745 Jena, Germany
  • 7Center for Ocean–Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida 32306-2741, USA
  • 8Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida 32306-4520, USA

Abstract. Eastern China (27–41°N, 110–123°E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5µm (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF–EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall–winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north–south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another.

We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17µgm−3 and PM2.5 by 35µgm−3 on average over fall–winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north–south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30µgm−3 and PM2.5 by 60µgm−3. For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north–south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF–EEMD package is freely available for noncommercial uses.

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Eastern China is heavily polluted by NO2, PM2.5, and other air pollutants. Our study uses EOF–EEMD to analyze the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes. Their regular diurnal cycles are mainly affected by human activities, while irregular day-to-day variations are dominated by weather processes representing synchronous variation or north–south opposing changes over Eastern China.
Eastern China is heavily polluted by NO2, PM2.5, and other air pollutants. Our study uses...
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