Abstract

Abstract. The urbanized region ofJing(Beijing)-Jin(Tianjin)-Ji (alias of Hebei province) and its nearby surrounding region (3JNS) is becoming China's most polluted area by haze, exceeding even the Yangtze and Pearl river deltas. Aside from pollutant emission, the meteorology of the planetary boundary layer (PBL) is the most important factor affecting haze pollution. Focusing on July 2008, the aerosol optical properties and PBL meteorology features closely related to haze formation were simulated in the 3JNS region using an online atmospheric chemical transport model. The relationship between regional PBL meteorology, PM2.5, and haze is discussed. Model results accurately simulated the aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter (ASY), validated by comparison with observations from the MODerate Resolution Imaging Spectroradiometer (MODIS), the China Aerosol Remote Sensing NETwork (CARSNET) and the Aerosol Robotic NETwork (AERONET). Modeled PBL wind speeds showed reasonable agreement with those from the National Centers for Environmental Prediction (NCEP) Reanalysis 2. A monthly mean AOD value as high as 1.2 was found from both model and observations, with a daily mean larger than 2.0 during haze episodes in the 3JNS region. Modeled and observed SSA values of 0.90–0.96 and ASY values of 0.72–0.74 demonstrated the high scattering characteristic of summer aerosols in this region. PBL wind speeds from modeled and NCEP data both showed a reversing trend of PM2.5 variation, illustrating the importance of the "PBL window shadow" in haze formation. Turbulence diffusion and PBL height had opposite phases to surface PM2.5, indicating that lower PBL height and weaker PBL turbulence diffusion are essential to haze formation. It is noted that homogeneous air pressure does not occur at the surface, but at an 850–950 hPa height during the haze episode. The momentum transmitting downward of the cold air from above the PBL to the low PBL and surface lead to an increase in surface wind speeds and haze dispersal.

Highlights

  • With its rapidly expanding urbanization, and both economic and industrial developments, China is faced with increasingly poor air quality and haze pollution

  • Chinese Unified Atmospheric Chemistry Environment (CUACE)/Gas is based on the Regional Acid Deposition Model (RADM) (Stockwell et al, 1990), which consists of 66 gaseous species, including five second-order organic aerosols (SOA); 21 photochemical reactions and 121 gas phase reactions are involved

  • Aerosol optical properties contributing to aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter (ASY) are the most direct and critical parameters for aerosol direct radiative forcing, radiative heating effects, and feedback to atmospheric circulation (Wang et al, 2006; Huang et al, 2006, 2009)

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Summary

Introduction

With its rapidly expanding urbanization, and both economic and industrial developments, China is faced with increasingly poor air quality and haze pollution. Wang et al.: Study of the interactions between aerosols and PBL meteorology rounding region (3JNS) These are all areas of high population, rapid economic growth, urbanization and energy consumption (Zhang et al, 2004; Chak et al, 2008; Che et al, 2009; Wu et al, 2010). Focusing on July 2008 over the 3JNS region, this paper outlines the methodology for the online calculation of aerosol optical features of different species based on an external mixing scheme, introduced into the GRAPES-CUACE atmospheric chemical model to simulate the aerosol optical features and PBL meteorology condition. Goddard Goddard KFETA scheme SFCLAY scheme MRF scheme SLAB scheme KESSLER scheme RADM II CUACE

Emissions
Online calculation of optical properties of externally mixed aerosols
Optical properties of aerosols
PBL meteorological features corresponding to the haze episode
Planetary boundary layer winds
Planetary boundary layer turbulence diffusion
Planetary boundary layer height
Patterns in planetary boundary layer air pressure fields
Conclusions
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