Air Quality Degradation by Mineral Dust over Beijing, Chengdu and Shanghai Chinese Megacities

Air pollution in Chinese megacities has reached extremely hazardous levels, and human activities are responsible for the emission or production of large amounts of particulate matter (PM). In addition to PM from anthropogenic sources, natural phenomena, such as dust storms over Asian deserts, may also emit large amounts of PM, which lead episodically to poor air quality over Chinese megacities. In this paper, we quantify the degradation of air quality by dust over Beijing, Chengdu and Shanghai megacities using the three dimensions (3D) chemistry transport model CHIMERE, which simulates dust emission and transport online. In the first part of our work, we evaluate dust emissions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Infrared Atmospheric Sounding Interferometer (IASI) satellite observations of aerosol optical depth, respectively, in the visible and the thermal infrared over source areas. PM simulations were also evaluated compared to surface monitoring stations. Then, mineral dust emissions and their impacts on particle composition of several Chinese megacities were analyzed. Dust emissions and transport over China were simulated during three years (2011, 2013 and 2015). Annual dust contributions to the PM 10 budget over Beijing, Chengdu and Shanghai were evaluated respectively as 6.6%, 9.5% and 9.3%. Dust outbreaks largely contribute to poor air quality events during springtime. Indeed it was found that dust significantly contribute for 22%, 52% and 43% of spring PM 10 events (for Beijing, Chengdu and Shanghai respectively).

[1]  P. Formenti,et al.  Three‐dimensional pathways of dust over the Sahara during summer 2011 as revealed by new Infrared Atmospheric Sounding Interferometer observations , 2020, Quarterly Journal of the Royal Meteorological Society.

[2]  Mikalai Filonchyk,et al.  Author Correction: Combined use of satellite and surface observations to study aerosol optical depth in different regions of China , 2019, Scientific Reports.

[3]  Xuelei Zhang,et al.  Multimodel simulations of a springtime dust storm over northeastern China: implications of an evaluation of four commonly used air quality models (CMAQ v5.2.1, CAMx v6.50, CHIMERE v2017r4, and WRF-Chem v3.9.1) , 2019, Geoscientific Model Development.

[4]  Boen Zhang,et al.  Estimation of PM2.5 Concentrations in China Using a Spatial Back Propagation Neural Network , 2019, Scientific Reports.

[5]  N. Mahowald,et al.  Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0) , 2019, Geoscientific Model Development.

[6]  M. Garay,et al.  Climatology of Asian dust activation and transport potential based on MISR satellite observations and trajectory analysis , 2019, Atmospheric Chemistry and Physics.

[7]  C. Clerbaux,et al.  The unintended consequence of SO2 and NO2 regulations over China: increase of ammonia levels and impact on PM2.5 concentrations , 2018, Atmospheric Chemistry and Physics.

[8]  Meng Li,et al.  Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions , 2018, Atmospheric Chemistry and Physics.

[9]  Bo Hu,et al.  Characteristics of PM2.5 mass concentrations and chemical species in urban and background areas of China: emerging results from the CARE-China network , 2018, Atmospheric Chemistry and Physics.

[10]  I. Sokolik,et al.  The Dust Direct Radiative Impact and Its Sensitivity to the Land Surface State and Key Minerals in the WRF‐Chem‐DuMo Model: A Case Study of Dust Storms in Central Asia , 2018 .

[11]  Jun Liu,et al.  Spatial and temporal evolution of natural and anthropogenic dust events over northern China , 2018, Scientific Reports.

[12]  Eleni Marinou,et al.  Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP , 2017 .

[13]  Jianping Huang,et al.  An overview of mineral dust modeling over East Asia , 2017, Journal of Meteorological Research.

[14]  S. Beirle,et al.  NOx emission trends over Chinese cities estimated from OMI observations during 2005 to 2015. , 2017, Atmospheric chemistry and physics.

[15]  Konstantinos Markakis,et al.  CHIMERE-2017 : from urban to hemispheric chemistry-transport modeling , 2017 .

[16]  A. Russell,et al.  Size distribution, directional source contributions and pollution status of PM from Chengdu, China during a long-term sampling campaign. , 2017, Journal of environmental sciences.

[17]  K. Gui,et al.  Heavy pollution episodes, transport pathways and potential sources of PM2.5 during the winter of 2013 in Chengdu (China). , 2017, The Science of the total environment.

[18]  Zifa Wang,et al.  Real-time observational evidence of changing Asian dust morphology with the mixing of heavy anthropogenic pollution , 2017, Scientific Reports.

[19]  P. Formenti,et al.  Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing , 2016 .

[20]  H. Kan,et al.  Long-term trend and spatial pattern of PM2.5 induced premature mortality in China. , 2016, Environment international.

[21]  Mengchu Zhou,et al.  Chemical composition of PM2.5 and meteorological impact among three years in urban Shanghai, China , 2016 .

[22]  Kebin He,et al.  Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model , 2015 .

[23]  C. Clerbaux,et al.  Temporal variations of flux and altitude of sulfur dioxide emissions during volcanic eruptions: implications for long-range dispersal of volcanic clouds , 2015 .

[24]  Daniel K. Zhou,et al.  Three‐dimensional distribution of a major desert dust outbreak over East Asia in March 2008 derived from IASI satellite observations , 2015 .

[25]  Yang Liu,et al.  Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013 , 2015, Environmental health perspectives.

[26]  D. Roberts,et al.  Statistical analysis of PM2.5 observations from diplomatic facilities in China , 2015 .

[27]  N. Mahowald,et al.  An improved dust emission model – Part 1: Model description and comparison against measurements , 2014 .

[28]  N. Mahowald,et al.  The size distribution of desert dust aerosols and its impact on the Earth system , 2014 .

[29]  Yuting Cheng,et al.  Evolution of aerosol chemistry in Xi'an, inland China, during the dust storm period of 2013 – Part 1: Sources, chemical forms and formation mechanisms of nitrate and sulfate , 2014 .

[30]  A. Piazzalunga,et al.  High secondary aerosol contribution to particulate pollution during haze events in China , 2014, Nature.

[31]  Dongxiao Niu,et al.  The energy supply and demand pattern of China: A review of evolution and sustainable development , 2013 .

[32]  Bin Zhao,et al.  Source, transport and impacts of a heavy dust event in the Yangtze River Delta, China, in 2011 , 2013 .

[33]  Gabriele Curci,et al.  CHIMERE 2013 : a model for regional atmospheric composition modelling , 2013 .

[34]  Renjian Zhang,et al.  Chemical composition of PM2.5 in an urban environment in Chengdu, China:Importance of springtime dust storms and biomass burning , 2013 .

[35]  N. Mahowald,et al.  Improved dust representation in the Community Atmosphere Model , 2012 .

[36]  L. Emmons,et al.  The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions , 2012 .

[37]  Michael Schulz,et al.  Global dust model intercomparison in AeroCom phase I , 2011 .

[38]  Daniel Sperling,et al.  China’s soaring vehicle population: Even greater than forecasted? , 2011 .

[39]  K. He,et al.  Characteristics of PM 2.5 speciation in representative megacities and across China , 2011 .

[40]  M. Haeffelin,et al.  Assessing in near real time the impact of the April 2010 Eyjafjallajökull ash plume on air quality , 2011 .

[41]  J. Kok,et al.  A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle , 2010, Proceedings of the National Academy of Sciences.

[42]  David M. Winker,et al.  CALIPSO lidar observations of the optical properties of Saharan dust: A case study of long‐range transport , 2008 .

[43]  L. Menut,et al.  An optimized particle size bin scheme for modeling mineral dust aerosol , 2006 .

[44]  Y. Wang,et al.  The contribution from distant dust sources to the atmospheric particulate matter loadings at XiAn, China during spring. , 2006, The Science of the total environment.

[45]  S. Bony,et al.  The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection , 2006 .

[46]  A. Russell,et al.  PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models , 2006 .

[47]  Benoit Laurent,et al.  Modeling mineral dust emissions from Chinese and Mongolian deserts , 2006 .

[48]  N. Mahowald,et al.  Change in atmospheric mineral aerosols in response to climate: Last glacial period, preindustrial, modern, and doubled carbon dioxide climates , 2006 .

[49]  L. Menut,et al.  Sensitivity of the Sandblasting Flux Calculations to the Soil Size Distribution Accuracy , 2005 .

[50]  R. J. Yamartino,et al.  A new air quality regime classification scheme for O3, NO2, SO2 and PM10 observations sites , 2005 .

[51]  Fabienne Maignan,et al.  Simulation of the mineral dust emission frequencies from desert areas of China and Mongolia using an aerodynamic roughness length map derived from the POLDER/ADEOS 1 surface products : Quantifying the radiative and biogeochemical impacts of mineral dust , 2005 .

[52]  K. He,et al.  Characterization of atmospheric mineral components of PM2.5 in Beijing and Shanghai, China. , 2005, The Science of the total environment.

[53]  Jean-Francois Lamarque,et al.  Interactive chemistry in the Laboratoire de Météorologie Dynamique general circulation model: Description and background tropospheric chemistry evaluation: INTERACTIVE CHEMISTRY IN LMDZ , 2004 .

[54]  V. Freudenthaler,et al.  Long-range transport of Saharan dust to northern Europe : The 11-16 October 2001 outbreak observed with EARLINET , 2003 .

[55]  Matthias Beekmann,et al.  Effect of biogenic volatile organic compound emissions on tropospheric chemistry during the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign in the Ile-de-France region , 2003 .

[56]  Ian G. McKendry,et al.  Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE-Asia: 2. Model simulation and validation , 2003 .

[57]  Y. Qian,et al.  Decreasing trends in sunshine duration over China for 1954–1998: Indication of increased haze pollution? , 2002 .

[58]  M. Noguer,et al.  Climate change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change , 2002 .

[59]  Y. Shao.  A model for mineral dust emission , 2001 .

[60]  M. Chin,et al.  Sources and distributions of dust aerosols simulated with the GOCART model , 2001 .

[61]  L. Gomes,et al.  Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas , 2001 .

[62]  T. Eck,et al.  Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols , 1999 .

[63]  Sandy P. Harrison,et al.  Dust sources and deposition during the last glacial maximum and current climate: A comparison of model results with paleodata from ice cores and marine sediments , 1999 .

[64]  Irina N. Sokolik,et al.  Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths , 1999 .

[65]  A. Nenes,et al.  ISORROPIA: A New Thermodynamic Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols , 1998 .

[66]  L. Gomes,et al.  Modeling the size distribution of a soil aerosol produced by sandblasting , 1997 .

[67]  G. Umlauf,et al.  Atmospheric gas-particle partitioning of organic compounds: Comparison of sampling methods , 1992 .

[68]  B. V. Leer,et al.  Towards the ultimate conservative difference scheme. IV. A new approach to numerical convection , 1977 .

[69]  J. Cuesta,et al.  Three-dimensional pathways of dust over the Sahara during summertime 2011 as revealed by new IASI observations , 2020 .

[70]  Dongsheng Chen,et al.  Trends of PM2.5 and Chemical Composition in Beijing, 2000-2015 , 2017 .

[71]  Bin Chen,et al.  Transport of East Asian dust storms to the marginal seas of China and the southern North Pacific in spring 2010 , 2017 .

[72]  Claude Derognat,et al.  An air quality forecasting system in Beijing--application to the study of dust storm events in China in May 2008. , 2012, Journal of environmental sciences.

[73]  L. Emmons,et al.  Geoscientific Model Development The Model of Emissions of Gases and Aerosols from Nature version 2 . 1 ( MEGAN 2 . 1 ) : an extended and updated framework for modeling biogenic emissions , 2012 .

[74]  Jean-Pierre Blanchet,et al.  A simulated climatology of Asian dust aerosol and its trans-Pacific transport. Part I: Mean climate and validation , 2006 .

[75]  Ian G. McKendry,et al.  A Simulated Climatology of Asian Dust Aerosol and Its Trans-Pacific Transport. Part II: Interannual Variability and Climate Connections , 2006 .

[76]  Leiming Zhang,et al.  A size-segregated particle dry deposition scheme for an atmospheric aerosol module , 2001 .

[77]  Oliver Wild,et al.  Fast-J: Accurate Simulation of In- and Below-Cloud Photolysis in Tropospheric Chemical Models , 2000 .

[78]  J. Pankow.  An absorption model of GAS/Particle partitioning of organic compounds in the atmosphere , 1994 .

[79]  G. Janssens‑Maenhout,et al.  “ HTAP _ v 2 : a mosaic of regional and global emission gridmaps for 2008 and 2010 to study hemispheric transport of air pollution , 2015 .