Source contributions of urban PM 2.5 in the Beijing–Tianjin–Hebei region: Changes between 2006 and 2013 and relative impacts of emissions and meteorology

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

[2]  Jiwen Fan,et al.  Incorporating an advanced aerosol activation parameterization into WRF‐CAM5: Model evaluation and parameterization intercomparison , 2015 .

[3]  John C. Lin,et al.  A method to quantitatively apportion pollutants at high spatial and temporal resolution: the Stochastic Lagrangian Apportionment Method (SLAM). , 2015, Environmental science & technology.

[4]  Yunping Chen,et al.  Estimating ground-level PM2.5 concentration using Landsat 8 in Chengdu, China , 2014, Asia-Pacific Environmental Remote Sensing.

[5]  Hongliang Zhang,et al.  Local and inter-regional contributions to PM2.5 nitrate and sulfate in China , 2014 .

[6]  Andrea Mazzino,et al.  An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx , 2014 .

[7]  Zifa Wang,et al.  A modeling study of source–receptor relationships in atmospheric particulate matter over Northeast Asia , 2014 .

[8]  Kebin He,et al.  Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China , 2014 .

[9]  Zifa Wang,et al.  Impacts of updated emission inventories on source apportionment of fine particle and ozone over the southeastern U.S. , 2014 .

[10]  Li Li,et al.  Source apportionment of fine particulate matter during autumn haze episodes in Shanghai, China , 2014 .

[11]  Qiang Zhang,et al.  The 2013 severe haze over southern Hebei, China: model evaluation, source apportionment, and policy implications , 2013 .

[12]  A. Megaritis,et al.  Contributions of local and regional sources to fine PM in the megacity of Paris , 2013 .

[13]  Alexis K.H. Lau,et al.  A study of control policy in the Pearl River Delta region by using the particulate matter source apportionment method , 2013 .

[14]  Yuan Cheng,et al.  Biomass burning contribution to Beijing aerosol , 2013 .

[15]  Renjian Zhang,et al.  Chemical characterization and source apportionment of PM 2 . 5 in Beijing : seasonal perspective , 2013 .

[16]  Ling Zhao,et al.  Chemical and Physical Characteristics of Moxibustion , 2013 .

[17]  Athanasios Nenes,et al.  Implementation of dust emission and chemistry into the Community Multiscale Air Quality modeling system and initial application to an Asian dust storm episode , 2012 .

[18]  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 .

[19]  Jianfeng Li,et al.  A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products , 2012 .

[20]  Qiang Zhang,et al.  A novel back‐trajectory analysis of the origin of black carbon transported to the Himalayas and Tibetan Plateau during 1996–2010 , 2012 .

[21]  Michael J. Burr,et al.  Source apportionment of fine particulate matter over the Eastern U.S. Part I: source sensitivity simulations using CMAQ with the Brute Force method , 2011 .

[22]  Source apportionment of fine particulate matter over the Eastern U.S. Part II: source apportionment simulations using CAMx/PSAT and comparisons with CMAQ source sensitivity simulations , 2011 .

[23]  S. Pandis,et al.  Contribution of long range transport to local fine particulate matter concerns , 2011 .

[24]  J. Randerson,et al.  Biomass burning contribution to black carbon , 2011 .

[25]  Greg Yarwood,et al.  Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model , 2008 .

[26]  S. Casadei,et al.  Application of Advanced Particulate Matter Source Apportionment Techniques in the Northern Italy Basin , 2008 .

[27]  Qi Ying,et al.  Source contributions to the regional distribution of secondary particulate matter in California , 2006 .

[28]  Robert Vet,et al.  A revised parameterization for gaseous dry deposition in air-quality models , 2003 .

[29]  C. Zender,et al.  Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology , 2003 .

[30]  Philip K. Hopke,et al.  Recent developments in receptor modeling , 2003 .

[31]  D. Jacob,et al.  Global modeling of tropospheric chemistry with assimilated meteorology : Model description and evaluation , 2001 .

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

[33]  A. Nenes,et al.  Continued development and testing of a new thermodynamic aerosol module for urban and regional air quality models , 1999 .

[34]  J. Seinfeld,et al.  Atmospheric Chemistry and Physics: From Air Pollution to Climate Change , 1998 .

[35]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[36]  Meng-Dawn Cheng,et al.  A receptor-oriented methodology for determining source regions of particulate sulfate observed at Dorset, Ontario , 1993 .

[37]  Paulette Middleton,et al.  A three‐dimensional Eulerian acid deposition model: Physical concepts and formulation , 1987 .

[38]  John G. Watson,et al.  The effective variance weighting for least squares calculations applied to the mass balance receptor model , 1984 .