Self-feedback LSTM regression model for real-time particle source apportionment.

[1]  Stanley F. Chen,et al.  A Gaussian Prior for Smoothing Maximum Entropy Models , 1999 .

[2]  R. Harrison,et al.  Comparison of three techniques for analysis of data from an Aerosol Time-of-Flight Mass Spectrometer , 2012 .

[3]  P. Cheng,et al.  Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles , 2011 .

[4]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[5]  A. Russell,et al.  Comparison and overview of PM 2.5 source apportionment methods , 2015 .

[6]  Yinchang Feng,et al.  Refined source apportionment of coal combustion sources by using single particle mass spectrometry. , 2018, The Science of the total environment.

[7]  P. Bhave,et al.  Source apportionment of fine particulate matter by clustering single-particle data: tests of receptor model accuracy. , 2001, Environmental science & technology.

[8]  Yinchang Feng,et al.  Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin, China , 2019, Atmospheric Environment.

[9]  P. Paatero.  The Multilinear Engine—A Table-Driven, Least Squares Program for Solving Multilinear Problems, Including the n-Way Parallel Factor Analysis Model , 1999 .

[10]  Philip K Hopke,et al.  Review of receptor modeling methods for source apportionment , 2016, Journal of the Air & Waste Management Association.

[11]  Z. Bai,et al.  Source Apportionment of PM10 in Four Cities of Northeastern China , 2012 .

[12]  Long-range sources of Toronto particulate matter (PM2.5) identified by Aerosol Laser Ablation Mass Spectrometry (LAMS) , 2004 .

[13]  Stig Hellebust,et al.  Source apportionment of PM 2.5 in Cork Harbour, Ireland using a combination of single particle mass spectrometry and quantitative semi-continuous measurements , 2010 .

[14]  S. Friedlander,et al.  A Chemical Element Balance for the Pasadena Aerosol , 1972 .

[15]  R. Harrison,et al.  Local and regional components of aerosol in a heavily trafficked street canyon in central London derived from PMF and cluster analysis of single-particle ATOFMS spectra. , 2015, Environmental science & technology.

[16]  I. J. Myung,et al.  Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .

[17]  Kui Chen,et al.  Influence of the sampling period and time resolution on the PM source apportionment: Study based on the high time-resolution data and long-term daily data , 2017 .

[18]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[19]  M. Shao,et al.  Understanding primary and secondary sources of ambient carbonyl compounds in Beijing using the PMF model , 2013 .

[20]  K. Prather,et al.  Aerosol time‐of‐flight mass spectrometry during the Atlanta Supersite Experiment: 1. Measurements , 2003 .

[21]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[22]  Armistead G. Russell,et al.  Using a new WALSPMF model to quantify the source contributions to PM2.5 at a harbour site in China , 2016 .

[23]  Qi Zhang,et al.  Real-time chemical characterization of atmospheric particulate matter in China: A review , 2017 .

[24]  Xiaofei Wang,et al.  Characteristics and source apportionment of ambient single particles in Tianjin, China: The close association between oxalic acid and biomass burning , 2020, Atmospheric Research.

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

[26]  Delbert J. Eatough,et al.  Source apportionment of 1 h semi-continuous data during the 2005 Study of Organic Aerosols in Riverside (SOAR) using positive matrix factorization , 2008 .

[27]  P. Paatero,et al.  Analysis of different modes of factor analysis as least squares fit problems , 1993 .

[28]  Yinchang Feng,et al.  Characteristics and sensitivity analysis of multiple-time-resolved source patterns of PM2.5 with real time data using Multilinear Engine 2 , 2016 .

[29]  S. Ying,et al.  The Effect of a Receding Saline Lake (The Salton Sea) on Airborne Particulate Matter Composition. , 2017, Environmental science & technology.