Recent advances in air pollution mixture resolutions

Hopke, Philip K.

doi:10.1016/j.microc.2020.105907

Title: Recent advances in air pollution mixture resolutions

Journal Article · 01 April 2021 · Microchemical Journal

DOI: https://doi.org/10.1016/j.microc.2020.105907 · OSTI ID:2274989

Hopke, Philip K.

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Sponsoring Organization:: USDOE

OSTI ID:: 2274989

Journal Information:: Microchemical Journal, Journal Name: Microchemical Journal Vol. 163 Journal Issue: C; ISSN 0026-265X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (38)

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Application of Optimally Scaled Target Factor Analysis for Assessing Source Contribution of Ambient PM₁₀ Escrig, Alberto; Monfort, Eliseo; Celades, Irina Journal of the Air & Waste Management Association, Vol. 59, Issue 11 https://doi.org/10.3155/1047-3289.59.11.1296	journal	November 2009
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Rotational tools for factor analytic models Paatero, Pentti; Hopke, Philip K. Journal of Chemometrics, Vol. 23, Issue 2 https://doi.org/10.1002/cem.1197	journal	February 2009
Understanding and controlling rotations in factor analytic models Paatero, Pentti; Hopke, Philip K.; Song, Xin-Hua Chemometrics and Intelligent Laboratory Systems, Vol. 60, Issue 1-2 https://doi.org/10.1016/S0169-7439(01)00200-3	journal	January 2002
Source Identification of Atlanta Aerosol by Positive Matrix Factorization Kim, Eugene; Hopke, Philip K.; Edgerton, Eric S. Journal of the Air & Waste Management Association, Vol. 53, Issue 6 https://doi.org/10.1080/10473289.2003.10466209	journal	June 2003
On the source contribution to Beijing PM2.5 concentrations Zíková, Naděžda; Wang, Yungang; Yang, Fumo Atmospheric Environment, Vol. 134 https://doi.org/10.1016/j.atmosenv.2016.03.047	journal	June 2016
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Comparison of hybrid receptor models to locate PCB sources in Chicago Hsu, Ying-Kuang; Holsen, Thomas M.; Hopke, Philip K. Atmospheric Environment, Vol. 37, Issue 4 https://doi.org/10.1016/S1352-2310(02)00886-5	journal	February 2003
Analysis of different modes of factor analysis as least squares fit problems Paatero, Pentti; Tapper, Unto Chemometrics and Intelligent Laboratory Systems, Vol. 18, Issue 2 https://doi.org/10.1016/0169-7439(93)80055-M	journal	February 1993
Global review of recent source apportionments for airborne particulate matter Hopke, Philip K.; Dai, Qili; Li, Linxuan Science of The Total Environment, Vol. 740 https://doi.org/10.1016/j.scitotenv.2020.140091	journal	October 2020
Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM _2.5 after the COVID-19 Outbreak Dai, Qili; Liu, Baoshuang; Bi, Xiaohui Environmental Science & Technology, Vol. 54, Issue 16 https://doi.org/10.1021/acs.est.0c02776	journal	July 2020
A residence time probability analysis of sulfur concentrations at grand Canyon National Park Ashbaugh, Lowell L.; Malm, William C.; Sadeh, Willy Z. Atmospheric Environment (1967), Vol. 19, Issue 8 https://doi.org/10.1016/0004-6981(85)90256-2	journal	January 1985
PM2.5 and gaseous pollutants in New York State during 2005–2016: Spatial variability, temporal trends, and economic influences Squizzato, Stefania; Masiol, Mauro; Rich, David Q. Atmospheric Environment, Vol. 183 https://doi.org/10.1016/j.atmosenv.2018.03.045	journal	June 2018
Methods for estimating uncertainty in factor analytic solutions Paatero, P.; Eberly, S.; Brown, S. G. Atmospheric Measurement Techniques, Vol. 7, Issue 3 https://doi.org/10.5194/amt-7-781-2014	journal	March 2014
Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources Masiol, Mauro; Squizzato, Stefania; Cheng, Meng-Dawn Aerosol and Air Quality Research, Vol. 19, Issue 4 https://doi.org/10.4209/aaqr.2018.09.0327	journal	January 2019
Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2 Amato, F.; Pandolfi, M.; Escrig, A. Atmospheric Environment, Vol. 43, Issue 17 https://doi.org/10.1016/j.atmosenv.2009.02.039	journal	June 2009
Approaches to reducing rotational ambiguity in receptor modeling of ambient particulate matter Hopke, Philip K. Chemometrics and Intelligent Laboratory Systems, Vol. 210 https://doi.org/10.1016/j.chemolab.2021.104252	journal	March 2021
Multivariate curve resolution applied to spectral data from multiple runs of an industrial process Tauler, Roma.; Kowalski, Bruce.; Fleming, Sydney. Analytical Chemistry, Vol. 65, Issue 15 https://doi.org/10.1021/ac00063a019	journal	August 1993
Effect of adding variables on rotational ambiguity in positive matrix factorization solutions Emami, Fereshteh; Hopke, Philip K. Chemometrics and Intelligent Laboratory Systems, Vol. 162 https://doi.org/10.1016/j.chemolab.2017.01.012	journal	March 2017
Analysis of Ambient Particle Size Distributions Using Unmix and Positive Matrix Factorization Kim, Eugene; Hopke, Philip K.; Larson, Timothy V. Environmental Science & Technology, Vol. 38, Issue 1 https://doi.org/10.1021/es030310s	journal	November 2003
Target transformation factor analysis as an aerosol mass apportionment method: A review and sensitivity study Hopke, Philip K. Atmospheric Environment (1967), Vol. 22, Issue 9 https://doi.org/10.1016/0004-6981(88)90066-2	journal	January 1988
Advanced Factor Analysis on Pittsburgh Particle Size-Distribution Data Special Issue ofAerosol Science and Technologyon Findings from the Fine Particulate Matter Supersites Program Zhou, Liming; Kim, Eugene; Hopke, Philip K. Aerosol Science and Technology, Vol. 38, Issue sup1 https://doi.org/10.1080/02786820390229589	journal	December 2004
Multivariate receptor modeling by N-dimensional edge detection Henry, Ronald C. Chemometrics and Intelligent Laboratory Systems, Vol. 65, Issue 2 https://doi.org/10.1016/S0169-7439(02)00108-9	journal	February 2003
Source apportionment of the ambient PM2.5 across St. Louis using constrained positive matrix factorization Amato, Fulvio; Hopke, Philip K. Atmospheric Environment, Vol. 46 https://doi.org/10.1016/j.atmosenv.2011.09.062	journal	January 2012
A study of the sources of acid precipitation in Ontario, Canada Zeng, Y.; Hopke, P. K. Atmospheric Environment (1967), Vol. 23, Issue 7 https://doi.org/10.1016/0004-6981(89)90409-5	journal	January 1989
Conditional bivariate probability function for source identification Uria-Tellaetxe, Iratxe; Carslaw, David C. Environmental Modelling & Software, Vol. 59 https://doi.org/10.1016/j.envsoft.2014.05.002	journal	September 2014
NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System Stein, A. F.; Draxler, R. R.; Rolph, G. D. Bulletin of the American Meteorological Society, Vol. 96, Issue 12 https://doi.org/10.1175/BAMS-D-14-00110.1	journal	December 2015
Comparison of sources of submicron particle number concentrations measured at two sites in Rochester, NY Kasumba, John; Hopke, Philip K.; Chalupa, David C. Science of The Total Environment, Vol. 407, Issue 18 https://doi.org/10.1016/j.scitotenv.2009.05.040	journal	September 2009
Multielemental characterization of urban roadway dust Hopke, Philip K.; Lamb, Robert E.; Natusch, David F. S. Environmental Science & Technology, Vol. 14, Issue 2 https://doi.org/10.1021/es60162a006	journal	February 1980
Long-term trends (2005–2016) of source apportioned PM2.5 across New York State Masiol, Mauro; Squizzato, Stefania; Rich, David Q. Atmospheric Environment, Vol. 201 https://doi.org/10.1016/j.atmosenv.2018.12.038	journal	March 2019
Source apportionment to support air quality planning: Strengths and weaknesses of existing approaches Thunis, P.; Clappier, A.; Tarrason, L. Environment International, Vol. 130 https://doi.org/10.1016/j.envint.2019.05.019	journal	September 2019
A factor analysis model of large scale pollution Blifford, Irving H.; Meeker, Gary O. Atmospheric Environment (1967), Vol. 1, Issue 2 https://doi.org/10.1016/0004-6981(67)90042-X	journal	March 1967
Self Modeling Curve Resolution Lawton, William H.; Sylvestre, Edward A. Technometrics, Vol. 13, Issue 3 https://doi.org/10.1080/00401706.1971.10488823	journal	August 1971

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