Block Tensor Decomposition for Source Apportionment of Air Pollution
The ambient particulate chemical composition data with three particle diameter sizes (2.5mm
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| creator | Hopke, Philip K Leung, Maggie Li, Na Navasca, Carmeliza |
| description | The ambient particulate chemical composition data with three particle
diameter sizes (2.5mm |
| doi_str_mv | 10.48550/arxiv.1110.4133 |
| format | Article |
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diameter sizes (2.5mm<D< 1.15mm, 1.15mm<D<0.34mm and 0.34mm<D<0.1mm) collected
at a major industrial center in Allen Park in Detroit, MI is examined. Standard
multiway (tensor) methods like PARAFAC and Tucker tensor decompositions have
been applied extensively to many chemical data. However, for multiple particle
sizes, the source apportionment analysis calls for a novel multiway factor
analysis. We apply the regularized block tensor decomposition to the collected
air sample data. In particular, we use the Block Term Decomposition (BTD) in
rank-(L;L;1) form to identify nine pollution sources (Fe+Zn, Sulfur with Dust,
Road Dust, two types of Metal Works, Road Salt, Local Sulfate, and Homogeneous
and Cloud Sulfate).]]></description><identifier>DOI: 10.48550/arxiv.1110.4133</identifier><language>eng</language><subject>Mathematics - Numerical Analysis</subject><creationdate>2011-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1110.4133$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1110.4133$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hopke, Philip K</creatorcontrib><creatorcontrib>Leung, Maggie</creatorcontrib><creatorcontrib>Li, Na</creatorcontrib><creatorcontrib>Navasca, Carmeliza</creatorcontrib><title>Block Tensor Decomposition for Source Apportionment of Air Pollution</title><description><![CDATA[The ambient particulate chemical composition data with three particle
diameter sizes (2.5mm<D< 1.15mm, 1.15mm<D<0.34mm and 0.34mm<D<0.1mm) collected
at a major industrial center in Allen Park in Detroit, MI is examined. Standard
multiway (tensor) methods like PARAFAC and Tucker tensor decompositions have
been applied extensively to many chemical data. However, for multiple particle
sizes, the source apportionment analysis calls for a novel multiway factor
analysis. We apply the regularized block tensor decomposition to the collected
air sample data. In particular, we use the Block Term Decomposition (BTD) in
rank-(L;L;1) form to identify nine pollution sources (Fe+Zn, Sulfur with Dust,
Road Dust, two types of Metal Works, Road Salt, Local Sulfate, and Homogeneous
and Cloud Sulfate).]]></description><subject>Mathematics - Numerical Analysis</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzz1PwzAQxnEvDKhlZ0L-Ail-iWN3DC1vUiWQyB5dnDvJahJHTovg21MD00m_4fT8GbuVYlM6Y8Q9pK_wuZEyg9T6mu0fhuiPvMFpiYnv0cdxjks4hThxushHPCePvJ7nmDKOOJ14JF6HxN_jMJwzrtkVwbDgzf9dsebpsdm9FIe359ddfSigMrpAg4KwVBak0JVwXakVku1cB4rQGmsrh6rfAqitIgG9J-g0oa9658rL3BW7-3v7W9HOKYyQvttc0-Ya_QMhDUWp</recordid><startdate>20111018</startdate><enddate>20111018</enddate><creator>Hopke, Philip K</creator><creator>Leung, Maggie</creator><creator>Li, Na</creator><creator>Navasca, Carmeliza</creator><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20111018</creationdate><title>Block Tensor Decomposition for Source Apportionment of Air Pollution</title><author>Hopke, Philip K ; Leung, Maggie ; Li, Na ; Navasca, Carmeliza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a653-e5e0fe427a103608b432ef7b8ba2fe757768e2d9aa292f0adcfab3fec6d884413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Mathematics - Numerical Analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Hopke, Philip K</creatorcontrib><creatorcontrib>Leung, Maggie</creatorcontrib><creatorcontrib>Li, Na</creatorcontrib><creatorcontrib>Navasca, Carmeliza</creatorcontrib><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hopke, Philip K</au><au>Leung, Maggie</au><au>Li, Na</au><au>Navasca, Carmeliza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Block Tensor Decomposition for Source Apportionment of Air Pollution</atitle><date>2011-10-18</date><risdate>2011</risdate><abstract><![CDATA[The ambient particulate chemical composition data with three particle
diameter sizes (2.5mm<D< 1.15mm, 1.15mm<D<0.34mm and 0.34mm<D<0.1mm) collected
at a major industrial center in Allen Park in Detroit, MI is examined. Standard
multiway (tensor) methods like PARAFAC and Tucker tensor decompositions have
been applied extensively to many chemical data. However, for multiple particle
sizes, the source apportionment analysis calls for a novel multiway factor
analysis. We apply the regularized block tensor decomposition to the collected
air sample data. In particular, we use the Block Term Decomposition (BTD) in
rank-(L;L;1) form to identify nine pollution sources (Fe+Zn, Sulfur with Dust,
Road Dust, two types of Metal Works, Road Salt, Local Sulfate, and Homogeneous
and Cloud Sulfate).]]></abstract><doi>10.48550/arxiv.1110.4133</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Mathematics - Numerical Analysis |
| title | Block Tensor Decomposition for Source Apportionment of Air Pollution |
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