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Statistical analysis of winter ozone exceedances in the Uintah Basin, Utah, USA
Because of the confluence of several factors (persistent multiday inversions, petroleum production, and snow cover), the Uintah Basin of eastern Utah, USA, exhibits high concentrations of winter ozone. A regression analysis is presented that successfully predicts daily ozone concentration with a sta...
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Published in: | Journal of the Air & Waste Management Association (1995) 2018-05, Vol.68 (5), p.403-414 |
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container_title | Journal of the Air & Waste Management Association (1995) |
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creator | Mansfield, Marc L |
description | Because of the confluence of several factors (persistent multiday inversions, petroleum production, and snow cover), the Uintah Basin of eastern Utah, USA, exhibits high concentrations of winter ozone. A regression analysis is presented that successfully predicts daily ozone concentration with a standard error of about 11 ppb. It also predicts with 90% accuracy whether any given day will exceed the National Ambient Air Quality Standard for ozone, 70 ppb. An analysis is introduced for calculating a "pseudo-lapse rate," a determination of inversion intensity in the absence of sounding data. By combining the model with historical meteorological data, it is possible to make long-range predictions about ozone formation. The odds of observing no exceedance days in any given season are 38%. The odds of only three or fewer exceedance days in any given season are 46%.
This paper provides an improved understanding of the scientific underpinnings of the winter ozone phenomenon and an ability to make long-range predictions. |
doi_str_mv | 10.1080/10962247.2017.1339646 |
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This paper provides an improved understanding of the scientific underpinnings of the winter ozone phenomenon and an ability to make long-range predictions.</description><subject>Air quality</subject><subject>Inversions</subject><subject>Lapse rate</subject><subject>Ozone</subject><subject>Petroleum production</subject><subject>Quality standards</subject><subject>Regression analysis</subject><subject>Snow cover</subject><subject>Standard error</subject><subject>Statistical analysis</subject><subject>Structural basins</subject><subject>Winter</subject><issn>1096-2247</issn><issn>2162-2906</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNpdkE1PwkAQhjdGI4j-BM0mXjxYnNndbtsjEr8SEg7AebNsp6GktNgtUfz1tgE8eJmZZJ53knkYu0UYIsTwhJBoIVQ0FIDREKVMtNJnrC9Qi0AkoM9Zv2OCDuqxK-_XACggji5ZT8QaFSjos-mssU3um9zZgtvSFnufe15l_CsvG6p59VOVxOnbEaW2dOR5XvJmRXzR7u2KP1ufl4980c5tnY2u2UVmC083xz5g89eX-fg9mEzfPsajSeBkrJoghiwKLUpwwhEqSbhMlSV0WsSZE-GSkJROZJpGTkut0ggxtklKqSONiRywh8PZbV197sg3ZpN7R0VhS6p23mCCIKWGEFr0_h-6rnZ1-6k3AqQSEmUUt1R4oFxdeV9TZrZ1vrH13iCYTrg5CTedcHMU3ubujtd3yw2lf6mTYfkLo3x5kA</recordid><startdate>201805</startdate><enddate>201805</enddate><creator>Mansfield, Marc L</creator><general>Taylor & Francis Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>7U7</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1233-7594</orcidid></search><sort><creationdate>201805</creationdate><title>Statistical analysis of winter ozone exceedances in the Uintah Basin, Utah, USA</title><author>Mansfield, Marc L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-80f75a130c2ce143e1bd4ae1c628fc25be1e4693dd7c6364d7118a9dedce6193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Air quality</topic><topic>Inversions</topic><topic>Lapse rate</topic><topic>Ozone</topic><topic>Petroleum production</topic><topic>Quality standards</topic><topic>Regression analysis</topic><topic>Snow cover</topic><topic>Standard error</topic><topic>Statistical analysis</topic><topic>Structural basins</topic><topic>Winter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mansfield, Marc L</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - 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A regression analysis is presented that successfully predicts daily ozone concentration with a standard error of about 11 ppb. It also predicts with 90% accuracy whether any given day will exceed the National Ambient Air Quality Standard for ozone, 70 ppb. An analysis is introduced for calculating a "pseudo-lapse rate," a determination of inversion intensity in the absence of sounding data. By combining the model with historical meteorological data, it is possible to make long-range predictions about ozone formation. The odds of observing no exceedance days in any given season are 38%. The odds of only three or fewer exceedance days in any given season are 46%.
This paper provides an improved understanding of the scientific underpinnings of the winter ozone phenomenon and an ability to make long-range predictions.</abstract><cop>United States</cop><pub>Taylor & Francis Ltd</pub><pmid>28614040</pmid><doi>10.1080/10962247.2017.1339646</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-1233-7594</orcidid><oa>free_for_read</oa></addata></record> |
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source | Taylor and Francis Science and Technology Collection |
subjects | Air quality Inversions Lapse rate Ozone Petroleum production Quality standards Regression analysis Snow cover Standard error Statistical analysis Structural basins Winter |
title | Statistical analysis of winter ozone exceedances in the Uintah Basin, Utah, USA |
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