<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-4999</journal-id>
<journal-title><![CDATA[Revista internacional de contaminación ambiental]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Int. Contam. Ambient]]></abbrev-journal-title>
<issn>0188-4999</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-49992019000100179</article-id>
<article-id pub-id-type="doi">10.20937/rica.2019.35.01.13</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[MODELO DE REGRESIÓN LINEAL MÚLTIPLE PARA ESTIMAR CONCENTRACIÓN DE PM1]]></article-title>
<article-title xml:lang="en"><![CDATA[MULTIPLE LINEAR REGRESSION MODEL TO ESTIMATE PM1 CONCENTRATION]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morantes-Quintana]]></surname>
<given-names><![CDATA[Giobertti Raúl]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rincón-Polo]]></surname>
<given-names><![CDATA[Gladys]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Santodomingo]]></surname>
<given-names><![CDATA[Narciso Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Simón Bolívar Departamento de Procesos y Sistemas ]]></institution>
<addr-line><![CDATA[Caracas ]]></addr-line>
<country>Venezuela</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Escuela Superior Politécnica del Litoral Facultad de Ingeniería Marítima Ciencias Biológicas, Oceánicas y Recursos Naturales ]]></institution>
<addr-line><![CDATA[Guayaquil ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<volume>35</volume>
<numero>1</numero>
<fpage>179</fpage>
<lpage>194</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-49992019000100179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-49992019000100179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-49992019000100179&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Durante 2014-2015, en el Valle de Sartenejas, Gran Caracas, Venezuela, se recolectaron muestras de material particulado (PM, por sus siglas en inglés) usando un impactador de cascada que segrega el PM en seis rangos de tamaños de partículas: &gt; 7.2 &#956;m, 3.0 a 7.2 &#956;m, 1.5 a 3.0 &#956;m, 0.95 a 1.5 &#956;m, 0.49 a 0.95 &#956;m y &lt; 0.49 &#956;m, junto con datos de la meteorología del lugar. Como complemento se recolectaron las fechas de ocurrencia de incendios forestales y lluvias para el periodo de muestreo, así como la precipitación acumulada histórica mensual de la Gran Caracas. El objetivo de esta investigación fue obtener un modelo lineal multivariante para la predicción de PM1 a partir de variables ambientales, meteorológicas y eventualidades físicas en una región intertropical del centro-norte de Venezuela. Utilizando la información del muestreo y de fuentes secundarias se construyó una matriz de datos con variables ambientales, meteorológicas y eventualidades capaces de predecir el comportamiento de las partículas finas (PM1) a partir de otros tamaños de partículas, temperatura, histórico de precipitación, ocurrencia de incendios y de lluvias. Finalmente, se construyó un modelo de regresión lineal múltiple para estimar concentraciones promedio de PM1 a partir de la ocurrencia de incendios forestales, la concentración de PM en el rango de 3.0 a 0.95 µm y el histórico del promedio mensual de precipitación acumulada. La varianza de PM1 se explica en más del 75 % de los casos a partir de esas variables (R2 = 0.759; p &lt; 0.000). Se validó el modelo usando el indicador de error de sesgo promedio, el cual subestima los valores pronosticados.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT During 2014-2015, in the Sartenejas Valley, Greater Caracas, Venezuela, samples of particulate matter (PM) were collected using a cascade impactor that segregates PM in six ranges of particle sizes: &gt; 7.2 &#956;m, 3.0-7.2 &#956;m, 1.5-3.0 &#956;m, 0.95-1.5 &#956;m, 0.49-0.95 &#956;m, and &lt; 0.49 &#956;m, together with local weather data. As a complement, we investigated the occurrence of forest fires and rains for the sampling period, as well as the monthly historical accumulated precipitation for the Greater Caracas. The objective of this investigation was to obtain a linear multivariate model for the prediction of PM1 from environmental, meteorological and physical eventualities in an inter-tropical region in the center-north of Venezuela. Making use of the information from sampling and information from secondary sources, a data matrix was constructed with environmental, meteorological and eventualities variables capable of predicting the behavior of fine particles (PM1) based on other PM sizes, temperature, historical precipitation, occurrence of fires and rains. Finally, a multiple linear regression model was constructed to estimate average concentrations of PM1 from the occurrence of forest fires, concentration of PM in the range of 3.0-0.95 &#956;m, and the historical average of monthly-accumulated precipitation. The variance of PM1 is explained in more than 75% from these variables (R2 = 0.759, p &lt;0.000). The model was validated using the average bias error indicator, which underestimates the predicted values.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[material particulado]]></kwd>
<kwd lng="es"><![CDATA[contaminación atmosférica]]></kwd>
<kwd lng="es"><![CDATA[correlación estadística]]></kwd>
<kwd lng="es"><![CDATA[modelo mulivariado]]></kwd>
<kwd lng="en"><![CDATA[particulate matter]]></kwd>
<kwd lng="en"><![CDATA[atmospheric pollution]]></kwd>
<kwd lng="en"><![CDATA[statistical correlation]]></kwd>
<kwd lng="en"><![CDATA[multivariate model]]></kwd>
</kwd-group>
</article-meta>
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