<?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>0187-6236</journal-id>
<journal-title><![CDATA[Atmósfera]]></journal-title>
<abbrev-journal-title><![CDATA[Atmósfera]]></abbrev-journal-title>
<issn>0187-6236</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>S0187-62362025000100028</article-id>
<article-id pub-id-type="doi">10.20937/atm.53488</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Modeling of the PM10 pollutant in Monterrey, Nuevo León, Mexico, with ARIMA, transfer functions and GARCH models]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez-Gómez]]></surname>
<given-names><![CDATA[Gustavo Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez-Guzmán]]></surname>
<given-names><![CDATA[Martha Elva]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Romero-Padilla]]></surname>
<given-names><![CDATA[Juan Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Macedo-Cruz]]></surname>
<given-names><![CDATA[Antonia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz-Solorio]]></surname>
<given-names><![CDATA[Carlos Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rendón-Sánchez]]></surname>
<given-names><![CDATA[Gilberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez-Castorena]]></surname>
<given-names><![CDATA[Edgar Vladimir]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Encinia-Uribe]]></surname>
<given-names><![CDATA[Vicente Vidal]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Nuevo León Facultad de Agronomía ]]></institution>
<addr-line><![CDATA[General Escobedo Nuevo León]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>39</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362025000100028&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362025000100028&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362025000100028&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Particles of matter smaller than 10 &#956;m (PM10) are significant pollutants due to their impact on health and the environment. This study analyzed daily PM10 data from the Obispado station (Monterrey, Nuevo León, Mexico) provided by the INECC, covering the period from 1997 to 2014. Climatological variables such as precipitation, wind speed (at 2 and 10 m), and atmospheric pressure, obtained from NASA&#8217;s POWER project, were included. Data were partitioned into training and testing sets using an 80-20% split, employing 3798 daily observations for training (2002 to 2012) and 950 observations for testing (2012 to 2014). Descriptive analysis and time series decomposition were performed to identify trends and seasonality. ARIMA models, univariate and with transfer functions incorporating meteorological variables, were applied. Augmented Dickey-Fuller and Ljung-Box tests validated the stationarity and residual independence assumptions. The ARIMA (1,1,1)(0,0,0)[365] model with transfer functions outperformed the univariate model. A significant relationship between meteorological variables and PM10 predictions was identified, supporting their use for short-term forecasting (&#8804; 10 days). Future studies should consider applying multivariate models with additional predictors and geostatistical approaches to improve spatiotemporal characterization.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Las partículas menores a 10 &#956;m (PM10) son contaminantes relevantes por su impacto en la salud y el ambiente. En este estudio se analizaron datos diarios de PM10 de la estación Obispado (Monterrey, Nuevo León, México), del INECC, entre 1997 y 2014. Se incluyeron variables climatológicas como precipitación, velocidad del viento (a 2 y 10 m) y presión atmosférica, obtenidas del proyecto POWER de la NASA. Los datos se dividieron en conjuntos de entrenamiento y prueba utilizando una proporción de 80-20%. Se emplearon 3798 observaciones diarias para entrenamiento (2002 a 2012) y 950 observaciones para prueba (2012 a 2014). Se realizó un análisis descriptivo y una descomposición de series temporales para identificar tendencias y estacionalidad. Se aplicaron modelos ARIMA univariados y con funciones de transferencia, incorporando variables climáticas. Las pruebas de Dickey-Fuller aumentada y Ljung-Box validaron la estacionalidad e independencia de los residuales. El modelo ARIMA (1,1,1)(0,0,0)[365] con funciones de transferencia fue más preciso que el univariado. Se identificó una relación significativa entre las variables climáticas y la predicción de PM10, lo cual respalda su uso para predicciones a corto plazo (&#8804; 10 días). Se recomienda aplicar modelos multivariados con más predictores y enfoques geoestadísticos para mejorar la caracterización espaciotemporal.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[time series]]></kwd>
<kwd lng="en"><![CDATA[transfer functions]]></kwd>
<kwd lng="en"><![CDATA[climate variables]]></kwd>
<kwd lng="en"><![CDATA[Granger causality test]]></kwd>
</kwd-group>
</article-meta>
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