<?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>2007-8196</journal-id>
<journal-title><![CDATA[Epistemus (Sonora)]]></journal-title>
<abbrev-journal-title><![CDATA[Epistemus (Sonora)]]></abbrev-journal-title>
<issn>2007-8196</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Sonora, División de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-81962025000100204</article-id>
<article-id pub-id-type="doi">10.36790/epistemus.v19i38.410</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Análisis de PM2.5 suspendidos en el noroeste de Hermosillo]]></article-title>
<article-title xml:lang="en"><![CDATA[Analysis of PM2.5 Suspended in Northwest Hermosillo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Domínguez Hurtado]]></surname>
<given-names><![CDATA[José Martín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cirett Galán]]></surname>
<given-names><![CDATA[Federico Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Torres Peralta]]></surname>
<given-names><![CDATA[Raquel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Estatal de Sonora Academia de Mecatrónico ]]></institution>
<addr-line><![CDATA[Navojoa Sonora]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Sonora Departamento Ingeniería Industrial ]]></institution>
<addr-line><![CDATA[Hermosillo Sonora]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Sonora Departamento Ingeniería Industrial ]]></institution>
<addr-line><![CDATA[Hermosillo Sonora]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>19</volume>
<numero>38</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-81962025000100204&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-81962025000100204&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-81962025000100204&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En la ciudad de Hermosillo, Sonora, se han empleado sensores de bajo costo para capturar datos sobre la contaminación por partículas PM2.5 y otros contaminantes atmosféricos. Dado que estos contaminantes han sido objeto de estudio durante las últimas décadas, es fundamental prever su comportamiento futuro. En este estudio, se utilizan modelos de aprendizaje automático para la predicción y el análisis de tendencias en los niveles de PM2.5. Los resultados preliminares indican que las concentraciones de contaminantes presentan una clara variabilidad estacional. La metodología propuesta sigue un enfoque sistemático para la preparación y análisis de datos en el contexto de los algoritmos de aprendizaje automático. Este enfoque incluye procesos de limpieza, exploración, tratamiento de valores atípicos y faltantes, escalado de datos categóricos, selección de características, y la partición de los datos en conjuntos de entrenamiento y prueba.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In Hermosillo, Sonora city, low-cost sensors have been used to capture data on PM2.5 particle pollution and other atmospheric pollutants. Since these pollutants have been studied over the past decades, it is essential to predict their future behavior. This study uses machine learning models for predicting and analyzing trends in PM2.5 levels. Preliminary results indicate that pollutant concentrations show clear seasonal variability. The proposed methodology follows a systematic approach for data preparation and analysis in the context of machine learning algorithms. This approach includes processes such as cleaning, exploration, handling outliers and missing values, scaling categorical data, feature selection, and partitioning the data into training and testing sets.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Calidad del aire]]></kwd>
<kwd lng="es"><![CDATA[PM2.5]]></kwd>
<kwd lng="es"><![CDATA[Estacionalidad]]></kwd>
<kwd lng="es"><![CDATA[Predicción]]></kwd>
<kwd lng="en"><![CDATA[Air Quality]]></kwd>
<kwd lng="en"><![CDATA[PM2.5]]></kwd>
<kwd lng="en"><![CDATA[Stationarity]]></kwd>
<kwd lng="en"><![CDATA[Prediction]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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