<?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>0188-4611</journal-id>
<journal-title><![CDATA[Investigaciones geográficas]]></journal-title>
<abbrev-journal-title><![CDATA[Invest. Geog]]></abbrev-journal-title>
<issn>0188-4611</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Geografía]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-46112020000100109</article-id>
<article-id pub-id-type="doi">10.14350/rig.59851</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación cartográfica de la vulnerabilidad frente a derrames de hidrocarburos en ductos. Consecuencias ambientales y sociales]]></article-title>
<article-title xml:lang="en"><![CDATA[Cartographic assessment of vulnerability to pipeline oil spills. Environmental and social consequences]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Daza-Leguizamón]]></surname>
<given-names><![CDATA[Omar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vera López]]></surname>
<given-names><![CDATA[Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chías]]></surname>
<given-names><![CDATA[Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Pedagógica y Tecnológica de Colombia Grupo de Investigación en Ingeniería Civil y Ambiental ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Pedagógica y Tecnológica de Colombia Instituto para la Investigación e Innovación en Ciencia y Tecnología de Materiales ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Autónoma de México Instituto de Geografía Geotecnología en Infraestructura Transporte y Sustentabilidad]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<numero>101</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-46112020000100109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-46112020000100109&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-46112020000100109&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La modelación y el análisis espacial han permitido mejorar la gestión de integridad de ductos de transporte de líquidos peligrosos gracias a que facilitan la identificación y la clasificación de tramos de los ductos de petróleo en función del grado de afectación que un potencial derrame pudiera ocasionar en el medio ambiente. Aunque existen herramientas de análisis basadas en Sistemas de Información Geográfica (SIG) para la identificación de tramos, no priorizan adecuadamente las actividades de gestión de integridad sobre la tubería, ya que sus resultados no incluyen un análisis integrado del territorio. En este artículo se presenta una metodología basada en el uso del Proceso de Análisis Jerárquico (PAJ) y el cálculo del costo mínimo acumulado (CMA) para clasificar puntos sobre tubería con afectación simultánea a varias áreas de alta consecuencia. El PAJ permitió establecer la vulnerabilidad relativa de cada uno de los elementos analizados del territorio. Con el cálculo del CMA se estimó el tiempo que tardaría el derrame, originado en la tubería, en alcanzar cada elemento. La vulnerabilidad relativa y el costo mínimo acumulado se utilizaron para calcular un índice de consecuencia que se asigna a puntos sobre el trazado del ducto. La metodología fue aplicada a un poliducto en el territorio colombiano para exponer las ventajas de identificar la afectación simultánea a varios elementos del lugar. Los resultados obtenidos permiten diferenciar adecuadamente puntos con diferente nivel de afectación, y son datos útiles para la implementación del análisis de riesgo en gestión de integridad de ductos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Modeling and spatial analysis led to improved management of the integrity of pipelines of hazardous liquids because it facilitates the identification and classification of sections of oil pipelines according to the extent of environmental affectation that may result from a potential spillage. Although analytical tools based on Geographic Information Systems are available for the identification of sections of pipelines, these fail to prioritize properly the management activities related to pipeline integrity, as the results thereof do not include an integrated analysis of the territory. This article outlines a methodology that combines spatial data on population, environmentally sensitive areas, infrastructure, streams, route of pipelines transporting hydrocarbons, to identify the indirect consequences of leakage of transported liquids. Our findings provide information related to the impact of spills, which are easily integrated to methodologies for integrity management based on risk analysis. The methodology is based on the use of the Hierarchical Analysis Process (PAJ, in Spanish) and the calculation of the Minimum Cumulative Cost (CMA) to group together points along pipelines with simultaneous impacts involving several heavily affected areas. PAJ defines each at-risk element in the territory and determines the relative vulnerability by calculating weighting factors. The CMA is calculated from the element of interest to each point in the pipe; this process leads to identifying the proximity between them based on a surface spill. The transportation cost is used mainly to modify the vulnerability as a function of distance, so that vulnerability decreases with increasing distance and the cost of transportation from the pipeline. Implementation requires the design of cartographic models to describe data processing and analysis and spatial information. The cartographic models designed include spatial analysis processes for calculating cost maps, CMA, algebraic overlay, and estimates of impact levels for specific pipeline points. PAJ contemplates the fulfilment of four phases: the first defined as an issue the need to classify pipelines according to the extent of environmental and social consequences. The second identified vulnerable elements in the territory and proposed a hierarchical three-level structure. The third set paired-comparison matrices, and calculated the weighting factors for each element at each level of the hierarchical structure with the Delphi method and a panel of experts. The fourth calculated the weighting factors for the last level using comparison values for intermediate pairs (Saaty, 2008). The spatial representation of the CMA of the spill requires a map of surface transport cost considering transport at ground level and through rivers. For ground transport, we used Manning&#8217;s equation assuming very wide rectangular cross sections (flat and convex plots) and parabolic sections (concave plots). For river transport, we included a constant k, which is higher than 1 for costs associated with water flows; this allows simulating the increased spill rate when transported by rivers. CMA represents the shortest time needed for a spill to travel the distance between the pipeline and any of the elements analyzed in the territory. The data required for calculation with the ArcGIS software are the map of costs, the digital elevation model, and vertical and horizontal cost factors. The layers of standardized minimum cost are overlaid algebraically using the weighting factors calculated previously. Finally, values are assigned to each point on the route of the pipeline. The proposed methodology offers significant advantages compared to traditional methods regarding the identification of sections that impact heavily affected areas. The implementation of cartographic models yielded a classification of pipeline points, considering an analysis that integrates environmental and social aspects of the territory. On the other hand, the impact index, expressed in a range of 0 to 1, can be easily integrated to risk assessment matrices in pipeline integrity management programs. Finally, the methodology is flexible and can be reproduced in other geographic areas with differing social and environmental characteristics.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Gestión de integridad]]></kwd>
<kwd lng="es"><![CDATA[vulnerabilidad]]></kwd>
<kwd lng="es"><![CDATA[análisis de riesgo]]></kwd>
<kwd lng="es"><![CDATA[modelo cartográfico]]></kwd>
<kwd lng="es"><![CDATA[análisis multicriterio]]></kwd>
<kwd lng="en"><![CDATA[management of integrity]]></kwd>
<kwd lng="en"><![CDATA[vulnerability]]></kwd>
<kwd lng="en"><![CDATA[risk analysis]]></kwd>
<kwd lng="en"><![CDATA[cartographic model]]></kwd>
<kwd lng="en"><![CDATA[multicriteria analysis]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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