Tropospheric O3 Model from Climatological Approaches in the Colombian Andes
We identified two climatological parameters that are key to modeling the behavior of tropospheric ozone (O3) produced in the urban area of a heavily polluted city in the Colombian Andes. These parameters are the relative humidity (RH) and total radiation intensity (I). In topographically constrained...
- Autores:
-
Nisperuza Toledo, Daniel José
Avendaño Tamayo, Efrén De Jesús
Rúa Cardona, Alex Fernando
Vásquez Londoño, Leidy Karina
Grajales Vargas, Heazel Janinne
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Tecnológico de Antioquia
- Repositorio:
- Repositorio Tdea
- Idioma:
- eng
- OAI Identifier:
- oai:dspace.tdea.edu.co:tdea/2886
- Acceso en línea:
- https://dspace.tdea.edu.co/handle/tdea/2886
- Palabra clave:
- Ozono troposférico
对流层臭氧
Oropospheric ozone
Tropospheric ozone
Ozone troposphérique
数学模型
气候参数
Climatological parameters
Parámetros climáticos
Modelo matemático
Mathematical model
Modèle mathématique
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| title |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| spellingShingle |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes Ozono troposférico 对流层臭氧 Oropospheric ozone Tropospheric ozone Ozone troposphérique 数学模型 气候参数 Climatological parameters Parámetros climáticos Modelo matemático Mathematical model Modèle mathématique |
| title_short |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| title_full |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| title_fullStr |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| title_full_unstemmed |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| title_sort |
Tropospheric O3 Model from Climatological Approaches in the Colombian Andes |
| dc.creator.fl_str_mv |
Nisperuza Toledo, Daniel José Avendaño Tamayo, Efrén De Jesús Rúa Cardona, Alex Fernando Vásquez Londoño, Leidy Karina Grajales Vargas, Heazel Janinne |
| dc.contributor.author.none.fl_str_mv |
Nisperuza Toledo, Daniel José Avendaño Tamayo, Efrén De Jesús Rúa Cardona, Alex Fernando Vásquez Londoño, Leidy Karina Grajales Vargas, Heazel Janinne |
| dc.subject.agrovoc.none.fl_str_mv |
Ozono troposférico 对流层臭氧 Oropospheric ozone Tropospheric ozone Ozone troposphérique 数学模型 |
| topic |
Ozono troposférico 对流层臭氧 Oropospheric ozone Tropospheric ozone Ozone troposphérique 数学模型 气候参数 Climatological parameters Parámetros climáticos Modelo matemático Mathematical model Modèle mathématique |
| dc.subject.proposal.none.fl_str_mv |
气候参数 Climatological parameters Parámetros climáticos |
| dc.subject.tee.none.fl_str_mv |
Modelo matemático Mathematical model Modèle mathématique |
| description |
We identified two climatological parameters that are key to modeling the behavior of tropospheric ozone (O3) produced in the urban area of a heavily polluted city in the Colombian Andes. These parameters are the relative humidity (RH) and total radiation intensity (I). In topographically constrained areas, the production of tropospheric O3, as a by-product during photo-oxidation of carbon monoxide (CO) and nitrogen oxides (NO and NO2) has received much attention over the last decades. Models used to describe O3 dynamics are based on computationally demanding techniques that require lots of input data, however. This study proposes a simple approach for describing O3. To that end, it evaluates fifteen empirical models based on the combination of four linear regressions: O3 against RH, temperature (T), wind speed (U), and I. Each model is driven by the analyzed climatological parameters over the period from 2012 to 2018 and further run using either daily or monthly averaged data. The best fitting model for monthly averaged data outperformed that for daily averaged data in both mathematical simplicity and accuracy; however, the differences between these models remained <0.4 percent. The results suggest that the O3 produced increases with I and decreases with RH. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2023-05-15T23:10:07Z |
| dc.date.available.none.fl_str_mv |
2023-05-15T23:10:07Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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0033-0124 |
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https://dspace.tdea.edu.co/handle/tdea/2886 |
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1467-9272 |
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0033-0124 1467-9272 |
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eng |
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eng |
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775 |
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4 |
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73 |
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The Professional Geographer |
| dc.relation.references.spa.fl_str_mv |
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Murray, and M. Lenzen. 2018. International trade linked with disease burden from airborne particulate pollution. Resources, Conservation and Recycling 129:1–11. doi: https://doi.org/10.1016/j.resconrec.2017.10.002. Zapata, C., and N. Cano. 2014. Inhalable particulate matter measurements (PM2.5) in the metropolitan area of Valle de Aburrá, Colombia. Journal of Engineering Research and Applications 4 (2):179–85 |
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Nisperuza Toledo, Daniel José97a59355-02da-4d24-aff9-990b2aaf4d2aAvendaño Tamayo, Efrén De Jesúse1190af9-a1d6-4dfe-bd61-9f8d5cc94c86Rúa Cardona, Alex Fernando191d78ce-69bd-4fa9-89a1-2e5f9b795e9bVásquez Londoño, Leidy Karina9be503d9-c430-4765-b129-f3984fce8b1dGrajales Vargas, Heazel Janinne4fd56385-f57c-40c0-b41a-759544ee806dAndes colombianos2023-05-15T23:10:07Z2023-05-15T23:10:07Z20210033-0124https://dspace.tdea.edu.co/handle/tdea/28861467-9272We identified two climatological parameters that are key to modeling the behavior of tropospheric ozone (O3) produced in the urban area of a heavily polluted city in the Colombian Andes. These parameters are the relative humidity (RH) and total radiation intensity (I). In topographically constrained areas, the production of tropospheric O3, as a by-product during photo-oxidation of carbon monoxide (CO) and nitrogen oxides (NO and NO2) has received much attention over the last decades. Models used to describe O3 dynamics are based on computationally demanding techniques that require lots of input data, however. This study proposes a simple approach for describing O3. To that end, it evaluates fifteen empirical models based on the combination of four linear regressions: O3 against RH, temperature (T), wind speed (U), and I. Each model is driven by the analyzed climatological parameters over the period from 2012 to 2018 and further run using either daily or monthly averaged data. The best fitting model for monthly averaged data outperformed that for daily averaged data in both mathematical simplicity and accuracy; however, the differences between these models remained <0.4 percent. The results suggest that the O3 produced increases with I and decreases with RH.在对哥伦比亚安第斯山脉一个严重污染城市产生的对流层臭氧的行为模拟中, 我们确定了两个关键的气候学参数：相对湿度和总辐射强度。在地形受限地区, 对流层臭氧是一氧化碳和氮氧化合物（一氧化氮和二氧化氮）的光氧化过程的副产品。过去几十年中, 对流层臭氧受到广泛的关注。然而, 描述臭氧动力学的模式, 依赖于基于大量输入数据的计算技术。本研究提出一个简单的方法来描述臭氧。为此, 本文基于四种线性回归组合（臭氧与相对湿度、温度、风速和总辐射强度）, 对15个经验模式进行了评估。每个模式都由2012年至2018年的气候参数所驱动, 并利用日平均或月平均数据进一步运行模式。月平均数据的最佳拟合模式, 在数学简单性和精度上均优于日平均数据。然而, 这些模式之间的差异在0.4%以内。结果表明, 臭氧随着总辐射强度的增加而增加, 随着相对湿度的增加而减少。Identificamos dos parámetros climatológicos que son claves para modelar el comportamiento del ozono troposférico (O3) producido en el área urbana de una ciudad altamente contaminada, en los Andes colombianos. Estos parámetros son la humedad relativa (RH) y la intensidad de la radiación total (I). En áreas constreñidas topográficamente la producción de O3 troposférico, como subproducto del proceso de foto-oxidación del monóxido de carbono (CO) y los óxidos de nitrógeno (NO y NO2), ha recibido considerable atención durante las últimas décadas. Sin embargo, los modelos que se usan para describir la dinámica del O3 se basan en técnicas computacionales que requieren grandes cantidades de datos. En este estudio se propone un enfoque simple para describir el O3. Con tal fin, se evalúan quince modelos empíricos basados en la combinación de cuatro regresiones lineales: O3 contra RH, temperatura (T), velocidad del viento (U), e I. Cada modelo es controlado por los parámetros climatológicos analizados a lo largo del período de 2012 a 2018 y por una corrida adicional usando datos promedio diarios o mensuales. El modelo más adecuado para los datos promedio mensuales superó al de los datos promedio diarios tanto en simplicidad matemática como en exactitud; sin embargo, las diferencias entre estos modelos permanecieron en <0.4 porciento. 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Journal of Engineering Research and Applications 4 (2):179–85784info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbOzono troposférico对流层臭氧Oropospheric ozoneTropospheric ozoneOzone troposphérique数学模型气候参数Climatological parametersParámetros climáticosModelo matemáticoMathematical modelModèle mathématiqueORIGINALTropospheric O3 Model from Climatological Approaches in the Colombian Andes.jpgTropospheric O3 Model from Climatological Approaches in the Colombian Andes.jpgSolo datos del documentoimage/jpeg481912https://dspace.tdea.edu.co/bitstream/tdea/2886/1/Tropospheric%20O3%20Model%20from%20Climatological%20Approaches%20in%20the%20Colombian%20Andes.jpg74b6b1fa8db722c7db7b135633a82540MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://dspace.tdea.edu.co/bitstream/tdea/2886/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessTHUMBNAILTropospheric O3 Model from Climatological Approaches in the Colombian Andes.jpg.jpgTropospheric O3 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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