Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation

Given the global problem of high levels of pollutants in the atmosphere, it is essential to use tools to measure and determine these levels. Unfortunately, it is impossible to have devices that allow direct pollutants' direct measurements in a place of interest. Due to this limitation, in this...

Full description

Autores:
Carrillo, G
Carrillo, G E
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/10023
Acceso en línea:
https://hdl.handle.net/20.500.12585/10023
https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012033
Palabra clave:
Air quality
Forecasting
Interpolation
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
title Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
spellingShingle Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
Air quality
Forecasting
Interpolation
LEMB
title_short Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
title_full Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
title_fullStr Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
title_full_unstemmed Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
title_sort Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolation
dc.creator.fl_str_mv Carrillo, G
Carrillo, G E
dc.contributor.author.none.fl_str_mv Carrillo, G
Carrillo, G E
dc.subject.keywords.spa.fl_str_mv Air quality
Forecasting
Interpolation
topic Air quality
Forecasting
Interpolation
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Given the global problem of high levels of pollutants in the atmosphere, it is essential to use tools to measure and determine these levels. Unfortunately, it is impossible to have devices that allow direct pollutants' direct measurements in a place of interest. Due to this limitation, in this work, a computer tool was developed to predict contaminants' behavior and their concentration levels in a reliable way. In this methodology, equations of the physics of motion were implemented to predict particles' behavior in a given area and an interpolation technique based on the Kriging method. In the initial stage, a preliminary analysis of the pollution data of the city of Bogota, Colombia, downloaded from the Air quality monitoring network of Bogota, Colombia, was performed. In the next stage, the variables of most significant interest in the analysis were defined, and the data to be characterized is explored. Finally, the selected method's calculation algorithm is implemented in Python, taking an ArcGIS library as a programming reference. From the results, it was possible to determine the contaminants' levels for some regions of Bogota, Colombia, between values of 0.067 to a maximum weight of 0.4039 ¼g/m3, for January 2013.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-02-16T15:05:05Z
dc.date.available.none.fl_str_mv 2021-02-16T15:05:05Z
dc.date.submitted.none.fl_str_mv 2021-02-12
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/lecture
dc.type.hasVersion.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv G Carrillo and G E Carrillo 2020 J. Phys.: Conf. Ser. 1708 012033
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/10023
dc.identifier.url.none.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012033
dc.identifier.doi.none.fl_str_mv 10.1088/1742-6596/1708/1/012033
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv G Carrillo and G E Carrillo 2020 J. Phys.: Conf. Ser. 1708 012033
10.1088/1742-6596/1708/1/012033
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/10023
https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/012033
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessRights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 7 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Journal of Physics: Conference Series 1708 (2020) 012033
institution Universidad Tecnológica de Bolívar
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spelling Carrillo, Ga1e8c876-c03d-48dd-a0bd-1fabfc144493Carrillo, G Ed1de93af-aa79-478f-9d48-0f302581d99f2021-02-16T15:05:05Z2021-02-16T15:05:05Z20202021-02-12G Carrillo and G E Carrillo 2020 J. Phys.: Conf. Ser. 1708 012033https://hdl.handle.net/20.500.12585/10023https://iopscience.iop.org/article/10.1088/1742-6596/1708/1/01203310.1088/1742-6596/1708/1/012033Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarGiven the global problem of high levels of pollutants in the atmosphere, it is essential to use tools to measure and determine these levels. Unfortunately, it is impossible to have devices that allow direct pollutants' direct measurements in a place of interest. Due to this limitation, in this work, a computer tool was developed to predict contaminants' behavior and their concentration levels in a reliable way. In this methodology, equations of the physics of motion were implemented to predict particles' behavior in a given area and an interpolation technique based on the Kriging method. In the initial stage, a preliminary analysis of the pollution data of the city of Bogota, Colombia, downloaded from the Air quality monitoring network of Bogota, Colombia, was performed. In the next stage, the variables of most significant interest in the analysis were defined, and the data to be characterized is explored. Finally, the selected method's calculation algorithm is implemented in Python, taking an ArcGIS library as a programming reference. From the results, it was possible to determine the contaminants' levels for some regions of Bogota, Colombia, between values of 0.067 to a maximum weight of 0.4039 ¼g/m3, for January 2013.7 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series 1708 (2020) 012033Prediction of particle level behavior in atmospheric air based on laws of physics of motion and geographic interpolationinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8544http://purl.org/coar/version/c_970fb48d4fbd8a85Air qualityForecastingInterpolationLEMBCartagena de IndiasPúblico generalYuan X, Zhang M, Wang Q, Wang Y, Zuo J 2017 Evolution analysis of environmental standards: Effectiveness on air pollutant emissions reduction J. Clean. Prod. 149 511Van Y, Perry S, Klemes J, Lee Ch 2018 A review on air emissions assessment Journal of Cleaner Production 194(1) 673Chen Q, Chen Y, Luo X, Hong Y, Hong, Z, Zhao Z 2019 Seasonal characteristics and health risks of PM 2.5 -bound organic pollutants in industrial and urban areas of a China megacity J. Environ. Manage. 245 273Li R., W Chen, Xiu, A, Zhao, Zhang H, Zhang S 2019 A comprehensive inventory of agricultural atmospheric particulate matters (PM 10 and PM 2.5) and gaseous pollutants (VOCs, SO 2, NH 3, CO, NOx and HC) emissions in China Ecol. Indic. 107 105609Sager L 2019 Estimating the effect of air pollution on road safety using atmospheric temperature inversions J. Environ. Econ. Manage. 98(251) 102250Sacks J, Lloyd J, Zhu Y, Anderton J, Jang C, B Hubbell, N Fann 2018 The Environmental Bene fi ts Mapping and Analysis Program e Community Edition (BenMAP e CE): A tool to estimate the health and economic bene fi ts of reducing air pollution Environ. Model. Softw. 104(2) 118Lee C, Tran M, Choo C, Tan C, Chiew Y 2020 Evaluation of air quality in Sunway City, Selangor, Malaysia from a mobile monitoring campaign using air pollution micro-sensors Environ. Pollut. 265 115058Ștefănu S, Öllerer K, Ion M 2019 National environmental quality assessment and monitoring of atmospheric heavy metal pollution - A moss bag approach J. Environ. Manage. 248 109224Teegavarapu R, Meskele T, Pathak C 2012 Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods Comput. Geosci. 40 28Beauchamp M, Malherbe L, Fouquet C, Létinoisand L, Tognet F 2018 A polynomial approximation of the traf fi c contributions for kriging- based interpolation of urban air quality model Environ. Model Softw. 105 132Morley D, Gulliver J 2018 A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment Environ. Model. Softw. 105 17Shen Q, Wang Y, Wang X, Liu X, Zhang X, Zhang S 2019 Comparing interpolation methods to predict soil total phosphorus in the Mollisol area of Northeast China Catena 174 59Shukla K, Kumar P, Mann G, Khare M 2020 Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi Sustain. Cities Soc. 54 101997Li L, Romary T, Caers J 2015 Universal kriging with training images Spat. Stat. 14 240Carrillo G 2016 Sistema Piloto para Estimación del PM10 Existente en el Aire Basados en Técnicas de Interpolación Geográfica (Colombia: Universidad del Norte)Sefair J, Espinosa M, Behrentz E, Medaglia A 2019 Computers, Environment and Urban Systems Optimization model for urban air quality policy design: A case study in Latin America Comput. Environ. 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