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...
- 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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|
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 |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_8544 |
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 |
bitstream.url.fl_str_mv |
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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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