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Proposing and investigating PCAMARS as a novel model for NO(2) interpolation
Effective measurement of exposure to air pollution, not least NO(2), for epidemiological studies along with the need to better management and control of air pollution in urban areas ask for precise interpolation and determination of the concentration of pollutants in nonmonitored spots. A variety of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394563/ https://www.ncbi.nlm.nih.gov/pubmed/30798406 http://dx.doi.org/10.1007/s10661-019-7253-2 |
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author | Yousefzadeh, Mohsen Farnaghi, Mahdi Pilesjö, Petter Mansourian, Ali |
author_facet | Yousefzadeh, Mohsen Farnaghi, Mahdi Pilesjö, Petter Mansourian, Ali |
author_sort | Yousefzadeh, Mohsen |
collection | PubMed |
description | Effective measurement of exposure to air pollution, not least NO(2), for epidemiological studies along with the need to better management and control of air pollution in urban areas ask for precise interpolation and determination of the concentration of pollutants in nonmonitored spots. A variety of approaches have been developed and used. This paper aims to propose, develop, and test a spatial predictive model based on multivariate adaptive regression splines (MARS) and principle component analysis (PCA) to determine the concentration of NO(2) in Tehran, as a case study. To increase the accuracy of the model, spatial data (population, road network and point of interests such as petroleum stations and green spaces) and meteorological data (including temperature, pressure, wind speed and relative humidity) have also been used as independent variables, alongside air quality measurement data gathered by the monitoring stations. The outputs of the proposed model are evaluated against reference interpolation techniques including inverse distance weighting, thin plate splines, kriging, cokriging, and MARS3. Interpolation for 12 months showed better accuracies of the proposed model in comparison with the reference methods. |
format | Online Article Text |
id | pubmed-6394563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-63945632019-03-15 Proposing and investigating PCAMARS as a novel model for NO(2) interpolation Yousefzadeh, Mohsen Farnaghi, Mahdi Pilesjö, Petter Mansourian, Ali Environ Monit Assess Article Effective measurement of exposure to air pollution, not least NO(2), for epidemiological studies along with the need to better management and control of air pollution in urban areas ask for precise interpolation and determination of the concentration of pollutants in nonmonitored spots. A variety of approaches have been developed and used. This paper aims to propose, develop, and test a spatial predictive model based on multivariate adaptive regression splines (MARS) and principle component analysis (PCA) to determine the concentration of NO(2) in Tehran, as a case study. To increase the accuracy of the model, spatial data (population, road network and point of interests such as petroleum stations and green spaces) and meteorological data (including temperature, pressure, wind speed and relative humidity) have also been used as independent variables, alongside air quality measurement data gathered by the monitoring stations. The outputs of the proposed model are evaluated against reference interpolation techniques including inverse distance weighting, thin plate splines, kriging, cokriging, and MARS3. Interpolation for 12 months showed better accuracies of the proposed model in comparison with the reference methods. Springer International Publishing 2019-02-23 2019 /pmc/articles/PMC6394563/ /pubmed/30798406 http://dx.doi.org/10.1007/s10661-019-7253-2 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Yousefzadeh, Mohsen Farnaghi, Mahdi Pilesjö, Petter Mansourian, Ali Proposing and investigating PCAMARS as a novel model for NO(2) interpolation |
title | Proposing and investigating PCAMARS as a novel model for NO(2) interpolation |
title_full | Proposing and investigating PCAMARS as a novel model for NO(2) interpolation |
title_fullStr | Proposing and investigating PCAMARS as a novel model for NO(2) interpolation |
title_full_unstemmed | Proposing and investigating PCAMARS as a novel model for NO(2) interpolation |
title_short | Proposing and investigating PCAMARS as a novel model for NO(2) interpolation |
title_sort | proposing and investigating pcamars as a novel model for no(2) interpolation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6394563/ https://www.ncbi.nlm.nih.gov/pubmed/30798406 http://dx.doi.org/10.1007/s10661-019-7253-2 |
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