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Proposing and investigating PCAMARS as a novel model for NO2 interpolation

Yousefzadeh, Mohsen ; Farnaghi, Mahdi LU ; Pilesjö, Petter LU and Mansourian, Ali LU (2019) In Environmental Monitoring and Assessment 191(3).
Abstract
Effective measurement of exposure to air pollution, not least NO2, 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 NO2 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... (More)
Effective measurement of exposure to air pollution, not least NO2, 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 NO2 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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Environmental Monitoring and Assessment
volume
191
issue
3
pages
12 pages
publisher
Springer
external identifiers
  • pmid:30798406
  • scopus:85062072465
ISSN
1573-2959
DOI
10.1007/s10661-019-7253-2
language
English
LU publication?
yes
id
063c4682-d59c-464e-b3ab-c5d43196ff43
date added to LUP
2019-02-25 11:29:39
date last changed
2020-01-16 03:46:44
@article{063c4682-d59c-464e-b3ab-c5d43196ff43,
  abstract     = {Effective measurement of exposure to air pollution, not least NO2, 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 NO2 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.},
  author       = {Yousefzadeh, Mohsen and Farnaghi, Mahdi and Pilesjö, Petter and Mansourian, Ali},
  issn         = {1573-2959},
  language     = {eng},
  number       = {3},
  publisher    = {Springer},
  series       = {Environmental Monitoring and Assessment},
  title        = {Proposing and investigating PCAMARS as a novel model for NO2 interpolation},
  url          = {http://dx.doi.org/10.1007/s10661-019-7253-2},
  doi          = {10.1007/s10661-019-7253-2},
  volume       = {191},
  year         = {2019},
}