Mapping and statistical analysis of NO2 concentration for local government air quality regulation

Jieun Ryu; Chan Park; Seong Woo Jeon

doi:10.3390/su11143809

Mapping and statistical analysis of NO₂ concentration for local government air quality regulation

Jieun Ryu, Chan Park, Seong Woo Jeon

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

Abstract

With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO₂. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO₂ concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO₂ concentration data, an administrative unit map was generated. When comparing the data for four NO₂ data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO₂ concentrations averaged by an administrative unit based on the interpolated NO₂ concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO₂ concentration by region using NO₂ concentrations for the administrative units.

Original language	English
Article number	3809
Journal	Sustainability (Switzerland)
Volume	11
Issue number	14
DOIs	https://doi.org/10.3390/su11143809
Publication status	Published - 2019

Bibliographical note

Funding Information:
Funding: This research was funded by Korea Environment Industry & Technology Institute(KEITI) through "The Chemical Accident Prevention Technology Development Project" project, funded by Korea Ministry of Environment(MOE)”, grant number “No. 2016001970001”, the National Research Foundation of Korea (NRF) through “? Study on Optimization and Model Development for the Assessment of Ecosystem Services for Fine Dust Reduction” Project, grant number “NRF-2018R1D1?1B07049160”, and by a Korea University.

Funding Information:
Funding: This research was funded by Korea Environment Industry & Technology Institute(KEITI) through “The Chemical Accident Prevention Technology Development Project” project, funded by Korea Ministry of Environment (MOE), grant number “No. 2016001970001”, the National Research Foundation of Korea (NRF) through “A Study on Optimization and Model Development for the Assessment of Ecosystem Services for Fine Dust Reduction” Project, grant number “NRF-2018R1D1A1B07049160”, and by a Korea University.

Publisher Copyright:
© 2019 by the authors.

Keywords

Cokriging
County level
Interpolation
Land use regression model
NO concentration map
Nitrogen dioxide
Satellite image
Urban forest

ASJC Scopus subject areas

Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Environmental Science (miscellaneous)
Energy Engineering and Power Technology
Management, Monitoring, Policy and Law

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/su11143809

Cite this

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title = "Mapping and statistical analysis of NO2 concentration for local government air quality regulation",

abstract = "With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO2. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO2 concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO2 concentration data, an administrative unit map was generated. When comparing the data for four NO2 data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO2 concentrations averaged by an administrative unit based on the interpolated NO2 concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO2 concentration by region using NO2 concentrations for the administrative units.",

keywords = "Cokriging, County level, Interpolation, Land use regression model, NO concentration map, Nitrogen dioxide, Satellite image, Urban forest",

author = "Jieun Ryu and Chan Park and Jeon, {Seong Woo}",

note = "Funding Information: Funding: This research was funded by Korea Environment Industry & Technology Institute(KEITI) through {"}The Chemical Accident Prevention Technology Development Project{"} project, funded by Korea Ministry of Environment(MOE)”, grant number “No. 2016001970001”, the National Research Foundation of Korea (NRF) through “? Study on Optimization and Model Development for the Assessment of Ecosystem Services for Fine Dust Reduction” Project, grant number “NRF-2018R1D1?1B07049160”, and by a Korea University. Funding Information: Funding: This research was funded by Korea Environment Industry & Technology Institute(KEITI) through “The Chemical Accident Prevention Technology Development Project” project, funded by Korea Ministry of Environment (MOE), grant number “No. 2016001970001”, the National Research Foundation of Korea (NRF) through “A Study on Optimization and Model Development for the Assessment of Ecosystem Services for Fine Dust Reduction” Project, grant number “NRF-2018R1D1A1B07049160”, and by a Korea University. Publisher Copyright: {\textcopyright} 2019 by the authors.",

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