Application of land use regression to regulatory air quality data in Japan.
A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe and North America. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Shizuoka, Japan. We used the annual-mean nitrogen dioxide (NO2) and suspended particulate matter (SPM) concentrations between April 2000 and March 2006 in the study area. SPM accounts for particulate matter with an aerodynamic diameter less than 8 microm (PM(8)). Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and physical component. Using geographical variables, we then constructed a model to predict the monitored levels of NO2 and SPM. The mean concentrations of NO2 and SPM were 35.75 microg/m(3) (standard deviation of 11.28) and 28.67 microg/m(3) (standard deviation of 4.73), respectively. The final regression model for the NO2 concentration included five independent variables. R(2) for the NO2 model was 0.54. On the other hand, the regression model for the SPM concentration included only one independent variable. R(2) for the SPM model was quite low (R(2) = 0.11). The present study showed that even if we used regulatory monitoring air quality data, we could estimate NO2 moderately well. This result could encourage the wide use of LUR models in Asian countries.
Kashima
Saori
S
Department of International Health, Okayama University Graduate School of Environmental Science, Okayama, Japan. saori_ksm@ybb.ne.jp
Yorifuji
Takashi
T
Tsuda
Toshihide
T
Doi
Hiroyuki
H
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D004784:Environmental Monitoring; D005843:Geography; D007564:Japan; D008962:Models, Theoretical; D009585:Nitrogen Dioxide / Q000032:analysis; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis; D012044:Regression Analysis; D001335:Vehicle Emissions / Q000032:analysis*
D000393:Air Pollutants; D052638:Particulate Matter; D001335:Vehicle Emissions; D009585:Nitrogen Dioxide