Abstract
This study aims to develop a pollution distribution model for estimating traffic related intra-urban concentrations of nitrogen dioxide (NO 2 ) levels. Weekly concentrations of NO 2 were measured at 45 different locations in the Historical Peninsula of Istanbul during spring, summer and winter seasons in 2010. The range of NO 2 was 14.2-155 µg/m 3 . A land use regressing (LUR) model was developed to explore the impact of independent variables on the measured levels. Independent model variables were selected based on land use characteristics, traffic and road network information, and meteorological data. Results suggest that 150 metre range is the most effective buffer zone for NO 2 distribution characteristics in the study area. Average wind speed and temperature data have significant influences (up to 25%) on the prediction performances. Better estimations were produced for spring and winter seasons, particularly for in land stations compared with costal ones. As a result, the overall prediction performance of the constructed model is satisfactory (R 2 = 0.64).
| Original language | English |
|---|---|
| Pages (from-to) | 24-40 |
| Number of pages | 17 |
| Journal | International Journal of Global Environmental Issues |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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Keywords
- Air pollution
- Clean cities
- Exposure modelling
- Geographic information systems
- Spatial regression
ASJC Scopus subject areas
- Geography, Planning and Development
- Management, Monitoring, Policy and Law
Cite this
Saving old cities : Land use regression model for traffic emissions in the Historical Peninsula of Istanbul. / Karaca, Ferhat; Yanik, Tugrul; Turkyilmaz, Ali.
In: International Journal of Global Environmental Issues, Vol. 18, No. 1, 01.01.2019, p. 24-40.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Saving old cities
T2 - Land use regression model for traffic emissions in the Historical Peninsula of Istanbul
AU - Karaca, Ferhat
AU - Yanik, Tugrul
AU - Turkyilmaz, Ali
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This study aims to develop a pollution distribution model for estimating traffic related intra-urban concentrations of nitrogen dioxide (NO 2 ) levels. Weekly concentrations of NO 2 were measured at 45 different locations in the Historical Peninsula of Istanbul during spring, summer and winter seasons in 2010. The range of NO 2 was 14.2-155 µg/m 3 . A land use regressing (LUR) model was developed to explore the impact of independent variables on the measured levels. Independent model variables were selected based on land use characteristics, traffic and road network information, and meteorological data. Results suggest that 150 metre range is the most effective buffer zone for NO 2 distribution characteristics in the study area. Average wind speed and temperature data have significant influences (up to 25%) on the prediction performances. Better estimations were produced for spring and winter seasons, particularly for in land stations compared with costal ones. As a result, the overall prediction performance of the constructed model is satisfactory (R 2 = 0.64).
AB - This study aims to develop a pollution distribution model for estimating traffic related intra-urban concentrations of nitrogen dioxide (NO 2 ) levels. Weekly concentrations of NO 2 were measured at 45 different locations in the Historical Peninsula of Istanbul during spring, summer and winter seasons in 2010. The range of NO 2 was 14.2-155 µg/m 3 . A land use regressing (LUR) model was developed to explore the impact of independent variables on the measured levels. Independent model variables were selected based on land use characteristics, traffic and road network information, and meteorological data. Results suggest that 150 metre range is the most effective buffer zone for NO 2 distribution characteristics in the study area. Average wind speed and temperature data have significant influences (up to 25%) on the prediction performances. Better estimations were produced for spring and winter seasons, particularly for in land stations compared with costal ones. As a result, the overall prediction performance of the constructed model is satisfactory (R 2 = 0.64).
KW - Air pollution
KW - Clean cities
KW - Exposure modelling
KW - Geographic information systems
KW - Spatial regression
UR - http://www.scopus.com/inward/record.url?scp=85064189372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064189372&partnerID=8YFLogxK
U2 - 10.1504/IJGENVI.2019.098893
DO - 10.1504/IJGENVI.2019.098893
M3 - Article
AN - SCOPUS:85064189372
VL - 18
SP - 24
EP - 40
JO - International Journal of Global Environmental Issues
JF - International Journal of Global Environmental Issues
SN - 1466-6650
IS - 1
ER -