Modelling NO2 Exposure in Greater Glasgow
Jonathan Gillespie [email protected]
University of Strathclyde
Aims
• To develop a land use regression model for nitrogen dioxide in Greater Glasgow • To apply this model to estimate residential exposure in a cohort group What is Land Use Regression Modelling?
Statistical approach based on nearby features and measured concentrations
Very low cost Good spatial prediction Applicable to multiple pollutants/ scales Easy to apply and understand No emission/met. data required
× Sampling prior to modelling × Network design crucial × May be temporally/spatially limited × No physical basis × Subjective
Extensively used in epidemiological studies Building and applying a Land Use Regression Model
1. Design monitoring network . 5 Local Authority monitoring networks 2. Measure pollutant . 181 monitoring locations
Monitoring Site Locations ¯
Monitoring Sites
0 1 2 4 Miles Building and applying a Land Use Regression Model
1. Design monitoring network . 5 Local Authority monitoring networks 2. Measure pollutant . 181 monitoring locations
3. Calculate proximal features in GIS
Building and applying a Land Use Regression Model
1. Design monitoring network . 5 Local Authority monitoring networks 2. Measure pollutant . 181 monitoring locations
3. Calculate proximal features in GIS . Length of roads
. Traffic (HGV/Total Traffic)
. Influence of Buildings
. Population
. Altitude
. Land use class Building and applying a Land Use Regression Model
1. Design monitoring network . 5 Local Authority monitoring networks 2. Measure pollutant . 181 monitoring locations
3. Calculate proximal features in GIS
4. Perform linear regression Evaluation of Model on Hold-Out Data
Model R2 0.73 Validation R2 0.82 Y = 0.82x + 8.1 Building and applying a Land Use Regression Model
1. Design monitoring network . 5 Local Authority monitoring networks 2. Measure pollutant . 181 monitoring locations
3. Calculate proximal features in GIS
4. Perform linear regression
5. Create pollution surface in GIS
Modelled NO2 Concentration in Greater Glasgow
Legend Modelled NO2 (ug/m3) 9.7 - 20 ¯ 20.1 - 30 30.1 - 40 40.1 - 50 50.1 - 60 601 - 70 70.1 - 80 80.1 - 90
0 1 2 4 Miles Building and applying a Land Use Regression Model
1. Design monitoring network
2. Measure pollutant
3. Calculate proximate features in GIS
4. Perform linear regression
5. Create pollution surface
. Participant locations from Glasgow blood 6. Geocode participant locations and pressure clinic estimate exposure . Health data available for follow up study
Patient Distribution
Legend
Patient Location Modelled NO2 (ug/m3) 9.7 - 20 ¯ 20.1 - 30 30.1 - 40 40.1 - 50 50.1 - 60 601 - 70 70.1 - 80 80.1 - 90
0 1 2 4 Miles Exposure Estimation for Cohort
Exposure Statistics Mean 26.6 Median 24.5 Minimum 9.7 Maximum 78.8 Percent > 40 µg/m3 7.5 Count 13679 Model Limitations
• Networks designed and run by 5 local authorities • Sampling bias from preferential sampling – 25% background sites? • Accuracy of GPS coordinates? • Consistency of measurements
• Only annual average for single year • Extrapolation is possible
• Exposure assessment limited to outdoor exposure at the home • Personal exposure may differ Summary
• Developed a simple LUR model for Greater Glasgow based on Local Authority data
• Highest concentrations were found in City Centre and localised hotspots
-3 • ~7 % of participants exposed to > 40 µg m NO2
• Participant health statistics are available for follow up work
Acknowledgments
• Dr Iain Beverland (University of Strathclyde) • Dr Scott Hamilton (Ricardo-AEA) • Professor Sandosh Padmanabhan (University of Glasgow)