The use of GIS in modelling exposure (theory) Kees de Hoogh Swiss - PowerPoint PPT Presentation

The use of GIS in modelling exposure (theory) Kees de Hoogh – Swiss TPH Environmental Exposures and Health Unit Department of Epidemiology and Public Health Andrea Ranzi – Arpae Reference Centre for Environment and Health Regional Agency for Prevention, Environment and Energy of Emilia-Romagna

INFORMATION FROM KEES DE HOOGH Dispersion models ADMS-Urbanis a commercial product you have to purchase( http://www.cerc.co.uk/environmental-software/ADMS-Urban-model.html) AERMOD is free but it is not very user friendly ( https://www.epa.gov/scram/air-quality-dispersion-modeling-preferred-and- recommended-models). 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 2 settings

European papers on satellite data modelling Kees de Hoogh, Harris Héritier, Massimo Stafoggia, Nino Künzli, Itai Kloog, Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland, Environmental Pollution, Volume 233, 2018, Pages 1147-1154,ISSN 0269-7491, https://doi.org/10.1016/j.envpol.2017.10.025 M. Stafoggia, J. Schwartz, C. Badaloni, T. Bellander, E. Alessandrini, G. Cattani, F. de' Donato, A. Gaeta, G. Leone, A. Lyapustin, M. Sorek-Hamer, K. de Hoogh, Q. Di, F. Forastiere, I. Kloog Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology Environ. Int., 99 (2017), pp. 234-244 https://doi.org/10.1016/j.envint.2016.11.024 Review of LUR modelling Gerard Hoek, Rob Beelen, Kees de Hoogh, Danielle Vienneau, John Gulliver, Paul Fischer, David Briggs, A review of land-use regression models to assess spatial variation of outdoor air pollution, Atmospheric Environment, Volume 42, Issue 33, 2008, Pages 7561-7578, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2008.05.057 . 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 3 settings

Learning outcomes At the end of this lecture, you should be able to: 1. Understand the concept of GIS; 2. Understand what role GIS can play in exposure assessment. School on IEHIA on air pollution and climate change in Mediterranean urban settings 25/04/18 4

Outline • Short introduction to GIS. • What role can GIS play in exposure assessment. • Examples of GIS functionality – i.e. proximity, buffering. • Example of use of GIS in exposure assessment studies School on IEHIA on air pollution and climate change in Mediterranean urban settings 25/04/18 5

Geographic information system (GIS) Disease cases Vector data Streets Census Elevation Raster data Land use Real world 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban settings 6

Geographic information system (GIS) • Managing spatial data Disease cases capture, integration, validation Vector data and quality control Streets • Mapping Census disease, environmental Elevation Raster data hazards and socio-economic Land use factors • Spatial modelling Real world linkage or integration of models 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 7 settings

Why does geography matter? Spatial variation in environmental hazards + Spatial variation in population distribution + Spatial variation in population characteristics (susceptibility) = Spatial variation in health outcomes 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 8 settings

Adding the spatial component Databases help answer: Who? What? When? Why? And How? • A GIS helps answering ques=ons about Where ? Loca=on: Where is it at? • GIS allows us to view, Trends: What has changed since ....? • understand, ques6on, PaCerns: What spa=al paCerns exist? • interpret and visualise data Modelling: What if ...? • in many ways that reveal In disease rates • rela6onships, pa:erns and trends in the form of maps, reports and charts 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 9 settings

GIS in Environment-Health Chain Land cover Roads Topography Monitoring data Population Pollution map Validation Exposure-response relationship Exposure map Health risk 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 10 settings

Sources of data Description Examples Georeferencing Existing maps Topography, land use, Lat/long; national grid administrative boundaries systems Routinely collected Census, mortality, hospital Census tracts, enumeration data admissions postcodes, addresses Satellite data Land cover, pollution Pixel Routine monitoring Pollution Monitoring sites (x,y) data Purpose-designed Health, SES, self-reported Postcodes, addresses household surveys exposure Environmental surveys Personal monitoring, field Map location (x,y), GPS surveys The key to using any data in GIS is by georeferencing (i.e. link to location) 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 11 settings

Data formats • Vector formats – Discrete representations of reality X,Y X,Y X,Y • Raster formats X,Y – Use square cells to model reality Reality Rows (motorway) X,Y Columns 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 12 settings

Vector data Real-world entities represented in three basic shapes Points Polygons (Areas/Regions) (address locations, Lines (Arcs/Routes) (administrative areas, land chimneys, pollution (roads, streams, use zones, exposure monitoring stations) disease vectors) zones) 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 13 settings

Raster data Real-world entities represented as regular grids • The relationship between cell size and the number of cells is expressed as the resolution of the raster • A finer resolution gives a more accurate and better quality image Columns Rows 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 14 settings

Attribute data For each location attribute information can be attached 4 2 1 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 15 settings

Overlay analysis Point-in-polygon Line-in-polygon Polygon-on-polygon Input layer Overlay layer Output layer inherits overlay layer’s attributes 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 16 settings

Proximity analysis distance Δ y � r 45 Δ x Buffering Distances 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 17 settings

Principle of interpolation and geostatisics Tobler’s first law of geography: Everything is related to everything else ...but near things are more related than distant things 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 18 settings

Modelling methods for point data Approach Example Description Proximity Voronoi Creates areas around each point tesselation containing locations nearest to that point Creates zone (buffer) of specified Buffering distance around point Distance Inverse- Weights each location in terms of functions distance inverse distance from monitoring site weighting Global Trend surface Fits global surface through data points interpolators analysis Local Kriging Fits series of local surfaces through interpolators data points

Air pollution modelling approaches NO 2 concentrations ( µ g/m3) Dispersion LUR IDW Nearest 28km monitor 45km 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 20 settings

Air pollution modelling approaches Type of model Description Application Proximity models measurement of the distance between the used with any type of source, it supposes a direct relationship subjects and the source of pollution. Continuous or discrete. Spatial interpolation Geostatistical models (i.e. kriging, IDW) to applicable in case of an adequate number of measurements. reconstruct the pollution values ​ in areas not covered by measurements Land Use regression statistical models on the relation between useful for differences within urban land characteristics and pollutant areas, they require measurements concentrations in a specific point campaigns Dispersion models mathematical models describing processes require detailed information, of pollutant diffusion reconstruct temporal and spatial variation of pollutant due to specific sources Remote sensing Analyses on satellite images to estimate required a calibration with measured atmospheric pollution at ground level data. Information both on spatial and temporal level Source apportionment statistical models to reconstruct the applicable when detailed pollution contribution of each emission source measures and chemical profiles of each source are available 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 21 settings

UK Municipal Solid Waste Incineration study Key Do municipal solid waste MSWI incinerators in operation following Post WID implementation of the EU Waste Pre WID Incineration Directive (2000/76/ EC) pose a risk to reproductive and infant health? Location of 21 operating MSWI in England and Wales between 2003-2010 25/04/18 School on IEHIA on air pollution and climate change in Mediterranean urban 22 settings