3 Data collection and pre-processing
3.2 GIS development
3.2.3 Predictor variables
The effectiveness of GIS-based analysis relies on the use of relevant and accurate data on variables that can help to predict spatial variations in the phenomena under consideration, in this case O3
concentrations. Deriving these potential predictor datasets is often time consuming, for most data need to be carefully checked, and subjected to a wide range of corrections and enhancements. Also, for prediction and mapping purposes these data need to be available (continuously) across the whole study area, including the unmonitored places (i.e. grid cells). Important predictor variables for
O3 were identified a priori and included roads, land cover, topography, meteorological factors, and
the distance to the sea; data are listed in Table 3.2.
Storing the data in the raster form makes handling and analysing easier than the vector format when the goal is to produce a concentration surface. Rasters typically contain only one single attribute; thus numerous rasters are needed to represent all the attributes of interest. Steps to create each of these are described in the subsequent sections. For some analyses, data need to be obtained for a window around a target cell – e.g. to represent the influence of emissions in surrounding areas. With vector data, these are usually extracted using buffering techniques.
With raster data, the equivalent procedure involves using a circular moving window of appropriate radius, along with relevant focal functions (sum, mean, range, and standard deviation). FOCALSUM, for example, is a moving window analysis whereby the sum of the values in a specific neighbourhood (i.e. window or cells surrounding the focal cell) is computed. The researcher specifies the size and shape of window.
Different window sizes were selected to represent the spatial zone of influence of different predictor variables. To represent local effects, windows ranging from 100 to 1000m were used; to reflect regional influences, window sizes of 5000 and 10000m were specified. As demonstrated in Figure 3.6, FOCALSUM for the 100m window consists of a neighbourhood of five grid cells, for which the values are added and the result allocated to the focal (i.e. centre) cell. This neighbourhood is passed, cell-by-cell, across the grid and the calculation repeated until the last grid cell of the study area is computed. Table 3.3 shows the FOCALSUMs used for each distance band.
After creating the original 100m grid for each of the relevant predictor variables, Model Builder in ArcGIS was used to construct the other window (or neighbourhood) averages and create the variables as a grid. These were then "intersected" with the monitoring sites using the equivalent tool for rasters, Extract values to points. As shown in Figure 3.7, the sequence of modelling was thus to run FOCALSUM statistics, extract values to points, update fields and export the results to DBF files. Figure 3.7 illustrates these steps to obtain land cover data for different window sizes; the same models were used to obtain all other GIS predictors simply by changing the references to dataset and folders.
Table 3.2 Overview of the predictor variables
Predictor GIS dataset Predictor variable Abbreviation Purpose Unit Source and resolution
Lan d c o ve r var iab le s
CORINE High density residential land Highdr Scavenge O3 Percentage CORINE land cover 100m grid- Version
13/2000 (CLC2000) from the EEA/Resolution (100m)
CORINE Low density residential land Lowdr Scavenge O3 Percentage
CORINE Industrial and commercial
land
Ind/Com Source of O3 precursors Percentage
CORINE Herbaceous land Herb Source of O3 precursors Percentage
CORINE Agriculture land Agri Source of O3 precursors
Depletion of O3
Percentage
CORINE Forest land Forst Source of O3 precursors
(BVOC)
Percentage
CORINE Open Space Opsp - Percentage
To p o gr ap h ic al var iab le s
CORINE Distance to sea D2S Increase O3 kilometre Derived from the distance between each grid
and the coast line
Altitude Altitude (height above sea
level)
Alt Increase O3 metre SRTM 90m Digital Elevation v4.1 produced by
NASA/Resolution (90m)
Altitude Topex Topex Decrease or Increase O3 metre Height difference between 100m window
and the mean of the surrounding 2000m cell centroids R o ad le n gth var iab le s
road network Motorways MR Scavenge O3 metre Eurostreets version 3.1 is a 1:10,000 digital
road network
road network Secondary Roads SR Scavenge O3 metre
road network Local Roads LR Scavenge O3 metre
M e te o ro lo gi cal fac to
rs NETCDF Surface solar radiation SSR Increase O3 w/s The European Commission Joint Research
Centre (JRC)
ERA Interim, monthly means of daily means derived from ECMWF/ resolution (40km)
NETCDF Total precipitation TP Depletion of O3 Mm
NETCDF Temperature TMP Increase O3 Co
Table 3.3 Window specifications based on grid cells (using FOCALSUM) Window size
(radius in m)
FOCALSUM “Circle” Distance (equivalent radius in cells)
Total number of grid cells within window 100m 1 5 300m 3 29 500m 5 81 1000m 10 317 5000m 50 7845 10000m 100 31417
Figure 3.7 Model builder to obtain the different land cover data within different window sizes
The following sections explain the variables that were selected in order to represent and enable modelling of the spatial variations in O3 across Europe at 100m resolution. The
importance of each variable in modelling O3 concentration is explained, the data source
cited, and the original resolution noted. Also, where necessary, any preprocessing (e.g. intersection, interpolation) that was applied to create these 100m GIS-variables is explained in detail.
3.2.3.1 Land cover variables
O3 is a secondary air pollutant, formed by a series of complex chemical reactions, as outlined
in section 2.1.1. Formation and loss are driven by two critical precursors: NOx and VOCs, in the presence of solar radiation (hv).
In the absence of detailed emissions data, land cover data were thus included in the analysis as proxies for emissions to the atmosphere, since they describe differences in source type (e.g. industry, residential land, forestry, agriculture) and, to some extent, source intensity (e.g. by defining densely populated areas or heavily trafficked zones).
Land cover nevertheless affects O3 concentrations in two, opposing ways. Some land cover
classes are indicators of O3 production, because they represent emission sources for O3
precursors, or situations where favourable conditions for chemical generation of O3 in the
atmosphere may occur. Other land cover types are likely to be associated with reduced O3
concentrations, because they are related to the release of O3 scavengers, or encourage
deposition of O3. In practice, these relationships with land cover are often complex and
contradictory. In the case of forestry and agricultural land, for example, both these roles may be at work. On the one hand, vegetation acts as a surface for dry deposition, especially during the day when stomata are open (Nowak et al., 2006, Fowler et al., 1998, Massman and Grantz 1995). At night, also, O3 concentrations tend to decline due to deposition on the
soil surface with no substitution by photochemical production. On the other hand, forestry and agriculture can be important emission sources for biogenic VOCs (isoprene and monotorpene), which are more reactive by 2-3 times than anthropogenic VOC (Carter, 1991). This leads to increased O3 concentrations (Chameides et al., 1988).
Land cover data in 100m resolution were derived from the CORINE Land Cover Map 2000. The database has been created by semi-automatic interpretation of data collected using
satellite-borne sensors and has a spatial resolution of approximately 25 ha. CORINE land cover data (CLC2000) were downloaded from the EEA web site13. CLC consists of 44 primary classes (Appendix, A section III). These were combined into 7 more general groups, as demonstrated in Table 3.4. This was done by reclassifying the original CLC grid using the CON (i.e. conditional) function in ArcMap Spatial Analyst to produce a new raster for each of the 7 classes.
These seven classes were specified on the basis of their influence on O3 formation and
dispersion. High density residential land represents areas of high population density, typically associated with more intense anthropogenic emissions of NOx, which scavenges O3.
Low density residential land comprises areas with lower population densities, which are typically associated with lower NOx emissions. Industrial and commercial land includes a range of different areas (e.g. industrial, commercial and construction). In general these can be expected to be sources of emissions of O3 precursors, such as NOx, CO and anthropogenic
VOC, which will either scavenge O3 or increase formation of O3.
Table 3.4 Definition of the 7 land cover domains derived as a combination of the 44 CLC classes
Abv. Land cover variables Description CLC Classesa
Highdr Lowdr Ind/Com Herb Agri Forst Opsp
High density residential land Low density residential land Industrial/commercial land Herbaceous land
Agriculture land Forest land Open Space
Continuous urban fabric Discontinuous urban fabric Industrial, commercial and construction units
Pastures, natural scrub and herbaceous vegetation Arable land, crops and heterogeneous agriculture Forest area
Beaches, rocks and open space with no vegetation 1 2 4-9 10-11,18,26- 29 12-17,19-22 23-25 30-34
a. CLC classes 35 to 44 representing wetland and water bodies were excluded because they do not characterise land
Three types of green area, varying in the density and height of the vegetation, have been defined: herbaceous land comprises areas of very low vegetation, mainly in the form of shrubs or grass, with few trees. Agriculture includes low, permanent or rotating crops
13
http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-raster/clc-2000-v13- 100m,accessedon March/2010
(excluding grass). Forests consist primarily of dense vegetation with tall and continuous tree cover (broad-leaved forest, coniferous forest, mixed forest). The three classes of green area (herbaceous, agriculture and forest) are the main sources of biogenic VOC, which contributes to O3 formation, but may also provide active surfaces for dry deposition. Finally,
open space comprises areas with no vegetation, such as beaches and rocks.
The FOCALSUM statistic in ArcMap was then applied to the seven land cover classes to produce grid cells for the different window sizes.
3.2.3.2 Topography
Topographic characteristics of the land are important because of their relationship with meteorological factors that might affect the distribution and transportation of O₃. For instance, O3 concentrations in Europe tend to increase in mountainous areas (Jonson et al.,
2006). Topographic exposure, such as the openness or lack thereof, may likewise influence atmospheric temperatures, and hence photochemical reactions, as well as exposure to prevailing winds which may act to accelerate the dispersal, mixing and deposition of O3.
Topography can therefore be used as a proxy for meteorological factors affecting O3
concentrations.
Three different topographic variables were derived for use in this study: altitude (height above mean sea level), topex (an index of topographic exposure) and distance to sea. Each of these is explained in turn.
1) Altitude
Altitude was obtained from the Shuttle Radar Topographic Mission (SRTM) v4.1 produced by NASA14, as ASCII files. The SRTM digital elevation data (DEM) is a high quality elevation data set covering over 80% of the globe, and the whole of this study area. It has a horizontal resolution of 90m at the equator, and data are provided in 5° x 5° tiles, in a geographic coordinate system (WGS84 datum). The vertical error of the DEM is stated to be less than 16m. Areas where water or heavy shadow prevented the quantification of elevation are indicated as "no-data".
14
available at http://www.cgiar-csi.org/data/elevation/item/45-srtm-90m-digital-elevation-database-v41 last accessed: 20 April 2012 in
The ASCII files were converted to integer rasters using the ArcGIS command ASCII TO RASTER. The tiles were then joined into a single raster dataset by using the command MOSIAC. Finally, the raster was re-projected into the chosen based project LAEA. Bilinear interpolation, which is the appropriate algorithm for continuous data, was used with the registration point set as (N400000, E200000). This bilinear interpolation will cause some smoothing of data because the output grid value is the weighted average of the nearest four cells, as shown in Figure 3.8.
Figure 3.8 Spatial resampling using bilinear interpolation
2) Topex
Topex refers to topographic exposure. It is computed by subtracting the mean altitude of the surrounding area from the altitude at the target area (sum of altitude values within a circular window around the focal cell. The resulting 100m altitude grid (Alt100) was used for
this purpose. A distance of 2000m was selected to represent the surrounding area, and was calculated using FOCALSUM (Alt2000). High (positive) Topex values indicate that the target
area represents a peak in the landscape, and is therefore relatively exposed; negative values indicate that it occupies a depression or valley, and is therefore sheltered (Figure 3.9). Topex will be zero if the topography is flat.
Figure 3.9 Illustrating the positive topex (a) and the negative topex (b)
The direction of the arrow points toward the higher ground between the 100m window (inner-ring) and surrounding neighbourhood (outer-2000m ring)
Topex was specifically calculated as follows:
Alt100 = Altitude for the surrounding 100m window size, based on the altitude grid
Alt2000 = sum of altitude values within a circular window around the focal cell. This
window has a radius of 20 cells (a total of 1,257 cells are contained within this window)
Alt2000-100 = The sum of altitude values in the "outer ring", computed by subtracting
altitude at 100m window from the previous result (e.g. Alt2000 - ALT100)
MAltouter = The mean altitude in the "outer ring", computed by dividing the value of
the "outer ring" by number of cells (eg. Alt2000-100 /1257)
Topex = The difference in altitude between the 100m window and surrounding area (e.g. Alt100 – MAltouter)
3.2.3.3 Distance to sea
Coastal areas comprise a distinctive O3 environment and are an important source of O3
precursors as these are areas of high photochemical activity. In addition to often being densely populated areas, there are several factors which act to influence O3 concentrations,
including short and long range transport, local emissions, meteorological phenomena influencing transport, dispersion and recirculation of pollutants, and photochemical activity. Recycling and trapping of pollution originates from the generally large heat differences
between ocean and land which produce a movement known as sea breeze circulation. Both horizontal and vertical coastal recirculation can occur, which can affect the air quality. Horizontal recirculation returns the air mass to its source area the next day, whereas the vertical recirculation currents return the air a few hundred metres down onto the land surface (Hsu, 1988). These factors increase O3 concentrations in coastal areas, by bringing in
sea breezes enriched with O3 (Klingberg et al., 2012). Distance to sea, therefore, provides a
potential proxy for these effects. Distance is computed as the straight line distance to the nearest body of open sea.
The following steps were used to create this data set (see appendix A, Section IV for full details).
1. The raster coastline from CORINE2000 (class 523) was converted into a coverage using the command CONVERSION. This was buffered by 20km to represent the boundary to the open water.
2. It is computationally intensive to compute the distance from each 100m cell to the coast; therefore centroids for a 1km grid for Europe were used instead. These were stored as coverage.
3. The NEAR command was used to compute the distance (in metres) from each 1km centroid to the nearest open water.
4. Distance to ocean, based on the 1km centroids, was then interpolated to the 100m level using inverse distance weighting and stored as a raster. Values from the resulting 100m distance to sea raster were extracted for the O3 monitoring sites
using Extract values to points.
5. This method for interpolating distance to sea was validated at the monitoring sites, by directly calculating the distance using the command NEAR between sites and open water. The correlation was found to be 0.99 at the monitoring sites.
3.2.3.4 Road length
Because a large proportion of the NOx emitted in Europe derives from road transport, data on road length for different road classes (local, secondary and major roads) were also obtained, to give a proxy for scavenging by transport-related NOx.
Road data were obtained from Eurostreets version 3.1, which is a 1:10,000 digital road network based on the TeleAtlas MultiNet TM. These data were obtained through the European Study of Cohorts for Air Pollution Effects (ESCAPE) project, and were converted from vector to a 100m raster by colleagues at Imperial College15. Eurostreets does not include traffic intensity data; however it does include a road classification (FRC code). To simplify the classification, FRC was reclassified into three groups as illustrated in Table 3.5.
Table 3.5 Selected road classes based on Eurostreets road classes
FRC Road classes New classes Abbreviation
0 Motorways Major roads MR
1 Roads not belonging to main road Major importance
2 Other Major roads
3 Seconds roads Secondary roads SR
4 Local connecting roads Local roads LR
5 Local roads of high importance
6 Local roads
Figure 3.10 Road classes Derived from Eurostreets version 3.1
15
Figure 3.10 shows the map of the whole study area, depicting only the first class, major road (MR); the inset for the city of Rome shows all three road types.
On obtaining the rasters for each road class (FRC), the following additional steps were performed to prepare these data for use in this study: firstly, reproject roads into the LAEA projection and create the 100m base polygon (grid) shapefile; secondly intersect road vectors with the base polygon; thirdly sum road length by FRC for each 100m polygon area; finally convert the polygons to rasters. These were then combined to create three road classes, as shown in Table 3.5. This was done using the PLUS command in ArcGIS. As per other variables, the FOCALSUM statistic was then applied to the three resulting road grids to produce grids for the different window sizes.
3.2.3.5 Meteorological factors
Important surface meteorological factors related to O3 concentration are cited as
temperature, wind speed, solar radiation, and precipitation (Tarasova and Karpetchko, 2003, Dueñas et al., 2002, Lou Thompson et al., 2001, odr guez and uerra, 2001, Dabdub et al., 1999). All four were used in this study.
The role of meteorological factors in O3 formation and dispersion can be summarised as
follows. In general, high O3 concentrations are observed in favourable photochemical
conditions, characterised by high temperature, high solar radiation (i.e. sunny) and in the presence of O3 precursors. In contrast, in overcast or rainy conditions, characterised by high
total precipitation and low sunlight due to the cloudiness, as well as low temperatures, O3
concentration is low due to the slow rate of photochemical reactions and to loss of O3 by
wet deposition (Andersson et al., 2007, Lelieveld and Crutzen, 1991).
The effect of wind speed on O3 concentrations is more complex, and depends on the specific
atmospheric conditions. One effect is to reduce O3 concentrations by encouraging dispersion
away from O3-rich areas. Downwind of these sources, however, the wind has the reverse
effect, of bringing in more O3-enriched air. In the vertical dimension, equally, contrasting
effects may occur. Where the boundary (i.e. ground) layer acts as a source of O3 due to
chemical generation, increasing wind speed reduces surface-level O3 concentration by
in the boundary layer is negative (i.e. if O3 concentrations are greater at higher levels in the