Assessing the Environmental Impact of Landfill sites in the

East Riding of Yorkshire

Gadogbe Mark

January 2014

INTRODUCTION

This project involved the identification of areas within the East Riding of Yorkshire County (ERYC)

with the highest sensitivity to any possible environmental impact from escape of landfill leachate

from landfill sites in the ERYC and the identification of specific landfill sites (three) that could pose

the highest risk in case of possible environmental pollution.

METHODOLOGY

The methodology applied involved a combination of GIS spatial analysis tools and multi criteria

evaluation. Figure 1 provides a schematic overview of the processing methodology and the essential

inputs and outputs. A study area was selected which comprise of eight (8) contiguous parishes out of

a total of 169 parishes stored in the parish dataset. This was done to limit the large amount of time

needed in carrying out the analysis for the entire East Riding (169 parishes). The study site (figure 2)

assessed for landfill impact comprise of the following selected parishes: Kilham, Carnaby, Burton

Agnes, Harpham, Barmston, Nafferton, Kelk and Driffield. Care was taken in the selection so as to

ensure landfill sites were located in all the eight parishes.

The first stage of the analysis involved setting the analysis extent to the parish layer and generating

raster grids for each of the seven criteria considered using the fuzzy classification approach of Multi

Criteria Evaluation modelling. The generated grids were for the following criteria: land-use type,

bedrock and surficial geology, elevation and slope, distance from residential areas, distance from

SSSIs, distance from coast, and distance from surface water.

ArcGIS spatial analyst tool, the raster calculator, was then used to add or overlay the contents of

each raster grid, applying appropriate weighting values for all the criteria to produce a grid (figure 3)

delineating areas with high and low sensitivity for environmental impact from landfill leachate in the

study area.

Four weight classes were defined and applied and depending on a criteria’s relative importance, it

was given a weighting of 1, 1.5, 2 or 2.5. For instance, the distance from residential areas criteria had

the highest weighting of 2.5 because it most directly affects the location of the landfill sites. It is

considered the most significant criteria because all other criteria been met, a landfill would most

likely still not be located if its choice location receives huge public disapproval such as locating in a

residential area or within a city.

Also, the criteria distance from SSSIs and distance from surface water were both given an equal

weighting of 2 because of the ecologically sensitive nature of these environments and as such the

huge impact even the smallest landfill leachate will produce in such environments. They were given

a lower weighting than the distance from residential areas criteria because it ranked slightly more

important than them based on discussions with local experts and literature review. The application of

the weights was to ensure that the various criteria are objectively ranked in order of significance to

the decision.

Figure 1: Flowchart showing the processing methodology

Set analysis mask

to parish layer;

processing extent

and raster analysis

cell size to

parishmask

Add selected study

sites to map as new

layer (studysites)

Merge and spatial

join ew053 to ew082

layers into one new

layer and convert to

raster (Geo_raster)

Raster Calculator: Ite_clas +

Slope_clas + Geo_clas ×1.5 + Coast_clas ×1.5 + Water_clas

×2 + SSSI_clas ×2 +

Towns_clas ×2.5

ArcToolbox →

Spatial Analyst

Tools → Distance

→ Euclidean

Distance

Towns_dist layer

Reclassify

Coast_dist layer

SSSI_dist layer

Water_dist layer

Towns_clas layer

Coast_clas layer

SSSI_clas layer

Water_clas layer

Geo_clas layer

Ite_clas layer

3D Analyst Tools →

Raster Surface →

Slope

Slope_Humb layer

Reclassify Slope_clas layer

Elev_clas layer

model_result Reclassify

Labelled

Sensitivity

map

Use select by

location tool to

select all landfill

sites in study area

and investigate

them

Select the three

landfill sites and

add to map as

new layer (landfills) Labelled Landfills

location map

Figure 2: Location of the study site relative to other parishes in the ERYC.

DISCUSSION AND INTERPRETATION OF RESULTS

Because of their environmental health, economic and ecological impacts, landfills are a critical issue

in urban planning (Charnpratheep et al., 1997). As a result, it is common to find many

recommendations being proposed by many studies involving landfills.

According to Dikshit et al. (2000), they should be situated at a fair distance away from biophysical

elements such as water, wetlands, critical habitats, and wells to reduce the risk of contamination

from landfill. Landfills should be located away from the coastline (Despotakis and Economopoulos,

2007), at a significant distance away from urban residential areas due to public concerns, such as

aesthetics, odour, noise, decrease in property value and health concerns (Tagaris et al., 2003; Zeiss

and Lefsrud, 1995) and a slope less than 12% would be suitable for prevention of contaminant runoff

(Lin and Kao, 1999) are common recommendations.

For this project, the study area (comprising of the eight parishes) falls under the Bridlington District

and had 35 landfill sites located within. The dominating land uses in the study comprise of arable,

mown/grazed grass and suburban uses. There were very little wetlands. Not all the data sources were

used in carrying out the analysis. For instance, the roads, railway tracks and electricity transmission

datasets were not used. This is because the seven criteria used were considered more crucial in the

determination of landfill impact to the environment.

Supposing the project was about determining the best sites for locating the landfills, these datasets

would have been given very significant consideration. For instance, landfills are often sited close to

transportation networks in order to save cost on collection and transportation, but in assessing their

impact on the environment these criteria may become not too significant. Also, because woodlands

are generally less sensitive to landfill leachate impacts, the woodland dataset was also not used.

Thus, the important datasets employed in the analysis included both vector and raster datasets such

as the land-use/land-cover (Ite), bedrock and surficial geology (ew053 to ew082), surface water line

and area, sites of specific scientific interest (sssi_er), landfill, coastline (coast_EY) the DEM of East

Riding (Humb_dem25) and the parish datasets. A resolution of 25m (same as for the Humber DEM)

was deemed most favourable for setting the analysis extent and producing the output grids because

data analysis in terms of processing speed and storage would be very costly if a higher resolution is

used.

However, even the 25m resolution quite affected the system’s cost effectiveness in terms of it been

quite computationally demanding. The large amounts of data considered made the system expensive

on computer time and space as it slowed down the pace of analysis and made the system take a long

time to process and produce required outputs. Studies of this nature have often been regarded as

complex and tedious because of the many different factors or criteria considered (Allanach, 1992)

and the increasingly sophisticated spatial analysis and modeling required (Chang et al, 2008).

Results of the environmental impact sensitivity grid (Figure 3) showed that out of the 35 landfill

sites, eleven (11) fell within the zone of high sensitivity of environmental impact from landfill

leachate. These included the Driffield (Negas/Eastgate) C, Bells Mils (Driffield), Kilham (Green

Lane), Gransmoor Quarry Site A and B, Gransmoor (Kelk), Gransmoor Lodge, Thorneholme,

Carnaby, Carnaby (Moor Lane) and Blakedale Farm landfill sites. The results also showed that

almost the entire area of Kelk and Driffield and a high proportion of Carnaby areas could be possibly

impacted by landfill leachate. The parish with the least impact was Kilham parish.

After careful assessment of the physiochemical danger of landfill sites taking into consideration area,

volume, waste and landfill type, leachate escape history and presence of nearby boreholes, the

Carnaby (Moor Lane), Gransmoor Quarry Site A and the Thorneholme landfill sites were identified

as having the highest environmental pollution potential (Figure 4).

Of the three, the Carnaby (Moor Lane) landfill site is the biggest (covering 15.8Ha in area and

2300000m3

in volume), has 5 boreholes (3 Private and 2 Public) in close proximity and has

household and commercial waste component (97%). The Gransmoor Quarry Site A landfill is also

very big, with water sources in close proximity but unknown waste composition as well as whether

capped or not. These unknown statuses could make it a potential hazard as it could contain

potentially dangerous chemical wastes. The Gransmoor Quarry Site B landfill though the second

biggest in the high sensitivity zone and with unknown cap status was not identified as one with

highest pollution potential because it contains 100% inert wastes which by their nature are not

considered harmful in landfill operation sense.

Figure 3: Map showing areas with high and low sensitivity for environmental impact of

leachate from landfill sites in the studied area (boundaries in blue).

Figure 4: Map showing the location of the three landfills within the study site (blue

boundaries) with the highest potential of causing significant environmental pollution.

Figure 5: Location of the three landfill sites without the sensitivity zones.

CONCLUSIONS

The unavailability of crisp data and the large number of data considered in the analysis made the use

of binary or Boolean classification approach fairly a limitation for this project.

Thus, fuzzy classification approach to multi criteria evaluation was deemed the most appropriate

method of choice for generating grids for the criteria under consideration. It was also more suitable

because of its superior ability of dealing quantitatively with imprecision in expressing the

importance of each criterion. According to Hall et al, (1992), it is a better way to handle uncertainty

and complexity in the application of MCE.

Another limitation observed is the cut-off values employed in defining the final site selection into

suitable and unsuitable. This can affect the number of sites that fall under each category as defining a

value to high will be restrictive and produce very few possible sites and vice versa.

Nonetheless, the methodology employed is deemed a valid methodology because it produced a good

site sensitivity grid when compared with empirical datasets and also because it is a commonly used

method for researches or analysis of this nature, for instance, in the study by Higgs (2006) which

used multi-criteria techniques and GIS in siting of a waste facility. Sener et al. (2006) also integrated

GIS and multicriteria decision analysis to solve the landfill site selection problem and developed a

ranking of the potential landfill areas based on a variety of criteria.

The criteria considered and the corresponding datasets employed for this project are however

deemed fundamental and thus not exhaustive. For a detailed and more efficient assessment, and for a

better management of environmental impacts, many other criteria and datasets need to be given great

consideration in future analyses of this nature.

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