Jagiellonian University, Kraków, Paris Lodron University of Salzburg
Marek Śnitkowski
ASSESSMENT OF TRAFFIC ROAD RISK IN BABORÓW
Msc thesis under the supervision of dr hab. Jacek Kozak
MSc thesis submitted in the framework of, and according to the requirements of the UNIGIS Master of Science programme (Geographical Information Science & Systems).
Kraków 2010
I declare that all sources used in the thesis were properly acknowledged. The thesis is fully my work and it was not and will not be submitted as a thesis elsewhere.
Date ………... Signature ………
Table of contents
1. Introduction ……….. 4
2. Aim of the study ………... 8
3. Study area ………... 10
3.1. Source of traffic noise in Baborów ………... 13
4. Data ……….. 15
5. Methods ………... 18
5.1. Initial stage ………... 18
5.2. Noise delimitation stage ………... 23
5.2.1. Interpolation technique ……….. 24
5.2.2. Setting interpolation parameters ……… 24
5.2.3. Creating noise traffic data ……….. 26
5.3. Final stage ………. 28
5.3.1. Calculation of M indicator ………. 34
6. Results ……….. 37
6.1. Maps of road traffic noise pollution ………. 37
6.1.1. Daytime ……….. 38
6.1.2. Nighttime ……… 40
6.2. Distribution of the M indicators ………..……….. 41
6.2.1. Daytime ……….. 41
6.2.2. Nighttime ………... 42
6.3. Total number of inhabitants endangered by traffic noise pollution ……….. 43
7. Discussion ……… 45
8. Conclusion ……… 47
References ……… 48
Annex: Figures ………. 50
Annex: Tables ………... 51
1. Introduction
Road traffic noise is a major part of environmental or community noise and nowadays it has become one of the most significant kinds of pollution. Noise can negatively influence human health and quality of life. It can disrupt sleep and causes major problems with conversation and concentration which affects performance, productivity, and social behavior. It may even cause aggression. Physiological responses of annoyance include: increased heart rate and blood pressure, elevated levels of stress and raise in hormone secretion. High levels of noise, which are common along major roads, can result in cardiovascular diseases and cumulative hearing damage over time.
Sensitivity to noise may vary significantly from one person to another. It depends on the time of exposure, age and health, or just individual proclivity for irritation. The young and the elderly people are usually more severely affected. Those with cardiovascular disease are at increased risk of negative noise pollution effects (Berglund et al. 2000;
Niemann, Mischke 2004; Babisch 2006; Den Boer, Schroten 2007).
However noise pollution effect on human health is well known, it is quite difficult to establish a single scale of noise values coordinated with possible negative effects, and final results are often disputable (Environment …1999; Berglund et al. 2000; Den Boer, Schroten 2007). One of the most recognized scales is presented below (Table 1.1).
Table 1.1. Selected examples of critical health effects in relation to noise indicator values for the outdoor environment according to WHO Community Noise Guidelines (2000)
Specific environment Critical health effect(s) LAeq*
[dB] Time base
[hours] LAmax*, fast [dB]
Outside bedrooms Sleep disturbance, window open
(outdoor values) 45 8 60
Outdoor living area
Moderate annoyance, daytime and evening
Serious annoyance, daytime and evening
50 55
16 16 School, playground
outdoor Annoyance (external source) 55 During
play Industrial, commercial
shopping and traffic areas, indoors and outdoors
Hearing Impairment 70 24 110
Public addresses, indoors
and outdoors Hearing Impairment 85 1 110
*LAeq - equivalent continuous A-weighted sound pressure level [dB]
*LAmax, fast - maximum A-weighted, passby, sound pressure level [dB]
Growing traffic volumes combined with adjacency of residential areas or living places often induce citizens’ complaints as a result of noise nuisance (Domańska 2005;
Anonymus 2008; Jabłonowska 2008). In such a situation measurements and
In Europe, solid foundations for standardization of environmental noise issues were included in Directive 2002/49/EC of the European Parliament and of the Council of 25 June 2002 relating to the assessment and management of environmental noise (European Commission, Official Journal of the European Communities 2002). This document provides elementary definitions related to noise including categories of noise emissions, comparable criteria for methodology for noise calculation and measurement, noise assessment. These had to be transposed into Member States laws. The directive also initiates common noise indicators defining as: “Physical scale for the description of environmental noise, which has a relationship with a harmful effect.”
Member states are bound to establish the areas referring to particular indicators and create a plan of counteracting the noise pollution. They also can introduce and use supplementary indicators and determine limit values, taking into account, inter alia, prevention criteria (Table 1.1)
Noise indicators which are implemented into Polish law are (European Commission, Official Journal of the Communities 2002):
• Lden - (day-evening-night noise indicator) shall mean the noise indicator in decibels (dB) for overall annoyance
• Lday - is a weighted long-term average sound level as defined in ISO 1996-2:
1987, determined over all the day periods of a year
• Levening - is a weighted long-term average sound level as defined in ISO 1996-2:
1987, determined over all the evening periods of a year
• Lnight - is a weighted long-term average sound level as defined in ISO 1996-2:
1987, determined over all the night periods of a year
The supplementary noise indicators established by Poland as a EU Member state, incorporated into Polish law are:
• LAeq D (LD) - is the equivalent continuous sound level in dB(A) over the period 06:00 – 22:00 hours (16h)
• LAeq N (LN) - is the equivalent continuous sound level in dB(A) over the period 22:00 – 6:00 hours (8h)
To quantify noise risk areas a so-called M indicator – noise immision indicator is used.
Its calculations require demographic data, because human population is a base subject to which risk is dedicated; the indicator allows for comparing risk in different areas and it is used at different scales (Rozporządzenie…2002; Profon-Acoustics…2010;
Strona wrocławskiego osiedla HUBY…2010).
According to the Polish law, permissible values of traffic noise (Table 1.2) are established for different kinds of noise and for different designations of areas (Rozporządzenie ... 2007).
Table 1.2. Permissible values of traffic noise established for different kind of noise and for different designations of the area. (Rozporządzenie ... 2007).
Permissible noise level expressed by means of equivalent continuous sound level A in dB – values determined for roads and railway lines
Designation of the area
daytime (LD) –
period of reference time equal to 16 hours
night time (LN) –
period of reference time equal to 8 hours
spa protection areas A
areas of hospitals outside the town 50 45
areas of single-family housing building areas with permanent or several-hour stay of children and youth areas of nursing homes
areas of hospitals inside the towns
55 50
areas of multi-family housing and collective housing
areas of housing with craftsman’s services areas of farmstead housing
recreation and holiday areas
60 50
areas in the downtown zone of cities over 100 000 residents with dense housing and concentration of administration,
commercial and service facilities
65 55
Basic precautions against noise pollution can be implemented when permissible noise values for proper functioning of an area are exceeded (Ustawa … 2001). Accurate delineation of such areas is fundamental for choosing technical or legal solutions to the noise problem. These might include construction of the anti-noise screens or selecting better location for settlements, away from busy roads which might affect the inhabitants due to the excessive noise levels.
Noise level is a basic piece of information required for spatial management and spatial planning. It is also essential in formulating environmental protection programs, ecophysiographic studies for standard documents required in spatial planning in Poland, like “Study of Local Preconditions and Directions of Spatial Development”, “Strategic Assessment of the Environmental Impact of the Regional Strategy” and “Spatial Management Plan”.
Nowadays we can access quickly developing informative web portals which inform us about noise level in our town in a form of an acoustic map. However, not all of them take into consideration the demography to an excessive noise risk calculation. That sort of information could provide us with complete data concerning the degree of the influence of traffic noise in the areas where we live.
2. Aim of the study
The aim of the dissertation is to present a methodology to assess traffic noise risk in a small township. As a case study area the town of Baborów in a Opole Voivodeship was selected.
Baborów was selected since inhabitants have complained there about road traffic noise.
Besides, it has available measurements done by environmental consulting company ECOPLAN which confirm that permissible noise values were exceeded (detailed noise data comes from ECOPLAN environmental database). I participated in spatial data creation for this place. I have decided to choose this town as an object of my master's thesis.
The information about the road traffic noise levels which can have harmful effect on human health is also enclosed in local spatial management plan for the town. This document indicates major roads as a main source of noise pollution in Baborów (Uchwała …. 2002). On the other hand Baborów has no noise map which could provide information about the areas where permissible noise values are exceeded.
As the permissible values of traffic noiseare formulated with special concern for human health, this thesis refers to estimation of possible risk of traffic noise pollution where the main subject are the inhabitants. The thesis is focused on places inhabited or regularly frequented by people who live or spend their time in the township and which are affected by road traffic noise.
This problematic issue is also visible in other small towns and villages in Opole Voivodeship and it is probably just as problematic in many other regions. The media provide us with news about local protests caused by frustration with traffic noise levels.
Traffic noise threats are also enclosed in local environmental protection elaborations (Bochenek 2009; Boczar, Boryczka 2005; Dragon 2009).
The noise map in an appropriate scale to represent inhabited areas, could prove if the complaints are legitimate and if permissible noise values are exceeded or not. There is no noise map calculated to a scale of inhabited places of stay for this area. It is important to show traffic noise risk as expressed by one universal and measurable indicator which makes it possible to single out homes where anti-noise activities should be done in the first place.
Currently available reports, maps and web mapping services show noise impacted areas basing on measurable and interpolated values, sometimes with relation to the designation of terrain, but the demographic aspects are usually not considered.
Indicator values are presented in the tabular form. However, it is done without consideration to the spatial aspects, which makes this way of presenting data insufficient.
One of the most commonly used indicators for noise mapping (M indicator) is spatially oriented and should be spatially presented and streamed on the Internet, in order to create a transparent source of information, available to anyone interested in noise risk data. It is also important to find a model method for efficient data processing and the best approach to establish any potential noise threatened areas in a most effective way using GIS techniques.
This study should thus answer the following questions:
• How many people are affected by traffic noise pollution in Baborów and what is the spatial distribution of the affected population ?
• Where the noise risk is the highest ?
• Where are the places where anti-noise activity should be started, concerning the simple building scale ?
• What are advantages and disadvantages of the applied methodology ?
3. Study area
Baborów is a small town in Upper Silesia in the south-west of Poland (Figure. 3.1). The town is also a centre of an urban-rural district (commune Baborów) located in the south- eastern part of Opole Voivodeship (Załącznik ….. 2005).
Figure 3.1. Baborów on a map of Poland (data source: geoportal.gov.pl)
Even though this dissertation does not concern local spatial planning studies, information from this source provides a detailed description of the study area for this master thesis. Such data are essential when stipulating, where permissible noise levels are exceeded in relation to the designation of the area and the appropriate noise values.
It allows us also to delineate places where risk assessment will need to be performed.
Local spatial planning studies, along with other spatial data, are necessary for further geoprocessing, in order to answer the problem posted in the aim of the study.
With regard to the aim of the study, the whole building area was limited to places inhabited or regularly frequented by people, like houses and public or private services.
Basing on this relation, further analysis proceeded only with reference to the places shown at Figure. 3.2 and listed below.
Figure 3.2. Built-up area within the study area of Baborów (source: Base map from Town office in Baborów and ECOPLAN MPZP vector data)
MW,Uc; - areas of multi-family housing and centres of services
MWU - areas of housing with predominance of multi-family housing and services MNU - areas of housing with predominance of single-family housing and services MRU - areas of housing with predominance of farmstead housing and services UO; UOp - areas of public services – education
Although there are medical services dispersed between housing and services area or as a separate healthcare services (UZ), there are no hospitals or nursing homes in this town.
Areas of housing with predominance of single-family housing and services are the largest part of study area. MNU covers has also the biggest buildings area, if we take into account particular type of terrains (Figure 3.3).
10%
0,3%
2,1%
7,1%
24,4%
56,1%
MNU MRU MWU MW, Uc UO UOp
Figure 3.3. Designation of the study area (source: calculated from ECOPLAN MPZP vector data)
Services in MNU are most often located in buildings with housing function that provide residential space for families. The second largest area of interest is MRU. It is located along two streets: Raciborska and Powstańców. The area is distinguished by the predominance of farm buildings. Services, farming and housing functions are united within single farmsteads. MW, Uc are major places with consolidated services areas, situated in the centre of the town. The area is characteristic for the accumulation of multistoried building including separate objects with exclusively services function.
Local government offices are also located there.
On the outskirts of MW, Uc, a small part of MWU is located, with similar building type as the aforementioned. The rest of this type areas are situated in the northern part of Baborów. The largest multi-family housing settlement is situated there.
The last two types of terrains (UO, UOp) have strictly one purpose, concerning permanent or several-hour stay of children and the youth. Their area is the smallest of all the selected types (6). In UO two schools are situated: a primary school for the youngest children at the Opawska Street and a primary and secondary school complex for older children at the Wiejska Street. In OUp, there are two kindergartens. The first, Maria Konopnicka Kindergarten is surrounded by the eastern part of MNU and located at Krakowska Street. The second, Adam Janiszewski Kindergarten is situated at the Powstańców Street.
3.1. Source of traffic noise in Baborów
The main source of noise in Baborów is the road traffic. Streets crossing building parts of the town run in many directions, connecting the town directly with nearby cities, town and villages. They are directed towards traffic junctions or just start and finish within the town area (Figure 3.4).
Figure 3.4. Major streets and routes in built-up area in Baborów (source: Base map from Town office in Baborów in vector data, Załącznik ….2005)
Areas along major streets and routes are potentially the most affected ones.
Following route connections cross or start in Baborów:
• Głubczyce – Racibórz, which is represented by council route number 1262, consist of: Raciborska, Dąbrowszczaków, Rynek, Głubczycka Streets. The longest part of this route adjoins MNU. It starts at Głubczycka Street, where buildings are aggregated along the road on both sides. Next, on the one side of Dąbrowszczaków Street, the route borders MWU and then it comes to the centre
of the town in the Rynek, crossing MW,Uc. After that, the route runs along Raciborska Street through MNU and MRU.
• Baborów – Nowa Cerekwia, which is represented by council route number 1225 and consists of: Opawska Street. This route starts in the town centre (MW,Uc) crossing MRU and passing OU at the primary school. At the end the route goes through MNU with only few buildings.
• Baborów – Szczyty (a traffic junction to county routes number 417 and 421), which is represented by council route number 1261 and consists of Krakowska Street.
• The road adjoins most of MNU, few buildings in MWU and is adjacent to UOp with Maria Konopnicka Kindergarten.
• Baborów – Szonów (a traffic junction to county route number 417), which is represented by council route number 1259, consist of Kościuszki Street. It adjoins MNU and MWU, where a few buildings only are located on one side of the road.
• Baborów – Dzielów which is represented by council route number 1277, consist of Powstanców Street. The road adjoins most of MRU, few buildings in MNU and is adjacent to UOp with Adam Janiszewski Kindergarten
• Baborów – national road number 38 (it starts in Kędzierzyn-Koźle and leads to Czech Republic), which is represented by council route number 1226.
In Baborów there are also two railway lines:
• Line number 177, which connects Racibórz – Baborów – Głubczyce – Racławice.
• Line number 195, which connects Baborów – Kędzierzyn-Koźle – Pilszcz.
Racibórz – Głubczyce line provides railway transport services for corn elevators and fodder production plant but it is only intermittently in use. Although the infrastructure for second line is still exists, the connections are currently canceled. At present neither line provides passenger transportation services. Summing up, railway traffic density in Baborów is hardly perceptible and cannot be considered a long-lasting traffic source of noise pollution (Uchwała … 2002; Załącznik ... 2005; Polskie ... 2009; Urząd ... 2009).
4. Data
The primary data and information for this thesis came from the town office of Municipality of Baborów, the database of the Ecoplan and WASKO S.A. The data was also completed using the Internet sources. The particular layers listed below are described also in Table 4.1:
• Base map
• Designation of area - local spatial planning studies
• Noise data
• Buildings
• Streets
• Demographic data
One of the most important and elementary sets of data was the base map of Baborów - a binary raster image (an image with cell values of 1 and 0). The map contains basic information about terrain and objects located in the town area.
The designation of an area has the form of vector data with attributes and a complete vector layer based on local spatial planning studies documentation (MPZP).
The noise data contains LAeq D values, referred further in this thesis as LD and LAeq N
values, referred further in this thesis as LN.
Noise measurements were modeled using SoundPlan software to 10x10 m matrix.
SoundPlan software is specialized software for noise and air pollution modeling (SoundPlan …… 2010).
Demographic data in xls table format, containing the number of inhabitants in relation to address (number of building/local, name of street) were used as well. Those data values were added to the database as a separate column.
The initial set of available data comes from year 2004. It consists of the base map, designation of area in vector format, noise measurements for the considered area. It was used to create new data, as required for further analysis.
Building and street database in vector format with attributes like: buildings location with addresses, functions, street location with names – this data set was created on the
basis of the base map of Baborów. The supplementary information was also added, basing on documentation available on the Internet, like route number or name of services objects (Uchwała … 2002; Załącznik ... 2005; Polskie ... 2009; Urząd ... 2009).
Table 4.1. Raster and vector data used in the study
Base map (raster)
Source: Town office in Baborów
Projected Coordinate System: PUWG 1965 zone IV scale 1:1000
Designation of area - local spatial planning studies (vector)
Source: Ecoplan
Projected Coordinate System: PUWG 1965 zone IV scale 1:1000
Noise data in the form of 10x10 m matrix (vector)
Source: Ecoplan
Projected Coordinate System: PUWG 1965 zone IV scale 1:1000
Buildings (vector)
Source: Digitized buildings (base map as a background) with attribute database
Projected Coordinate System: PUWG 1965 zone IV scale 1:1000
Streets (vector)
Source: Digitized centerlines of streets ( base map as a background ) with attribute database
Projected Coordinate System: PUWG 1965 zone IV scale 1:1000
Demographic data (attribute data)
Source: WASKO S.A
5. Methods
Methods which were used in this thesis were: attribute selection using SQL statements, spatial analysis based on spatial operators and interpolation. Those techniques were preceded by gathering of all needed information, which is applicable to the subject of the thesis. For efficient data processing ArcGIS ModelBuilder was used where all above techniques were included.
To indicate inhabitants affected by road traffic noise pollution it was necessary to set up several stages of work:
• initial stage – this stage was to prepare data concerning building area to be coherent with noise map and demographic data for further analysis,
• noise delimitation stage – this stage focused on interpolated noise data creation, especially traffic noise values over permissible levels for study area,
• final stage – this stage focused on data aggregation, final data processing and calculations for delineation of places where people can be affected by traffic noise pollution, quantifying the traffic noise risk by M – indicator, based on data obtained in previous steps (Stoter 1999; Mahdi et al. 2002).
For all the stages the ESRI desktop GIS software package - ArcGIS 9 series with ArcGIS Geostatical Analyst and ArcScan for ArcGIS extensions was used.
5.1. Initial stage
At the start of initial stage it was important to convert raster to vector data. Heads-up digitizing was used for this purpose. This is on-screen, manual data capture method, based on tracing a mouse over a scanned image displayed on a computer monitor, creating vectors (Stanley 2003).
In this way two feature classes were created:
• polygon features represented by buildings
• polyline features represented by major streets and routes (centerlines)
Vector data were essential for applicability in selection or spatial analysis based on location or assigned attributes.
Next step was to assign attributes for proper vector objects, including previously qualified, necessary information from base map, demographic data, documentation and web information (Table 5.1; Table 5.2). All the data give detailed information about buildings and allow focusing on places inhabited or regularly frequented by people in Baborów. They are indicated by a unique address of a building.
Table 5.1. Attributes of buildings
buildings
NAME Name of institution or service in building
ADDRESS Building address
Table 5.2. Attributes of major streets and routes
major streets and routes
NAME Name of street
NUMBER Number of council route
After that, polygon area were added to table attributes using calculate geometry function. To increase the speed of data processing the indexes were added (Figure 5.1;
Figure 5.2). At the end two feature classes with attributes were received: buildings.shp (Figure 5.3) and major streets and routes.shp (Figure 5.4).
Figure 5.1. Buildings – indices
Figure 5.2. Demography – indices
Figure 5.3. Data structure of feature class – buildings
Figure 5.4. Data structure of feature class – major streets and routes
In order to be coherent with building ADDRESS Field Names, attribute operation on demographic data was also performed. Using Calculate field function, number of building/ apartment [HOUSE_NR], name of street [STREET] were put to one column [ADDRESS] (Figure 5.5; Figure 5.6).
Figure 5.5. String calculation of demographic data
Figure 5.6. Data structure of table – demography
In the next step number of inhabitants were appended to building attribute database using ADRESS as a common join field (Figure 5.7).
Figure 5.7. Add Join function applied to adding demographic data to building database
To define traffic noise risk for human health, it was necessary to separate buildings which were inhabited from the rest of them. Only inhabited buildings were used for further analysis (Figure 5.8).
Figure 5.8. Inhabited buildings selection
In the next step particular attributes from local spatial planning studies (MPZP) were appended to buildings_inh attributes database (Figure 5.9).
Figure 5.9. Adding MPZP data to building database
At the initial stage, spatial planning, demography, buildings data were integrated.
Figure 5.10. Spatial planning, demography, buildings data integration
5.2 Noise delimitation stage
At this stage, noise data comes from LD and LN indicators, in the form of 10x10 m matrix was used to create noise maps. For this purpose, the first step was to choose a proper interpolation technique. The interpolation was essential in order to create a consistent noise value range zone, base on further geoprocessing and calculation had been done. Subsequently, noise isolines (vector) were created according to noise values from interpolated surface.
5.2.1 Interpolation technique
The major assumption of interpolator is to use already modeled noise values at particular location in the interpolated surface. Exact interpolators which force the resulting surface into define values fulfill those requirements. It is very important to take into account that an interpolator should provide good result with influence of local variation. This situation occurs where noise level are “quickly” reduced – at very short distance, for example behind buildings (Stoter 1999).
For the interpolation the Inverse Distance Weighted (IDW) method was chosen as a type of an exact interpolator. IDW bases on the neighborhood samples, with possibility to set up the number of points which have influence of unmeasured location, using power parameter. This feature has special significance for location with propagation obstruction, when the closest points are the most important for interpolation. Another advantage for IDW is the fact that the best results are obtained when measurements are evenly distributed throughout the area, like in this particular case. For this specific situation IDW is a better choice than another exact interpolator, Radial Basis Function (RBF). In contrast to IDW, RBF is inappropriate when, within a short distance big changes in surface value occur or there exists a suspicion of error or uncertainty (ESRI
… 2009).
5.2.2 Setting interpolation parameters
Setting interpolation parameters started with defining the area of calculation by changing ellipse size till all samples are covered. Next important adjustment was to determine neighborhood. To indicate local variation at the smallest scale it was important to use the measured values located closest to the unknowing places. Influence of measurements situated farther away is limited. Power value was also quite significant when considering this parameter. This and other adjustments were set up in the way to provide the smallest root mean square prediction error in cross validation (RMSPE). All settings used in interpolation were shown at figure 5.11.
Figure 5.11. Interpolation settings
The root-mean-squared standardized error was close to 1 and the mean prediction error was near zero (Figure 5.12; Figure 5.13) which means that predictions were valid and centered on the true values.
Figure 5.12. Predicted values and prediction errors (LD indicator data)
Figure 5.13. Predicted values and prediction errors (LN indicator data)
5.2.3 Creating noise traffic data
After the interpolated surface was created, it was important to set up breaks with range classes consistent with LD and LN indicators exceeding noise values. To do that, the manual classification method was used, with setting proper classes number separately for LD and LN indicators. This classification allows one to perform spatial analysis while taking into account the noise level exceeding threshold value for particular designation of terrain (Table 1.2).
To prepare noise data for further processing in ArcGIS ModelBuilder, the interpolated area was saved in layer (.lyr) format and exported to filled contours in vector format by GA Layer To Contour function (Figure 5.14; Figure 5.15).
Figure 5.14. GA Layer To Contour for LD indicator data
Figure 5.15. GA Layer To Contour for LN indicator data
Polygons referring to noise values over permissible levels were disconnected by proper class range using Select function (Figure 5.16; Figure 5.17).
Figure 5.16. LD indicator >55dB selection
Figure 5.17. LD indicator >60dB selection
At the noise stage, noise data was processed (Figure 5.18) concerning permissible values of traffic noise established for different kind of noise and for different designations of the area (Table 1.2). The output was used for further processing.
Figure 5.18. Noise data processing
5.3. Final stage
The output of selection was used for Spatial Join function where existing noise values over permissible levels were assigned to inhabited buildings located in particular designations of areas (Figure 5.19; Figure 5.20). Spatial Join function was also performed for LN indicator data in similar way.
Figure 5.19. Joining noise pollution and building data (LD indicator >55dB)
Figure 5.20. Joining noise pollution and building data (LD indicator >60dB)
In the next step, number of inhabited buildings from Baborów were limited to MPZP terrains which are referred to in Polish law about permissible values of traffic noise.
Figure 5.21. Selection of buildings located in LD indicator > 55dB zone to UO, UOp areas
For buildings located in the areas with permanent or several-hour stay of children and youth (UO, UOp) Selection function was used (Figure 5.21). It was final output of processing for these data (buildings_LDover55.shp).
For areas where LD is over 60dB and LN over 50dB, further processing was done and therefore it was necessary to create a layer format using Make Feature Layer function (Figure 5.22). During this conversion SQL selection was also used to extract housing with craftsmen’s services. Similar selection with the same SQL statement was done for buildings_LDover60.shp data.
Figure 5.22. Selection housing with craftsman’s services areas from MPZP data
To indicate all buildings from study area, which are partly or wholly located in the traffic noise pollution zones, the spatial analysis function was used. What is more, it was needed for M indicator calculation to acquire data about exceeding permissible LD and LN noise value assigned to a particular building.
In creating data for further M indicator calculation the first step was to use Select Layer by Attribute function (Figure 5.23). Housing with craftsman’s services buildings (buildings_LD_over60_Mu_Layer) was divided into groups of traffic noise related to inhabited buildings, differing by 1dB exceeding values.
Figure 5.23 Example of attribute selection of particular noise values group
At this part of the final stage, previously created and processed data was aggregated (Figure 5.24). Only for buildings located in the areas with permanent or several-hour stay of children and youth (UO, UOp) was obtained final output. For housing with craftsman’s services buildings, further computation have been processed.
Figure 5.24. Data aggregation
A single building was intersected by many noise contours (Figure 5.25), so in order to extract only these buildings intersected by the highest noise zone Select Layer By Location function was applied (Figure 5.26). Identical buildings crossed by equal- loudness contour with lower noise value(s) were removed.
Figure 5.25. Single building and equal-loudness contours assigned to it
Figure 5.26. Removing identical buildings from different noise values groups
The output in the form of selected features was saved using Copy Features function (Figure 5.27)
Figure 5.27. Copy Features function for selected before feature
Extracted groups with unique buildings were gathered in one file for further processing using Merge function (Figure 5.28).
Figure 5.28. Merging buildings with unique traffic noise values
The Aim of second part of the final stage was removing identical buildings from different noise values groups. End of the single “tree” processing was merge into one file with proper database structure (Figure 5.29). Output data should include preliminary information used for final M indicator computation.
Figure 5.29. Removing identical buildings from different noise values groups
5.3.1 Calculation of M indicator
Noise values over permissible level were subsequently extracted from noise attributes assigned to buildings using Calculate Field function (Figure 5.30). But firstly, new fields for calculation were added (Figure 5.31).
Figure 5.30. Calculating noise values over 60dB
Figure 5.31. Adding LDex_val field
Like in previous step new field for calculation were added (Figure 5.32).
Figure 5.32. Adding LDex_values_added field
Finally, all variable for M indicator were calculated (Figure 5.33). Final calculation could be processed according to the equation:
M = 0,1 m (100,1ΔL –1) where:
M – indicator value
ΔL – volume of exceeded permissible noise values [dB]
m – number of inhabitants placed on the area where permissible noise values are exceeded.
Figure 5.33. M indicator computation
The same way of proceeding was applied to M indicator processing based on LN data.
The Aim of the last part of model and final stage was exceeding noise values calculation (over 60dB and over 50dB) and M indicator computation (Figure 5.34).
6. Results
Results are referred to LD and LN indicators for particular building categories which were present in the study area (Table 6.1).
Table 6.1. Noise scale concerning permissible noise level and particular designation of area
Permissible noise level expressed by means of equivalent continuous sound level A in dB – values determined for roads and railway lines
Designation of the area MPZP terrains (symbols)
Daytime (LD) – period of reference time equal to 16 hours
Night time (LN) – period of reference time equal to 8 hours Building areas with permanent
or
several-hour stay of children and youth
UO; UOp 55 50
Areas of housing with
craftsman’s services MW,Uc ;MWU;
MNU; MRU 60 50
All results are presented in the form of:
a) maps
• map of permanent or several-hour stay of children and youth affected by road traffic noise pollution with assigned number of children,
• detailed maps of inhabitant’s places of stay, affected by road traffic noise pollution with assigned number of inhabitants,
• map of M indicators.
b) table with calculated values
• total number of inhabitants endangered by traffic noise pollution
Because there were no buildings which were completely within the zone of noise pollution, following results refer to buildings intersecting permissible noise values zones.
6.1 Maps of road traffic noise pollution
Even though there are no buildings situated wholly in the excessive noise zone, there are many of them which are partly located within it
6.1.1 Daytime
95 pupils
Op aw sk a S t.
´
Legend
LD > 60 LD > 55 MPZP UO
major streets and routes
buildings affected by road traffic noise pollution
primary school
Figure 6.1. Map of permanent or several-hour stay of children and youth affected by road traffic noise pollution with assigned number of children
If we analyse noise pollution during the day in the areas with permanent or several-hour stay of children and youth (LD indicator > 55 dB) we can say that only one area, the primary school situated at Opawska Street, where 95 pupils attend, can be threatened by noise (Figure 6.1).
Another group are the inhabitants living in the buildings located in the areas of housing with craftsmen's services (LD indicator > 60 dB). The highest number of inhabitants affected by such noise live at 5 Rynek Street, at a crossing of most of the busy roads (Figure 6.2).
Głubc
zycka St.
Opawska St.
Racib
orska St.
Dąb row
szcz akó
w S t.
Ryne k S
t.
Legend
major streets and routes LD > 60dB inhabitants number
1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 20 22 23 24 25 28 36
´
0 50 100Meters
Figure 6.2. Detailed maps of inhabitant’s places of stay, affected by road traffic noise pollution with assigned number of inhabitants where LD indicator > 60 dB
The highest number of inhabitants affected by traffic noise (during night and day) pollution was found to be at the Rynek 5 (market square) - 36 inhabitants affected.
If we take a closer look at the function of buildings at Rynek, including Rynek 5, the predominance of service function, or mixed service and housing function, is noticeable (Figure 3.1). According to the information from the Base map, in the center of this township concentrated settlement with dominance of more than one-story buildings prevail. That is why so many people populate this noise pollution zone
6.1.2 Night time
In the study area there were no public objects for permanent stay of children and youth, thus exceeding permissible LN indicator values during the night time would not be taken into account in this dissertation. Considering the noise levels presented in the night map (LN indicator > 50 dB) and comparing these to daytime noise levels map, we can notice that location of the highest number of citizens threatened by noise pollution is similar to those during a day (Figure 6.3).
Głubc
zycka St.
Opawska St.
Racib
orska St.
Dąb row
szcz aków
St.
Ryne k S
t.
Legend
major streets and routes LN > 50dB inhabitants number
1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 20 22 23 24 25 28 36
´
0 50 100Meters
Figure 6.3. Detailed maps of inhabitant’s places of stay, affected by road traffic noise pollution with assigned number of inhabitants where LN indicator > 50 dB
Analysing the distribution of Street. Along Raciborska Street few places are noticeable for high numbers of inhabitants affected by traffic noise. Along the Opawska Street, the
Other large groups of inhabitants affected by the traffic noise, we see that they are dispersed along Głubczycka Street and Dąbrowszczaków.
6.2. Distribution of the M indicator 6.2.1 Daytime
The highest values of M indicator delimitated multi-storied building located far away from center of the town (over 600m from market square at Rynek 5) at Głubczycka Street 32 (Figure 6.4). The highest note of M indicator value is a result of the fact that number of inhabitants are larger than in the second location (Raciborska Street 22) even though that both buildings are situated in the same zone where noise reach the maximum of exceeding noise values for day.
Głubc
zycka St.
Opawska St.
Racib
orska St.
Dąb row
szc zakó
w S t.
Legend
major streets and routes LD > 60dB M indicator
0,000 - 1,000 1,001 - 1,500 1,501 - 2,000 2,001 - 2,500 2,501 - 3,000 3,001 - 3,500 3,501 - 4,000 4,501 - 5,000 6,001 - 6,500
´
0 50 100Meters
Figure 6.4. M indicator assigned to inhabitant’s places of stay affected by noise pollution during the day
The highest M indicator values (LD indicator > 60dB):
Głubczycka Street 32 – 6.05 Raciborska Street 22 – 4.97
6.2.2 Night time
M indicator distribution at night repeated results on M indicator distribution during the day. The same two buildings have the highest M indicator results (Figure 6.4; Figure 6.5).
Głubc
zycka St.
Opawska St.
Racib
orska St.
Dąb row
szc zakó
w S t.
Legend
major streets and routes LN > 50dB M indicator
0,000 - 1,000 1,001 - 1,500 1,501 - 2,000 2,001 - 2,500 4,000 - 4,500 4,501 - 5,000
´
0 50 100Meters
Figure 6.5. M indicator assigned to inhabitant’s places of stay affected by noise pollution at night
The highest M indicator values (LN > 50dB):
Głubczycka Street 32 – 4.23 Raciborska Street 22 – 4.97
Analysing M indicator maps we can notice that the highest road traffic noise risk is visible at Głubczycka Street 32 and Raciborska Street 22. As opposed to M indicator values calculated on a day (6.05 and 4.97) (Figure 6.4), difference between the highest M indicator values at night is small and just noticeable (4.23 and 4.97) (Figure 6.5).
Going further in comparison, we can noticed that rates at Głubczycka Street 32 and Raciborska Street 22 are replaced themselves in ratings. Building with second note on day, have the highest M indicator value at night and vice versa.
At night object located on Raciborska Street 22 have higher M indicator values than on a day. It derives from the fact that is different noise scale, starting from 50 dB as a maximum permissible value and noise distribution are not exactly the same like during the day. Follow–up this fact, the most important factor for object at Głubczycka Street 32 was very high exceeding noise values at night, reaching 5 dB over permissible noise values, while building at Raciborska Street 22 was situated in a little bit more quiet zone.
Comparing to the results of M indicator on day, the same conclusion is clearly visible.
The highest results come from the combination of two elements: a large number of inhabitants and a high level of noise pollution. At these two locations, the anti-noise activities should be started first.
6.3 Total number of inhabitants endangered by traffic noise pollution
Areas of housing with craftsmen’s services located along Raciborska Street and Głubczycka Street are characterized by concentrated settlement, what is more, most of the multi-storied buildings are situated there. That is why over a hundred people there can be affected by noise pollution (Table 6.2). However, 130 people form only a little bit over 4% of total number inhabitants of Baborów (3114 inhabitants in total;
Wikipedia … 2010).
Table 6.2. Total number of citizens affected by noise pollution
Noise level Designation of the area LD > 60 dB
(max 67,6 dB) LD > 55 dB LN > 50 dB (max 56,93) Building areas with
permanent or
several-hour stay of children and youth
- 95 pupils -
Areas of housing with
craftsman’s services 130 inhabitants - 126 inhabitants
The highest noise value as a LD indicator reached in study area – 67,6 dB
The highest noise values over permissible level referring to places where people can be affected by traffic noise pollution in the study area for the day – 6dB
The highest noise value as a LN indicator reached in study area – 56,93 dB
The highest noise values over permissible level referring to places where people can be affected by traffic noise pollution in the study area at night – 5dB
The results noted during the day and these taken down at night are similar. Still, we need to consider the fact that the permissible values for LN indicator are much stricter for this type of area designation. It is exactly 10 dB difference between permissible values of traffic noise on day and at night. On the other hand traffic noise is generally bigger on day, that’s why the highest exceeded noise values for a day is over 7.5 dB while at night is below 7 dB.
In the scale of the whole study area only one place with permanent or several-hour stay of children and youth can be affected by traffic noise pollution. It is primary school situated at Opawska Street.
However permissible noise values were exceeded for this place, numbers of pupils are much fewer than in the school complex on Wiejska Street in the town centre (753 pupils)
7. Discussion
The object of this dissertation focused on a small town and the method and results presented above were applied just exactly to this one township, one of many in the area of the Opolskie Voivodship. It is quite difficult to compare all results to other places, even these similar in many respects to Baborów, because there are lack of so detailed assessments for towns with small number of inhabitants.
There are no references to demography in such studies. M indicator which brings together human and spatial aspect to indicate places where anti-noise activities should be starts first, is typically missing. Nonetheless some environmental elaborations include similar information which can be used to compare obtained results.
Table 7.1. LD and LN indicator data from different towns and villages – comparative table
(source: Bednarek 2009; Staliński 2004; Załącznik … 2005; Załącznik … 2008; Wikipedia …. 2010) Small towns
and villages Number of
inhabitants Max LAeq D (LD) Max LAeq N (LN) Major source of noise (route number and rank)
Baborów 3114 67,6dB 56,9dB 1262 – council route
Baranowo 2200 70,4dB 64,0dB E92 - national route
Brzeziny 12331 73,3dB 71,3dB 72 - national route
Marki 24000 74,3dB 73,2dB E67,8 - national route
Remertów 23144 74,4dB 72,3dB E77 - national route
Considering the same indicators used in calculation, we can say that the noise pollution, which in Baborów comes from road traffic, is rather low there (Table 7.1). It can probably be caused by lower flow capacity of routes crossing Baborów and less important communication functions. It limits traffic and therefore brings less noise that might threaten the inhabitants.
We can also say that according WHO scale of critical health effect(s) that the most serious health effects are annoyance and, possibly, sleep disturbance. As there is no noise pollution over 70 dB, hearing damage caused by traffic noise does not endanger citizens of Baborów in the study area (Table 1.1; Table 6.2).
The major aim of this thesis was to conduct traffic noise risk assessment but in an effective and efficient way using GIS techniques. Building up all geoprocessing task into a model using ArcGIS ModelBuilder allows us to input data and parameters for each tool and connect the processes together. The major advantages of this method, when compared to single separate processing are:
• maximization of productivity by avoiding duplication of processing effort,
• flexibility, validation and managing of data workflow,
• sharing common workflow model of geoprocessing between professionals Time-effectiveness is a very important aspect of method presented in this dissertation particularly when processing large volumes of data. This model enables complex operations which should be remembered in detail. What is more, it is easy to rank and compute the correlations between all independent variables in one workflow process.
Every mistake can be corrected or modified and connection rules can be implemented instantly. We can replace function and input data, and the whole processing scheme or only a part can be validated before running the process.
A methodical execution of a sequence of operations on geographic data can be exported to a file. Model in this form can be easily shared in a work group or published as a new ToolBox on a desktop or server (ESRI….2009). Managing all geoprocessing steps is much easier when we have all operations and data in one scheme:
On the other hand some disadvantages can appear:
• data pre-processing necessity,
• method availability limited only to ESRI software.
First of all, input data should be uniform. The same structure of attribute database, proper format, added indexes, should be suitable for model function are needed for the model to function, thus data conversion and rebuilding database are often necessary.
Secondly, geometrical and attribute data quality assessment (QA) should be done. It is essential to check the particular variables which will form the main components of calculation before they are implemented in a workflow. Available set of functions assigned to the software version can also be a source of certain limitations.
8. Conclusions
The thesis answered the questions put in the aim of study and proved that there truly exists traffic noise risk for inhabitants. Even though traffic noise values exceeded these permitted by Polish law, they are not so high as to cause hearing impairment or similar important and dangerous effects. On the other hand, the analysis confirms that such values could cause annoyance as an effect of traffic noise, what results in the inhabitants' complaints.
The dissertation presented above demonstrates important example of GIS application in the public field. Basic but detailed spatial data with proper geoprocessing and with the application of GIS analysis can be used in risk estimation in an effective and efficient way.
Such an analysis allows for indicating, in a single-building or a homestead scale, where the noise conflicts may arise. Pointing out proper objects can be helpful while estimating more accurately the costs of noise pollution prevention activities.
One of the reasons which induce traffic noise threat is the accumulation of residential places along the major routes in type of ribbon building. This is characteristic not only for Baborów but for other towns and villages. For buildings located in noise pollutions zones, appropriate technical precautions should be applied, like anti-noise windows or screens.
Noise protection activities should be implemented in the early stage of spatial planning.
Establishing areas with housing as the main purpose should be performed with proper consideration given to all the traffic noise pollution aspects. This way of thinking could locate settlements areas far away from noisy town centre. Information concerning inhabitants’ places of stay that are threatened by noise pollution should be easily accessible for everyone. The best way to publish these data is the use of webGIS portals. Presentation on the Internet ensures, on the one hand, the transparency, and on the other hand allows for flexibility in overlaying additional data like database address or type of buildings.
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