Pollution transport in the Boden garrison storm water

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Självständigt arbete Nr 59

Pollution transport in the Boden

garrison storm water

Pollution transport in the Boden

garrison storm water

Jonathan A. Udén

Uppsala universitet, Institutionen för geovetenskaper Kandidatexamen i Geovetenskap, 180 hp

Självständigt arbete i geovetenskap, 15 hp Tryckt hos Institutionen för geovetenskaper Geotryckeriet, Uppsala universitet, Uppsala, 2013.

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Självständigt arbete Nr 59

Pollution transport in the Boden

garrison storm water

Jonathan A Udén

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Sammanfattning

2012 så påbörjade Fortifikationsverket ett miljöprogram för sina fastigheter i norra Sverige. För Bodens garnison så betydde detta en utvärdering av sin

dagvattenhantering. Målet med detta arbete var att ta fram underlag gällande föroreningar i dagvattnet. Dagvattenledningarna vid Bodens garnison leder ut i Lule älven som i sin tur leder ut i östersjön. Årsvolymer av dagvatten skulle kopplas till föroreningskoncentrationer. Något som efter hand visade sig vara väldigt svårt, eftersom genomsnittliga koncentrationer över ett helt år är väldigt svårt att få fram. Förslag för förbättringar kring detta gjordes dock och delar av dem är redan

åtgärdade. En utvärdering gällande oljeföroreningar gjordes också. Dels beräknades maxflöden för varje utlopp för att se hur situationen skulle se ut vid ett större

oljeutsläpp som sammanfaller med regn. Flöden beräknades också vid olika regnintensitet för att utvärdera existerande oljefällors rening vid normala regn. Vid denna utvärdering visade det sig att samtliga utlopp är dåligt utrustade för ett större oljeutsläpp som sammanfaller med högre regnintensitet. Vid normala regn klarar samtliga oljefällor utom en av att rena det vatten som passerar.

Abstract

In 2012, the Swedish fortification agency started an environmental program for their real estate in northern Sweden. For the Boden garrison this meant an evaluation was needed for their storm water handling. The evaluation fell upon Grontmij AB in

Boden. This thesis concerns the pollution from the storm water pipes into its recipient Lule River. Its aim was also to evaluate the areas for the event of a bigger oil spill, since there are many mechanical garages within the garrison. The pollution

concerning oil was evaluated by calculating the flow in each outlet into the river, with different rain intensities. For other pollutants, the volumes of storm water each year, for every outlet were calculated. With concern for the snow melting process, it was also calculated for which period of the year would be interesting to keep an extra watch for pollutant concentrations and maximum flows. Results showed that for a 50-year period, none of the 50-years would have had their peak flow because of the snow melt. The results for evaluation of an oil spill showed that none of the outlets were equipped for an accident of such sort, should it coincide with a rainfall of a high intensity. The results also showed that one of the outlets had an oil trap only

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Background

The Boden garrison is owned and maintained by the Swedish fortifications agency. They are Sweden’s largest real estate owners (Fortification agency, 2013). Their main customer is the Swedish defense and they focus on secure buildings and infrastructure.

In the Boden garrison, storm water from the area is led through pipes out to the Lule River. As part of an environmental program, the Fortifications agency has worked out a control program together with the Swedish defense for the storm and wastewater. The program is to date very preliminary and is thought to have a more permanent form with the help of 2012-2014 tests. When this is done, testing and the control program are thought to be separated in two different reports instead of today’s single report. To be able to do this one should not be dependent on the other and a secure program has to be developed.

The environmental program started in 2012 and the first agenda was to decide which points in the network of pipes were interesting. The criteria were that the water was supposed to represent as much as possible of the garrison. All the outlets to the Lule River had to be represented and one should be able to separate areas from each other. 10 points were selected for the storm water and initial testing was done with one test in June 2012 and one in September 2012. Spring and fall was selected because this is a time of much activity in the areas. Activity means it is more likely for pollutants to appear. During this time of higher activity it is also more likely to occur bigger spills of for example oil. There are many garages in the garrison and military vehicles are often big, requiring big amounts of oil, gas and diesel. It is therefore important to calculate maximum flows of water in the storm water pipes. If a spill of oil occurs at the same time as a high flow of water, cleaning solutions have to be able to handle those circumstances.

Benchmarks for the storm water are partially from “The quality of urban storm water, 1994” by Malmqvist, Svensson and Fjellström. They have given

standard values which are widely cited and used. Gothenburg municipality has also given what they feel are good benchmark values. These values are for recipients of municipal storm water. These values are taken into consideration, simply because they are cited in a number of other reports and have been carefully been evaluated by the Gothenburg municipality. The fact that they are from 2008 means that they have taken into account the European water directive, which applies for every lake and stream in Sweden. There are no benchmarks for pollutants in the Lule River at the moment, which is why there is a need to use values from other projects. There is however an ongoing project to develop standard values for pollutants which are released into the Lule River. At a later stage of this program these might be used instead.

One of the outlets is special concerning pollutant loads. Outlet K108 B2004, which will later be referred to as outlet number 2, have pollutant

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2 the town heating plant which disposes of their process water through this outlet. Process water is used clean gases which results in high pollutant concentrations. Samples from this outlet should be interpreted with caution since the pollutant

concentrations from the heating plant are so high that storm water pollutants become insignificant.

Figure 1. Map over Sweden with Boden marked

(Google, Terrametrics, 2013)

Purpose/Goal

The purpose of the thesis is to evaluate storm water pollution from the Boden garrison into the Lule River. This is reached by calculating point specific and total volumes of storm water from the Boden garrison and combining these with data for pollutants. The data will be evaluated and discussed to reach a sufficient method for future testing. Existing oil traps will also be evaluated for the event of bigger oil spills.

Storm water

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3 There are a number of ways to handle storm water. In places with vast vegetation and small areas of roads, the storm water does not need any extra handling. Water that precipitates there, will most likely infiltrate into the ground and contribute to the groundwater storage or will be taken up by plants. In cities,

residential areas or industrial areas a method for moving the water is required. Since these areas have roads, pavement and other hard surfaces water cannot infiltrate, and rain will create puddles or floods. The most common way of dealing with this water is through a network of pipes and wells leading to a stream or river in the area. Local solutions for dealing with rain without moving the water could for example be slopes to a temporary pond.

The environmental role of pollutants transported by storm water has not always been important. Sewage water that carries many pollutants has often been led to the same waters as the storm water; therefore the pollutants contributed by storm water could be considered insignificant in comparison. In recent decades ways of handling wastewater have been greatly improved, and with that, the pollutant contribution of wastewater to the rivers decreases, in comparison to the amount contributed by storm water (Malmqvist, 1994).

The size of the recipient has an impact on which values for pollutants are more important. For a small stream or lake with seasonal variations in water level, storm water can have a big impact. A chock of pollutants with for example the spring floods, could be devastating, even if the yearly values are not much higher than for a very healthy stream. For a bigger river, the yearly volumes of pollutants are more interesting since it has a buffer capacity to handle sudden and temporary chocks of very contaminated water.

Sources of pollution

When it comes to sources of pollutants in storm water, there are a few to consider. Storm water picks up pollutants when running on surfaces with particles of pollution. These will be transported by the water to the recipient. An important aspect to consider is also the composition of the rain itself. On different occasions and places in time, rain can contain high values of strong pollutants such as mercury and sulfuric acid. It is therefore important to do a rainwater analysis for the same area as the outlets in order to get a background value to compare. If the storm water has high content of pollutants that are already in the rain as it falls, there is little idea of cleaning the storm water, since the recipient will get these pollutants anyway.

Roads and asphalt surfaces

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Roofs

This water generally has low particle content, but acidic pH because of the rain itself. If the roof has metals such as zinc or copper, these can be eroded by the acidic water and the storm water will get a high metal content. Depending on the height of a building and the amount of traffic around it lead deposition from exhausts can be found on the roof of the building, which is transported through the storm water. The storm water contributed by the roofs could also have a high content of bacteria because of bird droppings (Malmqvist 1994).

Snow

Pollutants from snow are the same as from roads and asphalt areas. The exhausts and particles from traffic get stuck in the snow and accumulate during the winter. During the melt period (April-May), pollutants that have been accumulating in the snow, all get released into the storm water pipes in a short time period. During winter is also the time of snow-cover, which means cars have spikes on the tires. This gives higher particle content from eroded asphalt and heavy metals from the spikes.

Quantifying pollution

When it comes to quantifying pollution, reliable concentration data is of great

importance. When measuring concentration of pollutants in storm water there are big uncertainties. Since pollutants in storm water are based on what is picked up from the surface, it is important to take into account the development of concentrations during a rainfall. After a long period of drought, the first rain falling will have a lot of pollutants to pick up. Meanwhile a rainfall late in a rain period will be clean in comparison. There are also big differences within each rain event. The more water has passed a spot, the cleaner it will be. This means concentrations should be lower towards the end of a rain event, when the last water passes the measuring point. Getting reliable data therefore requires extensive testing. The tests have to cover different scenarios of rain and take flow into account. Manual testing should therefore only be used as an initial way of raising awareness for high values of pollutants. Note that no pollutant should be excluded after manual point testing. It should only be used as a tool for raising interest in certain pollutants. There are however ways of getting more reliable data. Technical solutions that are able to make automated testing and relate concentrations, with flows for the water tested. During this project the fortification agency had a meeting at which the importance of these

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5 description see their webpage http://www.isco.com .The ISCO-2150 is then

connected to the ISCO GLS Mobile Sampler. The sampler uses a hose with suction to capture samples which are stored in a container inside the machine. It is possible to choose the preferable flow conditions for sampling. This means that one does not need to worry about missing the so important first “cleansing” rain after a dry period. It is however important to decide what is actually significant to look for. The results could be sensitive to flows and to the level that the hose is fixed at. Oil contamination for example will tend to stay at the surface during steady flow. This means that if it is the oil concentration that is of interest, sampling has to be done either directly after a very turbulent section, or on the top of the water. Suspended material on the other hand, is more likely to have a representative amount in the middle of the flow.

Initial water analyses were done in the spring and fall of 2012. The pollutants that were pointed out as interesting were N-tot, P-tot, Cu, Zn, As and Pb. Not all in the same outlets but all outlets had at least one of the pollutants.

Recipient

The recipient of Boden garrison’s storm water is the Lule River. The river then leads out to the Baltic Sea. The river has an average flow of 490 m3/s and average

maximum of 980 m3/s (SMHI, 2013).

Cleaning solutions

Oil traps

Oil traps are a way of separating oil, gasoline and other pollutants with a lower density than water, without having a lot of maintenance except draining the pollutant from the tank. An oil trap is based on the principle of gravity. The pollutant one wants to separate from the water has to have a density below 0,95 kg/dm3 which is just below water’s 1,0 kg/dm3

. As the polluted water enters the tank, the lighter oil will float on top of the water. By placing the outlet at a lower level than the inlet, the oil will get trapped within the tank (Naturvårdsverket, 2007). The outlets with an installed oil trap in the Boden garrison are more sophisticated. They have a lamella-filter. The principle is the same as for the simple oil trap but they are able to efficiently clean higher flows. They have a lamella which all the water has to pass. The lamella catches the oil and it travels to a separated section of the tank.

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Method

Calculations

The dimensioning of oil traps is done by calculating maximum flows and yearly volumes of water. Maximum flows were calculated using “The rational method”:

Eq 1. Where: ) )

There are a few terms that have to be met in order to use the rational method:

 The area should be close to rectangular

 Runoff coefficients with the same value should be evenly spread over the area

 Inflow times in different sections shouldn’t vary to much

 The method should be used in small, evenly exploited areas

(Lidström, 2012)

In order to meet these criteria some outlets were divided into rectangular sections. The outlets also had geographical reasons why they had to be divided. One outlet could represent several areas divided by one or more roads. This means all of the areas will not contribute to a maximum flow if the rain doesn’t last long enough, since it takes some time for water that falls high up in the pipes to travel all the way down to the outlet. Calculating the dimensioned rain intensity requires two separate equations. The first calculation is concerning general rain intensity:

Eq 2. Where: (Dahlström, 2010)

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7 Dahlström’s equation. They did this by comparing calculated data from a number of their own sites with calculated values. They found that the equation was well in line with their measurements for most cases. The exception was for rains with long duration and short return periods. These rains however, are not of concern for this project since maximum flow is of interest. There is a relationship between rain intensity and its duration time. The longer duration time it has the lesser will its intensity be (Lidström, 2012).

After the general rain intensity one has to specify the flow according to one’s section. This is done with the variable tc which is the flow time, the time it takes

for a drop of water to move from any point in the area to the point one wants to

calculate maximum flow. With this equation one gets the minimum duration of rainfall each section needs if it’s going to contribute to the maximum flow. The equation for flow time is:

Eq 3. Where: ) (Lidström, 2012)

The values for Lh80 were given by measuring the distance in GIS, using the

measurement tool. Sh were calculated taking height measurements for the highest

and lowest wells in the area of interest. The difference between those was divided by the length between them. Data for heights were hard to find. In some cases it’s not the highest and lowest points but wells relatively close to them. The lengths between them are always taken from the same wells as height data.

The runoff coefficient is a number between 1 and 0 representing the amount of water different surfaces contribute to the pipes, i.e. amount of water that doesn’t infiltrate the ground. 1 means all water that lands on a surface will eventually end up in the pipes. 0 means all of the water infiltrates the ground. The values used in this project are from “Swedish Water, P90, 2004”. Swedish water is a trade organization that represents Swedish companies dealing with water.

Table 1. Runoff coefficients depending on surface type Surface

Runoff coefficient

Roof 0.9

Concrete and asphalt, heavy sloped exposed bedrock 0.8

Plates with gravel joints 0.7

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sloped

Flat bedrock 0.3

Open area with gravel and graveled aisles 0.2 Park with rich vegetation, hill terrain 0.1 Cultivated areas, grass field, meadow 0-0.1

Flat forest 0-0.1

(Svenskt Vatten P90, 2004)

A collected runoff coefficient is calculated by adding the different surfaces within the section: Eq 4. Where: (Lidström, 2012)

Areas for each section were calculated using ArcGIS 10. Maps for all section were provided by the Fortification Agency. Catchment areas were then manually drawn for each outlet into the Lule River. At the first stage the only variable used when drawing was general slopes. These were then corrected considering roads, ditches, roof shapes, downspouts and neighboring surfaces. Finally, field observations were made in order to verify these initial settings. When the catchment areas were drawn, areal size of each surface type within each catchment area was calculated by the program.

Volumes of pollutants where calculated first by calculating volumes of water that every outlet contributes to the river per year. The catchment area of each outlet and its collected runoff coefficients were already calculated in calculations for the flow. These data were used to calculate average yearly volumes of water, with a correction for the precipitation that falls as snow. The amount of snow that eventually ends up in the pipes is separately calculated. This is because other variables have to be taken into account there, such as snow removal and evaporation. For the area around Boden, on average 40% of the annual precipitation falls as snow (SMHI, 2013). The rain that end up in the storm water pipes can be considered unaffected by the evaporation. During the time of rainfall, very little evaporation takes place and infiltration has already been taken into consideration in the runoff coefficient.

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9 Values for rainfall were taken from SMHIs tool air web (http://luftwebb.smhi.se). The tool calculates the precipitation at any given point in Sweden by interpolating the measured data from surrounding stations. The tool gives daily, monthly and annual values for temperature and precipitation. Corrections have already been made for precipitation loss because of wind. A 50-year long dataset (1961-2011) was used, which is also the longest available. From this data the mean annual precipitation was calculated.

The second part of pollutant calculations was to link these volumes of water with concentrations of pollutants. The later proved very hard to do, since reliable results were requested. When calculating volumes of water it has to be taken into

consideration the amount of precipitation that falls as snow.

Snow

When considering the amount of snow that contributes to the storm water,

evaporation has a smaller role than one might expect. In a study done by the Finnish hydrological bureau evaporation for 6 years where measured. During this time, on average only 3% of the snow evaporated each year (Finlands miljöcentral, 2013). How the snow melts is of importance to see, since it could potentially have an impact on the maximum runoff values. The idea is to get values for how many days the snow melted for a number of years. Also how much snow was melted for the same year. It is then possible to see both mean and maximum values for melted snow per day. The HBV-model was used to calculate the snow melting process. For using the HBV-model only two variables were needed. The daily precipitation and temperature. These data have already been collected with SMHIs tool luftweb. Each day is calculated based on two equations. The first is whether precipitation will add to the snowpack or if it falls as rain. This is done by setting a critical point in the temperature data, below which, precipitation will fall as snow. The second equation is for all values above the critical temperature. When these

temperatures occur, the snow will melt. Description

CFMAX= degree-day factor (mm oC-1 day-1)

This was set to 4 mm oC-1 day-1

CFR = refreezing coefficient

This was set to 0.05

TT = threshold temperature (oC)

For further explanation of the method see HBV-light manual. Available at

http://people.su.se/~jseib/HBV/HBV_manual_2005.pdf

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10 days between these two values was calculated. The maximum snow pack was then divided by the amount of days to get an average snow melt per day for that year. There are also tabled values for this done by Swedish Water in the P90 publication. These values are for two towns, both about 40km from Boden. The towns are Luleå and Råneå. They are both coastal cities while Boden is about 40 km from the coast. For Rånea the maximum melt for a single day was 26 mm and 6,0 l/s/ha. For

seventeen consecutive days the maximum was 11,2 mm/day and 2,6 l/s/ha. For Luleå the maximum values for a single day were 24,2 mm/day and 5,6 l/s/ha, and for seventeen consecutive days, maximum melt was 10,1 mm/day and 2,3 l/s/ha

(Svenskt vatten, 2004). These values were based on data for a ten year period.

Rainwater analysis

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Results

General rain intensity’s

calculated according to Dahlström (equation 2): Table 3. General rain intensities in l/s/hectare

Return period

(Years) Duration of rainfall

5 min 10 min 15 min 30 min 1 hour 2 hours

0.5 116.8 85.2 67.8 43.9 27.6 17.2 1 146.6 106.9 84.9 54.8 34.2 21.1 2 184.2 134.1 106.5 68.5 42.6 26.1 5 249.3 181.3 143.8 92.3 57.1 34.7 10 313.5 228.0 180.6 115.7 71.4 43.1 20 394.5 286.7 227.0 145.3 89.4 53.8 50 534.7 388.4 307.4 196.5 120.7 72.4 100 673.2 488.8 386.8 247.0 151.5 90.6

Here displayed in a chart:

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Maximum flow

Fig 4. Study area with marks for every outlet into the river

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1. SIB-outlet (K685 B2001)

Table 4. Maximum flow in l/s, depending on rain intensity Return

period

5 min 10 min 15 min 30 min 1 hour 2 hours

0.5 120.1 87.7 69.7 45.2 28.4 17.6 1 150.8 109.9 87.3 56.4 35.2 21.7 2 189.4 138.0 109.5 70.5 43.8 26.8 5 256.4 186.5 147.9 94.9 58.7 35.6 10 322.5 234.5 185.8 119.0 73.4 44.4 20 405.8 294.9 233.5 149.4 92.0 55.4 50 550.0 399.5 316.2 202.1 124.1 74.4 100 692.4 502.8 397.8 254.1 155.9 93.2

The maximum flow at the outlet, depending on different rain intensities (l/s) Oil trap

No oil trap

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2. Heating plant (K108 B2004)

period

5 min C 10 min A+B+C 15 min A+B+C 30 min A+B+C 1 hour A+B+C 2 hours A+B+C 0.5 156.2 206.9 164.6 106.6 66.9 41.7 1 196.1 259.5 206.1 133.0 83.1 51.2 2 246.4 325.7 258.4 166.3 103.4 63.3 5 333.4 440.2 349.0 224.0 138.6 84.1 10 419.4 553.4 438.5 280.9 173.4 104.7 20 527.7 696.0 551.2 352.7 217.2 130.7 50 715.3 942.9 746.3 477.0 293.0 175.7 100 900.5 1186.7 939.0 599.7 367.9 220.1

The letters stand for which sections were added for the different durations. For the 5 minute duration, sections with a travel time below 5 minutes were added. For a detailed view see appendix 3.

Oil trap No oil trap

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3. I19 Top (K105 B2284)

period

5 min A+B 10 min A+B 15 min A+B 30 min A+B 1 hour A+B 2 hours A+B 0.5 304.7 222.5 176.9 114.6 71.9 44.8 1 382.6 278.9 221.6 143.0 89.3 55.1 2 480.6 350.1 277.8 178.8 111.1 68.0 5 650.5 473.2 375.2 240.8 149.0 90.4 10 818.2 594.9 471.3 302.0 186.3 112.6 20 1029.5 748.1 592.5 379.1 233.4 140.5 50 1395.4 1013.5 802.2 512.7 314.9 188.8 100 1756.7 1275.6 1009.4 644.6 395.4 236.5

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4. I19 Mid (K105 B2286)

Table 7. Maximum flow in l/s, depending on rain intensity Return period

5 min A+B 10 min A+B 15 min A+B 30 min A+B 1 hour A+B 2 hours A+B 0.5 232.9 170.0 135.2 87.6 55.0 34.2 1 292.4 213.2 169.4 109.3 68.3 42.1 2 367.4 267.6 212.3 136.7 85.0 52.0 5 497.2 361.7 286.8 184.0 113.9 69.1 10 625.4 454.7 360.3 230.8 142.4 86.1 20 786.9 571.9 452.9 289.8 178.4 107.4 50 1066.6 774.7 613.2 391.9 240.7 144.3 100 1342.7 975.0 771.5 492.7 302.3 180.8

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5. I19 Bottom (K105 O92P)

Table 8. Maximum flow in l/s, depending on rain intensity Return period

5 min A 10 min A+B 15 min A+B+C 30 min A+B+C 1 hour A+B+C 2 hours A+B+C 0.5 277.9 564.1 501.8 324.9 204.0 127.0 1 348.9 707.2 628.4 405.5 253.2 156.1 2 438.4 887.6 787.8 507.0 315.2 192.9 5 593.3 1199.9 1064.0 682.9 422.5 256.5 10 746.2 1508.4 1336.7 856.5 528.5 319.3 20 939.0 1897.0 1680.3 1075.3 662.0 398.5 50 1272.7 2569.9 2275.2 1454.1 893.2 535.5 100 1602.2 3234.5 2862.7 1828.2 1121.5 670.9

Oil trap

092P - LOA 3000

Cleaning capacity, these values are estimations based on the closest model up and down in size.

- Class I 100 l/s - Class II 200 l/s Hydraulic capacity – 2000 l/s

Fig 9. Maximum flows for outlet 5 depending on rain intensity (l/s)

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6. P5 Top (K107 OA 092R)

Table 9. Maximum flow in l/s, depending on rain intensity

Return period (Years)

Duration of rainfall

5 min 10 min 15 min 30 min 1 hour 2 hours 0.5 740.0 540.2 429.7 278.2 174.7 108.7 1 929.0 677.3 538.1 347.2 216.8 133.7 2 1167.2 850.1 674.6 434.2 269.9 165.2 5 1579.7 1149.2 911.1 584.7 361.8 219.6 10 1986.9 1444.7 1144.6 733.4 452.5 273.4 20 2500.1 1816.9 1438.8 920.7 566.9 341.2 50 3388.6 2461.3 1948.2 1245.1 764.8 458.6 100 4266.1 3097.8 2451.3 1565.5 960.3 574.5

092R - Data missing, old oil trap

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7. P5 Mid (K107 OA 092X)

Return period

5 min 10 min 15 min 30 min 1 hour 2 hours 0.5 239.0 174.5 138.8 89.9 56.4 35.1 1 300.1 218.8 173.8 112.1 70.0 43.2 2 377.0 274.6 217.9 140.2 87.2 53.3 5 510.2 371.2 294.3 188.9 116.9 70.9 10 641.8 466.6 369.7 236.9 146.2 88.3 20 807.5 586.8 464.7 297.4 183.1 110.2 50 1094.5 795.0 629.3 402.2 247.0 148.1 100 1377.9 1000.6 791.8 505.6 310.2 185.5

The maximum flow at the outlet, depending on different rain intensities (l/s) Oil trap 092X - Alfa lamella OA 2800 Cleaning capacity - Class I 97 l/s - Class II 195 l/s Hydraulic capacity – 1500 l/s

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8. P5 Bottom (K107 OA 092Y)

Return period

0.5 135.4 98.9 78.6 50.9 32.0 19.9 1 170.0 123.9 98.5 63.5 39.7 24.5 2 213.6 155.6 123.4 79.4 49.4 30.2 5 289.1 210.3 166.7 107.0 66.2 40.2 10 363.6 264.4 209.4 134.2 82.8 50.0 20 457.5 332.5 263.3 168.5 103.7 62.4 50 620.1 450.4 356.5 227.8 140.0 83.9 100 780.6 566.8 448.5 286.5 175.7 105.1

092Y - Alfa lamella OA 2500 Cleaning capacity

- Class I 58 l/s - Class II 117 l/s Hydraulic capacity – 900 l/s

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9. A9 (K106 B2198)

Return period

5 min 10 min A+B 15 min A+B 30 min A+B 1 hour A+B 2 hours A+B 0.5 426.6 A 441.0 350.7 227.1 142.6 88.8 1 535.6 A 552.9 439.2 283.4 177.0 109.1 2 952.8 A+B 693.9 550.7 354.4 220.3 134.8 5 1289.4 A+B 938.1 743.7 477.3 295.3 179.3 10 1621.8 A+B 1179.2 934.3 598.6 369.4 223.2 20 2040.7 A+B 1483.0 1174.4 751.6 462.7 278.5 50 2766.0 A+B 2009.1 1590.2 1016.3 624.3 374.3 100 3482.2 A+B 2528.6 2000.9 1277.8 783.9 468.9

Oil trap

No oil trap

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10. P49 (K0116 Well 2012)

Return period

0.5 100.8 73.6 58.5 37.9 23.8 14.8 1 126.5 92.2 73.3 47.3 29.5 18.2 2 158.9 115.7 91.9 59.1 36.8 22.5 5 215.1 156.5 124.1 79.6 49.3 29.9 10 270.5 196.7 155.8 99.9 61.6 37.2 20 340.4 247.4 195.9 125.4 77.2 46.5 50 461.4 335.1 265.3 169.5 104.1 62.4 100 580.9 421.8 333.8 213.2 130.8 78.2

No oil trap

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Water and snow volumes

Water

Table 14. Volumes of water/year from rain in each outlet

Area (tot) Collected coefficient Water (m3) SIB 26879.7 0.3835 3614.4 Heating Plant 57816.6 0.4214 8543.3 I19 Topp 36515.9 0.7146 9150.3 I19 Mid 41470.2 0.4810 6994.1 I19 Bottom 132839.2 0.5571 25950.7 A9 106613.7 0.4852 18138.1 P5 topp 111387.5 0.5689 22221.3 P5 mid 30807 0.6644 7177.5 P5 bottom 26689.7 0.4345 4066.2 P49 18158.5 0.4752 3025.6 Snow

In the snow calculations regards have been taken to snow removal. 80% of the snow on asphalt, concrete, plates and gravel has been removed from the area.

Table 15. Amount of water as snow in each area/year without evaporation Snow (m3) SIB 1215,9 Heating Plant 2873,1 I19 Topp 2318,3 I19 Mid 2273,4 I19 Bottom 8599,5 A9 6439,5 P5 topp 6960,4 P5 mid 1711,3 P5 bottom 952,4 P49 2017,1

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24 Table 16. From the peak snow accumulation to the first snow -free days

water contributed by snow (mm) date of maximum snow accumulation Number of

days mm/day mm/hour

199.4 1962-04-06 21 9.50 0.79 68.0 1963-04-05 12 5.66 0.47 163.2 1964-04-12 23 7.10 0.59 377.4 1965-04-11 29 13.01 1.08 276.7 1966-04-24 22 12.58 1.05 224.3 1967-03-05 63 3.56 0.30 125.5 1968-03-24 28 4.48 0.37 192.1 1969-04-03 38 5.06 0.42 238.9 1970-04-29 13 18.37 1.53 124.3 1971-03-30 38 3.27 0.27 224.6 1973-03-09 62 3.62 0.30 241.4 1975-02-02 90 2.68 0.22 193.0 1976-04-09 27 7.15 0.60 208.2 1977-04-26 19 10.96 0.91 149.7 1978-03-28 47 3.18 0.27 177.0 1979-03-26 47 3.77 0.31 184.6 1980-03-29 31 5.96 0.50 269.9 1981-03-29 44 6.13 0.51 183.7 1982-03-15 44 4.17 0.35 206.7 1983-04-04 25 8.27 0.69 225.9 1984-04-04 25 9.03 0.75 176.7 1985-04-13 28 6.31 0.53 144.1 1986-03-14 51 2.83 0.24 130.4 1987-03-28 32 4.07 0.34 297.2 1988-03-30 42 7.08 0.59 133.4 1989-04-04 18 7.41 0.62 143.7 1990-02-19 49 2.93 0.24 148.1 1991-03-25 29 5.11 0.43 194.1 1993-03-13 49 3.96 0.33 316.5 1994-03-29 37 8.55 0.71 177.2 1997-04-12 29 6.11 0.51 252.4 1998-03-21 49 5.15 0.43 261.3 2000-04-05 28 9.33 0.78 141.9 2001-04-18 14 10.13 0.84 117.9 2002-03-25 20 5.90 0.49 169.4 2004-03-29 31 5.46 0.46 190.0 2005-03-23 35 5.43 0.45 147.0 2006-04-07 21 7.00 0.58 114.4 2007-03-11 32 3.58 0.30 164.3 2008-04-15 16 10.27 0.86 202.6 2009-04-08 22 9.21 0.77 228.8 2010-04-01 33 6.93 0.58 169.0 2011-03-01 47 3.60 0.30 192.4 Average 34.0 6.6 0.55

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25

Fig 15. Monthly snow accumulation (red), storm water due to rainfall (blue),

storm water due to snow -melt (green)

Values for precipitation that actually contribute to the storm water outlet nr 1 (SIB). The values are corrected for runoff coefficients but only the green bar is corrected for the 80% snow removal from trafficated surfaces. The dividing between red and blue is only the general months when snow occurs, to illustrate months with definitely only rain. In the calculations, true snow values have been used from the HBV-model previously explained. These values have roughly the same relationship between months in all of the outlets, even if other outlets have higher numbers. Also note that May is not always the month for all of the snow melt, but on average melting starts mid-april and last for 34 days, which equals a month. A month therefore had to be chosen in order to present the data clearly.

Rain water analysis

Table 17. Rain water analysis

Anions F- ppb Cl- ppb Br- ppb NO3- ppb SO42- ppb Ox2- ppb 102.391 172.389 1.416 631.917 5401.374 11.416 Cations Na+ ppb K+ ppb Ca2+ ppb Mg2+ ppb 3084.741 122.045 712.127 139.097

The analysis included all metals but most was in too low concentrations to be detected. 0 10 20 30 40 50 60

Jan Feb Mar Apr Maj Jun Jul Aug Sep Okt Nov Dec

[m

m

] Snowmelt

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26

Initial tests

Table 18. Quantified pollution from storm water analysis June 20t h -12

ELEMENT SAMPLE 4. K105 B2286 3. K105 B2284 5. K105 O92P 9. K106 B2198 6. K107 OA 092R 7. K107 OA 092X 8. K107 OA 092Y 2. K108 B2004 1. K685 B2001 pH 0.28 Conductivity mS/m TOC kg/year 0.08 0.07 N-tot kg/year 82.29 23.29 879.00 P-tot kg/year 1.17 0.64 8.26 1.55 0.58 As kg/year 0.92 Cd kg/year 0.01 0.04 Co kg/year Cr kg/year Cu kg/year 0.64 0.35 0.83 Mo kg/year Ni kg/year Pb kg/year 0.20 1.86 Zn kg/year 0.45 3.39 2.73 1.09 0.38 0.39 10.77

Calculated yearly volumes in each outlet from the concentrations tested in June 2012. Outlet nr 10 had no water in the pipes when samples were collected. Only concentrations above or close to benchmarks were calculated.

Table 19. Quantified pollution from storm water analysis September 27t h -12

ELEMENT SAMPLE 4. K105 B2286 3. K105 B2284 5. K105 O92P 9. K106 B2198 6. K107 OA 092R 7. K107 OA 092X 8. K107 OA 092Y 2. K108 B2004 1. K685 B2001 10. K0116 Well 2012 N-tot kg/year 51.90 P-tot kg/year 2.93 23.60 5.37 1.48 7.71 1.54 Zn kg/year 0.29 1.03 0.31 0.20

Calculated yearly volumes in each outlet from the concentrations tested in September 2012. Only concentrations above or close to benchmarks were calculated.

Discussion

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27 unknown. It is an old model and if the traps for other outlets could be used as an indicator, it is likely not dimensioned for the higher flows. Another outlet that could be of concern is outlet number 9, A9, especially for rainfall above 5 min duration and a 2 year return period since the outlet then uses the whole catchment area. This outlet has no oil trap installed which makes it as vulnerable no matter what intensity a rain has. A lack of capacity to clean 5 minute rain with 6 months return period is shared by all of the existing oil traps (outlet 5,7 and 8). This means that at least once every year, all of the existing traps will be unable to clean the storm water. One could however argue that the showers with a short duration will not be able to pick up all of the oil, meaning both oil spills and the general oil pollution from roads and asphalt surfaces. The oil trap unable to handle the largest number of flows has outlet 5 (I19 Bottom). This oil trap is only dimensioned for the showers with the lowest intensity. This means that most showers are going to wash out existing oil into the Lule river.

Two of the outlets, nr 2 (Heating Plant) and nr 5 (I19 Bottom), do not reach their maximum flow at rainfall with 5 minute duration. Since they both have sections with longer traveltime than this, they reach their maximum at 10 minute duration when all sections can contribute to the flow. The peak flow could be

somewhere in between since it was only calculated for 5 and 10 minute durations. In reality, they start contributing somewhere between and therefore have their peak flow below 10 minutes.

When installing the ISCO GLS and 2150 it is important that they record flows and take samples for the melting period. They should therefore be installed in the beginning of march when the melting could start. According to table 16 the melting could start as soon as late February. In the cases of early melt, the people installing the machines either has to be attentive for warmer weather or the

installation have to take place mid-February in what normally is freezing

temperatures. There is a clear connection between years with the most melting/day and a late start of the melt.

It is crucial for the results that catchment areas are correctly drawn. Hard ground is a common name for all surfaces that do not infiltrate water, such as asphalt and roof. By including sections of hard ground that will not contribute to the pipes, results could be misleading. For example a section of hard ground could be within reach of a well, but between the well and section is grass. The rain that fall on the hard ground would actually infiltrate the grass before reaching the well, and the maximum flow calculations would have a greater value than what is correct.

The pollutant loads are as previously described hard to decide because of an uncertainty in a true mean value for concentrations. Initial test will be

complemented with samples from the automatic sampler but even these values should be subject of further investigation. Initial samples show that there might be big differences in pollutants depending on the season. Likely because more pollutants have already been washed away in the fall.

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28 trafficated surfaces that lose their snow the likely most polluted snow is removed. This snow might still end up in the river when the temporary storages melt if they are close. The surfaces which have the storages could be subject of further investigation since pollutants will infiltrate the ground where piles are built.

It is clear from figure 14 that the snow melting period is the time of year that contributes the largest volumes of water, even with the extensive snow removal. It is however not likely that the peak flow will be reached during this period because of the melting contribution. Comparing the calculated maximum of 18,4 mm/day with Svenskt vatten’s tabled values for neighboring cities, 24,2 and 26,0 mm/day. Their values are higher but for a single day. The value in this thesis is an average for 13 consecutive days which means the maximum single day is likely much higher, but even 26,0 mm/day only gave 6,0 l/s/ha which becomes insignificant when adding to calculated rain intensities.

Conclusions

 Existing oil traps are only dimensioned for low rain intensities which mean they are vulnerable to accidental spills that coincide with heavy rain. Even without a spill they are not capable of handling the regular oil pollution from storm water should big amounts be picked up during heavy rain. Outlet 5 is likely not capable of cleaning any or at least very little of the storm water, regarding oil.

 The outlets without oil traps are in need of an evaluation regarding the risk of an oil spill.

 Maximum volumes of water come during the melt period but peak flows are not reached because of the melt.

 Existing water analysis are not sufficient for deciding quantities of pollutants but definitely raise concern for outlet 6 (P5 Top) and 2 (Heating plant).

Regarding outlet 2, definite concentrations of pollutants from the heating plant need to be set. Even though an exception has been made here, the initial analysis show values for nitrogen 30 times higher than the benchmark

maximum in concentrations. If these values are representative for the 30 000 m3 of water released from the heating plant. This means a staggering 870 kg of nitrogen is released into the Lule River each year. Fall samples were however not as bad. Further investigation is needed.

 The measures already taken, ordering automatic samplers, are definitely in the right direction. Even if definite quantities can’t be set even with these

machines, they will hopefully provide enough data to be able to decide whether further measures are needed, such as nitrogen- and phosphorus-filters.

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29

References

Malmqvist P-A, Svensson G, Fjellström C (1994) Dagvattnets sammansättning

VAV, VA-Forsk Rapport nr 1994-11

Svenskt Vatten AB (2004) Publikation P90, Stockholm: Ljungföretagen, 1st ed Lidström V (2012) Vårt vatten – Grundläggande lärobok i vatten- och avlopssteknik

Pp 129-147 1st ed, Svenskt Vatten AB, Solna

Dahlström B (2010) Rain intensity – a cloud physical contemplation Svenskt Vatten AB, Rapport nr 2010-05

Eriksson B (1981) Den potentiella evapotranspirationen i Sverige Rapport RMK 28, SMHI

Brandt M, Grahn G (1998) Avdunstning och avrinningskoefficient I Sverige,

1961-1990. Report Nr 73, SMHI

Naturvårdsverket (2007) Oljeavskiljare, FAKTA, 8283

Internet references

Swedish fortification agency, (2013)

http://www.fortv.se/?epslanguage=en&id=1229 2013-04-04 Rikstermsbanken, (2013)

http://www.rikstermbanken.se/rtb/visaTermpost.html?id=161091 2013-04-04 SMHI, Nederbördsdata i Sverige (2013)

http://www.smhi.se/klimatdata/meteorologi/nederbord/1.4172 2013-04-24 ISCO 2150 Area Velocity Module

http://www.isco.com/products/products3.asp?pl=2021010 2013-05-02 Finlands Miljöcentral

http://www.miljo.fi/default.asp?contentid=68807&lan=sv 2013-05-13 SMHI, Normal medelvattenföring i Sverige

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30

Appendix

Appendix 1 – Surface areas for each outlet

Table 1. Catchment areas for each outlet with surface types percentage

Area (m2) Roof Asfalt Concrete Plates Gravel Grass Forest Meadow

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31

Appendix 2 – Catchment areas

1. SIB-outlet (K685 B2001)

(39)

32

2.1. Heating plant top (K108 B2004)

(40)

33

2.2 Heating plant bottom (K108 B2004)

(41)

34

3. I19 Top (K105 B2284)

(42)

35

4. I19 Mid (K105 B2286)

(43)

36

5.1. I19 Bottom Top (K105 O92P)

(44)

37

5.2. I19 Bottom Mid (K105 O92P)

(45)

38

5.3. I19 Bottom Bottom (K105 O92P)

(46)

39

6. P5 Top (K107 OA 092R)

(47)

40

7. P5 Mid (K107 OA 092X)

(48)

41

8. P5 Bottom (K107 OA 092Y)

(49)

42

9.1. A9 Big (K106 B2198)

(50)

43

9.2. A9 Small (K106 B2198)

(51)

44

10. P49 (K0116 Well 2012)

(52)

45

Appendix 3 – Data for maximum flow

SIB-outlet (K685 B2001)

Travel time Table 3.1 Return period

0.5 4.4 4.9 5.2 6.0 7.0 8.1 1 4.1 4.5 4.9 5.6 6.5 7.6 2 3.8 4.2 4.5 5.2 6.1 7.1 5 3.5 3.8 4.1 4.8 5.5 6.5 10 3.2 3.6 3.8 4.4 5.2 6.1 20 3.0 3.3 3.6 4.1 4.8 5.6 50 2.7 3.0 3.2 3.7 4.4 5.1 100 2.5 2.8 3.0 3.5 4.1 4.8

The time it takes for the whole section to contribute water to the outlet, with different rain intensities Runoff Table 3.2 Return period

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46

Heating plant (K108 B2004)

Travel time Top2 A Table 3.3 Return period

0.5 7.1 7.4 7.6 8.1 8.7 9.5 1 6.9 7.1 7.4 7.8 8.4 9.1 2 6.7 6.9 7.1 7.6 8.1 8.8 5 6.4 6.7 6.9 7.3 7.8 8.4 10 6.3 6.5 6.7 7.1 7.5 8.1 20 6.1 6.3 6.5 6.9 7.3 7.9 50 5.9 6.1 6.3 6.6 7.0 7.5 100 5.8 6.0 6.1 6.4 6.8 7.3

(54)

47 Top1 (longest) B Table 3.4 Return period

0.5 8.7 9.0 9.2 9.6 10.2 10.9 1 8.5 8.8 9.0 9.4 9.9 10.6 2 8.3 8.6 8.8 9.2 9.7 10.3 5 8.1 8.3 8.5 8.9 9.3 9.9 10 8.0 8.2 8.4 8.7 9.1 9.6 20 7.9 8.0 8.2 8.5 8.9 9.4 50 7.7 7.9 8.0 8.3 8.7 9.1 100 7.6 7.7 7.9 8.1 8.5 8.9

The time it takes for the whole section to contribute water to the outlet, with different rain intensities Bottom C Table 3.5 Return period

0.5 3.6 4.0 4.3 4.9 5.7 6.6 1 3.3 3.7 4.0 4.6 5.3 6.2 2 3.1 3.4 3.7 4.2 4.9 5.8 5 2.8 3.1 3.4 3.9 4.5 5.3 10 2.6 2.9 3.1 3.6 4.2 4.9 20 2.4 2.7 2.9 3.3 3.9 4.6 50 2.2 2.4 2.6 3.0 3.5 4.2 100 2.0 2.3 2.4 2.8 3.3 3.9

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48 Runoff Topp2 Table 3.6 Return period

0.5 83.0 60.6 48.2 31.2 19.6 12.2 1 104.2 76.0 60.4 38.9 24.3 15.0 2 130.9 95.4 75.7 48.7 30.3 18.5 5 177.2 128.9 102.2 65.6 40.6 24.6 10 222.9 162.1 128.4 82.3 50.8 30.7 20 280.5 203.8 161.4 103.3 63.6 38.3 50 380.1 276.1 218.5 139.7 85.8 51.4 100 478.6 347.5 275.0 175.6 107.7 64.4

The sections contribution to the water flow, depending on different rain intensities (l/s)

Topp1 (Longest) Table 3.7 Return period (Years) Duration of rainfall

0.5 44.3 32.3 25.7 16.6 10.5 6.5 1 55.6 40.5 32.2 20.8 13.0 8.0 2 69.8 50.8 40.4 26.0 16.1 9.9 5 94.5 68.7 54.5 35.0 21.6 13.1 10 118.9 86.4 68.5 43.9 27.1 16.4 20 149.5 108.7 86.1 55.1 33.9 20.4 50 202.7 147.2 116.5 74.5 45.7 27.4 100 255.2 185.3 146.6 93.6 57.4 34.4

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49 Bottom Table 3.8 Return period

0.5 156.2 114.0 90.7 58.7 36.9 23.0 1 196.1 143.0 113.6 73.3 45.8 28.2 2 246.4 179.4 142.4 91.6 57.0 34.9 5 333.4 242.6 192.3 123.4 76.4 46.4 10 419.4 304.9 241.6 154.8 95.5 57.7 20 527.7 383.5 303.7 194.4 119.7 72.0 50 715.3 519.5 411.2 262.8 161.4 96.8 100 900.5 653.9 517.4 330.4 202.7 121.3

Total

For * minutes duration, all sections with a travel time below * minutes were added Table 3.9

Return period

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50

I19 Top (K105 B2284)

Travel time Top (Longest) A Table 3.10 Return period

0.5 4.6 4.9 5.1 5.7 6.3 7.1 1 4.3 4.6 4.9 5.4 6.0 6.8 2 4.2 4.4 4.7 5.1 5.7 6.4 5 3.9 4.2 4.4 4.8 5.3 6.0 10 3.7 4.0 4.2 4.6 5.1 5.7 20 3.6 3.8 4.0 4.4 4.8 5.4 50 3.4 3.6 3.8 4.1 4.5 5.1 100 3.3 3.5 3.6 3.9 4.3 4.8

The time it takes for the whole section to contribute water to the outlet, with different rain intensities Bottom B Table 3.11 Return period

0.5 2.7 3.0 3.3 3.7 4.4 5.1 1 2.5 2.8 3.0 3.5 4.1 4.7 2 2.4 2.6 2.8 3.3 3.8 4.4 5 2.2 2.4 2.6 3.0 3.4 4.0 10 2.0 2.2 2.4 2.7 3.2 3.8 20 1.9 2.1 2.2 2.6 3.0 3.5 50 1.7 1.9 2.0 2.3 2.7 3.2 100 1.6 1.7 1.9 2.2 2.5 3.0

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51 Runoff Top (Longest) Table 3.12 Return period

Bottom

Table 3.13 Return period

0.5 108.6 79.3 63.1 40.8 25.7 16.0 1 136.4 99.4 79.0 51.0 31.8 19.6 2 171.4 124.8 99.0 63.7 39.6 24.2 5 231.9 168.7 133.8 85.8 53.1 32.2 10 291.7 212.1 168.0 107.7 66.4 40.1 20 367.0 266.7 211.2 135.2 83.2 50.1 50 497.5 361.3 286.0 182.8 112.3 67.3 100 626.3 454.8 359.9 229.8 141.0 84.3

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52 Total

Return period

5 min A+B 10 min A+B 15 min A+B 30 min A+B 1 hour A+B 2 hours A+B 0.5 304.7 222.5 176.9 114.6 71.9 44.8 1 382.6 278.9 221.6 143.0 89.3 55.1 2 480.6 350.1 277.8 178.8 111.1 68.0 5 650.5 473.2 375.2 240.8 149.0 90.4 10 818.2 594.9 471.3 302.0 186.3 112.6 20 1029.5 748.1 592.5 379.1 233.4 140.5 50 1395.4 1013.5 802.2 512.7 314.9 188.8 100 1756.7 1275.6 1009.4 644.6 395.4 236.5 The total runoff at the outlet, depending on different rain intensities (l/s)

I19 Bottom (K105 O92P)

Travel time Top A Table 3.15 Return period

0.5 13.4 13.8 14.1 14.8 15.5 16.5 1 13.2 13.5 13.8 14.4 15.2 16.1 2 13.0 13.3 13.5 14.1 14.8 15.6 5 12.7 13.0 13.2 13.7 14.4 15.1 10 12.5 12.7 13.0 13.4 14.1 14.8 20 12.3 12.5 12.8 13.2 13.8 14.4 50 12.1 12.3 12.5 12.9 13.4 14.0 100 11.9 12.1 12.3 12.7 13.2 13.7

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53 Mid B Table 3.16 Return period

0.5 8.7 9.1 9.5 10.3 11.3 12.4 1 8.4 8.8 9.2 9.9 10.8 11.9 2 8.1 8.5 8.8 9.5 10.4 11.4 5 7.7 8.1 8.4 9.0 9.8 10.8 10 7.5 7.8 8.1 8.7 9.4 10.3 20 7.3 7.6 7.8 8.4 9.1 9.9 50 7.0 7.3 7.5 8.0 8.6 9.4 100 6.8 7.1 7.3 7.7 8.3 9.1

The time it takes for the whole section to contribute water to the outlet, with different rain intensities Bottom C Table 3.17 Return period

0.5 4.7 5.2 5.5 6.4 7.4 8.6 1 4.3 4.8 5.2 5.9 6.9 8.1 2 4.0 4.5 4.8 5.5 6.4 7.5 5 3.7 4.0 4.4 5.0 5.9 6.9 10 3.4 3.8 4.1 4.7 5.5 6.4 20 3.2 3.5 3.8 4.3 5.1 6.0 50 2.9 3.2 3.4 3.9 4.6 5.4 100 2.7 2.9 3.2 3.7 4.3 5.1

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54 Runoff Top Table 3.18 Return period (Years) Duration of rainfall

0.5 91.5 66.8 53.2 34.4 21.6 13.5 1 114.9 83.8 66.6 43.0 26.8 16.5 2 144.4 105.2 83.5 53.7 33.4 20.4 5 195.4 142.2 112.7 72.3 44.8 27.2 10 245.8 178.7 141.6 90.7 56.0 33.8 20 309.3 224.8 178.0 113.9 70.1 42.2 50 419.2 304.5 241.0 154.0 94.6 56.7 100 527.7 383.2 303.2 193.7 118.8 71.1

Mid

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55 Bottom

Total

Return period

(63)

56

I19 Mid (K105 B2286)

Travel time

Top (separated pipes) A

0.5 3.6 4.0 4.3 5.0 5.8 6.7 1 3.4 3.7 4.0 4.6 5.4 6.3 2 3.1 3.5 3.8 4.3 5.0 5.9 5 2.9 3.2 3.4 3.9 4.6 5.4 10 2.7 2.9 3.2 3.7 4.3 5.0 20 2.5 2.7 2.9 3.4 4.0 4.7 50 2.2 2.5 2.7 3.1 3.6 4.2 100 2.1 2.3 2.5 2.9 3.4 4.0

The time it takes for the whole section to contribute water to the outlet, with different rain intensities

Bottom (separated pipes) B

0.5 2.6 2.9 3.1 3.6 4.1 4.8 1 2.4 2.7 2.9 3.3 3.9 4.5 2 2.2 2.5 2.7 3.1 3.6 4.2 5 2.0 2.3 2.4 2.8 3.3 3.8 10 1.9 2.1 2.3 2.6 3.0 3.6 20 1.8 2.0 2.1 2.4 2.8 3.3 50 1.6 1.8 1.9 2.2 2.6 3.0 100 1.5 1.6 1.8 2.0 2.4 2.8

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57 Runoff Top Table 3.24 Return period

Bottom

Table 3.25 Return period

0.5 38.3 28.0 22.2 14.4 9.0 5.6 1 48.1 35.0 27.8 18.0 11.2 6.9 2 60.4 44.0 34.9 22.5 14.0 8.5 5 81.7 59.5 47.1 30.3 18.7 11.4 10 102.8 74.7 59.2 37.9 23.4 14.1 20 129.4 94.0 74.4 47.6 29.3 17.7 50 175.3 127.4 100.8 64.4 39.6 23.7 100 220.7 160.3 126.8 81.0 49.7 29.7

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58 Total

Return period

5 min A+B 10 min A+B 15 min A+B 30 min A+B 1 hour A+B 2 hours A+B 0.5 232.9 170.0 135.2 87.6 55.0 34.2 1 292.4 213.2 169.4 109.3 68.3 42.1 2 367.4 267.6 212.3 136.7 85.0 52.0 5 497.2 361.7 286.8 184.0 113.9 69.1 10 625.4 454.7 360.3 230.8 142.4 86.1 20 786.9 571.9 452.9 289.8 178.4 107.4 50 1066.6 774.7 613.2 391.9 240.7 144.3 100 1342.7 975.0 771.5 492.7 302.3 180.8 The total runoff at the outlet, depending on different rain intensities (l/s)

A9 (K106 B2198)

Travel time Top A Table 3.27 Return period

0.5 4.4 4.9 5.2 6.0 7.0 8.1 1 4.1 4.5 4.9 5.6 6.5 7.6 2 3.8 4.2 4.5 5.2 6.1 7.1 5 3.4 3.8 4.1 4.7 5.5 6.5 10 3.2 3.5 3.8 4.4 5.1 6.0 20 3.0 3.3 3.5 4.1 4.8 5.6 50 2.7 3.0 3.2 3.7 4.3 5.1 100 2.5 2.8 3.0 3.5 4.0 4.8

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59 Bottom B Table 3.28 Return period

0.5 5.7 6.3 6.8 7.8 9.1 10.5 1 5.3 5.9 6.3 7.3 8.5 9.9 2 4.9 5.5 5.9 6.8 7.9 9.2 5 4.5 5.0 5.3 6.2 7.2 8.4 10 4.2 4.6 5.0 5.7 6.7 7.8 20 3.9 4.3 4.6 5.3 6.2 7.3 50 3.5 3.9 4.2 4.8 5.6 6.7 100 3.3 3.6 3.9 4.5 5.2 6.2

The time it takes for the whole section to contribute water to the outlet, with different rain intensities Runoff Top Table 3.29 Return period

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60 Bottom Table 3.30 Return period (Years) Duration of rainfall

Total

Return period

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61

P5 Top (K107 OA 092R)

0.5 5.5 6.1 6.5 7.5 8.7 10.1 1 5.1 5.7 6.1 7.0 8.1 9.5 2 4.7 5.3 5.7 6.5 7.6 8.9 5 4.3 4.8 5.1 5.9 6.9 8.1 10 4.0 4.4 4.8 5.5 6.4 7.6 20 3.7 4.1 4.4 5.1 6.0 7.0 50 3.4 3.7 4.0 4.7 5.4 6.4 100 3.1 3.5 3.7 4.3 5.1 6.0

The time it takes for the whole section to contribute water to the outlet, with different rain intensities Runoff Table 3.33 Return period (Years) Duration of rainfall

(69)

62

P5 Mid (K107 OA 092X)

Traveltime Table 3.34 Return period

0.5 4.2 4.6 5.0 5.7 6.6 7.7 1 3.9 4.3 4.6 5.3 6.2 7.2 2 3.6 4.0 4.3 5.0 5.8 6.8 5 3.3 3.6 3.9 4.5 5.3 6.2 10 3.0 3.4 3.6 4.2 4.9 5.8 20 2.8 3.1 3.4 3.9 4.6 5.4 50 2.6 2.8 3.1 3.5 4.1 4.9 100 2.4 2.6 2.9 3.3 3.8 4.5

Runoff

Table 3.35

Return period

(70)

63

P5 Bottom (K107 OA 092Y)

0.5 3.4 3.8 4.1 4.7 5.4 6.3 1 3.2 3.5 3.8 4.4 5.1 5.9 2 3.0 3.3 3.5 4.1 4.7 5.5 5 2.7 3.0 3.2 3.7 4.3 5.1 10 2.5 2.8 3.0 3.4 4.0 4.7 20 2.3 2.6 2.8 3.2 3.7 4.4 50 2.1 2.3 2.5 2.9 3.4 4.0 100 2.0 2.2 2.3 2.7 3.2 3.7

Runoff

Table 3.37

Return period

(71)

64

P49 (K0116 Well 2012)

0.5 3.7 4.0 4.3 5.0 5.8 6.7 1 3.4 3.8 4.0 4.7 5.4 6.3 2 3.2 3.5 3.8 4.3 5.0 5.9 5 2.9 3.2 3.4 3.9 4.6 5.4 10 2.7 2.9 3.2 3.7 4.3 5.0 20 2.5 2.7 3.0 3.4 4.0 4.7 50 2.2 2.5 2.7 3.1 3.6 4.3 100 2.1 2.3 2.5 2.9 3.4 4.0

Runoff

Table 3.39

Return period

Pollution transport in the Boden garrison storm water

Självständigt arbete Nr 59

Pollution transport in the Boden

garrison storm water

Pollution transport in the Boden

garrison storm water

Jonathan A. Udén

Jonathan A. Udén

Självständigt arbete Nr 59

Pollution transport in the Boden

garrison storm water

Jonathan A Udén

Sammanfattning

Abstract

Contents

Background

Purpose/Goal

Storm water

Sources of pollution

Quantifying pollution

Recipient

Cleaning solutions

Method

Calculations

Rainwater analysis

Results

General rain intensity’s

Maximum flow

1. SIB-outlet (K685 B2001)

2. Heating plant (K108 B2004)

3. I19 Top (K105 B2284)

4. I19 Mid (K105 B2286)

5. I19 Bottom (K105 O92P)

6. P5 Top (K107 OA 092R)

7. P5 Mid (K107 OA 092X)

8. P5 Bottom (K107 OA 092Y)

9. A9 (K106 B2198)

10.

P49 (K0116 Well 2012)

Water and snow volumes

Rain water analysis

Initial tests

Discussion

Conclusions

References

Internet references

Appendix

Appendix 1 – Surface areas for each outlet

Appendix 2 – Catchment areas

Appendix 3 – Data for maximum flow

SIB-outlet (K685 B2001)

Heating plant (K108 B2004)

I19 Top (K105 B2284)

I19 Bottom (K105 O92P)

I19 Mid (K105 B2286)

A9 (K106 B2198)

P5 Top (K107 OA 092R)

P5 Mid (K107 OA 092X)

P5 Bottom (K107 OA 092Y)

P49 (K0116 Well 2012)