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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

The Good- & Socially Sustainable Street, from a Human Perspective : Focusing on the Relationship between Physical Environments and Social Life, with Hornsgatan in Stockholm as a Case Study

Thurell, Erik January 2012 (has links)
With over 300 years serving as a traffic route it is no wonder that Hornsgatan has the role of a major ‘artery’ in today’s Stockholm. This thesis will analyze and investigate how the street environment on Hornsgatan is affecting the people spending time on the street, and how a better street life and street quality can be brought out when it comes to enhance the social aspects for the street and its people. Through methods such as observations of the street and interviews with business owners, the case study of Hornsgatan have been based upon some theories by famous theorists when it comes to improving streets (and urban life) in cities, e.g.; Appleyard (1981), Jacobs (1993) and Gehl (2010). The results from the case study and the interviews show that Hornsgatan have both factors of what a ‘good’ and socially sustainable street should have; mix-use, the presence of people and social activities, active ground floors and different traffic modes, and factors that contribute to a less attractive street life; the heavy traffic, lack of places to sit, lack of greenery and lack of social activities for/between people. To reply the question if Hornsgatan is a ‘good’ and socially sustainable street or not, the answer is both yes and no. As Appleyard (1981) states, “[…] there is no single perfect street […]” (Appleyard 1981, p. 245). Instead each street and its situation are unique. However, with guidelines and aspects for how a street can be improved, at least some help can be brought out for its improvements. / Urban Form and Social Behaviour Research Project
2

Optimering av dammbindning på Hornsgatan med NORTRIP modellen / Optimization of Dust-Binding on Hornsgatan with the Nortrip Model

Tomasdottir, Tora January 2019 (has links)
Populärvetenskaplig sammanfattning Optimering av dammbindning på Hornsgatan med NORTRIP modellen Massan av luftburna partiklar med en diameter mindre än 10 µm (PM10) är en av de tuffaste miljökvalitetsnormerna att uppnå i Sverige. PM10 kommer från flera olika källor, både naturliga som havssalt och sand, samt antropogena som vägslitage, däckslitage, bromsslitage och avgaser. En stor uppkomstkälla till PM10 i luften slitage på grund av dubbdäcksanvändning. Uppvirvlingen är som störst i mars och april efter att snön smält, temperaturen stigit och vägbanan torkat upp. För att minska PM10 halten i luften kan en dammbindande saltlösning med lägre fryspunkt än vatten läggas ut på vägbanan. I Stockholm används saltlösningen CMA (kalciummagnesiumnitrat). Det är en typ av salt med liten påverkan på den urbana miljön. Vintersäsongen 2016–2017 lades CMA ut tre gånger i veckan på några utvalda gator i Stockholm mellan november och maj. En av dessa gator är Hornsgatan, som har undersökts i denna rapport. CMA är dyrt och resurskrävande att lägga ut. För att optimera utläggningen av CMA i Stockholm har spridningsmodellen NORTRIP (non-exhaust road traffic induced particle emissions) använts. Modellen använder meteorologiska data, trafikdata och data rörande saltning, sandning och städning för att räkna ut halten PM10 som spridits till luften. Den här modellen har använts för att testa några olika dammbindande scenarion på Hornsgatan i vilka CMA har lagts ut. Det har också testats, i NORTRIP, om PM10 halten i luften skulle minska genom utläggning av vatten på vägen. Vatten lades endast ut i modellen efter 15 mars med antagandet att temperaturen inte skulle sjunka under 0 °C efter datumet ifråga. De olika scenariona var utformade för att se om det var möjligt att minimera användandet av CMA men ändå hålla nere PM10 halten i luften. Alla scenarion jämfördes med scenariot där varken CMA eller vatten lades ut för att jämföra om PM10 i luften minskade. Ett resultat visade att det var bättre att lägga ut CMA varje dag under dammiga perioder än att sikta in sig på bara de dammigaste dagarna. PM10 i luften 2016 minskade med 4,7% när de 45 dammigaste dagarna behandlades med CMA. Det kan jämföras med en minskning på 6,5% när CMA applicerades under dammiga perioder under samma år. En annan slutsats var att det ger större effekt att lägga ut CMA i mars och april än mellan november och mars. PM10 i luften 2016 minskade med 2,1% om man började lägga ut CMA 1 november som planerat, och med 1,7% om utläggningen började i slutet av februari, när den dammiga säsongen börjar. Det resulterar i att endast en liten minskning av PM10 halten uppnåddes genom att börja behandla vägbanan med CMA den 1 november istället för i slutet av februari. Att börja lägga ut CMA i slutet av februari istället för 1 november skulle minska kostnaderna betydligt för staden. Resultaten visade även att ett tunt lager vatten (0,3 mm) utlagt på vägbanan mellan ordinarie dagar för dammbindning hade en betydande effekt på PM10 halten i luften. Vid vattenutläggning mellan dagarna för CMA utläggning efter 15 mars 2016 minskar PM10 i luften under 2016 med 1,4% utöver vad den skulle minskat med om inget vatten lagts ut. Den här rapporten visar att det är möjligt att optimera utläggningen av CMA på Hornsgatan. / Abstract Optimization of dust-binding on Hornsgatan with the NORTRIP model The mass of airborne particles with a diameter smaller than 10 µm (PM10) is one of the most difficult environmental quality standards addressed in Sweden. PM10 particles originates from a variety of sources; natural, like sea salt and sand, and human made like road wear, tire wear, brake wear and exhaust. A significant source of PM10 in the air is the usage of studded tires. The suspension typically occurs in March and April when the snow layer melts, temperature rises and the streets dry. A dry street is crucial for the road dust to suspend into the air. A way to prevent road dust to suspend in to the air is spraying the road with a salt solution that does not freeze at temperatures below 0 °C. In Stockholm a dust-binding substance called CMA (Calcium Magnesium Acetate) is used. It is a of salt with minimal negative side effects on the urban environment. CMA was applied on some specific streets in Stockholm three times a week between November and May winter season 2016–2017. One of the streets that gets treated with CMA is Hornsgatan which is the topic of this paper. Dust-binding substances are expensive and time consuming to apply to the streets. To optimize the appliance of CMA in Stockholm a non-exhaust road traffic induced particle emissions (NORTRIP) model has been used. The model uses meteorological data, traffic data combined with data on salting, sanding and cleaning to calculate PM10 suspension to the air. This model has been used to test different dust-binding scenarios on Hornsgatan in which CMA was applied. It has also been tested, in NORTRIP, if spraying the road with water could have a reductive effect on PM10 in the air. Water was only added to the model after the 15th of March because it was assumed the temperature would not sink below 0 °C after this date. The different scenarios were formed to see if it was possible to minimize the usage of CMA and still keep the PM10 level low. All scenarios were compared with the scenario of not applying any CMA or water to see how much PM10 in the air was reduced. One result showed that it is better to apply CMA every day during dusty periods rather than just manage to target the dustiest days alone. PM10 in the air 2016 was reduced by 4.7% when the 45 dustiest days were treated with CMA. This could be compared to a 6.5% reduction when CMA was applied during dusty periods. Another conclusion made was that applying CMA in March and April has a greater effect then applying CMA in November, December, January and February. PM10 in the air 2016 was reduced by 2.1% if the CMA treatment started on the 1st of November as planned, and by 1.7% if the treatment started in the end of February when the dusty season starts. That means there is only a small decrease of PM10 if the appliance of CMA starts in the end of February rather than the 1st of November. Reducing the days of CMA treatment would reduce the cost significantly for the city. It was also shown that a thin layer of water (0.3 mm) applied to the street between ordinary dust-binding days has a significant effect on PM10 in the air. Adding water to the street in between days of dust-binding after the 15th of March 2016 reduced suspended PM10 2016 in the air by 1.5% beyond what it would have been reduced without the water. This paper shows that it is possible to optimize the appliance of CMA.
3

Analys av luftkvaliteten på Hornsgatan med hjälp av maskininlärning utifrån trafikflödesvariabler / Air Quality Analysis on Hornsgatan using Machine Learning with regards to Traffic Flow

Teurnberg, Ellinor January 2023 (has links)
Denna studie har syftet att undersöka sambandet mellan luftföroreningar och olika fordonsvariabler, såsom årsmodell, bränsletyp och fordonstyp, på Hornsgatan i Stockholm. Studien avser att besvara vilka faktorer som har störst inverkan på luftkvaliteten. Utförandet baseras på maskininlärningsalgoritmerna Random Forest och Support Vector Regression, vilka jämförs utifrån R² och RMSE. Modellerna skapade med Random Forest överträffar Support Vector Regression för de olika luftföroreningarna. Den modell som presterade bäst var modellen för kolmonoxid vilken hade ett R²-värde på 99.7%. Den modell som gav prediktioner med lägst R²-värde, 68.4%, var modellen för kvävedioxid. Överlag var resultaten goda i relation till tidigare studier. Utifrån modellerna diskuteras variablers inverkan och olika åtgärder som kan införas i Stockholm Stad och på Hornsgatan för att förbättra luftkvaliteten. / This study aims to investigate the relationship between multiple air pollution and different vehicle variables, such as vehicle year, fuel type and vehicle type, on Hornsgatan in Stockholm. The study intends to answer which factors have the greatest impact on air quality. The implementation is based on the two machine learning algorithms Random Forest and Support Vector Regression, which are compared based on R² and RMSE. The models created with Random Forest outperform Support Vector Regression for the various air pollutants. The best performing model was the carbon monoxide model which had an R²-value of 99.7%. The model that gave predictions with the lowest R²-value, 68.4%, was the model for nitrogen dioxide. Overall, the results were good in relation to previous studies. With regards to these models, the impact of variables and different measures that can be introduced in the City of Stockholm and on Hornsgatan to improve air quality are discussed.

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