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Det är under all kritik att så många doktorander mår dåligt och inte färdigställer sina avhandlingar. Vi mer seniora akademiker har ett medmänskligt ansvar att etablera strukturer som tar hand om våra kollegor. Omtanke om våra doktoranders utveckling behövs då vi designar strukturer för doktorerande så väl som handledande. I denna artikel argumenterar jag för att omtanken om doktorandens utveckling bör vara en utgångspunkt i handledningsarbetet. Med hjälp av omtanke blir det enklare att: hitta rätt nivå för krav och mål, erkänna brister i handledarskapet, prata problem i satta maktrelationer, hantera utmaningar från den våldsamma akademin, med mera. Att bry sig om doktorandens utveckling innebär många olika saker under resans gång. Även om det i artikeln konkretiseras vad omtanke om doktorandens utveckling kan innebära, så är poängen snarare att det blir enklare att inse vad god handledning är, ifall omtanken om doktorandens väl är en utgångpunkt i handledningsarbetet., CARE AS A POINT OF DEPARTURE FOR ACADEMIC SUPERVISION It is appalling that so many doctoral students feel bad and do not complete their theses. We, more senior academics have a responsibility as fellow human beings to establish structures that take care of our colleagues. In this article, I argue that care for the doctoral student's development should be a starting point in supervision work. With care as a point of departure, it becomes easier to: find the right level for demands and goals, admit shortcomings in the supervision, talk about problems in fixed power relations, and deal with challenges from the violent academy, amongst others. Caring about the doctoral student's development means many different things during the journey. Even though the article details what care for the doctoral student's development can mean, the point is rather that it becomes easier to realize what good supervision is, if concern for the doctoral student's well-being is a starting point in the supervision work.Article Details

Exposure to excessive indoor radon causes around 500 lung cancer deaths in Sweden annually. However, until 2020, indoor radon measurements were only conducted in around 16% of Swedish single-family houses and 17% of multifamily houses. It is estimated that approximately 16% of single-family houses exceed the indoor radon reference level of 200 Bq/m3, and the corresponding situation in multifamily houses is unknown. Measuring indoor radon on an urban scale is complicated and costly. Statistical and machine learning, exploiting historical data for pattern identification, provides alternative approaches for assessing indoor radon risk in existing dwellings. By training MARS (Multivariate Adaptive Regression Splines) and Random Forest (RF) regression models with the data labels from the radon measurement records in the Swedish Energy Performance Certification registers, property registers, soil maps, and the radiometric grids, the correlations between response and predictive variables can be untangled. The interplay of the key features, including uranium and thorium concentrations, ventilation systems, construction year, basements, and the number of floors, and their impact magnitudes on indoor radon concentrations, are investigated in the study. The regression models tailored for different building classes were developed and evaluated. Despite the data complexity, the RF models can explain 28% of the variance in multifamily houses, 24% in all buildings, and 21% in single-family houses. To improve model fitting, more intricate supervised learning algorithms should be explored in the future. The study outcomes can contribute to prioritizing remediation measures for building stocks suspected of high indoor radon risk.

The presence of hazardous materials hinders the circular economy in construction and demolition waste management. However, traditional environmental investigations are costly and time-consuming, and thus lead to limited adoption. To deal with these challenges, the study investigated the possibility of employing registered records as input data to achieve in situ hazardous building materials management at a large scale. Through characterizing the eligible building groups in question, the risk of unexpected cost and delay due to acute abatement could be mitigated. Merging the national building registers and the environmental inventory from renovated and demolished buildings in the City of Gothenburg, a training dataset was created for data validation and statistical operations. Four types of inventories were evaluated to identify the building groups with adequate data size and data quality. The observations' representativeness was described by plotting the distribution of building features between the Gothenburg dataset and the training dataset. Evaluating the missing data and the positive detection rates affirmed that reports and protocols could locate hazardous materials in the building stock. The asbestos and polychlorinated biphenyl (PCB)-containing materials with high positive detection rates were highlighted and discussed. Moreover, the potential inventory types and building groups for future machine learning prediction were delineated through the cross-validation matrix. The novel study contributes to the method development for assessing the risk of residual hazardous materials in buildings., QC 20210825

Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential., (This article belongs to the Special Issue Energy Performance of Buildings)

The European building stock was renewed at a rapid pace during the period 1950–1975.In many European countries, the building stock from this time needs to be renovated, and thereare opportunities to introduce energy efficiency measures in the renovation process. Informationavailability and increasingly available analysis tools make it possible to assess the impact of policyand regulation. This article describes methods developed for analyzing investments in renovationand energy performance based on building ownership and inhabitant socio-economic informationdeveloped for Swedish authorities, to be used for the Swedish national renovations strategy in2019. This was done by analyzing measured energy usage and renovation investments made duringthe last 30 years, coupled with building specific official information of buildings and resident areacharacteristics, for multi-family dwellings in Gothenburg (N = 6319). The statistical analyses showthat more costly renovations lead to decreasing energy usage for heating, but buildings that havebeen renovated during the last decades have a higher energy usage when accounting for currentheating system, ownership, and resident socio-economic background. It is appropriate to includean affordability aspect in larger renovation projects since economically disadvantaged groups areover-represented in buildings with poorer energy performance.

In Sweden and many European countries, the building stock increased at a rapid pace during the period of 1950-1975. This aging building stock is in need of renovation which is a challenge and an opportunity in reaching the energy performance targets as well as decreasing societal inequities. The contribution of this thesis is to demonstrate how economic, social and resource usage challenges of renovating the building stock can be analyzed and described using comprehensive building-specific data. In this thesis, building ownership types, area socio-economic characteristics, building energy performance, and investments in renovation, are analyzed for the Gothenburg multi-family dwelling stock. Measured energy usage from the Swedish Energy Performance Certificate was used in the analysis. The data quality of the Energy Performance Certificate was assessed for the purpose of analyzing the building stock. The Energy Performance Certificates were matched with official building information from the Swedish Land Survey and area socio-economic information from Statistics Sweden. Using this dataset, rent increases due to renovations are estimated and compared. To broaden the analysis, a case study in a renovation project of multi-family dwellings in an economically disadvantaged area is also presented. In the case study renovation, the implemented energy usage reducing measure of volumetric billing of water was found to increase rents. The case study is used to exemplify how energy usage reducing renovation projects, with little consideration for social city development targets, can aggravate economic segregation. There is positive progress towards the 2020 targets of greenhouse gas emission reduction and improved energy efficiency in the Swedish housing sector; while there is little progress toward the Gothenburg city target to reduce segregation. The Swedish housing sector is rather part of the economic and geographic segregation process in Gothenburg. Renovation will be ne, Appended articles: I. Mangold M, Morrison G, Harder R, Hagbert P, and Rauch S. (2014) “TheTransformative Effect of the Introduction of Water Volumetric Billing in a DisadvantagedHousing Area in Sweden.” Water Policy 16 (5): 973-990. doi:10.2166/wp.2014.105. II. Mangold M, Österbring M, and Wallbaum H. (2015) “Review of Swedish ResidentialBuilding Stock Research.” International Journal of Environmental Sustainability, 11 (2).ISSN: 23251077. III. Mangold M, Österbring M, and Wallbaum H. (2015) “Handling Data UncertaintiesWhen Using Swedish Energy Performance Certificate Data to Describe Energy Usage inthe Building Stock.” Energy and Buildings 102: 328–36. IV. Mangold M, Österbring M, Wallbaum H, Tuvander L, and Femenias P. (2016) “SocioEconomic Impact of Renovation and Energy Retrofitting of the Gothenburg BuildingStock.” Energy and Buildings. 123: 41-49 doi:10.1016/j.enbuild.2016.04.033 V. Mangold M, Österbring M, Wallbaum H, Overland C, and Johansson T. “Buildingownership, renovation investments and energy performance - A study of multi-familydwellings in Gothenburg.” Submitted to Energy and Buildings. This thesis has been financially supported by Chalmers Infrastructure Engineering, SIRen, E2B2,Chalmers Area of Advance Energy, and the Swedish Research Council for Environment,Agricultural Sciences and Spatial Planning – Formas, within the Homes for Tomorrow project.

In low-income regions, drinking water is often derived from groundwater sources, which might spread diarrheal disease if they are microbiologically polluted. This study aimed to investigate the occurrence of fecal contamination in 147 improved groundwater sources in Juba, South Sudan and to assess potential contributing risk factors, based on bivariate statistical analysis. Thermotolerant coliforms (TTCs) were detected in 66% of the investigated sources, including 95 boreholes, breaching the health-based recommendations for drinking water. A significant association (p<. 0.05) was determined between the presence of TTCs and the depth of cumulative, long-term prior precipitation (both within the previous five days and within the past month). No such link was found to short-term rainfall, the presence of latrines or damages in the borehole apron. However, the risk factor analysis further suggested, to a lesser degree, that the local topography and on-site hygiene were additionally significant. In summary, the analysis indicated that an important contamination mechanism was fecal pollution of the contributing groundwater, which was unlikely due to the presence of latrines; instead, infiltration from contaminated surface water was more probable. The reduction in fecal sources in the environment in Juba is thus recommended, for example, through constructing latrines or designating protection areas near water sources. The study results contribute to the understanding of microbiological contamination of groundwater sources in areas with low incomes and high population densities, tropical climates and weathered basement complex environments, which are common in urban sub-Saharan Africa., QC 20150519

Quantifying differences in resource use and waste generation between individual households and exploring the reasons for the variations observed implies the need for disaggregated data on household activities and related physical flows. The collection of disaggregated data for water use, gas use, electricity use, and mobility has been reported in the literature and is normally achieved through sensors and computational algorithms. This study focuses on collecting disaggregated data for goods consumption and related waste generation at the level of individual households. To this end, two data collection approaches were devised and evaluated: (1) triangulating shopping receipt analysis and waste component analysis and (2) tracking goods consumption and waste generation using a smartphone. A case study on two households demonstrated that it is possible to collect quantitative data on goods consumption and related waste generation on a per unit basis for individual households. The study suggested that the type of data collected can be relevant in a number of different research contexts: eco-feedback; user-centered research; living-lab research; and life cycle impacts of household consumption. The approaches presented in this study are most applicable in the context of user-centered or living-lab research. For the other contexts, alternative data sources (e.g., retailers and producers) may be better suited to data collection on larger samples, though at a lesser level of detail, compared with the two data collection approaches devised and evaluated in this study.

As global and local visions for sustainable living environments are increasingly supported by policies and concrete practices in construction, the building and housing sector is seeking to mitigate its environmental impact as well as assume a greater social responsibility. The overarching policy objectives set to concretize what a sustainable housing development entails, however, tend to rely on equivocal terminology, allowing a varied interpretation by key industry practitioners. Though in line with an ecological modernization paradigm in policy, the promotion of a market-driven environmentalism in housing faces multiple challenges as varying interests and perspectives collide. Supported by empirical findings of a semi-structured interview study conducted with housing developers in a new ‘green’ urban district in Göteborg, Sweden, theoretical frameworks surrounding the paradoxical path towards a sustainable housing development are presented. Inconsistencies between outspoken ambitions; social dimensions; and the framing of efficiency in new housing are discussed. Possibilities for the housing sector are given in the recognition of new forms of development, where a systemic perspective is required in the alignment between how industry, policy and the market perceives housing development and what is actually sustainable., QC 20171108