Integrated decision-making about housing, energy and wellbeing: a qualitative system dynamics model.

BACKGROUND: The UK government has an ambitious goal to reduce carbon emissions from the housing stock through energy efficiency improvements. This single policy goal is a strong driver for change in the housing system, but comes with positive and negative "unintended consequences" across a broad range of outcomes for health, equity and environmental sustainability. The resulting policies are also already experiencing under-performance through a failure to consider housing as a complex system. This research aimed to move from considering disparate objectives of housing policies in isolation to mapping the links between environmental, economic, social and health outcomes as a complex system. We aimed to support a broad range of housing policy stakeholders to improve their understanding of housing as a complex system through a collaborative learning process. METHODS: We used participatory system dynamics modelling to develop a qualitative causal theory linking housing, energy and wellbeing. Qualitative interviews were followed by two interactive workshops to develop the model, involving representatives from national and local government, housing industries, non-government organisations, communities and academia. RESULTS: More than 50 stakeholders from 37 organisations participated. The process resulted in a shared understanding of wellbeing as it relates to housing; an agreed set of criteria against which to assess to future policy options; and a comprehensive set of causal loop diagrams describing the housing, energy and wellbeing system. The causal loop diagrams cover seven interconnected themes: community connection and quality of neighbourhoods; energy efficiency and climate change; fuel poverty and indoor temperature; household crowding; housing affordability; land ownership, value and development patterns; and ventilation and indoor air pollution. CONCLUSIONS: The collaborative learning process and the model have been useful for shifting the thinking of a wide range of housing stakeholders towards a more integrated approach to housing. The qualitative model has begun to improve the assessment of future policy options across a broad range of outcomes. Future work is needed to validate the model and increase its utility through computer simulation incorporating best quality data and evidence. Combining system dynamics modelling with other methods for weighing up policy options, as well as methods to support shifts in the conceptual frameworks underpinning policy, will be necessary to achieve shared housing goals across physical, mental, environmental, economic and social wellbeing.

Normal background levels of air and surface mould reserve in English residential building stock: a preliminary study towards benchmarks based on NAHA measurements.

This paper reports results obtained from a surface (both visually clean and dirty/dusty surfaces) and active (aggressive or activated) air testing scheme on 140 residential rooms in England, without visible water damage or mould growth, along with a few rooms with visible mould growth/water damage tested for comparison purposes. The aim was to establish normal background levels of mould in non-water-damaged interiors to benchmark a 'normal' indoor environment, and in turn when there is a need for further investigation, and, possibly, remediation. Air and surface mould was quantified based on the activity of ß-N-acetylhexosaminidase (EC 3.2.1.52; NAHA). The obtained readings showed a log-normal distribution. Ninety-eight percent of the samples obtained from visually clean surfaces were equal to or less than 25 relative fluorescence units (RFU), which is suggested to be the higher bound for the range which can be used as a success criterion for surface cleaning/remediation. Of samples obtained from visually dirty/dusty surfaces, around 98% were below 450 RFU, which is suggested to define the lower-bound for abnormally high levels of mould, rare even on dirty/dusty surfaces. Similarly, around 98% of the air samples were found to have 1700 RFU or below. Values above 1700 RFU are therefore deemed unlikely in a non-problem indoor environment and can be indicative of a possible problem inducing mould growth. The samples with values below 1700 were further divided into three proposed sub-categories. Finally, the obtained RFU values and the suggested benchmarks were compared to those obtained from 17 non-residential indoor environments tested previously in Copenhagen, and the benchmarks that are currently used in Danish national standards, and they were both found to be highly congruent, suggesting that local climate regimes and room functions might not be as influential on indoor mould levels as commonly thought, or that the nuances between England and Denmark in terms of these factors are not strong enough to lead to sizable changes in the typical indoor mould levels in these countries' building stocks.

Surface and passive/active air mould sampling: A testing exercise in a North London housing estate.

Despite indoor mould being one of the most common problems in residential properties in the UK, there are not any widely accepted methodologies for its measurement. This paper focusses on this problem of measurement and reports on the findings from a rigorous testing scheme carried out to quantify air and surface mould concentrations and particle counts within 71 rooms from 64 properties in North London, some with and some without visible mould. The aim was to investigate the potential of passive and active air sampling strategies (sampling from still and actively mixed air, respectively) to explain visible mould, and understand how home/room characteristics correlate with the obtained readings. Airborne mould levels were quantified using an Andersen sampler (passively and actively), as well as by a chemical method based on the quantification of the N-acetylhexosaminidase (NAHA) activity (actively), which was also used to quantify surface mould. The mould levels were then correlated against physical characteristics of the tested homes/rooms, collected by means of survey sheets developed as part of this study. The findings did not reveal any independent variable governing all or most of the response variables, but a complex analysis suggested that whether it is a house or a flat could depict mould levels in the air and on the surfaces. It was also shown that a robust testing protocol should combine air and surface based methods, and an active air sampling strategy leads to a more accurate appraisal of airborne mould levels. Finally, the results showed that while there is some correlation between visible mould (and other moisture induced problems such as condensation) and measured air mould concentrations, lack of visible mould within a room does not necessarily mean low air mould concentrations, and thus one should not rely solely on visual inspection.