Disparity Between Reality and Theoretical Models - Predicting Moisture and Mould Growth in New Zealand Houses

<p><b>A common goal amongst building practitioners is to create warmer, healthier, and drier houses. A key barrier to this is the presence of excessive moisture, the leading cause of mould growth within buildings. New Zealand Building Code Clause E3 ‘Internal Moisture’ has been set out to control internal moisture within a house, however, there is currently no prescribed method which practitioners can use to demonstrate compliance. Tools such as ASHRAE Standard 160 ‘Criteria for Moisture Control Design Analysis in Buildings’ can be used to predict internal conditions; however, studies have shown that such tools rely upon a range of possibly inappropriate assumptions and may not give accurate results. When looking specifically at ASHRAE Standard 160, the Indoor Design Temperature and Indoor Design Humidity application requires assumptions such as the presence of heating systems, a minimum heating setpoint, ventilation rates, and moisture generation rates of occupants. This research aimed to understand whether, considering the assumptions it makes, can ASHRAE Standard 160 be used in New Zealand to predict mould growth? It went on further to understand how the results produced by ASHRAE Standard 160 aligned with measured data?</b></p> <p>Using the yearlong records of New Zealand houses' external conditions (temperature and relative humidity) collected from the 2015 Pilot Housing Survey, two ‘Design Parameters’, the Indoor Design Temperature and Indoor Design Humidity (Simplified and Intermediate Method), were applied from ASHRAE Standard 160. These two ‘Design Parameters’ were the only two parameters assessed due to the limitations of the data that was able to be used from the Pilot Housing Survey. Other ‘Design Parameters’ in ASHRAE Standard 160 include exposure conditions and material properties, as well as a Full Parameter Calculation of Indoor Design Humidity, however, there was insufficient information from the 2015 Pilot Housing Survey to compare these parameters to. Having applied the Indoor Design Temperature and Indoor Design Humidity formula, year-long records of the same houses' internal conditions (temperature and relative humidity) were then used to identify discrepancies between the measured data and the theoretical conditions developed by ASHRAE Standard 160.</p> <p>To understand how discrepancies may be occurring, it was important first to understand the assumptions that ASHRAE Standard 160 is making when applying the Indoor Design Temperature and Indoor Design Humidity formula. The five most critical assumptions that these two ‘Design Parameter’ were implementing were:• A minimum heating setpoint of 21.1°C would be applied whenever the running 24- hour average outdoor temperature dropped below 18.3°C.</p> <p>• Under the Simplified Indoor Design Humidity, the indoor relative humidity was closely dependent on the running 24-hour average outdoor temperature.</p> <p>• The number of occupants in a house was dependant on the number of bedrooms within the house.</p> <p>• Each occupant generates approximately 3L per day.</p> <p>• The buildings' infiltration is either 0.2 ACH for a standard construction or 0.1 ACH foran airtight construction.</p> <p>Having compared and analysed the measured indoor conditions and the conditions outlined by ASHRAE Standard 160, a number of discrepancies became evident. This in turn suggested that the above assumptions that ASHRAE Standard 160 made in order to apply Indoor Design Temperature and Indoor Design Humidity (Simplified and Intermediate Method) are not reflective of New Zealand. The key conclusions from this research were:• The minimum heating setpoint of 21.1°C is not applicable in New Zealand houses.</p> <p>Instead, the application of the To24h + 2.8°C formula across all outdoor temperatures was favourable. Alternatively, further research could suggest a more applicable minimum heating setpoint for New Zealand.</p> <p>• Overall the Simplified Indoor Design Humidity is a more suitable method of determining the Indoor Design Humidity than the Intermediate Indoor Design HumidityIt was found that overall, the Simplified Indoor Design Humidity matched the measured indoor relative humidity better than the Intermediate Indoor Design Humidity. This was concluded to be due to the fact that the assumptions in the Intermediate Indoor Design Humidity did not reflect the reality of New Zealand houses. However, there is the possibility for the Intermediate Indoor Design Humidity to be altered to reflect the reality of New Zealand houses better.</p> <p>• The Intermediate Indoor Design Humidity parameters are altered to reflect the reality of New Zealand houses better.</p> <p>This research identified that the two main parameters, Design Moisture Generation and Design Ventilation Rate, do not reflect how New Zealanders occupy their houses. By undertaking further research into refining these parameters, the application of the Intermediate Indoor Design Humidity may become more suitable for New Zealand.</p> <p>Having identified discrepancies and the reasons for these discrepancies, this research began to investigate areas in which further research could improve the suitability of ASHRAE Standard 160 in New Zealand. This included additional information such as occupant moisture generation rates and any significant renovations on the houses, being gathered in future Pilot Housing Surveys. Further analysis could be undertaken on inputs such as the Moisture generation Rate and the Design Ventilation Rate by gathering this additional information. This in turn would allow for the alternative inputs to be analysed to understand how these ‘Design Parameters’ could be altered to reflect the reality of New Zealand houses better.</p>

The ageing trend and related socio-economic issues in Vietnam

Vietnam is among the most rapidly ageing countries in the world. Its ageing index of increased during the past 35 years. In 2019, the ageing index was 49% for the population aged 60 years and older and 33% for the population aged 65 and older. At the same time, the proportion of older people living alone has been increasing. The main objective of this study is to examine the problem of older people living alone in Vietnam using the logit regression and data from the 2014 Intercensal Population and Housing Survey. In 2014 the proportion of people living alone was 3.2% among people aged 60 years and older but was 16.4% among seniors aged 80 years and older. The regression analysis shows that women were more likely to live alone than men, and the probability of living alone was higher among older people with a lower assets level than those with a higher assets level.

The relationship between the built environment and subjective wellbeing – analysis of cross-sectional data from the English Housing Survey

This paper assesses how subjective wellbeing is related to housing and neighbourhood characteristics, controlling for personal variables. The secondary data analysis was based on the English Housing Survey, 2017: Housing Stock Data and the English Housing Survey: Fuel Poverty Dataset, 2017, collected in the period April 2016 to March 2018 (N = 9205). Subjective wellbeing was measured with four variables - life satisfaction, the perception of things being worthwhile in life, feeling happy and feeling anxious - that were dichotomized into low and high wellbeing. Logistic regression analysis showed that personal variables are most strongly related to wellbeing but that both housing and neighbourhood variables are also significantly related to it. Finding it difficult to keep the living room warm, being in fuel poverty, and finding it difficult to meet heating costs were associated with lower wellbeing. Low area satisfaction and not feeling safe were also significantly associated with lower wellbeing. The effects of variables are not constant across all four wellbeing measures used which raises the question ‘which wellbeing’ should be addressed. Results also showed that targeting householders with lowest wellbeing and hence in greatest need of wellbeing interventions based on publicly available data would be challenging. Finally, the research community needs to address methodological challenges around identifying the most appropriate covariates, defining wellbeing and considering the measurement of key variables.

Predicting Mobility: Who Is Forced to Move?

We use the 2013 American Housing Survey to examine which households are more likely to experience a forced move compared to a voluntary move. We examine how household vulnerability varies by racial and socioeconomic stratification, as well as other household demographics among homeowners and renters. We analyze household-level predictors of experiencing an inclusively defined forced move, including moves caused by disasters, private and government displacement, and eviction (for renters) or foreclosure (for homeowners). Comparing an inclusive definition of displacement to voluntary mobility, we find that lower levels of education, income, and the presence of a disabled household member increase the likelihood of displacement for homeowners. Among renters, the presence of children, older households, and being native-born increase the odds of displacement, but a female-headed household reduces them. When examining type-specific displacement, we find variation in who is most susceptible to experience a forced move.