It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.

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Opportunistic premise plumbing pathogens such as Pseudomonas aeruginosa and Naegleria fowleri are a growing concern in building water systems because of their potential risks to human health. The aim of this study was to determine the critical concentrations of P. aeruginosa and N. fowleri in water that are associated with meaningful public health risks. To determine these concentrations, a reverse quantitative microbial risk assessment (QMRA) was conducted. Environmental concentrations of P. aeruginosa and N. fowleri corresponding to the risk target of one micro-disability-adjusted life year (DALY) per person per year and 10−4 annual risks of illness were calculated for several applicable exposure scenarios. To calculate the concentration of P. aeruginosa, cleaning contact lenses with potentially contaminated tap water in the absence of an appropriate cleaning solution was considered. For N. fowleri, two exposure scenarios, recreational exposure (swimming) and nasal cleansing (via the use of a neti pot™ or similar device) were considered. The highest critical concentration for P. aeruginosa was found to be 33 CFU/L with a 95% confidence interval of (2.0, 118) for the drop exposure scenario using the 10−4 annual risk target. For N. fowleri, based on the DALY approach, critical concentrations were 0.000030 N. fowleri/L for swimming and 0.00000060 N. fowleri/L for neti pot™ use scenario. Considering heat inactivation, the critical concentration limits for P. aeruginosa using the DALY approach and the 10−4 annual risk target approach were found to be 0.55 CFU/L and 55 CFU/L, respectively. For N. fowleri, the 10−4 annual risk target approach resulted in 0.022 N. fowleri/L and the DALY approach resulted in 0.00000064 N. fowleri/L for the neti pot™ scenario. For P. aeruginosa, N50 (the median infective dose) and alpha (α) contributed the most and contact rates the least to the variability and uncertainty of the estimates for all the scenarios. For N. fowleri, N50 and contact rates contributed the most and α the least to the variability and uncertainty to calculate the concentrations for all the scenarios. The QMRA framework implemented in this research can be used to incorporate more information regarding opportunistic pathogens to inform management decisions, and to prioritize the best interventions regarding estimated reduction in infections caused by opportunistic pathogens.

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Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.