Background: Indoor air pollutants (IAPs) cause multiple health impacts. Prioritizing mitigation options that differentially affect individual pollutants and comparing IAPs with other environmental health hazards require a common metric of harm.Objectives: Our objective was to demonstrate a methodology to quantify and compare health impacts from IAPs. The methodology is needed to assess population health impacts of large-scale initiatives—including energy efficiency upgrades and ventilation standards—that affect indoor air quality (IAQ).Methods: Available disease incidence and disease impact models for specific pollutant–disease combinations were synthesized with data on measured concentrations to estimate the chronic heath impact, in disability-adjusted life-years (DALYs) lost, due to inhalation of a subset of IAPs in U.S. residences. Model results were compared with independent estimates of DALYs lost due to disease.Results: Particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5), acrolein, and formaldehyde accounted for the vast majority of DALY losses caused by IAPs considered in this analysis, with impacts on par or greater than estimates for secondhand tobacco smoke and radon. Confidence intervals of DALYs lost derived from epidemiology-based response functions are tighter than those derived from toxicology-based, interspecies extrapolations. Statistics on disease incidence in the United States indicate that the upper-bound confidence interval for aggregate IAP harm is implausibly high.Conclusions: The approach demonstrated in this study may be used to assess regional and national initiatives that affect IAQ at the population level. Cumulative health impacts from inhalation in U.S. residences of the IAPs assessed in this study are estimated at 400–1,100 DALYs lost annually per 100,000 persons.

The air leakage of a building envelope can be determined from fan pressurization measurements with a blower door. More than 70,000 air leakage measurements have been compiled into a database. In addition to air leakage, the database includes other important characteristics of the dwellings tested, such as floor area, year built, and location. There are also data for some houses on the presence of heating ducts, and floor/basement construction type.The purpose of this work is to identify house characteristics that can be used to predict air leakage. We found that the distribution of leakage normalized with floor area of the house is roughly lognormal. Year built and floor area are the two most significant factors to consider when predicting air leakage: older and smaller houses tend to have higher normalized leakage areas compared to newer and larger ones. Results from multiple linear regression of normalized leakage with respect to these two factors are presented for three types of houses: low-income, energy-efficient, and conventional. We demonstrate a method of using the regression model in conjunction with housing characteristics published by the US Census Bureau to derive a distribution that describes the air leakage of the single-family detached housing stock. Comparison of our estimates with published datasets of air exchange rates suggests that the regression model generates accurate estimates of air leakage distribution.

We present methods for analyzing commercial and industrial facility 15-minute-interval electric load data. These methods allow building managers to better understand their facility's electricity consumption over time and to compare it to other buildings, helping them to 'ask the right questions' to discover opportunities for demand response, energy efficiency, electricity waste elimination, and peak load management. We primarily focus on demand response. Methods discussed include graphical representations of electric load data, a regression-based electricity load model that uses a time-of-week indicator variable and a piecewise linear and continuous outdoor air temperature dependence, and the definition of various parameters that characterize facility electricity loads and demand response behavior. In the future, these methods could be translated into easy-to-use tools for building managers.

The performance metrics of airflow, sound, and combustion product capture efficiency (CE) were measured for a convenience sample of fifteen cooking exhaust devices, as installed in residences. Results were analyzed to quantify the impact of various device-and installationdependent parameters on CE. Measured maximum airflows were 70% or lower than values noted on product literature for 10 of the devices. Above-the-cooktop devices with flat bottom surfaces (no capture hood) -including exhaust fan/microwave combination appliances -were found to have much lower CE at similar flow rates, compared to devices with capture hoods. For almost all exhaust devices and especially for rear-mounted downdraft exhaust and microwaves, CE was substantially higher for back compared with front burner use. Flow rate, and the extent to which the exhaust device extends over the burners that are in use, also had a large effect on CE. A flow rate of 95 liters per second (200 cubic feet per minute) was necessary, but not sufficient, to attain capture efficiency in excess of 75% for the front burners. A-weighted sound levels in kitchens exceeded 56 dB when operating at the highest fan setting for all 14 devices evaluated for sound performance. Key WordsCarbon monoxide; Natural gas burners; Nitrogen dioxide; Range hood; Task ventilation; Unvented combustion. Practical ImplicationsNatural gas cooking burners and many cooking activities emit pollutants that can reach hazardous levels in homes. Venting range hoods and other cooking exhaust fans are thought to provide adequate protection when used. This study demonstrates that airflows of installed devices are often below advertised values and that less than half of the pollutants emitted by gas cooking burners are removed during many operational conditions. For many devices, achieving capture efficiencies that approach or exceed 75% requires operation at settings that produce prohibitive noise levels. While users can improve performance by preferentially using back burners, results suggest the need for improvements in hood designs to achieve high pollutant capture efficiencies at acceptable noise levels. CitationSinger BC, Delp WW, Apte MG, Price PN. (2012) Performance of installed cooking exhaust devices. Indoor Air, Online 02-Nov-2011.

This review examines current approximations and approaches that underlie the evaluation of transport properties for combustion modeling applications. Discussed in the review are: the intermolecular potential and its descriptive molecular parameters; various approaches to evaluating collision integrals; supporting data required for the evaluation of transport properties; commonly used computer programs for predicting transport properties; the quality of experimental measurements and their importance for validating or rejecting approximations to property estimation; the interpretation of corresponding states; combination rules that yield pair molecular potential parameters for unlike species from like species parameters; and mixture approximations. The insensitivity of transport properties to intermolecular forces is noted, especially the non-uniqueness of the supporting potential parameters. Viscosity experiments of pure substances and binary mixtures measured post 1970 are used to evaluate a number of approximations; the intermediate temperature range 1 < T* < 10, where T* is kT/ε, is emphasized since this is where rich data sets are available. When suitable potential parameters are used, errors in transport property predictions for pure substances and binary mixtures are less than 5 %, when they are calculated using the approaches of Kee et al.; Mason, Kestin, and Uribe; Paul and Warnatz; or Ern and Giovangigli. Recommendations stemming from the review include (1) revisiting the supporting data required by the various computational approaches, and updating the data sets with accurate potential parameters, dipole moments, and polarizabilities; (2) characterizing the range of parameter space over which the fit to experimental data is good, rather than the current practice of reporting only the parameter set that best fits the data; (3) looking for improved combining rules, since existing rules were found to under-predict the viscosity in most cases; (4) performing more transport property measurements for mixtures that include radical species, an important but neglected area; (5) using the TRANLIB approach for treating polar molecules and (6) performing more accurate measurements of the molecular parameters used to evaluate the molecular heat capacity, since it affects thermal conductivity, which is important in predicting flame development. (1) revisiting the supporting data required by the various computational approaches, and updating the data sets with accurate potential parameters, dipole moments, and polarizabilities; (2) characterizing the range of parameter space over which the fit to experimental data is good, rather than the current practice of reporting only the parameter set that best fits the data; (3) looking for improved combining rules, since existing rules were found to under-predict the viscosity in most cases; (4) performing more transport property measurements for mixtures that include radical species, an important but neglected area; (5) using the TRANLIB approach for treating polar...

Ecological regression is based on assumptions that are untestable from aggregate data. However, these assumptions seem more questionable in some applications than in others. There has been some research on implicit models of individual data underlying aggregate ecological regression modelling. We discuss ways in which these implicit models can be checked from aggregate data. We also explore the differences in applications of ecological regressions in two examples: estimating the effect of radon on lung cancer in the United States and estimating voting patterns for different ethnic groups in New York City.

h i g h l i g h t sCommercial Building Energy Saver is a powerful toolkit for energy retrofit analysis. CBES provides benchmarking, load shape analysis, and model-based retrofit assessment. CBES covers 7 building types, 6 vintages, 16 climates, and 100 energy measures. CBES includes a web app, API, and a database of energy efficiency performance. CBES API can be extended and integrated with third party energy software tools. a r t i c l e i n f o a b s t r a c tSmall commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.

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