Michael D. Sohn

Rapidly Locating and Characterizing Pollutant Releases in Buildings

Abstract Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a hypothetical pollutant release in a five-room building. Unknowns to the interpretation algorithm include location, duration, and strength of the source, and some building and weather conditions. Two sensor sampling plans and three levels of data quality are examined. Data interpretation in all examples is rapid; however, locating and characterizing the source with high probability depends on the amount and quality of data and the sampling plan.

Reconstructing population exposures from dose biomarkers: inhalation of trichloroethylene (TCE) as a case study

Physiologically based pharmacokinetic (PBPK) modeling is a well-established toxicological tool designed to relate exposure to a target tissue dose. The emergence of federal and state programs for environmental health tracking and the availability of exposure monitoring through biomarkers creates the opportunity to apply PBPK models to estimate exposures to environmental contaminants from urine, blood, and tissue samples. However, reconstructing exposures for large populations is complicated by often having too few biomarker samples, large uncertainties about exposures, and large interindividual variability. In this paper, we use an illustrative case study to identify some of these difficulties, and for a process for confronting them by reconstructing population-scale exposures using Bayesian inference. The application consists of interpreting biomarker data from eight adult males with controlled exposures to trichloroethylene (TCE) as if the biomarkers were random samples from a large population with unknown exposure conditions. The TCE concentrations in blood from the individuals fell into two distinctly different groups even though the individuals were simultaneously in a single exposure chamber. We successfully reconstructed the exposure scenarios for both subgroups — although the reconstruction of one subgroup is different than what is believed to be the true experimental conditions. We were however unable to predict with high certainty the concentration of TCE in air.

Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection

Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection Daniel]. Olsen, Nance Matson, Michael D. Sohn, Cody Rose, Junqiao Dudley, Sasank Goli, and Sila Kiliccote Lawrence Berkeley National Laboratory Marissa Hurnmon, David Palchak, Paul Denholm, and Jennie Iorgenson National Renewable Energy Laboratory September 2013

Analysis of U.S. residential air leakage database

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.

Factors affecting the concentration of outdoor particles indoors (COPI): Identification of data needs and existing data

The process of characterizing human exposure to particulate matter requires information on both particle concentrations in microenvironments and the time-specific activity budgets of individuals among these microenvironments. Because the average amount of time spent indoors by individuals in the US is estimated to be greater than 75%, accurate characterization of particle concentrations indoors is critical to exposure assessments for the US population. In addition, it is estimated that indoor particle concentrations depend strongly on outdoor concentrations. The spatial and temporal variations of indoor particle concentrations as well as the factors that affect these variations are important to health scientists. For them, knowledge of the factors that control the relationship of indoor particle concentrations to outdoor levels is particularly important. In this report, we identify and evaluate sources of data for those factors that affect the transport to and concentration of outdoor particles in the indoor environment. Concentrations of particles indoors depend upon the fraction of outdoor particles that penetrate through the building shell or are transported via the air handling (HVAC) system, the generation of particles by indoor sources, and the loss mechanisms that occur indoors, such as deposition. To address these issues, we (i) identify and assemble relevant information including the behavior of particles during air leakage, HVAC operations, and particle filtration; (ii) review and evaluate the assembled information to distinguish data that are directly relevant to specific estimates of particle transport from those that are only indirectly useful and (iii) provide a synthesis of the currently available information on building air-leakage parameters and their effect on indoor particle matter concentrations.

Protecting buildings from a biological or chemical attack: Actions to take before or during a release

This report presents advice on how to operate a building to reduce casualties from a biological or chemical attack, as well as potential changes to the building (e.g. the design of the ventilation system) that could make it more secure. It also documents the assumptions and reasoning behind the advice. The particular circumstances of any attack, such as the ventilation system design, building occupancy, agent type, source strength and location, and so on, may differ from the assumptions made here, in which case actions other than our recommendations may be required; we hope that by understanding the rationale behind the advice, building operators can modify it as required for their circumstances. The advice was prepared by members of the Airflow and Pollutant Transport Group, which is part of the Indoor Environment Department at the Lawrence Berkeley National Laboratory. The groups expertise in this area includes: tracer-gas measurements of airflows in buildings (Sextro, Thatcher); design and operation of commercial building ventilation systems (Delp); modeling and analysis of airflow and tracer gas transport in large indoor spaces (Finlayson, Gadgil, Price); modeling of gas releases in multi-zone buildings (Sohn, Lorenzetti, Finlayson, Sextro); and occupational health and safety experience related to building design and operation (Sextro, Delp). This report is concerned only with building design and operation; it is not a how-to manual for emergency response. Many important emergency response topics are not covered here, including crowd control, medical treatment, evidence gathering, decontamination methods, and rescue gear.

Anthrax Sampling and Decontamination: Technology Trade-Offs

The goal of this project was to answer the following questions concerning response to a future anthrax release (or suspected release) in a building: 1. Based on past experience, what rules of thumb can be determined concerning: (a) the amount of sampling that may be needed to determine the extent of contamination within a given building; (b) what portions of a building should be sampled; (c) the cost per square foot to decontaminate a given type of building using a given method; (d) the time required to prepare for, and perform, decontamination; (e) the effectiveness of a given decontamination method in a given type of building? 2. Based on past experience, what resources will be spent on evaluating the extent of contamination, performing decontamination, and assessing the effectiveness of the decontamination in abuilding of a given type and size? 3. What are the trade-offs between cost, time, and effectiveness for the various sampling plans, sampling methods, and decontamination methods that have been used in the past?

A Bayesian network model for the optimization of a chiller plant’s condenser water set point

To implement the condenser water set point optimization, one can employ a regression model. However, existing regression-based methods have difficulties to handle non-linear chiller plant behaviour. To address this problem, we develop a Bayesian network model and compare it to both a linear and a polynomial regression model via a case study. The results show that the Bayesian network model can predict the optimal condenser water set points with a lower root mean square deviation for both a mild month and a summer month than the linear and the polynomial models. The energy-saving ratios by the Bayesian network model are 25.92% and 1.39% for the mild month and the summer month, respectively. As a comparison, the energy-saving ratios by the linear and the polynomial models are less than 19.00% for the mild month and even lead to more energy consumption in the summer month (up to 3.73%).

Analysis of Fuel Cell Markets in Japan and the US: Experience Curve Development and Cost Reduction Disaggregation:

Author(s): Wei, Max; Smith, Sarah J.; Sohn, Michael D. | Abstract: Fuel cells are both a longstanding and emerging technology for stationary and transportation applications, and their future use will likely be critical for the deep decarbonization of global energy systems. As we look into future applications, a key challenge for policy-makers and technology market forecasters who seek to track and/or accelerate their market adoption is the ability to forecast market costs of the fuel cells as technology innovations are incorporated into market products. Specifically, there is a need to estimate technology learning rates, which are rates of cost reduction versus production volume. Unfortunately, no literature exists for forecasting future learning rates for fuel cells. In this paper, we look retrospectively to estimate learning rates for two fuel cell deployment programs: (1) the micro-combined heat and power (CHP) program in Japan, and (2) the Self-Generation Incentive Program (SGIP) in California. These two examples have a relatively broad set of historical market data and thus provide an informative and international comparison of distinct fuel cell technologies and government deployment programs. We develop a generalized procedure for disaggregating experience-curve cost-reductions in order to disaggregate the Japanese fuel cell micro-CHP market into its constituent components, and we derive and present a range of learning rates that may explain observed market trends. Finally, we explore the differences in the technology development ecosystem and market conditions that may have contributed to the observed differences in cost reduction and draw policy observations for the market adoption of future fuel cell technologies. The scientific and policy contributions of this paper are the first comparative experience curve analysis of past fuel cell technologies in two distinct markets, and the first quantitative comparison of a detailed cost model of fuel cell systems with actual market data. The resulting approach is applicable to analyzing other fuel cell markets and other energy-related technologies, and highlights the data needed for cost modeling and quantitative assessment of key cost reduction components.

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

Author(s): Smith, Sarah J.; Wei, Max; Sohn, Michael D. | Abstract: Retrospective experience curves are a useful tool for understanding historic technology development, and can contribute to investment program analysis and future cost estimation efforts. This work documents our development of an analysis approach for deriving retrospective experience curves with a variable learning rate, and its application to develop an experience curve for compact fluorescent lamps for the global and North American markets over the years 1990-2007. Uncertainties and assumptions involved in interpreting data for our experience curve development are discussed, including the processing and transformation of empirical data, the selection of system boundaries, and the identification of historical changes in the learning rate over the course of 15 years. In the results that follow, we find that that the learning rate has changed at least once from 1990-2007. We also explore if, and to what degree, public deployment programs may have contributed to an increased technology learning rate in North America. We observe correlations between the changes in the learning rate and the initiation of new policies, abrupt technological advances, including improvements to ballast technology, and economic and political events such as trade tariffs and electricity prices. Finally, we discuss how the findings of this work (1) support the use of segmented experience curves for retrospective and prospective analysis and (2) may imply that investments in technological research and development have contributed to a change in market adoption and penetration.