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Concentrations of air pollutants from vehicles are elevated along roadways, indicating that human exposure in transportation microenvironments may not be adequately characterized by centrally located monitors. We report results from ∼180 h of real-time measurements of fine particle and black carbon mass concentration (PM 2.5 , BC) and ultrafine particle number concentration (PN) inside a common vehicle, the auto-rickshaw, in New Delhi, India. Measured exposure concentrations are much higher in this study (geometric mean for ∼60 trip-averaged concentrations: 190 μg m −3 PM 2.5 , 42 μg m −3 BC, 280 × 10 3 particles cm −3 ; GSD ∼1.3 for all three pollutants) than reported for transportation microenvironments in other megacities. In-vehicle concentrations exceeded simultaneously measured ambient levels by 1.5× for PM 2.5 , 3.6× for BC, and 8.4× for PN. Short-duration peak concentrations (averaging time: 10 s), attributable to exhaust plumes of nearby vehicles, were greater than 300 μg m −3 for PM 2.5 , 85 μg m −3 for BC, and 650 × 10 3 particles cm −3 for PN. The incremental increase of within-vehicle concentration above ambient levels—which we attribute to in- and near-roadway emission sources—accounted for 30%, 68% and 86% of time-averaged in-vehicle PM 2.5 , BC and PN concentrations, respectively. Based on these results, we estimate that one’s exposure during a daily commute by auto-rickshaw in Delhi is as least as large as full-day exposures experienced by urban residents of many high-income countries. This study illuminates an environmental health concern that may be common in many populous, low-income cities.

This paper describes the origin, structure and continuing development of a model of time varying energy consumption in the US commercial building stock. The model is based on a flexible structure that disaggregates the stock into various categories (e.g. by building type, climate, vintage and life-cycle stage) and assigns attributes to each of these (e.g. floor area and energy use intensity by fuel type and end use), based on historical data and user-defined scenarios for future projections. In addition to supporting the interactive exploration of building stock dynamics, the model has been used to study the likely outcomes of specific policy and innovation scenarios targeting very low future energy consumption in the building stock. Model use has highlighted the scale of the challenge of meeting targets stated by various government and professional bodies, and the importance of considering both new construction and existing buildings.

Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model simulations, and ground measurements from 79 different countries to produce global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990 to 2010 and the year 2013. These estimates were applied to assess population-weighted mean concentrations for 1990-2013 for each of 188 countries. In 2013, 87% of the world's population lived in areas exceeding the World Health Organization Air Quality Guideline of 10 μg/m(3) PM2.5 (annual average). Between 1990 and 2013, global population-weighted PM2.5 increased by 20.4% driven by trends in South Asia, Southeast Asia, and China. Decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries. Population-weighted mean concentrations of ozone increased globally by 8.9% from 1990-2013 with increases in most countries-except for modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

The largest U.S. environmental health risk is cardiopulmonary mortality from ambient PM2.5. The concentration-response (C-R) for ambient PM2.5 in the U.S. is generally assumed to be linear: from any initial baseline, a given concentration reduction would yield the same improvement in health risk. Recent evidence points to the perplexing possibility that the PM2.5 C-R for cardiopulmonary mortality and some other major endpoints might be supralinear: a given concentration reduction would yield greater improvements in health risk as the initial baseline becomes cleaner. We explore the implications of supralinearity for air policy, emphasizing U.S.If C-R is supralinear, an economically efficient PM2.5 target may be substantially more stringent than under current standards. Also, if a goal of air policy is to achieve the greatest health improvement per unit of PM2.5 reduction, the optimal policy might call for greater emission reductions in already-clean locales-making "blue skies bluer"-which may be at odds with environmental equity goals. Regardless of whether the C-R is linear or supralinear, the health benefits of attaining U.S. PM2.5 levels well below the current standard would be large. For the supralinear C-R considered here, attaining the current U.S. EPA standard, 12 μg m(-3), would avert only ~17% (if C-R is linear: ∼ 25%) of the total annual cardiopulmonary mortality attributable to PM2.5.

Purpose Fine particulate matter (PM2.5) is considered to be one of the most important environmental factors contributing to the global human disease burden. However, due to the lack of broad consensus and harmonization in the life cycle assessment (LCA) community, there is no clear guidance on how to consistently include health effects from PM2.5 exposure in LCA practice. As a consequence, different models are currently used to assess life cycle impacts for PM2.5, sometimes leading to inconsistent results. In a global effort initiated by the United Nations Environment Programme (UNEP)/Society for Environmental Toxicology and Chemistry(SETAC) Life Cycle Initiative, respiratory inorganics’ impacts expressed as health effects from PM2.5 exposure were selected as one of the initial impact categories to undergo review with the goal of providing global guidance for implementation in life cycle impact assessment (LCIA). The goal of this paper is to summarize the current knowledge and practice for assessing health effects from PM2.5 exposure and to provide recommendations for their consistent integration into LCIA. Methods A task force on human health impacts was convened to build the framework for consistently quantifying health effects from PM2.5 exposure and for recommending PM2.5 characterization factors. In an initial Guidance Workshop,existing literature was reviewed and input from a broad range of internationally recognized experts was obtained and discussed. Workshop objectives were to identify the main scientific questions and challenges for quantifying health effects from PM2.5 exposure and to provide initial guidance to the impact quantification process.Results and discussion A set of 10 recommendations was developed addressing (a) the general framework for assessingPM2.5-related health effects, (b) approaches and data to estimate human exposure to PM2.5 using intake fractions, and (c) approaches and data to characterize exposure-response functions(ERFs) for PM2.5 and to quantify severity of the diseases attributed to PM2.5 exposure. Despite these advances, a number of complex issues, such as those related to non linearity of the ERF and the possible need to provide different ERFs for use in different geographical regions, require further analysis.Conclusions and outlook Questions of how to refine and improve the overall framework were analyzed. Data and models were proposed for harmonizing various elements of the health impact pathways for PM2.5. Within the next two years, our goal is to build a global guidance framework and to determine characterization factors that are more reliable for incorporating the health effects from exposure to PM2.5into LCIA. Ideally, this will allow quantification of the impacts of both indoor and outdoor exposures to PM2.5.

Traffic-related air pollution (TRAP) likely exerts a large burden of disease globally, and in many places, traffic is increasing dramatically. The impact, however, of urban form on the portion of population potentially exposed to TRAP remains poorly understood. In this study, we estimate portions of population potentially exposed to TRAP across seven global cities of various urban forms. Data on population distributions and road networks were collected from the best available sources in each city and from remote sensing analysis. Using spatial mapping techniques, we first overlaid road buffers onto population data to estimate the portions of population potentially exposed for four plausible impact zones. Based on a most likely scenario with impacts from highways up to 300 meters and major roadways up to 50 meters, we identified that the portions of population potentially exposed for the seven cities ranged from 23 to 96%. High-income North American cities had the lowest potential exposure portions, while those in Europe had the highest. Second, we adjusted exposure zone concentration levels based on a literature suggested multiplier for each city using corresponding background concentrations. Though Beijing and Mexico City did not have the highest portion of population exposure, those in their exposure zones had the highest levels of exposure. For all seven cities, the portion of population potentially exposed was positively correlated with roadway density and, to a lesser extent, with population density. These analyses suggest that urban form may influence the portion of population exposed to TRAP and vehicle emissions and other factors may influence the exposure levels. Greater understanding of urban form and other factors influencing potential exposure to TRAP may help inform interventions that protect public health. Keywords: Traffic-related air pollution, Population exposure, Urban form, Geographic Information Systems, Remote sensing, Buffer analysis, Building footprint

The developers of the Paharpur Business Center (PBC) and Software Technology Incubator Park in New Delhi, India offer an environmentally sustainable building with a strong emphasis on energy conservation, waste minimization and superior indoor air quality (IAQ). To achieve the IAQ goal, the building utilizes a series of air cleaning technologies for treating the air entering the building. These technologies include an initial water wash followed by ultraviolet light treatment and biolfiltration using a greenhouse located on the roof and numerous plants distributed throughout the building. Even with the extensive treatment of makeup air and room air in the PBC, a recent study found that the concentrations of common volatile organic compounds and aldehydes appear to rise incrementally as the air passes through the building from the supply to the exhaust. This finding highlights the need to consider the minimization of chemical sources in buildings in combination with the use of advanced air cleaning technologies when seeking to achieve superior IAQ. The goal of this project was to identify potential source materials for indoor chemicals in the PBC. Samples of building materials, including wood paneling (polished and unpolished), drywall, and plastic from a hydroponic drum that was part of themore » air cleaning system, were collected from the building for testing. All materials were collected from the PBC building and shipped to the Lawrence Berkeley National Laboratory (LBNL) for testing. The materials were pre-conditioned for two different time periods before measuring material and chemical specific emission factors for a range of VOCs and Aldehydes. Of the six materials tested, we found that the highest emitter of formaldehyde was new plywood paneling. Although polish and paint contribute to some VOC emissions, the main influence of the polish was in altering the capacity of the surface to accumulate formaldehyde. Neither the new nor aged polish contributed significantly to formaldehyde emissions. The VOC emission stream (excluding formaldehyde) was composed of up to 18 different chemicals and the total VOC emissions ranged in magnitude from 7 mu g/m2/h (old wood with old polish) to>500 mu g/m2/h (painted drywall). The formaldehyde emissions from drywall and old wood with either new or old polish were ~;;15 mu g/m2/h while the new wood material emitted>100 mu g/m2/h. However, when the projected surface area of each material in the building was considered, the new wood, old wood and painted drywall material all contributed substantially to the indoor formaldehyde loading while the coatings contributed primarily to the VOCs.« less

A Pilot Study of the Effectiveness of Indoor Plants for Removal of Volatile Organic Compounds in Indoor Air in a Seven-Story Office Building Michael G. Apte 1 and Joshua S. Apte 2 Lawrence Berkeley National Laboratory Environmental Energy Technologies Division Indoor Environment Department Berkeley, CA 94720 Energy Resources Group University of California, Berkeley Berkeley, CA 94720 April 27 2010 This work was sponsored by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State, and Community Programs, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the US DOE.

Window-Related Energy Consumption in the US Residential and Commercial Building Stock J oshua Apte and Dariush Arasteh, Lawrence Berkeley National Laboratory LBNL-60146 Abstract We present a simple spreadsheet-based tool for estimating window-related energy consumption in the United States. Using available data on the properties of the installed US window stock, we estimate that windows are responsible for 2.15 quadrillion Btu (Quads) of heating energy consumption and 1.48 Quads of cooling energy consumption annually. We develop estimates of average U-factor and SHGC for current window sales. We estimate that a complete replacement of the installed window stock with these products would result in energy savings of approximately 1.2 quads. We demonstrate that future window technologies offer energy savings potentials of up to 3.9 Quads. Introduction According to the US Department of Energy, in 2003 space conditioning in residential and commercial buildings was responsible for 9.19 quadrillion Btu (Quads) of site energy consumption, and 12.03 Quads of primary (source) consumption (US DOE Office of Energy Efficiency and Renewable Energy 2005). Although windows are generally understood to be an important driver of space conditioning energy consumption, few studies have directly investigated the energy impacts of windows at the national level. In this study, we introduce a simple spreadsheet-based tool for estimating the national energy impacts of windows in residential and commercial buildings. After presenting estimates of current window-related energy consumption in the US building stock, we discuss the energy savings potential of various technology scenarios. Methods and Results This section is divided into two sub-sections. In the first section, we describe the techniques and assumptions involved in estimating the window-related energy consumption of the US building stock. In the second section, we describe the process we used to estimate the energy-savings potential of various window technologies. Window-Related Energy Consumption of Building Stock General Approach In order to estimate the energy consumption attributable to the US window stock, we used a combination of top-down and bottom-up approaches. First, we compiled a set of estimates of total space conditioning energy consumption in the US building stock, broken down by sector (residential, commercial) and conditioning mode (heating, cooling). Second, for each of these four aggregated end use categories, we combined building energy simulations with market and survey data to estimate the fraction of total energy consumption attributable to windows. We then estimated the window-related space conditioning energy consumption for each end use category by multiplying total space conditioning energy consumption by the window-related fraction of that

Population Exposed in Toronto, Canada Motor vehicles represent a major source of air pollution emissions that probably contribute to acute and chronic health effects. In this study, we estimated the potential population exposed to high levels of traffic-related air pollution in the cities of developed and developing countries. Building on the proximity methods published in the Health Effects Institute Special Report 17, Chapter 3, we applied similar geographic information science techniques to estimate the population at risk in the cities of Beijing and New Delhi in Asia, Los Angeles and Toronto in North America, and Paris and Barcelona in Europe.

Strong policies to constrain increasing global use of light-duty vehicles (cars and light trucks) should complement fuel efficiency and carbon intensity improvements in order to meet international greenhouse gas emission and climate targets for the year 2050.