Kinetic modeling and experiments on gas uptake and chemical transformation of organic aerosol in the atmosphere

Aerosolpartikel beeinflussen das Klima durch Streuung und Absorption von Strahlung sowie als Nukleations-Kerne für Wolkentröpfchen und Eiskristalle. Darüber hinaus haben Aerosole einen starken Einfluss auf die Luftverschmutzung und die öffentliche Gesundheit. Gas-Partikel-Wechselwirkunge sind wichtige Prozesse, weil sie die physikalischen und chemischen Eigenschaften von Aerosolen wie Toxizität, Reaktivität, Hygroskopizität und optische Eigenschaften beeinflussen. Durch einen Mangel an experimentellen Daten und universellen Modellformalismen sind jedoch die Mechanismen und die Kinetik der Gasaufnahme und der chemischen Transformation organischer Aerosolpartikel unzureichend erfasst. Sowohl die chemische Transformation als auch die negativen gesundheitlichen Auswirkungen von toxischen und allergenen Aerosolpartikeln, wie Ruß, polyzyklische aromatische Kohlenwasserstoffe (PAK) und Proteine, sind bislang nicht gut verstanden.rn Kinetische Fluss-Modelle für Aerosoloberflächen- und Partikelbulk-Chemie wurden auf Basis des Pöschl-Rudich-Ammann-Formalismus für Gas-Partikel-Wechselwirkungen entwickelt. Zunächst wurde das kinetische Doppelschicht-Oberflächenmodell K2-SURF entwickelt, welches den Abbau von PAK auf Aerosolpartikeln in Gegenwart von Ozon, Stickstoffdioxid, Wasserdampf, Hydroxyl- und Nitrat-Radikalen beschreibt. Kompetitive Adsorption und chemische Transformation der Oberfläche führen zu einer stark nicht-linearen Abhängigkeit der Ozon-Aufnahme bezüglich Gaszusammensetzung. Unter atmosphärischen Bedingungen reicht die chemische Lebensdauer von PAK von wenigen Minuten auf Ruß, über mehrere Stunden auf organischen und anorganischen Feststoffen bis hin zu Tagen auf flüssigen Partikeln. rn Anschließend wurde das kinetische Mehrschichtenmodell KM-SUB entwickelt um die chemische Transformation organischer Aerosolpartikel zu beschreiben. KM-SUB ist in der Lage, Transportprozesse und chemische Reaktionen an der Oberfläche und im Bulk von Aerosol-partikeln explizit aufzulösen. Es erforder im Gegensatz zu früheren Modellen keine vereinfachenden Annahmen über stationäre Zustände und radiale Durchmischung. In Kombination mit Literaturdaten und neuen experimentellen Ergebnissen wurde KM-SUB eingesetzt, um die Effekte von Grenzflächen- und Bulk-Transportprozessen auf die Ozonolyse und Nitrierung von Protein-Makromolekülen, Ölsäure, und verwandten organischen Ver¬bin-dungen aufzuklären. Die in dieser Studie entwickelten kinetischen Modelle sollen als Basis für die Entwicklung eines detaillierten Mechanismus für Aerosolchemie dienen sowie für das Herleiten von vereinfachten, jedoch realistischen Parametrisierungen für großskalige globale Atmosphären- und Klima-Modelle. rn Die in dieser Studie durchgeführten Experimente und Modellrechnungen liefern Beweise für die Bildung langlebiger reaktiver Sauerstoff-Intermediate (ROI) in der heterogenen Reaktion von Ozon mit Aerosolpartikeln. Die chemische Lebensdauer dieser Zwischenformen beträgt mehr als 100 s, deutlich länger als die Oberflächen-Verweilzeit von molekularem O3 (~10-9 s). Die ROIs erklären scheinbare Diskrepanzen zwischen früheren quantenmechanischen Berechnungen und kinetischen Experimenten. Sie spielen eine Schlüsselrolle in der chemischen Transformation sowie in den negativen Gesundheitseffekten von toxischen und allergenen Feinstaubkomponenten, wie Ruß, PAK und Proteine. ROIs sind vermutlich auch an der Zersetzung von Ozon auf mineralischem Staub und an der Bildung sowie am Wachstum von sekundären organischen Aerosolen beteiligt. Darüber hinaus bilden ROIs eine Verbindung zwischen atmosphärischen und biosphärischen Mehrphasenprozessen (chemische und biologische Alterung).rn Organische Verbindungen können als amorpher Feststoff oder in einem halbfesten Zustand vorliegen, der die Geschwindigkeit von heterogenen Reaktionenen und Mehrphasenprozessen in Aerosolen beeinflusst. Strömungsrohr-Experimente zeigen, dass die Ozonaufnahme und die oxidative Alterung von amorphen Proteinen durch Bulk-Diffusion kinetisch limitiert sind. Die reaktive Gasaufnahme zeigt eine deutliche Zunahme mit zunehmender Luftfeuchte, was durch eine Verringerung der Viskosität zu erklären ist, bedingt durch einen Phasenübergang der amorphen organischen Matrix von einem glasartigen zu einem halbfesten Zustand (feuchtigkeitsinduzierter Phasenübergang). Die chemische Lebensdauer reaktiver Verbindungen in organischen Partikeln kann von Sekunden bis zu Tagen ansteigen, da die Diffusionsrate in der halbfesten Phase bei niedriger Temperatur oder geringer Luftfeuchte um Größenordnungen absinken kann. Die Ergebnisse dieser Studie zeigen wie halbfeste Phasen die Auswirkung organischeer Aerosole auf Luftqualität, Gesundheit und Klima beeinflussen können. rn

Using sulfur isotope fractionation to understand the atmospheric oxidation of SO 2

Sulfate aerosol plays an important but uncertain role in cloud formation and radiative forcing of the climate, and is also important for acid deposition and human health. The oxidation of SO2 to sulfate is a key reaction in determining the impact of sulfate in the environment through its effect on aerosol size distribution and composition. This thesis presents a laboratory investigation of sulfur isotope fractionation during SO2 oxidation by the most important gas-phase and heterogeneous pathways occurring in the atmosphere. The fractionation factors are then used to examine the role of sulfate formation in cloud processing of aerosol particles during the HCCT campaign in Thuringia, central Germany. The fractionation factor for the oxidation of SO2 by ·OH radicals was measured by reacting SO2 gas, with a known initial isotopic composition, with ·OH radicals generated from the photolysis of water at -25, 0, 19 and 40°C (Chapter 2). The product sulfate and the residual SO2 were collected as BaSO4 and the sulfur isotopic compositions measured with the Cameca NanoSIMS 50. The measured fractionation factor for 34S/32S during gas phase oxidation is αOH = (1.0089 ± 0.0007) − ((4 ± 5) × 10−5 )T (°C). Fractionation during oxidation by major aqueous pathways was measured by bubbling the SO2 gas through a solution of H2 O2

Species specific nitrogen isotope analysis by NanoSIMS

Der atmosphärische Kreislauf reaktiver Stickstoffverbindungen beschäftigt sowohl die Naturwissenschaftler als auch die Politik. Dies ist insbesondere darauf zurückzuführen, dass reaktive Stickoxide die Bildung von bodennahem Ozon kontrollieren. Reaktive Stickstoffverbindungen spielen darüber hinaus als gasförmige Vorläufer von Feinstaubpartikeln eine wichtige Rolle und der Transport von reaktivem Stickstoff über lange Distanzen verändert den biogeochemischen Kohlenstoffkreislauf des Planeten, indem er entlegene Ökosysteme mit Stickstoff düngt. Die Messungen von stabilen Stickstoffisotopenverhältnissen (15N/14N) bietet ein Hilfsmittel, welches es erlaubt, die Quellen von reaktiven Stickstoffverbindungen zu identifizieren und die am Stickstoffkeislauf beteiligten Reaktionen mithilfe ihrer reaktionsspezifischen Isotopenfraktionierung genauer zu untersuchen. rnIn dieser Doktorarbeit demonstriere ich, dass es möglich ist, mit Hilfe von Nano-Sekundärionenmassenspektrometrie (NanoSIMS) verschiedene stickstoffhaltige Verbindungen, die üblicherweise in atmosphärischen Feinstaubpartikeln vorkommen, mit einer räumlichen Auflösung von weniger als einem Mikrometer zu analysieren und zu identifizieren. Die Unterscheidung verschiedener stickstoffhaltiger Verbindungen erfolgt anhand der relativen Signalintensitäten der positiven und negativen Sekundärionensignale, die beobachtet werden, wenn die Feinstaubproben mit einem Cs+ oder O- Primärionenstrahl beschossen werden. Die Feinstaubproben können direkt auf dem Probenahmesubstrat in das Massenspektrometer eingeführt werden, ohne chemisch oder physikalisch aufbereited zu werden. Die Methode wurde Mithilfe von Nitrat, Nitrit, Ammoniumsulfat, Harnstoff, Aminosären, biologischen Feinstaubproben (Pilzsporen) und Imidazol getestet. Ich habe gezeigt, dass NO2 Sekundärionen nur beim Beschuss von Nitrat und Nitrit (Salzen) mit positiven Primärionen entstehen, während NH4+ Sekundärionen nur beim Beschuss von Aminosäuren, Harnstoff und Ammoniumsalzen mit positiven Primärionen freigesetzt werden, nicht aber beim Beschuss biologischer Proben wie z.B. Pilzsporen. CN- Sekundärionen werden beim Beschuss aller stickstoffhaltigen Verbindungen mit positiven Primärionen beobachtet, da fast alle Proben oberflächennah mit Kohlenstoffspuren kontaminiert sind. Die relative Signalintensität der CN- Sekundärionen ist bei kohlenstoffhaltigen organischen Stickstoffverbindungen am höchsten.rnDarüber hinaus habe ich gezeigt, dass an reinen Nitratsalzproben (NaNO3 und KNO3), welche auf Goldfolien aufgebracht wurden speziesspezifische stabile Stickstoffisotopenverhältnisse mithilfe des 15N16O2- / 14N16O2- - Sekundärionenverhältnisses genau und richtig gemessen werden können. Die Messgenauigkeit auf Feldern mit einer Rastergröße von 5×5 µm2 wurde anhand von Langzeitmessungen an einem hausinternen NaNO3 Standard als ± 0.6 ‰ bestimmt. Die Differenz der matrixspezifischen instrumentellen Massenfraktionierung zwischen NaNO3 und KNO3 betrug 7.1 ± 0.9 ‰. 23Na12C2- Sekundärionen können eine ernst zu nehmende Interferenz darstellen wenn 15N16O2- Sekundärionen zur Messung des nitratspezifischen schweren Stickstoffs eingesetzt werden sollen und Natrium und Kohlenstoff im selben Feinstaubpartikel als interne Mischung vorliegt oder die natriumhaltige Probe auf einem kohlenstoffhaltigen Substrat abgelegt wurde. Selbst wenn, wie im Fall von KNO3, keine derartige Interferenz vorliegt, führt eine interne Mischung mit Kohlenstoff im selben Feinstaubpartikel zu einer matrixspezifischen instrumentellen Massenfraktionierung die mit der folgenden Gleichung beschrieben werden kann: 15Nbias = (101 ± 4) ∙ f − (101 ± 3) ‰, mit f = 14N16O2- / (14N16O2- + 12C14N-). rnWird das 12C15N- / 12C14N- Sekundärionenverhältnis zur Messung der stabilen Stickstoffisotopenzusammensetzung verwendet, beeinflusst die Probematrix die Messungsergebnisse nicht, auch wenn Stickstoff und Kohlenstoff in den Feinstaubpartikeln in variablen N/C–Verhältnissen vorliegen. Auch Interferenzen spielen keine Rolle. Um sicherzustellen, dass die Messung weiterhin spezifisch auf Nitratspezies eingeschränkt bleibt, kann eine 14N16O2- Maske bei der Datenauswertung verwendet werden. Werden die Proben auf einem kohlenstoffhaltigen, stickstofffreien Probennahmesubstrat gesammelt, erhöht dies die Signalintensität für reine Nitrat-Feinstaubpartikel.

Test sperimentali e modellazione di una torre di lavaggio per la filtrazione di particolato sottile in impianti di combustione delle biomasse

This study investigates biomass and particulate matter also known as PM produced from the combustion of a domestic boiler powered by mais and how to separate PM from the stream of smoke output from the boiler using wet scrubber with structured packing. Sperimentations show the inefficiency of the separator used, so we provide an optimization of the structured packing changing geometric parameters as angle of the bend or thickness of the channels. In order to obtain a higher separation efficiency we remove the structured packinkg and introduce a packed bed composed of spheres of polyethylene with a diameter of 3 mm.

Aircraft noise, air pollution, and mortality from myocardial infarction

Objective: Myocardial infarction has been associated with both transportation noise and air pollution. We examined residential exposure to aircraft noise and mortality from myocardial infarction, taking air pollution into account. Methods: We analyzed the Swiss National Cohort, which includes geocoded information on residence. Exposure to aircraft noise and air pollution was determined based on geospatial noise and air-pollution (PM10) models and distance to major roads. We used Cox proportional hazard models, with age as the timescale. We compared the risk of death across categories of A-weighted sound pressure levels (dB(A)) and by duration of living in exposed corridors, adjusting for PM10 levels, distance to major roads, sex, education, and socioeconomic position of the municipality. Results: We analyzed 4.6 million persons older than 30 years who were followed from near the end of 2000 through December 2005, including 15,532 deaths from myocardial infarction (ICD-10 codes I 21, I 22). Mortality increased with increasing level and duration of aircraft noise. The adjusted hazard ratio comparing ≥60 dB(A) with <45 dB(A) was 1.3 (95% confidence interval = 0.96-1.7) overall, and 1.5 (1.0-2.2) in persons who had lived at the same place for at least 15 years. None of the other endpoints (mortality from all causes, all circulatory disease, cerebrovascular disease, stroke, and lung cancer) was associated with aircraft noise. Conclusion: Aircraft noise was associated with mortality from myocardial infarction, with a dose-response relationship for level and duration of exposure. The association does not appear to be explained by exposure to particulate matter air pollution, education, or socioeconomic status of the municipality.

A prospective study of the impact of air pollution on respiratory symptoms and infections in infants

Rationale: There is increasing evidence that short-term exposure to air pollution has a detrimental effect on respiratory health, but data from healthy populations, particularly infants, are scarce. Objectives: To assess the association of air pollution with frequency and severity of respiratory symptoms and infections measured weekly in healthy infants. Methods: In a prospective birth cohort of 366 infants of unselected mothers, respiratory health was assessed weekly by telephone interviews during the first year of life (19,106 total observations). Daily mean levels of particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3) were obtained from local monitoring stations. We determined the association of the preceding week's pollutant levels with symptom scores and respiratory tract infections using a generalized additive mixed model with an autoregressive component. In addition, we assessed whether neonatal lung function influences this association and whether duration of infectious episodes differed between weeks with normal PM10 and weeks with elevated levels. Measurements and Main Results: We found a significant association between air pollution and respiratory symptoms, particularly in the week after respiratory tract infections (risk ratio, 1.13 [1.02-1.24] per 10 μg/m(3) PM10 levels) and in infants with premorbid lung function. During times of elevated PM10 (>33.3 μg/m(3)), duration of respiratory tract infections increased by 20% (95% confidence interval, 2-42%). Conclusions: Exposure to even moderate levels of air pollution was associated with increased respiratory symptoms in healthy infants. Particularly in infants with premorbid lung function and inflammation, air pollution contributed to longer duration of infectious episodes with a potentially large socioeconomic impact.

Adaptations of intestinal macrophages to an antigen-rich environment

Intestinal macrophages, preferentially located in the subepithelial lamina propria, represent in humans the largest pool of tissue macrophages. To comply with their main task, i.e. the efficient removal of microbes and particulate matter that might have gained access to the mucosa from the intestinal lumen while maintaining local tissue homeostasis, several phenotypic and functional adaptations evolved. Most notably, microbe-associated molecular pattern (MAMP) receptors, including the lipopolysaccharide receptors CD14 and TLR4, but also the Fc receptors for IgA and IgG are absent on most intestinal Mø. Here we review recent findings on the phenotypic and functional adaptations of intestinal Mø and their implications for the pathogenesis of inflammatory bowel diseases.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Spatio-temporal Associations Between GOES Aerosol Optical Depth Retrievals and Ground-Level PM2.5

We assess the strength of association between aerosol optical depth (AOD) retrievals from the GOES Aerosol/Smoke Product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform and therefore provides dense temporal coverage with half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over space at fixed times are lower. Simple averaging over time actually reduces correlations over space dramatically, but statistical calibration allows averaging over time that produces strong correlations. These results and the data density of GASP AOD highlight its potential to help improve exposure estimates for epidemiological analyses. On average 40% of days in a month have a GASP AOD retrieval compared to 14% for MODIS and 4% for MISR. Furthermore, GASP AOD has been retrieved since November 1994, providing the possibility of a long-term record that pre-dates the availability of most PM2.5 monitoring data and other satellite instruments.

Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.

Reduced Bayesian Hierarchical Models: Estimating Health Effects of Simultaneous Exposure to Multiple Pollutants

Quantifying the health effects associated with simultaneous exposure to many air pollutants is now a research priority of the US EPA. Bayesian hierarchical models (BHM) have been extensively used in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for potential confounding of other pollutants and other time-varying factors. However, when the scientific goal is to estimate the impacts of many pollutants jointly, a straightforward application of BHM is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters, which often do not have an easy interpretation. In this paper we introduce a new BHM formulation, which we call "reduced BHM", aimed at analyzing clustered data sets in the presence of a large number of random effects that are not of primary scientific interest. At the first stage of the reduced BHM, we calculate the integrated likelihood of the parameter of interest (e.g. excess number of deaths attributed to simultaneous exposure to high levels of many pollutants). At the second stage, we specify a flexible random-effect distribution directly on the parameter of interest. The reduced BHM overcomes many of the challenges in the specification and implementation of full BHM in the context of a large number of nuisance parameters. In simulation studies we show that the reduced BHM performs comparably to the full BHM in many scenarios, and even performs better in some cases. Methods are applied to estimate location-specific and overall relative risks of cardiovascular hospital admissions associated with simultaneous exposure to elevated levels of particulate matter and ozone in 51 US counties during the period 1999-2005.

Two dimensional Lattice Boltzmann simulation of fluid flow through an idealized micro-structure of disordered media

This technical report discusses the application of Lattice Boltzmann Method (LBM) in the fluid flow simulation through porous filter-wall of disordered media. The diesel particulate filter (DPF) is an example of disordered media. DPF is developed as a cutting edge technology to reduce harmful particulate matter in the engine exhaust. Porous filter-wall of DPF traps these soot particles in the after-treatment of the exhaust gas. To examine the phenomena inside the DPF, researchers are looking forward to use the Lattice Boltzmann Method as a promising alternative simulation tool. The lattice Boltzmann method is comparatively a newer numerical scheme and can be used to simulate fluid flow for single-component single-phase, single-component multi-phase. It is also an excellent method for modelling flow through disordered media. The current work focuses on a single-phase fluid flow simulation inside the porous micro-structure using LBM. Firstly, the theory concerning the development of LBM is discussed. LBM evolution is always related to Lattice gas Cellular Automata (LGCA), but it is also shown that this method is a special discretized form of the continuous Boltzmann equation. Since all the simulations are conducted in two-dimensions, the equations developed are in reference with D2Q9 (two-dimensional 9-velocity) model. The artificially created porous micro-structure is used in this study. The flow simulations are conducted by considering air and CO2 gas as fluids. The numerical model used in this study is explained with a flowchart and the coding steps. The numerical code is constructed in MATLAB. Different types of boundary conditions and their importance is discussed separately. Also the equations specific to boundary conditions are derived. The pressure and velocity contours over the porous domain are studied and recorded. The results are compared with the published work. The permeability values obtained in this study can be fitted to the relation proposed by Nabovati [8], and the results are in excellent agreement within porosity range of 0.4 to 0.8.

Evaluating the Effectiveness of a Commercial Portable Air Purifier in Homes with Wood Burning Stoves: A Preliminary Study

Wood burning for residential heating is prevalent in the Rocky Mountain regions of the United States. Studies have shown that wood stoves can be a significant source of PM2.5 within homes. In this study, the effectiveness of an electrostatic filter portable air purifier was evaluated (1) in a home where a wood stove was the sole heat source and (2) in a home where a wood stove was used as a supplemental heat source. Particle count concentrations in six particle sizes and particle mass concentrations in two particle sizes weremeasured for ten 12-hour purifier on and ten purifier off trials in each home. Particle count concentrations were reduced by 61–85 percent. Similar reductions were observed in particle mass concentrations. These findings, although limited to one season, suggest that a portable air purifier may effectively reduce indoor particulate matter concentrations associated with wood combustion during home heating.