Dan Imre

Bio: Dan Imre is an academic researcher from Brookhaven National Laboratory. The author has contributed to research in topics: Particle & Particle size. The author has an hindex of 36, co-authored 63 publications receiving 3924 citations. Previous affiliations of Dan Imre include Pacific Northwest National Laboratory.

Evaporation Kinetics and Phase of Laboratory and Ambient Secondary Organic Aerosol

Abstract: Field measurements of secondary organic aerosol (SOA) find significantly higher mass loads than predicted by models, sparking intense effort focused on finding additional SOA sources but leaving the fundamental assumptions used by models unchallenged. Current air-quality models use absorptive partitioning theory assuming SOA particles are liquid droplets, forming instantaneous reversible equilibrium with gas phase. Further, they ignore the effects of adsorption of spectator organic species during SOA formation on SOA properties and fate. Using accurate and highly sensitive experimental approach for studying evaporation kinetics of size-selected single SOA particles, we characterized room-temperature evaporation kinetics of laboratory-generated α-pinene SOA and ambient atmospheric SOA. We found that even when gas phase organics are removed, it takes ∼24 h for pure α-pinene SOA particles to evaporate 75% of their mass, which is in sharp contrast to the ∼10 min time scale predicted by current kinetic models. Adsorption of “spectator” organic vapors during SOA formation, and aging of these coated SOA particles, dramatically reduced the evaporation rate, and in some cases nearly stopped it. Ambient SOA was found to exhibit evaporation behavior very similar to that of laboratory-generated coated and aged SOA. For all cases studied in this work, SOA evaporation behavior is nearly size-independent and does not follow the evaporation kinetics of liquid droplets, in sharp contrast with model assumptions. The findings about SOA phase, evaporation rates, and the importance of spectator gases and aging all indicate that there is need to reformulate the way SOA formation and evaporation are treated by models.

On the Evaporation Kinetics and Phase of Laboratory and Ambient Secondary Organic Aerosol

Abstract: Field measurements of secondary organic aerosol (SOA) find significantly higher mass loads than predicted by models, sparking intense effort focused on finding additional SOA sources but leaving the fundamental assumptions used by models unchallenged. Current air-quality models use absorptive partitioning theory assuming SOA particles are liquid droplets, forming instantaneous reversible equilibrium with gas phase. Further, they ignore the effects of adsorption of spectator organic species during SOA formation on SOA properties and fate. Using accurate and highly sensitive experimental approach for studying evaporation kinetics of size-selected single SOA particles, we characterized room-temperature evaporation kinetics of laboratory-generated α-pinene SOA and ambient atmospheric SOA. We found that even when gas phase organics are removed, it takes ∼24 h for pure α-pinene SOA particles to evaporate 75% of their mass, which is in sharp contrast to the ∼10 min time scale predicted by current kinetic models. Adsorption of “spectator” organic vapors during SOA formation, and aging of these coated SOA particles, dramatically reduced the evaporation rate, and in some cases nearly stopped it. Ambient SOA was found to exhibit evaporation behavior very similar to that of laboratory-generated coated and aged SOA. For all cases studied in this work, SOA evaporation behavior is nearly size-independent and does not follow the evaporation kinetics of liquid droplets, in sharp contrast with model assumptions. The findings about SOA phase, evaporation rates, and the importance of spectator gases and aging all indicate that there is need to reformulate the way SOA formation and evaporation are treated by models.

Nonequilibrium atmospheric secondary organic aerosol formation and growth

Abstract: Airborne particles play critical roles in air quality, health effects, visibility, and climate Secondary organic aerosols (SOA) formed from oxidation of organic gases such as α-pinene account for a significant portion of total airborne particle mass Current atmospheric models typically incorporate the assumption that SOA mass is a liquid into which semivolatile organic compounds undergo instantaneous equilibrium partitioning to grow the particles into the size range important for light scattering and cloud condensation nuclei activity We report studies of particles from the oxidation of α-pinene by ozone and NO3 radicals at room temperature SOA is primarily formed from low-volatility ozonolysis products, with a small contribution from higher volatility organic nitrates from the NO3 reaction Contrary to expectations, the particulate nitrate concentration is not consistent with equilibrium partitioning between the gas phase and a liquid particle Rather the fraction of organic nitrates in the particles is only explained by irreversible, kinetically determined uptake of the nitrates on existing particles, with an uptake coefficient that is 16% of that for the ozonolysis products If the nonequilibrium particle formation and growth observed in this atmospherically important system is a general phenomenon in the atmosphere, aerosol models may need to be reformulated The reformulation of aerosol models could impact the predicted evolution of SOA in the atmosphere both outdoors and indoors, its role in heterogeneous chemistry, its projected impacts on air quality, visibility, and climate, and hence the development of reliable control strategies

Deliquescence, Efflorescence, and Water Activity in Ammonium Nitrate and Mixed Ammonium Nitrate/Succinic Acid Microparticles

Abstract: Atmospheric aerosols have a direct impact on the earth’s radiation balance, an effect opposite in sign to that of the greenhouse gases. By scattering incoming solar radiation, either directly or indirectly as cloud particles, aerosols exert a cooling effect on the earth’s climate. In addition, they may provide catalytic sites for heterogeneous reactions to occur. The extent to which atmospheric particles will scatter light and be chemically reactive is directly related to their size, composition, and physical state. Tropospheric aerosols, both natural and anthropogenic in origin, are mostly composed of hygroscopic inorganic salts such as ammonium nitrate yet may contain organic compounds. These mixed particles may behave quite differently from the pure particles under changing relative humidity (RH) conditions. Therefore, in order to gain an understanding of these atmospheric processes and provide accurate input for regional climate models, it is imperative to understand the thermodynamics and kinetics of internally mixed particles under variable

From Agglomerates of Spheres to Irregularly Shaped Particles: Determination of Dynamic Shape Factors from Measurements of Mobility and Vacuum Aerodynamic Diameters

Abstract: With the advent of advanced real-time aerosol instrumentation, it has become possible to simultaneously measure individual particle mobility and vacuum aerodynamic diameters. This paper presents an experimental exploration of the effect of particle shape on the relationship between mobility and vacuum aerodynamic diameters. We make measurements on systems of three types: (1) Agglomerates of spheres, for which the density and the volume are known; (2) Ammonium sulfate, sodium chloride, succinic acid and lauric acid irregularly shaped particles of known density; and (3) Internally mixed particles, containing organics and ammonium sulfate, of unknown density and shape. For agglomerates of spheres we observe and quantify alignment effects in the Differential Mobility Analyzer (DMA), an important consequence of which is that mobility diameter of aspherical particles can be a function of DMA operating voltages. We report the first measurements of the dynamic shape factors (DSFs) in free molecular regime. We rep...

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The formation, properties and impact of secondary organic aerosol: current and emerging issues

Abstract: Secondary organic aerosol (SOA) accounts for a significant fraction of ambient tropospheric aerosol and a detailed knowledge of the formation, properties and transformation of SOA is therefore required to evaluate its impact on atmospheric processes, climate and human health. The chemical and physical processes associated with SOA formation are complex and varied, and, despite considerable progress in recent years, a quantitative and predictive understanding of SOA formation does not exist and therefore represents a major research challenge in atmospheric science. This review begins with an update on the current state of knowledge on the global SOA budget and is followed by an overview of the atmospheric degradation mechanisms for SOA precursors, gas-particle partitioning theory and the analytical techniques used to determine the chemical composition of SOA. A survey of recent laboratory, field and modeling studies is also presented. The following topical and emerging issues are highlighted and discussed in detail: molecular characterization of biogenic SOA constituents, condensed phase reactions and oligomerization, the interaction of atmospheric organic components with sulfuric acid, the chemical and photochemical processing of organics in the atmospheric aqueous phase, aerosol formation from real plant emissions, interaction of atmospheric organic components with water, thermodynamics and mixtures in atmospheric models. Finally, the major challenges ahead in laboratory, field and modeling studies of SOA are discussed and recommendations for future research directions are proposed.

Organic aerosol and global climate modelling: a review

Abstract: The present paper reviews existing knowledge with regard to Organic Aerosol (OA) of importance for global climate modelling and defines critical gaps needed to reduce the involved uncertainties. All pieces required for the representation of OA in a global climate model are sketched out with special attention to Secondary Organic Aerosol (SOA): The emission estimates of primary carbonaceous particles and SOA precursor gases are summarized. The up-to-date understanding of the chemical formation and transformation of condensable organic material is outlined. Knowledge on the hygroscopicity of OA and measurements of optical properties of the organic aerosol constituents are summarized. The mechanisms of interactions of OA with clouds and dry and wet removal processes parameterisations in global models are outlined. This information is synthesized to provide a continuous analysis of the flow from the emitted material to the atmosphere up to the point of the climate impact of the produced organic aerosol. The sources of uncertainties at each step of this process are highlighted as areas that require further studies.