scholarly journals Quantifying errors in the aerosol mixing-state index based on limited particle sample size

2020 ◽  
Vol 54 (12) ◽  
pp. 1527-1541
Author(s):  
J. T. Gasparik ◽  
Q. Ye ◽  
J. H. Curtis ◽  
A. A. Presto ◽  
N. M. Donahue ◽  
...  
Keyword(s):  
Sample Size ◽  
Mixing State ◽  
State Index ◽  
Aerosol Mixing State ◽  
Particle Sample

How many particles do we need to measure aerosol mixing state?

2020 ◽  
Author(s):  
Nicole Riemer ◽  
Jessica Gasparik ◽  
Qing Ye ◽  
Matthew West ◽  
Jeff Curtis ◽  
...  
Keyword(s):  
Sample Size ◽  
Single Particle ◽  
Measurement Techniques ◽  
Chemical Species ◽  
Finite Sample ◽  
Mixing State ◽  
Wide Range ◽  
Mixing States ◽  
The Impact ◽  
Aerosol Mixing State

<p>Atmospheric aerosols are evolving mixtures of different chemical species.  The term “aerosol mixing state” is commonly used to describe how different chemical species are distributed throughout a particle population.  A population is “fully internally mixed” if each individual particle consists of same species mixtures, whereas it is fully externally mixed if each particle only contains one species. Mixing state matters for aerosol health impacts and for climate-relevant aerosol properties, such as the particles’ propensity to form cloud droplets or the aerosol optical properties.</p><p>The mixing state metric χ quantifies the degree of internal or external mixing and can be calculated based on the particles’ species mass fractions. Several field studies have used this metric to quantify mixing states for different ambient environments using sophisticated single-particle measurement techniques. Inherent to these methods is a finite number of particles, ranging from a few hundred to several thousand particles, used to estimate the mixing state metric. </p><p>This study evaluates the error that is introduced in calculating χ due to a limited particle sample size.  We used the particle-resolved model PartMC-MOSAIC to generate a scenario library that encompasses a large number of reference particle populations and that represents a wide range of mixing states. We stochastically sub-sampled these particle populations using sample sizes of 10 to 10,000 particles and recalculated χ based on the sub-samples. This procedure mimics the impact of only having a limited sample size as it is common in real-world applications. The finite sample size leads to a consistent overestimation of χ, meaning that the populations appear more internally mixed than they are in reality. These findings are experimentally confirmed using single-particle SP-AMS measurement data from the Pittsburgh area. We also determined confidence intervals of χ for our sub-sampled populations. To determine χ within a range of  +/- 10 percentage points requires a sample size of at least 1000 particles.</p><p> </p>

scholarly journals Mixing state and compositional effects on CCN activity and droplet growth kinetics of size-resolved CCN in an urban environment

2012 ◽  
Vol 12 (21) ◽  
pp. 10239-10255 ◽  
Author(s):  
L. T. Padró ◽  
R. H. Moore ◽  
X. Zhang ◽  
N. Rastogi ◽  
R. J. Weber ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Water Soluble ◽  
Anthropogenic Sources ◽  
Droplet Growth ◽  
Mixing State ◽  
Aerosol Composition ◽  
Activation Kinetics ◽  
Aerosol Mixing State ◽  
Kinetics Of ◽  
Ccn Activity

Abstract. Aerosol composition and mixing state near anthropogenic sources can be highly variable and can challenge predictions of cloud condensation nuclei (CCN). The impacts of chemical composition on CCN activation kinetics is also an important, but largely unknown, aspect of cloud droplet formation. Towards this, we present in-situ size-resolved CCN measurements carried out during the 2008 summertime August Mini Intensive Gas and Aerosol Study (AMIGAS) campaign in Atlanta, GA. Aerosol chemical composition was measured by two particle-into-liquid samplers measuring water-soluble inorganic ions and total water-soluble organic carbon. Size-resolved CCN data were collected using the Scanning Mobility CCN Analysis (SMCA) method and were used to obtain characteristic aerosol hygroscopicity distributions, whose breadth reflects the aerosol compositional variability and mixing state. Knowledge of aerosol mixing state is important for accurate predictions of CCN concentrations and that the influence of an externally-mixed, CCN-active aerosol fraction varies with size from 31% for particle diameters less than 40 nm to 93% for accumulation mode aerosol during the day. Assuming size-dependent aerosol mixing state and size-invariant chemical composition decreases the average CCN concentration overprediction (for all but one mixing state and chemical composition scenario considered) from over 190–240% to less than 20%. CCN activity is parameterized using a single hygroscopicity parameter, κ, which averages to 0.16 ± 0.07 for 80 nm particles and exhibits considerable variability (from 0.03 to 0.48) throughout the study period. Particles in the 60–100 nm range exhibited similar hygroscopicity, with a κ range for 60 nm between 0.06–0.076 (mean of 0.18 ± 0.09). Smaller particles (40 nm) had on average greater κ, with a range of 0.20–0.92 (mean of 0.3 ± 0.12). Analysis of the droplet activation kinetics of the aerosol sampled suggests that most of the CCN activate as rapidly as calibration aerosol, suggesting that aerosol composition exhibits a minor (if any) impact on CCN activation kinetics.

scholarly journals Characterising mass-resolved mixing state of black carbon in Beijing using a morphology-independent measurement method

2020 ◽  
Vol 20 (6) ◽  
pp. 3645-3661 ◽  
Author(s):  
Chenjie Yu ◽  
Dantong Liu ◽  
Kurtis Broda ◽  
Rutambhara Joshi ◽  
Jason Olfert ◽  
...  
Keyword(s):  
Black Carbon ◽  
Atmospheric Aerosols ◽  
Particle Morphology ◽  
Urban Site ◽  
Inversion Method ◽  
Mixing State ◽  
State Index ◽  
Cloud Scavenging ◽  
Highly Correlated ◽  
State Mixing

Abstract. Refractory black carbon (rBC) in the atmosphere is known for its significant impacts on climate. The relationship between the microphysical and optical properties of rBC remains poorly understood and is influenced by its size and mixing state. Mixing state also influences its cloud scavenging potential and thus atmospheric lifetime. This study presents a coupling of a centrifugal particle mass analyser (CPMA) and a single-particle soot photometer (SP2) for the morphology-independent quantification of the mixing state of rBC-containing particles, used in the urban site of Beijing as part of the Air Pollution and Human Health–Beijing (APHH-Beijing) project during winter (10 November–10 December 2016) and summer (18 May–25 June 2017). This represents a highly dynamic polluted environment with a wide variety of conditions that could be considered representative of megacity area sources in Asia. An inversion method (used for the first time on atmospheric aerosols) is applied to the measurements to present two-variable distributions of both rBC mass and total mass of rBC-containing particles and calculate the mass-resolved mixing state of rBC-containing particles, using previously published metrics. The mass ratio between non-rBC material and rBC material (MR) is calculated to determine the thickness of a hypothetical coating if the rBC and other material followed a concentric sphere model (the equivalent coating thickness). The bulk MR (MRbulk) was found to vary between 2 and 12 in winter and between 2 and 3 in summer. This mass-resolved mixing state is used to derive the mass-weighted mixing state index for the rBC-containing particles (χrBC). χrBC quantifies how uniformly the non-rBC material is distributed across the rBC-containing-particle population, with 100 % representing uniform mixing. The χrBC in Beijing varied between 55 % and 70 % in winter depending on the dominant air masses, and χrBC was highly correlated with increased MRbulk and PM1 mass concentration in winter, whereas χrBC in summer varied significantly (ranging 60 %–75 %) within the narrowly distributed MRbulk and was found to be independent of air mass sources. In some model treatments, it is assumed that more atmospheric ageing causes the BC to tend towards a more homogeneous mixture, but this leads to the conclusion that the MRbulk may only act as a predictor of χrBC in winter. The particle morphology-independent and mass-based information on BC mixing used in this and future studies can be applied to mixing-state-aware models investigating atmospheric rBC ageing.

scholarly journals A Review of the Representation of Aerosol Mixing State in Atmospheric Models

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 168 ◽  
Author(s):  
Robin Stevens ◽  
Ashu Dastoor
Keyword(s):  
Optical Properties ◽  
Climate Models ◽  
Research Effort ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Heterogeneous Chemistry ◽  
Computationally Efficient ◽  
Atmospheric Models ◽  
Mixing State ◽  
Aerosol Mixing State

Aerosol mixing state significantly affects concentrations of cloud condensation nuclei (CCN), wet removal rates, thermodynamic properties, heterogeneous chemistry, and aerosol optical properties, with implications for human health and climate. Over the last two decades, significant research effort has gone into finding computationally-efficient methods for representing the most important aspects of aerosol mixing state in air pollution, weather prediction, and climate models. In this review, we summarize the interactions between mixing-state and aerosol hygroscopicity, optical properties, equilibrium thermodynamics and heterogeneous chemistry. We focus on the effects of simplified assumptions of aerosol mixing state on CCN concentrations, wet deposition, and aerosol absorption. We also summarize previous approaches for representing aerosol mixing state in atmospheric models, and we make recommendations regarding the representation of aerosol mixing state in future modelling studies.

scholarly journals Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing

2018 ◽  
Vol 18 (9) ◽  
pp. 6907-6921 ◽  
Author(s):  
Jingye Ren ◽  
Fang Zhang ◽  
Yuying Wang ◽  
Don Collins ◽  
Xinxin Fan ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Black Carbon ◽  
Cloud Condensation Nuclei ◽  
Field Measurements ◽  
Minor Role ◽  
Mixing State ◽  
Mixing Processes ◽  
Size Dependent ◽  
Aerosol Mixing State ◽  
Aerosol Chemical Composition

Abstract. Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external–internal size-resolved, abbreviated as EI–SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p∕m) were 0.90 – 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT–BK scheme) shows good closure with RCCN_p∕m of 1.0 –1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT–SR scheme) achieves better closure than the INT–BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI–SR and INT–SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p∕m of 0.66 – 0.75) when using the schemes of external mixtures with bulk (EXT–BK scheme) or size-resolved composition (EXT–SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

scholarly journals Single particle diversity and mixing state measurements

2014 ◽  
Vol 14 (12) ◽  
pp. 6289-6299 ◽  
Author(s):  
R. M. Healy ◽  
N. Riemer ◽  
J. C. Wenger ◽  
M. Murphy ◽  
M. West ◽  
...  
Keyword(s):  
Species Diversity ◽  
Ammonium Nitrate ◽  
Black Carbon ◽  
Mass Spectrometer ◽  
Single Particle ◽  
Organic Aerosol ◽  
Particle Mass ◽  
Mixing State ◽  
Mass Fractions ◽  
State Index

Abstract. A newly developed framework for quantifying aerosol particle diversity and mixing state based on information-theoretic entropy is applied for the first time to single particle mass spectrometry field data. Single particle mass fraction estimates for black carbon, organic aerosol, ammonium, nitrate and sulfate, derived using single particle mass spectrometer, aerosol mass spectrometer and multi-angle absorption photometer measurements are used to calculate single particle species diversity (Di). The average single particle species diversity (Dα) is then related to the species diversity of the bulk population (Dγ) to derive a mixing state index value (χ) at hourly resolution. The mixing state index is a single parameter representation of how internally/externally mixed a particle population is at a given time. The index describes a continuum, with values of 0 and 100% representing fully external and internal mixing, respectively. This framework was applied to data collected as part of the MEGAPOLI winter campaign in Paris, France, 2010. Di values are low (~ 2) for fresh traffic and wood-burning particles that contain high mass fractions of black carbon and organic aerosol but low mass fractions of inorganic ions. Conversely, Di values are higher (~ 4) for aged carbonaceous particles containing similar mass fractions of black carbon, organic aerosol, ammonium, nitrate and sulfate. Aerosol in Paris is estimated to be 59% internally mixed in the size range 150–1067 nm, and mixing state is dependent both upon time of day and air mass origin. Daytime primary emissions associated with vehicular traffic and wood-burning result in low χ values, while enhanced condensation of ammonium nitrate on existing particles at night leads to higher χ values. Advection of particles from continental Europe containing ammonium, nitrate and sulfate leads to increases in Dα, Dγ and χ. The mixing state index represents a useful metric by which to compare and contrast ambient particle mixing state at other locations globally.

Secondary aerosol formation alters CCN activity in the North China Plain

2021 ◽  
Author(s):  
Jiangchuan Tao ◽  
Ye Kuang ◽  
Nan Ma ◽  
Juan Hong ◽  
Yele Sun ◽  
...  
Keyword(s):  
Particle Size ◽  
North China Plain ◽  
North China ◽  
Particle Mass ◽  
Mixing State ◽  
Particle Size Range ◽  
The North ◽  
The North China Plain ◽  
Aerosol Mixing State ◽  
Ccn Activity

<p>The formation of secondary aerosols (SA, including secondary organic and inorganic aerosols, SOA and SIA) were the dominant sources of aerosol particles in the North China Plain and can result in significant variations of particle size distribution (PNSD) and hygroscopicity. Earlier studies have shown that the mechanism of SA formation can be affected by relative humidity (RH), and thus has different influences on the aerosol hygroscopicity and PNSD under different RH conditions. Based on the measurements of size-resolved particle activation ratio (SPAR), hygroscopicity distribution (GF-PDF), PM<sub>2.5</sub> chemical composition, PNSD, meteorology and gaseous pollutants in a recent field campaign McFAN (Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain) conducted at Gucheng site from November 16<sup>th</sup> to December 16<sup>th</sup> in 2018, the influences of SA formation on CCN activity and CCN number concentration (N<sub>CCN</sub>) calculation at super-saturation of 0.05% under different RH conditions were studied. Measurements showed that during daytime, SA formation could lead to a significant increase in N<sub>CCN</sub> and a strong diurnal variation in CCN activity. During periods with daytime minimum RH exceeding 50% (high RH conditions), SA formation significantly contributed to the particle mass/size changes in wide particle size range of 150 nm to 1000 nm, and led to an increase of N<sub>CCN</sub> in particle size range of 200 nm to 300 nm, while increases in particle mass concentration mainly occurred within particle sizes larger than 300nm. During periods with daytime minimum RH below 30% in (low RH conditions), SA formation mainly contributed to the particle mass/size and N<sub>CCN</sub> changes in particle sizes smaller than 300 nm. As a result, under the same amount SA formation induced mass increase, the increase of N<sub>CCN</sub> was weaker under high RH conditions, while stronger under low RH conditions. Moreover, the diurnal variations of aerosol mixing state (inferred from CCN measurements) due to SA formation was different under different RH conditions. If the variations of the aerosol mixing state were not considered, estimations of N<sub>CCN</sub> would bear significant deviations. By applying aerosol mixing state estimated by number fraction of hygroscopic particles from measurements of particle hygroscopicity or mass fraction of SA from measurements of particle chemical compositions, N<sub>CCN</sub> calculation can be largely improved with relative deviation within 30%. This study improves the understanding of the impact of SA formation on CCN activity and N<sub>CCN</sub> calculation, which is of great significance for improving parameterization of SA formation in aerosol models and CCN calculation in climate models.</p>

scholarly journals Toward Reduced Representation of Mixing State for Simulating Aerosol Effects on Climate

2017 ◽  
Vol 98 (5) ◽  
pp. 971-980 ◽  
Author(s):  
Laura Fierce ◽  
Nicole Riemer ◽  
Tami C. Bond
Keyword(s):  
Optical Properties ◽  
Cloud Condensation Nuclei ◽  
Mixing Time ◽  
Radiative Properties ◽  
Mixing State ◽  
Hygroscopic Properties ◽  
Model Aerosol ◽  
Aerosol Effects ◽  
Aerosol Mixing State ◽  
Individual Particles

Abstract Atmospheric aerosols affect Earth’s energy budget, and hence its climate, by scattering and absorbing solar radiation and by altering the radiative properties and the lifetime of clouds. These two major aerosol effects depend on the optical properties and the cloud-nucleating ability of individual particles, which, in turn, depend on the distribution of components among individual particles, termed the “aerosol mixing state.” Global models have moved toward including aerosol schemes to represent the evolution of particle characteristics, but individual particle properties cannot be resolved in global-scale simulations. Instead, models approximate the aerosol mixing state. The errors in climate-relevant aerosol properties introduced by such approximations may be large but have not yet been well quantified. This paper quantitatively addresses the question of to what extent the aerosol mixing state must be resolved to adequately represent the optical properties and cloud-nucleating properties of particle populations. Using a detailed benchmarking model to simulate gas condensation and particle coagulation, we show that, after the particles evolve in the atmosphere, simple mixing-state representations are sufficient for modeling cloud condensation nuclei concentrations, and we quantify the mixing time scale that characterizes this transformation. In contrast, a detailed representation of the mixing state is required to model aerosol light absorption, even for populations that are fully mixed with respect to their hygroscopic properties.

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