scholarly journals Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics

Atmosphere ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 15 ◽  
Author(s):  
Michael Hughes ◽  
John Kodros ◽  
Jeffrey Pierce ◽  
Matthew West ◽  
Nicole Riemer
Keyword(s):  
Machine Learning ◽  
Global Distribution ◽  
Mixing State ◽  
Aerosol Mixing State

scholarly journals Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

2020 ◽  
Author(s):  
Zhonghua Zheng ◽  
Jeffrey Curtis ◽  
Yu Yao ◽  
Jessica Gasparik ◽  
Valentine Anantharaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Global Scale ◽  
Earth System ◽  
Mixing State ◽  
Earth System Modeling ◽  
Submicron Aerosol ◽  
Aerosol Mixing State

scholarly journals Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

2020 ◽  
Author(s):  
Zhonghua Zheng ◽  
Jeffrey H. Curtis ◽  
Yu Yao ◽  
Jessica T. Gasparik ◽  
Valentine G. Anantharaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Global Scale ◽  
Earth System ◽  
Mixing State ◽  
Earth System Modeling ◽  
Submicron Aerosol ◽  
Aerosol Mixing State

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 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 Mapping the Potential Global Distribution of Red Imported Fire Ant (Solenopsis invicta Buren) Based on a Machine Learning Method

2020 ◽  
Vol 12 (23) ◽  
pp. 10182
Author(s):  
Shuai Chen ◽  
Fangyu Ding ◽  
Mengmeng Hao ◽  
Dong Jiang
Keyword(s):  
Machine Learning ◽  
Solenopsis Invicta ◽  
Central Africa ◽  
Global Distribution ◽  
Fire Ant ◽  
Machine Learning Method ◽  
Red Imported Fire Ant ◽  
Learning Method ◽  
Solenopsis Invicta Buren ◽  
Imported Fire Ant

As one of the most notorious invasive species, the red imported fire ant (Solenopsis invicta Buren) has many adverse impacts on biodiversity, environment, agriculture, and human health. Mapping the potential global distribution of S. invicta becomes increasingly important for the prevention and control of its invasion. By combining the most comprehensive occurrence records with an advanced machine learning method and a variety of geographical, climatic, and human factors, we have produced the potential global distribution maps of S. invicta at a spatial resolution of 5 × 5 km2. The results revealed that the potential distribution areas of S. invicta were primarily concentrated in southeastern North America, large parts of South America, East and Southeast Asia, and Central Africa. The deforested areas in Central Africa and the Indo-China Peninsula were particularly at risk from S. invicta invasion. In addition, this study found that human factors such as nighttime light and urban accessibility made considerable contributions to the boosted regression tree (BRT) model. The results provided valuable insights into the formulation of quarantine policies and prevention measures.

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