Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry is a high-dimensionality, large-data problem and thus may be suited to machine-learning algorithms. We show here the potential of a random forest regression algorithm to replace the gas-phase chemistry solver in the GEOS-Chem chemistry model. In this proof-of-concept study, we used one month of model output to train random forest regression models to predict the concentrations of each long-lived chemical species after integration based upon the physical and chemical conditions before the chemical integration. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for very long-lived species and the absolute concentration for shorter lived species. The skill of the machine learning algorithm is further improved by using a family approach for NO and NO2 rather than treating them independently.By replacing the numerical integrator with the random forest algorithm and running this model for one month, we find that the model is able to reproduce many of the features of the reference chemistry simulation. Replacing the integration methodology with a machine learning algorithm has the potential to be substantially faster. There are a wide range of applications for such an approach, e.g. to generate boundary conditions, for use in air quality forecasts or chemical data assimilation systems, etc

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it

Evaluating the impact of blowing-snow sea salt aerosol on springtime BrO and O3 in the Arctic

We use the GEOS-Chem chemical transport model to examine the influence of bromine release from blowingsnow sea salt aerosol (SSA) on springtime bromine activation and O3 depletion events (ODEs) in the Arctic lower troposphere. We evaluate our simulation against observations of tropospheric BrO vertical column densities (VCDtropo) from the GOME-2 (second Global Ozone Monitoring Experiment) and Ozone Monitoring Instrument (OMI) spaceborne instruments for 3 years (2007-2009), as well as against surface observations of O3. We conduct a simulation with blowingsnow SSA emissions from first-year sea ice (FYI; with a surface snow salinity of 0.1 psu) and multi-year sea ice (MYI; with a surface snow salinity of 0.05 psu), assuming a factor of 5 bromide enrichment of surface snow relative to seawater. This simulation captures the magnitude of observed March-April GOME-2 and OMI VCDtropo to within 17 %, as well as their spatiotemporal variability (r D 0:76-0.85). Many of the large-scale bromine explosions are successfully reproduced, with the exception of events in May, which are absent or systematically underpredicted in the model. If we assume a lower salinity on MYI (0.01 psu), some of the bromine explosions events observed over MYI are not captured, suggesting that blowing snow over MYI is an important source of bromine activation. We find that the modeled atmospheric deposition onto snow-covered sea ice becomes highly enriched in bromide, increasing from enrichment factors of ~ 5 in September-February to 10-60 in May, consistent with composition observations of freshly fallen snow. We propose that this progressive enrichment in deposition could enable blowing-snow-induced halogen activation to propagate into May and might explain our late-spring underestimate in VCDtropo. We estimate that the atmospheric deposition of SSA could increase snow salinity by up to 0.04 psu between February and April, which could be an important source of salinity for surface snow on MYI as well as FYI covered by deep snowpack. Inclusion of halogen release from blowing-snow SSA in our simulations decreases monthly mean Arctic surface O3 by 4-8 ppbv (15 %-30 %) in March and 8-14 ppbv (30 %-40 %) in April. We reproduce a transport event of depleted O3 Arctic air down to 40 N observed at many sub-Arctic surface sites in early April 2007. While our simulation captures 25 %-40 % of the ODEs observed at coastal Arctic surface sites, it underestimates the magnitude of many of these events and entirely misses 60 %-75 % of ODEs. This difficulty in reproducing observed surface ODEs could be related to the coarse horizontal resolution of the model, the known biases in simulating Arctic boundary layer exchange processes, the lack of detailed chlorine chemistry, and/or the fact that we did not include direct halogen activation by snowpack chemistry

Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models

Aromatic hydrocarbons, including benzene, toluene, and xylenes, play an important role in atmospheric chemistry, but the associated chemical mechanisms are complex and uncertain. Sparing representation of this chemistry in models is needed for computational tractability. Here, we develop a new compact mechanism for aromatic chemistry (GC13) that captures current knowledge from laboratory and computational studies with only 17 unique species and 44 reactions. We compare GC13 to six other currently used mechanisms of varying complexity in box model simulations of environmental chamber data and diurnal boundary layer chemistry, and show that GC13 provides results consistent with or better than more complex mechanisms for oxygenated products (alcohols, carbonyls, dicarbonyls), ozone, and hydrogen oxide (HOxOH+HO2) radicals. Specifically, GC13 features increased radical recycling and increased ozone destruction from phenoxy-phenylperoxy radical cycling relative to other mechanisms. We implement GC13 into the GEOS-Chem global chemical transport model and find higher glyoxal yields and net ozone loss from aromatic chemistry compared with other mechanisms. Aromatic oxidation in the model contributes 23%, 5%, and 8% of global glyoxal, methylglyoxal, and formic acid production, respectively, and has mixed effects on formaldehyde. It drives small decreases in global tropospheric OH (-2.2%), NOx (NO+NO2; -3.7%), and ozone (-0.8%), but a large increase in NO3 (+22%) from phenoxy-phenylperoxy radical cycling. Regional effects in polluted environments can be substantially larger, especially from the photolysis of carbonyls produced by aromatic oxidation, which drives large wintertime increases in OH and ozone concentrations

Radiative effect of clouds on tropospheric chemistry in a global three‐dimensional chemical transport model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94988/1/jgrd12465.pd

Biomechanical factors in atherosclerosis: mechanisms and clinical implications†

Blood vessels are exposed to multiple mechanical forces that are exerted on the vessel wall (radial, circumferential and longitudinal forces) or on the endothelial surface (shear stress). The stresses and strains experienced by arteries influence the initiation of atherosclerotic lesions, which develop at regions of arteries that are exposed to complex blood flow. In addition, plaque progression and eventually plaque rupture is influenced by a complex interaction between biological and mechanical factors—mechanical forces regulate the cellular and molecular composition of plaques and, conversely, the composition of plaques determines their ability to withstand mechanical load. A deeper understanding of these interactions is essential for designing new therapeutic strategies to prevent lesion development and promote plaque stabilization. Moreover, integrating clinical imaging techniques with finite element modelling techniques allows for detailed examination of local morphological and biomechanical characteristics of atherosclerotic lesions that may be of help in prediction of future events. In this ESC Position Paper on biomechanical factors in atherosclerosis, we summarize the current ‘state of the art' on the interface between mechanical forces and atherosclerotic plaque biology and identify potential clinical applications and key questions for future researc

Do little embryos make big decisions? How maternal dietary protein restriction can permanently change an embryo's potential, affecting adult health

Periconceptional environment may influence embryo development, ultimately affecting adult health. Here, we review the rodent model of maternal low-protein diet specifically during the preimplantation period (Emb-LPD) with normal nutrition during subsequent gestation and postnatally. This model, studied mainly in the mouse, leads to cardiovascular, metabolic and behavioural disease in adult offspring, with females more susceptible. We evaluate the sequence of events from diet administration that may lead to adult disease. Emb-LPD changes maternal serum and/or uterine fluid metabolite composition, notably with reduced insulin and branched-chain amino acids. This is sensed by blastocysts through reduced mammalian target of rapamycin complex 1 signalling. Embryos respond by permanently changing the pattern of development of their extra-embryonic lineages, trophectoderm and primitive endoderm, to enhance maternal nutrient retrieval during subsequent gestation. These compensatory changes include stimulation in proliferation, endocytosis and cellular motility, and epigenetic mechanisms underlying them are being identified. Collectively, these responses act to protect fetal growth and likely contribute to offspring competitive fitness. However, the resulting growth adversely affects long-term health because perinatal weight positively correlates with adult disease risk. We argue that periconception environmental responses reflect developmental plasticity and 'decisions' made by embryos to optimise their own development, but with lasting consequences