scholarly journals Improvements to the WRF-Chem 3.5.1 model for quasi-hemispheric simulations of aerosols and ozone in the Arctic

2017 ◽  
Vol 10 (10) ◽  
pp. 3661-3677 ◽  
Author(s):  
Louis Marelle ◽  
Jean-Christophe Raut ◽  
Kathy S. Law ◽  
Larry K. Berg ◽  
Jerome D. Fast ◽  
...  
Keyword(s):  
Gas Phase ◽  
Trace Gases ◽  
Surface Ozone ◽  
The Arctic ◽  
Surface Model ◽  
Gas Phase Chemistry ◽  
Surface Temperatures ◽  
Phase Chemistry ◽  
Mean Square Errors ◽  
Snow And Ice

Abstract. In this study, the WRF-Chem regional model is updated to improve simulated short-lived pollutants (e.g., aerosols, ozone) in the Arctic. Specifically, we include in WRF-Chem 3.5.1 (with SAPRC-99 gas-phase chemistry and MOSAIC aerosols) (1) a correction to the sedimentation of aerosols, (2) dimethyl sulfide (DMS) oceanic emissions and gas-phase chemistry, (3) an improved representation of the dry deposition of trace gases over seasonal snow, and (4) an UV-albedo dependence on snow and ice cover for photolysis calculations. We also (5) correct the representation of surface temperatures over melting ice in the Noah Land Surface Model and (6) couple and further test the recent KF-CuP (Kain–Fritsch + Cumulus Potential) cumulus parameterization that includes the effect of cumulus clouds on aerosols and trace gases. The updated model is used to perform quasi-hemispheric simulations of aerosols and ozone, which are evaluated against surface measurements of black carbon (BC), sulfate, and ozone as well as airborne measurements of BC in the Arctic. The updated model shows significant improvements in terms of seasonal aerosol cycles at the surface and root mean square errors (RMSEs) for surface ozone, aerosols, and BC aloft, compared to the base version of the model and to previous large-scale evaluations of WRF-Chem in the Arctic. These improvements are mostly due to the inclusion of cumulus effects on aerosols and trace gases in KF-CuP (improved RMSE for surface BC and BC profiles, surface sulfate, and surface ozone), the improved surface temperatures over sea ice (surface ozone, BC, and sulfate), and the updated trace gas deposition and UV albedo over snow and ice (improved RMSE and correlation for surface ozone). DMS emissions and chemistry improve surface sulfate at all Arctic sites except Zeppelin, and correcting aerosol sedimentation has little influence on aerosols except in the upper troposphere.

scholarly journals Improvements to the WRF-Chem model for quasi-hemispheric simulations of aerosols and ozone in the Arctic

2017 ◽  
Author(s):  
Louis Marelle ◽  
Jean-Christophe Raut ◽  
Kathy S. Law ◽  
Larry K. Berg ◽  
Jerome D. Fast ◽  
...  
Keyword(s):  
Gas Phase ◽  
Trace Gases ◽  
Surface Ozone ◽  
The Arctic ◽  
Surface Model ◽  
Gas Phase Chemistry ◽  
Surface Temperatures ◽  
Phase Chemistry ◽  
Mean Square Errors ◽  
Snow And Ice

Abstract. In this study, the WRF-Chem regional model is updated to improve simulated short-lived pollutants (aerosols, ozone) in the Arctic. Specifically, we include in WRF-Chem 3.5.1 (with SAPRC-99 gas-phase chemistry and MOSAIC aerosols) (1) a correction to the sedimentation of aerosols, (2) dimethylsulfide (DMS) oceanic emissions and gas-phase chemistry, (3) an improved representation of the dry deposition of trace gases over seasonal snow, (4) an UV-albedo dependence on snow and ice cover for photolysis calculations. We also (5) correct the representation of surface temperatures over melting ice in the Noah Land Surface Model and (6) couple and further test the recent KF-CuP (Kain-Fritsch + Cumulus Potential) cumulus parameterization that includes the effect of cumulus clouds on aerosols and trace gases. The updated model is used to perform quasi-hemispheric simulations of aerosols and ozone, which are evaluated against surface measurements of black carbon (BC), sulfate, and ozone, and airborne measurements of BC in the Arctic. The updated model shows significant improvements in terms of seasonal aerosol cycles at the surface, root mean square errors (RMSE) for surface ozone and aerosols and BC aloft, compared to the base version of the model and to previous large-scale evaluations of WRF-Chem in the Arctic. These improvements are mostly due to the inclusion of cumulus effects on aerosols and trace gases in KF-CuP (improved RMSE for surface BC and BC profiles, surface sulfate and surface ozone), the improved surface temperatures over sea ice (surface ozone, BC, and sulfate), and the updated trace gas deposition and UV-albedo over snow and ice (improved RMSE and correlation for surface ozone). DMS emissions and chemistry improve surface sulfate at all Arctic sites except Zeppelin, and correcting aerosol sedimentation has little influence on aerosols except in the upper troposphere.

Different atmospheric O3 chemical environment in industrial regions of China, 2016: implications for emission control strategies and impacts on the globe in the future

2020 ◽  
Author(s):  
Zhenze Liu ◽  
Ruth M. Doherty ◽  
Oliver Wild ◽  
Fiona M. O’Connor
Keyword(s):  
Air Pollution ◽  
Gas Phase ◽  
Action Plan ◽  
Surface Ozone ◽  
Pollution Prevention ◽  
River Delta ◽  
Gas Phase Chemistry ◽  
Emission Controls ◽  
Industrial Regions ◽  
Phase Chemistry

<p>Surface ozone (O<sub>3</sub>) pollution became the main cause of atmospheric pollution over industrial regions in China since 2013, due to the effective mitigation of fine particulate matter (PM<sub>2.5</sub>) by stringent emission controls by Air Pollution Prevention and Control Action Plan (APPCAP). O<sub>3</sub>, as a secondary photochemical pollutant, poses a challenge to control due to its non-linear chemical relationship to precursors – nitrogen oxides (NO<sub>x</sub>), carbon monoxide (CO) and volatile organic compounds (VOCs).</p><p>We hence investigated the differences of atmospheric chemistry environment in the main industrial regions with high emissions – North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Chongqing - in summer 2016, China by using a global climate-chemistry model, based on United Kingdom Chemistry and Aerosol (UKCA). Anthropogenic Multi-resolution Emission Inventory for China (MEIC) 2013 and Hemispheric Transport of Air Pollution (HTAP) emissions 2010 for the rest of globe were used but scaled to 2016 regionally and nationally separately. In addition, we improved the gas-phase chemistry scheme by adding more highly reactive VOC tracers to better simulate regional pollution. Diurnal cycles of O<sub>3</sub> and NO<sub>x</sub> have been evaluated and the results show very good model-observation comparisons after modifying the gas-phase chemistry scheme. Radical (OH, RO<sub>2</sub> and HO<sub>2</sub>), NO<sub>x</sub> and VOC concentrations have also been examined. O<sub>3</sub> production rates and budgets calculated based on these show the highest production rate in YRD and the lowest in PRD due to different NO<sub>x</sub> and VOC concentration levels.</p><p>To investigate the O<sub>3 </sub>sensitivity — VOC limited or NO<sub>x</sub> limited, we quantified the O<sub>3</sub> response to VOCs and NO<sub>x</sub> in total 64 scenarios by scaling NO<sub>x </sub>and VOCs emissions. O<sub>3</sub> isopleths suggest that most regions are VOC limited, but the sensitivities vary. O<sub>3</sub> in YRD is more sensitive to NO<sub>x</sub> emission change but PRD can be effectively controlled by decreasing VOC emissions. The ratio of H<sub>2</sub>O<sub>2</sub> to HNO<sub>3</sub> is applied to provide a quick examination method of O<sub>3</sub> sensitivity. The contribution of O<sub>3</sub> from China to the global O<sub>3</sub> burden compared with other continents has also been quantified. The results show that the relatively higher O<sub>3</sub> concentration in Asia is mainly contributed by China, and O<sub>3</sub> becomes more sensitive to VOCs. The model allows us to provide a quantitative assessment of different emission controls on mitigating O<sub>3</sub> over China and the impacts of Chinese emissions on the global O<sub>3</sub> burden.</p>

2D InP etching simulation under high density plasma of chlorine

2003 ◽  
Vol 782 ◽  
Author(s):  
A. Rhallabi ◽  
B. Liu ◽  
G. Marcos ◽  
J. P. Landesman
Keyword(s):  
Kinetic Model ◽  
Gas Phase ◽  
Surface Model ◽  
Plasma Parameters ◽  
Source Power ◽  
Gas Phase Chemistry ◽  
Etching Profile ◽  
Phase Chemistry ◽  
Etch Profile ◽  
Inp Surface

ABSTRACTA gas phase kinetic model of chlorine in an ICP reactor (Inductive Coupled Plasma) combined with a surface model have been developed to study the etching profile evolution of InP material. A gas phase chemistry model is used to predict the main neutral and charged specie fluxes impinging upon etched InP surface. These particle fluxes are then injected as input parameters into both a Monte-Carlo sheath model and a 2D surface model to predict the etch profile topography. The coupling between the gas kinetic model, sheath and surface models allows a direct prediction of the InP etch profile evolution versus reactor parameters (pressure, source power, Cl2 flow rate, DC bias on the substrate‥). A parametric study is carried out to show the role of some plasma parameters on etch rate, anisotropy and adsorbed InP surface state by chlorine.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. 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 long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Gas Phase Chemistry in Cellulose Fast Pyrolysis

2009 ◽  
Vol 48 (3) ◽  
pp. 1391-1399 ◽  
Author(s):  
R. Lanza ◽  
D. Dalle Nogare ◽  
P. Canu
Keyword(s):  
Gas Phase ◽  
Fast Pyrolysis ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Mass Spectrometry and Gas-Phase Chemistry of Anilines

ChemInform ◽  
2007 ◽  
Vol 38 (30) ◽  
Author(s):  
Marcos N. Eberlin ◽  
Daniella Vasconcellos Augusti ◽  
Rodinei Augusti
Keyword(s):  
Mass Spectrometry ◽  
Gas Phase ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Kinetic Analysis of C4 Alkane and Alkene Pyrolysis: Implications for SOFC Operation

2010 ◽  
Vol 7 (4) ◽  
Author(s):  
Ahmed Al Shoaibi ◽  
Anthony M. Dean
Keyword(s):  
Gas Phase ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Pyrolysis Kinetics ◽  
Fuel Conversion ◽  
Substantial Impact ◽  
Gas Phase Chemistry ◽  
Gas Phase Kinetics ◽  
Hydrocarbon Gas ◽  
Phase Chemistry

Pyrolysis experiments of isobutane, isobutylene, and 1-butene were performed over a temperature range of 550–750°C and a pressure of ∼0.8 atm. The residence time was ∼5 s. The fuel conversion and product selectivity were analyzed at these temperatures. The pyrolysis experiments were performed to simulate the gas-phase chemistry that occurs in the anode channel of a solid-oxide fuel cell (SOFC). The experimental results confirm that molecular structure has a substantial impact on pyrolysis kinetics. The experimental data show considerable amounts of C5 and higher species (∼2.8 mole % with isobutane at 750°C, ∼7.5 mole % with isobutylene at 737.5°C, and ∼7.4 mole % with 1-butene at 700°C). The C5+ species are likely deposit precursors. The results confirm that hydrocarbon gas-phase kinetics have substantial impact on a SOFC operation.

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