Emulation of an atmospheric gas-phase chemistry solver through deep learning: Case study of Chinese Mainland

2021 ◽  
pp. 101079
Author(s):  
Chang Liu ◽  
Hairui Zhang ◽  
Zhen Cheng ◽  
Juanyong Shen ◽  
Junhao Zhao ◽  
...  
2020 ◽  
Author(s):  
Basit Khan ◽  
Sabine Banzhaf ◽  
Edward C. Chan ◽  
Renate Forkel ◽  
Farah Kanani-Sühring ◽  
...  

Abstract. In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence resolving PALM model system 6.0. The new chemistry model is part of the PALM-4U components (read: PALM for you; PALM for urban applications) which are designed for application of PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3), has been utilised for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state to more complex mechanisms such as CBM4, which includes major pollutants namely O3, NO, NO2, CO, a simplified VOC chemistry and a small number of products. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure along with its various features, input requirements, its application and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study is comprised of part of Berlin, Germany, covering an area of 6.71 × 6.71 km with a horizontal resolution of 10 m. We used "PARAMETERIZED" emission mode of the chemistry model that only considers emissions from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. The results show importance of online photochemistry and dispersion of air pollutants in the urban boundary layer. The simulated NOx and O3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. CBM4 and SMOG mechanisms show better agreement with observations than the steady state PHSTAT mechanism.


2017 ◽  
Vol 5 (23) ◽  
pp. 5818-5823 ◽  
Author(s):  
Pontus Stenberg ◽  
Örjan Danielsson ◽  
Edvin Erdtman ◽  
Pitsiri Sukkaew ◽  
Lars Ojamäe ◽  
...  

We show by a combination of experiments and gas phase kinetics modeling that the combinations of precursors with the most well-matched gas phase chemistry kinetics gives the largest area of homoepitaxial growth of SiC.


2021 ◽  
Vol 14 (2) ◽  
pp. 1171-1193 ◽  
Author(s):  
Basit Khan ◽  
Sabine Banzhaf ◽  
Edward C. Chan ◽  
Renate Forkel ◽  
Farah Kanani-Sühring ◽  
...  

Abstract. In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence-resolving PALM model system 6.0 (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name). The new chemistry model is implemented in the PALM model as part of the PALM-4U (PALM for urban applications) components, which are designed for application of the PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3) has been utilized for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state (PHSTAT) to mechanisms with a strongly simplified volatile organic compound (VOC) chemistry (e.g. the SMOG mechanism from KPP) and the Carbon Bond Mechanism 4 (CBM4; Gery et al., 1989), which includes a more comprehensive, but still simplified VOC chemistry. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure and input requirements along with its various features and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study comprises part of Berlin, Germany. Emissions are considered using street-type-dependent emission factors from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied, and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. Even though the feedback of the model's aerosol concentrations on meteorology is not yet considered in the current version of the model, the results show the importance of online photochemistry and dispersion of air pollutants in the urban boundary layer for high spatial and temporal resolutions. The simulated NOx and O3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. The CBM4 and SMOG mechanisms show better agreement with observations than the steady-state PHSTAT mechanism.


2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans

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.


2009 ◽  
Vol 48 (3) ◽  
pp. 1391-1399 ◽  
Author(s):  
R. Lanza ◽  
D. Dalle Nogare ◽  
P. Canu

ChemInform ◽  
2007 ◽  
Vol 38 (30) ◽  
Author(s):  
Marcos N. Eberlin ◽  
Daniella Vasconcellos Augusti ◽  
Rodinei Augusti

Author(s):  
Ahmed Al Shoaibi ◽  
Anthony M. Dean

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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