Simulating the forest fire plume dispersion, chemistry, and aerosol formation using SAM-ASP version 1.0

Abstract. Biomass burning is a major source of trace gases and aerosols that can ultimately impact health, air quality, and climate. Global and regional-scale three-dimensional Eulerian chemical transport models (CTMs) use estimates of the primary emissions from fires and can unphysically mix them across large-scale grid boxes, leading to incorrect estimates of the impact of biomass burning events. On the other hand, plume-scale process models allow for explicit simulation and examination of the chemical and physical transformations of trace gases and aerosols within biomass burning smoke plumes, and they may be used to develop parameterizations of this aging process for coarser grid-scale models. Here we describe the coupled SAM-ASP plume-scale process model, which consists of coupling the large-eddy simulation model, the System for Atmospheric Modelling (SAM), with the detailed gas and aerosol chemistry model, the Aerosol Simulation Program (ASP). We find that the SAM-ASP version 1.0 model is able to correctly simulate the dilution of CO in a California chaparral smoke plume, as well as the chemical loss of NOx, HONO, and NH3 within the plume, the formation of PAN and O3, the loss of OA, and the change in the size distribution of aerosols as compared to measurements and previous single-box model results. The newly coupled model is able to capture the cross-plume vertical and horizontal concentration gradients as the fire plume evolves downwind of the emission source. The integration and evaluation of SAM-ASP version 1.0 presented here will support the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser grid-scale CTMs.

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Simulating Forest Fire Plume Dispersion, Chemistry, and Aerosol Formation Using SAM-ASP version 1.0

10.5194/gmd-2019-221 ◽

2019 ◽

Author(s):

Chantelle R. Lonsdale ◽

Matthew J. Alvarado ◽

Anna L. Hodshire ◽

Emily Ramnarine ◽

Jeffrey R. Pierce

Keyword(s):

Biomass Burning ◽

Process Model ◽

Large Scale ◽

Trace Gases ◽

Regional Scale ◽

Process Models ◽

Plume Dispersion ◽

Aerosol Formation ◽

Fire Plume ◽

The Impact

Abstract. Biomass burning is a major source of trace gases and aerosols that can ultimately impact health, air quality, and climate. Global and regional-scale three-dimensional Eulerian chemical transport models (CTMs) use estimates of the primary emissions from fires and can unphysically mix them across large-scale grid boxes, leading to incorrect estimates of the impact of biomass burning events. On the other hand, plume-scale process models allow for explicit simulation and examination of the chemical and physical transformations of trace gases and aerosols within biomass burning smoke plumes, and they may be used to develop parameterizations of this aging process for coarser grid-scale models. Here we describe the coupled SAM-ASP plume-scale process model, which consists of coupling the large-eddy simulation model, the System for Atmospheric Modelling (SAM) version 1.0, with the detailed gas and aerosol chemistry model, the Aerosol Simulation Program (ASP). We find that the SAM-ASP version 1.0 model is able to correctly simulate the dilution of CO in a California chaparral smoke plume, as well as the chemical loss of NOx, HONO, and NH3 within the plume, the formation of PAN and O3, the loss of OA, and the change in the size distribution of aerosols as compared to measurements and previous single-box model results. The newly coupled model is able to capture the cross-plume vertical and horizontal concentration gradients as the fire plume evolves downwind of the emission source. The integration and evaluation of SAM-ASP presented here will support the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser grid-scale CTMs.

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The Environmental Effects of the April 2020 Wildfires and the Cs-137 Re-Suspension in the Chernobyl Exclusion Zone: A Multi-Hazard Threat

Atmosphere ◽

10.3390/atmos12040467 ◽

2021 ◽

Vol 12 (4) ◽

pp. 467

Author(s):

Rocío Baró ◽

Christian Maurer ◽

Jerome Brioude ◽

Delia Arnold ◽

Marcus Hirtl

Keyword(s):

Air Quality ◽

Nuclear Power ◽

Transport Model ◽

Atmospheric Transport ◽

Point Of View ◽

Plume Dispersion ◽

Exclusion Zone ◽

Fire Plume ◽

Mean Values ◽

The Impact

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

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Vulnerability of coastal areas to increased aquifer saturation due to climate change

10.5194/egusphere-egu21-14870 ◽

2021 ◽

Author(s):

Alexandre Gauvain ◽

Ronan Abhervé ◽

Jean-Raynald de Dreuzy ◽

Luc Aquilina ◽

Frédéric Gresselin

Keyword(s):

Climate Change ◽

Sea Level ◽

Large Scale ◽

Regional Scale ◽

Drainage System ◽

Hydrological Regime ◽

Coastal Areas ◽

Western Coast ◽

Study Sites ◽

The Impact

Like in other relatively flat coastal areas, flooding by aquifer overflow is a recurring problem on the western coast of Normandy (France). Threats are expected to be enhanced by the rise of the sea level and to have critical consequences on the future development and management of the territory. The delineation of the increased saturation areas is a required step to assess the impact of climate change locally. Preliminary models showed that vulnerability does not result only from the sea side but also from the continental side through the modifications of the hydrological regime.We investigate the processes controlling these coastal flooding phenomena by using hydrogeological models calibrated at large scale with an innovative method reproducing the hydrographic network. Reference study sites selected for their proven sensitivity to flooding have been used to validate the methodology and determine the influence of the different geomorphological configurations frequently encountered along the coastal line.Hydrogeological models show that the rise of the sea level induces an irregular increase in coastal aquifer saturations extending up to several kilometers inland. Back-littoral channels traditionally used as a large-scale drainage system against high tides limits the propagation of aquifer saturation upstream, provided that channels are not dominantly under maritime influence. High seepage fed by increased recharge occurring in climatic extremes may extend the vulnerable areas and further limit the effectiveness of the drainage system. Local configurations are investigated to categorize the influence of the local geological and geomorphological structures and upscale it at the regional scale.

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Trace gases and organic aerosol at a rural site in Vietnam during large scale biomass burning

10.5194/egusphere-egu21-181 ◽

2021 ◽

Author(s):

Simone M. Pieber ◽

Dac-Loc Nguyen ◽

Hendryk Czech ◽

Stephan Henne ◽

Nicolas Bukowiecki ◽

...

Keyword(s):

Air Quality ◽

Chemical Composition ◽

Biomass Burning ◽

Large Scale ◽

Trace Gases ◽

Organic Aerosol ◽

Trace Gas ◽

Rural Site ◽

Air Masses ◽

Mixing Ratios

Open biomass burning (BB) is a globally widespread phenomenon. The fires release pollutants, which are harmful for human and ecosystem health and alter the Earth's radiative balance. Yet, the impact of various types of BB on the global radiative forcing remains poorly constrained concerning greenhouse gas emissions, BB organic aerosol (OA) chemical composition and related light absorbing properties. Fire emissions composition is influenced by multiple factors (e.g., fuel and thereby vegetation-type, fuel moisture, fire temperature, available oxygen). Due to regional variations in these parameters, studies in different world regions are needed. Here we investigate the influence of seasonally recurring BB on trace gas concentration and air quality at the regional Global Atmosphere Watch (GAW) station Pha Din (PDI) in rural Northwestern Vietnam. PDI is located in a sparsely populated area on the top of a hill (1466 m a.s.l.) and is well suited to study the large-scale fires on the Indochinese Peninsula, whose pollution plumes are frequently transported towards the site [1]. We present continuous trace gas observations of CO2, CH4, CO, and O3 conducted at PDI since 2014 and interpret the data with atmospheric transport simulations. Annually recurrent large scale BB leads to hourly time-scale peaks CO mixing ratios at PDI of 1000 to 1500 ppb around every April since the start of data collection in 2014. We complement this analysis with carbonaceous PM2.5 chemical composition analyzed during an intensive campaign in March-April 2015. This includes measurements of elemental and organic carbon (EC/OC) and more than 50 organic markers, such as sugars, PAHs, fatty acids and nitro-aromatics [2]. For the intensive campaign, we linked CO, CO2, CH4 and O3 mixing ratios to a statistical classification of BB events, which is based on OA composition. We found increased CO and O3 levels during medium and high BB influence during the intensive campaign. A backward trajectory analysis confirmed different source regions for the identified periods based on the OA cluster. Typically, cleaner air masses arrived from northeast, i.e., mainland China and Yellow sea during the intensive campaign. The more polluted periods were characterized by trajectories from southwest, with more continental recirculation of the medium cluster, and more westerly advection for the high cluster. These findings highlight that BB activities in Northern Southeast Asia significantly enhances the regional OA loading, chemical PM2.5 composition and the trace gases in northwestern Vietnam. The presented analysis adds valuable data on air quality in a region of scarce data availability.&#160;REFERENCES[1] Bukowiecki, N. et al. Effect of Large-scale Biomass Burning on Aerosol Optical Properties at the GAW Regional Station Pha Din, Vietnam. AAQR. 19, 1172&#8211;1187 (2019).[2] Nguyen, D. L, et al. Carbonaceous aerosol composition in air masses influenced by large-scale biomass burning: a case-study in Northwestern Vietnam. ACPD., https://doi.org/10.5194/acp-2020-1027, in review, 2020.

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Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Processes Discovered from Event Log Datasets

Applied Sciences ◽

10.3390/app10041493 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1493 ◽

Cited By ~ 1

Author(s):

Kwanghoon Pio Kim

Keyword(s):

Process Model ◽

Large Scale ◽

Process Mining ◽

Functional Integration ◽

Structural Complexity ◽

Integrated Approach ◽

Parallel Process ◽

Process Models ◽

Massively Parallel ◽

Event Log

In this paper, we propose an integrated approach for seamlessly and effectively providing the mining and the analyzing functionalities to redesigning work for very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. More precisely, we excogitate a series of functional algorithms for extracting the structural constructs and for visualizing the behavioral properness of those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

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Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

Atmospheric Measurement Techniques ◽

10.5194/amt-9-5591-2016 ◽

2016 ◽

Vol 9 (11) ◽

pp. 5591-5606 ◽

Cited By ~ 2

Author(s):

Eleonora Aruffo ◽

Fabio Biancofiore ◽

Piero Di Carlo ◽

Marcella Busilacchio ◽

Marco Verdecchia ◽

...

Keyword(s):

Biomass Burning ◽

Atmospheric Pressure ◽

Thermal Dissociation ◽

Ann Model ◽

Fire Plume ◽

Pressure Threshold ◽

Peroxy Nitrates ◽

Artificial Neural Network Ann ◽

The Impact

Abstract. Total peroxy nitrate ( ∑ PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1) ∑ PN concentrations higher than 6 times the standard deviation above the background and (2) ∑ PN concentrations higher than the 99th percentile of the ∑ PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold ( ∼  750 hPa) as ancillary parameter to ∑ PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.

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Data-Driven Adaptive Observer for Fault Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2012/832836 ◽

2012 ◽

Vol 2012 ◽

pp. 1-21 ◽

Cited By ~ 90

Author(s):

Shen Yin ◽

Xuebo Yang ◽

Hamid Reza Karimi

Keyword(s):

Fault Diagnosis ◽

Process Model ◽

Large Scale ◽

Process Models ◽

Physical Models ◽

Adaptive Observer ◽

Data Driven ◽

Process Data ◽

Tank System ◽

Residual Generator

This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

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Functional Integration with Process Mining and Process Analyzing for Structural and Behavioral Properness Validation of Discovered Processes from Event Log Datasets

10.20944/preprints202002.0122.v1 ◽

2020 ◽

Author(s):

Kwanghoon Kim

Keyword(s):

Process Model ◽

Large Scale ◽

Process Mining ◽

Functional Integration ◽

Structural Complexity ◽

Integrated Approach ◽

Parallel Process ◽

Process Models ◽

Massively Parallel ◽

Event Log

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

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Mid-21st century air quality at the urban scale under the influence of changed climate and emissions: case studies for Paris and Stockholm

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-27041-2015 ◽

2015 ◽

Vol 15 (19) ◽

pp. 27041-27085

Author(s):

K. Markakis ◽

M. Valari ◽

M. Engardt ◽

G. Lacressonnière ◽

R. Vautard ◽

...

Keyword(s):

Climate Change ◽

Urban Areas ◽

Large Scale ◽

Regional Scale ◽

Influential Factor ◽

Daily Average ◽

Pollutant Concentrations ◽

Emission Projections ◽

The Impact ◽

The City

Abstract. Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modeled at 4 and 1 \\unit{km} horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional scale emission projections over the study areas by comparing modeled pollutant concentrations between the fine and coarse scale simulations. We show that over urban areas with major regional contribution (e.g., the city of Stockholm) the bias due to coarse emission inventory may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modeling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily average and maximum) is up to −5 % for Paris and −2 % for Stockholm city. The joined climate benefit on PM2.5 and PM10 in Paris is between −10 and −5 % while for Stockholm we observe mixed trends up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily average and maximum ozone and 20 % for PM and through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily average ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting in a health impact perspective.

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Alignment of a Collaborative Resistance Model With a Change Management Process in Industry: A Case Study on Production Automation

Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC) ◽

10.1115/detc2020-22183 ◽

2020 ◽

Author(s):

Nicole Zero ◽

Joshua D. Summers

Keyword(s):

Change Management ◽

Process Model ◽

Large Scale ◽

Process Models ◽

Automation System ◽

Management Process ◽

Structured Interviews ◽

Resistance Model ◽

Production Automation

Abstract Current research and literature lack the discussion of how production automation is introduced to existing lines from the perspective of change management. This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen semi-structured interviews of eight engineers from three functional backgrounds, a process model was created to understand how the company of study introduced a new automation system into their existing production line, while also noting obstacles identified in the process. This process model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in literature to understand critical elements found within change management. The process that was revealed in the case study appeared to contain some elements of a design process as compared to traditional change management processes found in literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the objective analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the process.

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