The tagged species engineering model (TSEM)

McHenry, John N.; Binkowski, Francis S.; Dennis, Robin L.; Chang, Jenq Yang; Hopkins, Debra

doi:10.1016/0960-1686(92)90128-8

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…By the 1980's, "second-generation" models were utilized by the US Environmental Protection Agency (EPA) to address pressing issues: (1) the two-layer Regional Oxidant Model (ROM) was being applied to develop ozone control strategies for the NE US Corridor; (2) the Urban Airshed Model (UAM) was being used for similar purposes at urban scales within "confined" locations such as Los Angeles; and (3) the newer Regional Acid Deposition Model (RADM) was developed and deployed as part of an integrated assessment to reduce the effects of sulfuric acid deposition (e.g. McHenry, et al,1992). All of these air chemistry models were driven offline in retrospective simulation mode.…”

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confidence: 99%

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

McHenry

Vukovich

Hsu

2015

Journal of the Air & Waste Management Association

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Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

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confidence: 99%

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

McHenry

Vukovich

Hsu

2015

Journal of the Air & Waste Management Association

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“…Most divide the PM components into source-specific species that are tracked separately through the model (e.g. McHenry et al, 1992;Ying and Kleeman, 2006). These approaches are capable of accurately tracking the source contribution for secondary species.…”

Section: R Kranenburg Et Al: Source Apportionment Using Lotos-eurosmentioning

confidence: 99%

Source apportionment using LOTOS-EUROS: module description and evaluation

et al. 2013

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Abstract. To design effective mitigation strategies, the origin of emissions which produce air pollutants needs to be known. Contributors to air pollutants can be emission sources, like road traffic or industry, but also be more specified to emission from one location or from a specified time. Chemistry transport models can be used to assess the origin of air pollution across a large domain. However, in traditional simulations the information on origin is lost and brute force scenario studies are performed to assess the origin. Alternatively, one can trace the origin of air pollutants throughout a simulation using a labeling approach. In this paper we document and demonstrate a newly developed labeling module for the chemistry transport model LOTOS-EUROS which tracks the source allocation for all particulate matter components and precursor gases. Dedicated simulations confirmed that the new module functions correctly. The new module provides more accurate information about the source contributions than using a brute force approach with scenario runs as the chemical regime remains unchanged. An important advantage of the new module is the reduction of computation costs and analysis work associated with the calculations. The new module was applied to assess the origin of particulate nitrate across the Netherlands. Averaged across the Dutch territory, the main contributions to nitrate are derived from road and non-road transport as well as power plants. Overall, only one-fifth of the concentration derived from sources located inside the country. The new technology enables new research directions as improved information on pollution origin is desired for policy support as well as scientific applications.

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Development of regional corrosion maps for galvanized steel by linking the RADM engineering model with an atmospheric corrosion model

Spence

McHenry²

1994

Atmospheric Environment

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The tagged species engineering model (TSEM)

Cited by 10 publications

References 10 publications

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

Source apportionment using LOTOS-EUROS: module description and evaluation

Development of regional corrosion maps for galvanized steel by linking the RADM engineering model with an atmospheric corrosion model

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The tagged species engineering model (TSEM)

Cited by 10 publications

References 10 publications

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

Source apportionment using LOTOS-EUROS: module description and evaluation

Development of regional corrosion maps for galvanized steel by linking the RADM engineering model with an atmospheric corrosion model

Contact Info

Product

Resources

About

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM_2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing