Sub-Grid Scale Plume Modeling

Abstract

Multi-pollutant chemical transport models (CTMs) are being routinely used to predict the impacts of emission controls on the concentrations and deposition of primary and secondary pollutants. While these models have a fairly comprehensive treatment of the governing atmospheric processes, they are unable to correctly represent processes that occur at very fine scales, such as the near-source transport and chemistry of emissions from elevated point sources, because of their relatively coarse horizontal resolution. Several different approaches have been used to address this limitation, such as using fine grids, adaptive grids, hybrid modeling, or an embedded sub-grid scale plume model, i.e., plumein-grid (PinG) modeling. In this paper, we first discuss the relative merits of these various approaches used to resolve sub-grid scale effects in grid models, and then focus on PinG modeling which has been very effective in addressing the problems listed above. We start with a history and review of PinG modeling from its initial applications for ozone modeling in the Urban Airshed Model (UAM) in the early 1980s using a relatively simple plume model, to more sophisticated and state-of-the-science plume models, that include a full treatment of gas-phase, aerosol, and cloud chemistry, embedded in contemporary models such as CMAQ, CAMx, and WRF-Chem. We present examples of some typical results from PinG modeling for a variety of applications, discuss the implications of PinG on model predictions of source attribution, and discuss possible future developments and applications for PinG modeling. OPEN ACCESSAtmosphere 2011, 2 390

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

“…Alternatives for a high resolution of LOTOS-EUROS itself are the implementation of a plume in grid approach (Seigneur et al, 1983;Karamchandani et al, 2011;Rissman et al, 2013) and the (offline) coupling with plume or street models (Brandt et al, 2001;Kukkonen et al, 2016). An intermediate solution for the calculation of annual-average maps has been demonstrated for the Netherlands by combining the LOTOS-EUROS results with those of the Dutch Operational Prioritary Substances (OPS) model (Van Jaarsveld, 2004;Sauter et al, 2015), which is based on Gaussian dispersion.…”

Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1)

“…The multi-scale combination of Eulerian models with near-source models was developed initially for the treatment of plumes from tall stacks in the Los Angeles basin (Seigneur et al, 1983). Many other "plume-ingrid" (PinG) models have been developed over the following 3 decades (see Karamchandani et al, 2011, for an overview). Later PinG model development efforts have included PinG models for line sources, area sources, and volume sources using various modeling approaches (e.g., Cariolle et al, 2009;Karamchandani et al, 2009;Huszar et al, 2010;Jacobson et al, 2011;Briant and Seigneur, 2013;Holmes et al, 2014;Kim et al, 2014) in order to treat aircraft emissions, ship emissions, traffic emissions from roadways, and fugitive emissions from industrial sites.…”

Atmospheric Dispersion Modelling and Spatial Analysis to Evaluate Population Exposure to Pesticides from Farming Processes

“…For this reason, a second model that takes into account the effect of local meteorology on the atmospheric dispersion of pesticides was tested. In the literature, there are a number of applications of dispersion models to assess the dispersion of pesticides, both relying on pre-existing models, i.e., Gaussian or grid models [17][18][19][20][21][22], or by specifically developed models, such as AgDRIFT TM [23] (US Forest Service, USA), AGDISP TM [24] (USDA Forest Service, USA), PERFUM [25] (EPA, USA). However, these models are usually designed to work with a very limited number of sources for micro or local scale simulations.…”