High–resolution pollutant transport in the San Pedro Bay of California

Cohan, Alexander; Wu, Jun; Dabdub, Donald

doi:10.5094/apr.2011.030

Cited by 21 publications

(19 citation statements)

References 30 publications

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“…AERMOD is an integrated system of three modules: a steady-state dispersion model (which is a Gaussian model and designed for shortrange~50 km dispersion of air pollutants), AERMET (meteorological data pre-processor) and AERMAP (a terrain pre-processor). It can estimate emissions from point and line sources by treating them as area sources (Cohan et al, 2011), along with improved handling of ground dispersion (Silverman et al, 2007). Therefore, the current study considers vehicles used in an area of 4 km 2 as a single area source for estimating pollutant concentrations for these specific locations.…”

Section: Air Quality Modelingmentioning

confidence: 99%

Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach

Aggarwal

Jain

2015

Environment International

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Section: Air Quality Modelingmentioning

confidence: 99%

Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach

Aggarwal

Jain

2015

Environment International

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“…The same model was also used by Cesari et al (2016) for a case study of ship emissions in Brindisi, Italy. Another popular steady-state Gaussian plume model, AERMOD, recommended by United States Environmental Protection Agency (EPA) was also widely used by different groups (Gibson et al, 2013;Cohan et al, 2011). Abrutytė et al (2014) employed AERMOD to simulate the dispersion of NO x from ships in Klaipėda port.…”

Section: Introductionmentioning

confidence: 99%

“…Abrutytė et al (2014) employed AERMOD to simulate the dispersion of NO x from ships in Klaipėda port. AERMOD was also used by Fileni et al (2019) and Cohan et al (2011) to evaluate the contribution of ships on PM emissions in harbor cities. The Gaussian plume model is able to save a lot of computational cost; however, it suffers from several limitations, such as assuming a steady-state solution, a spatially uniform meteorology and straight line trajectories (Bluett et al, 2004), which make it not suitable under many conditions for air quality modeling.…”

Section: Introductionmentioning

confidence: 99%

Development of a moving point source model for shipping emission dispersion modeling in EPISODE–CityChem v1.3

et al. 2021

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Abstract. This paper demonstrates the development of a moving point source (MPS) model for simulating the atmospheric dispersion of pollutants emitted from ships under movement. The new model is integrated into the chemistry transport model EPISODE–CityChem v1.3. In the new model, ship parameters, especially speed and direction, are included to simulate the instantaneous ship positions and then the emission dispersion at different simulation time. The model was first applied to shipping emission dispersion modeling under simplified conditions, and the instantaneous and hourly averaged emission concentrations predicted by the MPS model and the commonly used line source (LS) and fixed point source (FPS) models were compared. The instantaneous calculations were quite different due to the different ways to treat the moving emission sources by different models. However, for the hourly averaged concentrations, the differences became smaller, especially for a large number of ships. The new model was applied to a real configuration from the seas around Singapore that included hundreds of ships, and their dispersion was simulated over a period of a few hours. The simulated results were compared to measured values at different locations, and it was found that reasonable emission concentrations were predicted by the moving point source model.

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“…One example of this phenomenon occurred in winter 2004 in Logan, Utah, United States: Temperatures reached −23.6°C, favoring thermal inversion and causing the worst air pollution episode ever recorded in the country (Malek et al ., 2006). In other metropolitan zones such as Mexico City (Mexico), Los Angeles (United States), and the Kathmandu Valley (Nepal), the transport of pollutants has been shown to be affected by variability in temperature, wind speed, solar radiation, and relative humidity (Shaw et al ., 2007; Cohan et al ., 2011; Mues et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

Influence of mixing height and atmospheric stability conditions on correlation of NO₂ columns and surface concentrations in a Mexico‐United States border region

Flores-Jiménez

García-Cueto

Santillán-Soto

et al. 2021

Atmospheric Science Letters

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The objective was to analyze how representative tropospheric NO 2 column densities are of surface NO 2 measurements under different atmospheric stability conditions in the air basin of two border cities: Calexico, United States, and Mexicali, Mexico. NO 2 columns were measured by the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite. NO 2 concentrations and meteorological parameters were also measured on the surface for comparison. Specifically, the correlations between OMI and surface NO 2 concentrations under different atmospheric stability conditions according to the Pasquill-Gifford (P-G) and Monin-Obukhov (M-O) classification schemes were determined for 2017 and 2018. During the passage of the satellite through the study area (11:00-13:00 UTC−8), unstable conditions were documented in both years.Good correlation was found between the surface NO 2 and OMI NO 2 column observations in the second semester of each year, particularly under unstable conditions as diagnosed by the P-G and M-O schemes applied in the first and second year, respectively. However, a weakening of these conditions occurs during the autumn-winter period. In both cases, the highest determination coefficients were found for Calexico, with values of 0.48 and 0.36 in 2017 and 2018, respectively; for Mexicali, the determination coefficients were 0.23 and 0.35, respectively. Under each atmospheric stability scheme, the mechanical and convective turbulence caused a decreasing trend in wind speed and solar radiation over the course of second semester of 2017 and in friction velocity, temperature, and sensible heat flux over the course of the same period for 2018. The negative trend of these parameters during the analyzed time frames helped to reduce the influence of unstable atmospheric conditions, favoring better correlations between satellite and surface NO 2 measurements. The methodology applied and results obtained herein can enable us to better understand the representativeness of OMI NO 2 data in arid border zones with extreme meteorological conditions.

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High–resolution pollutant transport in the San Pedro Bay of California

Cited by 21 publications

References 30 publications

Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach

Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach

Development of a moving point source model for shipping emission dispersion modeling in EPISODE–CityChem v1.3

Influence of mixing height and atmospheric stability conditions on correlation of NO₂ columns and surface concentrations in a Mexico‐United States border region

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High–resolution pollutant transport in the San Pedro Bay of California

Cited by 21 publications

References 30 publications

Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach

Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach

Development of a moving point source model for shipping emission dispersion modeling in EPISODE–CityChem v1.3

Influence of mixing height and atmospheric stability conditions on correlation of NO2 columns and surface concentrations in a Mexico‐United States border region

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Influence of mixing height and atmospheric stability conditions on correlation of NO₂ columns and surface concentrations in a Mexico‐United States border region