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Air pollution modelling
Published in Abhishek Tiwary, Ian Williams, Air Pollution, 2018
The traffic emissions estimated from PITHEM in the previous step were used as input to set up the air dispersion modelling using ADMS-Roads v3.1 (CERC, 2011). The model inputs comprised of traffic-generated emissions of CO, NO2, PM10, PM2.5 and meteorology. Long-term (annual average) outputs were obtained for a 250 m grid resolution applying an intelligent gridding along the roads. The latter generated additional output points on both sides of the modelled roads where the pollutant concentration gradients are the greatest. This was conducted in two stages – first, in the pre-run stage, up to 5000 extra receptor points were added in and around the roads in sets of 4, with at least one set of points added to each road segment that lies within the output grid; second, at the end of the model run, three sets of additional points were added between the first set of intelligent grid points for generating concentration output by linearly interpolating between the values at the first set of points. The second stage was conducted in ADMS-Roads after the model run by default, to create a regular Cartesian grid within the rectangular region specified (CERC, 2011).
Performance of AERMOD and CALPUFF models on SO2 and NO2 emissions for future health risk assessment in Tema Metropolis
Published in Human and Ecological Risk Assessment: An International Journal, 2019
Patrick Amoatey, Hamid Omidvarborna, Hannah A. Affum, Mahad Baawain
There are several advanced air quality dispersion models including the regulatory model for long transport dispersion called California Puff Model (CALPUFF) (Affum et al.2016), the US EPA Regulatory Model (AERMOD) (Seangkiatiyuth et al.2011), Industrial Source Complex Model (ISCST3) (Rama Krishna et al.2005), and Atmospheric Dispersion Modeling Software (ADMS) (Ali and Athar 2010). These models are developed based on the Gaussian plume model, which determines the vertical and horizontal spread of the plume, in both simple and complex terrains (Daly and Zannetti 2007). The models are being used to estimate the concentration level of different pollutants, which help to assess health risk assessment analysis. For example, Seangkiatiyuth et al. (2011) used AERMOD to assess the impact of NO2 emissions from a cement plant in Bangkok, Thailand. Mokhtar et al. (2014) assessed the health risk effect of SO2 from a coal-fired power plant by using AERMOD. AERMOD was employed for the prediction of hydrogen sulfide (H2S) emissions, a neighborhood claimed issue, from a sewage treatment plant (STP) in Oman (Baawain et al.2017). AERMOD predictions performed well with measured NOx and PM10 concentrations through the application of Weather Research Forecasting (WRF) model (Kumar et al.2017). Likewise, AERMOD was used to study the line sources of SO2 and NOx in Nova Scotia, Canada (Gibson et al.2013). Although AERMOD offers an opportunity to carry out a wide array of air quality applications, Mohan et al. (2011) concluded that AERMOD could underpredict suspended PM (SPM) with low bias between the measured the modeling results.
Sensitivity assessment of PM2.5 simulation to the below-cloud washout schemes in an atmospheric chemical transport model
Published in Tellus B: Chemical and Physical Meteorology, 2018
XINGCHENG LU, JIMMY C. H. FUNG
Other schemes are analysed here in addition to the two wet removal schemes (CAMx and CMAQ) described previously. The EMEP model is used widely in European countries to study air pollution issue (Simpson et al., 2012), and the BCW rate for particles in EMEP is calculated by the following equation (Scott, 1979): where A is 5.2 m3 kg−1 s−2 (an empirical coefficient), Pr is the precipitation rate (kgm−2 s−1), V is the raindrop terminal speed (set to be 5 ms−1), and is the collection efficiency from a look-up table (Simpson et al., 2012). CALPUFF (puff dispersion model) is another advanced non-steady-state air quality model that is preferred for simulation of the long range transport of ambient pollutants. In CALPUFF, the scavenging coefficient is set as a constant (10−4 s−1) multiplied by the rain rate (mm h−1), for sulphate and nitrate particles (Scire et al., 2000). The ADMS (Atmospheric Dispersion Modelling System) is an advanced atmospheric pollution dispersion model for calculating concentrations of atmospheric pollutants emitted both continuously from point, line, volume, and area sources. It was developed by Cambridge Environmental Research Consultants (CERC) of the United Kingdom. In the ADMS, the scavenging coefficient is expressed in the form of Equation (4) and the default coefficients a and b are equal to 10−4 and 0.64 (based on field measurement), respectively. The methods in the form of Equation (4) (parameterized by rain rate and empirical coefficients) proposed by Sparmacher et al. (1993), Baklanov and Sorensen (2001), and Wang et al. (2014c) are also compared in this study.