Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area

2006 ◽  
Vol 25 (2) ◽  
pp. 141-151 ◽  
Author(s):  
Ashok Kumar ◽  
Shobhit Dixit ◽  
Charanya Varadarajan ◽  
Abhilash Vijayan ◽  
Anand Masuraha
Keyword(s):  
Urban Area ◽  
Dispersion Model ◽  
Atmospheric Stability

scholarly journals Development of the city-scale chemistry transport model CityChem-EPISODE and its application to the city of Hamburg

2018 ◽  
Author(s):  
Matthias Karl
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Transport Model ◽  
Dispersion Model ◽  
Temporal Correlation ◽  
Urban Atmosphere ◽  
Aerosol Formation ◽  
Urban Background ◽  
New Model ◽  
The City

Abstract. This paper describes the City-scale Chemistry (CityChem) extension of the urban dispersion model EPISODE with the aim to enable chemistry/transport simulations of multiple reactive pollutants on urban scales. The new model is called CityChem-EPISODE. The primary focus is on the simulation of urban ozone concentrations. Ozone is produced in photochemical reaction cycles involving nitrogen oxides (NOx) and volatile organic compounds (VOC) emitted by various anthropogenic activities in the urban area. The performance of the new model was evaluated with a series of synthetic tests and with a first application to the air quality situation in the city of Hamburg, Germany. The model performs fairly well for ozone in terms of temporal correlation and bias at the air quality monitoring stations in Hamburg. In summer afternoons, when photochemical activity is highest, modelled median ozone at an inner-city urban background station was about 30 % lower than the observed median ozone. Inaccuracy of the computed photolysis frequency of nitrogen dioxide (NO2) is the most probable explanation for this. CityChem-EPISODE reproduces the spatial variation of annual mean NO2 concentrations between urban background, traffic and industrial stations. However, the temporal correlation between modelled and observed hourly NO2 concentrations is weak for some of the stations. For daily mean PM10, the performance of CityChem-EPISODE is moderate due to low temporal correlation. The low correlation is linked to uncertainties in the seasonal cycle of the anthropogenic particulate matter (PM) emissions within the urban area. Missing emissions from domestic heating might be an explanation for the too low modelled PM10 in winter months. Four areas of need for improvement have been identified: (1) dry and wet deposition fluxes; (2) treatment of photochemistry in the urban atmosphere; (3) formation of secondary inorganic aerosol (SIA); and (4) formation of biogenic and anthropogenic secondary organic aerosol (SOA). The inclusion of secondary aerosol formation will allow for a better sectorial attribution of observed PM levels. Envisaged applications of the CityChem-EPISODE model are urban air quality studies, environmental impact assessment, sensitivity analysis of sector-specific emission and the assessment of local and regional emission abatement policy options.

scholarly journals High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

2019 ◽  
Author(s):  
Daoyuan Yang ◽  
Shaojun Zhang ◽  
Tianlin Niu ◽  
Yunjie Wang ◽  
Honglei Xu ◽  
...  
Keyword(s):  
Urban Area ◽  
Real World ◽  
Vehicle Emissions ◽  
Large Scale ◽  
Emission Inventory ◽  
Road Traffic ◽  
Dispersion Model ◽  
Traffic Monitoring ◽  
Atmospheric Pollutants ◽  
Link Model

Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link, based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOX), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that non-local HDTs could for 29 % and 38 % of estimated total on-road emissions of NOX and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOX concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 μg m−3 of NOX in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

Construction and validation of an urban area flow and dispersion model on building scales

2013 ◽  
Vol 27 (6) ◽  
pp. 923-941 ◽  
Author(s):  
Bicheng Chen ◽  
Shuhua Liu ◽  
Yucong Miao ◽  
Shu Wang ◽  
Yuan Li
Keyword(s):  
Urban Area ◽  
Dispersion Model

The Change of Aerodynamic Parameters over Beijing Urban Area during 1991-2018

2020 ◽  
Author(s):  
Xiaoman Liu
Keyword(s):  
Wind Speed ◽  
Urban Area ◽  
Wind Direction ◽  
Wind Field ◽  
Atmospheric Stability ◽  
Meteorological Condition ◽  
Field Structure ◽  
Average Height ◽  
Growth Trend ◽  
Aerodynamic Parameters

<p>       Higher and denser building groups are the most concentrated reflection of urbanization on the underlying surface reconstruction. With the continuous city expanding, urban wind field structure was changed, also the aerodynamic parameters dependent on. Based on observational data (slow-response) collected at 15 levels on Beijing 325m meteorological tower from 1991-2018, time and vertical trends of atmospheric stability, wind direction, wind speed, aerodynamic parameters were analyzed. Through Sen's slope, Mann-Kendall trend test and mutation analysis, we believe that urbanization has made a significant influence on local meteorological condition, and all the above variables mutated around the year of 1999. Before 1999, the proportion of neutral and unstable conditions declined with a trend of -0.63% and -2.0% per year respectively, and increased with a trend of +0.08% and +0.06% per year after 1999. As for wind direction, the dominant wind direction below 47m turned from southwest/northwest before 1999 to southeast after 1999, while above 47m remain unchanged as southeast, reflecting that the action range of urban impact is clearly distinguished from that of atmospheric background field. In terms of wind speed, the annual mean value trended to decrease at -0.0019m/s per year, and vertical wind speed trended to increased with height (per meter) at m/s per year, which reflected the continuous enhancement of attenuation effect of complex underlying on the near-ground wind speed. Furthermore, we found that although there was indeed a weaken tendency for wind speed in Beijing urban areas, but near neutral wind speed maintained a growth trend under 140m during 1999-2018. It was possible the deal with urban wake effect, wind field structure mutation or turbulence effect. Aerodynamic parameters  and d have undergone significant changes during the peak stage of urbanization, and tended to develop steadily with a 7-years fluctuations trend after that. In the past 28 years, d has increased from 1.34m in 1991 to 26.19m in 2018, while  has decreased from 2.75m to 1.02m. This is due to the fact that the increase of buildings average height is the result of roughness superposition. If the 7-year fluctuations trend continues, d of Beijing urban area will soon enter the next uplift period, during which the wind speed may increase slightly under nearly neutral conditions, and the cleaning effect on the pollution may be gradually enhanced.</p><p> </p>

scholarly journals VISUALISING URBAN AIR QUALITY USING AERMOD, CALPUFF AND CFD MODELS: A CRITICAL REVIEW

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 355-363
Author(s):  
N. Ridzuan ◽  
U. Ujang ◽  
S. Azri ◽  
T. L. Choon
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Air Pollutants ◽  
Dispersion Model ◽  
Ambient Air ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Modelling ◽  
Modeling Tools ◽  
Wide Range

Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.

Air Pollution Meteorology

2019 ◽  
pp. 174-202
Author(s):  
Ravichandran C.
Keyword(s):  
Air Pollution ◽  
Indoor Air Pollution ◽  
Dispersion Model ◽  
Atmospheric Stability ◽  
Outdoor Air ◽  
Global Circulation ◽  
Composition And Structure ◽  
The Subject ◽  
Major Factors ◽  
Air Pollution Meteorology

Air pollution is a major environmental problem. It is divided into indoor air pollution and outdoor air pollution. The pollutants released in the outdoor atmosphere are under the control of meteorological conditions prevailing at the time of emission and after. Thus, the subject, air pollution meteorology, has emerged. It explains the significance of meteorological aspects and their influences on air pollutants emitted in the outdoor atmosphere. Prevailing winds and atmospheric stability are the two major factors that determine the dispersion of the pollutants. In this chapter, a brief outline is presented on composition and structure of atmosphere, and its processes such as radiation through the atmosphere, winds, global circulation, and atmospheric stability. Mixing height and ventilation coefficients are explained. The prediction of air quality using Gaussian dispersion model with the input of data of emission and meteorology is also explained.

Analysis of urban air quality in 6 European cities by lower cost sensors, Lagrangian urban dispersion modelling and traffic flow modelling: the TRAFAIR project

2020 ◽  
Author(s):  
Ohad Zivan ◽  
Alessandro Bigi ◽  
Giorgio Veratti ◽  
José Antonio Souto González ◽  
Lorena Marrodán ◽  
...  
Keyword(s):  
Air Quality ◽  
Traffic Flow ◽  
Urban Area ◽  
Lower Cost ◽  
Dispersion Model ◽  
Urban Air Quality ◽  
Urban Air ◽  
Electrochemical Cells ◽  
Dispersion Modelling ◽  
Traffic Data

<p>Most of worldwide population lives in urban areas, demanding for air quality information with a high spatio-temporal resolution. The most promising approaches for estimating urban air quality within the complex urban topography are small sensor networks and simulation models.</p><p>The TRAFAIR project focuses on understanding the role of traffic emissions on urban air quality by the combination of dispersion modelling, space- and time-resolved gas monitoring by lower cost sensors and realistic traffic flow rates by dynamic traffic model based on real time traffic data. Test cities of TRAFAIR are Modena, Florence, Pisa, Livorno, Zaragoza and Santiago de Compostela.</p><p>Depending on the size of the urban area, from 6 to 13 sensors units are deployed across each city since August 2019, providing estimates of NO, NO<sub>2</sub>, CO and O<sub>3</sub>, along with RH and temperature. Metal oxide sensors are deployed in Tuscany (Florence, Pisa, Livorno) and electrochemical cells are used elsewhere. The units are calibrated on a regular basis by co-location at the air quality regulatory stations and subsequently deployed across the town to monitor several representative locations (e.g. Low Emission Zones, hospital surroundings). For each sensor the raw readings (e.g. mV for electrochemical cells) are collected and a regression model (e.g. Random Forest) is applied to derive a calibration function, exploiting the data from the regulatory stations during co-location periods; for instance in Modena, the first short-term calibration provided a model with a Mean Absolute Error between 5 – 6 ppb and 2 – 4 ppb for NO and NO<sub>2</sub> respectively.</p><p>The sensors are used for both real-time urban air quality mapping and to test and validate the 24hr forecast service of NOx by the microscale lagrangian dispersion model GRAL. The simulation domains, covering the urban area of each TRAFAIR city, have a horizontal resolution of 4 m and allow to account for the presence of buildings. The dispersion model mainly focuses on NOx by traffic emissions, although domestic heating will be also included in the analysis. Vehicular emissions are based either upon historical traffic data (e.g. induction loops), or upon previously available traffic flow simulation, or upon traffic pattern reconstruction using a traffic flow model followed by a cluster analysis to group streets with similar pattern.</p><p>The final goal of the project is the development of a tool to support local policymakers and to inform citizenship about the quality of air and the impact of urban emission sources, particularly traffic. A secondary goal of the project is the development of a valuable QA/QC protocol for small sensor units and the optimization of the modelling chain for the forecast of traffic and domestic heating impact on local air quality at the urban scale.</p>

Sign in / Sign up

Export Citation Format

Share Document