Mathematical modeling of photochemical air pollution—I

Reynolds, Steven D.; Roth, Philip M.; Seinfeld, John H.

doi:10.1016/0004-6981(73)90214-x

Cited by 174 publications

(54 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first sub-grid scale treatment of plumes in 3-D air quality models, referred to as the Plume-Airshed Reactive-Interacting System (PARIS), was developed by Seigneur et al [68], who embedded a simple puff model into the Urban Airshed Model (UAM) [69], and used it to calculate ozone concentrations in urban areas such as Los Angeles, California, and St. Louis, Missouri. Morris et al [14,70] describe a plume-in-grid treatment in the variable grid version of UAM (UAM-V).…”

Section: Plume-in-grid Modelingmentioning

confidence: 99%

Sub-Grid Scale Plume Modeling

2011

View full text Add to dashboard Cite

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

show abstract

Section: Plume-in-grid Modelingmentioning

confidence: 99%

Sub-Grid Scale Plume Modeling

2011

View full text Add to dashboard Cite

show abstract

“…Long range transport, air quality over entire air shed, that is, large scale simulations are mostly done using Eulerian models. Reynolds (1973), Shir and Shieh (1974) applied Eulerian model for ozone and for SO 2 concentration simulation in urban areas, and Egan (1976) and Carmichael (1979) for regional scale sulfur. Holmes and Morawska (2006) used Eulerian model to calculate the transport and dispersion over long distances.…”

Section: Eulerian and Lagrangian Modelsmentioning

confidence: 99%

“…Holmes and Morawska (2006) used Eulerian model to calculate the transport and dispersion over long distances. The modeling studies by Reynolds (1973) on the Los Angeles basin formed the basis of the, the well-known Urban Air shed Model-UAM. Examples of Eulerian models are CALGRID model and ARIA Regional model or the Danish Eulerian Hemispheric Model (DEHM).…”

Section: Eulerian and Lagrangian Modelsmentioning

confidence: 99%

Urban Air Pollution Modeling

Srivastava¹,

Rao²

2011

Air Quality-Models and Applications

View full text Add to dashboard Cite

IntroductionAll life form on this planet depends on clean air. Air quality not only affects human health but also components of environment such as water, soil, and forests, which are the vital resources for human development. Urbanization is a process of relative growth in a country's urban population accompanied by an even faster increase in the economic, political, and cultural importance of cities relative to rural areas. Urbanization is the integral part of economic development. It brings in its wake number of challenges like increase in population of urban settlement, high population density, increase in industrial activities (medium and small scale within the urban limits and large scale in the vicinity), high rise buildings and increased vehicular movement. All these activities contribute to air pollution. The shape of a city and the land use distribution determine the location of emission sources and the pattern of urban traffic, affecting urban air quality (World Bank Reports 2002). The dispersion and distribution of air pollutants and thus the major factor affecting urban air quality are geographical setting, climatological and meteorological factors, city planning and design and human activities. Cities in the developing countries are characterized by old city and new development. The old cities have higher population density, narrow lanes and fortified structures. In order to ensure clean air in urban settlements urban planning and urban air quality management play an important role. New legislations, public awareness, growth of urban areas, increases in power consumption and traffic pose continuous challenges to urban air quality management. UNEP (2005) has identified niche areas Urban planning need to primarily focus on as:  Promotion of efficient provision of urban infrastructure and allocation of land use, thereby contributing to economic growth,  managing spatial extension while minimizing infrastructure costs,  improving and maintaining the quality of the urban environment and  Prerecording the natural environment immediately outside the urban area. Air quality modelling provides a useful support to decision making processes incorporating environmental policies and management process. They generate information that can be u s e d i n t h e d e c i s i o n m a k i n g p r o c e s s . T h e m a i n o b j e c t i v e s o f m o d e l s a r e : t o i n t e g r a t e observations, to predict the response of the system to the future changes, to make provision for future development without compromising with quality www.intechopen.com Air Quality -Models and Applications

show abstract

“…Johnson, et al 8 give results for three models developed under contract to EPA: two trajectory models, a moving well-mixed cell 150 and a Lagrangian column, 151 and a multibox, Eulerian model. 152 ' 153 Using the correlation coefficient and the root mean square error as statistics, results are shown for carbon monoxide (an inert pollutant) and for a photochemical product (oxidant). The difficulty in trying to compare the trajectory and grid point models is noted.…”

Section: Photochemical Modelingmentioning

confidence: 99%

A Critical Review

Turner¹

1979

Journal of the Air Pollution Control Association

View full text Add to dashboard Cite

There are many requirements for dispersion models in the Clean Air Act Amendments of 1977 including prevention of significant deterioration, air quality maintenance plans, and new source permits. The historical perspective of dispersion modeling includes both theoretical and practical developments, rural and urban field studies including tracer studies, plume rise, and urban modeling. Photochemical modeling includes both grid point and trajectory models. Off-the-shelf models include level terrain models for elevated point sources, urban point and area sources, and transportation sources; and complex terrain models for point sources. Models are needed for photochemical pollutants, sulfates, and nitrates; they should treat long range transport, topographic effects, build-up of urban concentrations, and aerodynamic downwash. Emphasis is placed herein upon proper interpretation of vertical temperature structure measurements, and the difficulties in determining the magnitude and locations of the highest and second highest concentrations in a year. Modeling improvements can be expected with representative on-site measurements of input parameters, and improved dispersion parameter schemes which must be verified with appropriate field investigations. Terms related to model evaluation are defined. A primary source of error, in comparing model estimates with monitored air quality, is not including the actual change of wind direction with height which determines elevated plume position. Measures for model evaluation will differ depending upon model use. The paper concludes asking for 1) encouragement of model development, 2) improvement of model inputs through on-site measurements, and 3) detailed field investigations.Mr. Turner is presently on assignment from the National

show abstract

Mathematical modeling of photochemical air pollution—I

Cited by 174 publications

References 18 publications

Sub-Grid Scale Plume Modeling

Sub-Grid Scale Plume Modeling

Urban Air Pollution Modeling

A Critical Review

Contact Info

Product

Resources

About