Air Pollution Modeling

Zannetti, Paolo

doi:10.1007/978-1-4757-4465-1

Cited by 241 publications

(80 citation statements)

References 184 publications

(244 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Backward time particle simulations thus enable implementation of a ''receptor-oriented framework'' [Uliasz and Pielke, 1990] that defines upstream influences on tracer observations at the receptor. The backward time particle locations map out the influence function I(x r ,t r j x,t), which quantitatively links sources and sinks S(x, t) to concentrations C(x r , t r ) of a conserved tracer at a receptor located at x r and time t r , following Holzer and Hall [2000] and Zannetti [1990]:…”

Section: Theoretical Background: Lagrangian Particle Dispersion Modelsmentioning

confidence: 99%

“…Particle models simulate both advection and dispersion in the PBL by explicitly incorporating turbulent velocity statistics in the trajectories of tracer particles [Stohl, 1998;Zannetti, 1990], expanding beyond traditional mean wind trajectory models that assume air parcels to be conserved entities [Stohl and Wotawa, 1993]. Particle locations are distributed without gridcell restrictions and can thus capture fine structures due to small-scale inhomogeneities in source distributions that are unresolved by gridded transport models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

et al. 2003

View full text Add to dashboard Cite

[1] We introduce a tool to determine surface fluxes from atmospheric concentration data in the midst of distributed sources or sinks over land, the Stochastic Time-Inverted Lagrangian Transport (STILT) model, and illustrate the use of the tool with CO 2 data over North America. Anthropogenic and biogenic emissions of trace gases at the surface cause large variations of atmospheric concentrations in the planetary boundary layer (PBL) from the ''near field,'' where upstream sources and sinks have strong influence on observations. Transport in the near field often takes place on scales not resolved by typical grid sizes in transport models. STILT provides the capability to represent near-field influences, transforming this noise to signal useful in diagnosing surface emissions. The model simulates transport by following the time evolution of a particle ensemble, interpolating meteorological fields to the subgrid scale location of each particle. Turbulent motions are represented by a Markov chain process. Significant computational savings are realized because the influence of upstream emissions at different times is modeled using a single particle simulation backward in time, starting at the receptor and sampling only the portion of the domain that influences the observations. We assess in detail the physical and numerical requirements of STILT and other particle models necessary to avoid inconsistencies and to preserve time symmetry (reversibility). We show that source regions derived from backward and forward time simulations in STILT are similar, and we show that deviations may be attributed to violation of mass conservation in currently available analyzed meterological fields. Using concepts from information theory, we show that the particle approach can provide significant gains in information compared to conventional gridcell models, principally during the first hours of transport backward in time, when PBL observations are strongly affected by surface sources and sinks.

show abstract

Section: Theoretical Background: Lagrangian Particle Dispersion Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

et al. 2003

View full text Add to dashboard Cite

show abstract

“…L has two different classification methods. One was estimated by classifying local meteorological data into stability classes using Turner classes [47] and then estimating 1/L as a function of stability class and z0 [48]. The other method uses Pasquill's [49] stability classification scheme to classify hourly local meteorological data into stability classes.…”

Section: Urban Forest Effects Model: Air Particulate Removed By Treesmentioning

confidence: 99%

The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing

Chen

Sun

et al. 2015

Atmosphere

View full text Add to dashboard Cite

Particulate matter is a serious source of air pollution in urban areas, where it exerts adverse effects on human health. This article focuses on the study of subduction of shelterbelts for atmospheric particulates. The results suggest that (1) the PM mass concentration is higher in the morning or both morning and noon inside the shelterbelts and lower mass concentrations at other times; (2) the particle mass concentration inside shelterbelt is higher than outside; (3) the particle interception efficiency of the two forest belts over the three months in descending order was PM10 > PM1 > PM2.5; and (4) the two shelterbelts captured air pollutants at rates of 1496.285 and 909.075 kg/month and the major atmospheric pollutant in Beijing city is PM10. Future research directions are to study PM mass concentration variation of shelterbelt with different tree species and different configuration.

show abstract

“…Zannetti (1990) classified dispersion models as Gaussian, Eulerian and Lagrangian models. Khare & Sharma (2002) classified models on the basis of their size, time horizon, and pollutants.…”

Section: Emission Output Processingmentioning

confidence: 99%

An integrated modelling approach to estimate urban traffic emissions

Misra

Roorda

MacLean

2013

Atmospheric Environment

View full text Add to dashboard Cite

Vehicular emissions contribute significantly to poor air quality in urban areas. An integrated modelling approach is adopted to estimate microscale urban traffic emissions. The modelling framework consists of a traffic microsimulation model, a microscopic emissions model, and two dispersion models. This framework is applied to a traffic network in downtown Toronto to evaluate summer time morning peak traffic emissions during weekdays for carbon monoxide and nitrogen oxide. The model predicted results are validated against sensor observations with a reasonably good fit. Availability of local estimates of ambient concentration is useful for accurate comparisons of total predicted concentrations with observed concentrations. Both predicted and observed concentrations are significantly smaller than the National Ambient Air Quality Objectives established by Environment Canada. Sensitivity analysis is performed on a set of input parameters and horizontal wind speed is found to be the most influential factor in pollutant dispersion.iii

show abstract

Air Pollution Modeling

Cited by 241 publications

References 184 publications

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model

The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing

An integrated modelling approach to estimate urban traffic emissions

Contact Info

Product

Resources

About