The accumulated particulate matter concentration at a given vertical column due to traffic sources in urban area has many important consequences. This task, however, imposes a major challenge, since the problem of realistic pollutant dispersion in an urban environment is a very demanding task, both theoretically and computationally. This is mainly due to the highly inhomogeneous three dimensional turbulent flow regime in the urban canopy roughness sublayer, which is far from “local equilibrium” between shear production and dissipation. We present here a mass-consistent urban Lagrangian stochastic model for pollutants dispersion, where the flow field is modeled using a hybrid approach by which we model the surface layer based on the typical turbulent scales, both of the canopy and in the surface layer inertial sub-layer. In particular it relies on representing the canopy aerodynamically as a porous medium by spatial averaging the equations of motion, with the assumption that the canopy is laterally uniform on a scale much larger than the buildings but smaller than the urban block/neighbourhood, i.e., at the sub-urban-block scale. Choosing the spatial representative averaging volume allows the averaged variables to reflect the characteristic vertical heterogeneity of the canopy but to smooth out smaller scale spatial fluctuations caused as air flows in between the buildings. This modeling approach serves as the base for a realistic and efficient methodology for the calculation of the accumulated concentration from multiple traffic sources for any vertical column in the urban area. The existence of multiple traffic sources impose further difficulty since the computational effort required is very demanding for practical uses. Therefore, footprint analysis screening was introduced to identify the relevant part of the urban area which contributes to the chosen column. All the traffic sources in this footprint area where merged into several areal sources, further used for the evaluation of the concentration profile. This methodology was implemented for four cases in the Tel Aviv metropolitan area based on several selected summer climatological scenarios. We present different typical behaviors, demonstrating combination of source structure, urban morphology, flow characteristics, and the resultant dispersion pattern in each case.

The Haifa Bay area (HBA) is a major metropolitan area in Israel, which consists of high volume transportation routes, major industrial complexes, and the largest international seaport in Israel. These, which lie relatively near densely populated residential areas, result in a multitude of air pollution sources, many of whose emissions are in the form of particulate matter (PM). Previous studies have associated exposure to such PM with adverse health effects. This potential consequence serves as the motivation for this study whose aim is to provide a realistic and detailed three-dimensional concentration field of PM, originating simultaneously from multiple sources. The IIBR in-house Lagrangian stochastic pollutant dispersion model (LSM) is suitable for this endeavor, as it describes the dispersion of a scalar by solving the velocity fluctuations in high Reynolds number flows. Moreover, the LSM was validated in urban field experiments, including in the HBA. However, due to the fact that the multiple urban sources reside within the canopy layer, it was necessary to integrate into the LSM a realistic canopy layer model that depicts the actual effect of the roughness elements’ drag on the flow and turbulent exchange of the urban morphology. This was achieved by an approach which treats the canopy as patches of porous media. The LSM was used to calculate the three-dimensional fields of PM10 and PM2.5 concentrations during the typical conditions of the two workday rush-hour periods. These were compared to three air quality monitoring stations located downstream of the PM sources in the HBA. The LSM predictions for PM2.5 satisfy all acceptance criteria. Regarding the PM10 predictions, the LSM results comply with three out of four acceptance criteria. The analysis of the calculated concentration fields has shown that the PM concentrations up to 105 m AGL exhibit a spatial pattern similar to the ground level. However, it decreases by a factor of two at 45 m AGL, while, at 105 m, the concentration values are close to the background concentrations.

A major challenge in accidental or unregulated releases is the ability to identify the pollutant source, especially if the location is in a large industrial area. Usually in such cases, only a few sensors provide non-zero signal. A crucial issue is therefore the ability to use a small number of sensors in order to identify the source location and rate of emission. The general problem of characterizing source parameters based on real-time sensors is known to be a difficult task. As with many inverse problems, one of the main obstacles for an accurate estimation is the non-uniqueness of the solution, induced by the lack of sufficient information. In this study, an efficient method is proposed that aims to provide a quantitative estimation of the source of hazardous gases or breathable aerosols. The proposed solution is composed of two parts. First, the physics of the atmospheric dispersion is utilized by a well-established Lagrangian stochastic model propagated backward in time. Then, a new algorithm is formulated for the prediction of the spacial expected uncertainty reduction gained by the optimal placement of an additional sensor. These two parts together are used to construct an adaptive decision support system for the dynamical deployment of detectors, allowing for an efficient characterization of the emitting source. This method has been tested for several scenarios and is shown to significantly reduce the uncertainty that stems from the insufficient information.

The general problem of characterizing gas source parameters based on concentration measurements is known to be a difficult task. As many inverse problems, one of the main obstacles for accurate estimation is the non-uniqueness of solution, induced by the lack of sufficient information. As the number of detectors is lowered, which is more than a plausible scenario for many practical situations, the number of possible solutions that can characterize the source increases dramatically, leading to severe errors. In this paper, a Lagrangian stochastic based method for identifying these suspected points, which will be referred to as 'degenerate space', is formulated and analysed. Then, a new procedure for quantitative prediction of the effect of deploying a new detector in space is used to design an adaptive scheme for source term estimation. This scheme has been tested for several scenarios, differing by the location of the initial detectors, and is shown to reduce dramatically the degeneracy formed by insufficient information. The combined formulation of degenerate space with the new adaptive scheme is shown to give improved accuracy, and in particular for a relatively small number of detectors.