A simple thermodynamical model to estimate the rate of depletion of nocturnal low level inversion layer

The low level inversion, be it that of ground based or elevated, plays a significant role in the dispersion of polluted particles and in aviation meteorology. The rate of rise of the ground based inversion top and the base of elevated inversion causes the decrease of inversion strength and thereby permits vertical mixing of pollutants as the stability of the atmosphere is reduced. A simple thermodynamical model using the global radiation and vertical temperature profile has been proposed to estimate the rate of rise of (i) the ground based inversion top and (ii) the base of the elevated inversion. The depth of inversion thus estimated can be used in the pollution/fog dispersion models. The model is simple and operationally practicable. The limitations of the model are also discussed.

Characterization of thin films from reflection and transmission ellipsometric parameters

Spectroscopic ellipsometry is a powerful tool for characterization of thin films / surfaces. To simultaneously extract optical constant and film thickness from ellipsometric parameters ψ and Δ, dispersion models of material’s refractive index and spectroscopic ellipsometry measurement have been often required. In this work, we propose an extraction method of optical parameters of thin films from the reflection and transmission ellipsometric parameters. This method necessitates neither spectroscopic information of ψ and Δ, nor dispersion models. Verification measurements were carried out with the single-point and imaging ellipsometers, respectively.

A field study to estimate the impact of noise barriers on mitigation of near road air pollution

Air pollution associated with vehicle emissions from roadways has been linked to a variety of adverse health effects. Wind tunnel and tracer studies show that noise barriers mitigate the impact of this pollution up to distances of 30 times the barrier height. Data from these studies have been used to formulate dispersion models that account for this mitigating effect. Before these models can be incorporated into Federal and State regulations, it is necessary to demonstrate their applicability under real-world conditions. This paper describes a comprehensive field study conducted in Riverside, CA, in 2019 to collect the data required to evaluate the performance of these models. Eight vehicles fitted with SF6 tracer release systems were driven in a loop on a 2-km stretch of Interstate 215 that had a 5-m tall noise barrier on the downwind side. The tracer, SF6, was sampled at over 40 locations at distances ranging from 5 to 200 m from the barrier. Meteorological data were measured with several 3-D sonic anemometers located upwind and downwind of the highway. The data set, corresponding to 10 h collected over 4 days, consists of information on emissions, tracer concentrations, and micrometeorological variables that can be used to evaluate barrier effects in dispersion models. An analysis of the data using a dispersion model indicates that current models are likely to overestimate concentrations, or underestimate the mitigation from barriers, at low wind speeds. We suggest an approach to correct this problem.

Does the Zero Carry Essential Information for Artificial Neural Network learning to simulate the contaminant transport in Urban Areas?

The release of hazardous materials in urbanized areas is a considerable threat to human health and the environment. Therefore, it is vital to detect the contamination source quickly to limit the damage. In systems localizing the contamination source based on the measured concentrations, the dispersion models are used to compare the simulated and registered point concentrations. These models are run tens of thousands of times to find their parameters, giving the model output’s best fit to the registration. Artificial Neural Networks (ANN) can replace in localization systems the dispersion models, but first, they need to be trained on a large, diverse set of data. However, providing an ANN with a fully informative training data set leads to some computational challenges. For example, a single simulation of airborne toxin dispersion in an urban area might contain over 90% of zero concentration in the positions of the sensors. This leads to the situation when the ANN target includes a few percent positive values and many zeros. As a result, the neural network focuses on the more significant part of the set - zeros, leading to the non-adaptation of the neural network to the studied problem. Furthermore, considering the zero value of concentration in the training data set, we have to face many questions: how to include zero, scale a given interval to hide the zero in the set, and include zero values at all; or limit their number? This paper will try to answer the above questions and investigate to what extend zero carries essential information for the ANN in the contamination dispersion simulation in urban areas. For this purpose, as a testing domain, the center of London is used as in the DAPPLE experiment. Training data is generated by the Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System.

An ensemble of state-of-the-art ash dispersion models: towards probabilistic forecasts to increase the resilience of air traffic against volcanic eruptions

High-quality volcanic ash forecasts are crucial to minimize the economic impact of volcanic hazards on air traffic. Decision-making is usually based on numerical dispersion modelling with only one model realization. Given the inherent uncertainty of such an approach, a multi-model multi-source term ensemble has been designed and evaluated for the Eyjafjallajökull eruption in May 2010. Its use for flight planning is discussed. Two multi-model ensembles were built: the first is based on the output of four dispersion models and their own implementation of ash ejection. All a priori model source terms were constrained by observational evidence of the volcanic ash cloud top as a function of time. The second ensemble is based on the same four dispersion models, which were run with three additional source terms: (i) a source term obtained from a model background constrained with satellite data (a posteriori source term), (ii) its lower-bound estimate and (iii) its upper-bound estimate. The a priori ensemble gives valuable information about the probability of ash dispersion during the early phase of the eruption, when observational evidence is limited. However, its evaluation with observational data reveals lower quality compared to the second ensemble. While the second ensemble ash column load and ash horizontal location compare well to satellite observations, 3D ash concentrations are negatively biased. This might be caused by the vertical distribution of ash, which is too much diluted in all model runs, probably due to defaults in the a posteriori source term and vertical transport and/or diffusion processes in all models. Relevant products for the air traffic management are horizontal maps of ash concentration quantiles (median, 75 %, 99 %) at a finely resolved flight level grid as well as cross sections. These maps enable cost-optimized consideration of volcanic hazards and could result in much fewer flight cancellations, reroutings and traffic flow congestions. In addition, they could be used for route optimization in the areas where ash does not pose a direct and urgent threat to aviation, including the aspect of aeroplane maintenance.

Unique stochastic Lagrangian particle dispersion models for inhomogeneous turbulent flows

It is well established that Lagrangian particle dispersion models, for inhomogeneous turbulent flows, must satisfy the ‘well-mixed condition’ of Thomson (J. Fluid Mech., vol. 180, 1987, pp. 529–556) in order to produce physically reasonable results. In more than one dimension, however, the well-mixed condition is not sufficient to define the dispersion model uniquely. The non-uniqueness, which is related to the rotational degrees of freedom of particle trajectories, permits models with trajectory curvatures and velocity autocorrelation functions which are clearly unphysical. A spin condition is therefore introduced to constrain the models. It requires an ensemble of particles with fixed initial position and velocity to have, at short times, expected angular momentum, measured relative to the mean position and velocity of an ensemble of fluid particles with initially random velocity, equal to the relative angular momentum of the mean flow at the ensemble mean location. The resulting unique model is found explicitly for the canonical example of inhomogeneous Gaussian turbulence and is characterised by accelerations which are exponential in the particle velocity. A simpler unique model with a quadratic acceleration is obtained using a weaker version of the spin condition. Unlike previous models, the unique models defined by the spin condition lead to particles having the correct (ensemble mean) angular speed in a turbulent flow in solid-body rotation. The properties of the new models are discussed in the settings of a turbulent channel flow and an idealised turbulent atmospheric boundary-layer flow.

A three-dimensional atmospheric dispersion model for Mars

Atmospheric local-to-regional dispersion models are widely used on Earth to predict and study the effects of chemical species emitted into the atmosphere and to contextualize sparse data acquired at particular locations and/or times. However, to date, no local-to-regional dispersion models for Mars have been developed; only mesoscale/microscale meteorological models have some dispersion and chemical capabilities, but they do not offer the versatility of a dedicated atmospheric dispersion model when studying the dispersion of chemical species in the atmosphere, as it is performed on Earth. Here, a new three-dimensional local-to-regional-scale Eulerian atmospheric dispersion model for Mars (DISVERMAR) that can simulate emissions to the Martian atmosphere from particular locations or regions including chemical loss and predefined deposition rates, is presented. The model can deal with topography and non-uniform grids. As a case study, the model is applied to the simulation of methane spikes as detected by NASA’s Mars Science Laboratory (MSL); this choice is made given the strong interest in and controversy regarding the detection and variability of this chemical species on Mars.