Derivation of the Gaussian plume model in three dimensions

Gaussian plume model is a common model to study advection diffusion equation which is solved in three dimensions by using Laplace transformation considering constant eddy diffusivity and wind speed power law. Different schemes such as Irwin, Power Law, Briggs and Standard methods are used to obtain crosswind integrated concentration. Statistical measures are used in this paper to know which is the best scheme which agrees with the observed concentration data obtained from Copenhagen, Denmark. The results of model are compared with observed data.

Crosswind integrated concentration for various dispersion parameter systems

LFkkuh; Lrj izdh.kZu ds fy, xkSlh;u fiPNd ekWMy ¼Gaussian Plume Model½ dk O;kid :i ls iz;ksx fd;k tkrk gSA vuqizLFk iou dh dqy lkanzrk Kkr djus ds fy, xkSlh;u lw= ¼QkWewyk½ dks laxfBr fd;k gSA vuqizLFk iou dh dqy lkanzrk dh x.kuk djus ds fy, izdh.kZu izkpyksa dh fHkUu&fHkUu iz.kkfy;ksa dk mi;ksx fd;k x;k gSA lrg Lrj esa Å¡pkbZ ds vuqlkj iou xfr dh fHkUurk dk o.kZu djus ds fy, ykxfjFehd foaM izksQkby dk mi;ksx fd;k x;k gSA blesa NksM+h tkus okyh izHkkoh Å¡pkbZ dks /;ku esa j[kk x;k gSA fHkUu fHkUu izdh.kZu izkpy iz.kkfy;ksa ds fy, iwokZuqekfur lkanzrkvksa vkSj dksisugsxu ds folj.k iz;ksx ls izkIr fd, x, izsf{kr vk¡dM+ksa dh rqyuk djus ds fy, lkaf[;dh; ifjekiksa dk mi;ksx fd;k x;k gSA The Gaussian plume model is the most widely used model for local scale dispersion. The Gaussian formula has been integrated to obtain the crosswind-integrated concentration. Different systems of dispersion parameters are used to calculate the crosswind integrated concentration. A logarithmic wind profile is used to describe the variation of wind speed with height in the surface layer. The effective release height was taken into consideration. Statistical measures are utilized in the comparison between the predicted concentrations for different dispersion parameter systems and the observed concentrations data obtained from Copenhagen diffusion experiment.

Simulating the Transport and Dispersal of Volcanic Ash Clouds With Initial Conditions Created by a 3D Plume Model

Volcanic ash transport and dispersion (VATD) models simulate atmospheric transport of ash from a volcanic source represented by parameterized concentration of ash with height. Most VATD models represent the volcanic plume source as a simple line with a parameterized ash emission rate as a function of height, constrained only by a total mass eruption rate (MER) for a given total rise height. However, the actual vertical ash distribution in volcanic plumes varies from case to case, having complex dependencies on eruption source parameters, such as grain size, speed at the vent, vent size, buoyancy flux, and atmospheric conditions. We present here for the first time the use of a three-dimensional (3D) plume model based on conservation laws to represent the ash cloud source without any prior assumption or simplification regarding plume geometry. By eliminating assumed behavior associated with a parameterized plume geometry, the predictive skill of VATD simulations is improved. We use our recently developed volcanic plume model based on a 3D smoothed-particle hydrodynamic Lagrangian method and couple the output to a standard Lagrangian VATD model. We apply the coupled model to the Pinatubo eruption in 1991 to illustrate the effectiveness of the approach. Our investigation reveals that initial particle distribution in the vertical direction, including within the umbrella cloud, has more impact on the long-range transport of ash clouds than does the horizontal distribution. Comparison with satellite data indicates that the 3D model-based distribution of ash particles through the depth of the volcanic umbrella cloud, which is much lower than the observed maximum plume height, produces improved long-range VATD simulations. We thus show that initial conditions have a significant impact on VATD, and it is possible to obtain a better estimate of initial conditions for VATD simulations with deterministic, 3D forward modeling of the volcanic plume. Such modeling may therefore provide a path to better forecasts lessening the need for user intervention, or attempts to observe details of an eruption that are beyond the resolution of any potential satellite or ground-based technique, or a posteriori creating a history of ash emission height via inversion.

Online treatment of eruption dynamics improves the volcanic ash and SO₂ dispersion forecast: case of the Raikoke 2019 eruption

In June 2019, the Raikoke volcano, Kuril Islands, emitted 0.4–1.8 × 109 kg of very fine ash and 1–2 × 109 kg of SO2 up to 14 km into the atmosphere. The eruption was characterized by several phases or puffs of different duration and eruption heights. Resolving such complex eruption dynamics is required for precise volcanic plume dispersion forecasts. To address this issue, we coupled the atmospheric model system ICON-ART (ICOsahedral Nonhydrostatic – Aerosols and Reactive Trace gases) with the 1-D plume model FPlume to calculate the eruption source parameters (ESPs) online. The main inputs are the plume heights for the different eruption phases that are geometrically derived from satellite data. An empirical relationship is used to derive the amount of very fine ash (particles < 32 µm), which is relevant for long range transport in the atmosphere. On the first day after the onset of the eruption, the modeled ash loading agrees very well with the ash loading estimated from AHI (Advanced Himawari Imager) observations due to the resolution of the eruption phases and the online treatment of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. We compared 6-hourly means of model and AHI data with respect to the structure, amplitude, and location (SAL-method) to further validate the simulated dispersion of SO2 and ash. In the beginning, the structure and amplitude values differed largely because the dense ash cloud leads to an underestimation of the SO2 amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters indicating a very good agreement of model and observations. Furthermore, we found a separation of the ash and SO2 plume after one day due to particle sedimentation, chemistry, and aerosol-radiation interaction. The results confirm that coupling the atmospheric model system and plume model enables detailed treatment of the plume dynamics (phases and ESPs) and leads to significant improvement of the ash and SO2 dispersion forecast. This approach can benefit the operational forecast of ash and SO2 especially in case of complex and non-continuous volcanic eruptions like the Raikoke 2019.

Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment

For environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90th percentile peak-to-mean factor, R90, was tested here by comparing it with the experimental results (Uttenweiler field experiment), considering different Probability Distribution Functions (PDFs): Gamma and the Modified Weibull. Seventy-two percent of the simulated mean concentration values fell within a factor 2 compared to the experimental ones: the model was judged acceptable. Both the modelled results for standard deviation, σC, and concentration intensity, Ic, overestimate the experimental data. This evidence can be due to the non-ideality of the measurement system. The propagation of those errors to the estimation of R90 is complex, but the ranges covered are quite repeatable: the obtained values are 1–3 for the Gamma, 1.5–4 for Modified Weibull PDF, and experimental ones from 1.4 to 3.6.