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We have developed an aggregation scheme for use with the Lagrangian atmospheric transport and dispersion model NAME (Numerical Atmospheric Dispersion modelling Environment), which is used by the London Volcanic Ash Advisory Centre (VAAC) to provide advice and guidance on the location of volcanic ash clouds to the aviation industry. The aggregation scheme uses the fixed pivot technique to solve the Smoluchowski coagulation equations to simulate aggregation processes in an eruption column. This represents the first attempt at modelling explicitly the change in the grain size distribution (GSD) of the ash due to aggregation in a model which is used for operational response. To understand the sensitivity of the output aggregated GSD to the model parameters, we conducted a simple parametric study and scaling analysis. We find that the modelled aggregated GSD is sensitive to the density distribution and grain size distribution assigned to the non-aggregated particles at the source. Our ability to accurately forecast the long-range transport of volcanic ash clouds is, therefore, still limited by real-time information on the physical characteristics of the ash. We assess the impact of using the aggregated GSD on model simulations of the 2010 Eyjafjallajökull ash cloud and consider the implications for operational forecasting. Using the time-evolving aggregated GSD at the top of the eruption column to initialize dispersion model simulations had little impact on the modelled extent and mass loadings in the distal ash cloud. Our aggregation scheme does not account for the density of the aggregates; however, if we assume that the aggregates have the same density of single grains of equivalent size, the modelled area of the Eyjafjallajökull ash cloud with high concentrations of ash, significant for aviation, is reduced by ∼ 2 %, 24 h after the start of the release. If we assume that the aggregates have a lower density (500 kg m−3) than the single grains of which they are composed and make up 75 % of the mass in the ash cloud, the extent is 1.1 times larger.

Epidemiological studies use direct and indirect methods to estimate exposure to air pollutants. In this paper, we compared the exposure to NO2 given by dispersion modeling and a fixed monitoring station and personal monitors during the daily routine of twenty-one children. Indoor-outdoor (I/O) ratios were used to represent differences between indoor and outdoor environments. A sensitivity test was performed to assess the impact of this parameter. Questionnaires were used to obtain subjects’ routines during campaigns. The results obtained using dispersion models agree with the individual differences in exposure assessed by personal monitors — if compared to a method relying solely on the monitoring station. Nevertheless, an overestimation occurred for children living near roadways. The exposure results revealed higher sensitivity to I/O ratios used than to children’s pathways between their usual destinations. The time spent outdoors (or indoors) and the concentration variation at the urban microscale (

Over-dispersed Poisson chain-ladder models are widely used in general insurance claims reserving. Although such models can accommodate the over-dispersion frequently observed in run-off triangles, they also impose an additional constraint of fixed variance to mean ratio across cells. In this paper, we relax this constraint and develop a flexible dispersion structure in a double Poisson chain-ladder model. The proposed model nests the classic over-dispersed Poisson model as a special case. A generalized likelihood ratio test is further proposed to compare different dispersion structures. In contrast to the existing claims reserving methods, our proposed method is more flexible in terms of the dispersion modelling. Simulation and empirical studies are conducted to demonstrate the importance of flexible dispersion modelling.

Ship emissions and coastal air pollution around China are expected to be alleviated with the gradual implementation of ship domestic emission control area (DECA) policies. However, a comprehensive post-assessment on the policy's effectiveness is still lacking. This study developed a series of high-spatiotemporal ship emission inventories around China from 2016 to 2019 based on an updated Ship Emission Inventory Model (SEIM v2.0) and analyzed the interannual changes in emissions under the influence of both ship activity increases and gradually promoted policies. In this model, NOx, SO2, PM and HC emissions from ships in China's inland rivers and the 200 Nm (nautical miles) coastal zone were estimated every day with a spatial resolution of 0.05∘×0.05∘ based on a combination of automatic identification system (AIS) data and the Ship Technical Specifications Database (STSD). The route restoration technology and classification of ocean-going vessels (OGVs), coastal vessels (CVs) and river vessels (RVs) has greatly improved our model in the spatial distribution of ship emissions. From 2016 to 2019, SO2 and PM emissions from ships decreased by 29.6 % and 26.4 %, respectively, while ship NOx emissions increased by 13.0 %. Although the DECA 1.0 policy was implemented in 2017, it was not until 2019 when DECA 2.0 came into effect that a significant emission reduction was achieved, e.g., a year-on-year decrease of 33.3 %, regarding SO2. Considering the potential emissions brought by the continuous growth of maritime trade, however, an even larger SO2 emission reduction effect of 39.8 % was achieved in these 4 years compared with the scenario without switching to cleaner fuel. Containers and bulk carriers are still the dominant contributors to ship emissions, and newly built, large ships and ships using clean fuel oil account for an increasingly large proportion of emission structures. A total of 4 years of consecutive daily ship emissions were presented for major ports, which reflects the influence of the step-by-step DECA policy on emissions in a timely manner and may provide useful references for port observation experiments and local policy making. In addition, the spatial distribution shows that a number of ships detoured outside the scope of DECA 2.0 in 2019, perhaps to save costs on more expensive low-sulfur oil, which would increase emissions in farther maritime areas. The multiyear ship emission inventory provides high-quality datasets for air-quality and dispersion modeling, as well as verifications for in situ observation experiments, which may also guide further ship emission control directions in China.

Atmospheric transport, dispersion, and deposition models (ATDMs) can support decision-making during nuclear emergencies; however, uncertainties in the ATDM results need to be carefully evaluated. T...

Quantification of SARS-CoV-2 in urban wastewaters has emerged as a cheap, efficient strategy to follow trends of active COVID-19 cases in populations. Moreover, mathematical models have been developed that allow the prediction of active cases following the temporal patterns of viral loads in wastewaters. In Mexico, no systematic efforts have been reported in the use of these strategies. In this work, we quantified SARS-CoV-2 in rivers and irrigation canals in the Mezquital Valley, Hidalgo, an agricultural region where wastewater from Mexico City is distributed and used for irrigation. Using quantitative RT-PCR, we detected the virus in six out of eight water samples from rivers and five out of eight water samples from irrigation canals. Notably, samples showed a general consistent trend of having the highest viral loads in the sites closer to Mexico City, indicating that this is the main source that contributes to detection. Using the data for SARS-CoV-2 concentration in the river samples, we generated a simplified transport model that describes the spatial patterns of dispersion of virus in the river. We suggest that this model can be extrapolated to other wastewater systems where knowledge of spatial patterns of viral dispersion, at a geographic scale, is required. Our work highlights the need for improved practices and policies related to the use of wastewater for irrigation in Mexico and other countries.

The modeling of atmospheric dispersion is the mathematical simulation of how pollutants are dispersed in the atmosphere. Based on the advection-diffusion equations describing the dispersion of pollutants, dispersal models are widely used to give a spatial variability of pollutants emitted mainly by agricultural activities and industrial facilities. In this context, an analytical model is presented to study the dispersion of pollutants in the atmospheric boundary layer. The solution procedure is based on dividing the planetary boundary layer into sub-domains, where in each sub-domain the eddy diffusivity and the wind speed take average values. The eddy diffusivity is expressed under unstable conditions and the wind speed is represented in its logarithmic form. The findings of the current study show that the developed model is successfully validated using data sets obtained from the Copenhagen diffusion experiments in unstable conditions, after this the model is numerically applied in order to observe, in a better way, the spread of the pollutant in the atmosphere.

There is a significant need in the current industrial scenario for methods to be formulated to treat dangerous chemicals most safely. Accidental release of toxic chemicals will result in emergencies. Hence, an emergency response plan (ERP) is inevitable. The most toxic chemicals in the water and wastewater sector are chlorine and hydrogen sulphide, whereas methane is a flammable gas. CAMEO software is used in this research to predict the region that toxic gas release impacts. This research deals with a sewage treatment plant ERP and control measures for methane and chlorine gases. The affected area of hazard will depend upon the weather conditions and the time of the accident. Comparing two different seasons, the impacted distance is more significant in summer than in winter. It is observed that the night and early morning is more dangerous than the afternoon and evening as it shows the larger impacted distance.

The Chernobyl Nuclear Power Plant (NPP) catastrophe of 1986 has been a milestone in the use of nuclear power for energy generation. After the accident, various topics have been discussed to evaluate the details of occurrence of the event and to understand its impacts on human, animal and plant life. One of the most controversial topics is the release height and homogeneity of radionuclides at release point in the atmosphere. Currently, there exists no definitive decision on the release height and vertical distribution pattern of radionuclides released from the Chernobyl accident. Based on this premise, this study focuses on the analysis of various possible release patterns along the vertical dimension and the potential influences on the atmospheric dispersion and total deposition with particular reference to Cs-137. For this purpose, some release pattern functions following uniform, Dirac delta, exponential, log-Pearson type III, and cumulative distribution functions along the z-axis were used to simulate the dispersion of Cs-137 released from the accident site. A total of 22 release patterns are produced using different maximum release heights (2000, 3000, and 4000 m). A Lagrangian particle dispersion model, FLEXPART, was then used to conduct simulations for these conditions to assess most coherent dispersion and deposition patterns. Model results from each release function were plotted, compared with each other and verified with measured data. In the functions where the release predominantly existed at lower levels, more extreme values were observed in the close vicinity of the source. Consequently, Dirac delta, log-Pearson type III (1), and exponential functions can be used as worst-case conditions at local scale. On the other hand, simulations also revealed that contamination spread to wider areas in cases where the release occurred from higher levels of the atmosphere. Therefore, log-Pearson type III (2) and cumulative distribution function can be considered more significant concerning a wider distribution of affected areas.

This paper demonstrates the development of a moving point source (MPS) model for simulating the atmospheric dispersion of pollutants emitted from ships under movement. The new model is integrated into the chemistry transport model EPISODE–CityChem v1.3. In the new model, ship parameters, especially speed and direction, are included to simulate the instantaneous ship positions and then the emission dispersion at different simulation time. The model was first applied to shipping emission dispersion modeling under simplified conditions, and the instantaneous and hourly averaged emission concentrations predicted by the MPS model and the commonly used line source (LS) and fixed point source (FPS) models were compared. The instantaneous calculations were quite different due to the different ways to treat the moving emission sources by different models. However, for the hourly averaged concentrations, the differences became smaller, especially for a large number of ships. The new model was applied to a real configuration from the seas around Singapore that included hundreds of ships, and their dispersion was simulated over a period of a few hours. The simulated results were compared to measured values at different locations, and it was found that reasonable emission concentrations were predicted by the moving point source model.

Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

Levels of vehicular emission pollutants (CO, NO2, and PM2.5) were measured along a major highway in Lagos, Nigeria using different state-of-the-art analytical techniques, while BREEZE ROAD dispersi...

The growing concern of the effects of potential releases of chemical, biological or radiological materials in populated areas has led to an increase in urban dispersion modelling over the past several decades. More recently, there has been a surge of research in the area of source term estimation (STE), in which inverse computational methods are used to predict a source (release) location and strength based on sensor readings. Many studies to date have focused on idealized, free-field scenarios estimating continuous or instantaneous gaseous releases. There have been limited efforts including geometry (e.g. terrain or urban structures) effects using computational fluid dynamics (CFD) and no efforts towards estimating highly complex, transient sources. The first contribution of this work is the development of a proposed methodology to approximate the strength and location of transient source terms, whether mobile or changing in strength. The transient prediction tool is demonstrated to accurately predict the location and strength of sources exhibiting low to moderate transient behaviour. For fast moving or rapidly changing sources, the model becomes heavily reliant on adequate sensor positioning. The second contribution of this work is to quantify the uncertainty of the STE tool given uncertain measurements in atmospheric conditions (e.g. wind speed, wind angle and surface roughness) which are often sparse and prone to variations. The uncertainty quantification study is performed on steady, instantaneous, mobile and variable-strength sources in an idealized free-field setting. The wind angle was found to have the most effect on the prediction of the release position. The true release location was within 10-90th percentiles, with standard deviations on the order of one CFD cell size, for all cases assessed indicating a robustness of the algorithm to handle uncertain inputs. The free-field analysis is used as a baseline for applying the uncertainty quantification to predictions in a full-scale urban environment using the Joint Urban 2003 experimentation. Despite uncertain atmospheric conditions in the urban setting, the predicted source location was generally in the correct vicinity, although sometimes in the adjacent upwind street. It is recommended that the uncertainty quantification be applied to a probabilistic prediction tool to quantify the uncertainty of a statistical source term representation. Further, the analysis could be applied for more complex, highly transient and multi-source scenarios to fully assess the robustness of the algorithm.

The development and validation of management practices to mitigate greenhouse gas (GHG) emissions from livestock require accurate emission measurements. This study assessed the accuracy of a practical inverse dispersion modelling (IDM) technique to quantify methane (CH4) emitted from a small cattle herd (16 animals) confined to a 63 m × 60 m experimental pen. The IDM technique calculates emissions from the increase in the CH4 concentration measured downwind of the animals. The measurements were conducted for 7 d. Two types of open-path (OP) gas sensors were used to measure concentration in the IDM calculation: a Fourier transform infrared spectrometer (IDM-FTIR) or a CH4 laser (IDM-Laser). The actual cattle emission rate was measured with a tracer-ratio technique using nitrous oxide (N2O) as the tracer gas. We found very good agreement between the two IDM emission estimates (308.1 ± 2.1 – mean ± SE – and 304.4 ± 8.0 g CH4 head−1 d−1 for the IDM-FTIR and IDM-Laser respectively) and the tracer-ratio measurements (301.9 ± 1.5 g CH4 head−1 d−1). This study suggests that a practical IDM measurement approach can provide an accurate method of estimating cattle emissions.

Particulate matter (PM) is one of the most dangerous pollutants in urban environments, and studies have linked it to many adverse health effects. Resuspended road dust makes up a large portion of PM. Therefore, monitoring and estimating the contribution of resuspended dust to PM is vital. In this study, emission rates of fine and coarse PM from different roads of Vellore City in Tamil Nadu, India, is estimated using silt loading protocol adapted by U.S. EPA. Silt load samples are collected from 8 different roads in the study region. The emission rates estimated using silt loading and fleet weight are then used to model the dispersion of PM throughout the study region using AERMOD. Hotspots of high PM concentration are identified at various locations frequented by commuters in the study region. Areas around educational institutions and hospitals in the region show highly elevated levels of PM. This study illustrates that resuspension of road dust can contribute significantly to PM in urban centers. Fine PM concentration exceeding 500 μg/m3 raises concerns about the health of commuters in the study region. This justifies the need for more regulations aiming at reducing the contribution of PM from resuspended road dust to overall urban PM.

We investigated the contribution of waste fires to air pollution. The annual emission of pollutants (CO, NOx, PM10, SO2) and greenhouse gases (CH4, CO2) were evaluated. The prediction of emissions is based on statistical data from 79 large fires that took place in Poland in 2018. We analyzed the spatial distribution of these fires along with the expected emission factor. The predicted emissions from all large waste fires was in total: 2.05 ± 0.10 Gg of CH4, 19.60 ± 0.90 Gg of CO, 196 ± 13 Gg of CO2, 0.963 ± 0.047 Gg of NOx, 5.26 ± 0.58 Gg of PM10, and 0.72 ± 0.12 Gg of SO2. For the evaluation of the consequences, we used the number of people exposed to PM10 emitted in one very big fire. Almost 6.5 million people were exposed to an additional 1-hour average concentration of PM10 higher than 10 μg/m3 and over 360 thousand were exposed to a concentration higher than 100 μg/m3.

Landfills are known to be a significant contributor to atmospheric methane, yet emissions are difficult to quantify because they are heterogeneous over a large area (up to 1 km^2) as well as unsteady in time. Many different measurement methods have been developed, each with limitations and errors due to various factors. The most important difference among different measurement methods is the size of the measurement footprint. Flux chambers have the smallest footprint, typically 1 m^2, radial plume mapping mass balance and eddy covariance can have footprints between 100 and 10,000 m^2, and aircraft-based mass balance and the tracer dilution method can measure whole landfill emissions. Whole landfill measurement techniques are considered the best because they account for spatial heterogeneity, and the tracer dilution method (TDM) in particular has gained popularity because it is relatively noninvasive and cost-effective.The TDM works by comparing ratios of methane and tracer gas plumes downwind of the landfill. A tracer gas such as acetylene is emitted from the landfill at a known, steady rate. Downwind plume transects are collected using a gas analyzer on a moving vehicle to obtain both methane and tracer gas concentrations. The idea behind the method is that at the transect measurement location, the gas plumes are well mixed enough that the ratio between the methane and tracer gas concentrations is approximately equal to the ratio between the methane and tracer gas emissions rates. Methane emissions are calculated by equating the ratio of concentrations to the ratio of emissions rates and solving for methane emissions. Field studies of the TDM with controlled methane releases over a flat field have quantified TDM-related measurement errors, but these errors are impossible to assess in real landfill measurements because the true landfill emissions are unknown.Numerical modeling of the TDM is an advantageous way to study the errors in TDM- measured emissions as well as how the error changes under a variety of different conditions. With a numerical model, the true methane emissions are prescribed, so they can be compared to the TDM-measured emissions to evaluate the method’s accuracy. The TDM is examined in this dissertation using numerical simulations with the Weather Research and Forecasting model (WRF). WRF is a mesoscale numerical weather prediction model with large-eddy simulation (LES) capabilities, allowing for high-resolution simulations with resolved large- scale turbulent motions. To examine the TDM, two real landfills in the U.S. are selected, with high resolution topography data and real atmospheric data informing the initial and boundary conditions of the model. The simulations are run with a nested grid configuration, starting from 2.25 km resolution and nesting to 150 m resolution and then 30 m horizontal grid resolution over the landfill area, with the lowest vertical level ∼15 m. To simulate the TDM, three components are needed: tracer emissions with a specified configuration and emissions rate, landfill emissions specified at every grid point on the surface of the landfill, and simulated transect measurements with a specified transect path and transect collection speed. In this dissertation, tracer emissions are prescribed as steady values at grid points on the surface of the landfill constituting various tracer configurations to be examined, and methane emissions are either prescribed as steady values or calculated using prescribed soil methane concentrations and a surface flux parameterization.To our knowledge, the work presented in this dissertation constitutes the first time WRF simulations have been used to examine the TDM. In the first part of this dissertation, steady landfill methane emissions are prescribed to study the effects of various aspects of the TDM setup and various external factors on the accuracy of the TDM-measured emissions. Factors tested include tracer location relative to the methane emissions hot spot of the landfill, distance from the landfill to the transect path, transect angle relative to the wind direction, and transect speed. Tracer location relative to the emissions hot spot is found to have the most significant effect on TDM accuracy, while transect angle relative to wind direction and transect vehicle speed are found to have negligible effects. The roles of wind direction and topography are also examined and found to have significant effects of the TDM’s accuracy.In the second part of the dissertation, the same landfill area is simulated, and a surface flux parameterization is added to WRF to introduce wind-dependent variability to the land- fill emissions. Significant standard deviations were seen in the TDM-measured emissions in the previous chapter despite the prescribed landfill emissions remaining constant, and when TDM-measured emissions for steady and unsteady emissions simulations are compared, the variability in the TDM-measured emissions is found to be essentially the same even though the variability in true emissions is significantly different, pointing to possible errors inherent in the TDM’s ability to capture true emissions short-term variability. TDM-measured emissions standard deviation and TDM error are compared for eight different time periods over two different days to see whether different times of day and different atmospheric conditions affect the TDM. The smallest measured standard deviations and smallest errors are seen at night on both days, and measured standard deviation increases over the course of the day for both days, with the largest standard deviations seen in late afternoon shortly before sunset. TDM percent error does not exhibit a noticeable diurnal trend. Two different tracer configurations are used for the TDM simulations to obtain a range of standard deviations and percent errors for an optimal and less ideal tracer placement.In the last part of the dissertation, a different landfill area is simulated and emphasis is placed on extensive comparison to field measurements. Four different days from different seasons are simulated to examine the seasonal and diurnal effects of wind-dependent variability on emissions. These simulations aim to help inform how limited measurement data can be used to extrapolate annual landfill methane emissions.

The dispersion of inhalable particulates (PM10) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of PM10 in coal mines using AERMOD and assess outcomes rendered by disparate digital elevation models (DEM). CartoDEM (10 m) generated using the rational polynomial coefficient method and publically available DEMs, i.e., SRTM (90 m), ASTER (30 m), CartoDEM (30 m), and FLAT, were processed for simulating complex terrain of coal mines. Modeled concentration predicted using different terrain inputs was compared with field measured values for evaluating performance metrics. This comparison suggested that SRTM and FLAT topography met lesser performance criteria in comparison with other input DEMs. The model performance was evaluated using Willmott’s index of agreement (dr) being 0.39, 0.41, and 0.47 for SRTM, ASTER, and CartoDEM, respectively. However, CartoDEM (10 m) showed a slight improvement with dr of 0.57. The results revealed that model performance improved due to the recentness of DEM rather than its resolution. Overburden dump, haulage routes, and railway siding shared the majority PM10 concentration load invariably in all model runs where peak concentration varied from 454 to 680 µg/m3. Categorically, complex terrain simulations of coal mines influenced dispersion models by altering emission sources’ interaction with pre-processor calculations of meteorological data. The work will help improve the performance of models in complex terrain and the selection of topographic parameterization for risk-based decisions.

We developed a LOcal-scale High-resolution atmospheric DIspersion Model using Large-Eddy Simulation (LOHDIM-LES) for the safety assessment of nuclear facilities and emergency responses to accidenta...

Introduction. Air pollution modeling is a powerful tool that allows developing scientifically justified solutions to reduce the risks posed by atmospheric emissions of pollutants. Problem Statement. Cloud computing infrastructures provide new opportunities for web-based air pollution forecasting systems. However the implementation of these capabilities requires changes in the architecture of the existing systems. Purpose. The purpose is to adapt the web service of forecasting the atmospheric pollution in Ukraine to operate in the cloud computing platform of the Ukrainian National Grid infrastructure. Materials and Methods. The web client – web server – cloud computing architecture was used. The calculation of the model is performed in the cloud infrastructure, while the client and server parts operate on separate computers. Results. With the developed service the forecast of air pollution is possible for every point at the territory of Ukraine for more than thirty substances, including chlorine, ammonia, hydrogen sulfide and others. The forecast is performed using the data of the WRF-Ukraine numerical weather prediction system and visualized through a web interface. The capabilities of the developed system were demonstrated by the example of simulation of air pollution in part of Kyiv affected by the releases from the Energia incineration plant during pollution episode in September, 2019. The total releases of toluene gas from incineration plant and from the fire on spontaneous waste landfill, which is located a few km from Kyiv, were estimated and analyzed. For the considered period the fire could bring considerable additional amounts of pollutants to the studied region. The confidence interval for the maximum airborne concentration for the considered period is estimated from 0.7 to 2.1 mg·m-3 which is higher than the permissible value (0.6 mg· m-3). Conclusions. The presented system could be used by institutions responsible for response to environmental accidents. Keywords: air pollution, atmospheric dispersion, web-systems, cloud computing. Introduction. Air pollution modeling is a powerful tool that allows developing scientifically justified solutions to reduce the risks posed by atmospheric emissions of pollutants. Problem Statement. Cloud computing infrastructures provide new opportunities for web-based air pollution forecasting systems. However the implementation of these capabilities requires changes in the architecture of the existing systems. Purpose. The purpose is to adapt the web service of forecasting the atmospheric pollution in Ukraine to operate in the cloud computing platform of the Ukrainian National Grid infrastructure. Materials and Methods. The web client – web server – cloud computing architecture was used. The calculation of the model is performed in the cloud infrastructure, while the client and server parts operate on separate computers. Results. With the developed service the forecast of air pollution is possible for every point at the territory of Ukraine for more than thirty substances, including chlorine, ammonia, hydrogen sulfide and others. The forecast is performed using the data of the WRF-Ukraine numerical weather prediction system and visualized through a web interface. The capabilities of the developed system were demonstrated by the example of simulation of air pollution in part of Kyiv affected by the releases from the Energia incineration plant during pollution episode in September, 2019. The total releases of toluene gas from incineration plant and from the fire on spontaneous waste landfill, which is located a few km from Kyiv, were estimated and analyzed. For the considered period the fire could bring considerable additional amounts of pollutants to the studied region. The confidence interval for the maximum airborne concentration for the considered period is estimated from 0.7 to 2.1 mg·m-3 which is higher than the permissible value (0.6 mg· m-3). Conclusions. The presented system could be used by institutions responsible for response to environmental accidents.

This paper presents a methodology for estimating fleet emission rates from measured roadside concentrations. By filtering measurements based on meteorological conditions, including effective wind speeds above 2 m ⋅ s − 1 and periods where the receptor is downwind, we find our simplified approach can compare well with the more sophisticated Research LINE source (RLINE) model. We applied our method to two years of roadside air pollution and traffic measurements at a Toronto, Canada, highway site to estimate minutely emission rates (ER, mass∙m−1∙s−1) and emission factors (EF, mass∙vehicle−1∙ km−1) for carbon dioxide (CO2), nitrogen oxides (NOX), carbon monoxide (CO), black carbon (BC), particulate matter mass less than 2.5 μm in diameter (PM2.5), particle number (PN), and ozone (O3) over a two-year period. Re-entering these emission rates to a multi-lane RLINE model showed favorable agreement between predicted and measured concentrations for all pollutants with 85-87% of predicted concentrations falling within a factor of two of measured. A multiple-input linear regression was used to determine light-duty vehicle (LDV) and medium/heavy-duty vehicle (MDV + HDV)-specific emission factors, which fell in or near ranges previously reported for all pollutants. More generally, the method proposed here can allow researchers to easily measure emission rates and factors from roadways using near-road concentration measurements and simple analysis methods, and can exclude some or all micrometeorological inputs, allowing researchers to perform inverse dispersion modeling in regions where such inputs are unavailable. The results also provide updated data on Canadian vehicle emissions and refine the relationships between emissions and traffic composition and speed.

In case of a major accident involving airborne emissions of harmful gases, a temporary portable meteorological station may be used to improve atmospheric dispersion modelling (ADM) for protection of people and the environment. While the meteorological station provides signal values in real time, ADM results for the future are of particular interest for planning purposes. It is possible to use the current measured value as a future input to the ADM but it is suboptimal. It is also possible to use model output statistics (MOS) to predict the future local weather information from numerical weather prediction (NWP) models, while available operational NWP models are in general too coarse to be used directly in fine-resolution ADM. MOS models are obtained through machine learning and the training data sets in most traditional uses of MOS are big, which is beneficial for modelling. We envision using MOS in an emergency and for a location of a temporary meteorological station. We use windowing for online data selection to explore its accuracy when the amount of available training data is very limited, which is expected in an emergency situation. We show that MOS for wind vector with 1 day of training data greatly improves on the numerical weather predictions and the persistence model, so its use in such an emergency would be advantageous.

This paper presents an analysis of data from a wind tunnel study conducted to examine the dispersion of emissions at the edges of near-road noise barriers. The study is motivated by the concern that a barrier positioned downwind of a roadway may guide highly polluted plumes along the barrier leading to heightened concentrations as the plume spills around and downwind of the barrier end. The wind tunnel database consists of measurements of dispersion around a simulated roadway segment with various noise barrier configurations. Each roadway segment simulated in the wind tunnel had full-scale equivalent dimensions of 135 m long. Barrier segments, 135 m long with a height (H) of 6 m, were located on the downwind side of the source at a distance of 18 m from it (measured perpendicularly from the line source). Examination of the concentration patterns associated with the cases indicates that 1) vertical mixing induced by barriers persists at crosswind distances up to the edge (lateral end) of the barrier and downwind distances of x/H = 10, 2) concentration levels at all heights below z/H = 1 increase towards the edge of the barrier at downwind distances less than x/H = 7, and 3) concentration is well mixed in the vertical at the edge of the barrier, and the levels can be higher than in the middle of the barrier even when the source ends at the edge of the barrier. We have formulated a parameterization that captures the major features of these observations and can be incorporated in models such as RLINE.

Over the years, decline in air quality has been connected to the growing rate of urbanization and increasing number of vehicles on the roads. Most of the pollutants emitted from vehicular activities have been observed to have adverse effects on individuals as well as the atmosphere. Although, the focus of this study is to develop an emission dispersion model to predict the concentration of specific air pollutants with distance, the application of geostatistical technique such as Kriging interpolation to study the spatial distribution of pollutants from vehicular emissions around the study area was also exemplified. Seven (7) georeferenced points, namely, Ugbowo main gate, Ekosodin junction, Agen junction, Super D junction, Nitel junction, Okhunmwun junction and Oluku market junction were used for data collection. Pollutants from vehicular emissions, namely, nitrogen dioxide (NO2), carbon monoxide (CO) including the total radiation were monitored in the morning and evening for a period of 35 days (7th July to 12th August 2020) with the aid of portable toxic gas monitors and radiation alert meters. Other parameters of interest, which were also measured include maximum temperature and wind speed using infra-red thermometers and portable anemometer respectively. To ascertain the quality of the data, selected preliminary analysis, namely, test of normality, test of homogeneity, outlier detection and reliability test were done. Result of the study showed a high concentration of NO2, CO and total radiation around Ugbowo main gate and Okhunmwun community and environs especially during the peak hours of evening (5.0 p.m. – 6.0 p.m.) when the traffic load is high.