Multi-scenario validation of the robust inversion method with biased plume range and values

Release rate estimation is a vital means of revealing the emission process of radionuclides and assessing the environmental consequences in an emergency. Inverse modeling is widely used in emergency cases, but is vulnerable to plume biases in atmospheric dispersion modeling. One promising solution is a model called "Simultaneously Estimates the Release rate And Corrects both the plume range and Transport pattern" (SERACT). This study investigates the feasibility and behavior of SERACT based on four wind tunnel experiments replicating complex dispersion scenarios with both dense buildings and heterogeneous topography. SERACT's performance is compared with that of Tikhonov-regularized inversion and its predecessor. The results demonstrate that SERACT successfully corrects the modeled plume biases and simultaneously improves the release rate estimations in all four complex local-scale scenarios. The release rates retrieved by SERACT provide better agreement with the true release rates than those given by the other methods for all scenarios, with an average deviation of only 5.83%. After correction, the simulated plume reproduces the concentrations at all sites and achieves the best Pearson correlation coefficient (1.00) and fraction of simulations within a factor of 2 of the measurements (1.00); these values are 7.33 and 2.09 times higher, respectively, than those of simulations using release rates obtained using Tikhonov-regularized inversion.

Spatial and temporal distribution of endotoxins, antibiotic resistance genes and mobile genetic elements in the air of a dairy farm in Germany

Antimicrobial resistance (AMR) is a serious issue that is continuously growing and spreading, leading to a dwindling number of effective treatments for infections that were easily treatable with antibiotics in the past. Animal farms are a major hotspot for AMR, where antimicrobials are often overused, misused, and abused, in addition to overcrowding of animals. In this study, we investigated the risk of AMR transmission from a farm to nearby residential areas by examining the overall occurrence of endotoxins, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) in the air of a cattle farm. We assessed various factors, including the season and year, day and nighttime, and different locations within the farm building and its vicinity. The most abundant ARGs detected were tetW, aadA1, and sul2, genes that encode for resistances towards antibiotics commonly used in veterinary medicine. While there was a clear concentration gradient for endotoxin from the middle of the farm building to the outside areas, the abundance of ARGs and MGEs was relatively uniform among all locations within the farm and its vicinity. This suggests that endotoxins preferentially accumulated in the coarse particle fraction, which deposited quickly, as opposed to the ARGs and MGEs, which might concentrate in the fine particle fraction and remain longer in the aerosol phase. The occurrence of the same genes found in the air samples and in the manure indicated that ARGs and MGEs in the air mostly originated from the cows, continuously being released from the manure to the air. Although our atmospheric dispersion model indicated a relatively low risk for nearby residential areas, farm workers might be at greater risk of getting infected with resistant bacteria and experiencing overall respiratory tract issues due to continuous exposure to elevated concentrations of endotoxins, ARGs and MGEs in the air of the farm.

Atmospheric dispersion and transmission of Legionella from wastewater treatment plants: A 6-year case-control study

Legionnaires Disease incidence has risen in the Netherlands in recent years. For the majority of the cases, the source of infection is never identified. Two Dutch wastewater treatment plants (WWTPs) have previously been identified as source of outbreaks of Legionnaires Disease (LD) among local residents. The objective of this study is to examine if LD patients in the Netherlands are more exposed to aerosols originating from WWTPs than controls.

METHODS

An atmospheric dispersion model was used to generate nationwide exposure maps of aerosols from 776 WWTPs in the Netherlands. Municipal sewage treatment plants and industrial WWTPs were both included. Exposure of LD cases and controls at the residential address was compared, in a matched case-control design using a conditional logistic regression. Cases were notified LD cases with onset of disease in the period 2013-2018 in the Netherlands (n = 1604).

RESULTS

Aerosols dispersed over a large part of the Netherlands, but modelled concentrations are estimated to be elevated in close proximity to WWTPs. A statistically significant association was found between LD and the calculated annual average aerosol concentrations originating from WWTPs (odds-ratio: 1.32 (1.06-1.63)). This association remained significant when the two outbreak-related WWTPs were removed from the analysis (odds-ratio: 1.28 (1.03-1.58)).

CONCLUSION

LD cases were more exposed to aerosols from WWTPs than controls. This indicates that exposure to aerosols dispersed from WWTPs caused Legionnaires Disease in residents living near WWTPs in the period 2013-2018. In order to investigate which characteristics of WWTPs are associated with an increased LD risk, the WWTP database should be updated and more data is needed on the presence and survival of aerosolized Legionella bacteria to improve the Legionella dispersion modelling. Furthermore, it is recommended to further investigate how aerosol dispersion of WWTPs can effectively be reduced in order to reduce the potential health risk.

Uncertainties in atmospheric dispersion modelling during nuclear accidents

Predictions of the atmospheric dispersion of radionuclides accidentally released from a nuclear power plant are influenced by two large sources of uncertainty: one associated with the meteorological data employed, and one with the source term, i.e. the temporal evolution of the amount and physical and chemical properties of the release. A methodology is presented for quantitative estimation of the variability of the prediction of atmospheric dispersion resulting from both sources of uncertainty. The methodology, which allows for efficient calculation, and thus is well suited for real-time assessment, is applied to a hypothetical accidental release of radionuclides.

Validation of source detective system of the radionuclides measured in the atmosphere using a field tracer experiment

A source detective system has been developed to estimate unknown source regions and release rates of radionuclides released into the air from covert nuclear activities and accidents. This system is composed of trajectory, atmospheric dispersion, and source term estimation models. Simulated results were compared with the measurements of a field tracer experiment performed at the Yeonggwang nuclear power plant in Korea in May 1996. Two trajectories among five computed backward trajectories moved toward the original release point, and the comparative results contained some error due to single operation of the backward trajectory model. An atmospheric dispersion model was used to minimize the error of the trajectory model and to improve the accuracy of the source detective system. The results generated by the trajectory and atmospheric dispersion models together agreed better with the measurements than those obtained using the trajectory model alone.

Human Q fever incidence is associated to spatiotemporal environmental conditions

Airborne pathogenic transmission from sources to humans is characterised by atmospheric dispersion and influence of environmental conditions on deposition and reaerosolisation. We applied a One Health approach using human, veterinary and environmental data regarding the 2009 epidemic in The Netherlands, and investigated whether observed human Q fever incidence rates were correlated to environmental risk factors. We identified 158 putative sources (dairy goat and sheep farms) and included 2339 human cases. We performed a high-resolution (1 × 1 km) zero-inflated regression analysis to predict incidence rates by Coxiella burnetii concentration (using an atmospheric dispersion model and meteorological data), and environmental factors - including vegetation density, soil moisture, soil erosion sensitivity, and land use data - at a yearly and monthly time-resolution. With respect to the annual data, airborne concentration was the most important predictor variable (positively correlated to incidence rate), followed by vegetation density (negatively). The other variables were also important, but to a less extent. High erosion sensitive soils and the land-use fractions "city" and "forest" were positively correlated. Soil moisture and land-use "open nature" were negatively associated. The geographical prediction map identified the largest Q fever outbreak areas. The hazard map identified highest hazards in a livestock dense area. We conclude that environmental conditions are correlated to human Q fever incidence rate. Similar research with data from other outbreaks would be needed to more firmly establish our findings. This could lead to better estimations of the public health risk of a C. burnetii outbreak, and to more detailed and accurate hazard maps that could be used for spatial planning of livestock operations.

Impacts of C-uptake by plants on the spatial distribution of 14C accumulated in vegetation around a nuclear facility-Application of a sophisticated land surface 14C model to the Rokkasho reprocessing plant, Japan

The impacts of carbon uptake by plants on the spatial distribution of radiocarbon (¹⁴C) accumulated in vegetation around a nuclear facility were investigated by numerical simulations using a sophisticated land surface ¹⁴C model (SOLVEG-II). In the simulation, SOLVEG-II was combined with a mesoscale meteorological model and an atmospheric dispersion model. The model combination was applied to simulate the transfer of ¹⁴CO₂ and to assess the radiological impact of ¹⁴C accumulation in rice grains during test operations of the Rokkasho reprocessing plant (RRP), Japan, in 2007. The calculated ¹⁴C-specific activities in rice grains agreed with the observed activities in paddy fields around the RRP within a factor of four. The annual effective dose delivered from ¹⁴C in the rice grain was estimated to be less than 0.7 μSv, only 0.07% of the annual effective dose limit of 1 mSv for the public. Numerical experiments of hypothetical continuous atmospheric ¹⁴CO₂ release from the RRP showed that the ¹⁴C-specific activities of rice plants at harvest differed from the annual mean activities in the air. The difference was attributed to seasonal variations in the atmospheric ¹⁴CO₂ concentration and the growth of the rice plant. Accumulation of ¹⁴C in the rice plant significantly increased when ¹⁴CO₂ releases were limited during daytime hours, compared with the results observed during the nighttime. These results indicated that plant growth stages and diurnal photosynthesis should be considered in predictions of the ingestion dose of ¹⁴C for long-term chronic releases and short-term diurnal releases of ¹⁴CO₂, respectively.

Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements

Urban vehicle emission models have been utilized to calculate pollutant concentrations at both microscopic and macroscopic levels based on vehicle emission rates which few researches have been able to validate. The objective of our research is to estimate urban roadside emissions and calibrate it with in-field measurement data. We calculated the vehicle emissions based on localized emission rates, and used an atmospheric dispersion model to estimate roadside emissions. A non-linear regression model was applied to calibrate the localized emission rates using in-field measurement data. With the calibrated emission rates, emissions on urban roadside can be estimated with a high accuracy.