Measured Concentration Data Research Articles

A Method for Assessing Background Concentrations near Sources of Strong CO2 Emissions

In the quantification model of emission intensity of emission sources, the estimation of the background concentration of greenhouse gases near an emission source is an important problem. The traditional method of estimating the background concentration of greenhouse gases through statistical information often results in a certain deviation. In order to solve this problem, we propose an adaptive estimation method of CO2 background concentrations near emission sources in this work, which takes full advantage of robust local regression and a Gaussian mixture model to achieve accurate estimations of greenhouse gas background concentrations. It is proved by experiments that when the measurement error is 0.2 ppm, the background concentration estimation error is only 0.08 mg/m3, and even when the measurement error is 1.2 ppm, the background concentration estimation error is less than 0.4 mg/m3. The CO2 concentration measurement data all show a good background concentration assessment effect, and the accuracy of top-down carbon emission quantification based on actual measurements should be effectively improved in the future.

Air pollutant emissions and reduction potentials from municipal solid waste incineration in China

China fully implemented the new emission standards in 2016 to further reduce the emissions of air pollutants from the municipal solid waste (MSW) incineration industry; however, the implementation effect of the new standards remains unknown. This study developed the first nationwide air pollutant emission inventory of MSW incineration plants in China based on the measured concentration data from China's continuous emissions monitoring systems (CEMS) network, and activity level data from the China Urban Construction Statistical Yearbook, to evaluate the effectiveness of implementing the new emission standards and estimate the future reduction potentials. Our results demonstrated that the overall standard-reaching proportions of particulate matter (PM), sulfur dioxide (SO2), nitrogen oxide (NOX), hydrogen chloride (HCl) and carbon monoxide (CO) were 98.8%, 99.3%, 99.4%, 99.4% and 97.6%, respectively, by comparing with the corresponding concentration limits of new emission standards. The total emissions of PM, SO2, NOX, HCl and CO from 412 MSW incineration plants in 2019 were 1.9, 6.2, 50.8, 4.3 and 6.6 kt yr−1, respectively, which is 33.6–75.8% lower than those in 2015, mainly due to the sharp decrease in emission factors. Pollutant emission hotspots were mainly concentrated in eastern and central and southern regions with large populations and well-developed economies. The analysis of future scenario results shows that despite the continuous increase of MSW incineration amount in the future, if the government strengthens pollutant emission standards and comprehensively implements waste sorting, total emissions and emission factors of air pollutants could be further reduced by 25.8–72.7% and 59.8–81.2%, respectively, by 2050. These findings provide helpful insights into future policymaking and technology selection for China and other countries seeking to reduce pollutant emissions from the MSW incineration industry.

Explication on distribution patterns of volatile organic compounds in petro-chemistry and oil refineries of China using a species-transport model and health risk assessment

Volatile organic compounds (VOCs) from industrial emissions have attracted great attention due to their negative effects on human, but there is lack of deterministic air quality model for VOC emissions. In this study, airborne VOCs from a typical petrochemical and oil refinery region, Lanzhou, Gansu province of China, were on-site measured. The regional pollution patterns were investigated using a species transport model and the health risks were evaluated. The spatial distribution of VOCs showed that 87.5 % of the airborne VOCs were benzene, toluene, ethylbenzene, and xylene having higher concentration (146 μg/m3) in the north direction oil refinery industrial areas. The concentrations of toluene and benzene were as high as 41.5 and 33.3 μg/m3 in the 4 km2 area away from the petrochemical emission source, respectively, and the concentration of o−/m + p-xylene was up to 79.7 μg/m3. Based on the measured concentration data, the numerical results showed that the accumulation of high concentration of VOC species by mass transfer in the region is related to the atmospheric diffusion driven by downward-moving air over the valley areas. Non-carcinogenic risk assessments showed that airborne benzene exposure had acceptable hazard quotient of 0.185 for adults, which was 1.8 times of children's (0.102), whereas it was found that a high carcinogenic risk (>10−4) from benzene in several sampling sites and diffuse distance become significant for carcinogenic risk. This study verified the effectiveness of VOC atmospheric diffusion model through a large number of on-site monitoring data, providing data support for model-based risk assessment.

Bayesian Inverse Modelling for Probabilistic Multi-Nuclide Source Term Estimation Using Observations of Air Concentration and Gamma Dose Rate

In case of a release of hazardous radioactive matter to the atmosphere from e.g., a nuclear power plant accident, atmospheric dispersion models are used to predict the spatial distribution of radioactive particles and gasses. However, at the early stages of an accident, only limited information about the release may be available. Thus, there is a need for source term estimation methods suitable for operational use shortly after an accident. We have developed a Bayesian inverse method for estimating the multi-nuclide source term describing a radioactive release from a nuclear power plant. The method provides a probabilistic source term estimate based on the early available observations of air concentration and gamma dose rate by monitoring systems. The method is intended for operational use in case of a nuclear accident, where no reliable source term estimate exists. We demonstrate how the probabilistic formulation can be used to provide estimates of the released amounts of each radionuclide as well as estimates of future gamma dose rates. The method is applied to an artificial case of a radioactive release from the Loviisa nuclear power plant in southern Finland, considering the most important dose-contributing nuclides. The case demonstrates that only limited air concentration measurement data may be available shortly after the release, and that to a large degree one will have to rely on gamma dose rate observations from a frequently reporting denser monitoring network. Further, we demonstrate that information about the core inventory of the nuclear power plant can be used to constrain the release rates of certain radionuclides, thereby decreasing the number of free parameters of the source term.

GenES-MDA: A generic open-source software package to solve inverse problems via the Ensemble Smoother with Multiple Data Assimilation

Ensemble Kalman filter methods have been successfully applied for data assimilation and parameter estimation through inverse modeling in various scientific fields. We have developed a new generic software package for the solution of inverse problems implementing the Ensemble Smoother with Multiple Data Assimilation (genES-MDA). It is an open-source, platform-independent Python-based program. Its aim is to facilitate the management and configuration of the ES-MDA through several programming tools that help in the preparation of the different steps of ES-MDA. genES-MDA has a flexible workflow that can be easily adapted for the implementation of different variants of the ensemble Kalman filter and for the solution of generic inverse problems. This paper presents a description of the package and some application examples. genES-MDA has been tested in three synthetic case studies: the solution of the reverse flow routing for the estimation of the inflow hydrograph to a river reach using observed water levels and a calibrated forward model of the river system, the identification of a hydraulic conductivity field using piezometric observations and a known forward flow model, and the estimation of the release history of a contaminant spill in an aquifer from measured concentration data and a known flow and transport model. The results of all these tests have demonstrated the flexibility of genES-MDA and its capabilities to efficiently solve different types of inverse problems.

Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

Lidar study of unusual winter Saharan dust loads above Sofia, Bulgaria: impacts on the local weather and troposphere

We report results of lidar studies of the atmospheric aerosols present above the city of Sofia during four episodes of wintertime Saharan dust intrusions in the last decade. The optical and microphysical properties of the detected aerosols, as well as the aerosol layering and dynamics, are retrieved and characterized. By combining lidar results with in situ PM10 concentration measurement data and air-transport modeling/forecast data, the effects of mixing and interaction of desert aerosols with local ones are analyzed as dependent on the altitude range, phase, and intensity of the dust load events. Included in the analysis are also meteorological radiosonde data, which allow us to ascertain the direct and strong influence of the desert air and aerosols on the local atmospheric conditions and meteorological parameters and thus, indirectly, on essential components of the local weather, such as the seasonal thermal regime and the regimes of cloud formation and precipitation. Using online resources, color maps of air temperature anomalies in the region for the days of lidar measurements are also provided based on climatological data over the three preceding decades. The results presented are in conformity with previously published statistical data of other authors and indicate that the wintertime intrusions of warm air masses from North Africa carrying Saharan dust to Europe, and particularly to the Balkans, rare and atypical until recently, nowadays show a trend of increasing in terms of frequency and intensity, pointing to deviations in the seasonal regime of the involved intercontinental air circulation systems.

Challenges in Measuring the Recovery of SARS-CoV-2 from Wastewater.

Wastewater-based epidemiology is an emerging tool for tracking the spread of SARS-CoV-2 through populations. However, many factors influence recovery and quantification of SARS-CoV-2 from wastewater, complicating data interpretation. Specifically, these factors may differentially affect the measured virus concentration, depending on the laboratory methods used to perform the test. Many laboratories add a proxy virus to wastewater samples to determine losses associated with concentration and extraction of viral RNA. While measuring recovery of a proxy virus is an important process control, in this piece, we describe the caveats and limitations to the interpretation of this control, including that it typically does not account for losses during RNA extraction. We recommend reporting the directly measured concentration data alongside the measured recovery efficiency, rather than attempting to correct the concentration for recovery efficiency. Even though the ability to directly compare SARS-CoV-2 concentrations from different sampling locations determined using different methods is limited, concentration data (uncorrected for recovery) can be useful for public health response.

Leak localization using distributed sensors and machine learning for hydrogen releases from a fuel cell vehicle in a parking garage

The detection and localization of hydrogen releases are of vital importance in large confined spaces to enable safe commercial penetration of hydrogen energy into society. In the present study, a hydrogen leak localization system was developed based on measured concentration data and a machine learning system. A scale parking garage model was used to experimentally model fuel cell vehicle leaks in a confined space with helium used as a surrogate of hydrogen for safety reasons. Twelve gas sensors were placed along the ceiling to measure the helium concentrations. The leak position was changed in each test to gather a range of concentration data. Then, the measured helium concentrations were used as input data to train the machine learning models. Two machine learning algorithms were used to locate the leak position, an artificial neural network (ANN) and the K-DTW algorithm. The helium concentrations recorded by the twelve sensors at a specific time point were used as the input data for the ANN model, while time-series concentration data was used as the input for the K-DTW algorithm. The results show that the ANN model can predict leak locations that were not included in the training datasets and that the K-DTW algorithm can classify the unknown leak location to the given location label. The ANN prediction accuracy was 78.4%. The K-DTW algorithm identified the leak location with 87.5% accuracy. The localization technology developed in the present work can provide safe monitoring of large parking garages and the model accuracy can be improved by feeding more training data to the machine learning models.

Studi pengaruh erupsi abu vulkanik gunung agung terhadap hasil pengukuran partikel tersuspensi

Mount Agung with an altitude of 3.142 meters above sea level is located in Karangasem. This mountain is one of the active volcanoes in Indonesia. Some dangerous impacts caused by the eruption of Mount Agung are acid rain, earthquakes, lava/lava flow, landslides/mudflow, smoke from lava, pyroclastic, rain of volcanic ash, and rocks and also volcanic gas. One of them which is Rain of volcanic ash can cause blisters on the skin and eyes as well as respiratory irritation. For long-term effects can cause silicosis (lung disease resulting from dust) and chronic lung disease. This study was conducted to determine the Mount Agung volcanic ash eruption effect on the results of SPM concentration measurements at the Jembrana Climatology Station using comparative data and sample data obtained from SPM, VONA concentration measurement data, and the HYSPLIT Volcanic Ash (NOAA) models.

Effect of reversible lanes on the concentration field of road-traffic-generated fine particulate matter (PM2.5)

Fine particulate matter (PM2.5) poses a potential threat to human health. The effects on PM2.5 before and after implementing a reversible lane were analysed based on a road segment. A simulation experiment was conducted, and the results were verified with normalized and measured concentration data. The PM2.5 concentration fields were studied in a series of cases with three different aspect ratios and two wind speeds. The average PM2.5 concentrations at y = 0.5, y = 1.5, and y = 2.0 m after the implementation of the reversible lane decreased by 2.35 %, 2.45 %, and 2.70 %, respectively. The effect of implementing a reversible lane on reducing PM2.5 concentration was outstanding when the aspect ratio H/W was 2:1, followed by H/W = 1:1 and H/W = 1:2. When the aspect ratio H/W was 2:1 and wind speeds were 1.2 and 3.6 m/s, the average PM2.5 concentrations at y = 1.5 m after the implementation of the reversible lane decreased by 84.14 % and 45.45 %, respectively. These findings will assist in designing streets with appropriate aspect ratios, developing an orderly strategy to implement variable lanes throughout an entire road network, and controlling road traffic flows to reduce the potential public harm of PM2.5.

Environmental prioritization of pesticide in the Upper Citarum River Basin, Indonesia, using predicted and measured concentrations

A novel screening method was developed to prioritize aquatic and human health risks of pesticides based on usage data, runoff modelling and effect prediction. An important asset of this new method is that it does not require measured concentration data, which are often unavailable or difficult to obtain in low- and middle-income countries like Indonesia. The method was applied to prioritize 31 agricultural pesticides used in the Upper Citarum River Basin in West Java, Indonesia. Ranking of pesticides based on predicted concentrations generally showed good agreement with ranking based on concentrations measured by passive sampling. The individual pesticide intake through the consumption of river water was predicted to cause negligible human health risks, but substantial aquatic risks (i.e. PEC/PNEC >1) were predicted for profenofos (5.2.E+01), propineb (3.6.E+01), chlorpyrifos (2.6.E+01), carbofuran (1.7.E+01), imidacloprid (9.4.E+00), methomyl (7.6.E+00) and chlorantraniliprole (3.6.E+00). In order to protect the aquatic environment, water managers are advised to take measures to reduce the use and runoff of these pesticides in the UCRB. The screening assessment can be further refined by performing additional effect studies for some pesticides, pesticide mixtures and validation of the predicted water concentrations by targeted measurements.

Gas Source Declaration With Tetrahedral Sensing Geometries and Median Value Filtering Extreme Learning Machine

Gas source localization (including gas source declaration) is critical for environmental monitoring, pollution control and chemical safety. In this paper we approach the gas source declaration problem by constructing a tetrahedron, each vertex of which consists of a gas sensor and a three-dimensional (3D) anemometer. With this setup, the space sampled around a gas source can be divided into two categories, i.e. inside (“source in”) and outside (“source out”) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the “source in” or “source out” cases, we propose to directly take raw gas concentration and wind measurement data as features, and apply a median value filtering based extreme learning machine (M-ELM) method. Our experimental results show the efficacy of the proposed method, yielding accuracies of 93.2% and 100% for gas source declaration in the regular and irregular tetrahedron experiments, respectively. These results are better than that of the ELM-MFC (mass flux criterion) and other variants of ELM algorithms.

Therapeutic drug monitoring of levetiracetam in newborns

Neonatal seizures in full-term and preterm infants represent a common neurological syndrome. Levetiracetam (LEV) is one of the new and widely prescribed second- or third-line antiepileptic drug for the treatment of seizures. Routine therapeutic drug monitoring of LEV was not recommended due to its almost ideal pharmacokinetic profile: linear pharmacokinetics, predictable dose-concentration relationship, wide therapeutic index, favourable safety profile, and unlikely clinically significant drug-drug pharmacokinetic interactions. In newborns, drug pharmacokinetics may be under the influence of maturation process. LEV pharmacokinetics in newborns appears to be age (gestational, postnatal) dependent and highly variable within the age ranges. These aspects make therapeutic drug monitoring a useful procedure for therapy optimization in this specific patient population. In population modeling based on therapeutic drug monitoring and nonlinear mixed effects models, covariates were found that should significantly affect the LEV clearance and volume of distribution in newborns — creatinine clearance and total body weight. Using of these regression equations can help to adjust the LEV doses without the patient's measured concentration data. But the significant magnitudes of the interindividual variability remaining in these final regression models justify the need for therapeutic drug monitoring and Bayesian adaptive control for personalization of LEV dosage regimens in neonates.

Quantifying and Qualifying Alloys Based on Level of Homogenization: A U-10Mo Alloy Case Study

Abstract Homogenization heat treatment is performed to attain uniformity in microstructure which is helpful to achieve the desired workability and microstructure in final products and, eventually, to gain predictive and consistent performance. Fabrication of low-enriched uranium alloys with 10 wt% molybdenum (U-10Mo) fuel plates involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the performance in the nuclear reactor. To ensure uniform homogenization, a statistical method is proposed to quantify and characterize the molybdenum concentration variation in U-10Mo fuel plates by analyzing the molybdenum concentration measurement data from scanning electron microscopy energy dispersive spectroscopy line-scan. Statistical tolerance intervals (TI) are employed to determine the qualification of the U-10Mo fuel plate. We formulate an argument for the minimum number of independent samples to define fuel plate qualification if no molybdenum measurement data are available in advance and demonstrate that the given TI requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication costs. The statistical strategy developed in this paper can be implemented for other applications especially in the field of material manufacturing to assess qualification requirements and monitor and improve the process design.

Simulation and analysis of load shifting and energy saving potential of CO2-based demand-controlled ventilation in a sports training center

This paper aims to evaluate and characterize the impact of optimizing the operation of the HVAC system through maintaining dynamic CO2-based Demand-Controlled ventilation (DCV) on the electricity load profile and energy consumption of the sports training center of Leibniz University Hannover. The actual ventilation control scheme, in which the operation of the HVAC system is operated with a two-stage volume flow controller based on indoor CO2 concentration is improved through two steps to avoid overventilation and reduce power consumption. For this purpose, a detailed multi-zone model of the sports center and energy supply system has been developed in TRNSYS. In the first step, a multi-stage control scenario is implemented considering the occupancy schedules and indoor CO2 concentration measurement data. In the second step, based on an indoor CO2 concentration model, a predictive control scenario is developed and applied. Aiming at characterizing the influence of these operation scenarios on the power consumption of the building, the annual electricity load profiles of the simulation cases will be analyzed and compared with the actual load profile of the building based on the technical planning documents and data provided by building management system (BMS). Simulation results show that utilizing predictive CO2-based DCV leads to a reduction of the peak load electricity by almost 2 kW and the base load by 5 kW as well as decreasing the annual energy consumption by 40 %.

Source term estimation in the presence of nuisance signals

Many source-term estimation algorithms for atmospheric releases assume the measured concentration data are influenced only by the releases of interest. However, there are situations where identifying a short-term release from an unknown location in the presence of long-term releases from a different location is of interest. One such example is determining if part or all of a typical magnitude concentration of a radioactive isotope in a sampler came from a nuclear explosion, such as the explosion announced by DPRK in 2013, while medical isotope facilities and nuclear power plants were also operating in the region.An estimation algorithm has been developed for the case where a short-duration release is confounded by a long-term nuisance signal associated with an additional release location. The technique is demonstrated using synthetic release data for a hypothetical medical isotope production facility and a hypothetical puff release from a different location. The algorithm successfully determines the location (within 30 km) and time-varying release rate (within a factor of 2) for the medical isotope production facility and the location (within 60 km), time (within 6 h), and release magnitude (within a factor of 4) of the puff release.

Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.

Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. The approach presented is able to identify reliable time-varying individualized glucose-insulin models.

Lessons learned from atmospheric modeling studies after the Fukushima nuclear accident: Ensemble simulations, data assimilation, elemental process modeling, and inverse modeling

Modeling studies on the atmospheric diffusion and deposition of the radiocesium associated with the Fukushima Daiichi Nuclear Power Plant accident is reviewed here, with a focus on a research collaboration between l'Institut de Radioprotection et de Surete Nucleaire (IRSN)—the French institute in charge of evaluating the consequences of nuclear accidents and advising authorities in case of a crisis—and the Meteorological Research Institute (MRI) of the Japan Meteorological Agency—an operational weather forecasting center in Japan. While the modelers have come to know that wet deposition is one of the key processes, the size of its influence is unknown. They also know that the simulation results vary, but they do not know exactly why. Under the research collaboration, we aimed to understand the atmospheric processes, especially wet deposition, and to quantify the uncertainties of each component of our simulation using various numerical techniques, such as ensemble simulations, data assimilation, elemental process modeling, and inverse modeling. The outcomes of these collaborative research topics are presented in this paper. We also discuss the future directions of atmospheric modeling studies: data assimilation using the high temporal and spatial resolution surface concentration measurement data, and consideration of aerosol properties such as size and hygroscopicity into wet and dry deposition schemes. Copyright © 2018 by The Geochemical Society of Japan.

SELF-ORGANIZING MAP BASED SURROGATE MODELS FOR CONTAMINANT SOURCE IDENTIFICATION UNDER PARAMETER UNCERTAINTY

Identification of unknown groundwater contaminant sources is a complex problem. The complexities arise mainly due to the uncertainties related to the hydrogeologic information, sparsity of measurement data and unavoidable concentration measurement errors. The process of contaminant source identification with sparse and limited concentration measurement data especially when the hydrogeologic parameters are uncertain requires an efficient procedure. The existing methodologies to tackle this problem in real world cases usually require huge computational time and the solutions may be non-unique. The goal of this study is to evaluate a developed methodology to characterize the groundwater contamination sources in a heterogeneous, multi layered aquifer. This developed methodology utilizes the Self Organizing Maps (SOM) algorithm to design the surrogate models for source characterization. The most important advantages is that in this methodology, the trained SOM based surrogate models is directly utilized for groundwater contaminant source characterization without the necessity of using a separate linked simulation optimization model. The performance of the developed methodology is evaluated by using deterministic hydraulic conductivity values, and uncertain hydraulic conductivity values. These results indicate that the developed methodology could efficiently approximate groundwater flow and transport simulation models, and also characterize unknown groundwater contaminant sources in terms of location, magnitude and release history.