Continuous Air Monitor Research Articles

Data-driven approach enabling post-operation evaluation of air conditioning performance regarding thermal conditions attained indoors

Data-driven approaches become more and more attractive in building performance analysis including performance assessment of heat, ventilation, and air conditioning (HVAC) systems. Their popularity is associated with the increasing availability of large amounts of high-quality building-related measurement data. The study is focused on a data-driven approach that enables post-operation evaluation of AC performance based on indoor air monitoring. It allows for the identification of the classifying solution which recognizes the monitoring data that is representative of thermal conditions during AC operation. The major novelty consists in proposing the classification performance criterion, which is F1w-Score (the weighted F1-Score), and the procedure of weights selection. The procedure utilizes Hellinger distance (H). It is applied to measure the similarity between the binary empirical probability distribution of T and RH (EPDtr), associated with AC operation, and the prediction of this distribution (EPDes), made by the classifier. The approach was tested on three years of data from continuous indoor air monitoring in the IT engineers' room equipped with an AC unit. The best weights of F1w-Score were wPPV = 0.85 and wTPR = 0.15. The best classifying solution was identified using F1w-Score. It involved: Logistic Regression as the classifier and feature vector being Fourier spectrum of 16 min long fragments of RH and T time series. The performance of the best classifying solution was high as indicated by F1w-Score = 0.89 ± 0.05. The corresponding H(EPDtr, EPDes) = 0.17 ± 0.04 pointed at the capability to identify the measurement data that characterizes indoor conditions during AC operation well. This work contributes to the development of data-driven approaches serving diagnostics of HVAC systems. It draws attention to the problem of defining the classification performance criterion which takes into account the ultimate, domain-specific use of the classification results.

Simple Moving Average Applied to "ISO Method" CPAM Concentration Estimates: An Unexpected Result.

A surprisingly large amount of variance reduction has been observed when filtering International Organization for Standardization (ISO) "ISO Method" continuous particulate air monitor (CPAM) airborne radioactivity concentration estimates with a simple three-point moving average. This processing has relatively little lag relative to the amount of variance reduction obtained. The key factor producing this effect is the specific autocorrelation structure of the estimated concentrations, which are based on taking first differences of integrated-count data; this scheme results in successive count differences that contain a common count value between them. The observed variance reduction factor has also been derived analytically.

Detection limit variation against coarse aerosol during airborne contamination measurement with continuous air monitor

Abstract In nuclear facilities, the mandatory airborne contamination surveillance is operated by continuous air monitors (CAMs). It samples the ambient air on a filter and measures the deposited activity. It is designed to trig an alarm whenever the measured activity concentration exceeds a defined threshold. However, in some sites, such as dismantling nuclear sites, a high rate of false alarm is experienced, mainly for artificial alpha. It has been shown that false alarms are directly related to a sudden variation of the aerosol mass sizes distribution, i.e. a wrong detection limit (DL) evaluation. Experiments on the ICARE tests bench have been carried out to compare the CAM’s DL and an estimated DL as a function of the aerosol characteristics, to the measured one. This new estimation shows significant improvements over the previous one and highlights the need to consider aerosol characteristics for a correct airborne contamination measurement.

Effect of Agar Weight Loss with Passive Continuous Air Monitoring According GMP Annex 1 2023

The GMP Annex 1 2023 requests that microbial environmental monitoring by active and passive microbial air samplers is actuated by continuous air monitoring. A prolonged sampling produces a loss of moisture in the agar of the Petri dish and therefore it is necessary that during the “continuous time” the dryness of the nutrient media is under control to guarantee a regular growing of the cfu. The purpose of this paper is to evaluate if the agar of settle plate (which volume was reduced with a treatment under sterile laminar flow before aspiration) has still the correct nutrient characteristics during the incubation. The results of the study are summarised in Table 1 and 2. The average loss of humidity after 4 hours in the first part of test under laminar flow was 14,7%. In the second part of test, the Petri dishes aspirated the air in a normal open environment and the average growth after incubation (indicated in cfu/ plate), in comparation with control plates with the original volume of agar, was 87%. The acceptance criteria using the US Pharmacopeia Validation Recommendation for microbiological examination (chapter ) is that the test plates had to recover < 70% of the challenge.

Highly sensitive portable laser absorption spectroscopy formaldehyde sensor using compact spherical mirror multi-pass cell

A portable sensor with an optical case dimension of 46 × 28 × 16 cm3 based on laser absorption spectroscopy for highly sensitive measurement of formaldehyde was reported. A compact improved spherical mirror multi-pass cell consisting of two 5-cm diameter spherical mirrors separated by 17.7 cm was developed. The cell offered a sample volume of 350 mL and an optical absorption path length of 50.6 m, giving a high path length-to-volume ratio of 14.6 cm−2. The sampling response time was less than 1 s, allowing the application of rapid spectral background subtraction technique to effectively suppress optical fringes. The achieved detection limit was 650 pptv (1σ, 1 s), corresponding to a minimum detection absorption coefficient of 2.3 × 10−9 cm−1. The capability of the HCHO sensor was demonstrated by two days of continuous indoor air monitoring. The developed portable sensor is suitable for mobile and fast indoor air quality measurements.

SCIENTIFIC SUBSTANTIATION OF ENVIRONMENTAL MONITORING SYSTEMS AT THE REGIONAL LEVEL USING THE EXAMPLES OF PM2.5, PM10 AND OZONE IN WARTIME CONDITIONS

SCIENTIFIC SUBSTANTIATION OF ENVIRONMENTAL MONITORING SYSTEMS AT THE REGIONAL LEVEL USING THE EXAMPLES OF PM2.5, PM10 AND OZONE IN WARTIME CONDITIONS

Numerical Simulation of a CAM-Measured Spectra Influenced by Coarse Aerosol

In nuclear facilities, the mandatory atmosphere surveillance is operated by Continuous Air Monitors. This standalone instrument is designed to measure the airborne aerosol activity concentration and to trig an alarm signal when a predetermined activity concentration is exceeded. However, a rapid resuspension event of coarse aerosol leads to a measurement error: the airborne aerosol activity concentration is over-evaluated. Prior results have shown that the coarse aerosol deposit disturbs the background evaluation for the radioactivity measurement. The interactions between radioactive aerosols (with radon daughters) and coarse non-radioactive aerosols have to be investigated by running together aerosol models and nuclear simulations. Therefore, this paper investigates different ways to represent an aerosol deposit in numerical simulations. We developed two numerical aerosol deposit models that we integrated into Geant4, a tool for the simulation of the passage of radiations through matter, and then compared these to experimental results. The simplest model was discarded, and by using the second model, we managed to correctly frame our simulation results as an experimental measurement: an aerosol has been correctly considered in a nuclear simulation. By combining theory, simulations, and experimentations on both aerosol science and nuclear physics, this research will be able to improve the comprehension of monitors’ behaviour in delicate situations and, more broadly, the filtration of aerosols using radioactivity.

SARS-CoV-2 Surveillance in Indoor Air Using Electrochemical Sensor for Continuous Monitoring and Real-Time Alerts.

The severe acute respiratory syndrome related coronavirus 2 (SARS-CoV-2) has spread globally and there is still a lack of rapid detection techniques for SARS-CoV-2 surveillance in indoor air. In this work, two test rigs were developed that enable continuous air monitoring for the detection of SARS-CoV-2 by sample collection and testing. The collected samples from simulated SARS-CoV-2 contaminated air were analyzed using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The test rigs utilized two air sampling methods: cyclone-based collection and internal impaction. The former achieved a limit of detection (LoD) of 0.004 cp/L in the air (which translates to 0.5 cp/mL when tested in aqueous solution), lower than the latter with a limit of 0.029 cp/L in the air. The LoD of 0.5 cp/mL using the UFC-19 sensor in aqueous solution is significantly lower than the best-in-class assays (100 cp/mL) and FDA EUA RT-PCR test (6250 cp/mL). In addition, the developed test rig provides an ultra-fast method to detect airborne SARS-CoV-2. The required time to test 250 L air is less than 5 min. While most of the time is consumed by the air collection process, the sensing is completed in less than 2 s using the UFC-19 sensor. This method is much faster than both the rapid antigen (<20 min) and RT-PCR test (<90 min).

Impact of the Coarse Indoor Non-radioactive Aerosols on the Background Radon Progenies' Compensation of a Continuous Air Monitor.

This paper addresses the problem of false positive alarm when using a continuous air monitor (CAM) in decommissioning sites of nuclear facilities. CAMs are used to measure airborne activity and play an important role in the radiation protection of workers likely to be exposed to radioactive aerosols. Monitors usually sample aerosols on a membrane filter. Radioactive particles sampled are detected through the alpha and beta decays that they emit. These latter ionizing particles are measured online by spectrometry thanks to a Passivated Implanted Planar Silicon detector (PIPS). Alpha and beta decays, in this context, come mainly from the natural radon progeny (218Po, 214Pb, and so on) and, in the case of radioactive contamination, also from artificial radionuclides such as 239Pu or 137Cs. The aim of the CAM is to alert the workers when the artificial airborne activity occurs, always considering the presence of a variable background due to the natural particulate airborne activity. The CAM-specific algorithm considers this background dynamically and continuously, often by using a constant parameter. However, non-radioactive aerosols are also sampled on the membrane filter. These latter make the discrimination more difficult as they lead to the deterioration of the alpha-energy spectrum. In this paper, the effect of coarse non-radioactive aerosols on the CAM response is highlighted with four aerosol size-distributions. The evolution of the background is characterized as a function of the aerosol mass sampled, with the example of a simple algorithm. Thus, in this paper, results show a positive correlation of the background with the aerosol mass sampled by the CAM. In addition, results highlight at least two different evolutionary trends according to the aerosol size distribution. An explanation of these evolutions is given by considering the penetration profile of the natural radioactive aerosols in the granular deposit above the CAM filter. The main consequence of these results is that the background could not be considered as proportional to radon progeny as it is currently used.

Development of an in-situ continuous air monitor for the measurement of highly radioactive alpha-emitting particulates ([formula omitted]-aerosols) under high humidity environment

In the long-lasting decommissioning of the Fukushima Daiichi Nuclear Power Station (1F), the dismantling of nuclear fuel debris (NFD) remaining in the damaged reactors is an unavoidable but significant issue with many technical difficulties. The dismantling is presumed to involve mechanical and/or thermal processing of NFD, generating significant concentrations of particulates containing α-radionuclides (α-aerosols) that pose significant health risk upon inhalation. In order to minimize the radiation exposure of workers with α-aerosols during the dismantling/decommissioning process at 1F, it is essential to monitor the concentration of α-aerosols at the point of initial generation, i.e. inside the primary containment vessels (PCV) of the damaged reactors. Toward this end, an in-situ monitoring system for α-aerosols (in-situ alpha air monitor: IAAM) was developed and its technical performance was investigated under the conditions expected for the actual environments at 1F. IAAM was confirmed to fulfill four technical requirements: (1) steady operation under high humidity, (2) operation without using filters, (3) capability of measuring high counting rates of α-radiation, and (4) selective measurement of α-radiation even under high radiation background with β/γ-rays. IAAM is capable of selectively measuring α-aerosols with a concentration of 3.2 × 102 Bq/cm 3 or higher without saturation under high humid environment (100%-relative humidity) and under high background with β/γ-radiation (up to 100 mSv/h of γ-radiation). These results demonstrate promising potential of IAAM to be utilized as a reliable monitoring system for α-aerosols during the dismantling of NFD, as well as the whole long-lasting decommissioning of 1F.

A Preliminary Study of Radon Equilibrium Factor at a Tourist Cave in Okinawa, Japan

The International Commission on Radiological Protection (ICRP) issued its Publication 137, Occupational Intakes of Radionuclides: Part 3 in which the radon equilibrium factor is fixed as 0.4 for tourist caves; however, several studies have reported a different value for the factor and its seasonal variation has also been observed. In this study, the radon concentration, equilibrium equivalent radon concentration and meteorological data were measured, and the equilibrium factor was evaluated in a tourist cave, Gyokusen-do Cave located in the southern part of Okinawa Island in southwestern Japan. Radon concentrations were measured with an AlphaGUARD and their corresponding meteorological data were measured with integrated sensors. Equilibrium equivalent radon concentration was measured with a continuous air monitor. The measured radon concentrations tended to be low in winter and high in summer, which is similar to previously obtained results. By contrast, the equilibrium factor tended to be high in winter (0.55 ± 0.09) and low in summer (0.24 ± 0.15), with a particularly large fluctuation in summer. It was concluded that measurements in different seasons are necessary for proper evaluation of radon equilibrium factor.

Prediction of COVID-19 Cases from the Nexus of Air Quality and Meteorological Phenomena: Bangladesh Perspective

An integrated approach was used to estimate the number of COVID-19 patients related to air quality and meteorological phenomena. Additionally, the air quality during pre-lockdown, lockdown, and post-lockdown stages of the COVID-19 pandemic was assessed to determine the effect of the infection containment measures taken in Bangladesh during the pandemic. The air quality was assessed based on measurements of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), black carbon, particulate matter (PM2.5 and PM10), and aerosol optical depth. Time-averaged maps of these parameters have been generated from NASA’s (National Aeronautics and Space Administration) website. Values of these parameters have also been collected from a continuous air monitoring station (CAMS) located in Bangladesh’s north-western city Rajshahi. The comparison shows that lockdown during the pandemic has brought significant improvements in air quality. However, the improvement was not sustained, since rapid increases in the air pollutant concentrations were observed in the post-lockdown period. Furthermore, Pearson correlation coefficients between each air quality variable and the daily new COVID-19 case rates were calculated. Different meteorological variables during the same time periods were determined to observe the variation in Rajshahi city. Relationships of these variables with the case rates were also established. Additionally, statistical analyses of the obtained data have been conducted for the measured variables using the Kruskal–Wallis test to assess the differences in the observed data among the pre-lockdown, lockdown, and post-lockdown periods. Dunn’s “Q” test was employed to test if the variables showed significance statistical difference during the Kruskal–Wallis test for pairwise comparisons. From the study, it has been observed that both meteorological variables and air quality parameters have significant relationship with daily new COVID-19 case rates. Both positive and negative associations of these parameters with the COVID-19 case rates have been observed. Very high air pollution has been observed in the post-lockdown period. Thus, it is recommended that appropriate authorities undertake corrective measures to protect the environment in cities with large populations. This study provides guidance for decision makers and health officials for future research and potentially reducing the spread of COVID-19.

Actinide in Air (Rn-Progeny Rejected) Alpha Spectroscopy With Tensioned Metastable Fluid Detectors

Abstract This article discusses outcome of research for deriving a methodology and apparatus for ascertaining for the presence of ultratrace-level actinides in air from their alpha emission signatures, while remaining blind to the relatively large (1000× higher activity) alpha emissions from Rn-progeny. Apparatus and techniques were developed to collect and characterize alpha-emitting nuclides of Rn-progeny and actinides in air on a polycarbonate 3 μm pore size continuous air monitor (CAM) filter. A wet-chemistry approach was developed and validated for successfully separating the Rn-progeny alpha emitting isotopes of Po-214 and Po-218, while extracting the actinides (U, Pu, Am) in a fluid mixture that is suitable for conduct of alpha spectroscopy with a centrifugally tensioned metastable fluid detector (CTMFD). The resulting α-TMFD technology was compared against the state-of-art “Alpha-Sentry™” Continuous Air Monitor (CAM) system commonly utilized world-wide. Results indicate that the α-TMFD technology can potentially offer complementary and superior performance in multiple performance categories, and ∼18× improvement in the time to detect (e.g., at 0.02 derived air concentration (DAC) within ∼3 h, versus ∼70 h for Alpha-Sentry) for actinides of interest while also remaining ∼100% blind to ∼103× higher Rn-progeny background—with the added potential for offering few keV scale energy resolution without resorting to peak shape fitting, versus ∼300–400 keV for existing CAM systems.

Evaluation of an airborne alpha and beta particle detection capability using an environmental continuous air monitor system

We evaluate the ability of the Canberra Alpha Beta Environmental Continuous Air Monitor (ECAM) to detect and quantify airborne radiological contamination. The ECAM essentially consists of a passively-implanted planar silicon (PIPS) detector near a particulate filter through which outside air is pulled. Three years’ worth of background measurements on three different systems were assessed and calibrated to compensate for changing conditions and develop an average background response for the systems. The ECAM was also exposed to several radionuclides of interest, including 235U and 239Pu, to measure the response to alpha and beta particle sources. Both standard calibration sources and custom sources consisting of aqueous radioisotope solutions absorbed into clean filters. The ECAM responses to these sources were then scaled to quantities of interest and injected on the averaged background. Various alarm algorithms were evaluated on the source-injected spectra for minimum detectable air concentration for a given false alarm rate. Even in the worst case, the ECAM was able to detect radionuclides of interest at 10% of the Derived Response Level (DRL) for each isotope based on early-phase Protective Action Guides (PAG). Quantification of the radionuclides was also evaluated for the various algorithms, with mixed results, but overall clearly indicating the optimal algorithms for alpha and beta particle alarm and quantification. Finally, a limited evaluation of the beta particle detection efficiency points to a detection energy threshold of approximately 290 keV.

Technical Details of the Sigma Factor Alarm Method within Alpha CAMs.

Some test must be applied to the low-level count data from alpha continuous air monitors (CAMs) to determine if the count is statistically significant (i.e., significantly above background). The method should be as sensitive as possible, automatically desensitizing when required due to radon progeny, yet in such a manner as to not exceed an acceptable false-alarm rate. The test that has been historically used within many alpha CAMs to accomplish these goals is the so-called sigma factor approach. In this paper, this approach is discussed with respect to four common CAM algorithms. These algorithms are referred to as the theoretical best possible algorithm, the 4-ROI algorithm, the tail-fitting algorithm, and the peak-fitting algorithm. Relative sensitivities are estimated for each algorithm.

Gross Alpha-Based Continuous Air Monitor: Mathematical Modeling, Measurements, and Interpretation

The detection and measurement of transuranic activity in ambient air by counting alpha particles is confounded due to the presence of short-lived alpha-emitting isotopes due to radon and thoron. This paper describes an algorithm intended for use in a gross alpha-based continuous air monitor. The algorithm is capable of generating the variation of gross alpha count rate with time from air filter samples using in situ–measured radon progeny data. The required input of atom concentration can be obtained from a simple experimental technique, which is discussed here.

Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection

Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitoring systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here, an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional neural networks (CNNs), were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.

Neural network modeling methods for predicting the air parameters in the city of Tuzla

According to the report of the World Health Organization, the city of Tuzla is the second in the world, and the first in Europe in terms of the number of diseases caused by air pollution. Tuzla Canton since 2003 has continuous air monitoring. Concentrations of individual pollutants exceed hourly, daily and annual limit values. In this paper, based on the existing results of air monitoring and meteorological data, using statistical methods and neural network modeling methods, unique and reliable models for predicting the concentration of NO2 in the air for the City of Tuzla have been developed. The results obtained using these models can be used in strategic decision-making processes and activities related to air quality control and management. This paper, on the example of the City of Tuzla, showed that using existing air monitoring data, concentrations of pollutants can be predicted for a longer period of time, using artificial intelligence methods. Reliable models with a high correlation coefficient can be obtained. In the case of a short or long interruption of the measurement of pollutant concentrations for the City of Tuzla with the help of models, which are the result of this work, it is possible to predict the concentrations of pollutants and plan to take measures based on them.

A REMOTE CONTINUOUS AIR MONITORING SYSTEM FOR MEASURING AIRBORNE ALPHA CONTAMINATION.

We developed a remote continuous air monitoring (RCAM) system. The RCAM system consisted of a personal air monitor and a robot. The personal air monitor (poCAMon, SARAD, Germany) had a 400-mm2 ion-injected silicon detector and a membrane air filter with 25 mmφ. The personal air monitor provides the alpha energy spectra for any measurement time interval. Demonstration measurements were taken underground at the Mizunami Underground Research Laboratory and at a poorly ventilated concrete building. The RCAM system was remotely operated and successfully measured the 222Rn progeny even though the relative humidity was almost 100%. In the measured alpha spectra, the peaks of 218Po (6.0-MeV alpha) and 214Po (7.7-MeV alpha) were clearly identified. Our developed monitor is promising for alpha dust monitoring in a high gamma-ray environment or contaminated areas where a worker cannot safely physically enter.

Trend Characteristics of Atmospheric Particulate Matters in Major Urban Areas of Bangladesh

The urban areas of Bangladesh suffer from severe air quality problem especially in dry season (November–April) when the PM concentrations frequently rise to 7–8 times of the WHO guideline value. The Clean Air and Sustainable Environment (CASE) Project of the Department of Environment has deployed countrywide continuous air monitoring systems to regularly monitor the air quality of the urban areas of Bangladesh. In this paper hourly concentrations of PM10 and PM2.5 captured using β-attenuation method from 2013 to April 2018 in six important cities located in different regions of the country were exhaustively analyzed. Statistical analyses, diurnal and seasonal trends, and polar plots of PM concentrations were examined. Long range sources were spotted by Concentration Weighted Trajectory (CWT) method, where the trajectories were calculated using HYSPLIT-4 model. The analyses identified cities in the middle of the country (Dhaka, Narayanganj, Gazipur) as the most polluted ones while the city to the northeast region (Sylhet) was the least polluted. Average PM10 concentrations at Dhaka, Chattogram, Narayanganj, Gazipur, Sylhet and Barisal stations in dry seasons (November–April) were found 238.7±155.4, 190.7±108.5, 303.6±161.4, 227.3±142.7, 151.7±105.0 and 170.7±108.4 μg m−3 respectively whereas those in wet seasons (May–October) were 75.0±51.6, 55.5±40.8, 102.4±84.4, 60.6±48.5, 52.7±38.3, and 54.4±41.6 μg m−3 respectively. Correlative study of diurnal variations in PM concentrations and meteorological parameters revealed that the congenial meteorology aided in developing higher concentrations of both PM10 and PM2.5 during nighttime. Sources located to the northwestern districts (Naogao, Rangpur, Bogura) were traced by the CWT method contributing to the air pollution in other regions of the country. Outside the boundary, sources in Nepal, and Delhi-NCR and Uttar Pradesh regions of India could have contributed to fine particles at the middle of the country.