In a post-pandemic scenario, indoor air monitoring may be required seeking to safeguard public health, and therefore well-defined methods, protocols, and equipment play an important role. Considering the COVID-19 pandemic, this manuscript presents a literature review on indoor air sampling methods to detect viruses, especially SARS-CoV-2. The review was conducted using the following online databases: Web of Science, Science Direct, and PubMed, and the Boolean operators “AND” and “OR” to combine the following keywords: air sampler, coronavirus, COVID-19, indoor, and SARS-CoV-2. This review included 25 published papers reporting sampling and detection methods for SARS-CoV-2 in indoor environments. Most of the papers focused on sampling and analysis of viruses in aerosols present in contaminated areas and potential transmission to adjacent areas. Negative results were found in 10 studies, while 15 papers showed positive results in at least one sample. Overall, papers report several sampling devices and methods for SARS-CoV-2 detection, using different approaches for distance, height from the floor, flow rates, and sampled air volumes. Regarding the efficacy of each mechanism as measured by the percentage of investigations with positive samples, the literature review indicates that solid impactors are more effective than liquid impactors, or filters, and the combination of various methods may be recommended. As a final remark, determining the sampling method is not a trivial task, as the samplers and the environment influence the presence and viability of viruses in the samples, and thus a case-by-case assessment is required for the selection of sampling systems.

No abstract

With the advent of autonomous vehicles, detection of the occupants' posture is crucial to tackle the needs of infotainment interaction or passive safety systems. Generative approaches have been recently proposed for human body pose in-car detection, but this type of approaches requires a large training dataset for a feasible accuracy. This requirement poses a difficulty, given the substantial time required to annotate such large amount of data. In the in-car scenario, this requirement risk increases even further, since a robust human body pose ground-truth system capable of working in it is needed but inexistent. Currently, the gold standard for human body pose capture is based on optical systems, requiring up to 39 visible markers for a plug-in gait model, which in this case are not feasible given the occlusions inside the car. Other solutions, such as inertial suits, also have limitations linked to magnetic sensitivity and global positioning drift. In this paper, a system for the generation of images for human body pose detection in an in-car environment is proposed. To this end, we propose to smartly combine inertial and optical systems to suppress their individual limitations: By combining the global positioning of 3 visible head markers provided by the optical system with the inertial suit's relative human body pose, we obtain an occlusion-ready, drift-free full-body global positioning system. This system is then spatially and temporally calibrated with a time-of-flight sensor, automatically obtaining in-car image data with (multi-person) pose annotations. Besides quantifying the inertial suit inherent sensitivity and accuracy, the feasibility of the overall system for human body pose capture in the in-car scenario was demonstrated. Our results quantify the errors associated with the inertial suit, pinpoint some sources of the system's uncertainty and propose how to minimize some of them. Finally, we demonstrate the feasibility of using system generated data (which was made publicly available), independently or mixed with two publicly available generic datasets (not in-car), to train 2 machine learning algorithms, demonstrating the improvement in their algorithmic accuracy for the in-car scenario.

A técnica MIMS (membrane introduction mass spectrometry) foi utilizada para monitorar a formação de clorofórmio durante a cloração de suspensões aquosas de várias espécies brasileiras de algas verdes e azuis (Microcystis panniformis, Selenastrum sp., Scenedesmus sp., Monoraphidium sp. (strain 354), Monoraphidium sp. (strain 960), and Staurastrum sp.). Foram avaliadas as influências de parâmetros como temperatura, pH, concentração inicial de hipoclorito de sódio, filtração e tempo de reação. Foi constatado que o teor de clorofórmio é fortemente dependente da espécie de alga e também é favorecido com o aumento da temperatura, pH, dosagem de cloro inicial e do tempo de reação. Amostras de suspensões de algas submetidas a filtração produziram menores quantidades de clorofórmio em comparação com as amostras brutas.Membrane introduction mass spectrometry (MIMS) was used to perform on-line monitoring of the chloroform formation via the chlorination of aqueous suspensions of several green and blue-green Brazilian algae (Microcystis panniformis, Selenastrum sp., Scenedesmus sp., Monoraphidium sp. (strain 354), Monoraphidium sp. (strain 960), and Staurastrum sp.). The influence of major parameters, such as temperature, pH, initial concentration of sodium hypochloride, filtration, and reaction time, on chloroform formation was evaluated. It was verified that the chloroform formation is strongly dependent on the alga type and is favored by high temperatures, pH, sodium hypochloride initial concentration and reaction time. Finally, filtered algae samples produce smaller amounts of chloroform in comparison to the rough suspension.

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.