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.

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.

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.

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.

In this paper, a toolchain for the generation of realistic synthetic images for human body pose detection in an in-car environment is proposed. The toolchain creates a customized synthetic environment, comprising human models, car, and camera. Poses are automatically generated for each human, taking into account a per-joint axis Gaussian distribution, constrained by anthropometric and range of motion measurements. Scene validation is done through collision detection. Rendering is focused on vision data, supporting time-of-flight (ToF) and RGB cameras, generating synthetic images from these sensors. Ground-truth data is then generated, comprising the car occupants' body pose (2D/3D), as well as full body RGB segmentation frames with different body parts' labels. We demonstrate the feasibility of using synthetic data, combined with real data, to train distinct machine learning agorithms, demonstrating the improvement in their algorithmic accuracy for the in-car scenario.

Over the next years, the number of autonomous vehicles is expected to increase. This new paradigm will change the role of the driver inside the car, and so, for safety purposes, the continuous monitoring of the driver/passengers becomes essential. This monitoring can be achieved by detecting the human body pose inside the car to understand the driver/passenger's activity. In this paper, a method to accurately detect the human body pose on depth images acquired inside a car with a time-of-flight camera is proposed. The method consists in a deep learning strategy where the architecture of the convolutional neural network used is composed by three branches: the first branch is used to estimate the confidence maps for each joint position, the second one to associate different body parts, and the third branch to detect the presence of each joint in the image. The proposed framework was trained and tested in 8820 and 1650 depth images, respectively. The method showed to be accurate, achieving an average distance error between the detected joints and the ground truth of 7.6 pixels and an average accuracy, precision, and recall of 95.6%, 96.0%, and 97.8% respectively. Overall, these results demonstrate the robustness of the method and its potential for in-car body pose monitoring purposes.

O objetivo do trabalho foi analisar o desafio da cidade de Manaus-AM em universalizar o acesso aos sistemas de abastecimento de água e esgotamento sanitário. O estudo tem característica descritiva com abordagem qualitativa e foi desenvolvido por meio da análise da série histórica do Sistema Nacional de Informações sobre o Saneamento (SNIS), que abarcou informações e indicadores operacionais de 2002 a 2015, como extensão da rede de água, extensão da rede de esgotos; índice de atendimento urbano de água; índice de atendimento urbano de esgoto. Concluiu-se que há significativa diferença entre o abastecimento de água e o esgotamento sanitário, sendo que o último atende a pouco mais de 10% da população da cidade. Considerou-se que o maior desafio da cidade é a ampliação do acesso ao sistema de esgotamento sanitário, principalmente no que tange às obras de infraestrutura urbana e à garantia da adesão da população aos sistemas.

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