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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.

As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology.

Monitoring on urban water environment and analysis of engineering improvement measures are intricate and time-consuming tasks. In previous studies, the integration of hydrodynamic and water quality models and geographical information system (GIS) usually takes three approaches: loose coupling, tight coupling, and full coupling. However, this paper adopted a special loose coupling approach—case-based reasoning (CBR) to develop an integrated decision support system. This was characterized by invoking the case base stored in the GIS platform as the output of the model. The fused capability of model’s water quality predication and strong spatial data processing analysis of GIS can be realized at the same time by integration. The functionality of the integrated system was illustrated through a case study of Chaohu, a medium-sized city in China, which includes case retrieval, result interpretation, and the visual display in the GIS platform. Results verified the feasibility and operability of the developed method. As a useful tool, the integrated decision support system makes it simpler and more convenient for decision makers to make decisions efficiently and quickly.