Indoor Air Quality in Healthcare Units—A Systematic Literature Review Focusing Recent Research

The adequate assessment and management of indoor air quality in healthcare facilities is of utmost importance for patient safety and occupational health purposes. This study aims to identify the recent trends of research on the topic through a systematic literature review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. A total of 171 articles published in the period 2015–2020 were selected and analyzed. Results show that there is a worldwide growing research interest in this subject, dispersed in a wide variety of scientific journals. A textometric analysis using the IRaMuTeQ software revealed four clusters of topics in the sampled articles: physicochemical pollutants, design and management of infrastructures, environmental control measures, and microbiological contamination. The studies focus mainly on hospital facilities, but there is also research interest in primary care centers and dental clinics. The majority of the analyzed articles (85%) report experimental data, with the most frequently measured parameters being related to environmental quality (temperature and relative humidity), microbiological load, CO2 and particulate matter. Non-compliance with the WHO guidelines for indoor air quality is frequently reported. This study provides an overview of the recent literature on this topic, identifying promising lines of research to improve indoor air quality in healthcare facilities.

Particulate Matter in an Urban–Industrial Environment: Comparing Data of Dispersion Modeling with Tree Leaves Deposition

Particulate matter represents a serious hazard to human health, and air quality models contribute to the understanding of its dispersion. This study describes particulate matter with a ≤10 μm diameter (PM10) dynamics in an urban–industrial area, through the comparison of three datasets: modeled (TAPM—The Air Pollution Model), measured concentration (environmental control stations—ECS), and leaf deposition values. Results showed a good agreement between ECS and TAPM data. A steel plant area was used as a PM10 emissions reference source, in relation to the four sampling areas, and a distance/wind-based factor was introduced (Steel Factor, SF). Through SF, the three datasets were compared. The SF was able to describe the PM10 dispersion values for ECS and leaf deposition (r2 = 0.61–0.94 for ECS; r2 = 0.45–0.70 for leaf); no relationship was found for TAPM results. Differences between measured and modeled data can be due to discrepancies in one district and explained by a lack of PM10 inventory for the steel plant emissions. The study suggests the use of TAPM as a suitable tool for PM10 modeling at the urban scale. Moreover, tree leaves are a low-cost tool to evaluate the urban environmental quality, by providing information on whether and when data from leaf deposition can be used as a proxy for air pollution concentration. Further studies to include the re-suspension of particles as a PM10 source within emission inventories are suggested.

Development of Artificial Intelligence Climate Chamber and Experimental Study on Crop Phenotype Acquisition

Background: The deficiencies of traditional artificial climate chambers in phenotypic collection and analysis were improved to achieve the high-throughput acquisition of crop phenotypes during the growth period. This paper has developed an artificial intelligence climate cabin with functions of crop cultivation management and phenotype acquisition during the whole growth period. This research also established an environmental control system, a crop phenotype monitoring system and a crop phenotype acquisition system with environmental parameter adjustment and crop image collection. Phenotypic feature extraction and other functions were carried out in the cultivation experiment, and phenotype acquisition of wheat was performed under different nitrogen fertiliser application rates. Comparison and analyses were performed by the systematic and manual measurement values of crop phenotype characteristics, and the acquisition of wheat table was evaluated based on artificial intelligence climate cabin. The goodness of fit of the model was used to classify data.Results: During the different growth periods of wheat, the correlation analysis between the systematic and manual measurement values of its leaf area, plant height and canopy temperature showed that the obtained correlation coefficient r was greater than 1, and the fitting determination coefficient R2 was greater than 0.7156, with errors. The coefficient root mean square error was less than 2.42, indicating that the two were positively correlated, and their correlation was excellent. Conclusion: The results verified the feasibility and applicability of the artificial intelligence climate cabin to study the phenotypic characteristics of crops.

Evaluating the Effect of Cover Materials on Greenhouse Microclimates and Thermal Performance

This study compared and analyzed changes in the microclimate and thermal environment inside single-span greenhouses covered with a single layer of plastic film, polycarbonate (PC), and glass. The results of the experiment show that the PC-covered greenhouse was the most favorable for managing the nighttime heating effect during the cold season. However, the glass-covered greenhouse was found to be the most favorable for managing the cooling effect during the hot season. Although the plastic-covered greenhouse was inexpensive and easy to install, the air temperature inside varied significantly, and it was difficult to control its indoor environment. The thermal load leveling values showed that the PC-covered greenhouse had the lowest variation, confirming its superiority in terms of environmental control and energy savings. In terms of the overall heat transfer, heat was generally transferred from the interior to the exterior of the greenhouses. In the plastic-covered greenhouse, however, heat was transferred in the opposite direction at night due to the influence of radiant cooling. The occurrence of the minimum and maximum heat transfer values had a tendency similar to that of the occurrence of the minimum and maximum air temperatures inside the greenhouses.

A Review of Ventilation and Environmental Control of Underground Spaces

Because of rapid urbanization, traffic problems, and other factors, underground spaces have been used more in the twenty-first century. Large underground spaces are required for underground city, metro, tunnel, mine, industrial and agricultural engineering, and civil air defense engineering. Underground spaces with varying thermal, ventilation, and lighting environments can face problems of comfort, health, and safety. High temperatures, high humidity, difficulty in flue gas emission, harmful microorganisms, radon, and physical and psychological problems are examples of issues. Air quality control technologies for underground spaces, such as ventilation, dehumidification, natural energy utilization, smoke extraction, and ventilation resistance reduction, are discussed. Ventilation for smoke-proofing/evacuation is also extensively addressed.

Climate Changes and Atmospheric Pollution

The objective of this chapter is to present the central concepts, parameters, and methods for the monitoring of climate changes, with a focus on air pollution, and the possible global and regional impacts of climate changes as well. There are plant species used as bioindicators that have a high sensitivity or ability to accumulate environmental pollutants. Another method that this chapter will present is the use of receiver models that employ both mathematical and statistical approaches to quantify the individual contribution of a given number of emission sources in the composition of a sample. The data presented in this chapter will provide reliable bases and methodologies for environmental control, supporting the adoption of more restrictive policies.

City Vertical Gardening

The countermeasures taken during the COVID-19 pandemic opened discussions regarding their status as temporal or ephemeral as they designated the positive environmental effects of the COVID-19 anthropause. The necessity to think about city transformation in times of environmental and health crises has revealed a number of digital tools and greening practices that might shape new policy and planning models to affront global challenges. Among these tools, a number of ‘urban acupuncture' activities have revealed the role of greening and gardening in urban spaces and how they assist in tackling challenges of environmental sustainability and city resilience. The authors investigate the contribution of vertical gardening (VG) as urban health enhancer and its prospects within smart city. They select and assess two case studies that integrate synergies between VG and machine learning (ML) approaches in an effort to showcase the tools' combined effect in realizing environmental control. These experiments imply hints for potential future research and implementation to broaden environments.