This paper summarizes the presently available knowledge about the association between low-frequency noise and its effects on health. A database was constructed with a total of 142 articles published between 2016 and 2019 regarding low-frequency noise exposure and its effects on health. A total of 39 articles were analysed in depth. The articles were divided into categories according to the effects on human health addressed. Regarding the emitting source, there was a greater number of articles addressing issues related to sources of environmental noise and noise from wind turbines. As for the effects generated on human health, there was a greater number of articles referring to the effects on sleep disorders, discomfort, sensitivity to and irritability from noise, annoyance, hearing loss, and cardiovascular diseases, and these effects are analysed in more detail in the present article.

Green infrastructure plays a fundamental role in mitigating the effects of urban heat island. Vegetation may trap particulates and absorb pollutants like ozone, thus improving air quality. Understanding how green infrastructure reduces urban heat island and air pollution within specific urban zones can provide valuable insights for better urban design, improved environmental quality, and increased resident well-being. This study addresses the impact of green infrastructure deprivation on urban heat island effects, air pollution, and heat-related health risks in Porto, Portugal. The study employs a monitoring network to analyse the spatial distribution of air temperature and humidity throughout the city, although with specific gaps in coverage. With a focus on the role of urban green infrastructure in mitigating air urban heat island effects, this paper uses the data from Porto Digital's monitoring network between 2019 and 2022. Heat risk index assesses vulnerability to heat-related health risks by integrating land surface temperature, land cover, and demographic data through remote sensing. Green infrastructure mapping is conducted to quantify the spatial distribution of vegetation elements in the study area. The data analysis from 2019 to 2022 reveals that urban heat island intensity is more pronounced during the summer and at night. Approximately 32.6% of Porto is in areas with a high heat risk index, indicating increased vulnerability to heat-related health risks. The study finds that limited green infrastructure exacerbates this vulnerability, particularly in socioeconomically disadvantaged areas. Additionally, persistent air pollution hotspots, including elevated levels of ozone and particulate matter, contribute to the intensity of urban heat island. These findings underscore the need for integrating green infrastructure into urban planning to mitigate urban heat island and air pollution, improve urban resilience, and promote environmental justice.

Green spaces have become the most threatened by urban growth, and the decline in these areas is a main cause of environmental and social problems with implications for human health and well-being. Vertical greenery systems have been proposed as a solution to restore the connection between the city and nature, particularly in compact and dense cities, where horizontal space is limited. This paper provides a literature review to examine the influence of outdoor and indoor vertical greenery systems on human health and well-being. The Web of Science and Scopus databases were chosen to survey peer-reviewed documents published until October 2022. A total of 73 documents were selected by the search. Over 71% of the documents were published over the last four years, and most of them focused on the environmental and thermal benefits of vertical greenery systems. Although these benefits could bring health and well-being outcomes, they were not objectively measured and quantified. Other additional gaps and guidelines for future research were also identified and discussed. This review could be helpful for researchers and urban planners in developing vertical greenery to create healthy and more sustainable cities.

As the monitoring of carbon dioxide is an important proxy to estimate the air quality of indoor and outdoor environments, it is essential to obtain trustful data from CO₂ sensors. However, the use of widely available low-cost sensors may imply lower data quality, especially regarding accuracy. This paper proposes a new approach for enhancing the accuracy of low-cost CO₂ sensors using an extremely randomized trees algorithm. It also reports the results obtained from experimental data collected from sensors that were exposed to both indoor and outdoor environments. The indoor experimental set was composed of two metal oxide semiconductors (MOS) and two non-dispersive infrared (NDIR) sensors next to a reference sensor for carbon dioxide and independent sensors for air temperature and relative humidity. The outdoor experimental exposure analysis was performed using a third-party dataset which fit into our goals: the work consisted of fourteen stations using low-cost NDIR sensors geographically spread around reference stations. One calibration model was trained for each sensor unit separately, and, in the indoor experiment, it managed to reduce the mean absolute error (MAE) of NDIR sensors by up to 90%, reach very good linearity with MOS sensors in the indoor experiment (r² value of 0.994), and reduce the MAE by up to 98% in the outdoor dataset. We have found in the outdoor dataset analysis that the exposure time of the sensor itself may be considered by the algorithm to achieve better accuracy. We also observed that even a relatively small amount of data may provide enough information to perform a useful calibration if they contain enough data variety. We conclude that the proper use of machine learning algorithms on sensor readings can be very effective to obtain higher data quality from low-cost gas sensors either indoors or outdoors, regardless of the sensor technology.