Several epidemiologic and toxicological studies have commonly viewed ambient fine particulate matter (PM_2.5), defined as particles having an aerodynamic diameter of less than 2.5 µm, as a significant potential danger to human health. PM_2.5 is mostly absorbed through the respiratory system, where it can infiltrate the lung alveoli and reach the bloodstream. In the respiratory system, reactive oxygen or nitrogen species (ROS, RNS) and oxidative stress stimulate the generation of mediators of pulmonary inflammation and begin or promote numerous illnesses. According to the most recent data, fine particulate matter, or PM_2.5, is responsible for nearly 4 million deaths globally from cardiopulmonary illnesses such as heart disease, respiratory infections, chronic lung disease, cancers, preterm births, and other illnesses. There has been increased worry in recent years about the negative impacts of this worldwide danger. The causal associations between PM_2.5 and human health, the toxic effects and potential mechanisms of PM_2.5, and molecular pathways have been described in this review.

Indoor air pollution (IAP) is a serious threat to human health, causing millions of deaths each year. A plethora of pollutants can result in IAP; therefore, it is very important to identify their main sources and concentrations and to devise strategies for the control and enhancement of indoor air quality (IAQ). Herein, we provide a critical review and evaluation of the major sources of major pollutant emissions, their health effects, and issues related to IAP-based illnesses, including sick building syndrome (SBS) and building-related illness (BRI). In addition, the strategies and approaches for control and reduction of pollutant concentrations are pointed out, and the recent trends in efforts to resolve and improve IAQ, with their respective advantages and potentials, are summarized. It is predicted that the development of novel materials for sensors, IAQ-monitoring systems, and smart homes is a promising strategy for control and enhancement of IAQ in the future.

Chitosan is a natural polymer that has been widely utilized for many purposes in the food, textile, agriculture, water treatment, cosmetic and pharmaceutical industries. Based on its characteristics, including biodegradability, non-toxicity and antimicrobial properties, it has been employed effectively in wound healing applications. Importantly, however, it is necessary to improve chitosan’s capacities by combination with zinc oxide (ZnO), titanium dioxide (TiO₂) and silver (Ag) nanoparticles (NPs). In this review of many of the latest research papers, we take a closer look at the antibacterial effectiveness of chitosan combined with ZnO, TiO₂ and Ag NPs and also evaluate the specific wound healing application potentials.

Ultraviolet (UV) radiation has been demonstrated to cause skin disorders, including sunburn and relative symptoms of prolonged exposure. It has been reported that sunscreens have beneficial effects in reducing the incidence of skin disorders (sunburn, skin aging, and immunosuppression) through their ability to absorb, reflect, and scatter UV. Many commercial products have recently been manufactured from not only usual organic and inorganic UV filters, but also hybrid and botanical ingredients using typical formulations (emulsion, gel, aerosol, and stick). Particularly, these products have been supplemented with several preeminent properties to protect against the negative effects of not only UVB, but also UVA. However, the use of sunscreen has faced many challenges, including inducing photoallergic dermatitis, environment pollution, and deficiency of vitamin D production. Therefore, consumers should efficiently apply suitable products to improve sun protection. as well as to avoid the side effects of sunscreen.

This study investigated the effects of particulate matter (PM) on human skin diseases by conducting a systematic review of existing literature and performing a meta-analysis. It considered articles reporting an original effect of PM on human skin. From among 918 articles identified, 13 articles were included for further consideration after manual screening of the articles resulted in the exclusion of articles that did not contain data, review articles, editorials, and also articles in languages other than English. Random-effects models and forest plots were used to estimate the effect of PM on the skin by Meta-Disc analysis. According to people’s reports of exposure and negative skin effects (atopic dermatitis (AD), eczema, and skin aging, etc.) due to air pollution, the summary relative risk (odds ratio) of PM₁₀ was determined to be 0.99 (95% confidence interval (CI) 0.89–1.11) whereas PM_2.5 was determined to be 1.04 (95% CI 0.96–1.12). Simultaneously, there was a different extent of impact between PM₁₀ and PM_2.5 on atopic dermatitis (AD) for those of young age: the odds ratio of PM₁₀ and PM_2.5 were 0.96 (95% CI 0.83–1.11; I² = 62.7%) and 1.05 (95% CI 0.95–1.16; I² = 46%), respectively. Furthermore, the results suggest an estimated increase of disease incidence per 10 μg/m³ PM of 1.01% (0.08–2.05) due to PM₁₀ and 1.60% (0.45–2.82) due to PM_2.5. Following the results, PM₁₀ and PM_2.5 are associated with increased risks of human skin diseases, especially AD, whose risk is higher in infants and school children. With its smaller size and a high concentration of metals, PM_2.5 is more closely related to AD in younger people, compared to PM₁₀.

The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches’ many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.