Analysis of genotoxic effects on plants exposed to high traffic volume in urban crossing intersections

Assessment of genotoxicity of air pollution in urban areas using an integrated model of passive biomonitoring

Atmospheric pollution is a major public health issue and has become increasingly critical for human health. Urban atmospheric pollution is typically assessed through physicochemical indicators aligned with environmental legislation parameters, providing data on air quality levels. While the effects of pollution on sensitive organisms serve as a warning for public health decision-makers, there remains a need to explore the interpretation of environmental data on pollutants. The use of species adapted to urban environments as sentinels enables continuous and integrated monitoring of environmental pollution implications on biological systems. In this study, we investigated the use of the plant species Tradescantia pallida as a biomonitor to evaluate the genotoxic effects of atmospheric pollution under diverse vehicular traffic conditions. T. pallida was strategically planted at the leading urban intersections in Uberlândia, Brazil. During COVID-19 pandemic lockdowns, we compared indicators such as physical, biological, and traffic data at different intersections in residential and commercial zones. The reduction in vehicular traffic highlighted the sensitivity of plant species to changes in air and soil pollutants. T. pallida showed bioaccumulation of heavy metals Cd and Cr in monitored areas with higher traffic levels. Additionally, we established a multiple linear regression model to estimate genotoxicity using the micronucleus test, with chromium concentration in the soil (X1) and particulate matter (PM) in the atmosphere (X2) identified as the primary independent variables. Our findings provide a comprehensive portrait of the impact of vehicular traffic changes on PM and offer valuable insights for refining parameters and models of Environmental Health Surveillance.

A comprehensive overview of genotoxicity and mutagenicity associated with outdoor air pollution exposure in Brazil

This review examined the mutagenicity and genotoxicity associated with exposure to outdoor air pollutants in Brazil. A search was performed on the Web of Science database using a combination of keywords that resulted in 134 articles. After applying exclusion criteria, a total of 75 articles were obtained. The articles were classified into three categories: (1) studies with plants and animals, (2) in vitro studies, and (3) human biomonitoring. The investigations were conducted in 11 of 27 Brazilian states with the highest prevalence in the southeast and south regions. Only 5 investigations focused on the effects of burning biomass on the quality of outdoor air. Plants, especially Tradescantia pallida, were the main air pollution biomonitoring tool. When available, a significant association between levels of air pollutants and genetic damage was described. Among the in vitro studies, Salmonella/microsome is the most used test to evaluate mutagenesis of outdoor air in Brazil (n = 26). Human biomonitoring studies were the least frequent category (n = 18). Most of the investigations utilized micronucleus bioassay, in oral mucosa cells (n = 15) and lymphocytes (n = 5), and the comet assay (n = 6). The analysis in this study points to the existence of gaps in genotoxicity studies and our findings indicate that future studies need to address the variety of potential sources of pollution existing in Brazil. In addition to extent of the impacts, consideration should be given to the enormous Brazilian biodiversity, as well as the determination of the role of socioeconomic inequality of the population in the observed outcomes.

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

The research community of environmental economics has had a growing interest for the exploration of artificial intelligence (AI)-based systems to provide enriched efficiencies and strengthened human knacks in daily live maneuvers, business stratagems, and society evolution. In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs). The governing relations of EESs were presented in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters. The reference datasets of EESs were assembled using the Adams numerical solver for different EES scenarios and were used as targets of IBNs-BR to find the approximate solutions. Comparative studies based on convergence curves on the mean square error (MSE) and absolute deviation from the reference results were used to verify the correctness of IBNs-BR for solving EESs, i.e., MSE of around 10−9 to 10−10 and absolute error close to 10−5 to 10−7. The endorsement of results was further validated through performance evaluation by means of error histogram analysis, the regression index, and the mean squared deviation-based figure of merit for each EES scenario.