On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes

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.

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

A biological assessment of environmental quality was performed using the tropical plant species Tradescantia pallida (Rose) D.R. Hunt. var. purpurea exposed to different levels of air contamination in urban intersections with high volume of vehicle traffic. Air quality (average daily levels of particulate material in the PM_{1, 2.5, 10} fractions) and traffic volume in crossing intersections were monitored for 30 days before the collection of plants. Frequency of micronuclei and pollen abortivity in inflorescences collected at different intersections with gradual levels of traffic volume were evaluated as biomarkers of genotoxicity. In addition, the concentrations of bioaccumulated heavy metals in the leaves of the collected plants were also investigated. The proposed biological assessment model found a positive association between the environmental variables (traffic volume; concentration of particulate material) and biological effects (leaf concentration of Cr and Cd; micronucleus frequencies and pollen abortivity).