Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design.

Several feature extraction approaches for QSPR modelling in Cheminformatics are discussed in this paper. In particular, this work is focused on the use of these strategies for predicting mechanical properties, which are relevant for the design of polymeric materials. The methodology analysed in this study employs a feature learning method that uses a quantification process of 2D structural characterization of materials with the autoencoder method. Alternative QSPR models inferred for tensile strength at break (a well-known mechanical property of polymers) are presented. These alternative models are contrasted to QSPR models obtained by feature selection technique by using accuracy measures and a visual analytic tool. The results show evidence about the benefits of combining feature learning approaches with feature selection methods for the design of QSPR models.

Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures.

New descriptors of main and side chains for polymers with high molecular weight are presented in order to predict the glass-transition temperature (T(g)) by means of T(g)/M ratio. They were obtained by molecular modeling for the middle unit in a series of three repeating units (trimer). Taken together with other classic descriptors calculated for the entire trimeric structure, the ones that correlated better with the property were selected by using a variable selection method. Only three descriptors were chosen: main chain surface area (SA(MC)), side chain mass (M(SC)) and number of rotatable bonds (RBN), where the first two descriptors belong to the set of the new ones proposed. By means of a multi-layer perceptron (MLP) neural network a good prediction model (R²=0.953 and RMS=0.25 K mol/g) was achieved and internally (R²=0.964 and RMS=0.41 K mol/g) and externally (R²=0.933 and RMS =0.47 K mol/g) validated. The dataset included 88 polymers. The selected descriptors and the quality of the obtained model demonstrate the advantages of capturing through computational molecular modeling the structural characteristics of the polymers' main and side chains in the prediction of T(g)/M.