Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects.

Air pollution is a prevailing environmental problem in cities worldwide. The future vehicle electrification (VE), which in Europe will be importantly fostered by the ban of thermal engines from 2035, is expected to have an important effect on urban air quality. Machine learning models represent an optimal tool for predicting changes in air pollutants concentrations in the context of future VE. For the city of Valencia (Spain), a XGBoost (eXtreme Gradient Boosting package) model was used in combination with SHAP (SHapley Additive exPlanations) analysis, both to investigate the importance of different factors explaining air pollution concentrations and predicting the effect of different levels of VE. The model was trained with 5 years of data including the COVID-19 lockdown period in 2020, in which mobility was strongly reduced resulting in unprecedent changes in air pollution concentrations. The interannual meteorological variability of 10 years was also considered in the analyses. For a 70% VE, the model predicted: 1) improvements in nitrogen dioxide pollution (-34% to -55% change in annual mean concentrations, for the different air quality stations), 2) a very limited effect on particulate matter concentrations (-1 to -4% change in annual means of PM2.5 and PM10), 3) heterogeneous responses in ground-level ozone concentrations (-2% to +12% change in the annual means of the daily maximum 8-h average concentrations). Even at a high VE increase of 70%, the 2021 World Health Organization Air Quality Guidelines will be exceeded for all pollutants in some stations. VE has a potentially important impact in terms of reducing NO2-associated premature mortality, but complementary strategies for reducing traffic and controlling all different air pollution sources should also be implemented to protect human health.

Overview of the Market of Electric Cars by Multilogistic Curves

Sigmoid functions (growth function, logistic function, evolution function, etc.) are used to describe, study and forecast several phenomena of the life. In some cases (for example, in case of the COVID-19 disease), the phenomenon has several waves, which needs to apply multilogistic (multiwave logistic) curves in order to perform realistic investigation. In product design, the logistic curve can describe the lifecycle of a product. A product lifecycle can be finished by the significant decrease of the market, but in some cases, several new developments and innovations can regenerate the increase of the market by starting a new boom. This renewing process can invoke several waves of the phenomenon, which will make necessary the application of multilogistic curves for the correct study. This multiwave behaviour of the product lifecycle makes this phenomenon very similar to the time history of the COVID-19 disease which also has several waves, because of the newer and newer virus variants. Analysis and comparison of several phenomena described by logistic curves, or bi- logistic, tri- logistic or multilogistic curves can be made easier by the application of the EBSYQ (Evolutionary Based SYstem of Qualification and comparison of group achievements) comparison and qualification system. The similarity between the multiwave characteristics of the product lifecycle and coronavirus time history makes possible to apply several results, skills and methods of comparison and investigation, which were developed and used previously during the analysis of several waves of the disease also for the case of product lifecycle analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Overview of the Market of Electric Cars by Multilogistic Curves

Sigmoid functions (growth function, logistic function, evolution function, etc.) are used to describe, study and forecast several phenomena of the life. In some cases (for example, in case of the COVID-19 disease), the phenomenon has several waves, which needs to apply multilogistic (multiwave logistic) curves in order to perform realistic investigation. In product design, the logistic curve can describe the lifecycle of a product. A product lifecycle can be finished by the significant decrease of the market, but in some cases, several new developments and innovations can regenerate the increase of the market by starting a new boom. This renewing process can invoke several waves of the phenomenon, which will make necessary the application of multilogistic curves for the correct study. This multiwave behaviour of the product lifecycle makes this phenomenon very similar to the time history of the COVID-19 disease which also has several waves, because of the newer and newer virus variants. Analysis and comparison of several phenomena described by logistic curves, or bi- logistic, tri- logistic or multilogistic curves can be made easier by the application of the EBSYQ (Evolutionary Based SYstem of Qualification and comparison of group achievements) comparison and qualification system. The similarity between the multiwave characteristics of the product lifecycle and coronavirus time history makes possible to apply several results, skills and methods of comparison and investigation, which were developed and used previously during the analysis of several waves of the disease also for the case of product lifecycle analysis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.