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.

Housework and Parenting during the Lockdowns in France: How Have Socio-Economic and Gender Inequalities Changed?

The lockdowns imposed during the COVID-19 pandemic had an unprecedented impact on people's time use. This article analyses the changes in time spent on household tasks and parenting by men and women during the lockdowns of the spring and autumn of 2020 in France, by social category, education, working arrangements and family configurations, using data from the major longitudinal EpiCov survey. The time spent on housework was high in the spring of 2020 and caring for children was particularly time consuming. This additional domestic and parental burden affected both women and men, but women continued to perform the majority of the housework, in spite of the similar working conditions between the sexes during this period. During the first lockdown, women at the top of the social hierarchy, who generally perform fewer household chores, spent far more time than usual on these tasks, thereby temporarily reducing social differences. © 2022, Institut National de la Statistique et des Etudes Economiques. All rights reserved.