摘要：Anthropogenic environmental pollution is a known and indisputable issue, and the importance of searching for reliable mathematical models that help understanding the underlying process is witnessed by the extensive literature on the topic. In this article, we focus on the temporal aspects of the processes that govern the concentration of pollutants using typical explanatory variables, such as meteorological values and traffic flows. We develop a novel technique based on multiobjective optimization and linear regression to find optimal delays for each variable, and then we apply such delays to our data to evaluate the improvement that can be obtained with respect to learning an explanatory model with standard techniques. We found that optimizing delays can, in some cases, improve the accuracy of the final model up to 15%.
