Showing papers by "Silvana Di Sabatino published in 2019"

Estimating pro-environmental potential for the development of mobility-based informational intervention: a data-driven algorithm

Abstract: Informational interventions are important to bring positive changes in attitudes and perception among individuals. In relation to the individual’s mobility behavior, habits, attitudes, and perceptions are difficult to change. Therefore, it is vital to identify relatively soft aspects of travel behavior with a potential to reduce the negative impacts of mobility on the environment and individual health. This paper provides a methodological framework and describes the development of a computational algorithm that helps to identify soft changes in the travel behavior. The algorithm is based on a variety of different data sources such as activity-travel diaries and related constraint information, meteorological conditions, bicycle and public transport supply data, and emissions and air pollutant concentrations data. A variety of rules that are part of the algorithm are derived from the transport modeling literature, where constraints and factors were examined for activity-travel decisions. Three major aspects of activity-travel behavior, such as reduced car use, cold start of car engines, and participation in non-mandatory outdoor activities are considered in assessing pro-environmental potential. The algorithm is applied to collected small datasets from citizens of Hasselt (Belgium), Bologna (Italy), and Guildford (UK). A significant replaceable potential for car trips within 3 km to cycling and car trips to public transport has been found. The replaceable potential of excessive cold starts and participation in non-mandatory outdoor activities were also found, to some extent, to bring positive changes in the environment. In future research, these identified potentials are reported back to individuals with their consequence as part of a mobility-based informational intervention.

Future impacts of the reforestation policy on the atmospheric parameters in Ireland: a sensitivity study including heat discomfort impacts on humans and livestock

Abstract: The increase of temperature attributed to anthropogenic emissions is projected to continue in future climate scenarios. Protocols and policies are being put in place in several European countries to reduce both emissions and impact of human activities on climate. The Irish Reforestation policy is a good example of such protocols. Nevertheless often contemplated policies do not take into account their potential effects on the atmospheric variables. This study aims to assess the influence of the increase of vegetation cover over Ireland, on surface temperature, livestock and human heat comfort, using the Weather Research Forecast (WRF-ARW 3.7.1) model. Multi-scale numerical simulations are performed under two scenarios: (i) a “control scenario” con- sidering no change in vegetation cover with respect to the prescribed one and (ii) a “green scenario” with increased tree cover based on the introduced Irish Reforestation policy. To simulate this policy, the cropland and vegetative mo- saic is substituted with evergreen broad leaf forest, increasing the total forest area from 19.7% to 36.2% of the land in the analyzed domain. This change in vegetation cover increases the temperature over the simulated domain up to 0.7oC and, moreover, it enhances both human and livestock heat discom- fort during the day-time, with different magnitude all over the domain. It is concluded that the reforestation policy, which is introduced to mitigate the climate warming and greenhouse gas emissions, causes a further increase in temperature along with heat discomfort to both human and livestock.