Air pollution, in particular high air pollution events, increase the effects of climate change on human health. It is necessary to undertake actions to prevent and minimize these adverse events with the aim of supporting a policy for environmental sustainable development.

A simple conceptual model is presented to describe the near-surface flow of a long, partially urbanized valley of slope \(\beta \) located normal to a coastline, considering forcing due to differential surface temperatures between the sea, undeveloped (rural) land and urban area. Accordingly, under weak synoptic conditions and when the coastal and urban (thermally induced pressure-gradient) forcing are in phase with that of the valley thermal circulation, the mean flow velocity \(U\) is parameterized by the cumulative effects of multiple forcing: \(U = \varGamma w_*\beta ^{1/3} +C(g\alpha \varDelta TL)^{1/2}\). This accounts for the coastal/urban forcing due to surface-air buoyancy difference \(g\alpha \Delta T\) over a distance \(L\). Here \(\varGamma \) and \(C\) are constants and \(w_*\) the convective velocity. Comparisons with data of the Meteo-diffusion field experiment conducted in a coastal semi-urbanized valley of Italy (Biferno Valley) reveal that the inferences of the model are consistent with observed valley flow velocities as well as sharp morning and prolonged evening transitions. While the experimental dataset is limited, the agreement with observations suggests that the model captures essential dynamics of valley circulation subjected to multiple forcing. Further observations are necessary to investigate the general efficacy of the model.

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New Atmospheric Pollution Indicators and Tools to Support Policy for Environmental Sustainable Development

Near-surface flow in complex terrain with coastal and urban influence

Both the World Health Organization (WHO) with its 2015 "Climate and Health Country Profile Project" and the Istituto Superiore di Sanita (ISS) with its 2018 "Health and Climate Change", agree on the emergency generated by the climate change and concerning health problems. The mitigation strategy suggested by the Intergovernmental Panel On Climate Change (IPCC) against greenhouse gas emissions and their effects on climate change, has not yet yielded the desired results. It is therefore necessary to focus on adaptation strategies, to immediately counter the effects of Climate Change (CC) on most vulnerable people and environments, by increasing their resilience through local interventions and targeted resilience actions. Coordinated resilience actions are necessary to combat the effects of CC especially in urban areas. Useful tools to manage and optimize resilience actions are Artificial Neural Networks (ANN) in complex and dynamic domains as cities are. The case of ANN applied to a city is presented as an example to increase the climate resilience of health local systems. In the current state of knowledge, ANN prove to be the most advanced and global solution to coordinate and manage a set of resilience actions in urban areas.

New Atmospheric Pollution Indicators and Tools to Support Policy for Environmental Sustainable Development

Citations

Near-surface flow in complex terrain with coastal and urban influence

Resilience actions to counteract the effects of climate change and health emergencies in cities: the role of artificial neural networks.

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