2. Case study: Milan, Italy

Introduction

The analysis framework developed within the project is tested on the city of Milan (Northern Italy). Milan is located in one of the most densely populated European regions, the Padana Plain, affected by poor wind circulation and high urbanization which favour the stagnation of pollutants as well as the persistency of a historically well-known Urban Heat Island (UHI) [ 6 ]. Beside these environmental peculiarities – which, nevertheless, are common to many medium-sized cities in Europe – the Municipality of Milan has recently embraced a valuable Open Data Policy. The vast amount of available open datasets allows both citizens and researchers to explore a number of urban characteristics and dynamics which were hardly – if not at all – accessible until the recent past. In the context of the D4CA challenge, this information is combined with other global datasets (either available under open data licenses or kindly provided by private companies) to bring valuable contributions to the study of urban climate issues. For all these reasons, Milan seems to be a valuable testing ground for the implementation of the analyses which represent the core research activities of this phase of the project.

Experiment outline

The analysis of available data has allowed to address two main questions related to the study of urban heating issues in the city of Milan. The first question concerns the assessment of LCZ - Local Climate Zone [ 7 ] mapping performance offered by satellite imagery having different spatial and spectral resolutions, i.e. Sentinel-2 and PlanetScope imagery. LCZ maps are then used as input to implement the Local Scale Urban Parameterization Scheme (LUMPS) [ 8 ] to simulate surface heat fluxes characterizing Milan urban areas for different months in year 2016. The output consists of surface energy maps at different resolutions depicting the morphology of the UHI in Milan.

The second question regards the identification of possible links between local thermal anomalies and heavy traffic events, assuming that the latter contribute to the city energy flows and that this contribution is discoverable from the available datasets. Only few studies exist in literature, which analyze the correlation between traffic and temperature (see e.g. [ 9 ] and [ 10 ]), especially when considering an exploratory data approach. In this project a preliminary analysis is carried out, which detects traffic and thermal anomalies by exploiting time series of observations collected from different environmental and traffic sensors at known locations within the city. The anomalies detected are then compared in order to evaluate to what extent heavy traffic events are connected to thermal anomalies in a reasonably closed spatial and temporal interval.

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