MEASURING THE INFLUENCE OF A MOTORWAY CONSTRUCTION ON LAND SURFACE TEMPERATURE USING LANDSAT THERMAL DATA: A CASE STUDY IN THE METROPOLITAN CITY OF MILAN

Cities have been identified as a landmark for climate change, being among the direct targets of its negative feedbacks. The combined effect of climate change and rapidly growing urbanization is exacerbating the urban heat island phenomenon in cities worldwide. The availability of multiple geo-data sources including satellite remote sensing products is significantly empowering the investigation of its driving factors. This is a crucial step to implement ad hoc mitigation and adaptation strategies. In view of the above, the goal of this study is to measure the effect of a motorway on the Land Surface Temperature (LST) space-time patterns by leveraging Landsat 5 and 8 thermal data of the period from 2006 to 2022. The study area is around the motorway A58 and connected roads in the Metropolitan City of Milan (northern Italy). LST patterns are investigated along the motorway track and in the neighbouring areas before and after the motorway construction, in both the cold and warm seasons. Results show that the motorway significantly affects the LST distribution during summer with a median increase of 2.5 °C along the road track with respect to the surrounding area. The warming effect is also recorded in the road buffers with decreasing LST with increasing distance from the road. On the contrary, no meaningful variation in terms of LST is measured in winter. These experiments provide insightful measures of the effect of a highway on the local climate conditions in an urban area, thus representing crucial pieces of information for driving evidence-based urban planning activities

Multiple teeth replacement with endosseous one-piece yttrium-stabilized zirconia dental implants

Objectives: The purpose of this study is to clinically and radiographically evaluate survival and success rate of multiple zirconia dental implants positioned in each patient during a follow-up period of at least 12 months up to 48 months. Study Design: Eight patients were treated for multiple edentulism with 29 zirconia dental implants. All implants received immediate temporary restorations and 6 months after surgery were definitively restored. 6 months to 4 years after implant insertion, a clinical-radiographic evaluation was performed in order to estimate peri-implant tissues health and peri-implant marginal bone loss. Results: Survival rate within follow-up period was therefore 100%. The average marginal bone loss (MBL) from baseline to 6 months was +1.375±0.388 mm; from 6 months to 1 year was +0.22±0.598 mm; from 1 year to 2 years was -0.368±0.387 mm; from 2 years to 3 years was -0.0669±0.425 mm; from 3 years to 4 years +0.048±0.262 mm. The mean marginal bone loss at 4 years from the implants insertion was +1.208 mm. Conclusions: According to several studies, when using a radiographic criterion for implant success, marginal bone loss below 0.9-1.6 mm during the first year in function can be considered acceptable. In our work, radiographic measurements of MBL showed values not exceeding 1.6 mm during the first year of loading and also 1 year up to 4 years after surgery further marginal bone loss was minimal and not significant. This peri-implant bone preservation may be associated to the absence of micro-gap between fixture and abutment since zirconia dental implants are one-piece implant. Moreover, zirconia is characterized by high biocompatibility and it accumulates significantly fewer bacteria than titanium

A combined Remote Sensing and GIS-based method for Local Climate Zone mapping using PRISMA and Sentinel-2 imagery

In the last decade, several methods have been developed for Local Climate Zone (LCZ) mapping, encompassing Remote Sensing and Geographic Information Systems (GIS) −based procedures. Combined approaches have also been proposed to compensate for intrinsic limitations that characterized their separate application. Recent work has disclosed the potential of hyperspectral satellite imagery for improving LCZ identification. However, the use of hyperspectral data for LCZ mapping is yet to be fully unfolded. A combined Remote Sensing and GIS-based method for LCZ mapping is proposed to exploit the integration of hyperspectral PRISMA and multispectral Sentinel-2 images with ancillary urban canopy parameter layers. Random Forest algorithm is applied to the feature sets to obtain the LCZ classification. The method is tested on the Metropolitan City of Milan (Italy), for the period from February to August 2023. A spectral separability analysis is carried out to investigate the improvement in LCZ identification using PRISMA in comparison to Sentinel-2 data, as well as improvements in LCZ spectral separability on PRISMA pan-sharpened images. The resulting maps’ quality is evaluated by extracting accuracy metrics and performing inter-comparisons with maps computed from the LCZ Generator benchmark tool. Inter-comparisons yield promising results with a mean Overall Accuracy increase of 16% using PRISMA for each LCZ class. Furthermore, we find that PRISMA improves the detection of LCZs compared to Sentinel-2, with a mean Overall Accuracy increase of 5%, in line with the higher spectral separability of PRISMA spectral signatures computed on the training samples

MAPPING LOCAL CLIMATE ZONES WITH MULTIPLE GEODATA AND THE OPEN DATA CUBE: INSIGHTS OF DOMAIN USER REQUIREMENTS AND OUTLOOKS OF THE LCZ-ODC PROJECT

Rapid urbanization and climate change are intensifying the urban heat island (UHI) phenomenon across cities worldwide. There is a pressing need to implement evidence-based mitigation and adaptation strategies as well as to develop tools for effectively measuring the impact of such actions on UHI patterns. In this context, the Local Climate Zone (LCZ) concept is a well-established classification system commonly used for the assessment of UHI. With this in mind, we present here the LCZ-ODC project aiming to develop a methodology for LCZ mapping in the Metropolitan City of Milan (northern Italy) by leveraging multiple geospatial data and cutting-edge software tools, including the Open Data Cube (ODC). A key aim of the project is to develop user-oriented solutions facilitating the exploitation of the generated LCZ maps for different application tasks. In this paper, we first present a brief overview of the methodologies and data sources used in the literature for LCZ mapping. Then, we introduce the LCZ-ODC project, with a focus on the end-user requirements which were gathered through a questionnaire distributed to a sample of potential stakeholders. The primary objective of the survey was to collect insights and consolidate requirements related to the key features of LCZ maps that will be produced within the project. The outcomes of the survey play a pivotal role in guiding the project’s development phase, ensuring that the project outputs will effectively address the identified end-user needs

PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping

The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort

Sequence-Specific Features of Short Double-Strand, Blunt-End RNAs Have RIG-I- and Type 1 Interferon-Dependent or -Independent Anti-Viral Effects

Pathogen-associated molecular patterns, including cytoplasmic DNA and double-strand (ds)RNA trigger the induction of interferon (IFN) and antiviral states protecting cells and organisms from pathogens. Here we discovered that the transfection of human airway cell lines or non-transformed fibroblasts with 24mer dsRNA mimicking the cellular micro-RNA (miR)29b-1* gives strong anti-viral effects against human adenovirus type 5 (AdV-C5), influenza A virus X31 (H3N2), and SARS-CoV-2. These anti-viral effects required blunt-end complementary RNA strands and were not elicited by corresponding single-strand RNAs. dsRNA miR-29b-1* but not randomized miR-29b-1* mimics induced IFN-stimulated gene expression, and downregulated cell adhesion and cell cycle genes, as indicated by transcriptomics and IFN-I responsive Mx1-promoter activity assays. The inhibition of AdV-C5 infection with miR-29b-1* mimic depended on the IFN-alpha receptor 2 (IFNAR2) and the RNA-helicase retinoic acid-inducible gene I (RIG-I) but not cytoplasmic RNA sensors MDA5 and ZNFX1 or MyD88/TRIF adaptors. The antiviral effects of miR29b-1* were independent of a central AUAU-motif inducing dsRNA bending, as mimics with disrupted AUAU-motif were anti-viral in normal but not RIG-I knock-out (KO) or IFNAR2-KO cells. The screening of a library of scrambled short dsRNA sequences identified also anti-viral mimics functioning independently of RIG-I and IFNAR2, thus exemplifying the diverse anti-viral mechanisms of short blunt-end dsRNAsThe work was supported by the Swiss National Science Foundation (31003A_179256/1 to UFG, and 320030_205097 to JPS), the Swiss National Science Foundation SystemsX RTD InfectX (51RT 0_126008 to UFG and CvM), and the University Research Priority Program of the University of Zurich (URPP) ITINERARE – Innovative Therapies in Rare Diseases to JPS.Peer reviewe

Population Space–Time Patterns Analysis and Anthropic Pressure Assessment of the Insubric Lakes Using User-Generated Geodata

Human activities are one of the main causes of lake-water pollution and eutrophication. The study of human pressure around lakes is of importance to understand its effects on the lakes natural resources. Social media data is a valuable space–time-resolved information source to detect human dynamics. In this study, user-generated geodata, namely users’ location records provided by the Facebook Data for Good program, are used to assess population patterns and infer the magnitude of anthropic pressure in the areas surrounding the Insubric lakes (Maggiore, Como and Lugano) between Northern Italy and Southern Switzerland. Patterns were investigated across different lakes’ neighbouring areas as well as seasons, days of the week, and day hours in the study period May 2020–August 2021. Two indicators were conceived, computed and mapped to assess the space–time distribution of users around lakes and infer the anthropic pressure. The highest pressure was found around lakes Maggiore and Como coastal areas during weekends in summer (up to +14% average users presence than weekdays in winter), suggesting tourism is the primary accountable reason for the pressure. Contrarily, around lake Lugano, the population dynamic is mostly affected by commuters or weekly workers, where the maximum pressure occurs during weekdays in all seasons (+6.6% average users presence than weekends). Results provide valuable input to further analyses connected, for example, to the correlation between human activities and lake-water quality and/or prediction models for anthropic pressure and tourism fluxes on lakes that are foreseen for the future development of this work

Population Space–Time Patterns Analysis and Anthropic Pressure Assessment of the Insubric Lakes Using User-Generated Geodata

Human activities are one of the main causes of lake-water pollution and eutrophication. The study of human pressure around lakes is of importance to understand its effects on the lakes natural resources. Social media data is a valuable space–time-resolved information source to detect human dynamics. In this study, user-generated geodata, namely users’ location records provided by the Facebook Data for Good program, are used to assess population patterns and infer the magnitude of anthropic pressure in the areas surrounding the Insubric lakes (Maggiore, Como and Lugano) between Northern Italy and Southern Switzerland. Patterns were investigated across different lakes’ neighbouring areas as well as seasons, days of the week, and day hours in the study period May 2020–August 2021. Two indicators were conceived, computed and mapped to assess the space–time distribution of users around lakes and infer the anthropic pressure. The highest pressure was found around lakes Maggiore and Como coastal areas during weekends in summer (up to +14% average users presence than weekdays in winter), suggesting tourism is the primary accountable reason for the pressure. Contrarily, around lake Lugano, the population dynamic is mostly affected by commuters or weekly workers, where the maximum pressure occurs during weekdays in all seasons (+6.6% average users presence than weekends). Results provide valuable input to further analyses connected, for example, to the correlation between human activities and lake-water quality and/or prediction models for anthropic pressure and tourism fluxes on lakes that are foreseen for the future development of this work

Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements

With a concentration of people, activities, and infrastructures, urban areas are particularly vulnerable to the negative effects of climate change. Among others, the intensification of the Urban Heat Island (UHI) effect is leading to an increased impact on citizen health and the urban ecosystem. In this context, this study aims to investigate the effect of urban morphology and land cover composition—which are established by exploiting the Local Climate Zone (LCZ) classification system—on two urban climate indicators, i.e., Land Surface Temperature (LST) and air temperature. The study area is the Metropolitan City of Milan (northern Italy). LCZ and LST maps are derived by leveraging satellite imagery and building height datasets. Both authoritative and crowdsourced in situ measurements are used for the analysis of air temperature. Several experiments are run to investigate the mutual relation between LCZ, LST, and air temperature by measuring LST and air temperature patterns in different LCZs and periods. Besides a strong temporal correlation between LST and air temperature, results point out vegetation and natural areas as major mitigating factors of both variables. On the other hand, higher buildings turn out to increase local air temperature while buffering LST values. A way lower influence of building density is measured, with compact building areas experiencing slightly higher air temperature yet no significant differences in terms of LST. These outcomes provide valuable tools to urban planners and stakeholders for implementing evidence-based UHI mitigation strategies