Urban seismic monitoring in Brasília, Brazil

Urban seismology has gained scientific interest with the development of seismic ambient noise monitoring techniques and also for being a useful tool to connect society with the Earth sciences. The interpretation of the sources of seismic records generated by sporting events, traffic, or huge agglomerations arouses the population’s curiosity and opens up a range of possibilities for new applications of seismology, especially in the area of urban monitoring. In this contribution, we present the analysis of seismic records from a station in the city of Brasilia during unusual episodes of silencing and noisy periods. Usually, cultural noise is observed in high-fequency bands. We showed in our analysis that cultural noise can also be observed in the low-frequency band, when high-frequency signal is attenuated. As examples of noisy periods, we have that of the Soccer World Cup in Brazil in 2014, where changes in noise are related to celebrations of goals and the party held by FIFA in the city, and the political manifestations in the period of the Impeachment trial in 2016, which reached the concentration of about 300,000 protesters. The two most characteristic periods of seismic silence have been the quarantine due to the COVID-19 pandemic in 2020, and the trucker strike that occurred across the country in 2018, both drastically reducing the movement of people in the city.

Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

An Early Beginning of Citizen Science: Adolescents Experiencing Urban Energy Usages and Air Pollution

Here, we report on the process and development of high school science projects, which were inspired by a citizen science program focused on urban monitoring. We gathered and discussed two 1980s projects’ data, involving 2600 students, 80 teachers, 15 scientists and 20 stakeholders. We added recent survey data from speaking with the former participants. Our analysis revealed key findings: (1) the process of a student-driven science investigation engages students in the scientific practices; (2) it is important to bring together scientists, teachers and students, reflecting the importance of multi-dimensional learning; and (3) citizen science was born before the 1990s, when the term came into use. Our findings have implications for awareness of urban environmental issues and the links between the education system and society, young people working together with public and private managers and the science and technology sector instilling ideas on sustainability in the entire society.

Wukong

Querying graph data is becoming increasingly prevalent and important across many application domains, like social networking, urban monitoring, electronic payment, and semantic webs. In the last few years, we have ben working on improving the performance of graph querying by leveraging new hardware features and system designs. Moving towards this goal, we have designed and developed Wukong, a distributed in-memory framework that provides low latency and high throughput for concurrent query processing over large and fast-evolving graph data. This article overviews our architecture and presents four systems that aim to satisfy diverse challenging requirements on graph querying (e. g. high concurrency, evolving graphs, workload heterogencity, and locality preserving). Our systems also significantly outperform state-of-the-art systems in both latency and throughput, usually by orders of magnitude.

Sources and pathways of biocides and their transformation products in 2ha urban district

<p>Biocides used as film protection products to prevent algae and fungi growth on facades wash off during rain events and represent a potential risk to the environment. So far, urban monitoring studies focused mainly on large heterogeneous urban areas. Thus, little information about individual sources and entry pathways were obtained. However, this is important to understand the potential risk of biocide entry to groundwater.</p><p>This study investigates biocide emissions from a 2 ha residential area, 13 years after construction has ended. Investigated substances represent commonly used biocides for film protection, i.e. Terbutryn, Diuron and Octylisothiazolinone (OIT) and some of their known transformation products (TPs, Diuron-Desmethyl, Terbumeton, 2-Hydroxy-Terbutylazin and Terbutryn-Desethyl). We used existing urban infrastructure for efficient monitoring and applied a three-step approach to (a) determine the overall relevance of biocides, (b) identify source areas and long-term emission and (c) characterize entry pathways into surface- and groundwater.</p><p>Initial sampling in the swale system gave an integrated signal from the entire district and confirmed the relevance of biocide leaching, more than a decade after construction. Concentrations peaked at 174 ng/L for Diuron and 40 ng/L for Terbutryn during a high magnitude event and were above PNEC values. During later events, transformation products were detected, though at lower concentrations. For all substances, source areas were identified in a second step. Artificial elution experiments confirmed expected sources, i.e. façades, but we also found additional sources through sampling of rainfall downpipes from flat roofs. A small part of the roof façade was repainted two years before sampling and thereby showed a magnitude higher leaching rates than the remaining façades. Since all biocide wash-off arrived on a flat roof and was drained by rainfall down pipes, we could estimate net biocide emission and arrived at 155 mg Diuron, 17 mg Terbutryn, 12 mg OIT and 17 mg Diuron-Desmethyl from a 10 m<sup>2</sup> painted façade area over a time period of two years. In a third step, we characterized entry pathways comparing samples from a drainage pipe that collected road runoff (surface pathway) with two others that collected infiltrated water on top of an underground garage (soil pathway). All drainage pipes showed Terbutryn, two of them also Diuron but none OIT. The drainage pipe representing the surface pathway showed a smaller number of individual transformation products but similar concentrations of parent compounds. One pipe representing the soil pathway had highest concentrations of Terbutryn and its TPs which suggests a high leaching potential of this biocide also away from concentrated infiltration in urban stormwater management infrastructure.</p>

Urbanisation-induced climate warming in Great Britain

<p>In Great Britain (GB) 5.8% of the total land area is considered urban, yet the wider impact of Urban Heat Islands (UHIs) beyond city scales has not been fully explored. Through scaling data from a high-resolution urban monitoring network we estimate the current (2014) spatial daily-mean urban warming across GB to be 0.04°C [0.02 °C – 0.06°C]. Despite this GB-wide contribution appearing small (94% of the land cover is still rural), half of GB's population currently live in areas with average daily-mean UHIs of 0.4°C. GB is also experiencing rapid urbanisation, with urban land cover expanding from 4.3 to 5.8% between 1975 and 2014. Purely due to urbanisation in this period, we estimate GB as a whole is warming at a rate that is both equivalent and in addition to ~3% of the background surface-level climate change (i.e. natural and greenhouse gas induced). In areas with the greatest urban expansion, we find UHI-induced warming rates are up to three times this average. Although our study only applies to GB, the simplicity of our method means that it can be equally applied to other countries. Urbanisation is undeniably a global phenomenon with urban expansion in many countries far exceeding that found in GB.</p>

Mobile-Based Sensing Scheme to Minimize Battery Power Consumption for Urban Monitoring Systems

In urban monitoring systems, mobile sensing is imperative to acquire data from sensors and relay them to a cloud server. Mobile devices can be used anytime and anywhere, enabling communication with pervasive sensing in various conditions to obtain the data. Reliable data acquisition has been required in urban monitoring systems from the macroscale to the microscale. However, a broadcast method for the data acquisition process may lead to the increased battery power consumption of mobile devices. Managing the battery power consumption of mobile devices is essential for reliable data acquisition. In this paper, we propose an urban monitoring system with an optimization algorithm in which a cloud server broadcasts a communication request that includes battery power consumption and the data acquisition quantity of mobile devices. Game theoretic optimization is formulated with a decision process. We derive a best response and Nash equilibrium for mobile communication with sensors and a cloud server. Evaluation results demonstrate that the proposed system can guarantee a low battery power consumption, as well as acquire the desired data quantity.

MeSmart-Pro: Advanced Processing at the Edge for Smart Urban Monitoring and Reconfigurable Services

With reference to the MeSmart project, the Municipality of Messina is making a great investments to deploy several types of cameras and digital devices across the city for carrying out different tasks related to mobility management, such as traffic flow monitoring, number plate recognition, video surveillance etc. To this aim, exploiting specific devices for each task increases infrastructure and management costs, reducing flexibility. On the contrary, using general-purpose devices customized to accomplish multiple tasks at the same time can be a more efficient solution. Another important approach that can improve the efficiency of mobility services is moving computation tasks at the Edge of the managed system instead of in remote centralized serves, so reducing delays in event detection and processing and making the system more scalable. In this paper, we investigate the adoption of Edge devices connected to high-resolution cameras to create a general-purpose solution for performing different tasks. In particular, we use the Function as a Service (FaaS) paradigm to easily configure the Edge device and set up new services. The key results of our work is deploying versatile, scalable and adaptable systems able to respond to smart city’s needs, even if such needs change over time. We tested the proposed solution setting up a vehicle counting solution based on OpenCV, and automatically deploying necessary functions into an Edge device. From experimental results, it results that computing performance at the Edge overtakes the performance of a device specifically designed for vehicle counting under certain conditions and thanks to our reconfigurable functions.