Real-Time Visual Analytics for Air Quality

Raise collective awareness about the daily levels of humans exposure to toxic chemicals in the air is of great significance in motivating citizen to act and embrace a more sustainable life style. For this reason, Public Administrations are involved in effectively monitoring urban air quality with high-resolution and provide understandable visualization of the air quality conditions in their cities. Moreover, collecting data for a long period can help to estimate the impact of the policies adopted to reduce air pollutant concentration in the air. The easiest and most cost-effective way to monitor air quality is by employing low-cost sensors distributed in urban areas. These sensors generate a real-time data stream that needs elaboration to generate adequate visualizations. The TRAFAIR Air Quality dashboard proposed in this paper is a web application to inform citizens and decision-makers on the current, past, and future air quality conditions of three European cities: Modena, Santiago de Compostela, and Zaragoza. Air quality data are multidimensional observations update in real-time. Moreover, each observation has both space and a time reference. Interpolation techniques are employed to generate space-continuous visualizations that estimate the concentration of the pollutants where sensors are not available. The TRAFAIR project consists of a chain of simulation models that estimates the levels of NO and NO2 for up to 2 days. Furthermore, new future air quality scenarios evaluating the impact on air quality according to changes in urban traffic can be explored. All these processes generate heterogeneous data: coming from different sources, some continuous and others discrete in the space-time domain, some historical and others in real-time. The dashboard provides a unique environment where all these data and the derived statistics can be observed and understood

Synthesis and crystal structure determination of the bicyclic [bis(μ -P,N-1-benzyl-2-imidazolyl-diphenylphosphine)(μ -O,O'-diperchlorate)dimercury(II)][diperchlorate].

In the attempt to test the oxidizing capability of group 12 perchlorate salts in the regards of phosphane ligands, the 1:1 molar ratio reactions between a donor P, N phosphane ligand (1-benzyl-2-imidazolyl-diphenylphosphine, L) and the respective salts have been performed. In all the cases no ligand oxidation was observed while the metal complexation was highlighted. The species L2M2(ClO4)4 was formed with an increasing predominance from Cd<Zn<Hg. The complex L3M(ClO4)2 was the only solid product isolated in the case of cadmium and the predominant species in solution both for zinc and cadmium reactions. The crystal structure of [bis(μ-P,N-1-benzyl-2-imidazolyl-diphenylphosphine)(μ-O,O’-diperchlorate)dimercury(II)][diperchlorate] shows two centrosymmetric Hg atoms as a part of an eight member cycle formed by two bridging (Bzim)Ph2P ligands coordinated in a head-to-tail mode with P and N as donor atoms. Each Hg atom interacts with the oxygen atoms of two symmetry related molecules of ClO4- that bridge the metal centres forming a second eight member cycle, roughly normal to the first one

University of Rome Carbon-14 Dates III

This material was digitized as part of a cooperative project between Radiocarbon and the University of Arizona Libraries.The Radiocarbon archives are made available by Radiocarbon and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform February 202

N-[2-(1-hydrazonoethyl)-3-benzofuranyl]-p-toluenesulfonamide

The structure of the title compound, C17H17N3O3S, has been determined. It consists of two planar moieties, the benzofuran and S-aryl systems, which form an angle of 65.6(1)degrees with one another

Anomaly Detection and Repairing for Improving Air Quality Monitoring

Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants’ concentrations can support the identification of urban areas with poor air quality and raise citizens’ awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms’ performances

University of Rome Carbon-14 Dates V

This material was digitized as part of a cooperative project between Radiocarbon and the University of Arizona Libraries.The Radiocarbon archives are made available by Radiocarbon and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform February 202

An arrow-caused lesion in a late Upper Palaeolithic human pelvis.

Study of a case of healed trauma due to an arrow-point wound in an Upper Paleolithic female individual (San Teodoro 4) found in a burial in the San Teodoro cave in Sicily