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To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates. © 2023 by the authors.

This paper reports a study on the User experience (UX) of STEM (e.g., Science, Technology, Engineering, and Mathematics) students using smart glasses and an interactive Augmented Reality (AR) educational app. The results show that the AR app provides a good UX, despite the presence of some form factor issues. Students’ usability ratings for both the AR app and the smart glasses, are positively correlated with the students’ perceived learning. The results of this study can be used as a guideline to design and develop further immersive e-learning technologies. © 2022 IEEE Computer Society. All rights reserved.

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO₂, PM_2.5, SO₂, and O₃) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO₂, −42.17%, for PM_2.5, and −63.98%, for NO₂. The reduction of this latter gas has induced an increase of O₃ by +26.54%.

The concentration peaks of atmospheric pollutants are the most challenging and important phenomena in air quality forecasting. The fact that these elevated levels of pollution do not seem to follow any specific pattern explains why current models still struggle to provide an accurate prediction of these harmful events for human health. The present study tackles this issue by testing several supervised learning methods to discriminate between peak and no peak of concentrations of five contaminants: NO2, CO, SO2, PM2.5, and O3. The classification performance of ensemble decision tree (gradient boosting machine (GBM)) models and ensemble deep learning (EDL) models are compared. The results reveal that the EDL outperforms the GBM model. An analysis of the variable importance (SHapley additive exPlanations (SHAP)) shows that both temporal and meteorological features have an impact on the proposed models. In particular, time of day and wind speed are the most important features to explain the performance of the ensemble DL models.

We combine current literature in software quality metrics with an attitude validation study with industry practitioners, to establish how quality metrics can be used for data-driven approaches. We also propose a simple metric nomenclature and map our findings into a decision making model for easy adoption and utilization of data-driven decision-making methods.

We combine current literature in software quality metrics with an attitude validation study with industry practitioners, to establish how quality metrics can be used for data-driven approaches. We also propose a simple metric nomenclature and map our findings into a decision making model for easy adoption and utilization of data-driven decision-making methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In the light of the 21st century, after two devastating world wars, humanity still has not learned to solve their conflicts through peaceful negotiations and dialogue. Armed conflicts, both international and within a single state, still cause devastation, displacement, and death all over the world. Not to mention the consequences that war has on the environment. Due to a lack of published research about war impact on modern air quality, this work studies air pollution evolution during the first months of the Russian-Ukrainian conflict. Satellite images of NO2, CO, O3, SO2, and PM2.5 over Ukrainian territory and PM2.5 land monitoring data for Kyiv were analyzed. The results showed that NO2 and PM2.5 correlated the most with war activities. CO and O3 levels increased, while SO2 concentrations reduced four-fold as war intensified. Drastic increases in pollution (especially PM2.5) from bombing and structural fires, raise additional health concerns, which might have serious implications for the exposed local and regional populations. This study is an invaluable proof of the impact any armed conflict has on air quality, the population, and environment. © 2022 by the authors.

Political and economic protests build-up due to the financial uncertainty and inequality spreading throughout the world. In 2019, Latin America took the main stage in a wave of protests. While the social side of protests is widely explored, the focus of this study is the evolution of gaseous urban air pollutants during and after one of these events. Changes in concentrations of NO2, CO, O3 and SO2 during and after the strike, were studied in Quito, Ecuador using two approaches: (i) inter-period observational analysis; and (ii) machine learning (ML) gradient boosting machine (GBM) developed business-as-usual (BAU) comparison to the observations. During the strike, both methods showed a large reduction in the concentrations of NO2 (31.5–32.36%) and CO (15.55–19.85%) and a slight reduction for O3 and SO2. The GBM approach showed an exclusive potential, especially for a lengthier period of predictions, to estimate strike impact on air quality even after the strike was over. This advocates for the use of machine learning techniques to estimate an extended effect of changes in human activities on urban gaseous pollution. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM_2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM_2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall - 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Particulate matter (PM) is one of the key pollutants causing health risks worldwide. While the preoccupation for increased concentrations of these particles mainly depends on their sources and thus chemical composition, some regions are yet not well investigated. In this work the composition of chemical elements of atmospheric PM₁₀ (particles with aerodynamic diameters ≤ 10 µm), collected at the urban and suburban sites in high elevation tropical city, were chemically analysed during the dry and wet seasons of 2017-2018. A large fraction (~ 68%) of PM₁₀ composition in Quito, Ecuador is accounted for by water-soluble ions and 16 elements analysed using UV/VIS spectrophotometer and Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Hierarchical clustering analysis was performed to study a correlation between the chemical composition of urban pollution and meteorological parameters. The suburban area displays an increase in PM₁₀ concentrations and natural elemental markers during the dry (increased wind intensity, resuspension of soil dust) season. Meanwhile, densely urbanized area shows increased total PM₁₀ concentrations and anthropogenic elemental markers during the wet season, which may point to the worsened combustion and traffic conditions. This might indicate the prevalence of cardiovascular and respiratory problems in motorized areas of the cities in the developing world.

Complex inhalable particles have become one of the main causes to trigger health problems worldwide. While the level of concern depends on the chemical composition of these particles, some regions are poorly studied, particularly, the Andes. In this work, the chemical characterization of atmospheric PM10 filter samples, collected between January and October of 2017, was carried out for the first time in the world's highest capital, Quito, Ecuador. This study investigates PM10 relation with meteorological variables and criteria pollutants. Average PM10 concentrations ranged from 24.9 μg m−3 to 26.2 μg m−3, with some alarming peaks during the episodes of fires and New Year's celebration. The major elements at study sites were Ca, Na, S, Mg, P, K, Fe, Si and Al, while the major water-soluble ion was SO42−. Meteorology plays an important role at this complex terrain city. Factor analysis showed natural dust and soil resuspension as the main source of particulate matter. Moreover, two less urbanized sites showed evidence of industrial activities or airport emissions, while the central city site showed a very strong signal of traffic-related pollution. These results are compared with representative cities around the world. As is the case in developing countries, low-quality diesel fuel is recognized for emitting large amounts of heavy metals, resulting in higher levels of those tracers in traffic flow areas. This work demonstrates the problems facing a midsize city, such as the lack of stricter regulations and, thus compromised air quality. This may imply serious respiratory and cardiovascular health effects.