Magnetorheological elastomer (MRE) materials have the potential to be used in a wide range of applications that require long-term service in hostile environments. These widespread applications will ...result in the emergence of MRE-specific durability issues, where durability refers to performance under in-service environmental conditions. In response, the outdoor tropical climatic environment, combined with the effects of weathering, will be the primary focus of this paper, specifically the photodegradation of the MRE. In this study, MRE made of silicone rubber (SR) and 70 wt% micron-sized carbonyl iron particles (CIP) were prepared and subjected to mechanical and rheological testing to evaluate the effects under natural weathering. Magnetorheological elastomer samples were exposed to the natural weathering conditions of a tropical climate in Kuala Lumpur, Malaysia, for 30 days. To obtain a comprehensive view of MRE degradation during natural weathering, mechanical testing, rheology, and morphological evaluation were all performed. The mechanical and rheological properties test results revealed that after 30 days of exposure and known meteorological parameters, Young’s modulus and storage modulus increased, while elongation at break decreased. The degradation processes of MRE during weathering, which are responsible for their undesirable change, were given special attention. With the help of morphological evidence, the relationship between these phenomena and the viscoelastic properties of MRE was comprehensively defined and discussed.
Submarine groundwater discharge (SGD) is an important driver of coastal biogeochemical budgets worldwide. Radon (222Rn) has been widely used as a natural geochemical tracer to quantify SGD, but field ...measurements are time consuming and costly. Here, we use deep learning to predict coastal seawater radon in SGD‐impacted regions. We hypothesize that deep learning could resolve radon trends and enable preliminary insights with limited field observations of groundwater tracers. Two deep learning models were trained on global coastal seawater radon observations (n = 39,238) with widely available inputs (e.g., salinity, temperature, water depth). The first model used a one‐dimensional convolutional neural network (1D‐CNN‐RNN) framework for site‐specific gap filling and producing short‐term future predictions. A second model applied a fully connected neural network (FCNN) framework to predict radon across geographically and hydrologically diverse settings. Both models can predict observed radon concentrations with r2 > 0.76. Specifically, the FCNN model offers a compelling development because synthetic radon tracer data sets can be obtained using only basic water quality and meteorological parameters. This opens opportunities to attain radon data from regions with large data gaps, such as the Global South and other remote locations, allowing for insights that can be used to predict SGD and plan field experiments. Overall, we demonstrate how field‐based measurements combined with big‐data approaches such as deep learning can be utilized to assess radon and potentially SGD beyond local scales.
Key Points
We show how deep learning can predict coastal seawater radon, a popular groundwater discharge tracer
One model, intended for short‐term forecasts and gap filling, correctly predicted radon (r2 > 0.80) at hydrologically diverse study sites
A second model accurately predicts radon (r2 > 0.76) using only water depth, temperature, salinity, air temperature, and wind speed
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•Green lettuce was cultivated both organically and conventionally.•Changes in the levels of plant phenols and mineral content were examined.•Higher plant phenols and antioxidant ...capacity were found in the conventional system.•Higher yield and plant mineral content were found in the organic farming system.•The results were discussed in relation to the agronomic and fertilization systems.
The present paper aims to investigate phenolic profiles, antioxidant capacity and mineral composition of lettuce (Lactuca sativa L., var. ‘Maravilla de Verano’) grown under conventional (CON) and an organic (ORG) systems with four different fertilization treatments. The polyphenolic profiles of leaf extracts were determined by ultra-high-performance liquid chromatography (UHPLC), the levels of mineral elements by means of inductively coupled plasma mass spectrometry, whereas total phenolic content and antioxidant capacity were determined spectrophotometrically. Yield, soil and meteorological parameters were measured. In all the fertilization treatments, total phenolic acids and flavonols in CON were significantly higher compared to ORG. A trend parallel to that of single phenols was observed for total phenolic content and total antioxidant capacity. Plant mineral distribution revealed significant changes between CON and ORG systems in some plant macronutrients (N, Mg, S, Na, Fe) and micronutrients (Se, Mn, Mo). The differences among fertilization treatments for all the parameters considered were also discussed. From the overall analysis of the results, the higher content of phenolics observed in CON system could be associated to the presence of more stressful conditions, in terms of plant and/or soil mineral deficits. On the other hand, the adoption of an organic management determined higher yields and a better plant mineral balance.
The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar ...radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021. Spearman and Kendall correlation coefficients were used to investigate the association. The results showed that wind speed, air temperature, and solar radiation have positive and strong correlations with the confirmed cases and deaths in the cold season (autumn and winter 2020–2021). The total COVID-19 cases negatively correlated with relative humidity but were not significant in all seasons. Besides, PM2.5 strongly correlated with COVID-19 confirmed cases for the summer of 2020. The death distribution by age group showed the highest deaths for those aged 60–69. The highest number of deaths was 41% in the summer of 2020. The study provided useful information about the COVID-19 health emergency and meteorological parameters, which can be used for future health disaster planning, adopting prevention strategies and providing healthcare procedures to protect against future infraction transmission.
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•Meteorological parameters have positive correlation with COVID-19 infections.•Relative humidity has negative and irrelevant correlation with COVID-19 infection.•PM2.5 has only a positive correlation with COVID-19 infection in summer 2020.•Age group 60–69 years has the highest deaths average during the current study.•Percentage of vaccinated people did not exceed 3% of the population end August 2021.
This paper presents a rural exemplar house built in San Francisco de Raymina (a high Andean village 3700 masl) in southern Peru that integrates passive and sustainable solar heating techniques. A ...climatic analysis of this village was carried out using measurements of meteorological parameters recorded throughout a whole year. The annually averaged temperature, relative humidity and horizontal daily solar energy were 8.3 °C, 73.1% and 5.2 kWh/m2, respectively. The temperatures outside and inside the most rural dwellings are almost the same, so they do not offer any protection specially, during nights when the temperature can reach values below zero. The thermal behavior of the house was modeled with the m2m tool, and an experimental validation was carried out. With the use of m2m, it was possible to create an energy balance during the month of June 2014 (the winter cold and dry season) to determine the energy loss/gain contributions by each wall and to assess how air exchanges (the flow rates of which were deduced using an inversion approach, as they could not be directly measured) between the exterior and interior influence the thermal behavior of the whole house. Infiltration contributed approximately 48.6% of the daily energy losses, while the main solar gains were from the skylights (21.8%) and the adobe walls, which absorbed heat during the day and released heat at night.
Climate change over the past several decades prompted this preliminary investigation into the possible effects of global warming on the climatological behavior of U.S. tornadoes for the domain ...bounded by 30°–50°N and 80°–105°W. On the basis of a warming trend over the past 30 years, the modern tornado record can be divided into a cold “Period I” from 1954 to 1983 and a subsequent 30-year warm “Period II” from 1984 to 2013. Tornado counts and days for (E)F1–(E)F5, significant, and the most violent tornadoes across a 2.5° × 2.5° gridded domain indicate a general decrease in tornado activity from Period I to Period II concentrated in Texas/Oklahoma and increases concentrated in Tennessee/Alabama. These changes show a new geographical distribution of tornado activity for Period II when compared with Period I. Statistical analysis that is based on field significance testing and the bootstrapping method provides proof for the observed decrease in annual tornado activity in the traditional “Tornado Alley” and the emergence of a new maximum center of tornado activity. Seasonal analyses of both counts and days for tornadoes and significant tornadoes show similar results in the spring, summer, and winter seasons, with a substantial decrease in the central plains during summer. The autumn season displays substantial increases in both tornado counts and significant-tornado counts in the region stretching from Mississippi into Indiana. Similar results are found from the seasonal analysis of both tornado days and significant-tornado days. This temporal change of spatial patterns in tornado activity for successive cold and warm periods may be suggestive of climate change effects yet warrants the climatological study of meteorological parameters responsible for tornado formation.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The tropospheric delay acquired by the Global Navigation Position System (GPS) Precise Point Positioning (PPP) is continuous and steady, less affected by rainfall. The Water Vapor Radiometer (WVR) ...can provide real‐time meteorological parameters but is more sensitive to high‐frequency information in troposphere. To explore the use of WVR‐retrieved tropospheric delay and assist other geodetic techniques for atmospheric correction, the tropospheric delay from WVR and co‐located GPS at Shanghai, Beijing, Kunming, and Urumqi stations in China are compared. For the inconsistent values of WVR‐PPP zenith wet delay, the variations of the tropospheric delay from WVR and GPS before and after the rainfall were statistically analyzed. The results suggest that, for the rain rate ranging from 0.1 to 50 mm/hr, the impact of rainfall on WVR could last from 10 min before to 30 min after the rainfall. With filtering WVR data based on meteorological parameters and rain rate, the zenith wet delay between WVR and PPP at Shanghai shows good consistency, the root mean square (RMS) is 6.11 mm, correlation is 0.997, and the RMS in the other three stations ranges from 16.35 to 25.16 mm (correlation ranges in 0.794–0.951). The analysis indicates that the tropospheric delay of WVR is reliable to be applied to space geodetic techniques correction in real‐time with filtering to reduce the effect of rainfall, water vapor, and liquid water variability.
Key Points
Comparison of zenith wet delay(ZWD) from water vapor radiometer, co‐located GPS station observations in high temporal resolution
Quantitative analysis of the magnitude and duration of rainfall effects on the zenith wet delay from water vapor radiometer and GPS
Such data analysis and processing might be valuable for assimilating multi‐source data on tropospheric wet delay
High-resolution spatiotemporal wind speed mapping is useful for atmospheric environmental monitoring, air quality evaluation and wind power siting. Although modern reanalysis techniques can obtain ...reliable interpolated surfaces of meteorology at a high temporal resolution, their spatial resolutions are coarse. Local variability of wind speed is difficult to capture due to its volatility. Here, a two-stage approach was developed for robust spatiotemporal estimations of wind speed at a high resolution. The proposed approach consists of geographically weighted ensemble machine learning (Stage 1) and downscaling based on meteorological reanalysis data (Stage 2). The geographically weighted machine learning method is based on three base learners, which are an autoencoder-based deep residual network, XGBoost and random forest, and it incorporates spatial autocorrelation and heterogeneity to boost the ensemble predictions. With reanalysis data, downscaling was introduced in Stage 2 to reduce bias and spatial abrupt (non-natural) variation in the predictions inferred from Stage 1. The autoencoder-based residual network was used in Stage 2 to adjust the difference between the averages of the fine-resolution predicted values and the coarse-resolution reanalysis data to ensure consistency. Using mainland China as a case study, the geographically weighted regression (GWR) ensemble predictions were shown to perform better than individual learners’ predictions (with an approximately 12–16% improvement in R2 and a decrease of 0.14–0.19 m/s in root mean square error). Downscaling further improved the predictions by reducing inconsistency and obtaining better spatial variation (smoothing). The proposed approach can also be applied for the high-resolution spatiotemporal estimation of other meteorological parameters or surface variables involving remote sensing images (i.e. reliable coarsely resolved data), ground monitoring data and other relevant factors.
An increasing attention of citizens and policy-makers is devoted to the monitoring and modelling of urban traffic-related air pollution (TRAP), as there is a demonstrated relationship among this and ...human health effects (e.g. circulatory and ischemic heart diseases, lung cancer, asthma onset in children and adults, and acute lower respiratory infections in children). In this work, we investigate the capability of the ENVI- met® software to reproduce the concentrations of pollutants, emitted from vehicular traffic, and the meteorological parameters, both measured by a specific monitoring station, to evaluate its potential use for the TRAP prediction. Starting from the meteorological and traffic flow data of a specific day, a number of simulations, with different configurations, have been run and the results (temporal and spatial distribution of meteorological parameters and pollutants concentrations) have been compared with the monitored data, provided by the ARPAS (Agenzia Regionale per la Protezione dell’Ambiente della Sardegna – Regional Agency for the Protection of the Sardinian Environment) and measured by the weather station and the air quality monitoring station CENCA1 in Cagliari (Italy). The results of these comparisons are encouraging and can help, among the others, in better understanding the urban traffic pollutant dispersion and in optimizing the location of the air quality monitoring stations.
The structural temperature distribution, especially temperature difference caused by solar radiation, has a great impact on the deformation and curvature of the concrete slab tracks of high-speed ...railways. Previous studies mainly focused on the temperature prediction of slab tracks, while how the temperature distribution is affected by environmental conditions has been rarely investigated. Based on the integral transformation method, this work presents an analytical method to determine and decompose the temperature distribution of the concrete slab track. A field temperature test of a half-scaled specimen of concrete slab track was conducted to validate the developed methodology. In the proposed method, we decompose the temperature distribution of the slab track into an initial temperature component and a boundary temperature component. Then, the boundary temperature components caused by solar radiation and atmospheric temperature are investigated, respectively. The results show that the solar radiation plays a significant role in the nonlinear temperature distribution, while the atmospheric temperature has little effect. By contrast, the temperature change in the slab surface resulting from the atmospheric temperature accounts on average for only 5% in the hot weather condition. The proposed method establishes a relation between the structural temperature and meteorological parameters (i.e., the solar radiation and atmospheric temperature). Consequently, the temperature distribution of the concrete slab track is predicted via the meteorological parameters.
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