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In the context of improving aircraft safety, this work focuses on creating and testing a graphene-based ice detection system in an environmental chamber. This research is driven by the need for more accurate and efficient ice detection methods, which are crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors tested under varied and controlled atmospheric conditions exhibit a remarkable potential to enhance ice detection systems for aircraft. Their lightweight, efficient, and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

The present paper describes a tool developed in-house for the modeling of free-falling water droplet cooling processes. A two-way coupling model is employed to account for the interactions between the droplets and the carrier fluid, following a Eulerian–Lagrangian approach. In addition, a stochastic separated flow technique is employed, involving random sampling of the fluctuating fluid velocity. In physical modeling, two empirical correlations are considered for determining the heat and mass transfer coefficients, with the possibility of accounting for vibrations. The numerical results indicate the preponderance of the interactions between droplet and carrier fluid at various humidity ratios.

In April 2004, the federal government published a Code of Practice for the Environmental Management of Road Salts. The code is designed to help municipalities and other road authorities better manage road salt use to reduce environmental harm while maintaining road safety. And while the government did not ban road salts, the discussion around the issue has encouraged city managers to combine improvements in salt storage and use with new chemicals and technologies. Examples include: 1. anti-icing, 2. pre-wetting, 3. electronic spreader controls, 4. a road weather information system, and 5. fixed automated spray technology and advanced road weather information systems. The result of all of this has been new responses to winter conditions across Canada, with some impressive results.

\"Mr. [Sye Rosin] just thought about money, not his workers,\" they said in a statement. \"If he cared about [Donald Poisson], he would have helped pay for things or even send a card.\" \"In assessing a fine, I am in no way attempting to set a dollar value on Donalds life,\" said [Ernie Bobowski]. \"Every life is precious.\"

The owner of a Saskatchewan snow-removal business has been fined $34,000 in the crushing death of a teenage worker in 2003. Three charges under Saskatchewan's Occupational Health and Safety Act and associated regulations were laid against Sye Rosin, owner of Sye Rosin Snow Removal, following the death of Donald Poisson, 18, two winters ago.

Airline safety is perhaps the major concern of air travelers. This concern is more heightened when traveling in inclement weather such as during snow and ice storms. Since the traditional methods of de-icing airplanes to prevent the occurrence of tragedy have negative environmental consequences, several efforts have been exerted to find an alternative method. One such effort led to the development of Infratek system that uses a patented formula to remove ice and snow through focused radiant heat. The system takes into account the fact that planes come in a variety of configurations by providing the exact amount of low-intensity, high-output radiant energy necessary to de-ice each individual aircraft. The system has been proven to be more cost-effective and environmentally friendly than traditional methods.

Satellite radar altimeters like CryoSat‐2 estimate sea ice thickness by measuring the return‐time of transmitted radar pulses, reflected from the sea ice and ocean surface, to measure the radar freeboard. Converting freeboard to thickness requires an assumption regarding the fractional depth of the snowpack from which the radar waves backscatter (α)$(\\alpha )$ . We derive sea ice thickness from CryoSat‐2 radar freeboard data with incremental values for α$\\alpha $ , for the 2010–2021 winter periods. By comparing these to sea ice thickness estimates derived from upward‐looking sonar moorings, we find that α$\\alpha $values between 35%–80% result in the best representation of interannual variability observed over first‐year ice, reduced to <${< } $ 55% over multi‐year ice. The underestimating bias in retrievals caused by optimizing this metric can be removed by reducing the waveform retracking threshold to 20%–50%. Our results pave the way for a new generation of ‘partial penetration’ sea ice thickness products from radar altimeters. Plain Language Summary Satellite altimeters like CryoSat‐2 can be used to estimate sea ice thickness by estimating how far sea ice floes stick out above the waterline. This is done by measuring the time taken for radar waves to travel to the surface of the ice floe and back to the altimeter. All current winter sea ice thickness estimates assume that the radar waves return entirely from the sea ice surface, and not from the overlying snow cover. A growing body of research suggests this may not be the case, with weather and snow conditions affecting the fraction of the detected radar power that comes from the ice surface. We consider how well CryoSat‐2 estimates capture whether the ice is thicker or thinner than usual at a given time of year. We find that its skill is highest when we assume that 35%–80% of the radar power comes from the sea ice surface, and 20%–65% comes from the snow surface. However, improving this aspect of skill makes the sea ice thickness estimates too low. To address this, we show that a simple change in the waveform processing method can counter this bias. Key Points CryoSat‐2 retrievals of sea ice thickness have historically been tuned to minimize bias rather than to capture interannual variability We use upward‐looking sonar moorings to tune the treatment of both waveform retracking and snowpack penetration by radar waves Tuning to optimize interannual variability indicates partial penetration for all retracking thresholds

Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. The fall speed of ice particles is a critical parameter for the representation of ice clouds and snow in atmospheric numerical models, as it determines the rate of removal of ice from the modelled clouds. Fall speed is also required for snowfall predictions alongside other properties such as ice particle size, cross-sectional area, and shape. For example, shape is important as it strongly influences the scattering properties of these ice particles and thus their response to remote sensing techniques. This work analyzes fall speed as a function of particle size (maximum dimension), cross-sectional area, and shape using ground-based in situ measurements. The measurements for this study were done in Kiruna, Sweden, during the snowfall seasons of 2014 to 2019, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). The resulting data consist of high-resolution images of falling hydrometeors from two viewing geometries that are used to determine particle size (maximum dimension), cross-sectional area, area ratio, orientation, and the fall speed of individual particles. The selected dataset covers sizes from about 0.06 to 3.2 mm and fall speeds from 0.06 to 1.6 m s−1. Relationships between particle size, cross-sectional area, and fall speed are studied for different shapes. The data show in general low correlations to fitted fall speed relationships due to large spread observed in fall speed. After binning the data according to size or cross-sectional area, correlations improve, and we can report reliable parameterizations of fall speed vs. particle size or cross-sectional area for part of the shapes. For most of these shapes, the fall speed is better correlated with cross-sectional area than with particle size. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically oriented particles fall faster on average. However, most particles for which orientation can be defined fall horizontally.

Meteorological forecast and climate models require good knowledge of the microphysical properties of hydrometeors and the atmospheric snow and ice crystals in clouds, for instance, their size, cross-sectional area, shape, mass, and fall speed. Especially shape is an important parameter in that it strongly affects the scattering properties of ice particles and consequently their response to remote sensing techniques. The fall speed and mass of ice particles are other important parameters for both numerical forecast models and the representation of snow and ice clouds in climate models. In the case of fall speed, it is responsible for the rate of removal of ice from these models. The particle mass is a key quantity that connects the cloud microphysical properties to radiative properties. Using an empirical relationship between the dimensionless Reynolds and Best numbers, fall speed and mass can be derived from each other if particle size and cross-sectional area are also known. In this study, ground-based in situ measurements of snow particle microphysical properties are used to analyse mass as a function of shape and the other properties particle size, cross-sectional area, and fall speed. The measurements for this study were done in Kiruna, Sweden, during snowfall seasons of 2014 to 2019 and using the ground-based in situ Dual Ice Crystal Imager (D-ICI) instrument, which takes high-resolution side- and top-view images of natural hydrometeors. From these images, particle size (maximum dimension), cross-sectional area, and fall speed of individual particles are determined. The particles are shape-classified according to the scheme presented in our previous study, in which particles sort into 15 different shape groups depending on their shape and morphology. Particle masses of individual ice particles are estimated from measured particle size, cross-sectional area, and fall speed. The selected dataset covers sizes from about 0.1 to 3.2 mm, fall speeds from 0.1 to 1.6 m s−1, and masses from 0.2 to 450 µg. In our previous study, the fall speed relationships between particle size and cross-sectional area were studied. In this study, the same dataset is used to determine the particle mass, and consequently, the mass relationships between particle size, cross-sectional area, and fall speed are studied for these 15 shape groups. Furthermore, the mass relationships presented in this study are compared with the previous studies. For certain crystal habits, in particular columnar shapes, the maximum dimension is unsuitable for determining Reynolds number. Using a selection of columns, for which the simple geometry allows the verification of an empirical Best-number-to-Reynolds-number relationship, we show that Reynolds number and fall speed are more closely related to the diameter of the basal facet than the maximum dimension. The agreement with the empirical relationship is further improved using a modified Best number, a function of an area ratio based on the falling particle seen in the vertical direction.

Ice and snow reduce the road surface coefficient of friction, leading to economic losses and jeopardizing driving safety. Salt-storage asphalt pavements often fail to extend pavement life effectively. This limitation is caused by insufficient slow-release performance in the de-icing technology. In this study, a slow-release salt storage asphalt mixture was developed to mitigate the hazards posed by ice and snow on roadways. The pavement performance and ice and snow melt inhibition performance of slow-release salt storage asphalt mixtures were investigated. Fuzzy analysis was employed to optimize the dosage of these aggregates. Results show that the salt within the asphalt matrix was observed to dissolve, migrate, and diffuse effectively. The impact on pavement performance varied with different aggregate dosages. Fuzzy analysis was employed to evaluate pavement performance and ice and snow suppression and an optimal dosage of 40% was determined. At this dosage, the salt storage asphalt mixture showed enhanced pavement performance and effective ice and snow melting inhibition. These findings support the development of active de-icing and snow removal technologies, improving driving safety on icy roads during winter.