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This paper develops a framework for extracting sub-canopy topography from the TanDEM-X digital elevation model (DEM) by fusing ALOS-2 PARSAR-2 interferometric synthetic aperture radar (InSAR) coherence and Global Ecosystem Dynamics Investigation (GEDI) data. The main idea of this method is to estimate the forest height signals caused by the limited penetration of the X-band into the canopy from the TanDEM-X DEM. To achieve this goal, a spaceborne repeat-pass InSAR coherent scattering model is first used to estimate the forest height by the ALOS-2 PARSAR-2 InSAR coherence (APIC), taking the GEDI canopy height as the reference. Then, a linear regression model of the TanDEM-X DEM Vegetation Bias (TDVB) depending on the forest height and the fraction of vegetation cover (FVC) is established and used to estimate the sub-canopy topography. The proposed method was validated by the data of the Amazon rainforest and a boreal forest in Canada. The results showed that the proposed method extracted the sub-canopy topography at the study sites in the tropical forest and boreal forest with the root mean square error of 4.0 m and 6.33 m, respectively, and improved the TanDEM-X DEM accuracy by 75.7% and 39.7%, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between wheat trading countries using both descriptive and statistical inference methods for complex networks. The global Wheat Trade Network (WTN) and its evolving topological characteristics is analysed for the periods 2009–2013 and 2014–2018, using the Food and Agriculture Organization (FAO) data. The network characterisation measures in both periods are rather stable. There are some differences, however, in the magnitude of some measures (e.g., connectivity or disassortativity), and a higher degree of inequality in the distribution of the number of partners and the distribution of trade volume in the period 2014–2018. An Exponential Random Graph Model (ERGM) has been applied to identify significant determinants associated with the presence/absence of trade links between countries. The results indicate that wheat trade links are driven mainly by country openness, reciprocity (mutual importer-exporter relationship), whether the exporting country is US or Canada and the geographical location of importer and exporter. Other factors, such as the economic size of the importing country, if the importing country is US or Canada and the land surface of the exporting country also contribute less to capture interlinkages of WTN. [ABSTRACT FROM AUTHOR]

Slip-induced falls are among the most common causes of major occupational injuries and economic loss in Canada. Identifying the risk factors associated with slip events is key to developing preventive solutions to reduce falls. One factor is the slip-resistance quality of footwear, which is fundamental to reducing the number of falls. Measuring footwear slip resistance with the recently developed Maximum Achievable Angle (MAA) test requires a trained researcher to identify slip events in a simulated winter environment. The human capacity for information processing is limited and human error is natural, especially in a cold environment. Therefore, to remove conflicts associated with human errors, in this paper a deep three-dimensional convolutional neural network is proposed to detect the slips in real-time. The model has been trained by a new dataset that includes data from 18 different participants with various clothing, footwear, walking directions, inclined angles, and surface types. The model was evaluated on three types of slips: Maxi-slip, midi-slip, and mini-slip. This classification is based on the slip perception and recovery of the participants. The model was evaluated based on both 5-fold and Leave-One-Subject-Out (LOSO) cross validation. The best accuracy of 97% was achieved when identifying the maxi-slips. The minimum accuracy of 77% was achieved when classifying the no-slip and mini-slip trials. The overall slip detection accuracy was 86% with sensitivity and specificity of 81% and 91%, respectively. The overall accuracy dropped by about 2% in LOSO cross validation. The proposed slip detection algorithm is not only beneficial for footwear manufactures to improve their footwear slip resistance quality, but it also has other potential applications, such as improving the slip resistance properties of flooring in healthcare facilities, commercial kitchens, and oil drilling platforms. [ABSTRACT FROM AUTHOR]

Heavy oil and bitumen supply the vast majority of energy resources in Canada. Different methods can be implemented to produce oil from such unconventional resources. Surfactants are employed as an additive to water/steam to improve an injected fluid's effectiveness and enhance oil recovery. One of the main fractions in bitumen is asphaltene, which is a non-symmetrical molecule. Studies of interactions between surfactants, anionic, and non-anionic, and asphaltene have been very limited in the literature. In this paper, we employed molecular dynamics (MD) simulation to theoretically focus on the interactions between surfactant molecules and different types of asphaltene molecules observed in real oil sands. Both non-anionic and anionic surfactants showed promising results in terms of dispersant efficiency; however, their performance depends on the asphaltene architecture. Moreover, a hydrogen/carbon (H/C) ratio of asphaltenes plays an inevitable role in asphaltene aggregation behavior. A higher H/C ratio resulted in decreasing asphaltene aggregation tendency. The results of these studies will give a deep understanding of the interactions between asphaltene and surfactant molecules. [ABSTRACT FROM AUTHOR]

With increasing urbanization, climate change poses an unprecedented threat, and climate-sensitive urban management is highly demanded. Mitigating climate change undoubtedly requires smarter urban design tools and techniques than ever before. With the continuous evolution of geospatial technologies and an added benefit of analyzing and virtually visualizing our world in three dimensions, the focus is now shifting from a traditional 2D to a more complicated 3D spatial design and assessment with increasing potential of supporting climate-responsive urban decisions. This paper focuses on using 3D city models to calculate the mean radiant temperature (Tmrt) as an outdoor thermal comfort indicator in terms of assessing the spatiotemporal distribution of heat stress on the district scale. The analysis is done to evaluate planning scenarios for a district transformation in Montreal/Canada. The research identifies a systematic workflow to assess and upgrade the outdoor thermal comfort using the contribution of ArcGIS CityEngine for 3D city modeling and the open-source model of solar longwave environmental irradiance geometry (SOLWEIG) as the climate assessment model. A statistically downscaled weather profile for the warmest year predicted before 2050 (2047) is used for climate data. The outcome shows the workflow capacity for the structured recognition of area under heat stress alongside supporting the efficient intervention, the tree placement as a passive strategy of heat mitigation. The adaptability of workflow with the various urban scale makes it an effective response to the technical challenges of urban designers for decision-making and action planning. However, the discovered technical issues in data conversion and wall surface albedo processing call for the climate assessment model improvement as future demand. [ABSTRACT FROM AUTHOR]

It is often believed that the settlement and integration of skilled immigrants is moderately easy in Canada, and that skilled immigrants do well in Canada after a brief adjustment period. However, numerous barriers prevent the effective integration of skilled immigrants in the mainstream society. Despite being famous for its Federal Skilled Worker Program, which includes the immigration of skilled workers through Express Entry, Canada shows disappointing results in the economic and social outcomes of the integration of skilled immigrants. This has socioeconomic implications for the immigrants and affects their health and wellbeing. Therefore, there is a need for all those who are involved with immigrant integration to explore and be conversant about the contexts and issues faced by skilled newcomers in Canada. In reviewing the academic and grey literature on the settlement and integration of skilled immigrants in Canada, this paper highlights the challenges faced by skilled immigrants in Canada and the needs experienced by them in facing these challenges. It provides an overview of the experiences and expectations of skilled immigrants related to their settlement and integration in Canada. This paper indicates a need to evaluate the availability of immigrant services focused on skilled immigrants and the effectiveness of the existing support offered to them by various government and non-government agencies. [ABSTRACT FROM AUTHOR]

Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen's kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data. [ABSTRACT FROM AUTHOR]

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points. [ABSTRACT FROM AUTHOR]

In this study, we aimed to molecularly characterize 14 whole genome sequences of chicken astrovirus (CAstV) isolated from samples obtained from white chick syndrome (WCS) outbreaks in Western Canada during the period of 2014–2019. Genome sequence comparisons showed all these sequences correspond to the novel Biv group from which no confirmed representatives were published in GenBank. Molecular recombination analyses using recombination detection software (i.e., RDP5 and SimPlot) and phylogenetic analyses suggest multiple past recombination events in open reading frame (ORF)1a, ORF1b, and ORF2. Our findings suggest that recombination events and the accumulation of point mutations may have contributed to the substantial genetic variation observed in CAstV and evidenced by the current seven antigenic sub-clusters hitherto described. This is the first paper that describes recombination events in CAstV following analysis of complete CAstV sequences originated in Canada. [ABSTRACT FROM AUTHOR]

Total hatching egg set (for both egg production chicks and broilers) in the Agriculture and Agri-Food Canada report 2017 was over 1.0 billion. With the fertility rate for this year observed to be around 82%, there were about 180 million unhatched eggs (worth over 300 million Canadian dollars) incubated in Canada for the year 2017 alone. These non-hatching (non-fertile) eggs can find useful applications as commercial table eggs or low-grade food stock if they can be detected early and isolated accordingly preferably prior to incubation. The conventional method of chicken egg fertility assessment termed candling, is subjective, cumbersome, slow, and eventually inefficient, leading to huge economic losses. Hence, there is a need for a non-destructive, fast and online prediction technology to assist with early chicken egg fertility identification problem. This paper reviewed existing non-destructive approaches including ultrasound and dielectric measurements, thermal imaging, machine vision, spectroscopy, and hyperspectral imaging. Hyperspectral imaging was extensively discussed, being an emerging new technology with great potential. Suggestions were finally proffered towards building futuristic robust model(s) for early detection of chicken egg fertility. [ABSTRACT FROM AUTHOR]

Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on the roughness and moisture content of the surface, SAR offers unique potential for wetland monitoring. However, there are still challenges in applying SAR for mapping complex wetland environments. The backscattering similarity of different wetland classes is one of the challenges. Choosing the appropriate SAR specifications (incidence angle, frequency and polarization), based on the wetland type, is also a subject of debate and should be investigated more thoroughly. The geometric distortion of SAR imagery and loss of coherency are other remaining challenges in applying SAR and its processing techniques for wetland studies. Hence, this study provides a systematic meta-analysis based on compilation and analysis of indexed research studies that used SAR for wetland monitoring. This meta-analysis reviewed 172 papers and documented an upward trend in usage of SAR data, increasing usage of multi-sensor data, increasing integration of C- and L- bands over other configurations and higher classification accuracy with multi-frequency and multi-polarized SAR data. The highest number of wetland research studies using SAR data came from the USA, Canada and China. This meta-analysis highlighted the current challenges and solutions for wetland monitoring using SAR sensors. [ABSTRACT FROM AUTHOR]

Accurate visualization of air temperature distribution can be useful for various thermal analyses in fields such as human health and heat transfer of local area. This paper presents a novel approach to measuring air temperature from midwave hyperspectral Fourier transform infrared (FTIR) imaging in the carbon dioxide absorption band (between 4.25–4.35 μ m). In this study, the proposed visual air temperature (VisualAT) measurement is based on the observation that the carbon dioxide band shows zero transmissivity at short distances. Based on analysis of the radiative transfer equation in this band, only the path radiance by air temperature survives. Brightness temperature of the received radiance can provide the raw air temperature and spectral average, followed by a spatial median-mean filter that can produce final air temperature images. Experiment results tested on a database obtained by a midwave extended FTIR system (Telops, Quebec City, QC, Canada) from February to July 2018 show a mean absolute error of 1.25 ∘ K for temperature range of 2.6−26.4 ∘ C. [ABSTRACT FROM AUTHOR]

Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants' responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada. [ABSTRACT FROM AUTHOR]

Through an empirical investigation of the historical relationship between the destination climate and tourist arrivals in the Caribbean, this study presents the first revealed preference evaluation of a climate index informed by tourists' stated climatic preferences for coastal-beach tourism (i.e., a sun-sand-surf or 3S travel market). The goal of this multi-organization collaboration was to examine the potential application of a newly designed climate index—the Holiday Climate Index (HCI):Beach—for three Caribbean destinations (Antigua and Barbuda, Barbados, Saint Lucia). This paper provides an overview of the evolution of climate indices, including the development of the (HCI):Beach. To test the validity of climate indices for a beach travel market, daily climate ratings based on outputs from the Tourism Climate Index and the HCI were correlated with monthly arrivals data from Canada (a key source market) at an island destination scale. The results underscore the strength of the new index, with each destination scoring consistently higher using the HCI:Beach, including a stronger relationship (R2) between index scores and tourist arrivals. These findings demonstrate the value of combining stated and revealed preference methodologies to predict tourism demand and highlight opportunities for future research. [ABSTRACT FROM AUTHOR]

High temperature superconducting (HTS) power cables are a potential solution for the grid integration of offshore wind farms since the HTS cable can conduct bulk wind power at low voltage levels. However, the transient current through the HTS cable in cases of low voltage ride through (LVRT) operation has a negative impact on the HTS cable operation due to the quenching phenomenon. This paper analyzes the impact of LVRT control strategies on the HTS cable operation. In addition, a coordinated control of wind turbines for LVRT improvement of an offshore wind farm is proposed. The feasibility of the HTS cable application for the grid connection of offshore wind farms is also discussed in this study. The proposed controller is designed for the wind turbine generator based on a type-4 permanent magnet synchronous generator. In the proposed controller, the transient current through the HTS cable is reduced by regulating the machine side power during fault conditions. The feasibility of the proposed controller is validated in the PSCAD/EMTDC program (Manitoba Hydro International Ltd., Winnipeg, Manitoba, Canada, version 4.2.1). The effects of transient current on the cable temperatures and resistances are analyzed in this study. Simulation results show that the proposed control strategy could reduce the transient current and temperature rise of the HTS cable. [ABSTRACT FROM AUTHOR]

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications. [ABSTRACT FROM AUTHOR]

This paper proposes a new control scheme for the low frequency AC transmission (LFAC) system aiming at extending the point-to-point configuration to form a multi-terminal electrical energy network. The multi-terminal low frequency ac (MT-LFAC) system configuration is based on the use of modular multilevel matrix converters (M3Cs) and virtual synchronous generator (VSG) control. The M3C is the next ac/ac converter generation, which is used as an interface with the conventional AC network and the LFAC electrical energy system. Application of VSG control is proposed to enable proper power sharing, to provide synchronization of each terminal, and frequency stabilization, thus, to offer multiterminal forming capability. Two different operation modes are applied in the system to damp the frequency deviation after a dynamic perturbation, which provides additional stabilization feature to the VSG. Frequency restoration mode and commanded mode of power sharing are applied as dynamic states to validate the robustness of the VSG control system. Besides, to solve the negative impact of low X/R ratio in the LFAC electrical energy system, we enhance the VSG control by proposing a virtual-impedance-based solution, which increases the output total impedance on the low frequency side and prevents the coupling between P and Q. The operation of the proposed system is examined by simulation results with a precise model of M3Cs in the PSCAD/ EMTDC software environment (version 4.2.1, Winnipeg, MB, Canada). [ABSTRACT FROM AUTHOR]

Remote and isolated communities in Canada experience gaps in access to stable energy sources and must rely on diesel generators for heat and electricity. However, the cost and environmental impact resulting from the use of fossil fuels, especially in local energy production, heating, industrial processes and transportation are compelling reasons to support the development and deployment of renewable energy hybrid systems. This paper presents a computer model for economic analysis and risk assessment of a wind–diesel hybrid system with compressed air energy storage. The proposed model is developed from the point of view of the project investor and it includes technical, financial, risk and environmental analysis. Robustness is evaluated through sensitivity analysis. The model has been validated by comparing the results of a wind–diesel case study against those obtained using HOMER (National Renewable Energy Laboratory, Golden, CO, United States) and RETScreen (Natural Resources Canada, Government of Canada, Canada) software. The impact on economic performance of adding energy storage system in a wind–diesel hybrid system has been discussed. The obtained results demonstrate the feasibility of such hybrid system as a suitable power generator in terms of high net present value and internal rate of return, low cost of energy, as well as low risk assessment. In addition, the environmental impact is positive since less fuel is used. [ABSTRACT FROM AUTHOR]

Entropy applications in hydrometric network design problems have been extensively studied in the most recent decade. Although many studies have successfully found the optimal networks, there have been assumptions which could not be logically integrated into their methodology. One of the major assumptions is the uncertainty that can arise from data processing, such as time series simulation for the potential stations, and the necessary data quantization in entropy calculations. This paper introduces a methodology called ensemble-based dual entropy and multiobjective optimization (EnDEMO), which considers uncertainty from the ensemble generation of the input data. The suggested methodology was applied to design hydrometric networks in the Nelson-Churchill River Basin in central Canada. First, the current network was evaluated by transinformation analysis. Then, the optimal networks were explored using the traditional deterministic network design method and the newly proposed ensemble-based method. Result comparison showed that the most frequently selected stations by EnDEMO were fewer and appeared more reliable for practical use. The maps of station selection frequency from both DEMO and EnDEMO allowed us to identify preferential locations for additional stations; however, EnDEMO provided a more robust outcome than the traditional approach. [ABSTRACT FROM AUTHOR]

This paper studies control strategies for an AC/AC system based on a modular multilevel matrix converter (M3C) when an asymmetric fault occurs in the secondary side ac system. Firstly, the operating principle of M3C is briefly introduced and verified by simulation. Then, based on its mathematical model by double αβ0 transformation, the decoupled control strategies for the primary side and secondary side systems are designed. In view of the asymmetric fault condition of the secondary side system, the positive sequence and negative sequence components of voltages and currents are separated and extracted, and then a proportional resonant controller (PR) is used to regulate the positive and negative sequence currents at the same time to realize decoupled current control in the αβ reference frames. The capacitor voltage balancing control, which consists of an inter-subconverter balancing control and an inner-subconverter balancing control, is realized by adjusting four circulating currents. Finally, the proposed control strategy is validated by simulation in the PSCAD/EMTDC software (Manitoba HVDC Research Center, Canada). The result shows that during the period of the BC-phase short-circuit fault occurring in the secondary side system, the whole system can still operate stably and transmit a certain amount of active power, according to their set values. Furthermore, the capacitor voltages are balanced, with a slight increase during the fault period. The simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

There is a large body of research showing that psychological treatment can be effectively delivered via the internet, and Digital Mental Health Services (DMHS) are now delivering those interventions in routine care. However, not all attempts to translate these research outcomes into routine care have been successful. This paper draws on the experience of successful DMHS in Australia and Canada to describe ten lessons learned while establishing and delivering internet-delivered cognitive behavioural therapy (ICBT) and other mental health services as part of routine care. These lessons include learnings at four levels of analysis, including lessons learned working with (1) consumers, (2) therapists, (3) when operating DMHS, and (4) working within healthcare systems. Key themes include recognising that DMHS should provide not only treatment but also information and assessment services, that DMHS require robust systems for training and supervising therapists, that specialist skills are required to operate DMHS, and that the outcome data from DMHS can inform future mental health policy. We also confirm that operating such clinics is particularly challenging in the evolving funding, policy, and regulatory context, as well as increasing expectations from consumers about DMHS. Notwithstanding the difficulties of delivering DMHS, we conclude that the benefits of such services for the broader community significantly outweigh the challenges. [ABSTRACT FROM AUTHOR]

This study examines the angular distribution of scattered solar radiation associated with wildfire smoke aerosols observed over boreal forests in Canada during the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) campaign. First, it estimates smoke radiative parameters (550 nm optical depth of 3.9 and single scattering albedo of 0.90) using quasi-simultaneous multiangular and multispectral airborne measurements by the Cloud Absorption Radiometer (CAR). Next, the paper estimates the broadband top-of-atmosphere radiances that a satellite instrument such as the Clouds and the Earth's Radiant Energy System (CERES) could have observed, given the narrowband CAR measurements made from an aircraft circling about a kilometer above the smoke layer. This estimation includes both an atmospheric correction that accounts for the atmosphere above the aircraft and a narrowband-to-broadband conversion. The angular distribution of estimated radiances is found to be substantially different than the angular model used in the operational data processing of CERES observations over the same area. This is because the CERES model is a monthly average model that was constructed using observations taken under smoke-free conditions. Finally, a sensitivity analysis shows that the estimated angular distribution remains accurate for a fairly wide range of smoke and underlying surface parameters. Overall, results from this work suggest that airborne CAR measurements can bring some substantial improvements in the accuracy of satellite-based radiative flux estimates. [ABSTRACT FROM AUTHOR]

Work that addresses the cumulative impacts of resource extraction on environment, community, and health is necessarily large in scope. This paper presents experiences from initiating research at this intersection and explores implications for the ambitious, integrative agenda of planetary health. The purpose is to outline origins, design features, and preliminary insights from our intersectoral and international project, based in Canada and titled the "Environment, Community, Health Observatory" (ECHO) Network. With a clear emphasis on rural, remote, and Indigenous communities, environments, and health, the ECHO Network is designed to answer the question: How can an Environment, Community, Health Observatory Network support the integrative tools and processes required to improve understanding and response to the cumulative health impacts of resource development? The Network is informed by four regional cases across Canada where we employ a framework and an approach grounded in observation, "taking notice for action", and collective learning. Sharing insights from the foundational phase of this five-year project, we reflect on the hidden and obvious challenges of working across scales, sectors, and sites, and the overlap of generative and uncomfortable entanglements associated with health and resource development. Yet, although intersectoral work addressing the cumulative impacts of resource extraction presents uncertainty and unresolved tensions, ultimately we argue that it is worth staying with the trouble. [ABSTRACT FROM AUTHOR]

This paper evaluates the effect of inclement weather conditions on the travel demand for three classes of vehicles for a primary highway in the province of Alberta, Canada. The demand variables are passenger cars, trucks, and total traffic. It is well known from previous studies that adverse weather conditions such as low temperatures and heavy snowfall cause variation in traffic flow patterns. A winter weather model, based on the dummy variable regression model, was developed to quantify the variations in traffic volume due to snowfall and temperature changes. To establish the relationships, vehicular data was collected from six weigh-in-motion (WIM) sites, and the weather data associated with the WIM sites was collected from nearby weather stations. The study revealed that the variation in truck traffic, due to inclement weather conditions, was insignificant compared to variation in passenger car traffic. This study also investigated the temporal transferability of the developed winter weather model to test if a model can be applied irrespective of the time when it was developed. In addition, an attempt was made to check if the model coefficients could be optimized differently for different classes of traffic for estimating correct traffic variations. To evaluate transferability, the performance of both dummy variable regression and naive (without dummy variables) models was investigated. The results revealed that the dummy variable regression models show better performance for passenger car traffic and total traffic and naive winter weather models give better results for truck traffic. [ABSTRACT FROM AUTHOR]

Coastal management often relies on large-scale flood mapping to produce sea level rise assessments where the storm-related surge is considered as the most important hazard. Nearshore dynamics and overland flow are also key parameters in coastal flood mapping, but increase the model complexity. Avoiding flood propagation processes using a static flood mapping is less computer-intensive, but generally leads to overestimation of the flood zone, especially in defended urban backshore. For low-lying communities, sea level rise poses a certain threat, but its consequences are not only due to a static water level. In this paper, the numerical process-based model XBeach is used in 2D hydrodynamic mode (surfbeat) to reproduce an observed historical flood in Maria (eastern Canada). The main goal is to assess the impacts of a future storm of the same magnitude in the horizon 2100 according to an increase in sea level rise. The model is first validated from in situ observations of waves and water levels observed on the lower foreshore. Based on field observations of a flood extent in 2010, the simulated flooded area was also validated given a good fit (59%) with the actual observed flood. Results indicate that the 2010 storm-induced surge generated overwash processes on multiple areas and net landward sediment transport and accumulation (washover lobes). The flood was caused by relatively small nearshore waves (Hs < 1 m), but despite small water depth (>1.2 m), high flow velocities occurred in the main street (U > 2 m/s) prior to draining in the salt marsh. The impact of sea level rise on the low-lying coastal community of Maria could induce a larger flood area in 2100, deeper floodwater, and higher flow velocities, resulting in higher hazard for the population. [ABSTRACT FROM AUTHOR]