► This paper examines the influence of weather conditions on walking and cycling modes. ► Younger individuals’ walking/cycling are more negatively affected by cold temperature. ► Wind speed and ...showers negatively influences cyclists more strongly than pedestrians. ► Females’ tendency to bike is more negatively affected by cold temperatures than males.
Three weather sensitive models are used to explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data are restricted to non-captive commuters who have the option of selecting among five basic modes of auto driver, auto passenger, transit, bike and walk. Daily trip rates in various weather conditions are assessed. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.
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•Porous graphene foam composites are explored for the waterproof dual-mode sensor.•The sensor has high sensitivity over large strain range with ultralow detection limit.•The sensor ...also exhibits high temperature sensitivity over a wide linear range.•The waterproof sensor detects movement and temperature in dry and wet conditions.•It captures subtle pulse waveforms from distal arteries and skeletal muscle motions.
Soft multimodal sensors in practical applications for health monitoring and human–machine interfaces require them to show sufficiently high sensitivity over wide sensing rages, rapid response and excellent durability, and waterproof property. Herein, we demonstrate a dual-mode sensor based on a porous graphene foam composite to achieve the aforementioned challenging yet attractive performance parameters for strain and temperature sensing. The resulting dual-mode sensor exhibits a strain sensitivity of 2212.5 in the wide piecewise linear range of 0–65%, a rapid response of 0.11 s, an ultralow detection limit of 0.0167%, and outstanding stability over 15,000 cycles. The sensor can also detect temperature with a high sensitivity of 0.97 × 10-2 °C -1 over a wide linear range of 10–185 °C and a small detection limit for sensing both low- and high-temperature environments. Taken together with the waterproof property, the dual-mode sensor can accurately monitor the large, small, and even subtle motions and temperature variations in both dry and underwater conditions. The capability to detect the subtle yet rapidly changing motions from distal arteries and skeletal muscles paves the way for the development of future soft multimodal, waterproof electronic sensing devices toward human–computer interaction, health monitoring and early disease prevention, and personalized medicine.
Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still ...lower than those of general remote sensing classification. In order to improve the performance, previous attempts have been made to develop new features, classifiers, and rules, whereas few studies have investigated the in-depth causes and the corresponding solutions. In this paper, after reviewing the related literature, some of the fundamental difficulties hindering the improvement of LMRS are disclosed and discussed. Firstly, landslides do not have distinguishable spectral, spatial, or temporal characteristics, as they may actually be covered by other land covers. Secondly, the surface features of a landslide can vary greatly, affected by the different geological factors, geomorphological factors, hydrological factors, weather conditions, and other factors. Thirdly, the differences in the surface features are often remarkable and nonnegligible, and thus it is difficult to identify a landslide with only a few simple criteria. Finally, some solutions to the above difficulties are suggested. It is expected that the accuracy and applicability of LMRS could be greatly improved, by exploiting big data, utilizing the deep learning technique, and modelling the surface spatial structure of the landslide.
The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW ...are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Radar and camera information fusion sensing methods are used to solve the inherent shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the main hardware and camera as ...the auxiliary hardware framework. At the same time, the Mahalanobis distance is used to match the observed values of the target sequence. Data fusion based on the joint probability function method. Moreover, the algorithm was tested using actual sensor data collected from a vehicle, performing real-time environment perception. The test results show that radar and camera fusion algorithms perform better than single sensor environmental perception in severe weather, which can effectively reduce the missed detection rate of autonomous vehicle environment perception in severe weather. The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.
•Live fuels were the main driver of the severity of crown-convective fires in pine forests.•Vegetation vertical structure, fire history and weather also affected fire severity.•Physical properties ...had a minor influence on the severity of crown-convective fires.•Low-density LiDAR had a high potential for evaluating pre-fire fuel structure.
The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity.
The new SARS-CoV-2 coronavirus, which causes the COVID-19 disease, was reported in Wuhan, China, in December 2019. This new pathogen has spread rapidly around more than 200 countries, in which Spain ...has one of the world's highest mortality rates so far. Previous studies have supported an epidemiological hypothesis that weather conditions may affect the survival and spread of droplet-mediated viral diseases. However, some contradictory studies have also been reported in the same research line. In addition, many of these studies have been performed considering only meteorological factors, which can limit the reliability of the results. Herein, we report a spatio-temporal analysis for exploring the effect of daily temperature (mean, minimum and maximum) on the accumulated number of COVID-19 cases in the provinces of Spain. Non-meteorological factors such as population density, population by age, number of travellers and number of companies have also been considered for the analysis. No evidence suggesting a reduction in COVID-19 cases at warmer mean, minimum and maximum temperatures has been found. Nevertheless, these results need to be interpreted cautiously given the existing uncertainty about COVID-19 data, and should not be extrapolated to temperature ranges other than those analysed here for the early evolution period.
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•No evidence of a relationship between COVID-19 cases and temperature was found.•Results should not be extrapolated to other temperature ranges.•These results should be interpreted carefully due to data uncertainty and confounders.•It is important to account for non-meteorological, spatial and temporal effects.
Transmission line galloping often causes structural and electrical failures, which is a serious threat to the security of transmission systems. Through analysing the influence factors of galloping, ...it reveals that weather conditions are the most significant excitation factors and conductors of any voltage level and region may gallop when the apt-galloping weather conditions are satisfied. This study proposes an early warning method for transmission line galloping based on support vector machine (SVM) and AdaBoost bi-level classifiers. First, a prediction model of apt-galloping weather conditions based on an SVM classifier is built through data mining of historical weather parameters in regions where galloping frequently occurred. When the forecast weather conditions of a particular region satisfy the apt-galloping weather conditions, the conductor type, cross-section and span of transmission line are further considered to realise early warning of galloping through an AdaBoost classifier. Finally, the historical galloping events of a power grid are adopted to verify the validity of the proposed methods. The test results indicate that both the accurate classification rate and accurate warning rate are above 90%, whereas the missed warning rate is below 10%. The models are suitable for early warning of transmission line galloping and can provide important decision support for operation staff of power grid.
Changes in global fire activity are influenced by a multitude of factors including land‐cover change, policies, and climatic conditions. This study uses 17 climate models to evaluate when changes in ...fire weather, as realized through the Fire Weather Index, emerge from the expected range of internal variability due to anthropogenic climate change using the time of emergence framework. Anthropogenic increases in extreme Fire Weather Index days emerge for 22% of burnable land area globally by 2019, including much of the Mediterranean and the Amazon. By the midtwenty‐first century, emergence among the different Fire Weather Index metrics occurs for 33–62% of burnable lands. Emergence of heightened fire weather becomes more widespread as a function of global temperature change. At 2 °C above preindustrial levels, the area of emergence is half that for 3 °C. These results highlight increases in fire weather conditions with human‐caused climate change and incentivize local adaptation efforts to limit detrimental fire impacts.
Plain Language Summary
Observed increases in the frequency and severity of fire weather have been observed across portions of the globe over the past half century. We used climate models to identify where and when anthropogenic climate change causes fire weather conditions to exceed that of natural variability. Modeling results show that emergence for some fire weather indices is already under way for a sizable portion of the globe, including much of southern Europe and the Amazon, and with an expansion of this area with continued warming over the twenty‐first century. These findings suggest substantial increases in fire potential in regions where vegetation abundance and ignitions are not limiting, highlighting the urgency to adapt to changes in fire disturbances and hazards.
Key Points
Anthropogenic climate change is projected to enhance fire weather across most burnable global land surfaces during the twenty‐first century
Emergence of fire weather conditions from natural variability is modeled to occur in the first half of the twenty‐first century in many regions
Extent of fire‐weather emergence is twice as large when global temperature surpasses 3 °C, compared to 2 °C, above preindustrial levels
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