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1.
J Hazard Mater ; 443(Pt A): 130131, 2023 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-36240586

RESUMO

Efficient and safe recovery of high-viscosity marine crude oil spills is still a worldwide challenge. High-viscosity crude oil is difficult to be removed by traditional adsorbent materials. Although some recent developments in photothermal or electric-thermal oil-absorbing materials, the vertical heat transfer inside and the potential hazard of electrical leakage are difficult to be guaranteed. In order to overcome these problems, we polymerized dopamine (DA) in situ on the skeleton surface of the commercial melamine sponge (MS), and further coated the full-wavelength light-absorbing Fe3O4 NPs-Graphene (HF-G) on it to obtain the superhydrophobic sponge with excellent photothermal conversion effect, heat conductivity and magnetic heating capabilities (HF-G/PDA@MS). When the thickness of sponge is 5 mm, the HF-G/PDA@MS shows excellent vertical heat conductivity ability, and can absorb about 80 g/g. It also can be combined with an extra electric-heating device to achieve continuous heating to reduce the viscosity and recover crude oil at night or extreme weather. In addition, the temperature of HF-G/PDA@MS can reach about 40 °C by electromagnetic induction heater, indicating that we can use multiple energies-assisted modes to heat the HF-G/PDA@MS to. This work provides a promising solution and theoretical support for all-weather solving offshore crude oil spills pollution and recovery.


Assuntos
Petróleo , Interações Hidrofóbicas e Hidrofílicas , Água/química , Tempo (Meteorologia)
2.
Sci Total Environ ; 856(Pt 2): 159114, 2023 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-36181825

RESUMO

Knowledge of the evaporation rate from rock surfaces is critical for obtaining the water flux in the rock-atmosphere interphase, for understanding moisture distribution, and for quantification of damage from salt crystallization within the rock. Evaporation from rocks is a poorly understood, yet important process. We present a study on evaporation from 10 lithologies, including sedimentary, igneous, and metamorphic granular rocks. The evaporation rate was measured from rock cores with a set vaporization plane depth in a humid temperate continental climate during at least eight observation periods for eight months. The measured evaporation rate varied over four orders of magnitude (0.4-2447 mm/year), being dependent on the vaporization plane depth, lithology, and climate seasonality at the site. The evaporation rate from the rock cores was calculated based on Fick's law. The calculations reasonably followed the measured values. Using contrasting, yet field-realistic values in the calculation, virtual time series of the seasonal evaporation rate from natural rock outcrops in three different climates (arid, semi-arid, humid) were constructed. This revealed possible annual evaporative losses from the rock outcrops (0.1 mm-896 mm). Within the range of observed values, the evaporation rate was mostly influenced by the vaporization plane depth (by up to 2.2 orders of magnitude), which was followed by: lithology (up to 1.1 order of magnitude), local climate (up to 1.0 order of magnitude), and climate seasonality (up to 0.8 order of magnitude). Thus, our study shows the key role of the vaporization plane depth in the evaporation rate. This approach can find employment in a large number of investigations such as in the evaporation estimates and hydrologic balance in rock landforms and rocky slopes, hydrologic processes in the shallow rock subsurface, living conditions of endolithic and epilithic organisms, weathering processes, and in the protection of carved or rock constructed cultural heritage.


Assuntos
Atmosfera , Clima , Atmosfera/química , Tempo (Meteorologia)
3.
Appl Ergon ; 106: 103899, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36191404

RESUMO

Night foggy road conditions limit visibility distance of drivers and are associated with higher accident and fatality rates than other weather conditions. Therefore, ensuring road visibility in night foggy road is critical. However, it is difficult to reproduce fog on a real road and only a few studies have researched foggy road conditions and visibility in a laboratory as a small scale. Previous studies have suggested that a color temperature of road lighting is related to visibility. However, many have only investigated the effects of relative transmittance in limited indoor experiments, and the impacts of differences in transmittance on visibility have thus far not been studied in real-scale conditions. In this study, a real-scale test involving 91 subjects was conducted to investigate how the visibility distance under night foggy conditions is affected by different lighting color temperatures. Based on the real scale experiments, the correlation between the visibility distance and lighting color temperature was derived. Road lighting with a low color temperature (i.e., yellow) was found to provide longer visibility distances than that with high color temperatures under night foggy conditions having measured visibility of approximately 102m. The impact of the differences in lighting color increased as the visibility distance decreased. In contrast, road lighting with a high color temperature (i.e., white) improved driver visibility in higher-visibility conditions. Therefore, this study confirmed the correlation between lighting color temperature and visibility distance for different visibility conditions and could serve as a foundation for the development of roadway design standards as well as future studies.


Assuntos
Acidentes de Trânsito , Iluminação , Humanos , Temperatura , Tempo (Meteorologia)
4.
Sci Total Environ ; 857(Pt 2): 159500, 2023 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-36265629

RESUMO

Chemical boundary conditions (BCs) are important inputs for regional chemical transport models. In this study, we use the brute-force method (BFM), process analysis (PA) and response surface model (RSM) to quantify the effects of BCs on simulated O3 concentrations in different regions of China by the weather research and forecasting with chemistry (WRF-Chem) model. We combine the model with an integrated gas-phase reaction rate (IRR) tool to further analyze the changes in the O3 chemical mechanisms. Our results show that the simulated O3 concentrations in western cities are significantly affected by the O3 in the BCs (BC-O3), which can increase the maximum simulated O3 concentration, such as in Lanzhou (36.6 µg/m3, 26.3 %), Wuhai (30.1 µg/m3, 25.5 %) and Urumqi (50.7 µg/m3, 41.2 %). In contrast, O3 generation in the eastern region is dominated by emissions. Subsequently, we compare the reaction rate changes in O3 generation and consumption under the effects of BC-O3 in the western city of Urumqi and the eastern city of Beijing. The results show that in Beijing, the O3 concentration and the related chemical reaction rates undergo little change, while in Urumqi, the concentration and reaction rates have significant differences. The BC-O3 significantly accelerates the O3 photochemical reaction process in Urumqi, resulting in increased O3 generation and consumption reaction rates; additionally, there may be a chemical reaction pathway for the formation of O3: BC-O3 + NO → NO2 + hv → O + O2 → O3. BC-O3 transmission is the main pathway of changes in the simulated O3 concentration in the study area, and the chemical reactions between BC-O3 and local pollutants are primarily characterized by O3 consumption. In conclusion, the study shows the importance of BCs for regional model simulation while providing supporting information for O3 formation in model studies.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Ozônio , Poluentes Atmosféricos/análise , Ozônio/análise , Monitoramento Ambiental/métodos , Tempo (Meteorologia) , China , Poluição do Ar/análise
5.
Sensors (Basel) ; 22(21)2022 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-36366275

RESUMO

With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.


Assuntos
Inteligência Artificial , Condução de Veículo , Algoritmos , Tempo (Meteorologia)
6.
Sci Rep ; 12(1): 19803, 2022 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-36396735

RESUMO

Landscape evolution is driven by tectonics, climate and surface denudation. In New Zealand, tectonics and steep climatic gradients cause a dynamic landscape with intense chemical weathering, rapid soil formation, and high soil losses. In this study, soil, and elemental redistribution along two adjacent hillslopes in East Otago, New Zealand, having different landscape settings (ridge versus valley) are compared to identify soil weathering and erosion dynamics. Fallout radionuclides (239+240Pu) show that over the last ~ 60 years, average soil erosion rates in the valley (~ 260 [t km-2 year-1]) are low compared to the ridge (~ 990 [t km-2 year-1]). The ridge yields up to 26% lower soil weathering intensity than the topographical-protected valley. The lowest soil weathering intensity is found at both hilltop positions, where tors (residual rocks) are present and partially disintegrate. The soil weathering intensity increases with distance from tors, suggesting that tors rejuvenate the chemical weathering signature at the hilltop positions with fresh material. The inversed and decreasing weathering degree with all soil depth indicates that the fresh mineral contribution must be higher at the soil surface than at the bedrock weathering front. Higher erosion rates at the exposed ridge may be partially attributed to wind, consistent with rock abrasion of tors, and low local river sediment yields (56 [t km-2 year-1]). Thus, the East Otago spatial patterns of soil chemistry and erosion are governed by tor degradation and topographic exposure.


Assuntos
Solo , Tempo (Meteorologia) , Nova Zelândia , Rios
7.
Sci Rep ; 12(1): 19768, 2022 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-36396755

RESUMO

Heat waves are often termed as the silent killer and have become even more important as recent studies suggest that the heat wave have become second most devastating extreme weather events in terms of human deaths and losses. It is also been largely realised by scientific community that it is not just the high temperatures which are responsible for the gruesome effect of heat waves but several other meteorological parameters play a vital role in aggravating the impact and causing much more damages. In view of the above the attention of scientific community, weather forecasters as well as disaster managers has shifted to also take into account the different meteorological parameters like maximum and minimum temperatures, relative humidity, wind speed, duration/spell of heat waves and its intensity which are aggravating the impact of heat stress. In this background, this study is undertaken as an attempt to quantify the effect of different meteorological parameters on heat wave on different regions of India for different summer months (March, April, May and June). In this study the impact of individual meteorological parameter as well their cumulative effect is studied based on data of 30 years (1981-2010) for 300 stations. The effect of different meteorological parameters is identified for different months for different regions of the country. Also the cumulative scores are calculated for different regions considering different meteorological parameters, as a first initiative to perform heat hazard analysis and zonation over the entire country. This could serve as initial step for planning mitigation and adaptation strategies throughout the country. These scores as thresholds for different regions may be also useful for operational forecaster's for early impact based warning services as well as for the disaster managers, for taking effective and timely actions.


Assuntos
Transtornos de Estresse por Calor , Humanos , Temperatura Alta , Tempo (Meteorologia) , Estações do Ano , Resposta ao Choque Térmico
8.
Environ Monit Assess ; 195(1): 51, 2022 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-36316588

RESUMO

Wheat is the important food grain and is cultivated in many Indian states: Punjab, Haryana, Uttar Pradesh, and Madhya Pradesh, which contributes to major crop production in India. In this study, popular statistical approach multiple linear regression (MLR) and time series approaches Time Delay Neural Network (TDNN) and ARIMAX models were envisaged for wheat yield forecast using weather parameters for a case study area, i.e., Junagarh district, western Gujarat region situated at the foot of Mount Girnar. Weather data corresponds to 19 weeks (42nd to 8th Standard Meteorological Week, SMW) during crop growing season was used for prediction of wheat yield using these statistical techniques and were evaluated for their predictive capability. Furthermore, trend analysis among weather parameters and crop yield was also carried out in this study using non-parametric Mann-Kendall test and Sen's slope method. Significant negative correlation was observed between wheat yield and some of the weekly weather variables, viz., maximum temperature (48, 49, 50, 51, 52, and 4th SMW), and total rainfall (50, 51, and 1st SMW) while positive correlation was observed with morning relative humidity (49 and 3rd SMW). Study indicated that forecast error varied from 1.80 to 10.28 in MLR, 0.79 to 7.79 in ARIMAX (2,2,2), - 3.09 to 10.18 in TDNN (4,5) during model training period (1985-2014). The MAPE value shows that the time series data predicted less than 5% of variation, whereas the conventional MLR technique indicated more than 7% variation. Both ARIMAX and TDNN approaches indicated better performance during model training periods, i.e., 1985-2014 and 1985-2015, while former performed well during the forecast periods 1985-2016 and 1985-2017. Overall, the study indicated that the ARIMAX approach can be used consistently for 4 years using the same model.


Assuntos
Agricultura , Monitoramento Ambiental , Triticum , Grão Comestível/crescimento & desenvolvimento , Estações do Ano , Triticum/crescimento & desenvolvimento , Tempo (Meteorologia) , Índia , Previsões
10.
Sci Rep ; 12(1): 19830, 2022 Nov 18.
Artigo em Inglês | MEDLINE | ID: mdl-36400908

RESUMO

Understanding how weather conditions affect animal populations is essential to foresee population changes in times of global climate shifts. However, assessing year-round weather impacts on demographic parameters is hampered in migratory animals due to often unknown occurrence in space and time. We addressed this by coupling tracking and weather data to explain extensive variation in apparent survival across 19 years in a northern European population of little ringed plovers (Charadrius dubius). Over 90% (n = 21) of tracked individuals followed migration routes along the Indo-European flyway to south India. Building on capture-recapture histories of nearly 1400 individuals, we found that between-year variation in precipitation during post-breeding staging in northern South Asia explained 47% of variation in apparent adult survival. Overall, the intensity of the monsoon in South Asia explained 31-33% of variability in apparent survival. In contrast, weather conditions in breeding, final non-breeding and pre-breeding quarters appeared less important in this species. The integration of multi-source data seems essential for identifying key regions and periods limiting population growth, for forecasting future changes and targeting conservation efforts.


Assuntos
Charadriiformes , Tempo (Meteorologia) , Animais , Estações do Ano , Clima , Mudança Climática
11.
Curr Biol ; 32(21): R1240-R1242, 2022 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-36347235

RESUMO

An impressive long-term study of Greater Ani birds reveals fluctuating selection for group size. In wet years, with abundant food, larger groups enjoy greater protection from predators. In dry years, however, larger groups suffer greater nestling mortality relative to smaller groups.


Assuntos
Aves , Tempo (Meteorologia) , Animais
12.
JASA Express Lett ; 2(10): 104001, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-36319210

RESUMO

Sonic boom measurements from recent flight tests have provided an opportunity to investigate effects of microphone installation on sonic boom waveforms, spectra, and metric levels in support of NASA X-59 flight test planning. While those flight tests used N-wave aircraft (F-18s), modeling studies were also conducted using source characteristics for a shaped low-boom aircraft. Of particular interest were the effects of receiver height on boom waveforms and metrics at elevated receiver positions, microphone installation, and local ground cover type. Reductions of more than 2 dB in A-weighted sound exposure level and perceived level were shown for 1.6 ft (0.48 m) microphone heights for 35º ray elevation angle. Measured and modeled results are described in this letter.


Assuntos
Aeronaves , Som , Tempo (Meteorologia)
13.
Chaos ; 32(10): 103115, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-36319290

RESUMO

On average once every four years, the Tropical Pacific warms considerably during events called El Niño, leading to weather disruptions over many regions on Earth. Recent machine-learning approaches to El Niño prediction, in particular, Convolutional Neural Networks (CNNs), have shown a surprisingly high skill at relatively long lead times. In an attempt to understand this high skill, we here use data from distorted physics simulations with the intermediate-complexity Zebiak-Cane model to determine what aspects of El Niño physics are represented in a specific CNN-based classification method. We find that the CNN can adequately correct for distortions in the ocean adjustment processes, but that the machine-learning method has far more trouble in dealing with distortions in upwelling feedback strength.


Assuntos
El Niño Oscilação Sul , Tempo (Meteorologia) , Física
14.
Environ Monit Assess ; 195(1): 50, 2022 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-36316488

RESUMO

Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The "Gulab" cyclonic storm formed on the 24th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the "Gulab" cyclonic event. The Otsu's thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km2), followed by Telangana (~2040 km2) and Odisha (~1132 km2), and the least in Chhattisgarh (~93.75 km2). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years.


Assuntos
Clima Extremo , Monitoramento Ambiental/métodos , Inundações , Produtos Agrícolas , Água , Tempo (Meteorologia)
15.
Proc Natl Acad Sci U S A ; 119(47): e2207536119, 2022 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-36375064

RESUMO

Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in [Formula: see text] geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.


Assuntos
Mudança Climática , Tempo (Meteorologia) , Estados Unidos , Teorema de Bayes , Estações do Ano , Temperatura
16.
J R Soc Interface ; 19(196): 20210865, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36382379

RESUMO

Globally, the spread and severity of COVID-19 have been distinctly non-uniform. Seasonality was suggested as a contributor to regional variability, but the relationship between weather and COVID-19 remains unclear and the focus of attention has been on outdoor conditions. Because humans spend most of their time indoors and because most transmission occurs indoors, we here, instead, investigate the hypothesis that indoor climate-particularly indoor relative humidity (RH)-may be the more relevant modulator of outbreaks. To study this association, we combined population-based COVID-19 statistics and meteorological measurements from 121 countries. We rigorously processed epidemiological data to reduce bias, then developed and experimentally validated a computational workflow to estimate indoor conditions based on outdoor weather data and standard indoor comfort conditions. Our comprehensive analysis shows robust and systematic relationships between regional outbreaks and indoor RH. In particular, we found intermediate RH (40-60%) to be robustly associated with better COVID-19 outbreak outcomes (versus RH < 40% or >60%). Together, these results suggest that indoor conditions, particularly indoor RH, modulate the spread and severity of COVID-19 outbreaks.


Assuntos
COVID-19 , Humanos , COVID-19/epidemiologia , Umidade , Tempo (Meteorologia) , Temperatura
17.
PLoS One ; 17(11): e0269022, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36395111

RESUMO

In 2013, Thailand was ranked second in the world in road accident fatalities (RAFs), with 36.2 per 100,000 people. During the Songkran festival, which takes place during the traditional Thai New Year in April, the number of road traffic accidents (RTAs) and RAFs are markedly higher than on regular days, but few studies have investigated this issue as an effect of festivity. This study investigated the factors that contribute to RAFs using various count regression models. Data on 20,229 accidents in 2015 were collected from the Department of Disaster Prevention and Mitigation in Thailand. The Poisson and Conway-Maxwell-Poisson (CMP) distributions, and their zero-Inflated (ZI) versions were applied to fit the data. The results showed that RAFs in Thailand follow a count distribution with underdispersion and excessive zeros, which is rare. The ZICMP model marginally outperformed the CMP model, suggesting that having many zeros does not necessarily mean that the ZI model is required. The model choice depends on the question of interest, and a separate set of predictors highlights the distinct aspects of the data. Using ZICMP, road, weather, and environmental factors affected the differences in RAFs among all accidents, whereas month distinguished actual non-fatal accidents and crashes with or without deaths. As expected, actual non-fatal accidents were 2.37 times higher in April than in January. Using CMP, these variables were significant predictors of zeros and frequent deaths in each accident. The RAF average was surprisingly higher in other months than in January, except for April, which was unexpectedly lower. Thai authorities have invested considerable effort and resources to improve road safety during festival weeks to no avail. However, our study results indicate that people's risk perceptions and public awareness of RAFs are misleading. Therefore, nationwide road safety should instead be advocated by the authorities to raise society's awareness of everyday personal safety and the safety of others.


Assuntos
Acidentes de Trânsito , Tempo (Meteorologia) , Humanos , Distribuição de Poisson , Gestão da Segurança , Citidina Monofosfato
18.
Sci Rep ; 12(1): 20037, 2022 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-36414682

RESUMO

Hemorrhagic fever with renal syndrome (HFRS), caused by hantavirus, is a serious public health problem in China. Despite intensive countermeasures including Patriotic Health Campaign, rodent control and vaccination in affected areas, HFRS is still a potential public health threat in China, with more than 10,000 new cases per year. Previous epidemiological evidence suggested that meteorological factors could influence HFRS incidence, but the studies were mainly limited to a specific city or region in China. This study aims to evaluate the association between monthly HFRS cases and meteorological change at the country level using a multivariate distributed lag nonlinear model (DLNM) from 2004 to 2018. The results from both univariate and multivariate models showed a non-linear cumulative relative risk relationship between meteorological factors (with a lag of 0-6 months) such as mean temperature (Tmean), precipitation, relative humidity (RH), sunshine hour (SH), wind speed (WS) and HFRS incidence. The risk for HFRS cases increased steeply as the Tmean between - 23 and 14.79 °C, SH between 179.4 and 278.4 h and RH remaining above 69% with 50-95 mm precipitation and 1.70-2.00 m/s WS. In conclusion, meteorological factors such as Tmean and RH showed delayed-effects on the increased risk of HFRS in the study and the lag varies across climate factors. Temperature with a lag of 6 months (RR = 3.05) and precipitation with a lag of 0 months (RR = 2.08) had the greatest impact on the incidence of HFRS.


Assuntos
Epidemias , Febre Hemorrágica com Síndrome Renal , Tempo (Meteorologia) , Humanos , China/epidemiologia , Febre Hemorrágica com Síndrome Renal/epidemiologia , Incidência , Meteorologia
19.
Sci Rep ; 12(1): 20024, 2022 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-36414672

RESUMO

Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial-temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, time headway), road characteristics (e.g. road type, number of lanes), weather data (e.g. daily rainfall, snow depth, temperature), and calendar variables (e.g. day of the week, public holidays, month, year) are used for model calibration. Three different decision tree algorithms, including, Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) have been employed to predict crash frequency. Results show that based on the traditional evaluation measures, the XGBosst is better for the explanation and interpretation of the factors affecting crash frequency, while the RF model is better for detecting trends and forecasting crash frequency. According to the results, the traffic flow rate, road type, year of the crash, and wind speed are the most influencing variables in predicting crash frequency on rural roads. Forecasting the high and medium risk segment-day in the rural network can be essential to the safety management plan. This risk will be sensitive to real traffic data, weather forecasts and road geometric characteristics. Seventy percent of high and medium risk segment-day are predicted for the case study.


Assuntos
Acidentes de Trânsito , População Rural , Humanos , Segurança , Árvores de Decisões , Tempo (Meteorologia)
20.
Sensors (Basel) ; 22(19)2022 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-36236229

RESUMO

Groundwater resource assessment and forecasting in mountain areas requires the monitoring of two conditions, local meteorological conditions, and springs' groundwater parameters. The reliability of the monitoring data and conditions are linked to the technical instrumentation, multiparametric probes, and sensors. This paper presents a set of attractive tools and sensors for springs' groundwater resource monitoring and assessment in mountain basins. Data from the combination of weather station sensors with spring flow-rate instruments, installed in the alpine Mascognaz basin, can guarantee an entire understanding of how one set of parameters can affect other results, defining consequential cause-and-effect relationships. Since a large part of the Alpine groundwater bodies are exploited for drinking purposes, understanding the evolution of their rechange processes requires making the right economic and instrumental investments aimed at using them according to forecast predictions and sustainable development goals.


Assuntos
Água Subterrânea , Nascentes Naturais , Compreensão , Monitoramento Ambiental , Reprodutibilidade dos Testes , Tempo (Meteorologia)
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