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1.
Appl Ergon ; 98: 103598, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34607162

RESUMO

Data-linked Next Generation Weather Radar (NEXRAD) images can be delayed up to 20 min in the cockpit. Pilots' underappreciating or ignoring the time delay may be the major cause of two fatal accidents. No studies have connected spatial awareness with accidents. This study evaluated how delayed radar information affects the spatial awareness of pilots at three levels of analysis. Thirty-one student pilots and flight instructors completed three sequential estimation tasks (i.e., the current location of storms, the current relative distance to storms, and the future relative distance to storms). Fifty-four weather scenarios were developed for three factors (storm speeds, delays, displays) and presented to pilots. The results indicated that delays and the storm speed significantly affected the three levels of spatial awareness. Participants' estimation accuracy was the lowest under long delay and fast speed in the current location estimation, under medium delay and speed in the current distance estimation, and under short delay and slow speed in the future distance estimation. Spatial awareness could be high under the long delay and fast speed conditions if pilots had no time limits. Thus, pilots can process 20-min delayed radar information. However, there were no differences in estimation accuracy between the static and animation displays in any of the conditions. Well-designed features on displays, such as scale or distance measuring tools, can aid pilots' spatial estimation and support all levels of spatial awareness.


Assuntos
Aviação , Pilotos , Conscientização , Humanos , Radar , Tempo (Meteorologia)
2.
Chemosphere ; 287(Pt 4): 132428, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34606899

RESUMO

Being detrimental to human health and vegetation growth, ground-level ozone (O3) is becoming a huge concern as an air pollutant. The processes of formation, diffusion, transformation, and transport of O3 in the atmosphere are highly affected by meteorological conditions such as solar radiation, temperature, precipitation, and wind. Chemical transport models (CTMs) are widely used in simulating O3 pollution with two main inputs of the meteorological condition and emission inventory. Meteorological inputs play a crucial role in the model simulation accuracy especially in areas where emission has been well constrained such as the United States (U.S.). However, most O3 simulations today still use only one set of meteorological input, which leaves room for model performance improvement by using ensemble meteorological conditions. In this study, O3 over the Southeast U.S. was simulated for one week in the summer of each year from 2016 to 2018 by using ensemble meteorological inputs offered by Short Range Ensemble Forecast products. The predictions were conducted through the Weather Research and Forecasting model coupled with Chemistry. The calculated ensemble prediction results got at least 66.7% improvement in agreement with O3 observations compared with single runs in the three selected cities (Miami, Atlanta, and Baton Rouge) from 2016 to 2018. This study emphasized the accuracy and provided a new idea of using ensemble meteorological inputs to improve O3 prediction than using traditional single meteorology by CTMs.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Ozônio , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental , Humanos , Meteorologia , Ozônio/análise , Estados Unidos , Tempo (Meteorologia)
3.
Sci Total Environ ; 804: 150165, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-34509853

RESUMO

This paper is based on the fact that some climatic variables show a preferential directionality and grant a markedly anisotropic character to the weathering system acting on rocks. The aim of this work is to quantify the anisotropic degree of the weathering system and its effects on rock erosion. For this purpose, a new methodology based on the vector analysis of directional and time-dependent parameters is proposed to quantify the annual or seasonal anisotropy of the weathering system. Results show that, on the one hand, wind-driven rain and solar radiation are the most anisotropic variables, being north and east the most intense directions for wind-driven rain and southeast for solar radiation, in the case of the San José Tower, the reference monument of this study. On the other hand, the ranking from the most to the least eroded façades of the tower are: east (maximum recession depth of 26.77 mm) > south (15.53 mm) ≈ west (13.56 mm) > north (6.37 mm). Solar radiation and indirect processes arising therefrom are the most important weathering agents in the semiarid Mediterranean climate, whilst wind-driven rain is the main erosion factor especially due to its torrential character. According to our results, weathering and erosion agents are strongly anisotropic, which emphasizes the importance of integrating the anisotropic character of the weathering system in preventive strategies against surface deterioration of monuments. In this sense, this paper advances the United Nations' 2030 Agenda for Sustainable Development.


Assuntos
Chuva , Tempo (Meteorologia) , Anisotropia , Espanha , Vento
4.
Sensors (Basel) ; 21(21)2021 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-34770422

RESUMO

Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação , Previsões , Tempo , Tempo (Meteorologia)
5.
Sensors (Basel) ; 21(21)2021 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-34770466

RESUMO

Flood control and water resources management require monitoring the water level in rivers and streams. Water level measurement techniques increasingly consider image processing procedures. Most of the systems use a staff gauge to support the waterline detection. However, these techniques can fail when applied to urban stream channels due to water undulation, debris on the water surface, and traces of rain captured by the camera, and other adverse effects on images can be quite dramatic on the results. The importance of considering these effects is that they are usually associated with the variation in the water level with the occurrence of rain. The technique proposed in this work uses a larger detection zone to minimize the effects that tend to obstruct the waterline. The developed system uses an infrared camera to operate during the day and night. Images acquired in different weather conditions helped to evaluate the proposed technique. The water level measurement accuracy was about 1.8 cm for images taken during the day and 2.8 cm for images taken at night. During short periods of heavy rain, the accuracy was 2.6 cm for the daytime and 3.4 cm for the nighttime. Infrared lighting can improve detection accuracy at night. The developed technique provides good accuracy under different weather conditions by combining information from various detection positions to deal with waterline detection issues.


Assuntos
Rios , Água , Inundações , Humanos , Chuva , Tempo (Meteorologia)
6.
Sensors (Basel) ; 21(21)2021 Oct 31.
Artigo em Inglês | MEDLINE | ID: mdl-34770575

RESUMO

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.


Assuntos
Pedestres , Acidentes de Trânsito , Algoritmos , Humanos , Percepção , Tempo (Meteorologia)
7.
Sensors (Basel) ; 21(21)2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34770586

RESUMO

Images based on RGB pixel values were used to measure the extinction coefficient of aerosols suspended in an atmospheric state. The pixel values of the object-image depend on the target-object reflection ratio, reflection direction, object type, distances, illumination intensity, atmospheric particle extinction coefficient, and scattering angle between the sun and the optical axes of the camera, among others. Therefore, the imaged intensity cannot directly provide information on the aerosol concentration or aerosol extinction coefficient. This study proposes simple methods to solve this problem, which yield reasonable extinction coefficients at the three effective RGB wavelengths. Aerosol size information was analogized using the RGB Ångström exponent measured at the three wavelengths for clean, dusty, rainy, Asian dust storm, and foggy days. Additionally, long-term measurements over four months showed reasonable values compared with existing PM2.5 measurements and the proposed method yields useful results.


Assuntos
Poluentes Atmosféricos , Aerossóis , Poluentes Atmosféricos/análise , Luz , Estações do Ano , Tempo (Meteorologia)
8.
Sensors (Basel) ; 21(21)2021 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-34770634

RESUMO

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Poluição do Ar/prevenção & controle , Cidades , Ecossistema , Monitoramento Ambiental , Humanos , Aprendizado de Máquina , Tempo (Meteorologia)
9.
Artigo em Inglês | MEDLINE | ID: mdl-34769832

RESUMO

This paper describes the functional development of the ClimApp tool (available for free on iOS and Android devices), which combines current and 24 h weather forecasting with individual information to offer personalised guidance related to thermal exposure. Heat and cold stress assessments are based on ISO standards and thermal models where environmental settings and personal factors are integrated into the ClimApp index ranging from -4 (extremely cold) to +4 (extremely hot), while a range of -1 and +1 signifies low thermal stress. Advice for individuals or for groups is available, and the user can customise the model input according to their personal situation, including activity level, clothing, body characteristics, heat acclimatisation, indoor or outdoor situation, and geographical location. ClimApp output consists of a weather summary, a brief assessment of the thermal situation, and a thermal stress warning. Advice is provided via infographics and text depending on the user profile. ClimApp is available in 10 languages: English, Danish, Dutch, Swedish, Norwegian, Hellenic (Greek), Italian, German, Spanish and French. The tool also includes a research functionality providing a platform for worker and citizen science projects to collect individual data on physical thermal strain and the experienced thermal strain. The application may therefore improve the translation of heat and cold risk assessments and guidance for subpopulations. ClimApp provides the framework for personalising and downscaling weather reports, alerts and advice at the personal level, based on GPS location and adjustable input of individual factors.


Assuntos
Temperatura Baixa , Tempo (Meteorologia) , Aclimatação , Previsões , Temperatura Alta , Humanos
10.
Front Public Health ; 9: 718846, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34722435

RESUMO

Background: Carbon monoxide (CO) poisoning is the leading cause of poisoning death worldwide, but associations between CO poisoning and weather remain unclear. Objective: To quantify the influence of climate parameters (e.g., temperature, relative humidity, and wind speed) on the incidence risk of acute CO poisoning in Taiwan. Methods: We used negative binomial mixed models (NBMMs) to evaluate the influence of weather parameters on the incidence risk of acute CO poisoning. Subgroup analyses were conducted, based on the seasonality and the intentionality of acute CO poisoning cases. Results: We identified a total of 622 patients (mean age: 32.9 years old; female: 51%) with acute CO poisoning in the study hospital. Carbon monoxide poisoning was associated with temperature (beta: -0.0973, rate ratio (RR): 0.9073, p < 0.0001) but not with relative humidity (beta: 0.1290, RR: 1.1377, p = 0.0513) or wind speed (beta: -0.4195, RR: 0.6574, p = 0.0806). In the subgroup analyses, temperature was associated with the incidence of intentional CO poisoning (beta: 0.1076, RR: 1.1136, p = 0.0333) in spring and unintentional CO poisoning (beta: -0.1865, RR: 0.8299, p = 0.0184) in winter. Conclusion: Changes in temperature affect the incidence risk for acute CO poisoning, but the impact varies with different seasons and intentionality in Taiwan. Our findings quantify the effects of climate factors and provide fundamental evidence for healthcare providers to develop preventative strategies to reduce acute CO poisoning events.


Assuntos
Intoxicação por Monóxido de Carbono , Adulto , Intoxicação por Monóxido de Carbono/epidemiologia , Feminino , Humanos , Estudos Retrospectivos , Estações do Ano , Taiwan/epidemiologia , Tempo (Meteorologia)
11.
Artigo em Inglês | MEDLINE | ID: mdl-34769662

RESUMO

The current study site of the project Inform@Risk is located at a landslide prone area at the eastern slopes of the city of Medellín, Colombia, which are composed of the deeply weathered Medellín Dunite, an ultramafic Triassic rock. The dunite rock mass can be characterized by small-scale changes, which influence the landslide exposition to a major extent. Due to the main aim of the project, to establish a low-cost landslide early warning system (EWS) in this area, detailed field studies, drillings, laboratory and mineralogical tests were conducted. The results suggest that the dunite rock mass shows a high degree of serpentinization and is heavily weathered up to 50 m depth. The rock is permeated by pseudokarst, which was already found in other regions of this unit. Within the actual project, a hypothesis has for the first time been established, explaining the generation of the pseudokarst features caused by weathering and dissolution processes. These parameters result in a highly inhomogeneous rock mass and nearly no direct correlation of weathering with depth. In addition, the theory of a secondary, weathering serpentinization was established, explaining the solution weathering creating the pseudokarst structures. This contribution aims to emphasize the role of detailed geological data evaluation in the context of hazard analysis as an indispensable data basis for landslide early warning systems.


Assuntos
Deslizamentos de Terra , Cidades , Colômbia , Geologia , Tempo (Meteorologia)
12.
J Healthc Eng ; 2021: 1463299, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34804444

RESUMO

Objective: To investigate the influence of cold weather on setup errors of patients with chest and pelvic disease in radiotherapy. Methods: The image-guided data of the patients were collected from the Radiotherapy Center of Cancer Hospital Affiliated to Guangxi Medical University from October 2020 to February 2021. During this period, the cold weather days were December 15, 16, and 17, 2020, and January 7 and 8, 2021. For body fixation in radiotherapy, an integrated plate and a thermoplastic mold were employed in 18 patients with chest disease, while an integrated plate and a vacuum pad were applied in 19 patients with pelvic disease. All patients underwent cone beam computed tomography (CBCT) scans in the first five treatments and once a week thereafter. The obtained data were registered to the planning CT image to get the setup errors of the patient in the translational direction including X, Y, and Z axes and rotational direction including R X , R Y , and R Z . Then, the Mann-Whitney U test was performed. The expansion boundary values of the chest and pelvis were calculated according to the formula M PTV=2.5∑+0.7δ. Results: A total of 286 eligible results of CBCT scans were collected. There were 138 chest CBCT scans, including 26 taken in cold weather and 112 in usual weather, and 148 pelvic CBCT scans, including 33 taken in cold weather and 115 in usual weather. The X-, Y-, and Z-axis translational setup errors of patients with chest disease in the cold weather group were 0.16 (0.06, 0.32) cm, 0.25 (0.17, 0.52) cm, and 0.35 (0.21, 0.47) cm, respectively, and those in the usual weather group were 0.14 (0.08, 0.29) cm, 0.23 (0.13, 0.37) cm, and 0.18 (0.1, 0.35) cm, respectively. The results indicated that there was a statistical difference in the Z-axis translational error between the cold weather group and the usual weather group (U = 935.5; p=0.005 < 0.05), while there was no statistical difference in the rotational error between the two groups. The external boundary values of X, Y, and Z axes in the cold weather group were 0.57 cm, 0.92 cm, and 0.99 cm, respectively, and those in the usual weather group were 0.57 cm, 0.78 cm, and 0.68 cm, respectively. There was no significant difference in the translational and rotational errors of patients with pelvic disease between the cold weather group and the usual weather group (p < 0.05). The external boundary values of X, Y, and Z axes were 0.63 cm, 0.79 cm, and 0.68 cm in the cold weather group and 0.61 cm, 0.79 cm, and 0.61 cm in the usual weather group, respectively. Conclusion: The setup error of patients undergoing radiotherapy with their bodies fixed by an integrated plate and a thermoplastic mold was greater in cold weather than in usual weather, especially in the ventrodorsal direction.


Assuntos
Tomografia Computadorizada de Feixe Cônico , Planejamento da Radioterapia Assistida por Computador , China , Humanos , Tempo (Meteorologia)
13.
Medicina (Kaunas) ; 57(11)2021 Nov 08.
Artigo em Inglês | MEDLINE | ID: mdl-34833434

RESUMO

Background: The objective of this study was to evaluate the impact of weather factors on stroke parameters. Methods: This retrospective study analyzed the records of stroke patients concerning the influence of meteorological conditions and moon phases on stroke parameters. Results: The study group consisted of 402 patients aged between 20 and 102; women constituted 49.8% of the subjects. Ischaemic stroke was diagnosed in 90.5% of patients and hemorrhagic stroke was diagnosed in 9.5% of patients. The highest number of hospitalizations due to stroke was observed in January (48 events); the lowest number was observed in July (23 events). There was no statistically significant correlation between the meteorological parameters on the day of onset and the preceding day of stroke and the neurological status (NIHSS) of patients. Mean air temperature on the day of stroke and the day preceding stroke was significantly lower in the group of patients discharged with a very good functional status (≤2 points in modified Rankin scale (mRS)) compared to the patients with a bad functional status (>2 points in mRS); respectively: 7.98 ± 8.01 vs. 9.63 ± 7.78; p = 0.041 and 8.13 ± 7.72 vs. 9.70 ± 7.50; p = 0.048). Humidity above 75% on the day of stroke was found to be a factor for excellent functional state (RR 1.61; p = 0.016). The total anterior circulation infarcts (in comparison with stroke in the other localization) were more frequent (70%) during a third quarter moon (p = 0.011). The following parameters had a significant influence on the number of stroke cases in relation to autumn having the lowest number of onsets: mean temperature (OR 1.019 95% CI 1.014-1.024, p < 0.000), humidity (OR 1.028, CI 1.023-1.034, p < 0.0001), wind speed (OR 0.923, 95% CI 0.909-0.937, p < 0.0001), insolation (OR 0.885, 95% CI 0.869-0.902, p < 0.0001), precipitation (OR 0.914, 95% CI 0.884-0.946, p < 0.0001). Conclusion: Air humidity and air temperature on the day of stroke onset as well as air temperature on the day preceding stroke are important for the functional status of patients in the acute disease period. A combination of the following meteorological parameters: lowered mean temperature and low sunshine, high humidity and high wind speed all increase the risk of stroke during the winter period. High humidity combined with high precipitation, low wind speed and low sunshine in the autumn period are associated with the lowest stroke incidence risk. A possible relationship between phases of the moon and the incidence requires further investigation.


Assuntos
Isquemia Encefálica , Acidente Vascular Cerebral , Adulto , Idoso , Idoso de 80 Anos ou mais , Isquemia Encefálica/epidemiologia , Feminino , Humanos , Conceitos Meteorológicos , Pessoa de Meia-Idade , Prevalência , Estudos Retrospectivos , Estações do Ano , Acidente Vascular Cerebral/epidemiologia , Acidente Vascular Cerebral/etiologia , Temperatura , Tempo (Meteorologia) , Adulto Jovem
14.
Sensors (Basel) ; 21(22)2021 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-34833842

RESUMO

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.


Assuntos
Raios Ultravioleta , Tempo (Meteorologia) , Clima , Humanos , Redes Neurais de Computação , Estações do Ano
15.
Sensors (Basel) ; 21(22)2021 Nov 22.
Artigo em Inglês | MEDLINE | ID: mdl-34833845

RESUMO

Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.


Assuntos
Algoritmos , Condução de Veículo , Coleta de Dados , Redes Neurais de Computação , Tempo (Meteorologia)
16.
Zhonghua Liu Xing Bing Xue Za Zhi ; 42(8): 1445-1452, 2021 Aug 10.
Artigo em Chinês | MEDLINE | ID: mdl-34814566

RESUMO

Objective: To identify the threshold of a health warning system based on the association of apparent temperature and years of life lost (YLL). Methods: Daily mortality records and meteorological data were collected from 364 Chinese counties for 2006-2017. Distributed lag nonlinear model and multivariate Meta-analyses were applied to estimate the association between the apparent temperature and YLL rate. A regression tree model was employed to estimate the warning thresholds of the apparent temperature. Stratified analyses were further conducted by age and cause of death. Results: The daily YLL rate was 23.6/105. The mean daily apparent temperature was 15.7 ℃. U-shaped nonlinear associations were observed between apparent temperature and YLL rate. The actual temperature-caused YLL rate for the elderly was higher than the young population. The daily excess deaths rate increased with the higher effect levels. Conclusions: Regression tree model was employed to define the warning threshold for meteorological health risk. The present study provides theoretical support for the weather-related health warning system.


Assuntos
Temperatura Baixa , Temperatura Alta , Idoso , China/epidemiologia , Humanos , Dinâmica não Linear , Temperatura , Tempo (Meteorologia)
17.
Environ Monit Assess ; 193(12): 835, 2021 Nov 20.
Artigo em Inglês | MEDLINE | ID: mdl-34800190

RESUMO

Lakes, the main entities of lacustrine environments, are a rich archive of environmental and geogenic changes in terms of compositional variation of water and sediment. Water and sediment samples (N = 173) were collected during 2013-2014 from the Wular Lake, one of the important fresh lakes within the Indian landmass. The study provides insights on the solutes acquisition mechanism and provenance of ionic constituents within the lake water and the sediments. Besides, the impact of catchment attributes on the lake system was in addition assessed. The hydrochemical results suggest that the chemical weathering of silicate and carbonates within the catchment shapes the lake water chemistry and characterizes the facies pattern into a hybrid type. The geochemical results of the lake sediments demonstrate that the improved abrasion rates and ensuant settling of detritus into the lake are closely linked with the prominent physical weathering over chemical weathering. The new finding of the present study is that sediments represent an unweathered basalt compositional trend, plausible provenance from mafic rocks, experiencing low to moderate degree of chemical weathering. The study found that increased encroachment within the lake catchment due to continued anthropogenic forcing is the primary source contributing the organic matter (OM) as well as the higher levels of Cl, NO3, SO4, and P to the lake. These findings corroborate with the land use-land cover changes (from the last 50 years) within the lake catchment in significantly deteriorating the lake system. The study recommends that the ongoing conversion of lake peripheral areas into urban settlement and agro-horticulture land by filling activities should be restricted.


Assuntos
Lagos , Poluentes Químicos da Água , Monitoramento Ambiental , Sedimentos Geológicos , Poluentes Químicos da Água/análise , Tempo (Meteorologia)
18.
Sci Rep ; 11(1): 22027, 2021 11 11.
Artigo em Inglês | MEDLINE | ID: mdl-34764317

RESUMO

Rising temperature levels during spring and summer are often argued to enable lifting of strict containment measures even in the absence of herd immunity. Despite broad scholarly interest in the relationship between weather and coronavirus spread, previous studies come to very mixed results. To contribute to this puzzle, the paper examines the impact of weather on the COVID-19 pandemic using a unique granular dataset of over 1.2 million daily observations covering over 3700 counties in nine countries for all seasons of 2020. Our results show that temperature and wind speed have a robust negative effect on virus spread after controlling for a range of potential confounding factors. These effects, however, are substantially larger during mealtimes, as well as in periods of high mobility and low containment, suggesting an important role for social behaviour.


Assuntos
COVID-19/epidemiologia , Humanos , Umidade , Pandemias , Fatores de Risco , SARS-CoV-2/isolamento & purificação , Estações do Ano , Comportamento Social , Temperatura , Tempo (Meteorologia) , Vento
19.
Geospat Health ; 16(2)2021 11 03.
Artigo em Inglês | MEDLINE | ID: mdl-34730321

RESUMO

Dengue is a complex disease with an increasing number of infections worldwide. This study aimed to analyse spatiotemporal dengue outbreaks using geospatial techniques and examine the effects of the weather on dengue outbreaks in the Klang Valley area, Kuala Lumpur, Malaysia. Daily weather variables including rainfall, temperature (maximum and minimum) and wind speed were acquired together with the daily reported dengue cases data from 2001 to 2011 and converted into geospatial format to identify whether there was a specific pattern of the dengue outbreaks. The association between these variables and dengue outbreaks was assessed using Spearman's correlation. The result showed that dengue outbreaks consistently occurred in the study area during a 11-year study period. And that the strongest outbreaks frequently occurred in two high-rise apartment buildings located in Kuala Lumpur City centre. The results also show significant negative correlations between maximum temperature and minimum temperature on dengue outbreaks around the study area as well as in the area of the high-rise apartment buildings in Kuala Lumpur City centre.


Assuntos
Dengue , Cidades , Dengue/epidemiologia , Surtos de Doenças , Humanos , Malásia/epidemiologia , Tempo (Meteorologia)
20.
F1000Res ; 10: 911, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34745565

RESUMO

Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94% accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application.


Assuntos
Asma , Telemedicina , Asma/epidemiologia , Humanos , Redes Neurais de Computação , Temperatura , Tempo (Meteorologia)
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