Responses in the breeding parameters of the collared flycatcher to the changing climate

Global climate change involves various aspects of climate, including precipitation changes and declining surface wind speeds, but studies investigating biological responses have often focused on the impacts of rising temperatures. Additionally, related long-term studies on bird reproduction tend to concentrate on breeding onset, even though other aspects of breeding could also be sensitive to the diverse weather aspects. This study aimed to explore how multiple aspects of breeding (breeding onset, hatching delay, breeding season length, clutch size, fledgling number) were associated with different weather components. We used an almost four-decade-long dataset to investigate the various aspects of breeding parameters of a collared flycatcher (Ficedula albicollis) population in the Carpathian Basin. Analyses revealed some considerable associations, for example, breeding seasons lengthened with the amount of daily precipitation, and clutch size increased with the number of cool days. Parallel and opposing changes in the correlated pairs of breeding and weather parameters were also observed. The phenological mismatch between prey availability and breeding time slightly increased, and fledgling number strongly decreased with increasing mistiming. Our results highlighted the intricate interplay between climate change and the reproductive patterns of migratory birds, emphasizing the need for a holistic approach. The results also underscored the potential threats posed by climate change to bird populations and the importance of adaptive responses to changing environmental conditions.

Changes in wet bulb globe temperature and risk to heat-related hazards in Bangladesh

The rise in temperatures and changes in other meteorological variables have exposed millions of people to health risks in Bangladesh, a densely populated, hot, and humid country. To better assess the threats climate change poses to human health, the wet bulb globe temperature (WBGT) is an important indicator of human heat stress. This study utilized high-resolution reanalysis data from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF ERA5) to analyze the spatiotemporal changes in outdoor WBGT across Bangladesh from 1979 to 2021, employing Liljegren's model. The study revealed an increase in the annual average WBGT by 0.08-0.5 °C per decade throughout the country, with a more pronounced rise in the southeast and northeast regions. Additionally, the number of days with WBGT levels associated with high and extreme risks of heat-related illnesses has shown an upward trend. Specifically, during the monsoon period (June to September), there has been an increase of 2-4 days per decade, and during the pre-monsoon period (March to May), an increase of 1-3 days per decade from 1979 to 2021. Furthermore, the results indicated that the escalation in WBGT has led to a five-fold increase in affected areas and a three-fold increase in days of high and extreme heat stress during the monsoon season in recent years compared to the earlier period. Trend and relative importance analyses of various meteorological variables demonstrated that air temperature is the primary driver behind Bangladesh's rising WBGT and related health risks, followed by specific humidity, wind speed, and solar radiation.

The impact of wind speed measurement method on MRT and PET values in limited air flow conditions on warm, sunny days

Wind speed is an important variable in the assessment of thermal comfort. Different types of meteorological devices provide different accuracy of air velocity (va) measurements, which under limited air flow conditions, may result in a discrepancy in actual thermal stress level. Simultaneous measurements on warm summer days, performed with a cup anemometer and hot-wire probe, prove that too high starting threshold of the first of these sensors can lead to a discrepancy of actual wind speed, and as a consequence can distort MRT (estimated with globe thermometers) and PET values on average up to 10 °C and 1 °C, respectively.

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss

The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.

Estimation of regional and at-site quantiles of extreme winds under flood index procedure

Extreme winds are becoming more common among environmental events with the most catastrophic societal consequences. A regional frequency analysis of Daily Annual Maximum Wind Speed (DAMWS) is necessary not only for a comprehensive understanding of wind hazards but also for infrastructure design and safety, wind energy potential, disaster risk reduction, insurance and risk assessment in a particular region of study. This study investigated regional frequency analysis of DAMWS of Baluchistan and Sindh provinces of Pakistan. L-moments regionalization techniques along with flood index procedure were applied to DAMWS records of 21 stations from 1990 to 2019 across the study area. We intended to find the regional frequency distribution for maximum winds and predict the returns for extreme winds events in the future. Only one station namely Lasbella was found to be discordant. With the help of cluster analysis, the remaining 20 stations were further divided into two homogeneous. Heterogeneity measures validate that both regions are homogenous with allotted stations. Regional quantiles for both regions are estimated through best-fit probability distribution among Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type 3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV). Robustness of GLO distribution compared to GEV distribution is assessed through Monte Carlo simulations of relative bias and relative root mean square error. Findings clearly show that GLO distribution is the best for regional modeling. Furthermore, with the help of index flood procedure we determined at-site quantiles of all stations for various return periods. These estimated quantiles are of valuable information for various sectors, including infrastructure, energy, disaster management, and climate resilience, leading to improved planning, development, and risk reduction in the face of wind-related hazards in Sindh and Balochistan provinces of Pakistan.

Long-term spatiotemporal analysis of the climate related impact on the transmission rate of COVID-19

BACKGROUND

The association between weather conditions and the spread of COVID-19 was demonstrated by previous studies but focused on specific countries or investigated shorter periods of duration limiting the interpretation of the results.

AIM

To make an international comprehensive insight into the association between the weather conditions and the spread of COVID-19 by spanning many regions in the Northern and Southern hemispheres over a period of two years for the COVID-19 Outbreak.

METHODS

The data were analyzed by using statistical description, linear and multiple regressions, and the Spearman rank correlation test. Daily and weekly COVID-19 cases, the average temperatures, Wind Speed, the amount of precipitation as well as the relative humidity rates were collected from Irbid, Jordan as the main location of analyses, as well as comparison cities and countries in both hemispheres.

RESULTS

we found that certain climate variables are significant factors in determining the transmission rate of COVID-19 worldwide. Where, The temperature in the northern hemisphere regions was the most important climate factor that affects the increase in the transmission rate of COVID-19 (Northern Hemisphere rs = -0.65; Irbid rs = -0.74995; P < 0.001), While in southern hemisphere, the climate factor that affects the increase in the transmission rate of COVID-19 was the humidity (rs = 0.55; P < 0.01), In addition, we found the negligible and oscillated effect of wind speed on the transmission rate of COVID-19 worldwide. Moreover, we found that in Irbid 82% of COVID-19 cases were in the fall and winter seasons, while in summer the percentage of COVID-19 cases didn't exceed 3% during the total study period.

CONCLUSION

This study can help develop international strategies and policies against COVID-19-related pandemic peaks, especially during the colder seasons in the Northern Hemisphere regions from the first month of fall to the last month of winter.

On the development of new cosine-based probabilistic methods with applications to univariate and bivariate analyses of the wind speed energy

So far in the literature, a number of probability distributions have been successfully implemented for analyzing the wind speed and energy data sets. However, there is no published work on modeling and analyzing the wind speed and energy data sets with probability distributions that are introduced using trigonometric functions. In the existing literature, there is also a lack of studies on implementing the bivariate trigonometric-based probability distributions for modeling the wind speed and energy data sets. In this paper, we take up a meaningful effort to cover these interesting research gaps. Thus, we first incorporate a cosine function and introduce a new univariate probability distributional method, namely, a univariate modified cosine-G (UMC-G) family. Using the UMC-G method, a new probability distribution called a univariate modified cosine-Weibull (UMC-Weibull) distribution is studied. We apply the UMC-Weibull distribution for analyzing the wind energy data set taken from the weather station at Sotavento Galicia, Spain. Furthermore, we also introduce a bivariate version of the UMC-G method using the Farlie-Gumble-Morgenstern copula approach. The proposed bivariate distributional method is called a bivariate modified cosine-G (BMC-G) family. A special member of the BMC-G distributions called a bivariate modified cosine-Weibull (BMC-Weibull) distribution is introduced. We apply the BMC-Weibull distribution for analyzing the bivariate data set representing the wind speed and energy taken from the weather station at Sotavento Galicia. Using different statistical tools, we observe that the UMC-Weibull and BMC-Weibull are the best-suited models for analyzing the wind speed and energy data sets.

The unprecedented 2022 extreme summer heatwaves increased harmful cyanobacteria blooms

Heatwaves are increasing and expected to intensify in coming decades with global warming. However, direct evidence and knowledge of the mechanisms of the effects of heatwaves on harmful cyanobacteria blooms are limited and unclear. In 2022, we measured chlorophyll-a (Chla) at 20-s intervals based on a novel ground-based proximal sensing system (GBPSs) in the shallow eutrophic Lake Taihu and combined in situ Chla measurements with meteorological data to explore the impacts of heatwaves on cyanobacterial blooms and the potential relevant mechanisms. We found that three unprecedented summer heatwaves (July 4-15, July 22-August 16, and August 18-23) lasting a total of 44 days were observed with average maximum air temperatures (MATs) of 38.1 ± 1.9 °C, 38.7 ± 1.9 °C, and 40.2 ± 2.1 °C, respectively, and that these heatwaves were characterized by high air temperature, strong PAR, low wind speed and rainfall. The daily Chla significantly increased with increasing MAT and photosynthetically active radiation (PAR) and decreasing wind speed, revealing a clear promotion effect on harmful cyanobacteria blooms from the heatwaves. Moreover, the combined effects of high temperature, strong PAR and low wind, enhanced the stability of the water column, the light availability and the phosphorus release from the sediment which ultimately boosted cyanobacteria blooms. The projected increase in heatwave occurrence under future climate change underscores the urgency of reducing nutrient input to eutrophic lakes to combat cyanobacteria growth and of improving early warning systems to ensure secure water management.

An evaluation of childhood carbon monoxide intoxications in a rural area using the Beaufort wind scale

Climatic changes are known to affect CO intoxications. The purpose of this study was to examine childhood CO intoxications with the Beaufort wind scale (BWS) classification of wind speeds. The demographic data (age and sex) and information concerning the hour, day, and month of presentation to the emergency department for cases diagnosed with CO intoxication over a 7-year period between 2015 and 2021 in the pediatric emergency department of a tertiary training and research hospital in a rural area were examined. Wind speeds (m/s) measured on the days of presentation to the emergency department were recorded. The wind category on the BWS on the day of intoxication was then determined. Four hundred twenty-two patients, with a mean age of 95.12 ± 59.4 (1-215) months, 218 (51.7%) girls and 204 (48.3%) boys were diagnosed with CO intoxication over the 7-year study period. A comparison of wind speeds on the days of presentation to hospital revealed a significantly higher wind speed in 2020 than in the other years (p<0.001). A comparison of the groups in terms of the BWS revealed a significant difference between the years of presentation to hospital (p:0.001). This is the first study to investigate CO intoxications in the pediatric emergency department with the BWS. A significant association was observed between wind speed based on the BWS and childhood CO intoxications. Further studies evaluating wind in the rural setting and CO intoxications are now needed for protection against such intoxications.

Quantifying the influence of transmission path characteristics on urban railway noise

Every ambient noise study employs the source-path-receiver structure to explore the overall behaviour of sound. Noise levels are affected by changes in distance, intervening barriers, and atmospheric conditions along the transmission path between the source and the receiver. The objective of this study is to quantify the influence of transmission path characteristics for a realistic time-varying moving line source. In this context, railway noise was considered to explore the variance of noise in an urban setting over a variety of measuring distances, including 25, 50, 100, and 200 m, with variables such as air temperature, humidity, and wind condition. The data corresponding to 106 trains was collected for analysis, and it was observed that the effect of the wind was more significant for larger distances between the source and the receiver. When the sound levels were measured in two opposite wind directions, a considerable noise level difference was observed. For every 1 m/s increase in wind speed, within a distance of 50 m, the average sound attenuation induced by the upwind phenomena was 0.2 dBA. The impact of air temperature changes on received sound level from a moving source was insignificant within the range of temperatures considered in the study. The effect of humidity is less at shorter distances but at larger distances, increasingly attenuates noise levels. Analysis of variance was performed on the selected variables to determine whether the means of each group were significantly different from each other and found that train speed had a more significant impact on railway noise compared to other parameters.

An extreme wind speed climatology - Atmospheric driver identification using neural networks

Extreme wind speeds are a significant climate risk, potentially endangering human lives, causing damage to infrastructure, affecting maritime and aviation activity, along with the optimal operation of wind energy conversion systems. In this context, accurate knowledge of return levels for various return periods of extreme wind speeds and their atmospheric circulation drivers is essential for effective risk management. In this paper, location-specific extreme wind speed thresholds are identified and return levels of extremes are estimated using the Peaks-Over-Threshold method of the Extreme Value Analysis framework. Furthermore, using an environment-to-circulation approach, the key atmospheric circulation patterns that cause extreme wind speeds are identified. The data used for this analysis are hourly wind speed data, mean sea level pressure and geopotential at 500 hPa from the ERA5 reanalysis dataset, at a horizontal resolution of 0.25° × 0.25°. The thresholds are selected utilizing the Mean Residual Life plots, while the exceedances are modeled with the General Pareto Distribution. The diagnostic metrics exhibit satisfactory goodness-of-fit and the maxima of extreme wind speed return levels are located over marine and coastal areas. The optimal Self-Organizing-Map (2 × 2) is selected using the Davies-Bouldin criterion, and the atmospheric circulation patterns are related to the cyclonic activity in the area. The proposed methodological framework can be applied to other areas, that are endangered by extreme phenomena or in need of accurately assessing the principal drivers of extremes.

Weather associations with physical activity, sedentary behaviour and sleep patterns of Australian adults: a longitudinal study with implications for climate change

BACKGROUND

Weather is a potentially important influence on how time is allocated to sleep, sedentary behaviour and physical activity across the 24-h day. Extremes of weather (very hot, cold, windy or wet) can create undesirable, unsafe outdoor environments for exercise or active transport, impact the comfort of sleeping environments, and increase time indoors. This 13-month prospective cohort study explored associations between weather and 24-h movement behaviour patterns.

METHODS

Three hundred sixty-eight adults (mean age 40.2 years, SD 5.9, 56.8% female) from Adelaide, Australia, wore Fitbit Charge 3 activity trackers 24 h a day for 13 months with minute-by-minute data on sleep, sedentary behaviour, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) collected remotely. Daily weather data included temperature, rainfall, wind, cloud and sunshine. Multi-level mixed-effects linear regression analyses (one model per outcome) were used.

RESULTS

Ninety thousand eight hundred one days of data were analysed. Sleep was negatively associated with minimum temperature (-12 min/day change across minimum temperature range of 31.2 °C, p = 0.001). Sedentary behaviour was positively associated with minimum temperature (+ 12 min/day, range = 31.2 oC, p = 0.006) and wind speed (+ 10 min/day, range = 36.7 km/h, p< 0.001), and negatively associated with sunshine (-17 min/day, range = 13.9 h, p < 0.001). LPA was positively associated with minimum temperature (+ 11 min/day, range = 31.2 °C, p = 0.002), cloud cover (+ 4 min/day, range = 8 eighths, p = 0.008) and sunshine (+ 17 min/day, range = 13.9 h, p < 0.001), and negatively associated with wind speed (-8 min/day, range = 36.7 km/h, p < 0.001). MVPA was positively associated with sunshine (+ 3 min/day, range = 13.9 h, p < 0.001) and negatively associated with minimum temperature (-13 min/day, range = 31.2 oC, p < 0.001), rainfall (-3 min/day, range = 33.2 mm, p = 0.006) and wind speed (-4 min/day, range = 36.7 km/h, p < 0.001). For maximum temperature, a significant (p < 0.05) curvilinear association was observed with sleep (half-U) and physical activity (inverted-U), where the decrease in sleep duration appeared to slow around 23 °C, LPA peaked at 31 oC and MVPA at 27 °C.

CONCLUSIONS

Generally, adults tended to be less active and more sedentary during extremes of weather and sleep less as temperatures rise. These findings have the potential to inform the timing and content of positive movement behaviour messaging and interventions.

TRIAL REGISTRATION

The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

Estimation of wind characteristics at different topographical conditions using doppler remote sensing instrument-a comparative study using optimization algorithm

This study uses novel evolutionary algorithms and computational techniques to analyze wind potential on flat, complex coastal, and offshore sites utilizing mast as well as remote sensing data. The wind data were recorded using remote sensing technique and conventional technique. The optimum Weibull parameters are estimated using nine methods. The genetic algorithm, particle swarm optimization, and TLBO algorithms are compared and evaluated. The goodness of fit test, such as root mean square error test (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and chi-square test (X²), is used to evaluate the accuracy of the selected methods. Parameter estimates are used to compute wind densities. The TLBO and PSO algorithms outperformed genetic algorithms in terms of efficiency. This research compares remote sensing measurements to cup anemometer measurements.

The relationship between PM10 and meteorological variables in the mega city Istanbul

PM₁₀, one of the air pollutants, occurs regularly in İstanbul during the winter months, namely in December, January, and February. PM₁₀ pollutant is affected by numerous factors. Among these factors are various meteorological variables and climatological factors. This article aims to determine the relationship between PM₁₀ and meteorological variables (wind speed, wind direction, temperature, and relative humidity) and to interpret these results. PM₁₀ and meteorological data were examined between 2011 and 2018. To determine the relationship, multiple linear regression, Pearson's correlation coefficient (PCC), Spearman's rank correlation, Kendall Tau correlation, autocorrelation function (ACF), cross-correlation function (CCF), and visuals were determined using the R program (open-air) packages. In the study, the relationship between wind, temperature, and relative humidity with PM₁₀ was determined, and it was observed that the PM₁₀ concentration was maximum between January and February. PM₁₀ concentrations have a positive relationship with relative humidity and wind direction, while a negative relationship with wind speed and temperature was observed. The correlation values for relative humidity and temperature were found to be 0.01 and - 0.15, respectively. Furthermore, the relationship between wind speed and PM₁₀ was calculated from multiple linear regression model, and the estimated value was - 0.12 while looking at the wind direction value, it was approximately 0.03.

The association between wind speed and the risk of injuries among preschool children: New insight from a sentinel-surveillance-based study

BACKGROUND

Injuries among preschool children are an important public health concern worldwide. Significant gaps remain in understanding the potential impact of wind speed on injuries among preschoolers. We aimed to clarify the association and its variation across subgroups to capture the vulnerability features.

METHODS

Using a case-crossover design and conditional logistic regression model, we compared the exposure to wind speed right before the injury events (case period) with that of control periods to determine the excess rate (ER) of injury on each of 0-3 lag days in Guangzhou, 2016-2020. Results were also stratified by sociodemographic characteristics of patients, basic characteristics of injury events, and clinical features of injuries to identify the most vulnerable subgroups of preschoolers.

RESULTS

Higher wind speed was significantly associated with an increased risk of injuries among preschoolers on lag 0, reaching an ER of 2.93 % (95 % confidence interval [CI] = 0.87, 5.03), but not on other lag days. The results of the stratified analyses showed that children under 3-year-old (3.41 %; 95 % CI = 0.36, 6.55), boys (3.66 %; 95 % CI = 1.04, 6.35), and non-locally registered children (3.65; 95 % CI = 0.02, 7.40) were more prone to wind-related injuries. Falls (2.67 %; 95 % CI = 0.11, 5.30) were the main cause of wind-related injuries, and taking transportation was the main activity when injuries occurred (13.16 %; 95 % CI = 4.45, 22.60). Additionally, injuries involving buildings/grounds/obstacles (4.69 %; 95 % CI = 1.66, 7.81) and the occurrence of sprain/strain (7.60 %; 95 % CI = 0.64, 15.04) showed a positive association with wind speed.

CONCLUSIONS

Higher wind speed was associated with a significantly elevated rate of injuries among preschoolers without delayed effects, where children under 3-year-old, boys, and non-locally registered subgroups were more susceptible to wind-related injuries. This study may provide new insights for refining the prevention measures against wind-related injuries among preschoolers.

Onshore wind farms do not affect global wind speeds or patterns

The proportion of global electricity generated by wind is increasing. There are concerns that onshore wind farms may affect local winds and/or patterns, with impacts on local ecosystems. Global-scale evaluations of these impacts are lacking. To investigate this issue, we used TerraClimate and ERA5 datasets covering the years 1980-1999 to judge the impact of onshore wind farms on wind speeds (at 10 m and 100 m elevations) and their distribution patterns. Winds were compared in two periods approximately representing periods without (1980-1999) and with (2001-2020) large-scale wind farms in existence. The TerraClimate dataset shows that 10 m wind speeds decreased at wind farm locations, while the wind speed distribution patterns did not change significantly. However, in the densest wind farm areas, the 10 m wind speeds actually increased. Analysis of the ERA5 data showed no significant changes in 10 m and 100 m wind speeds or distribution patterns at wind farm locations. The influence of wind farms on local and global wind speeds was slight and far less than that of oceanic/atmospheric oscillations. In the long term, the potential for onshore wind farms to reduce global wind speeds or affect their distribution patterns is very small.

Evaluation of suitability of wind speed probability distribution models: a case study from Tamil Nadu, India

The optimal design and performance monitoring of wind farms depend on the precise assessment of spatial and temporal distribution of wind speed. The aim of this research is to investigate the appropriateness of nine popular probability distribution models (exponential, gamma, generalised extreme value, inverse Gaussian, Kumaraswamy, log-logistic, lognormal, Nakagami, and Weibull) for the assessment of wind speed distribution (WSD) at 10 sites situated at topographically distinct locations in Tamil Nadu, India, based on 39 years of data. The results suggest that a single distribution cannot produce best fit for all the stations. On an individual level, the generalised extreme value distribution provided the most suitable fit for majority of the stations, followed by the Kumaraswamy distribution. The Kumaraswamy distribution has performed well even if the WSD of the station is negatively skewed. Hence, based on the ranking and performance consistency, the Kumaraswamy distribution can be preferred irrespective of the topographical heterogeneity of the stations.

Application of a microplastic trap to the determination of the factors controlling the lakebed deposition of microplastics

Microplastics (MPs) in aquatic environments are hard to degrade, easy to transport, and potentially hazardous to biota. Previous studies of MPs in lakes have shown that their deposition is a significant process controlling both their lateral dispersal from a source, and their concentration within the water column. However, the lakebed depositional rates of MPs have predominantly been determined using laboratory experiments and/or through model simulations that may not fully reflect field conditions. In this paper, lacustrine depositional rates in Lake Ulansuhai were documented using an MP trap that allowed for the assessment and quantification of the depositional rates of MPs of differing size, density, and shape at three sampling sites over five different time periods. The results showed that the downward flux for all types of MPs near the lakebed was correlated with wind speed. Higher wind speeds led to the resuspension of greater amounts of MPs in the lakebed sediments and the transport of greater amounts of MPs from the lake inlet to the lake interior and outlet along the hydrologic flow directions. Consequently, higher wind speeds increased the abundance of MPs at the sediment-water interface and intensified the vertical mixing of MPs in the lake water, resulting in a higher depositional flux of MPs. Particles of differing size, shape, and density exhibited different depositional rates. In general, fragmentary, larger size, and higher density MPs were more likely to be deposited. Thus, size and shape have a strong effect on the migration and deposition of HDMPs in Lake Ulansuhai.

Estimation methods for the ratio of medians of three-parameter lognormal distributions containing zero values and their application to wind speed data from northern Thailand

Wind speed has an important impact on the formation and dispersion of fine particulate matter (PM), which can cause several health problems. During the transition from the winter to the summer season in northern Thailand, the wind speed has been low for longer than usual, which has resulted in fine PM accumulating in the air. Motivated by this, we have identified a need to investigate wind speed due to its effect on PM formation and dispersion and to raise awareness among the general public. The hourly windspeed can be approximated by using confidence intervals for the ratio of the medians of three-parameter lognormal distributions containing zero values. Thus, we constructed them by using fiducial, normal approximation, and Bayesian methods. By way of comparison, the performance measures for all ofthe proposed methods (the coverage percentage, lower and upper error probabilities (LEP and UEP,respectively), and expected length) were assessed via Monte Carlo simulation. The results of Monte Carlo simulation studies show that the Bayesian method provided coverage percentages close to the nominal confidence level and shorter intervals than the other methods. Importantly, it maintained a good balance between LEP and UEP even for large variation and percentage of zero-valued observations. To illustrate the efficacy of our proposed methods, we applied them to hourly wind speed data from northern Thailand.

Association between meteorological factors and elderly falls in injury surveillance from 2014 to 2018 in Guangzhou, China

Introduction

With rapid increase in the aging population, falls injuries have become an important public health problem. However, limited data have been reported on the associations between meteorological factors and falls injuries in the elderly. This study assessed the epidemiology of falls injuries and explored this association in the elderly in Guangzhou, China.

Methods

Data on elderly falls injury cases and meteorological variables from 2014 to 2018 in Guangzhou were collected from the Guangzhou Injury Monitoring System and Guangzhou Meteorological Bureau, respectively. The monthly average data on falls injuries and meteorological factors were applied to the data analysis. These correlations were conducted using Pearson correlation analysis. A multiple linear regression model was used to estimate the effects of meteorological factors on falls injuries in the elderly in Guangzhou, China.

Results

Accounting for 49.41% of causes of elderly injury were falls in the Guangzhou Injury Monitoring System from 2014 to 2018, which occupied first place for five consecutive years. The monthly number of elderly falls injury cases was lowest in April and highest in December, and had a positive correlation with monthly mean wind speed (r = 0.187, P < 0.01) and a negative correlation with monthly atmospheric pressure (r = -0.142, P < 0.05). A multiple linear regression model was constructed (F = 10.176, P < 0.01), which explained 23.7% of the variances (R ² = 0.237). Monthly mean wind speed (β = 76.85, P < 0.01) and monthly mean atmospheric pressure (β = -3.162, P < 0.01) were independent factors affecting monthly elderly falls injuries.

Conclusions

Falls are the primary cause of injury among elderly people in Guangzhou, China. Meteorological factors are related to falls injuries in the elderly population. Decreasing activity during high wind and low atmospheric pressure weather may help reduce the number of elderly falls injury cases.

An open design for a low-cost open-loop subsonic wind tunnel for aerodynamic measurement and characterization

A wind tunnel is an essential device for aerodynamic modeling and measurement. High cost and relatively huge size with no open market design hinder the wind tunnel from being widely available in laboratory design for universities and small R&D companies, particularly in a developed country with limited research funding. Thus, most aerodynamic modeling and measurement are done by simulating through computer software which leads to high deviation as the nature of wind is unpredictable. This project aims to provide an open design for a relatively low-cost wind tunnel that universities and R&D companies can quickly adapt. An open design for an open-loop wind tunnel is presented in this article. The proposed wind tunnel design is specifically intended to help the researcher with the aerodynamic measurement with minimum cost for building, customizable design, and reliable measurement. The components, parts, and equipment use the widely available part, which can be obtained across the globe. Validation and characterization are done using software simulation and actual measurement through the device. The proposed design can meet the criteria for aerodynamic measurement and can help the researcher provide a better analysis by combining the actual measurement and software simulation.

Teleconnections between large-scale oceanic-atmospheric patterns and interannual surface wind speed variability across China: Regional and seasonal patterns

Great attention has been paid to the long-term decline in terrestrial near-surface wind speed (SWS) in China. However, how the SWS varies with regions and seasons and what modulates these changes remain unclear. Based on quality-controlled and homogenized terrestrial SWS data from 596 stations, the covarying SWS patterns during the Asian Summer Monsoon (ASM) and the Asian Winter Monsoon (AWM) seasons are defined for China using empirical orthogonal function (EOF) analysis for 1961-2016. The dominant SWS features represented by EOF1 patterns in both seasons show a clear decline over most regions of China. The interannual variability of the EOF1 patterns is closely related to the Northeast Asia Low Pressure (NEALP) and the Arctic Oscillation (AO), respectively. The EOF2 and EOF3 patterns during ASM (AWM) season describe a dipole mode of SWS between East Tibetan Plateau and East China Plain (between East Tibetan Plateau and Northeast China), and between Southeast and Northeast China (between Northeast China and the coastal areas of Southeast China), respectively. These dipole structures of SWS changes are closely linked with the oceanic-atmospheric oscillations on interannual scale.

Historical seasonal changes in prescribed burn windows in California

Prescribed (Rx) burns are conducted on days when the meteorological thresholds of maximum air temperature, relative humidity, and wind speeds are all met (burn window) in order to ensure safe Rx burn practices. Limited burn windows have been consistently identified as one of the most important constraints for conducting Rx burns in California. We investigate whether burn windows across California can be extended from the typical fall season to include other opportune seasons for facilitating specific management objectives. We quantify the seasonal Rx burn efficiencies by assessing the frequency and burned areas using an aggregate of Rx datasets, and we compute the seasonal spatiotemporal trends in the number of days the set of meteorological parameters are met over thirty-five years (1984 to 2019), using the gridMET 4 km dataset. Our results indicate that while fall burns are most frequently executed (40% of the time), the spring (and to a lesser extent winter) seasons yield efficient Rx burns similar to fall because greater acres are being consumed with less burns. In addition, winter and spring seasons experience burn window opportunities (70-90% of the time) over larger areas than the other seasons, and this is predominantly over forested regions in Northern California. Our results also indicate that burn windows in the winter and spring are decreasing at a rate of one day per year over a larger spatial area than that of summer and fall. This decrease is primarily driven by changes in the number of days the relative humidity thresholds are met. Policymakers recognize the critical importance that Rx burns have on a multitude of ecosystem restoration factors, fire behavior dynamics, and firefighter safety. Therefore, there is a need to capitalize on these additional burn windows before these opportunities become less feasible in the future.

Impact of COVID-19 lockdown on the atmospheric boundary layer and instability process over Indian region

The abrupt reduction in the human activities during the first lockdown of the COVID-19 pandemic created unprecedented changes in the background atmospheric conditions. Several studies reported the anthropogenic and air quality changes observed during the lockdown. However, no attempts are made to investigate the lockdown effects on the Atmospheric Boundary Layer (ABL) and background instability processes. In this study, we assess the lockdown impacts on the ABL altitude and instability parameters (Convective Available Potential Energy (CAPE) and Convective Inhibition Energy (CINE)) using WRF model simulations. Results showed a unique footprint of COVID-19 lockdown in all these parameters. Increase in the visibility, surface temperature and wind speed and decrease in relative humidity during the lockdown is noticed. However, these responses are not uniform throughout India and are significant in the inland compared to the coastal regions. The spatial variation of temperature (wind speed) and relative humidity shows an increase and decrease over the Indo Gangetic Plain (IGP) and central parts of India by 20% (100%) and 40%, respectively. Increase (80%) in the ABL altitude is larger over the IGP and central parts of India during lockdown of 2020 compared to similar time period in 2015-2019. This increase is attributed to the stronger insolation due to absence of anthropogenic activity and other background conditions. At the same time, CAPE decreased by 98% in the IGP and central parts of India, where it shows an increase in other parts of India. A prominent strengthening of CINE in the IGP and a weakening elsewhere is also noticed. These changes in CAPE and CINE are mainly attributed to the dearth of saturation in lower troposphere levels, which prevented the development of strong adiabatic ascent during the lockdown. These results provide a comprehensive observation and model-based insight for lockdown induced changes in the meteorological and thermo-dynamical parameters.

Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology

The urban form and extreme microclimate events can have an important impact on the energy performance of buildings, urban comfort and human health. State-of-the-art building energy simulations require information on the urban microclimate, but typically rely on ad-hoc numerical simulations, expensive in-situ measurements, or data from nearby weather stations. As such, they do not account for the full range of possible urban microclimate variability and findings cannot be generalized across urban morphologies. To bridge this knowledge gap, this study proposes two data-driven models to downscale climate variables from the meso to the micro scale in arbitrary urban morphologies, with a focus on extreme climate conditions. The models are based on a feedforward and a deep neural network (NN) architecture, and are trained using results from computational fluid dynamics (CFD) simulations of flow over a series of idealized but representative urban environments, spanning a realistic range of urban morphologies. Both models feature a relatively good agreement with corresponding CFD training data, with a coefficient of determination R² = 0.91 (R² = 0.89) and R² = 0.94 (R² = 0.92) for spatially-distributed wind magnitude and air temperature for the deep NN (feedforward NN). The models generalize well for unseen urban morphologies and mesoscale input data that are within the training bounds in the parameter space, with a R² = 0.74 (R² = 0.69) and R² = 0.81 (R² = 0.74) for wind magnitude and air temperature for the deep NN (feedforward NN). The accuracy and efficiency of the proposed CFD-NN models makes them well suited for the design of climate-resilient buildings at the early design stage.

Short-term wind speed prediction using hybrid machine learning techniques

Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R². The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.

Widespread decline in winds promoted the growth of vegetation

Vegetation dynamics are sensitive to climate change. Wind is an important climate factor that can affect carbon fluxes by altering carbon uptake and emission rates; however, the impact of wind has not been fully considered in previous studies; therefore, exploring the characteristics of vegetation responses to wind speed is crucial to sustainable natural resource utilization and ecological restoration. In this study, the global leaf area index (LAI) from 1984 to 2013 was used to investigate the vegetation spatial heterogeneities, change processes, and relative contributions of climate change. The differences in vegetation responses to climate factors, such as precipitation (PRE), temperature (TEM), and wind speed (WD), were compared by considering the effects of wind. The results revealed that (1) the global vegetation (86.24%) exhibited a greening trend, among which evergreen broad-leaved forests (0.0052 a^-1) changed the most. (2) The wind speed explained 31.54% of the vegetation variations, which is higher than the contribution of other factors. (3) Reduction of wind speed had a positive impact on vegetation changes. The contribution of climate to vegetation growth increased by 8.14% when considering the effects wind speed, particularly in India and South America. Wind speed effects were essential for enhancing the vegetation dynamics assessment and improving the prediction accuracy of the model.

Utilization of GOCI data to evaluate the diurnal vertical migration of Microcystis aeruginosa and the underlying driving factors

Cyanobacterial blooms are one of the most severe ecological problems affecting lakes. The vertical migration of cyanobacteria in the water column increases the uncertainty in the formation and disappearance of blooms, which may be closely associated with light, temperature, and wind speed. However, it is difficult to quantitatively evaluate the influencing factors of cyanobacteria vertical movement in natural environment compared to the laboratory experimental environment. Besides, both field survey and laboratory experiment method have the difficulties in determining the diurnal vertical migration of cyanobacteria at the synoptic lake scale. In this study, based on the diurnal dynamics of cyanobacterial bloom intensity (CBI) observed by the Geostationary Ocean Color Imager (GOCI) from 2011 to 2019, the daily variations, floating rate, and sinking rate of Microcystis aeruginosa were calculated in the natural environment. Then, the effects of light, temperature, and wind speed on the vertical migration of M. aeruginosa were analysed from the perspectives of day, night, and season. The results are as follows: the records of three typical patterns of diurnal CBI exhibited strong seasonal variability from the 9-year statistics; at night, the buoyancy recovery rate of cyanobacterial colonies increased with temperature, so that at temperature >15 °C and wind speed <3 m s^-1, CBI reached the maximum of the whole day at 08:16; the sinking rate of M. aeruginosa was positively correlated with the cumulated light energy at both synoptic and pixel scale; the upward migration speed of M. aeruginosa was positively correlated with the maximum wind speed of the day before cyanobacterial bloom. Therefore, the severer cyanobacterial blooms were often observed by satellite images after strong winds. The analysis of diurnal variation, floating rate, and sinking rate of M. aeruginosa will expand our knowledge for further understanding the formation mechanism of cyanobacterial blooms and for improving the accuracy of model simulation to predict the hourly changes in cyanobacterial blooms in Lake Taihu.

A method for measuring the emissions of in situ agricultural plastic film microplastics by ultraviolet and mechanical abrasion

Agricultural plastic film (APF) is widely used in modern agriculture. Under natural environmental conditions, the structure, surface properties and mechanical properties of APFs change because of sunlight, wind and other factors and gradually break into debris, resulting in the generation of microplastics (MPs). Studies have reported that the MPs concentration in soil is positively correlated with the use intensity and duration of APFs. Unfortunately, to the best of our knowledge, no method to measure the emissions of in situ APFs has been developed. In this study, the effects of mechanical abrasion driven by wind on MPs fragmentation by polyethylene (PE) and polyvinyl chloride (PVC) APFs with the increase of exposure time were investigated. Meanwhile, based on the release rate model of PS fragmented MPs under natural sunlight, a modified model to quantify the effect of ultraviolet (UV) radiation exposure duration on the production of APF fragmented MPs was developed. Based on these models, the amount of MPs produced from APFs in farmland in China was estimated. The national annual MPs mass emissions from APFs in agricultural soil were approximately 5 × 10⁴ to 6.8 × 10⁴ tons in 2018 due to wind and 6.5 × 10³ tons due to sunlight, and the total emission level due to both wind and sunlight was 5.1 × 10⁴ to 7.0 × 10⁴ tons. Compared with that of wind, the contribution of UV radiation to MPs emission is smaller. Our estimates are comparable to data reported in previous studies, indicating that our models have good practical applications and are of great significance for predicting MPs production from APFs in farmland.

An AIAPO MPPT controller based real time adaptive maximum power point tracking technique for wind turbine system

Nowadays, the energy demand is increasing all over the world and conventional energy sources like fossil fuels are gradually emitting less harmful gases (as greenhouse gases). Therefore, the renewable energy (RE) sources are affordable and sustainable, which is essential to increase the demand for power generation. This manuscript proposes a novel Artificial Intelligence Based Adaptive P&O (AIAPO) for real-time adaptive hybrid Maximum Power Point Tracking (MPPT) controller to attain Maximum Power Point (MPP) from the Wind Turbine (WT) system The major objective of the proposed method is "to increase the mathematical calculation of the controller design and eliminate the disadvantage of the conventional MPPT and fuzzy logic (FL) controller". In the proposed method, the optimum perturbation is computed with respect to the variation of WS by FL controller. This optimum perturbation is fed into adaptive P&O technique that is desirable duty-cycle generated for dc-dc power converter using proposed system to achieve the MPP tracking and to enhance the efficiency of the proposed framework. It is estimated that these features can improve the power track by decreasing the steady-state fluctuations of the output power as well as improve the transient performance. Real-time outcomes with novel tracking technique is likened to the existing perturb & observe (P&O), fuzzy logic (FL) depend maximum power point tracking techniques for Wind Turbine Induction Generator (WTIG) system. The proposed algorithm is used to improve the results and to compare the power fluctuations on MPPT with variable wind speed (WS). The statistical analysis of proposed and existing techniques like P&O, FL and SVM are also analyzed. In the proposed method, the best value attains 230.5365, worst value attains 210.5934, mean value attains 230.952 and standard deviation attains 0.05314.

Weather forecasting based on data-driven and physics-informed reservoir computing models

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from

The aim of the SOLETE dataset is to support researchers in the meteorological, solar and wind power forecasting fields. Particularly, co-located wind and solar installations have gained relevance due to the rise of hybrid power plants and systems. The dataset has been recorded in SYSLAB, a laboratory for distributed energy resources located in Denmark. A meteorological station, an 11 kW wind turbine and a 10 kW PV array have been used to record measurements, transferred to a central server. The dataset includes 15 months of measurements from the 1st June 2018 to 1st September 2019 covering: Timestamp, air temperature, relative humidity, pressure, wind speed, wind direction, global horizontal irradiance, plane of array irradiance, and active power recorded from both the wind turbine and the PV inverter. The data was recorded at 1 Hz sampling rate and averaged over 5 min and hourly intervals. In addition, there are three Python source code files accompanying the data file. RunMe.py is a code example for importing the data. MLForecasting.py is a self-contained example on how to use the data to build physics-informed machine learning models for solar PV power forecasting. Functions.py contains utility functions used by the other two.

Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia

The novel coronavirus (COVID-19) outbreak has left a major impact on daily lifestyle and human activities. Many recent studies confirmed that the COVID-19 pandemic has human-to-human transmissibility. Additional studies claimed that other factors affect the viability, transmissibility, and propagation range of COVID-19. The effect of weather factors on the spread of COVID-19 has gained much attention among researchers. The current study investigates the relationship between climate indicators and daily detected COVID-19 cases in Saudi Arabia, focusing on the top five cities with confirmed cases. The examined climate indicators were temperature (°F), dew point (°F), humidity (%), wind speed (mph), and pressure (Hg). Using data from Spring 2020 and 2021, we conducted spatio-temporal correlation, regression, and time series analyses. The results provide preliminary evidence that the COVID-19 pandemic spread in most of the considered cities is significantly correlated with temperature (positive correlation) and pressure (negative correlation). The discrepancies in the results from different cites addressed in this study suggest that non-meteorological factors need to be explored in conjunction with weather attributes in a sufficiently long-term analysis to provide meaningful policy measures for the future.

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

Convergence in critical fuel moisture and fire weather thresholds associated with fire activity in the pyroregions of Mediterranean Europe

Wildfires are becoming an increasing threat to many communities worldwide. There has been substantial progress towards understanding the proximal causes of increased fire activity in recent years at regional and national scales. However, subcontinental scale examinations of the commonalities and differences in the drivers of fire activity across different regions are rare in the Mediterranean zone of the European Union (EUMed). Here, we first develop a new classification of EUMed pyroregions, based on grouping different ecoregions with similar seasonal patterns of burned area. We then examine the thresholds associated with fire activity in response to different drivers related to fuel moisture, surface meteorology and atmospheric stability. We document an overarching role for variation in dead fuel moisture content (FM_d), or its atmospheric proxy of vapor pressure deficit (VPD), as the major driver of fire activity. A proxy for live fuel moisture content (EVI), wind speed (WS) and the Continuous Haines Index (CH) played secondary, albeit important, roles. There were minor differences in the actual threshold values of FM_d (10-12%), EVI (0.29-0.36) and CH (4.9-5.5) associated with the onset of fire activity across pyroregions with peak fire seasons in summer and fall, despite very marked differences in mean annual burned area and fire size range. The average size of fire events increased with the number of drivers exceeding critical thresholds and reaching increasingly extreme values of a driver led to disproportionate increases in the likelihood of a fire becoming a large fire. For instance, the percentage of fires >500 ha increased from 2% to 25% as FM_d changed from the wettest to the driest quantile. Our study is among the first to jointly address the roles of fuel moisture, surface meteorology and atmospheric stability on fire activity in EUMed and provides novel insights on the interactions across fire activity triggers.

Association between coronavirus cases and seasonal climatic variables in Mediterranean European Region, evidence by panel data regression

The coronavirus pandemic is one of the most fast-spreading diseases in the history, and the transmission of this virus has crossed rapidly over the whole world. In this study, we intend to detect the effect of temperature, precipitation, and wind speed on the Coronavirus infected cases throughout climate seasons for the whole year of epidemic starting from February 20, 2020 to February 19, 2021 with considering data patterns of each season separately; winter, spring, summer, autumn, in Mediterranean European regions, whereas those are located at the similar temperature zone in southern Europe. We apply the panel data approach by considering the developed robust estimation of clustered standard error which leads to achieving high forecasting accuracy. The main finding supports that temperature and wind speed have significant influence in reducing the Coronavirus cases at the beginning of this epidemic particularly in the first-winter, spring, and early summer, but they have very weak effects in the autumn and second-winter. Therefore, it is important to take into account the changes throughout seasons, and to consider other indirect factors which influence the virus transmission. This finding could lead to significant contributions to policymakers in European Union and European Commission Environment to limit the Coronavirus transmissions. As the Mediterranean region becomes more crowded for tourism purposes particularly in the summer season.

Effect of wind speed variation on rainfed wheat production evaluated by the CERES-Wheat model

Climate is one of the major factors affecting crop phenology and yield. In most previous studies, impacts of temperature (T) and rainfall (R) on crop development, growth, and yield were investigated, while the effect of wind speed (WS) has so far not been assessed. In this study, the influence of WS alteration on rainfed wheat production was evaluated in arid and semi-arid environments during a 25-year period in northeast Iran. In so doing, various climatic scenarios were defined using T, R, and WS changes, and then applied to the CERES-Wheat model included in DSSAT v4.7.5. The results showed that WS variation can alter total ET (planting to harvest) from -12.1 to +8.9%, aboveground biomass from -8.4 to +11.0%, water use efficiency from -13.4 to +19.7%, and grain yield from -11.2 to +15.3%. These changes were in many cases related to the climatic conditions. It was also revealed that in a greater amount of rainfall and shorter growing season (i.e., less drought stress), the WS variation had the stronger impact on total ET; while for aboveground biomass, water use efficiency, and grain yield, the greatest effect of WS variation was detected under the water scarcity conditions (i.e., low rainfall). The results demonstrate that wind speed needs to be better considered in climate change impact studies, in particular in water-scarce regions.

Climate factors and dengue fever occurrence in Makassar during period of 2011-2017

OBJECTIVE

Dengue fever is a global burden because of high cases number. Climate factors became determinant of the mosquito's growth. This study aimed to analyze the relationship between climate factors (humidity, temperature, wind speed, rainfall) and dengue cases in Makassar during 2011-2017.

METHODS

It was quantitative study located in Makassar. Data were analyzed by General Estimating Equation (GEE). Gee was used to showing the model of variables. This study used secondary data from Health District Office of Makassar to get Dengue Cases Data and Meteorological, Climatological, and Geophysical Agency of Makassar for monthly climate data.

RESULTS

The result showed significant correlation between climate variables that have been researched which were temperature, humidity, rainfall, and wind speed to dengue fever cases.

CONCLUSIONS

As conclusion, the humidity had strongest correlation to dengue fever cases. It also showed positive correlation, while others showed negative correlation.

Interaction between meteorological parameters and COVID-19: an ecological study on 406 authorities of the UK

Understanding the factors affecting COVID-19 transmission is critical in assessing and mitigating the spread of the pandemic. This study investigated the transmissibility and death distribution of COVID-19 and its association with meteorological parameters to study the propagation pattern of COVID-19 in UK regions. We used the reported case and death per capita rate (as of November 13, 2020; before mass vaccination) and long-term meteorological data (temperature, humidity, precipitation, wind speed, and visibility) in 406 UK local authority levels based on publicity available secondary data. We performed correlation and regression analysis between COVID-19 variables and meteorological parameters to find the association between COVID-19 and independent variables. Student's T and Mann-Whitney's tests were used to analyze data. The correlation and regression analyses revealed that temperature, dew point, wind speed, and humidity were the most important factors associated with spread and death of COVID-19 (P <0.05). COVID-19 cases negatively correlated with humidity in areas with high population density, but the inverse in low population density areas. Wind speeds in low visibility areas, which are considered polluted air, may increase the spread of disease (r=0.42, P <0.05) and decrease the spread in high visibility areas (r=-0.16, P <0.05). Among low (T <10°C) and high (T >10°C) temperature areas, the average incidence rates were 2056.86 (95% confidence interval (CI): 1909.49-2204.23) and 1446.76 (95% CI: 1296.71-1596.81). Also, COVID-19 death per capita rates were 81.55 (95% CI: 77.40-85.70) and 69.78 (95% CI: 64.39-75.16) respectively. According to the comprehensive analysis, the spread of disease will be suppressed as the weather warms and humidity and wind speed decrease. Different environmental conditions can increase or decrease spread of the disease due to affecting spread of disease vectors and by altering people's behavior.

Improving the preparedness against an oil spill: Evaluation of the influence of environmental parameters on the operability of unmanned vehicles

When an oil spill occurs, a prompt response reduces significantly the impact. The preparedness and contingency plans are essential to identify the most appropriate technologies. Unmanned and autonomous vehicles (UAVs) is emerging as a powerful tool of strategic potential in the observation, oil tracking and damage assessment of an oil spill. The SpilLess project explored the suitability of these devices to be the first-line response to an oil spill. This work analyses the operational requirements related to environmental parameters following a two steps approach: 1) Environmental characterization from long wind and waves time series and modelling; 2) Definition of the optimal periods for operating each UAVs. We have defined the periods in which each of these facilities acts best, confirming that the operational limits of UAVs are not significantly more restrictive than the traditional operations. UAVs should be included in contingency plans as available tools to fight against oil spills.

Wind and solar resource assessment and prediction using Artificial Neural Network and semi-empirical model: case study of the Colombian Caribbean region

This work is focused on the importance of developing and promoting the use of wind and solar energy resources in the Colombian Caribbean coast. This region has a considerable interest for the development of solar technology due to the available climatic characteristics. Therefore, a detailed solarimetric analysis has been carried out in the department of San Andrés, Providencia and Santa Catalina, located in the Colombian Caribbean region, using a semi-empirical radiation model, based on the Bird & Hulstrom model, and the parameterizations of the Mächler & Iqbal model, which allowed obtaining an average total irradiation value of 6.5 kWh/m²day. In addition, a statistical analysis of the wind resource was carried out based on meteorological data, which yielded an average multiannual wind speed of 3.4 m/s, and a maximum wind speed of 15.2 m/s during the month of October. The meteorological input data used for this analysis were provided by the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM), in order to perform initial calculations and obtain a climatic profile of the areas with clear, medium and cloudy atmospheres throughout the year. Regarding the comparative study, the analysis was complemented with a prediction of solar radiation using Artificial Neural Networks (ANN), where irradiance could be predicted with a fairly good agreement, which was validated with a Root Mean Square Error (RMSE) of 0.87 using the temperature, the relative humidity, the pressure and the wind speed as the input data.

Analysis of some factors related to dust storms occurrence in the Sistan region

Dust storms over the Sistan region in East Iran are associated with predominant northwest winds (called 120-day winds) which promote desertification, including drying of the Hamoun wetlands. These storms are more frequent in spring and summer seasons in the Sistan region. The study aims to examine the relationship between vegetation cover and wind speed with dust storms intensity in order to understand the behavior of dust sources using satellite remote sensing data (AOD) between 2000 and 2019. Based on the time series, the study period can be divided into three parts based on the following characteristics: high dust intensity (2004), moderate relative intensity of value in all parameters studied (2005 to 2014), and dust reduction (2015-2019). Time series analysis shows a negative relationship between AOD and wind speed owing presumably to vegetative cover changes during years that wind speed has increased. Based on multiple regression analysis by monthly time scales that conforms time series result, monthly NDVI is significantly related to AOD. Analysis of the 3 hourly wind data suggests a positive relationship between wind and dust, and effective thresholds for dust erosion based on wind speeds are proposed for the Sistan region.

Local dispersion characteristics of dust in large open-air piles under the action of one-way wind

A large amount of dust particles produced by the wind in an open-air pile is one of the important reasons for air pollution. Studying the law of dust diffusion in local areas is of great significance for the atmospheric particulate control. In this study, a pile of sodium carbonate in a large open-air pile in Weifang, China, is regarded as the research object. The dispersion characteristics of dust particles around the pile under the action of unidirectional wind are studied through wind tunnel test and numerical simulation. The complex atmospheric environment is simplified as unidirectional wind, and the influence of different wind speeds on the dispersion of particles with diverse sizes in the pile is studied. Although a large gap exists between the assumption and the real atmospheric environment, this study provides a reference for the evaluation of the pollution scope of blowing dust and prevention and control of pollution. Results show that a high-concentration range of the dust exists near the pile behind the wind direction and may continue to spread to the height due to the influence of a whirlpool, and the dispersion distance and width can increase with the increase in wind speed. The increase in particle diameter increases the kinetic energy loss of particles for the fluid. Under the same starting speed, the dispersion distance of dust decreases with the increase in particle diameter. With the increase in particle diameter, the dust concentration distribution presents the trend of interior hollowing and high-concentration area fragmenting.

Influence of natural and anthropogenic sound sources on the soundscape of the Cananéia estuary, southeastern Brazil

The aim of this study is to characterize the background noise and abiotic and anthropogenic sound sources in the sector with greater anthropogenic use of the Cananéia estuary. The results show that the relative amplitude of background noise decreased with the increase of frequency range, and was higher with greater number of vessels, wind speed and during flood tide. Weekends and vacation periods were shown to be important dates during which background noise increased in the region. The influence of the tide and the wind speed on the relative amplitude was dependent on the frequency range analyzed. Therefore, both abiotic and anthropogenic sound sources were observed to be important factors regarding an increase in background noise in Cananéia. The importance of the continuity of vessel regulation in the region and of future studies that identify whether such noises alter parameters from the sound repertoire of the cetacean species in the region.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.

Association between meteorological factors and daily new cases of COVID-19 in 188 countries: A time series analysis

By 31 December 2020, Coronavirus disease 2019 (COVID-19) had been prevalent worldwide for one year, and most countries had experienced a complete seasonal cycle. The role of the climate and environment are essential factors to consider in transmission. We explored the association between global meteorological conditions (including mean temperature, wind speed, relative humidity and diurnal temperature range) and new cases of COVID-19 in the whole past year. We assessed the relative risk of meteorological factors to the onset of COVID-19 by using generalized additive models (GAM) and further analyzed the hysteresis effects of meteorological factors using the Distributed Lag Nonlinear Model (DLNM). Our findings revealed that the mean temperature, wind speed and relative humidity were negatively correlated with daily new cases of COVID-19, and the diurnal temperature range was positively correlated with daily new cases of COVID-19. These relationships were more apparent when the temperature and relative humidity were lower than their average value (21.07°Cand 66.83%). The wind speed and diurnal temperature range were higher than the average value(3.07 m/s and 9.53 °C). The maximum RR of mean temperature was 1.30 under -23°C at lag ten days, the minimum RR of wind speed was 0.29 under 12m/s at lag 24 days, the maximum RR of range of temperature was 2.21 under 28 °C at lag 24 days, the maximum RR of relative humidity was 1.35 under 4% at lag 0 days. After a subgroup analysis of the countries included in the study, the results were still robust. As the Northern Hemisphere enters winter, the risk of global covid-19 remains high. Some countries have ushered in a new round of COVID-19 epidemic. Thus, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19 in winter.

Effects of ambient conditions on solar assisted heat pump systems: a review

Solar assisted heat pump (SAHP) systems aim to increase the performance of heat pumps by supporting with solar energy using various heating modes, whose performances depend on ambient conditions. In the literature, SAHP systems are classified by structure types, and the heating mode options are not considered as the main priority of the design but a result of the structure of the system. This paper aimed to investigate the effect of ambient conditions on heating modes, and identify the preferable ambient condition ranges for each SAHP system depends on their heating modes, by using a narrative review of 47 recent studies, that shed light on the problem. For this purpose, direct solar heating (DSH), air source heat pump (ASHP), solar source heat pump (SSHP) and solar-air source heat pump (S/ASHP) modes were stated as the basic heating modes of SAHP systems. In accordance with the literature, SAHP systems were classified as direct expansion solar assisted heat pump (DX-SAHP) and series, parallel and dual source indirect expansion solar assisted heat pumps (IDX-SAHP). Solar irradiation, ambient temperature, relative humidity and wind speed has been taken as major ambient conditions to investigate. Reviewed studies indicated that, parallel and series IDX-SAHP are preferable in high solar irradiations about 800 W/m² with their DSH mode options. Frosting on evaporator is prevented in DX-SAHP with its S/ASHP mode, and even in the presence of frosting, unlike IDX-SAHPs ASHP mode, freezing is a factor that improves performance on flat evaporator in cold and humid conditions. This study indicates that there are obscure areas for future studies to focus on for a better comparison between SAHP types. Moreover, proposed novel designs of this paper, such as solar preheating of air in IDX-SAHP systems to add S/ASHP mode as an option, might enhance the performance and applicability of SAHP systems.

Spatial characteristics of the PM2.5/PM10 ratio and its indicative significance regarding air pollution in Hebei Province, China

Particulate matter (PM) is the primary air pollutant in northern China. The PM_2.5/PM₁₀ ratio has been used increasingly as an indicator to reflect anthropogenic PM pollution, but its advantages compared with individual PM_2.5 or PM₁₀ concentrations have not been proven sufficiently by experimental data. By dividing Hebei Province (China) into seven natural ecological regions, this study investigated the spatial characteristics of the PM_2.5/PM₁₀ ratio and its relationships with PM_2.5, PM₁₀, economic density, and wind speed. Results showed that the PM_2.5/PM₁₀ ratio decreased from east to west and from south to north, with an annual average value in 2019 of 0.439-0.559. The characteristics of the spatial variation of the PM_2.5/PM₁₀ ratio were different to those of either PM_2.5 or PM₁₀ concentration, indicating that PM pollution reflected by the PM_2.5/PM₁₀ ratio is not entirely consistent with that by PM_2.5 and PM₁₀ concentrations. In comparison with PM_2.5 or PM₁₀ concentration, the PM_2.5/PM₁₀ ratio had higher (lower) correlation with economic density (wind speed), indicating that the PM_2.5/PM₁₀ ratio is a better indicator used to reflect the intensity of anthropogenic emissions of PM pollutants. According to the characteristics of the spatial variations of the PM_2.5/PM₁₀ ratio and the PM_2.5 and PM₁₀ concentrations, the seven ecological regions of Hebei Province were categorized into four different types of atmospheric PM pollution: "three low regions," "three high regions," "one high and two low regions," and "one low and two high regions." This reflects the comprehensive effect of the intensity of anthropogenic PM emissions and the atmospheric diffusion conditions.

Wind speed pattern data and wind energy potential in Pakistan: current status, challenging platforms and innovative prospects

Energy is an essential parameter for the economic growth and sustainable development of any country. Due to the rapid increase in energy demand, depletion of fossil fuels and environmental concerns, many developing and developed countries are moving towards alternative renewable resources such as solar energy, wind energy and biomass. Wind energy as a renewable energy source is gaining a lot of significant attention. Wind energy is a sustainable solution to produce energy having potential benefits such as clean source, reduced toxic gases emission and environmental friendly protocol for operation. Pakistan is among the top countries facing the worst energy crisis due to different political and financial issues. Pakistan is blessed with a huge potential of wind energy having all the basic requirements such as windy regions and good wind speed for harnessing energy. Pakistan can utilize the potential of wind energy to reduce the problem of energy outrage in the country and also take steps towards green economy from conventional fuel economy. This critical review highlights the current status, potential and the steps taken in the past and present to overcome the energy shortage in Pakistan by employing wind energy. Outlook on wind speed data, deployment of wind energy, environmental effect of wind energy and its barriers in the adoption are discussed with recommendations and suggestions to utilize this clean energy in an effective way. Graphical abstract.

Influences of weather-related parameters on the spread of Covid-19 pandemic - The scenario of Bangladesh

OBJECTIVE

Weather parameters such as temperature, humidity, air quality index and wind speed are the important factors influencing the infectious diseases like Covid-19. Therefore, this study aims to discuss and analyse the relation between weather parameters and the spread of Coronavirus disease (Covid-19) from the perspective of Bangladesh.

METHODS

Correlation among weather parameters and infection and death rate were established using several graphical plots and wind rose diagrams, Kendall and Spearman correlation and appropriate discussion with relevancy and reference. Information presented in this study has been extracted from 1st April 2020 to 30th December 2020.

RESULTS

Analyses show that with the decrease in temperature, infection rate increased significantly. Also, the number of infection increases as wind speed increases. As the absolute humidity rate of Bangladesh is almost constant; therefore, the authors are unable to predict any relation of absolute humidity with the number of infection. Further, the prediction for the number of infections based on the wind direction for the several regions of seven divisions in Bangladesh is vulnerable for the upcoming several months.

CONCLUSION

This study has analysed the dependency of weather parameters on a number of infections along with predicting the upcoming danger zones.