Cross-scale assessments of the impacts and resilience of subtropical montane cloud forests to chronic seasonal droughts and episodic typhoons.

Montane cloud forests (MCFs) are ecosystems frequently immersed in fog and are vital for the terrestrial hydrological cycle and biodiversity hotspots. However, the potential impacts of climate change, particularly intensified droughts and typhoons, on the persistence of ecosystems remain unclear. Our study conducted cross-scale assessments using 6-year (2016-2021) ground litterfall and 21-year (2001-2021) satellite greenness data (the Enhanced Vegetation Index [EVI] and the EVI anomaly change [ΔEVI% ]), gross primary productivity anomaly change (ΔGPP% ), and meteorological variables (the standardized precipitation index [SPI] and wind speed). We found a positive correlation between summer EVI and ΔGPP% with the SPI-3 (3-month time scale), while winter litterfall showed a negative correlation. Maximum typhoon daily wind speed was negatively correlated with summer and the monthly ΔEVI% and ΔGPP% . These findings suggest vegetation damage and productivity loss were related to drought and typhoon intensities. Furthermore, our analysis highlighted that chronic seasonal droughts had more pronounced impacts on MCFs than severe typhoons, implying that high precipitation and frequent fog immersion do not necessarily mitigate the ramifications of water deficit on MCFs but might render MCFs more sensitive and vulnerable to drought. A significant negative correlation between the summer and winter ΔEVI% and ΔGPP% of the same year, suggesting disturbance severity during summer may facilitate vegetation regrowth and carbon accumulation in the subsequent winter. This finding may be attributed to the ecological resilience of MCFs, which enables them to recover from the previous summer. In the long-term, our results indicated an increase in vegetation resilience over two decades in MCFs, likely driven by rising temperatures and elevated carbon dioxide levels. However, the enhancement of resilience might be overshadowed by the potential intensified droughts and typhoons in the future, potentially causing severe damage and insufficient recovery times for MCFs, thus raising concerns about uncertainties regarding their sustained resilience.

Measurement of microclimates in a warming world: problems and solutions.

As the world warms, it will be tempting to relate the biological responses of terrestrial animals to air temperature. But air temperature typically plays a lesser role in the heat exchange of those animals than does radiant heat. Under radiant load, animals can gain heat even when body surface temperature exceeds air temperature. However, animals can buffer the impacts of radiant heat exposure: burrows and other refuges may block solar radiant heat fully, but trees and agricultural shelters provide only partial relief. For animals that can do so effectively, evaporative cooling will be used to dissipate body heat. Evaporative cooling is dependent directly on the water vapour pressure difference between the body surface and immediate surroundings, but only indirectly on relative humidity. High relative humidity at high air temperature implies a high water vapour pressure, but evaporation into air with 100% relative humidity is not impossible. Evaporation is enhanced by wind, but the wind speed reported by meteorological services is not that experienced by animals; instead, the wind, air temperature, humidity and radiation experienced is that of the animal's microclimate. In this Commentary, we discuss how microclimate should be quantified to ensure accurate assessment of an animal's thermal environment. We propose that the microclimate metric of dry heat load to which the biological responses of animals should be related is black-globe temperature measured on or near the animal, and not air temperature. Finally, when analysing those responses, the metric of humidity should be water vapour pressure, not relative humidity.

Sea spray aerosol concentration modulated by sea surface temperature.

Natural aerosols in pristine regions form the baseline used to evaluate the impact of anthropogenic aerosols on climate. Sea spray aerosol (SSA) is a major component of natural aerosols. Despite its importance, the abundance of SSA is poorly constrained. It is generally accepted that wind-driven wave breaking is the principle governing SSA production. This mechanism alone, however, is insufficient to explain the variability of SSA concentration at given wind speed. The role of other parameters, such as sea surface temperature (SST), remains controversial. Here, we show that higher SST promotes SSA mass generation at a wide range of wind speed levels over the remote Pacific and Atlantic Oceans, in addition to demonstrating the wind-driven SSA production mechanism. The results are from a global scale dataset of airborne SSA measurements at 150 to 200 m above the ocean surface during the NASA Atmospheric Tomography Mission. Statistical analysis suggests that accounting for SST greatly enhances the predictability of the observed SSA concentration compared to using wind speed alone. Our results support implementing SST into SSA source functions in global models to better understand the atmospheric burdens of SSA.

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.

Study on the Improvement of Droplet Penetration Effect by Nozzle Tilt Angle under the Influence of Orthogonal Side Wind.

This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 µL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations.

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions.

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

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).

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.

Back-Projected Signal-Based Self-Interferometric Phase Analysis Technique for Sea Surface Observation Using a Single Scatterometer System.

This manuscript presents a self-interferometric phase analysis technique for sea surface observation using a single scatterometer system. The self-interferometric phase is proposed to complement the imprecise analysis results due to the very meager signal strength measured at a high incident angle of more than 30°, which is a vulnerability of the existing analysis method using the Doppler frequency based on the backscattered signal strength. Moreover, compared to conventional interferometry, it is characterized by the phase-based analysis using consecutive signals from a single scatterometer system without any auxiliary system or channel. To apply the interferometric signal process on the moving sea surface observation, it is necessary to secure a reference target; however, this is hard to solve in practice. Hence, we adopted the back-projection algorithm to project the radar signals onto a fixed reference position above the sea surface, where the theoretical model for extracting the self-interferometric phase was derived from the radar-received signal model applying the back-projection algorithm. The observation performance of the proposed method was verified using the raw data collected at the Ieodo Ocean Research Station in Republic of Korea. In the observation result for wind velocity at the high incident angles of 40° and 50°, the self-interferometric phase analysis technique shows a better performance of a correlation coefficient of more than about 0.779 and an RMSE (root-mean-square error) of about 1.69 m/s compared to the existing method of a correlation coefficient of less than 0.62 and RMSE of more than 2.46 m/s.

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution.

Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD-GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD-GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.

Scalable Lightweight IoT-Based Smart Weather Measurement System.

The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost weather station, where human activities can be significantly affected by enhancing the production of clean energy based on the known direction of the wind. Meanwhile, common weather stations are neither affordable nor customizable for specific applications. Moreover, due to weather forecast changes over time and location within the same city, it is not efficient to rely on a limited number of weather stations that may be located far away from a recipient's location. Therefore, in this paper, we focus on presenting a low-cost weather station that relies on an artificial intelligence (AI) algorithm that can be distributed across a WTEG area with minimal cost. The proposed study measures multiple weather parameters, such as wind direction, wind velocity (WV), temperature, pressure, mean sea level, and relative humidity to provide current measurements to recipients and AI-based forecasts. In addition, the proposed study consists of several heterogeneous nodes and a controller for each station in a target area. The collected data can be transmitted through Bluetooth low energy (BLE). The experimental results reveal that the proposed study matches the standard of the National Meteorological Center (NMC), with a nowcast measurement of 95% accuracy for WV and 92% for wind direction (WD).

The Application of a Self-Made Integrated Three-in-One Microsensor and Commercially Available Wind Speed Sensor to the Cold Air Pipe of the Heating, Ventilation, and Air Conditioning in a Factory for Real-Time Wireless Measurement.

In this study, the integrated three-in-one (temperature, humidity, and wind speed) microsensor was made through the technology of the Micro-electro-mechanical Systems (MEMS) to measure three important physical quantities of the internal environment of the cold air pipe of the Heating, Ventilation and Air Conditioning (HVAC) in the factory, plan the installation positions of the integrated three-in-one microsensor and commercially available wind speed sensor required by the internal environment of the cold air pipe, and conduct the actual 310-h long term test and comparison. In the experiment, it was also observed that the self-made micro wind speed sensor had higher stability compared to the commercially available wind speed sensor (FS7.0.1L.195). The self-made micro wind speed sensor has a variation range of ±200 mm/s, while the commercially available wind speed sensor a variation range of ±1000 mm/s. The commercially available wind speed sensor (FS7.0.1L.195) can only measure the wind speed; however, the self-made integrated three-in-one microsensor can conduct real-time measurements of temperature and humidity according to the environment at that time, and use different calibration curves to know the wind speed. As a result, it is more accurate and less costly than commercially available wind speed sensors. The material cost of self-made integrated three-in-one microsensor includes chemicals, equipment usage fees, and wires. In the future, factories may install a large number of self-made integrated three-in-one microsensors in place of commercially available wind speed sensors. Through real-time wireless measurements, the self-made integrated three-in-one microsensors can achieve the control optimization of the HVAC cold air pipe's internal environment to improve the quality of manufactured materials.

Distribution of Phlebotomus argentipes Annandale & Brunetti, 1908 in the Anuradhapura district, North Central Sri Lanka.

Background & objectives: Phlebotomus argentipes Annandale & Brunetti, 1908 (Diptera: Psychodidae) is the main vector responsible for the transmission of Leishmania donovani (Laveran & Mesnil, 1903) Ross, 1903 in the subcontinent of India. It is the potential vector of cutaneous leishmaniasis in Sri Lanka. The present study determined ecological factors that influence the abundance of P. argentipes in areas with high disease prevalence in the Anuradhapura district, North Central Sri Lanka. Methods: CDC light traps and yellow sticky traps were used for sampling, and abundance was recorded throughout 12 months with selected environmental parameters namely, relative humidity, wind speed, and temperature. The relationships between the abundance of P. argentipes with mean temperature, % relative humidity, and wind speed were tested with regression analysis. The temporal distribution of the vector population was tested with a time series analysis. Results: The study identified the most preferable microhabitats of P. argentipes: shrubs, unclear areas, gardening areas, wet soil areas with leaf litter, and termite hills. The results indicated that the abundance of P. argentipes was highly dependent on mean temperature (P = 0.00, R2 = 68%), and a high number of P. argentipes was recorded for a low mean temperature range of 24.7-27.3°C. Furthermore, the abundance of P. argentipes exhibited an increasing trend with high humidity levels of 72-88% (P = 0.00, R2 = 91.6%). Interpretation & conclusion: These findings may help predict the temporal variation of the potential vector population with studied ecological parameters and contribute to a successful vector management strategy with thorough knowledge of the behavioral pattern of P. argentipes.

Experimental investigation of the effect of surgical masks on outdoor thermal comfort in Xiamen, China.

The COVID-19 pandemic has significantly changed people's lifestyles, and wearing surgical masks in outdoor public spaces has become commonplace. However, few studies have explored the impact of wearing masks on outdoor thermal comfort in different seasons. From May 2021 to February 2022, a series of longitudinal experiments were conducted in Xiamen, China to examine the effect of wearing surgical masks on outdoor thermal comfort. Forty-two participants took part in the experiments with and without masks. During the experiments, the thermal perceptions of the subjects and environmental thermal parameters were collected. Differences in outdoor thermal comfort between subjects wearing masks and those not wearing masks were determined in summer, autumn, and winter. Results showed that 1) the subjects wearing masks had lower neutral temperatures, and this difference was particularly pronounced in summer and exacerbated by walking; 2) in warm environments, masks reduced thermal comfort, and discomfort associated with masks was worse when walking than when sitting; 3) wearing masks significantly worsened facial comfort and increased chest discomfort, as summer turned to winter, the impact of masks on facial comfort decreased; 4) radiation and air temperature were the environmental parameters with the greatest impact on outdoor thermal sensation. Subjects who wore masks preferred lower temperatures, radiation, and humidity, and higher wind speeds.

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.

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

PM10, one of the air pollutants, occurs regularly in Istanbul 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.

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.

Understanding the Potential Drivers for Respiratory Syncytial Virus Rebound During the Coronavirus Disease 2019 Pandemic.

Nonpharmaceutical interventions (NPIs) were widely introduced to combat the coronavirus disease 2019 (COVID-19) pandemic. These interventions also likely led to substantially reduced activity of respiratory syncytial virus (RSV). From late 2020, some countries observed out-of-season RSV epidemics. Here, we analyzed the role of NPIs, population mobility, climate, and severe acute respiratory syndrome coronavirus 2 circulation in RSV rebound through a time-to-event analysis across 18 countries. Full (re)opening of schools was associated with an increased risk for RSV rebound (hazard ratio [HR], 23.29 [95% confidence interval {CI}, 1.09-495.84]); every 5°C increase in temperature was associated with a decreased risk (HR, 0.63 [95% CI, .40-.99]). There was an increasing trend in the risk for RSV rebound over time, highlighting the role of increased population susceptibility. No other factors were found to be statistically significant. Further analysis suggests that increasing population susceptibility and full (re)opening of schools could both override the countereffect of high temperatures, which explains the out-of-season RSV epidemics during the COVID-19 pandemic.