Sunshine Duration Research Articles

Intraday analyses on weather-induced sentiment and stock market behavior

We examine the intraday relationship between weather conditions and investor sentiment in the Korean equity market. By uncovering the intermediary role of weather-induced sentiment in affecting market variables, we suggest a potential link between weather conditions, sentiment, and market dynamics. High temperatures, humidity, and cloud coverage negatively affect investors’ moods, while strong winds and long sunshine durations have a positive impact. Although only cloud coverage marginally impacts stock returns, weather factors significantly affect market turnover, volatility, and illiquidity, especially when they alter sentiment dynamics.

Atlantyk Północny a klimat Europy. Mechanizmy wpływu. Część 1

The work discusses the mechanisms operating within the climate system leading to variability and changes in the atmospheric climate over Europe. The first part discusses the operation of the main factor regulating the variability of atmospheric circulation, which is the North Atlantic thermohaline circulation (NA THC). The variability of NA THC, through its influence on the shaping of the mid-tropospheric circulation and the distribution of geopotential heights, determines the variability of the lower circulation, and thus the variability of climatic elements over Poland and Europe. The analysis showed that the variability of NA THC largely explains the variability of sunshine duration, air temperature and relative humidity, but does not satisfactorily explain the variability of other climatic elements.

Spatiotemporal changes in tourism climate comfort in Sichuan Province from 1961 to 2022

AbstractBased on the daily temperature, wind speed, relative humidity, sunshine hours and precipitation in Sichuan Province from 1961 to 2022, the spatiotemporal variation characteristics of tourism climate comfort in Sichuan Province were analysed using the temperature–humidity index (THI), precipitation index (P), sunshine duration index (SSD), wind chill index (WCI) and tourism climate index (TCI). The results show that the average annual TCI in Sichuan Province over the past 62 years is “acceptable” or above and shows a significant increasing trend at a rate of 0.018 per decade. On average, the best travel comfort period in Sichuan Province is from March to May and from October to November. The TCI increased significantly in cold seasons such as February and November with rates of 0.064 and 0.053 per decade, respectively. The increase in TCI is mainly controlled by the increase in THI and WCI, both of which are related to the increase in temperature, with a greater effect in cold seasons and at higher elevations. Most of China's 5A‐level tourist attractions in Sichuan Province (14 of 17) have an annual TCI of “acceptable” or better. Similarly, the top 3 months for climate comfort at these attractions tend to be March to May and October, with ratings reaching “good” or even “very good.” For the other three destinations, all characterized by glaciers and mountains, the top 3 months for climate comfort are concentrated in the warm months. The overall tourism climate comfort of Sichuan Province is increasing, and the climate is changing in a positive way for the tourism industry.

Climate resilience in aquaculture: A cluster analysis of Chinese coastal regional production and farming practices in the face of climatic shifts 2017–2021

This study conducts a comprehensive analysis of the aquaculture industry across 11 coastal regions in eastern China from 2017 to 2021 to assess their adaptability and resilience in the face of climate change. Cluster analysis was employed to examine regional variations in aquaculture adaptation by analyzing data on annual average temperatures, annual extreme high/low temperatures, annual average relative humidity, annual sunshine duration, and total yearly precipitation alongside various aquaculture practices. The findings reveal that southern regions, such as Fujian and Guangdong, demonstrate higher adaptability and resilience due to their stable subtropical climates and advanced aquaculture technologies. In contrast, northern regions like Liaoning and Shandong, characterized by more significant climatic fluctuations, exhibit varying degrees of cluster changes, indicating a continuous need to adjust aquaculture strategies to cope with climatic challenges. Additionally, the study explores the specific impacts of climate change on species selection, disease management, and water resource utilization in aquaculture, emphasizing the importance of developing region-specific strategies. Based on these insights, several strategic recommendations are proposed, including promoting species diversification, enhancing disease monitoring and control, improving water quality management techniques, and urging governmental support for policies and technical guidance to enhance the climate resilience and sustainability of the aquaculture sector. These strategies and recommendations aim to assist the aquaculture industry in addressing future climate challenges and fostering long-term sustainable development.

Characteristics, Impact Factors and Potential Sources of PM2.5 Pollution for Hainan Island

Based on the environmental monitoring data and meteorological observational data in Hainan Island from 2015 to 2021, the PM2.5-polluted characteristics, influencing factors, and potential contributing regions were analyzed using the backward trajectory simulation, cluster analysis, potential source analysis function （PSCF）, and concentration weight trajectory （CWT） methods. The results showed that PM2.5 in Hainan Island had an obvious seasonal variation, with the highest in winter （22.6 μg·m-3）, followed by that in autumn and spring （17.38 and 16.53 μg·m-3, respectively）, with the lowest in summer （9.79 μg·m-3）. In the past seven years, there were 30 days in Hainan Island in which PM2.5 concentration exceeded the standard. The annual average and four seasons of PM2.5 showed a significant downward trend, and the climatic change rates were -0.97 （annual mean）, -1.09 （spring）, -0.61 （summer）, -0.83 （autumn）, and -1.25 （winter） μg·（m3·a）-1. PM2.5 in Hainan Island was highly correlated with gaseous pollutants, with correlation coefficients of 0.471 （SO2）, 0.633 （NO2）, 0.479 （CO）, and 0.773 （O3-8h）, all passing a significance level of 0.01. PM2.5 was positively correlated with average wind speed and atmospheric pressure and negatively correlated with precipitation, relative humidity, sunshine duration, average temperature, and total solar radiation. Among them, average temperature, relative humidity, and total solar radiation were the main dominant meteorological factors on PM2.5 in Hainan Island. Backward trajectory and potential source analysis revealed that PM2.5 concentration was high （≥20 μg·m-3） in winter and autumn, which was influenced by airflow from inland regions, and Fujian, Zhejiang, Hunan, Jiangxi, Guangdong, and Guangxi provinces were the main potential sources of PM2.5 in Hainan Island.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Machine learning models for daily net radiation prediction across different climatic zones of China

Net radiation (Rn), a critical component in land surface energy cycling, is calculated as the difference between net shortwave radiation and longwave radiation at the Earth’s surface and holds significant importance in crop models for precision agriculture management. In this study, we examined the performance of four machine learning models, including extreme learning machine (ELM), hybrid artificial neural networks with genetic algorithm models (GANN), generalized regression neural networks (GRNN), and random forests (RF), in estimating daily Rn at four representative sites across different climatic zones of China. The input variables included common meteorological factors such as minimum and maximum temperature, relative humidity, sunshine duration, and shortwave solar radiation. Model performance was assessed and compared using statistical parameters such as the correlation coefficient (R2), root mean square errors (RMSE), mean absolute errors (MAE), and Nash–Sutcliffe coefficient (NS). The results indicated that all models slightly underestimated actual Rn, with linear regression slopes ranging from 0.810 to 0.870 across different zones. The estimated Rn was found to be comparable to observed values in terms of data distribution characteristics. Among the models, the ELM and GANN demonstrated higher consistency with observed values, exhibiting R2 values ranging from 0.838 to 0.963 and 0.836 to 0.963, respectively, across varying climatic zones. These values surpassed those of the RF (0.809–0.959) and GRNN (0.812–0.949) models. Additionally, the ELM and GANN models showed smaller simulation errors in terms of RMSE, MAE, and NS across the four climatic zones compared to the RF and GRNN models. Overall, the ELM and GANN models outperformed the RF and GRNN models. Notably, the ELM model's faster computational speed makes it a strong recommendation for Rn estimates across different climatic zones of China.

Seasonal Incidence of Scirtothrips dorsalis and Bemisia tabaci on Chilli with Studies on Comparative Efficacy of Insecticides and Botanical Oils

The study conducted at Sam Higginbottom University of Technology and Sciences in Prayagraj during the 2019 and 2020 kharif seasons provided comprehensive insights into insect population dynamics and insecticide efficacy against thrips and whiteflies in chilli cultivation. Key findings revealed significant pest population fluctuations, with thrips peaking at 16 per plant during the 43rd Standard Meteorological Week (SMW) in late October for both years. Whitefly populations reached up to 17.6 per plant, peaking during the 44th week in 2019 and the 43rd week in 2020, both in late October. Correlation analyses showed significant negative relationships between insect populations and temperature, wind velocity, and rainfall, while a positive correlation was observed with sunshine duration. Multiple linear regression models demonstrated that weather parameters collectively explained 69.8% and 70.4% of thrips population variability in 2019 and 2020, respectively, and 87.7% and 17.2% of whitefly population variability. Stepwise regression models revealed that in 2019, maximum temperature and rainfall jointly accounted for 46% of pest population variability. In 2020, the addition of wind velocity to these factors explained 83% and 56% of variability in thrips and whitefly populations, respectively. Insecticide efficacy evaluation showed acetamiprid as most effective against thrips (92.14% reduction) and imidacloprid against whiteflies (88.89% reduction). Thiamethoxam and imidacloprid demonstrated comparable effectiveness. Economic analysis highlighted imidacloprid's superiority, providing effective pest control, the highest chili yield, and the best cost-benefit ratios (1:8.30 in 2019 and 1:5.38 in 2020).

The Mutual Effects of Pinching and Promalin® on Root Architecture in Two Boxwood Species

Boxwoods are constantly pinched to shape and form hedges, and Promalin® application is used to boost their shoot yield. Having good root development is crucial for the healthy development of plants; however, the effects of such applications on plant root development have generally been ignored in previous studies. Hence, this study evaluated the effects of applications on the root growth of two boxwood species (Buxus sempervirens L. and Buxus balearica Lam.) during the summer dormancy period. Pinching and Promalin® (0, 1 000, 2 000, and 4 000 ppm) were applied to one-year-old seedlings at three different times. Results showed that pinching had a negative effect on root development in both species and both years. Root development was lower in pinched plants than in control plants, which were not pinched. Promalin® at 2 000 ppm was found to be the most effective application on the root parameter. Root development was also higher in the second year than in the first year, depending on temperature, humidity, and the duration of sunshine. It is recommended to apply 2 000 ppm of Promalin® and not prune B. sempervirens. However, if pinching is to be performed, application of Promalin® will be useful to improve root development. For B. balearica, applying pinching and Promalin® together will help increase root development depending on the application time (in periods when the temperature is high).

Relationship Between Environmental Meteorological Factors and the Incidence of Epistaxis in Different Age Groups in Yangzhou.

Objective: To investigate the relationship between environmental and meteorological factors and the incidence of epistaxis in different age groups in Yangzhou, as well as to provide a reference and theoretical basis for epistaxis prevention and treatment. Methods: The patients with epistaxis who were treated in Northern Jiangsu People's Hospital of Jiangsu Province from January 2016 to December 2020 were reviewed, and the relationship between the local environmental meteorological factors at the time of onset and the incidence of epistaxis in different age groups was analyzed, and the potential environmental meteorological risk factors of epistaxis in each age group were determined by Stepwise logistic regression. Results: From 2016 to 2020, there were 24,407 cases of epistaxis, mostly males and children. The effects of O3 concentration, average humidity, average temperature, NO2 concentration, sunshine duration, average wind speed, CO concentration, and temperature difference on the study population were statistically significant (P < .05). Analysis by age group showed that there were differences in environmental and meteorological factors related to epistaxis in different age groups. Conclusions: In Yangzhou, epistaxis is more prevalent among males and children. The environmental meteorological factors are related to the incidence of epistaxis in Yangzhou, among which the average humidity and temperature difference are negatively correlated with the incidence of epistaxis. In contrast O3 concentration, average temperature, NO2 concentration, sunshine duration, average wind speed, and CO concentration are positively correlated with epistaxis occurrence. However, the impact of these environmental and meteorological factors varies in different age groups.

Empowering the solar energy landscape: The techno-economic analysis of grid-connected PV power plants in Uganda

Solar PV power is still under-utilized despite the abundance of solar radiation in Uganda. There is need for empowering renewable energy landscape through unlocking the technical and economic feasibility of solar photovoltaic power. We analyzed data from 56 locations for the techno-economic and environmental assessment of photovoltaic power facilities in Uganda. This was based on weather data availability and accessibility to the national power grid. Analysis of the energy generation and different input factors was done using PVsyst 7.2. A three stage approach to losses was adopted: absorption of sunlight, conversion to DC and DC to AC conversion. Findings indicate that most of the countryside is suitable for construction of large scale grid-connected photovoltaic power facilities. Due to longer sunshine duration and stronger Global Horizontal Irradiance (GHI) which are associated with high energy yield, northern Uganda performed better than the rest of the country, making it a preferential siting for large scale grid-connected photovoltaic facilities. South western Uganda performed the poorest. After a thorough energy accounting and a list of all performance metrics, the viability of investing in grid-connected photovoltaic power facilities was assessed.

Applying and Improving Pyranometric Methods to Estimate Sunshine Duration for Tropical Site

Applying and Improving Pyranometric Methods to Estimate Sunshine Duration for Tropical Site

Effects of environment and human activities on rice planting suitability based on MaxEnt model.

Rice is one of the major food crops, and the study of suitable planting areas for rice plays an important role in improving rice yield and optimizing the production layout. This study used Maximum Entropy (MaxEnt) model to simulate and predict the distribution of suitable rice planting areas in China from 1981 to 2020 by combining the climate, soil, and human activities, analyzed the spatial and temporal changes of suitable rice planting areas in China, and determined the main factors affecting rice planting suitability. The results indicated that the main factors influencing the distribution of suitable planting areas for rice in China were gross domestic product (GDP), population density (Pop), and annual sunshine duration (Sun), with human activities playing a dominant role. The high suitable planting areas of rice were mainly distributed in Hubei, Hunan, Jiangxi, Anhui, Guangdong, southeastern Sichuan and western Guizhou. The total suitable planting areas for rice were 346.00 × 104 km2, 345.66 × 104 km2, 347.01 × 104 km2, and 355.57 × 104 km2 from 1981 to 1990, 1991 to 2000, 2001 to 2010 and 2011 to 2020, respectively. With the passage of time, the area of unsuitable areas for rice gradually decreased, and the area of medium suitable areas increased, with large changes in the area of high- and low-suitable areas. Moreover, due to the transfer of a large number of rural laborers to the cities in recent years, the tension between people and land caused by the population explosion has led to the increasing impact of Pop on rice suitable areas and the relatively weakening of the impact of GDP on rice production interventions. The results can be used to provide scientific evidence for the management of rice cultivation and food production safety, with a view to reducing the impacts of climate change on agricultural production in the context of global climate change.

Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

Air quality forecasting is vital for managing and mitigating the adverse effects of air pollution on human health, crops, and the environment. This study aims to forecast daily time series of ozone and fine particulate matter (PM) concentrations using a meta-hybrid deep NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model. Two datasets were utilised: (a) data on meteorological parameters (temperature, air pressure, relative humidity) and air pollutant concentrations (particulate matter, ozone, dioxide of carbon, volatile organic compounds, formaldehyde) provided by a sensor model A3 situated in the centre of Craiova city, and (b) data on wind direction, wind speed, and sunshine duration provided by the National Meteorological Administration. The data sets covered a time interval from December 10, 2020, to January 05, 2024. Initially, a statistical analysis was conducted to assess the correlation between variables. Results revealed that ozone concentration is primarily influenced by meteorological variables such as temperature (r = 0.79), sunshine duration (r = 0.55), and relative humidity (r = −0.48), and secondarily by air pollution indicators including VOC (r = −0.34), PM concentrations (r = −0.34), and CO2 (r = −0.3). In the subsequent stage, thirteen Machine Learning (ML) models were employed in conjunction with an integral feature selection (IFS) method to identify the best combinations of predictor variables for predicting ozone and PMs. Finally, a deep NARMAX model was developed to forecast the next periods of ozone and PMs based on the optimal combinations identified earlier. Results indicated the selection of sixty best models for ozone forecasting and four best models for PMs. The R2 values surpassed 0.97 for ozone and exceeded 0.8 for PMs, demonstrating the efficacy of the forecasting approach.

Interactive effects of fine particular matter and its constituents and sunshine duration on the risk of intrahepatic cholestasis of pregnancy

Interactive effects of fine particular matter and its constituents and sunshine duration on the risk of intrahepatic cholestasis of pregnancy

The impact of interaction between particular matter and sunshine duration on neonatal hyperbilirubinemia

The impact of interaction between particular matter and sunshine duration on neonatal hyperbilirubinemia

A quick comparison model on optimizing the efficiency of photovoltaic panels in collecting solar radiation

Few scholars study light efficiency of solar-cell arrays in theory, while it is difficult to experimentally determine the maximum capacity of a photovoltaic panel to collect solar radiation. This paper proposes a solar energy comparison model (SECM), considering the sunshine duration changes every day to optimize the solar radiation collection model in an ideal state for a whole year, which is easy to use, and can quickly obtain the optimal tilt angle of photovoltaic panels and the solar radiation collecting efficiency enhancement of intelligent light tracking photovoltaic panels. The results show that the sunshine duration is an important factor affecting the solar radiation received by photovoltaic panels. In regions from 66°34′N to 66°34′S, intelligent light tracking photovoltaic panels can increase the collected solar radiation by at least 63.55%, up to 122.51% compared to stationary photovoltaic panels during the effective light time, which is much higher than what most people generally thought. And the advantage of intelligent light tracking photovoltaic panels is more obvious in high latitudes, with a longer and more variable sunshine duration. These findings provide a theoretical basis for the solar radiation collection and photovoltaic panels.

Meteorological factors and rhegmatogenous retinal detachment in metropolitan France

Rhegmatogenous retinal detachment (RRD) is a sight-threatening condition with rising global incidence. Identifying factors contributing to seasonal variations in RRD would allow a better understanding of RRD pathophysiology. We therefore performed a retrospective case series study investigating the relationship between RRD occurrence and meteorological factors throughout metropolitan France (the METEO-POC study), particularly the mean temperature over the preceding 10-day period (T-1). Adult patients having undergone RRD surgery and residing in one of the three most populated urban areas of each French region were included (January 2011–December 2018). The study involved 21,166 patients with idiopathic RRD (61.1% males, mean age 59.8–65.1 years). RRD incidence per 100,000 inhabitants increased from 7.79 to 11.81. RRD occurrence was not significantly associated with mean temperature over T-1 in the majority of urban areas (31/36). In a minority of areas (5/36) we observed correlations between RRD incidence and mean temperature over T-1, however these were extremely weak (r = 0.1–0.2; p < 0.05). No associations were found between RRD incidence and secondary outcomes: mean daily temperature over the 10 days prior T-1, minimum/maximum temperatures, rainfall, duration of sunshine, atmospheric pressure, overall radiation, relative humidity, wind speed. Overall, we found no relationships between meteorological parameters and RRD occurrence.

Covariate-dependent spatio-temporal covariance models

Geostatistical regression models are widely used in environmental and geophysical sciences to characterize the mean and dependence structures for spatio-temporal data. Traditionally, these models account for covariates solely in the mean structure, neglecting their potential impact on the spatio-temporal covariance structure. This paper addresses a significant gap in the literature by proposing a novel covariate-dependent covariance model within the spatio-temporal random-effects model framework. Our approach integrates covariates into the covariance function through a Cholesky-type decomposition, ensuring compliance with the positive-definite condition. We employ maximum likelihood for parameter estimation, complemented by an efficient expectation conditional maximization algorithm. Simulation studies demonstrate the superior performance of our method compared to conventional techniques that ignore covariates in spatial covariances. We further apply our model to a PM2.5 dataset from Taiwan, highlighting wind speed’s pivotal role in influencing the spatio-temporal covariance structure. Additionally, we incorporate wind speed and sunshine duration into the covariance function for analyzing Taiwan ozone data, revealing a more intricate relationship between covariance and these meteorological variables.

Adapting Seasonal Rice Cultivation Strategies for Food Security in Response to Climate Change Impacts

An in-depth examination of the effects of climate change on rice yield in China, encompassing various rice types, is crucial for ensuring the nation’s food security. This study develops an “economy-climate” theoretical model and employs Panel Corrected Standard Error Estimation (PCSE) on panel data spanning from 1978 to 2018, sourced from China’s primary grain-producing regions. The analysis delves into the impact of climate variables, including precipitation, temperature, and sunshine duration, on overall rice production and different rice types. Overall, the findings reveal a nonlinear relationship between precipitation, temperature, sunshine duration, and rice yield, characterized by an “inverted U-shaped” pattern. However, significant variations exist in the effects on different rice types across China’s main grain-producing areas. Increasing precipitation generally enhances early rice production across provinces and also augments mid-season and one-season-late rice production in the Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province. Conversely, it reduces mid-season and one-season-late rice output in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, and Hunan. Sichuan Province sees a rise in temperature favoring early and double-season-late rice production, unlike other provinces. For mid-season and one-season-late rice, temperature increases benefit output in Heilongjiang Province but not in other regions. Additionally, prolonged sunshine duration boosts early and double-season-late rice production across all provinces but does not have the same effect on mid-season and one-season-late rice in China’s primary grain-producing areas.