Search

Previous epidemiological studies have reported a short-term association between ambient temperature and suicide risk. To gain a clearer understanding of this association, it is essential to differentiate the risk factors for intentional self-harm (ISH) from those specifically associated with suicide deaths. Therefore, this study aims to examine whether the association between daily temperature and ISH or suicide deaths differs by age and sex. Between 2014 and 2019, cases of emergency room visits related to ISH and suicide deaths in Seoul were identified. A time-stratified case-crossover design was used to adjust for temporal trends and seasonal variation. A distributed lag nonlinear model was used to analyze the nonlinear and time-delayed effect of ambient temperature on ISH and suicide deaths. Positive associations were observed between temperature and both ISH and suicide deaths. For ISH, the relative risk (RR) was high at 1.17 (95% confidence interval (CI): 1.03, 1.34) for a temperature of 25.7 °C compared with 14.8 °C. The RR for suicide death was higher than those for ISH, at 1.43 (95% CI: 1.03, 2.00) for a temperature of 33.7 °C. These associations varied by age and sex, with males and females aged 35–64 years showing increased susceptibility to suicide deaths. This study provides detailed evidence that unusually high temperatures, both anomalous and out of season, may trigger suicidal behaviors, including both ISH and suicide deaths. [ABSTRACT FROM AUTHOR]

The study presents analysis of 1 year 15 min cycle radon data using artificial neural network (ANN) in an attempt to estimate earthquake prediction time near Indo-Burman subduction line. The region was found to be seismically active and radon anomalies respond well to it, when majority of the radon anomalies peaks and selected earthquakes correlated. The observation also shows that application of non-linear technique ANN to a non-linear data like radon seems a promising approach in predicting geophysical phenomena at one of the most seismically active region of the world. [ABSTRACT FROM AUTHOR]

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model's performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals. [ABSTRACT FROM AUTHOR]

The episodes of elevated concentrations of different gaseous pollutants and particulate matter (PM) are of major concern worldwide, especially in city agglomerations. Krakow is an example of an urban–industrial agglomeration with constantly occurring PM10 air limit value exceedances. In recent years, a number of legislative actions have been undertaken to improve air quality in this area. The multitude of factors affecting the emergence of cases of very high air pollutant concentrations makes it difficult to analyze them using simple statistical methods. Machine learning (ML) methods can be an adequate option, especially when proper amounts of credible data are available. The main aim of this paper was to examine the influence of various factors (including main gaseous pollutant concentrations and some meteorological factors) on the effect of high PM10 concentration episodes in the ambient air in Krakow (Poland) using the random forest algorithm. The original methodology based on the PM10 limit and binary classification of cases with and without the occurrence of high concentration episodes was developed. The data used were derived from routine public air quality monitoring and a local meteorological station. A range of random forest classification models with various predictor sets and for different subsets of the observations coupled with variable importance analysis were performed. The performance of the algorithm was assessed using confusion matrices. The variable importance rankings revealed, among other things, the dominant impact of the mixing layer height on elevated PM10 concentration episode formation. This research work showed the usefulness of the random forest algorithm in identifying factors contributing to poor air quality, even in the absence of reliable emission data. [ABSTRACT FROM AUTHOR]

Air is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people's health is air pollution, and appropriate precautions should be taken to monitor and anticipate its quality in advance. Among all the countries, the air quality in India is decreasing daily, which is a matter of concern to the health department. Many studies use machine learning and Deep learning methods to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. Many research studies confuse researchers and readers about how to proceed with further research. This paper aims to give every detail of the considered air pollutants and brief about the techniques used, their advantages, and challenges faced during pollutant prediction, which leads to a better understanding of the techniques before starting any research related to air pollutant prediction. This paper has given numerous prospective questions on air pollution that piqued the study's interest. This study discussed various machine and deep learning methods and optimization techniques. Despite all the discussed machine learning and deep learning techniques, the paper concluded that more datasets, better learning techniques, and a variety of suggestions would enhance interpretability while maintaining high accuracy for air pollution prediction. The purpose of this review is also to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s). [ABSTRACT FROM AUTHOR]

The study of the surface energy balance characteristics of different ecosystems in the Qinghai Plateau is of great significance for a deeper understanding of land surface processes, the water cycle, and global climate change. This study aims to compare the seasonal variations in energy balance and partitioning of four typical ecosystems on the Qinghai Plateau—swamp meadows, subalpine mountain meadows, alpine shrublands, and alpine deserts. Mantel analysis and path analysis were used to explore the regulatory mechanisms of meteorological elements on energy fluxes and the Bowen ratio (β). The results showed the following: (1) Net radiation (Rn), sensible heat flux (H), and latent heat flux (LE) all exhibited a single-peak pattern of change, and the energy partitioning was closely related to the hydrothermal conditions. Swamp meadows and subalpine mountain meadows were dominated by LE throughout the year and the growing season, while H dominated in the non-growing season. Meanwhile, alpine shrublands and alpine deserts were dominated by H throughout the year. (2) β reflected the characteristics of turbulent fluxes variations and the moisture level of the underlying surface. Swamp meadows and subalpine mountain meadows were relatively moist, with the value of β all being less than 1. Alpine shrublands and deserts were comparatively arid, with the values of β all exceeding 1. The energy closure rate ranged from 48% to 90%, with better energy closure conditions observed during the growing season compared to the non-growing season. (3) Meteorological factors collectively regulated the variations in energy fluxes and its partitioning, with H and LE being primarily influenced by Rn, relative humidity (RH), and soil moisture (Ms). β was significantly affected by RH, Ms, and the saturated vapor pressure deficit (VPD). The sensitivity of the ecosystems to changes in fluxes increased with decreasing moisture, especially in alpine deserts, with Ms, VPD and RH being the most affected. Swamp meadows were significantly associated with air temperature (Ta), soil temperature (Ts), and wind speed; subalpine mountain meadows with Ta and Ts; and alpine shrublands with Ta. These results provided a basis for further analyses of the energy balance characteristics and partitioning differences of different ecosystems on the Qinghai Plateau. [ABSTRACT FROM AUTHOR]

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation. [ABSTRACT FROM AUTHOR]

The meteorological and tobacco leaf weight data from 53 districts (counties) in the tobacco-growing region of central and eastern Guizhou from 2010 to 2021 were selected to analyze the influence of meteorological factors on the leaf weight of flue-cured tobacco. Four linear and non-linear models were established using four meteorological factors on an annual scale (four-factor model) and multiple meteorological factors on a ten-day scale (multi-factor model), respectively. The accuracy and stability of the models were validated to explore the advantages and disadvantages of different models in the central and eastern regions of Guizhou. The results indicated: (1) the average single leaf weight of the lower, cutters, and upper leaves in the tobacco-growing areas of central and eastern Guizhou in the past 12 years were 5.7g, 9.2g, and 10.8g, respectively. The variation range in single leaf weight was smaller in the lower leaves than in the cutters and upper leaves. (2) Based on the modeling with four factors, the BP neural network algorithm model for the single leaf weight of lower leaves showed the best simulation effect, with R² of 0.26 and RMSE of 0.84g. The simulation effect of the random forest algorithm model exhibited the best simulation performance, with R² of 0.33 and RMSE of 1.08g for single leaf weight in the cutters leaf, and R² of 0.16 and RMSE of 1.59g for single leaf weight in the upper leaf. When model with multiple factors, the random forest algorithm model for the single leaf weight of the lower and upper leaves performed best, with R² of 0.22 and RMSE of 0.85g for the lower leaves and R² of 0.16 and RMSE of 1.57g for the upper leaves. For the cutters leaves, the BP neural network algorithm model demonstrated the optimal simulation effect, with R² of 0.18 and RMSE of 1.14g. (3) The accuracy of the multiple-factor stepwise regression algorithm for predicting the single-leaf weight of the lower leaves was 34.86%, while the simulation accuracy of other algorithms for the same leaf position was generally above 70%. The multi-factor BP neural network model predicted the highest accuracy of 86.24% in the lower single leaf weights, while the random forest model predicted the highest accuracy of 89.91% in the cutters single leaf weights under four-factor. The random forest algorithm with multifactor had the highest prediction accuracy of 84.4% for the upper leaf weight. (4) Based on the four-factor algorithm model for predicting the single-leaf weight in the central and eastern regions of Guizhou in 2021, the accuracy of the four algorithmic models for predicting the single-leaf weight of the cutters and lower leaves showed little difference. The accuracy of the random forest algorithm for predicting the single-leaf weight of the upper leaves was the highest, reaching 91%, which was significantly higher than the other three algorithmic models. Under the multi-factor condition, the average simulated accuracy of the various models for the lower, middle, and upper parts was 81%, 74%, and 85% respectively. (5) At the city-level dimension, under the condition of four factors, the average accuracy of the BP neural network model for simulating the single-leaf weight of the lower leaves was 84%. There was no significant difference in the simulation results of the four algorithmic models for the cutters leaves. The upper single leaf weight simulation resulted in the highest average accuracy of 92% with the random forest algorithm. Under the condition of multiple factors, the average accuracy of the linear regression model for simulating the single leaf weight of the lower leaves was 91%, while for the upper leaves, the linear regression model also had the highest average accuracy of 88%. In summary, for the average single-leaf weight of tobacco leaves in the central and eastern regions of Guizhou, using the four-factor BP neural network and random forest algorithm can better simulate the impact of meteorological factors on the single-leaf weight of tobacco leaves, providing scientific basis for the regulation of tobacco leaf production. [ABSTRACT FROM AUTHOR]

Background: Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work. Methods: In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values. Results: Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P < 0.05). The minimum RHU value was 70.5%, the lowest TEM value was 16.0 °C, and there was no sunlight exposure for 9 consecutive days. The highest O3 concentration reached 164 µg/m3. The range of these factors were consistent with the risk factor range of A (H3N2). The common influenza A (H3N2) variant genotype in 2022 was 3 C.2a1b.2a.1a. It was more accurate to predict influenza A (H3N2) with meteorological forecast values than with actual values only. Conclusion: The extreme weather conditions of sustained low temperature and wet rain may have been important driving factors for the abnormal influenza A (H3N2) epidemic. A low vaccination rate, new mutated strains, and insufficient immune barriers formed by natural infections may have exacerbated this epidemic. Meteorological forecast values can aid in the early prediction of influenza outbreaks. This study can help relevant departments prepare for influenza outbreaks during extreme weather, provide a scientific basis for prevention strategies and risk warnings, better adapt to climate change, and improve public health. [ABSTRACT FROM AUTHOR]

The apple orchards in Liaoning, one of the four major apple-producing areas in Bohai Bay, Northeast China, play a crucial role in regulating the carbon sink effect. However, there is limited information on the variation in carbon flux and its influential factors in apple orchards in this region. To address this, CO2 flux data were monitored throughout the entire apple growth seasons from April to November in 2017 and 2018 in the apple (Malus pumila Mill. cv Hanfu) orchard in Shenyang, China. The energy closure of the apple orchard was calculated, and variations in net ecosystem exchange (NEE) at different time scales and its response to environmental factors were analyzed. Our results showed that the energy balance ratio of the apple was 0.74 in 2017 and 1.38 in 2018. NEE was generally positive in April and November and negative from May to October, indicating a strong carbon sink throughout the growth season. The daily average NEE ranged from − 0.103 to 0.094 mg m−2 s−1 in 2017 and from − 0.134 to 0.059 mg m−2 s−1 in 2018, with the lowest values observed in June and July. NEE was negatively correlated with net radiation, atmospheric temperature, saturated vapor pressure deficit, and soil temperature. These findings provide valuable insights for predicting carbon flux in orchard ecosystems within the context of global climate change. [ABSTRACT FROM AUTHOR]

Background: Mushroom poisoning is a significant food safety issue in Guizhou Province, China. Studies on this topic are essential for its prevention and control. We aimed to analyze the epidemiological characteristics of mushroom poisoning and study the correlation between its cases and meteorological factors in Guizhou Province. Methods: We collected data on mushroom poisoning and meteorological factors in Guizhou Province in 2023. A descriptive analysis was conducted on the epidemiological features of mushroom poisoning and meteorological factors. We used Spearman correlation analysis and the generalized additive model to examine the relationship between meteorological factors and the number of mushroom poisoning cases. Results: In 2023, mushroom poisoning cases in Guizhou Province were concentrated among individuals aged 20–59. Clinical symptoms were primarily gastrointestinal symptoms and occurrences peaked from June to October, mainly in the northeastern region of the province. Most incidents occurred in households. In 72 mushroom poisoning incidents where species were identified, 33 poisonous mushrooms were found. The number of mushroom poisoning cases in Guizhou Province was positively correlated with each meteorological factor(P < 0.05). The generalized additive model showed a significant nonlinear relationship between DGT, PRE, RHU, SSD, and the number of mushroom poisoning cases (P < 0.05). Interaction analysis showed that the risk of mushroom poisoning in Guizhou Province increased with the rising values of any two of these four meteorological factors. Conclusion: Mushroom poisoning incidents in Guizhou Province are characterized by high-risk groups, seasonality, and specific high-incidence regions and places. Public awareness for high-risk groups and early warnings for high-incidence regions and places should be strengthened every summer and fall. There is a correlation between meteorological factors and the number of mushroom poisoning cases, suggesting that these factors could serve as early warning indicators for the prevention and control of mushroom poisoning. [ABSTRACT FROM AUTHOR]

Bioaerosols, including airborne bacteria, are significant pollutants affecting both indoor and outdoor air quality, with implications for human health. Despite extensive research on indoor air quality, there is a notable lack of comprehensive data on ambient bacterial concentrations and their interactions with pollutants and meteorological factors. This review focuses on bacterial aerosols in the atmosphere, measured using the culture-based method, considered the "gold standard" for microorganism detection and identification. Studies reveal significant variability in bacterial concentrations across different environments and seasons, influenced by factors such as temperature, humidity, wind speed, solar radiation, and precipitation, underscoring the need for further research and monitoring to enhance health risk assessments and mitigation strategies. The presence of air pollutants such as particulate matter (PM) and ozone (O3) further complicates these dynamics. The authors emphasize the need for more extensive research on outdoor bacterial aerosols and recommend that future studies prioritize detailed bioaerosol characterization to establish comprehensive exposure standards in ambient air, thereby improving public health protection and environmental management practices. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme climate events disrupts the regional water budget balance and leads to changes in the dry and wet conditions of the surface, making the water surplus and deficit more complex and variable. To explore the quantitative relationship between the spatiotemporal evolution of dry and wet conditions and meteorological factors in the Hexi Corridor under changing environmental conditions, the relative moisture index (MI) and FAO Penman–Monteith (FAO P–M) model were combined to construct a partial differential quantitative attribution model for dry and wet variations affected by climate factors in the Hexi Corridor. The results show that: (1) MI in the Hexi Corridor increased significantly (Z = 2.341) during 1960–2019, showing a wet-trend change, and the degree of drought increased from southeast to northwest in the Hexi Corridor. (2) The order of drought degree in four seasons is as follows: winter (− 0.95), spring (− 0.93), autumn (− 0.89) and summer (− 0.83). (3) The frequency of extreme drought, severe drought, moderate drought, and mild drought within 60 years of 21 meteorological stations accounted for 28.38%, 50.48%, 8.85%, and 7.38%, respectively, and the frequency above severe drought was the highest. (4) The sensitivity of meteorological factors gradually increased from northwest to southeast, and MI was the most sensitive to the change of precipitation (P), followed by net radiation (Rn), wind speed (u2), mean temperature (Tmean), relative humidity (RH) and maximum temperature (Tmax). MI was the least sensitive to the change of minimum temperature (Tmin). P is the most important meteorological variable that contributes to the increase of MI, followed by u2, Tmean, and Tmin. Rn, Tmax, and RH have the least influence, and the total contribution of the seven meteorological factors is 85.59%. Compared with the reference evapotranspiration, P is the main factor affecting the dry and wet variations in Hexi Corridor. [ABSTRACT FROM AUTHOR]

Mansonia mosquito species are recognised as a significant vector of human pathogens, primarily transmitting the filarial nematode, Brugia malayi. In central Thailand, the three most prevalent Mansonia species are Mansonia annulifera, Mansonia indiana and Mansonia uniformis. This study explored the influence of seasonal changes on the phenotypic variation of these Mansonia species in central Thailand using the geometric morphometrics (GM). To ensure accurate species identification, we integrated GM techniques with DNA barcoding, examining distinctions in both phenotype and genotype among the species. The intraspecific genetic divergence ranged from 0.00% to 1.69%, whereas the interspecific genetic divergence ranged from 10.52% to 16.36%. The clear distinction between intra‐ and interspecific distances demonstrated the presence of a barcoding gap, confirming the successful differentiation of the three Mansonia mosquito species through DNA barcoding. Similarly, the interspecies GM assessment for classifying Mansonia species demonstrated a high degree of accuracy, with an overall performance of 98.12%. Exploring seasonal variation in the three Mansonia species revealed wing variations across different seasons, and pronounced variations appearing in the cool season. Regarding their association with meteorological factors, Ma. annulifera and Ma. uniformis showed significant positive correlations with temperature (p < 0.05), and Ma. uniformis also displayed a significant negative correlation with atmospheric pressure (p < 0.05). The insights from this study will deepen our understanding of the adaptive patterns of Mansonia mosquitoes in Thailand's central region, paving the way for enhanced disease surveillance related to these vectors. [ABSTRACT FROM AUTHOR]

Existing evidence suggested that the risk of tuberculosis (TB) infection was associated to the variations in temperature and PM2.5. A total of 9,111 cases of TB were reported in Ningxia Hui Autonomous Region, China from 2013 to 2015 on a daily basis, and 57.2% of them were male. The TB risk was more prominent for a lower temperature in males (RR of 1.724, 95% CI: 1.241, 2.394), the aged over 64 years (RR of 2.241, 95% CI: 1.554, 3.231), and the high mobility occupation subpopulation (RR of 2.758, 95% CI: 1.745, 4.359). High concentration of PM2.5 showed a short-term effect and was only associated with an increased risk in the early stages of exposure for the female, and aged 36–64 years group. There were 15.06% (1370 cases) of cases of TB may be attributable to the temperature, and 2.94% (268 cases) may be attributable to the increase of PM2.5 exposures. Low temperatures may be associated with significantly increase in the risk of TB, and high PM2.5 concentrations have a short-term association on increasing the risk of TB. Strengthening the monitoring and regular prevention and control of high risk groups will provide scientific guidance to reduce the incidence of TB. [ABSTRACT FROM AUTHOR]

Aim: Long‐term population studies provide invaluable insights into natural variability, particularly in ecology and conservation. We reconstruct the 31‐year dynamics of the European hamster population in a vast area within Central Europe. We identify key meteorological factors influencing population dynamics and project the potential impact of climate change on their conservation. Location: Poland. Methods: As an indicator of hamster population changes, we utilized data on the prey of the Lesser Spotted Eagle (Clanga pomarina) collected over 31 years (1993–2023) in the geographical region where the European hamster coexists. The reliability of ornithological data was validated. Results: Meteorological factors play a critical role in driving hamster population changes. We found a strong correlation between population growth and warm weather patterns in October and November, as well as favourable conditions in March of the following year. These weather patterns allow hamsters to store larger food supplies and enter hibernation in better physiological condition, resulting in improved habitat and nutritional conditions upon emergence from hibernation in spring. Main Conclusions: Our findings underscore the importance of considering meteorological factors in conservation risk assessments, especially when planning and evaluating hamster reintroduction programmes. The significant influence of meteorological conditions raises concerns about the future prospects of this species in the context of ongoing climate change. Disturbances during critical hamster periods, including unseasonal and prolonged temperature drops in spring following relatively warm winters, pose substantial risks to conservation efforts. [ABSTRACT FROM AUTHOR]

Due to intense population density and rapid economic development, ozone (O3) pollution is serious in the Huaihe River Economic Belt (HREB) of China. Based on pollutant and meteorological observation data from 2015 to 2020, many interdisciplinary methods, e.g., Kernel Density Estimation (KDE), Standard Deviation Ellipse (SDE), and Multiple Linear Regression (MLR), were employed to investigate the spatio-temporal distribution and the meteorological driving force of O3 in 27 cities in the HREB. The results revealed that the annual mass concentration of O3 increased in 2015~2018 and then decreased in 2019. The seasonal O3 concentration displayed a bimodal structure, with highs in spring (122.3 μg/m³) and summer (134.7 μg/m³), and low in autumn (99.1 μg/m³) and winter (64.4 μg/m³). Spatially, the O3 concentration in the northeastern HREB was higher than in the southwestern HREB. SDE analysis indicated that southeast Shangqiu (33.80°N-33.89°N, 116.33°E-116.40°E) was the center of gravity for O3 concentrations in the HREB. The cities with severe O3 pollution were clustered in the northern HREB, forming the high-high (HH) type, and those in the southern HREB with low O3 concentrations forming the low-low (LL) type. Meteorological factors, including temperature, pressure, and sunshine duration, had a relatively significant impact on O3 concentration. Based on the results of the MLR analysis, meteorological factors can explain 82.2%~18.2% (60.5% on average) of the O3 variation in the HREB. The O3 in the northern HREB was more affected by meteorological conditions than in the southern HREB. [ABSTRACT FROM AUTHOR]

Ornamental plant breeding enables the selection of cultivars with desired features from numerous genotypes; however, this process is time-consuming and resource-demanding. Aiming to establish a pre-selection protocol that can facilitate the selection of dwarf rose varieties, the connection between anatomical and histological characteristics and the vegetative growth of rose cultivars was examined. To assess the adaptive potential of the studied cultivars, intra-annual cambial dynamics were explored relative to the observed meteorological fluctuations during the growing season. The investigation included six garden rose cultivars from the 'Reka' and 'Pixie' collections, bred under semi-arid open-field conditions in Serbia. Plant height ranged from 20 to 68 cm, with differing growth habits and types. Vegetative growth was significantly correlated with the xylem/phloem ratio, the proportion of total vessel area relative to cross-sectional and xylem areas, vessel-related features, and porosity (correlation coefficients up to 0.78). Regeneration via cambial activity and the formation of false rings were observed in five of the six cultivars studied, with meteorological analysis suggesting that precipitation and temperature triggered cambial reactivation. This approach effectively targets key parameters in the selection of dwarf and climate-resilient rose cultivars, facilitating the development of reliable pre-selection criteria. [ABSTRACT FROM AUTHOR]

In arid and semiarid regions, vegetation provides essential ecosystem services, especially retarding the desertification process. Vegetation assessment through remote sensing data is crucial in understanding ecosystem responses to climatic factors and large-scale human activities. This study analyzed vegetation cover changes in the Ulan Buh Desert from 1989 to 2019, focusing on the impacts of human activities and key meteorological factors. The results showed that both climatic and human activities contributed to an increasing trend in vegetation cover (normalized difference vegetation index (NDVI)) over the 30-year period. Temperature and precipitation significantly affected the NDVI in the desert, with temperature having a more substantial influence. The combined impact of average temperature and precipitation on the NDVI was notable. Human activities and meteorological factors caused the vegetation restoration area in the desert to be approximately 35% from 1989 to 2019. Human activities were the primary influencers, responsible for about 60% of vegetation restoration across the study area. Especially from 2004 to 2019, the conversion to farmland driven by human activities dominated the region's NDVI increase. The research underscores the importance of considering both climatic and human factors in understanding and managing ecosystem dynamics in arid areas like the Ulan Buh Desert. By integrating these factors, policymakers and land managers can develop more effective strategies for sustainable ecosystem management and combating desertification. [ABSTRACT FROM AUTHOR]

Urban green space can effectively alleviate air pollution, in which vegetation structure plays an important role. However, these green spaces with varying vegetation structures exist in different environmental backgrounds of the city. By analyzing the influence of the different environmental backgrounds on the dust retention effect of green spaces with varying vegetation structures, green spaces can be truly utilized as a solution in alleviating air pollution. Therefore, according to the typical characteristics of landscape patterns and different coverage ratios of green areas in Xi'an city, China, the matrices of urban landscape were divided into three types, which include "green space", "grey-green mixed space" and "gray space." In each environmental background, urban green space was divided into three levels: horizontal structure, species composition and vertical structure. Subsequently, 13 types of green spaces with different vegetation structures and three hard (no vegetation present) squares as control groups were selected. A one-year on-site monitoring was conducted on urban green spaces and concentrations of TSP, PM10, PM2.5 and PM1. The results showed that: (1) In the green space, the concentrations of PM1 and PM2.5 were relatively higher. In the grey-green mixed space, the average concentration of air particle of all four particle sizes was the lowest. In the gray space, the concentrations of PM10 and TSP were more concentrated. (2) Under the same matrices, due to the different locations of the plots, the concentration of air particles of different sizes was significantly different. Under the different urban environmental backgrounds, temperature, relative humidity, wind speed and air pressure all showed the same trend in the change of air particle concentration. (3) The one-layer green space structure was most suitable for planting. Considering the green space, the coniferous one-layered green space (CO) structure was recommended. The partly-closed broad-leaved one-layered green space (P-CBO) was found to be more suitable for the grey-green mixed space. Considering the gray space in the city center, it was suggested to plant the closed mixed coniferous and broad-leaved one-layered green space (CMO) structure. The findings provide empirical support for the future collocation of urban green vegetation structure and the improvement of urban air quality. [ABSTRACT FROM AUTHOR]

Abstract Acute respiratory infections (ARIs) are the most common issue in pediatric clinical practice. They pose a significant public threat, with high morbidity and mortality rates worldwide. Aiming at understanding the epidemiological characteristics of respiratory pathogens and their risk factors among children in eastern Guangdong, China. Samples obtained from 15,993 children hospitalized with ARIs in eastern Guangdong Province were tested for 14 pathogens via multiplex polymerase chain reaction (PCR) from May 2019 to July 2023. The number of hospitalizations for ARIs was correlated with pathogens, age, meteorological parameters, and the pandemic of COVID-19. The data were analyzed by different statistical methods. Among all the samples, the positive rate with ARIs accounted for 68.94% (11,026/15,993) in hospitalized patients. Cytomegalovirus (CMV) (24.49%), Streptococcus pneumoniae (SP) (20.54%), and Respiratory Syncytial Virus (RSV) (14.16%) were the top three pathogens with the greatest infection rates. Among hospitalized patients, there were more single infections in pediatric patients (40.91%, P

The high carbon monoxide content in the urban atmosphere is one of the most important indicators of poor air quality in megacities such as Moscow. This study is to evaluate the importance of wind speed, air temperature, and relative air humidity for predicting the concentrations of carbon monoxide for the day ahead using a simplified one-dimensional quasistationary statistical model. It is shown that the concentration of carbon monoxide in the Moscow atmosphere is determined by a combination of internal (previous days CO concentration) and external (meteorological conditions) factors. The variation of carbon monoxide concentration at one station differs from the variation at another station due to the differences in local conditions. Taking into account wind speed and air temperature increases the predictive value of the onedimensional quasi-stationary statistical model for most of the stations. In contrast to wind, relative air humidity decreases the predictive value of the model for most of the stations. This means that meteorological factors considered in this study could have different effects on predicting carbon monoxide concentration in the case of Moscow. The data from the Balchug weather station, located in the city center, offers a more accurate CO concentration forecast for most Moscow stations compared to the VDNKh weather station. For a more complete description of the influence of meteorological conditions on the predicted low concentration of gases, it is useful to take into account the model wind direction, surface air pressure, and the intensity of mixing in the urban boundary layer.

Abstract Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P

Abstract Air is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people’s health is air pollution, and appropriate precautions should be taken to monitor and anticipate its quality in advance. Among all the countries, the air quality in India is decreasing daily, which is a matter of concern to the health department. Many studies use machine learning and Deep learning methods to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. Many research studies confuse researchers and readers about how to proceed with further research. This paper aims to give every detail of the considered air pollutants and brief about the techniques used, their advantages, and challenges faced during pollutant prediction, which leads to a better understanding of the techniques before starting any research related to air pollutant prediction. This paper has given numerous prospective questions on air pollution that piqued the study’s interest. This study discussed various machine and deep learning methods and optimization techniques. Despite all the discussed machine learning and deep learning techniques, the paper concluded that more datasets, better learning techniques, and a variety of suggestions would enhance interpretability while maintaining high accuracy for air pollution prediction. The purpose of this review is also to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s).

Abstract Background Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work. Methods In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values. Results Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P

Abstract Background Mushroom poisoning is a significant food safety issue in Guizhou Province, China. Studies on this topic are essential for its prevention and control. We aimed to analyze the epidemiological characteristics of mushroom poisoning and study the correlation between its cases and meteorological factors in Guizhou Province. Methods We collected data on mushroom poisoning and meteorological factors in Guizhou Province in 2023. A descriptive analysis was conducted on the epidemiological features of mushroom poisoning and meteorological factors. We used Spearman correlation analysis and the generalized additive model to examine the relationship between meteorological factors and the number of mushroom poisoning cases. Results In 2023, mushroom poisoning cases in Guizhou Province were concentrated among individuals aged 20–59. Clinical symptoms were primarily gastrointestinal symptoms and occurrences peaked from June to October, mainly in the northeastern region of the province. Most incidents occurred in households. In 72 mushroom poisoning incidents where species were identified, 33 poisonous mushrooms were found. The number of mushroom poisoning cases in Guizhou Province was positively correlated with each meteorological factor(P

Abstract The frequent occurrence of extreme climate events disrupts the regional water budget balance and leads to changes in the dry and wet conditions of the surface, making the water surplus and deficit more complex and variable. To explore the quantitative relationship between the spatiotemporal evolution of dry and wet conditions and meteorological factors in the Hexi Corridor under changing environmental conditions, the relative moisture index (MI) and FAO Penman–Monteith (FAO P–M) model were combined to construct a partial differential quantitative attribution model for dry and wet variations affected by climate factors in the Hexi Corridor. The results show that: (1) MI in the Hexi Corridor increased significantly (Z = 2.341) during 1960–2019, showing a wet-trend change, and the degree of drought increased from southeast to northwest in the Hexi Corridor. (2) The order of drought degree in four seasons is as follows: winter (− 0.95), spring (− 0.93), autumn (− 0.89) and summer (− 0.83). (3) The frequency of extreme drought, severe drought, moderate drought, and mild drought within 60 years of 21 meteorological stations accounted for 28.38%, 50.48%, 8.85%, and 7.38%, respectively, and the frequency above severe drought was the highest. (4) The sensitivity of meteorological factors gradually increased from northwest to southeast, and MI was the most sensitive to the change of precipitation (P), followed by net radiation (R n), wind speed (u 2), mean temperature (T mean), relative humidity (RH) and maximum temperature (T max). MI was the least sensitive to the change of minimum temperature (T min). P is the most important meteorological variable that contributes to the increase of MI, followed by u 2, T mean, and T min. R n, T max, and RH have the least influence, and the total contribution of the seven meteorological factors is 85.59%. Compared with the reference evapotranspiration, P is the main factor affecting the dry and wet variations in Hexi Corridor.

The presented research covers a significant part of the period of the COVID-19 pandemic and studies the role of environmental factors in formation of population mortality from main causes. The main causes of mortality among the population of Odesa Region during the studied period include diseases of circulatory system, COVID-19, neoplasms, external causes, diseases of digestive organs and diseases of respiratory organs. The aforesaid causes constitute 94-95% of all causes of mortality in Odesa Region. However, out of these six classes of diseases, only diseases of circulatory system, COVID-19 and diseases of respiratory organs and some of their nosological forms have simultaneous peaks in mortality, during which mortality of the Odesa Region population from all causes almost doubles. Over time, there is a certain redistribution of frequency of the causes of mortality among the six main causing diseases of the population`s mortality in the region. But this does not explain occurrence of simultaneous peaks in mortality from diseases of circulatory system, COVID-19, and diseases of respiratory organs. Formation of simultaneous peaks in mortality from non-infectious and infectious diseases cannot be explained by the influence of social or anthropogenic factors. This indicates the need to address a number of environmental factors of natural origin – meteorological, biometeorological and heliocosmic. The study shows the possibility of an existing linear statistical connection between these environmental factors and the mortality of the Odesa Region population from such classes of diseases as diseases of circulatory system, COVID-19 and diseases of respiratory organs, as well as the relevant nosological forms. The results of the correlation analysis between mortality rates from major diseases and the environmental factors studied showed weak correlations between them. It is fully consistent with the World Health Organization's view of the role of environmental conditions in shaping public health.

Abstract 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

Abstract The burden of rheumatoid arthritis (RA) has gradually elevated, increasing the need for medical resource redistribution. Forecasting RA patient arrivals can be helpful in managing medical resources. However, no relevant studies have been conducted yet. This study aims to construct a long short-term memory (LSTM) model, a deep learning model recently developed for novel data processing, to forecast RA patient arrivals considering meteorological factors and air pollutants and compares this model with traditional methods. Data on RA patients, meteorological factors and air pollutants from 2015 to 2022 were collected and normalized to construct moving average (MA)- and autoregressive (AR)-based and LSTM models. After data normalization, the root mean square error (RMSE) was adopted to evaluate models’ forecast ability. A total of 2422 individuals were enrolled. Not using the environmental data, the RMSEs of the MA- and AR-based models’ test sets are 0.131, 0.132, and 0.117 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they are 0.110, 0.130, and 0.112 for the univariate LSTM models. Considering meteorological factors and air pollutants, the RMSEs of the MA- and AR-based model test sets were 0.142, 0.303, and 0.164 when the training set: test set ratio is 2:1, 3:1, and 7:1, while they were 0.108, 0.119, and 0.109 for the multivariable LSTM models. Our study demonstrated that LSTM models can forecast RA patient arrivals more accurately than MA- and AR-based models for datasets of all three sizes. Considering the meteorological factors and air pollutants can further improve the forecasting ability of the LSTM models. This novel method provides valuable information for medical management, the optimization of medical resource redistribution, and the alleviation of resource shortages.

Objective: To assess the correlation between atmospheric pollutants, meteorological factors, and emergency department visits for respiratory diseases in Haikou City. Methods: Daily data on atmospheric pollutants, meteorological factors, and emergency department visits for respiratory diseases in Haikou City from 2018 to 2021 were collected. The Spearman rank correlation test was used to analyze the correlation, and a distributed lag non-linear model was employed to analyze the health effects and lag impacts of environmental factors. Subgroup analyses were conducted based on sex and age. Results: According to the criteria of International Classification of Diseases (ICD-10: J00-J99), a total of 221 913 cases were included, accounting for 21.3% of the total emergency department visits in Haikou City. For every 1 °C increase in temperature, the risk of emergency department visits increased by 1.029% (95% CI 1.016%-1.042%). Relative humidity greater than 80% reduced the risk of visits, while higher atmospheric pressure (>1 010 hpa) also decreased the likelihood of daily emergency department visits. Higher concentrations of PM25 (30-50 μg/m3), PM10 (>60 μg/m3), and O3 (75-125 μg/m3) were associated with increased visits. Higher temperatures (>25 °C) have a greater impact on females and children aged 0-14 years, while males are more sensitive to low atmospheric pressure. Individuals aged 65 and above exhibited increased sensitivity to O3 concentration, and the effects of PM2.5, PM10, and O3 are more pronounced in individuals over 14 years old. Conclusions: Short-term exposure to high temperatures, particulate matter pollutants (PM25 and PM10), and ozone (O3) is associated with increased emergency department visits for respiratory diseases.

Jakarta, the capital city of Indonesia, is located in a tropical region with abundant sunlight and high temperatures year-round. Ozone and particulate matter (PM) are critical parameters causing unhealthy air pollution. Meteorological data were obtained from the NASA Power website. This study aims to explore the relationship between ozone formation and meteorological factors in Jakarta. Ozone air quality data were measured using the Backman model 950A ozone Analyzer, which detects concentrations as low as 0.05 ppm, with measurements taken every 40 seconds. From January to October 2023, ozone concentrations increased during the dry months of May to October, with the highest hourly value recorded at 263 μg/m³. During this period, average temperatures ranged from 27-29°C, rainfall was 0.3-5.6 mm, wind speeds were 3.14-4.64 m/s, wind direction was 92-171 degrees, and air humidity was 74-82%. Significant episodes were identified on (i) May 5-9, (ii) July 12-15, (iii) September 6-7, (iv) September 13-14, (v) September 21-22, and (vi) October 29-30, 2023. Daily, monthly, and seasonal ozone variations aligned with meteorological conditions, showing higher concentrations during the dry months. Further studies, including photochemical modeling, are required to identify dominant factors causing high ozone concentrations during these episodes. Understanding NOx or VOC emission sensitivities is crucial for effective ozone abatement strategies in Jakarta.

Abstract Although the independent effects of ambient CO, temperature or humidity on stroke have been confirmed, it is still unclear where there is an interaction between these factors and who is sensitive populations for these. The stroke hospitalization and ambient CO, temperature, humidity data were collected in 22 Counties and districts of Ningxia, China in 2014–2019. The lagged effect of ambient CO, temperature or humidity were analyze by the generalized additive model; the interaction were evaluated by the bivariate response surface model and stratified analysis with relative excessive risk (RERI). High temperature and CO levels had synergistic effects on hemorrhagic stroke (RERI = 0.05, 95% CI 0.033–0.086) and ischemic stroke (RERI = 0.035, 95% CI 0.006–0.08). Low relative humidity and CO were synergistic in hemorrhagic stroke (RERI = 0.192, 95% CI 0.184–0.205) and only in ischemic stroke in the elderly group (RERI = 0.056, 95% CI 0.025–0.085). High relative humidity and CO exhibited antagonistic effects on the risk of ischemic stroke hospitalization in both male and female groups (RERI = − 0.088, 95% CI − 0.151to − 0.031; RERI = − 0.144, 95% CI − 0.216 to − 0.197). Exposure to CO increases the risk of hospitalization related to hemorrhagic and ischemic strokes. CO and temperature or humidity interact with risk of stroke hospitalization with sex and age differences.

Abstract Background Acute otitis media (AOM) is a prevalent childhood acute illness, with 13.6 million pediatric office visits annually, often stemming from upper respiratory tract infections (URI) and affected by environmental factors like air pollution and cold seasons. Methods Herein, we made use of territory-wide hospitalization data to investigate the relationships between meteorological factors, air pollutants, influenza infection, and AOM for children observed from 1998 to 2019 in Hong Kong. Quasi-Poisson generalized additive model, combined with a distributed-lag non-linear model, was employed to examine the relationship between weekly AOM admissions in children and weekly influenza-like illness-positive (ILI +) rates, as well as air pollutants (i.e., oxidant gases, sulfur dioxide, and fine particulate matter), while accounting for meteorological variations. Results There were 21,224 hospital admissions due to AOM for children aged ≤ 15 years throughout a 22-year period. The cumulative adjusted relative risks (ARR) of AOM were 1.15 (95% CI, 1.04–1.28) and 1.07 (95% CI, 0.97–1.18) at the 95th percentile concentration of oxidant gases (65.9 ppm) and fine particulate matter (62.2 μg/m3) respectively, with reference set to their medians of concentration. The ARRs exhibited a monotone increasing trend for all-type and type-specific ILI + rates. Setting the reference to zero, the cumulative ARRs of AOM rose to 1.42 (95% CI, 1.29–1.56) at the 95th percentile of ILI + Total rate, and to 1.07 (95% CI, 1.01–1.14), 1.19 (95% CI, 1.11–1.27), and 1.22 (95% CI, 1.13–1.32) for ILI + A/H1N1, A/H3N2, and B, respectively. Conclusions Our findings suggested that policy on air pollution control and influenza vaccination for children need to be implemented, which might have significant implications for preventing AOM in children.

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. [ABSTRACT FROM AUTHOR]

The quality of the air exerts considerable effects on human health, and meteorological factors affect air quality. The relationships between meteorological factors and air quality parameters are complex dependency correlations. This article is based on the air quality monitoring data and meteorological monitoring data obtained from a monitoring station in Binjiang District, Hangzhou City, China, spanning from 01:00 on 14 April 2021 to 23:00 on 31 December 2021. The Informer model was used to explore the air quality parameters' response to meteorological factors. By analyzing 12 different kinds of 2-Minute Average Wind Speed (2-MAWSP), 10-Minute Average Wind Speed (10-MAWSP), and Maximum Wind Speed (MXSPD); 16 different kinds of Hourly Precipitation (HP) and Air Temperature (AT); 11 different kinds of Relative Humidity (RH); and 8 different kinds of Station Pressure (STP), the following results were obtained: (1) The influence of wind speed on various air quality parameters is multifaceted and lacks a standardized form, potentially influenced by factors like wind direction and geographical location. One clear effect of wind speed is evident in the levels of particulate matter 10 (with an aerodynamic diameter smaller than 10 μm, PM10), as the values of this parameter first decrease and then increase with increasing wind speed. (2) HP has an evident reducing effect on most air quality parameters, including particulate matter (including PM2.5 and PM10), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2), as well as nitrogen oxides (NOx). (3) The increase in AT has a clear reducing effect on the concentration of NO2, while the trend for the concentrations of PM10 and NOx is one of initial decrease followed by a gradual rise. (4) RH only reduces the concentrations of SO2 and PM10. (5) With the rise in STP, the concentrations of most air quality parameters generally rise as well, except for the decrease in NOx concentration. This can give some indications and assistance to meteorological and environmental departments for improving air quality. This model can be used for a performance analysis and the forecasting of multi-parameter non-correlated systems. [ABSTRACT FROM AUTHOR]

Based on the Chaozhou urban area's hourly air quality concentration data from 2014 to 2020, the annual, monthly and daily variation characteristics of PM2.5-O3 complex pollution were analyzed. The reanalysis data were used to classify the weather situation, and combined with the meteorological data from Chaozhou National Station in the corresponding period, the relationship between PM2.5-O3 and synoptic patterns and meteorological factors was analyzed in this paper. The results showed that the annual mean ρ(PM2.5), ρ(O3-8 h) and the number of days of complex pollution in the Chaozhou urban area showed a decreasing trend during 2014--2020. The highest monthly mean ρ(PM2.5) appeared in March, the highest monthly mean ρ(O3-8 h) appeared in October, and the lowest of both pollutants appeared in June. The most complex pollution occurred from October to April of the following year. PM2.5 and O3 pollution had certain interactions. When the daily average concentration of one pollutant was higher, the concentration of the other pollutant was correspondingly higher, and the daily range of pollutant concentration also increased. When air pollution was severe, the peak time of O3 appeared at 15:00, and the peak time of PM2.5 appeared at 19: 00--20: 00. The weather patterns that caused the complex pollution in Chaozhou were mainly the degenerative ridge of high pressure, anticyclone, subtropical high and degenerative ridge of high pressure, and uniform pressure field, which mainly appeared in autumn, winter and spring, and a few appeared in early summer. The four weather types appeared in different seasons, but generally speaking, sunny and dry weather was the main meteorological factor for the occurrence of complex pollution. [ABSTRACT FROM AUTHOR]

【Objective】The impact of meteorological factors on the single leaf weight of flue-cured tobacco were explored, and a model for single leaf weight of flue-cured tobacco in the western region of Guizhou Province based on multiple meteorological factors were constructed to provide reference basis for tobacco production decision-making.【Method】Based on meteorological elements and data about single leaf weight of field flue-cured tobacco in the western tobacco planting area of Guizhou Province from 2010 to 2021, four artificial intelligence and statistical algorithms, including BP neural network, random forest, linear regression and stepwise regression, were used to analyze the trend of change in single leaf weight of tobacco in the western tobacco planting area of Guizhou Province in the past 12 years. Based on this, the relationship between meteorological elements and single leaf weight of tobacco was studied, and a model of multiple meteorological elements and single leaf weight of tobacco was constructed and validated.【Result】In the western tobacco growing areas of Guizhou Province in the past 12 years, the average single leaf weight of the lower leaves was 6.25 g, the average single leaf weight of the middle leaves was 9.95 g, and the average single leaf weight of the upper leaves was 10.81 g; The single leaf weight of the middle leaves showed little change, while the lower and upper leaves showed a trend of increasing weight. Using four artificial intelligence and statistical algorithms, a model for single leaf weight was established based on four meteorological elements (temperature in mature period, precipitation in fast growing period, sunshine hours in growing period and available time in growing period, hereinafter referred to as the four elements) and meteorological elements (multiple elements) from March to September during the critical periods of tobacco growth. The model was simulated year by year from 2010 to 2021, and it found that although the BP neural network algorithm had the highest simulation accuracy when establishing the model, however, when simulating actual single leaf weight, the stepwise regression algorithm model based on multiple factors had the best simulation effect. Compared with other models, it could simulate the peak and valley values of single leaf weight for each leaf position year by year, followed by the random forest algorithm.【Conclusion】The stepwise regression and random forest algorithm have good simulation effects on the single leaf weight of flue-cured tobacco in the western region of Guizhou Province. Therefore, when establishing a prediction model for single leaf weight of flue-cured tobacco, the meteorological factors prediction can be considered to provide scientific basis for tobacco production decision-making. [ABSTRACT FROM AUTHOR]

Potato virus Y (PVY) is a major tobacco (Nicotiana tabacum L.) pathogen that causes severe crop losses. We studied the influence of meteorological factors and a population of twelve aphid species on the development of PVY in field-grown tobacco from 1996 to 2010 in Poland. Three PVY-susceptible tobacco varieties were used in the study. The mean virus incidence ranged from 18% in 2010 to almost 99% in 1996, 2004, and 2009. For determining the relationship between tobacco plant infection and meteorological conditions and aphid populations, logistic regression analysis was used. It was found that the probability of PVY infection is significantly dependent on the average air temperature, relative humidity, number of days with an average temperature of at least 25 °C, and the abundance of Aphis fabae and Brachycaudus helichrysi. The probability of infection of tobacco plants with potato virus Y decreased with increasing air temperature and relative humidity. In addition, with each subsequent day with a temperature of at least 25 °C, the risk of infection decreased by 24%. Furthermore, it was often observed that high populations of Aphis fabae and Brachycaudus helichrysi were associated with a high incidence of virus infection in tobacco plants. [ABSTRACT FROM AUTHOR]

In this study, the medium-early maturing varieties ('HQY971' and 'QY1719') and the medium-late maturing varieties ('SY21' and 'SY28') were used to analysis the effects of meteorological factors on the grain water content of rapeseed varieties at different maturity stages. The Logistic power model was used to fit the active accumulated temperature (≥ 0°C) and grain water content of four rapeseed varieties from pollination to harvest between 2019 and 2020. The results showed that: (1) the grain water content of the four rapeseed varieties showed a slow decline first and then a rapid decline at the pollination-harvest stage. The average grain water content of the medium-early maturing varieties at physiological maturity stage was higher than that of the medium-late maturing cultivars. The average grain water content at harvest stage decreased with the extension of growth period. Among them, the grain dehydration rate of the medium-early maturing variety 'HQY 971' was 9.66 percentage points·d-1, which was significantly different from the other three rapeseed varieties. (2) The trend water content fitting curves of medium-early maturing varieties and mid-late maturing varieties showed an "S" shaped change trend. Moreover, the meteorological water content of medium-early maturing varieties was greatly affected by meteorological factors, while the meteorological water content of mid-late maturing varieties was less affected by meteorological factors. (3) The dehydration of rapeseed varieties at different maturity stages was mainly affected by meteorological factors such as average temperature, maximum temperature, minimum temperature and accumulated temperature. The average temperature (-1.125) had an indirect effect on the meteorological water content of the mid-early maturing varieties, while the maximum temperature (-0.347), minimum temperature (-0.472) and accumulated temperature (-0.936) had a direct effect. For the mid-late maturing varieties, the average temperature (-0.847) was indirect, while the maximum temperature (-0.288), minimum temperature (-0.250) and accumulated temperature (-0.877) were direct effects. (4) The path analysis showed that the R2 of each factor to the determinants of meteorological water content was 0.8433 (mid-early maturing varieties) and 0.8130 (mid-late maturing varieties), respectively, which indicated that accumulated temperature had the greatest impact on the meteorological water content of rapeseed varieties at different maturity stages. The fitting results of all equations in the stepwise regression analysis reached the extremely significant level, indicating that the influence of meteorological factors on meteorological water content can be explained to a certain extent. [ABSTRACT FROM AUTHOR]

Revealing the dynamic changes of water and heat fluxes in straw-covered farmland ecosystems can provide a theoretical basis for the quantitative regulation of efficient water use by crops under agronomic mulching. Two treatments of straw mulching (S) and none mulching (N) were set up in the experiment. The dynamic changes of net radiation flux (Rn), latent heat flux (λET), sensible heat flux (H) and soil heat flux (G) of farmland energy flux elements under straw mulching in North China Plain and their responses to meteorological factors were studied by using Bowen ratio energy balance method and measured data. The results showed that the diurnal variation of Rn, λET and H in each growth period showed an obvious single-peak pattern. Compared with N treatment, S treatment mainly reduced the daily peak of Rn, λET and H in each growth period in both wheat and maize growing seasons. However, from the perspective of daily mean value, S treatment increased the proportion of λET in Rn in wheat growing season, and the proportion of λET in Rn in the total growth period of S treatment (68.8%) was slightly higher than that of N treatment (68.4%). The proportion of λET to Rn in the total growth period of S treatment (65.0%) was lower than that of N treatment (65.9%). The meteorological factor that had the greatest influence on instantaneous λET in wheat growing season and maize growing season was Rn, and the other factors had little influence. Each meteorological factor affects instantaneous λET through direct and indirect effects. The influence of Rn on instantaneous λET is mainly reflected in the direct effect, and the other factors are mainly reflected in the indirect effect. [ABSTRACT FROM AUTHOR]

In the rapidly changing climate of arid desert regions, evaluating the comprehensive characteristics of humidity levels is crucial for agricultural, urban, and infrastructural planning, as well as for minimizing potential public health impacts. We investigated variability and trends of humidity levels in major Saudi Arabian cities during 1982–2022, focusing on the influence of meteorological factors such as average, maximum and minimum temperature, rainfall, and windspeed. Employing the Probability Density Function and descriptive statistics, variability of climatic factors was analyzed. The Mann–Kendall Test (MKT) and Innovative Trend Analysis (ITA) were employed to identify monthly and annual trends. The magnitude and changing patterns were determined by calculating Sen's Slope and ITA slope. Findings of the MKT and ITA showed similar trends in humidity levels across all the cities. ITA result revealed that humidity in Riyadh and Taif decreased at a rate of 0.012% and 0.016% per year, respectively, while increased in Jeddah, Makkah, and Madinah at a 0.05 confidence level. The influence of climatic factors on humidity was assessed using Pearson's correlation coefficients, multiple regression model, and wavelet transform coherence (WTC) for each city, pinpointing temperature as the key driver of humidity variability. The dominance of temperature features was corroborated by strong power spectrums in the WTC across various time periods and scales. The in-depth analysis of humidity dynamics in this study provides critical insights for the development of climate-resilient infrastructure and formulation of public health strategies in Saudi Arabian cities. [ABSTRACT FROM AUTHOR]

Flash drought (FD) has attracted much attention due to its severe stress on vegetation ecosystems. Yet to date, the impacts of FD on vegetation ecosystems in different regions have not been fully evaluated and explored, especially for ecologically fragile areas. In this study, we identified the FD events in the Loess Plateau from 2000 to 2023 based on the attenuation rate in soil moisture percentile over time. The evolution process of FD, the driving roles of meteorological conditions and the responses of different vegetation types to FD were explored by vegetation indicators such as solar-induced chlorophyll fluorescence (SIF), SIFyield, SIF-RCI, etc. The results showed that FD events were predominantly concentrated in wetter areas with dense vegetation, with the highest frequency being 29. Meteorological factors contributed differently to the occurrence and development of FD. The responses of vegetation to FD were not only related to vegetation types (cropland was more sensitive to FD than forest and grassland) but were also significantly influenced by background climate. The SIFyield anomaly of vegetation was more sensitive than SIF anomaly and SIF-RCI. The results advance our understanding of the formation mechanisms of FD and facilitate the exploration of vegetative photosynthetic responses to FD. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exploring NDVI variation and what drives it on the Qinghai–Tibet Plateau can strategically inform environmental protection efforts in light of global climate change. For this analysis, we obtained MODIS NDVI data collected during the vegetative growing season, vegetation types for the region, and meteorological data for the same period from 2001 to 2020. We performed Theil–Sen trend analysis, Mann–Kendall significance testing, spatial autocorrelation analysis, and Hurst index calculation to review the spatiotemporal changes in NDVI characteristics on the plateau for various vegetation types. We used the correlation coefficients from these analyses to investigate how the NDVI responds to temperature and precipitation. We found the following: (1) Overall, the Qinghai–Tibet Plateau NDVI increased throughout the multi-year growing season, with a much larger area of improvement (65.68%) than of degradation (8.83%). (2) The four main vegetation types were all characterized by improvement, with meadows (72.13%) comprising the largest portion of the improved area and shrubs (18.17%) comprising the largest portion of the degraded area. (3) The spatial distribution of the NDVI had a strong positive correlation and clustering effect and was stable overall. The local clustering patterns were primarily low–low and high–high clustering. (4) The Hurst index had an average value of 0.46, indicating that the sustainability of vegetation is poor; that is, the trend of vegetation change in the growing season in a large part of the Qinghai–Tibet Plateau in the future is opposite to that in the past. (5) The plateau NDVI correlated positively with air temperature and precipitation. However, the correlations varied geographically: air temperature had a wide influence, whereas precipitation mainly influenced meadows and grassland in the northern arid zone. The overall temperature-driven effect was stronger than that of precipitation. This finding is consistent with the current research conclusion that global warming and humidification promote vegetation growth in high-altitude areas and further emphasizes the uniqueness of the Qinghai–Tibet Plateau as a climate-change-sensitive area. This study also offers a technical foundation for understanding how climate change impacts high-altitude ecosystems, as well as for formulating ecological protection strategies for the plateau. [ABSTRACT FROM AUTHOR]

To improve the energy efficiency of ships and reduce greenhouse gas (GHG) emissions, the implementation of energy-efficient operation measures is particularly important. Driven by this, this study was dedicated to improving the accuracy of ship fuel oil consumption (FOC) prediction and laying the foundation for optimizing energy-efficient operations. Firstly, we combined voyage reports and meteorological data and constructed six datasets containing different features. These features comprise navigation-related features encompassing sailing speed, displacement and trim, as well as meteorological features encompassing wind, wave, sea current, sea water salinity and sea water temperature. Secondly, we conducted experiments with 14 popular ML models on the datasets and compared the prediction performance of different models by a new scoring system. Finally, we explored the advantages and disadvantages of each dataset based on the model performance scoring results and analyzed the effects of related meteorological factors on FOC during navigation. The key findings of the proposed work were that extra trees (ET), random forest (RF), XGBoost, and LightGBM had good fitting and generalization performance. Set5, the dataset containing the most complete meteorological data, achieved the best prediction results. In particular, it had an R 2 (test) of 0.9317 on the ET model, which was 1.97% higher than the R 2 (test) of the dataset using only voyage reports. The conclusions can assist shipping companies in constructing a ship FOC prediction framework and developing ship fuel-saving strategies. [ABSTRACT FROM AUTHOR]

Any change in a meteorological variable can affect an environmental element's evapotranspiration over time. This paper studies the dynamic causal relationships between evapotranspiration and meteorological parameters, which have long-term equilibrium relationships in Lahore, Pakistan, during the period from October 2004 to March 2020. The Augmented Dicky Fuller test reveals that the ARDL Approach to Cointegration is used for forecasting ET (kg m−2 s−1). The results depicted that geopotential height, surface air temperature, relative humidity at the surface, surface wind speed, tropopause height, surface skin temperature, and water vapor mass mixing ratio have a statistically significant long-run relationship with evapotranspiration. The results also revealed that cloud fraction, relative humidity at the surface, surface air temperature, total surface precipitation, water vapor mass mixing ratio, and tropopause height have a statistically significant short-run relationship with evapotranspiration. The CF (t - 1) has the strongest effect on ET (kg m−2 s−1) in the short and long run. Speed of adjustment ( ECT t - 1 ) shows a highly significant and negative relation, which indicates that after a 1 unit increase in the period, evapotranspiration (kg m−2 s−1) will go toward long-run equilibrium with a rate of 66.4%. The forecasted evapotranspiration time plot shows a long-term trend with seasonal components from April 2020 to March 2025. [ABSTRACT FROM AUTHOR]

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination ( R 2 ), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R 2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models' precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models' effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions. [ABSTRACT FROM AUTHOR]

A long-term monitoring experiment was conducted to investigate the spatial and temporal variation rule of the slab track temperature in cold regions using statistical analysis methods. The sensitivity of track temperature to meteorological factors was analyzed using the finite element method. The study found that temperature, solar radiation intensity, and full-section temperature of the track structure exhibit a reciprocal change with a daily cycle. Additionally, the temperature change of each structural layer lags with increasing depth. The measured data showed that the maximum positive and negative temperature gradient of the track slab exceeded the standard design value (90 °C/m, − 45 °C/m), suggesting that the design value of positive and negative temperature gradient in cold regions should be 95 °C/m and − 55 °C/m, respectively. Furthermore, the finite element model analysis indicated that the overall temperature of the track slab is strongly influenced by the daily average temperature, the solar radiation intensity, and the wind speed, while the temperature gradient of the track slab is strongly influenced by the daily temperature amplitude and solar radiation intensity. The maximum positive temperature gradient warning value of the track slab under extreme high-temperature weather was found to be 100 °C/m. These findings can provide valuable insights for the optimization design and temperature effect research of CRTS III prefabricated slab tracks in cold regions. [ABSTRACT FROM AUTHOR]

Based on ozone (O3) monitoring data for Xiangtan and meteorological observation data for 2020–2022, we examined ozone pollution characteristics and the effects of meteorological factors on daily maximum 8-h average ozone (O3-8h) concentrations in Xiangtan. Thus, we observed significant increases as well as notable seasonal variations in O3-8h concentrations in Xiangtan during the period considered. The ozone and temperature change response slope (KO3-T) indicated that local emissions had no significant effect on O3-8h generation. Further, average O3-8h concentration and maximum temperature (Tmax) values showed a polynomial distribution. Specifically, at Tmax < 27 °C, it increased almost linearly with increasing temperature, and at Tmax between 27 and 37 °C, it showed an upward curvilinear trend as temperature increased, but at a much lower rate. Then, at Tmax > 37 °C, it decreased with increasing temperature. With respect to relative humidity (RH), the average O3-8h concentration primarily exceeded the standard value when RH varied in the range of 45–65%, which is the key humidity range for O3 pollution, and the inflection point for the correlation curve between O3-8h concentration and RH appeared at ~55%. Furthermore, at wind speeds (WSs) below 1.5 m∙s−1, O3-8h concentration increased rapidly, and at WSs in the 1.5–2 m∙s−1 range, it increased at a much faster rate. However, at WSs > 2 m∙s−1, it decreased slowly with increasing WS. O3-8h concentration also showed the tendency to exceed the standard value when the dominant wind directions in Xiangtan were easterly or southeasterly. [ABSTRACT FROM AUTHOR]