The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran. Study Design: An ecological study. METHODS: In this ecological research, three main ML techniques, including decision trees, random forest, and extreme gradient boosting (XGBoost), have been applied to correlate meteorological parameters and air pollutants with infection transmission, hospitalization, and death due to COVID-19 from 1 October 2020 to 1 March 2022. These parameters and pollutants included particulate matter (PM2), sulfur dioxide (SO2 ), nitrogen dioxide (NO2 ), nitric oxide (NO), ozone (O3 ), carbon monoxide (CO), temperature (T), relative humidity (RH), dew point (DP), air pressure (AP), and wind speed (WS). RESULTS: Based on the three ML techniques, NO2 (lag 5 day), CO (lag 4), and T (lag 25) were the most important environmental features affecting the spread of COVID-19 infection. In addition, the most important features contributing to hospitalization due to COVID-19 included RH (lag 28), T (lag 11), and O3 (lag 10). After adjusting for the number of infections, the most important features affecting the number of deaths caused by COVID-19 were NO2 (lag 20), O3 (lag 22), and NO (lag 23). CONCLUSION: Our findings suggested that epidemics caused by COVID-19 and (possibly) similarly viral transmitted infections, including flu, air pollutants, and meteorological parameters, can be used to predict their burden on the community and health system. In addition, meteorological and air quality data should be included in preventive measures.

Asymmetric association between meteorological factors and human infections with hemorrhagic fever with renal syndrome: A 16-year ecological trend study in Shaanxi, China.

Objective: Hemorrhagic fever with renal syndrome (HFRS) continues to pose a significant threat to global health. This study aimed to investigate both the long- and short-term asymmetric impacts of variations in meteorological variables on HFRS. Methods: The reported monthly HFRS incidence data from Shaanxi between 2004 and 2019, along with corresponding meteorological data, were collected to conduct an ecological trend analysis. Subsequently, the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models were used to examine the long- and short-term asymmetric effects of climate variables on HFRS incidence. Results: Overall, a reduction in HFRS incidence was observed in Shaanxi from 2004 to 2019, with an average annual percentage change of -0.498 % (95 %CI -13.247 % to 12.602 %). HFRS incidence peaked in December and reached its lowest point in March each year. A 1 mm increase in aggregate precipitation (AP) was associated with a 4.3 % rise in HFRS incidence, while a 1 mm decrease contributed to a 3.7 % increase, indicating a long-term asymmetric impact (Wald long-term asymmetry test [WLT] = 9.072, P = 0.003). In the short term, a 1 % decrease in mean relative humidity (MRH) led to a 5.7 % decline in HFRS incidence (Wald short-term asymmetry test [WSR] = 5.978, P = 0.015). Additionally, changes in meteorological variables showed varied effects: ΔMWV(+) at a 1-month lag had a significant positive short-term effect on HFRS; ΔMRH(+) at a 3-month lag, ΔAP(+) at a 2-month lag, ΔAP(-) at a 1-month lag, ΔASH(+) at a 1-month lag, and ΔASH(-) at a 3-month lag all exhibited strong negative short-term impacts on HFRS incidence. Conclusions: Weather variability plays a significant role in influencing HFRS incidence, with both long- and short-term asymmetric and/or symmetric effects. Utilizing the NARDL model through a One Health lens offers promising opportunities for enhancing HFRS control measures.

Spatiotemporal evolution of dry and wet and quantitative analysis of the influence of meteorological factors based on MI and the FAO P-M model.

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.

Temporal changes in net ecosystem CO2 exchange and influential factors in an apple orchard in Northeast China.

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.

Seasonal and spatial variations in concentration, diversity, and antibiotic resistance of ambient bioaerosols in an arid region.

The airborne microbiome significantly influences human health and atmospheric processes within Earth's troposphere and is a crucial focus for scientific research. This study aimed to analyze the composition, diversity, distribution, and spatiotemporal characteristics of airborne microbes in Qatar's ambient air. Air samples were collected using a sampler from ten geographically or functionally distinct locations during a period of one year. Spatial and seasonal variations significantly impacted microbial concentrations, with the highest average concentrations observed at 514 ± 77 CFU/m3 for bacteria over the dry-hot summer season and 134 ± 31 CFU/m3 for fungi over the mild winter season. Bacterial concentrations were notably high in 80% of the locations during the dry-hot summer sampling period, while fungal concentrations peaked in 70% of the locations during winter. The microbial diversity analysis revealed several health-significant bacteria including the genera Chryseobacterium, Pseudomonas, Pantoea, Proteus, Myroides, Yersinia, Pasteurella, Ochrobactrum, Vibrio, and fungal strains relating to the genera Aspergillus, Rhizopus Fusarium, and Penicillium. Detailed biochemical and microscopic analyses were employed to identify culturable species. The strongest antibiotic resistance (ABR) was observed during the humid-hot summer season, with widespread resistance to Metronidazole. Health risk assessments based on these findings indicated potential risks associated with exposure to high concentrations of specific bioaerosols. This study provides essential baseline data on the natural background concentrations of bioaerosols in Qatar, offering insights for air quality assessments and forming a basis for public health policy recommendations, particularly in arid regions.

Association of ambient temperature with intentional self-harm and suicide death in Seoul: a case-crossover design with a distributed lag nonlinear model.

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.

Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach.

Suicide is a severe public health issue globally. Accurately identifying high-risk lung cancer patients for suicidal behavior and taking timely intervention measures has become a focus of current research. This study intended to construct dynamic prediction models for identifying suicide risk among lung cancer patients. Patients were sourced from the Surveillance, Epidemiology, and End Results database, while meteorological data was acquired from the Centers for Disease Control and Prevention. This cohort comprised 455, 708 eligible lung cancer patients from January 1979 to December 2011. A Cox proportional hazard regression model based on landmarking approach was employed to explore the impact of meteorological factors and clinical characteristics on suicide among lung cancer patients, and to build dynamic prediction models for the suicide risk of these patients. Additionally, subgroup analyses were conducted by age and sex. The model's performance was evaluated using the C-index, Brier score, area under curve (AUC) and calibration plot. During the study period, there were 666 deaths by suicide among lung cancer patients. Multivariable Cox results from the dynamic prediction model indicated that age, marital status, race, sex, primary site, stage, monthly average daily sunlight, and monthly average temperature were significant predictors of suicide. The dynamic prediction model demonstrated well consistency and discrimination capabilities. Subgroup analyses revealed that the association of monthly average daily sunlight and monthly average temperature with suicide remained significant among female and younger lung cancer patients. The dynamic prediction model can effectively incorporate covariates with time-varying to predict lung cancer patients' suicide death. The results of this study have significant implications for assessing lung cancer individuals' suicide risk.

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models.

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.

Spatial analysis of the prevalence of abdominal obesity in middle-aged and older adult people in China: exploring the relationship with meteorological factors based on gender differences.

Background: In recent years, the incidence of abdominal obesity among the middle-aged and older adult population in China has significantly increased. However, the gender disparities in the spatial distribution of abdominal obesity incidence and its relationship with meteorological factors among this demographic in China remain unclear. This gap in knowledge highlights the need for further research to understand these dynamics and inform targeted public health strategies. Methods: This study utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the incidence of abdominal obesity among the middle-aged and older adult population in China. Additionally, meteorological data were collected from the National Meteorological Information Center. Using Moran's I index and Getis-Ord Gi* statistical methods, the spatial distribution characteristics of abdominal obesity incidence were examined. The influence of various meteorological factors on the incidence of abdominal obesity in middle-aged and older adult males and females was investigated using the q statistic from the Geodetector method. Furthermore, Multi-Scale Geographically Weighted Regression (MGWR) analysis was employed to explore the impact of meteorological factors on the spatial heterogeneity of abdominal obesity incidence from a gender perspective. Results: The spatial distribution of abdominal obesity among middle-aged and older adult individuals in China exhibits a decreasing trend from northwest to southeast, with notable spatial autocorrelation. Hotspots are concentrated in North and Northeast China, while cold spots are observed in Southwest China. Gender differences have minimal impact on spatial clustering characteristics. Meteorological factors, including temperature, sunlight, precipitation, wind speed, humidity, and atmospheric pressure, influence incidence rates. Notably, temperature and sunlight exert a greater impact on females, while wind speed has a reduced effect. Interactions among various meteorological factors generally demonstrate bivariate enhancement without significant gender disparities. However, gender disparities are evident in the influence of specific meteorological variables such as annual maximum, average, and minimum temperatures, as well as sunlight duration and precipitation, on the spatial heterogeneity of abdominal obesity incidence. Conclusion: Meteorological factors show a significant association with abdominal obesity prevalence in middle-aged and older adults, with temperature factors playing a prominent role. However, this relationship is influenced by gender differences and spatial heterogeneity. These findings suggest that effective public health policies should be not only gender-sensitive but also locally adapted.

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.

Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.

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.

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.

Particle-ozone complex pollution under diverse synoptic weather patterns in the Yangtze River Delta region: Synergistic relationships and the effects of meteorology and chemical compositions.

There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a "seesaw" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.

Interaction between ambient CO and temperature or relative humidity on the risk of stroke hospitalization.

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.

High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data.

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R 2 of 0.125 (original ConvLSTM: RMSE = 0.702, R 2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R 2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

Relationships Between Meteorological Factors and Mongolian Gerbils and Its Flea Burdens - Xilingol League, Inner Mongolia Autonomous Region, China, 2012-2021.

Introduction: Plague is a significant global infectious disease, its spread is linked to host and flea populations. Meteorological conditions can impact flea populations and host densities, hence influencing plague outbreaks. Investigating the connection between meteorological factors, flea populations, and rodent densities in Inner Mongolia's natural plague foci can aid in predicting and managing plague outbreaks. Methods: Monthly data on flea index, rodent density, meteorological factors, and normalized difference vegetation index (NDVI) were collected for the study area. Generalized additive modeling (GAM) was used to analyze the non-linear and lag effects of meteorological factors on flea index and rodent density. Structural equation modeling (SEM) was employed to investigate the relationships among meteorological factors, NDVI, flea index, and rodent density. Results: GAM analysis revealed that temperature, precipitation, relative humidity, and NDVI had significant linear, non-linear, and time-lagged impacts on the density of Mongolian gerbils and the flea index. SEM analysis indicated that meteorological factors could directly influence the density and flea index of Mongolian gerbils, or indirectly impact NDVI, subsequently influencing gerbil density and the flea index. Conclusions: Meteorological factors primarily influence gerbil density and flea index indirectly by affecting NDVI and the relationship between flea index and gerbil density. This study offers additional support for the significance of meteorological factors and NDVI in influencing the vector-rodent system, offering valuable insights for predicting and managing plague outbreaks.

Global warming contributes to reduction in the intensity of Artemisia pollen seasons in Lublin, central-eastern Poland.

INTRODUCTION AND OBJECTIVE: Species of the genus Artemisia (Asteraceae) are weeds and ruderal plants growing in northern temperate regions of the world. Many of them are used in medicine and the cosmetic industry and for culinary purposes. Pollen grains of plants of this genus contain the most important aeroallergens. MATERIAL AND METHODS: An aerobiological study conducted with the volumetric method in Lublin in 2001-2022. Trend lines for the season parameters were established. Spearman's correlation and stepwise regression analyses were carried out to determine relationships between various parameters of the pollen season and meteorological factors. PCA analysis was also carried out to visually compare the pollen seasons. RESULTS: In Lublin, central-eastern Poland, the Artemisia pollen season lasted on average from the second ten days of July to the end of August, with its beginning depending on the temperature in April and May. The highest pollen concentrations were mainly recorded in the first half of August and were largely dependent on the mean temperature in June and July. The second peak in the pollen season recorded in September was associated with the presence of Artemisia annua pollen. Intense sunshine in June and the higher temperatures in June and July resulted in significant reduction in the Artemisia annual pollen sum (by 65%) over 22 years. Artemisia vulgaris is abundant in the Lublin region and contributes substantially to the amount of Artemisia pollen in the aeroplankton. CONCLUSIONS: The downward trend in the amount of Artemisia pollen was a result of the increase in temperatures observed in the summer months, and the declining rainfall rates. The global warming effect is extremely unfavourable for plants of Artemisia vulgaris, as they require moist soil substrates for growth.

Epidemic Characteristics and Meteorological Risk Factors of Hemorrhagic Fever With Renal Syndrome in 151 Cities in China From 2015 to 2021: Retrospective Analysis.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) continues to pose a significant public health threat to the population in China. Previous epidemiological evidence indicates that HFRS is climate sensitive and influenced by meteorological factors. However, past studies either focused on too-narrow geographical regions or investigated time periods that were too early. There is an urgent need for a comprehensive analysis to interpret the epidemiological patterns of meteorological factors affecting the incidence of HFRS across diverse climate zones. OBJECTIVE: In this study, we aimed to describe the overall epidemic characteristics of HFRS and explore the linkage between monthly HFRS cases and meteorological factors at different climate levels in China. METHODS: The reported HFRS cases and meteorological data were collected from 151 cities in China during the period from 2015 to 2021. We conducted a 3-stage analysis, adopting a distributed lag nonlinear model and a generalized additive model to estimate the interactions and marginal effects of meteorological factors on HFRS. RESULTS: This study included a total of 63,180 cases of HFRS; the epidemic trends showed seasonal fluctuations, with patterns varying across different climate zones. Temperature had the greatest impact on the incidence of HFRS, with the maximum hysteresis effects being at 1 month (-19 ºC; relative risk [RR] 1.64, 95% CI 1.24-2.15) in the midtemperate zone, 0 months (28 ºC; RR 3.15, 95% CI 2.13-4.65) in the warm-temperate zone, and 0 months (4 ºC; RR 1.72, 95% CI 1.31-2.25) in the subtropical zone. Interactions were discovered between the average temperature, relative humidity, and precipitation in different temperature zones. Moreover, the influence of precipitation and relative humidity on the incidence of HFRS had different characteristics under different temperature layers. The hysteresis effect of meteorological factors did not end after an epidemic season, but gradually weakened in the following 1 or 2 seasons. CONCLUSIONS: Weather variability, especially low temperature, plays an important role in epidemics of HFRS in China. A long hysteresis effect indicates the necessity of continuous intervention following an HFRS epidemic. This finding can help public health departments guide the prevention and control of HFRS and develop strategies to cope with the impacts of climate change in specific regions.

Characteristics of surface ozone pollution and its correlations with meteorological factors: the case of Xiangtan.

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.

Hydro-meteorological aspects of soil fluorides in semi-arid soils using microwave remote sensing.

Soil fluoride is a critical determinant of soil fertility, human health and crop productivity. Soil fluoride can be increased by climatic conditions, irrigation water and anthropogenic activity, and it is important to control fluoride by understanding the complex relationships between atmospheric conditions and water systems. In this research, a detailed focus is on the hydrological and meteorological aspects of soil fluorides in semi-saturated and saturated soils to discuss the impact of irrigation, capillary rise and the combination of rainfall and anthropogenic activities such as fertilizer application on the soils in the dry spell and monsoon seasons of 2021 and 2022. A Sentinel-1 data can be used to estimate fluoride levels to the above soil conditions. In an effort to estimate fluoride levels in different hydro-meteorological scenarios, we have put forward a hypothesis that focuses on understanding the potential connections between hydro-meteorological factors (precipitation, groundwater levels, and temperature) and the levels of fluoride. The findings indicate that the extensive use of groundwater for irrigation leads to a rise in fluoride levels, posing a significant threat to crop health over time. Furthermore, the combined effects of irrigation and upheaval leaching on fluoride levels have shown strong statistical conformity (R2 > 0.85) with the relevant field-measured fluoride data for the year 2022. Importantly, areas affected by F upheaval are more sensitive to the sand and clay percentage in the soil because potential and dispersion behaviour enlarge the capillaries to decelerate the upward movement. A region-based discussion details the factors contributing to the increase of fluoride in soil helpful in taking remedial measures and mitigation plans.