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The sustainable management of energy sources such as wind plays a crucial role in supplying electricity for both residential and industrial purposes. For this, accurate wind data are essential to bring sustainability in energy output estimations for wind stations. The choice of an appropriate distribution function significantly affects the actual wind data, directly influencing the estimated energy output. While the Weibull function is commonly used to describe wind speed at various locations worldwide, the variability of weather information across wind sites varies significantly. Probabilistic forecasting offers comprehensive probability information for renewable generation and load, assisting decision-making in power systems under uncertainty. Traditional probabilistic forecasting techniques based on machine learning (ML) rely on prediction uncertainty derived from previous distributional assumptions. This study utilized a Bayesian Recurrent Neural Network (BNN-RNN), incorporating prior distributions for weight variables in the RNN network layer and extending the Bayesian networks. Initially, a periodic RNN processes data for wind energy prediction, capturing trends and correlation characteristics in time-series data to enable more accurate and reliable energy production forecasts. Subsequently, the wind power meteorological dataset was analyzed using the reciprocal entropy approach to reduce dimensionality and eliminate variables with weak connections, thereby simplifying the structure of the prediction model. The BNN-RNN prediction model integrates inputs from RNN-transformed time-series data, dimensionality-reduced weather information, and time categorization feature data. The Winkler index is lower by 3.4%, 32.6%, and 7.2%, respectively, and the overall index of probability forecasting pinball loss is reduced by 51.2%, 22.3%, and 10.7%, respectively, compared with all three approaches. The implications of this study are significant, as they demonstrate the potential for more accurate wind energy forecasting through Bayesian optimization. These findings contribute to more precise decision-making and bring sustainability to the effective management of energy systems by proposing a Bayesian Recurrent Neural Network (BNN-RNN) to improve wind energy forecasts. The model further enhances future estimates of wind energy generation, considering the stochastic nature of meteorological data. The study is crucial in increasing the understanding and application of machine learning by establishing how Bayesian optimization significantly improves probabilistic forecasting models that would revolutionize sustainable energy management. [ABSTRACT FROM AUTHOR]

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning.

Abstract Objective At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue. Methods Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day. Results In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B. Conclusions The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

Objective: At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue. Methods: Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day. Results: In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B. Conclusions: The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza. [ABSTRACT FROM AUTHOR]

Developing air pollution monitoring systems utilizing Unmanned Aerial Vehicles (UAVs) and a real-time platform is a significant advancement in environmental monitoring. The paper described a system incorporating multiple sensors, including particulate matter, temperature and humidity sensors, and a barometric pressure sensor to assess air quality parameters using UAVs at different altitudes. The system monitors air quality parameters from 0 to 120 meters at various altitudes. This vertical profiling capability provides a more detailed understanding of air quality variations in the vertical dimension, which is crucial for identifying pollution sources and patterns. The system leverages the Internet of Things (IoT) platform for realtime data collection, transmission, and analysis. The study suggests that the developed system could serve as an alternative for particulate matter monitoring at an urban scale. Combining UAVs, air quality sensors, and IoT technologies offers a comprehensive and dynamic approach to air quality monitoring in urban environments. [ABSTRACT FROM AUTHOR]

Large sample datasets of in situ evapotranspiration (ET) measurements with well documented data provenance and quality assurance are critical for water management and many fields of earth science research. We present a post-processed ET oriented dataset at daily and monthly timesteps, from 161 stations, including 148 eddy covariance flux towers, that were chosen based on their data quality from nearly 350 stations across the contiguous United States. In addition to ET, the data includes energy and heat fluxes, meteorological measurements, and reference ET downloaded from gridMET for each flux station. Data processing techniques were conducted in a reproducible manner using open-source software. Most data initially came from the public AmeriFlux network, however, several different networks (e.g., the USDA-Agricultural Research Service) and university partners provided data that was not yet public. Initial half-hourly energy balance data were gap-filled and aggregated to daily frequency, and turbulent fluxes were corrected for energy balance closure error using the FLUXNET2015/ONEFlux energy balance ratio approach. Metadata, diagnostics of energy balance, and interactive graphs of time series data are included for each station. Although the dataset was developed primarily to benchmark satellite-based remote sensing ET models of the OpenET initiative, there are many other potential uses, such as validation for a range of regional hydrologic and atmospheric models.

Amidst urban expansion, numerous ecological challenges have surfaced, notably the swift alterations in the net primary productivity (NPP), presenting complexities in discerning their scale and spatial distribution. Leveraging meteorological data, multi-modal remote sensing datasets, field sampling data, and a digital elevation model (DEM), this research amalgamates field assessments, model simulations, and cutting-edge remote sensing techniques to craft a city-scale NPP estimation model for vegetation rooted in the Carnegie-Ames-Stanford Approach (CASA). This improved CASA model facilitated the computation of monthly NPP for Wuhan over 2017–2020. Our analysis divulged NPP spatiotemporal trends and distribution nuances in Wuhan, and assessed meteorological influences on the same. Salient conclusions encompass: (1) Wuhan's annual NPP maximum between 984.40 and 1310.51 gC/(m2 a), averaging from 316.78 to 430.32 gC/(m2 a), and culminating in totals from 170.15 to 231.60 × 1010 gC/a. (2) Monthly NPP trajectories in Wuhan exhibited a unimodal pattern: ascending from January to June, peaking in July–August, and tapering off through September-December, with a spatial distribution characterized by lower NPP values centrally and higher values peripherally. (3) Regionally, temperature markedly influenced NPP over precipitation. This research underpins urban ecological planning, offering empirical insights and strategic guidance for fostering a sustainable coexistence between humans and their environment, and charting future urban development paradigms.

Abstract Background The spread of COVID-19 has brought challenges to health, social and economic systems around the world. With little to no prior immunity in the global population, transmission has been driven primarily by human interaction. However, as with common respiratory illnesses such as influenza some authors have suggested COVID-19 may become seasonal as immunity grows. Despite this, the effects of meteorological conditions on the spread of COVID-19 are poorly understood. Previous studies have produced contrasting results, due in part to limited and inconsistent study designs. Methods This study investigates the effects of meteorological conditions on COVID-19 infections in England using a Bayesian conditional auto-regressive spatio-temporal model. Our data consists of daily case counts from local authorities in England during the first lockdown from March–May 2020. During this period, legal restrictions limiting human interaction remained consistent, minimising the impact of changes in human interaction. We introduce a lag from weather conditions to daily cases to accommodate an incubation period and delays in obtaining test results. By modelling spatio-temporal random effects we account for the nature of a human transmissible virus, allowing the model to isolate meteorological effects. Results Our analysis considers cases across England’s 312 local authorities for a 55-day period. We find relative humidity is negatively associated with COVID-19 cases, with a 1% increase in relative humidity corresponding to a reduction in relative risk of 0.2% [95% highest posterior density (HPD): 0.1–0.3%]. However, we find no evidence for temperature, wind speed, precipitation or solar radiation being associated with COVID-19 spread. The inclusion of weekdays highlights systematic under reporting of cases on weekends with between 27.2–43.7% fewer cases reported on Saturdays and 26.3–44.8% fewer cases on Sundays respectively (based on 95% HPDs). Conclusion By applying a Bayesian conditional auto-regressive model to COVID-19 case data we capture the underlying spatio-temporal trends present in the data. This enables us to isolate the main meteorological effects and make robust claims about the association of weather variables to COVID-19 incidence. Overall, we find no strong association between meteorological factors and COVID-19 transmission.

With growing urbanization, water contamination has become a problem. The water quality is assessed using physicochemical parameters and requires manual collection. Moreover, physicochemical parameters are insufficient for water quality monitoring as heavy rainfalls and abundance of air pollutants cause water pollution. Thus, considering natural factors as influencing parameters and the latest technology for easy and global coverage for sampling, water quality monitoring is modified. This study investigates Rawal watershed with (a) physicochemical, (b) air pollutants like nitrogen dioxide (NO2), and (c) meteorological variables like wind speed for June 2018 to September 2022. Correlation and regression analysis are performed. The results show negative correlations for NO2 with total dissolved solids (TDS) (ranging, 0.51–0.85), turbidity (range, 0.53–0.65), pH (range, 0.5–0.75), and dissolved oxygen (DO) (range, 0.5–0.82), and positive correlation with electric conductivity (EC) (range, 0.54–0.85). The regression analysis with LightGBM, multi-layer perceptron (MLP), and support vector machine (SVM) is applied with air pollutants, and meteorological parameters taken as independent variables giving root-mean-square error (RMSE) (ranging, 0.015–0.18). MLP gave an RMSE of 0.18 and 0.003 for TDS and pH, respectively. SVM performed well for DO, turbidity, and EC with RMSE ranging from 0.015 to 0.027. Moreover, floods on August 2022 are taken as a case study. HIGHLIGHTS Impact assessment of air pollutants on physicochemical parameters.; Meteorological features can have a moderate impact on water quality, i.e., wind speed with chl-α, EC, DO, and TDS, and air temperature with DO and TDS in August and September.; Machine learning approaches, i.e., LightGBM, MLP, and SVM, are applied for the analysis.; Floods can have a negative impact on water quality introducing an excess of pollutants and nutrients in water.;

This multicenter study aimed to investigate the relationship between varicella incidence and meteorological factors including mean temperature, relative humidity, sunshine duration, diurnal temperature difference, wind speed, and rainfall, as previous studies have produced varying results. Our study also sought to identify potential sources of heterogeneity. Data on reported daily varicella numbers and meteorological factors were collected for 14 cities in Yunnan Province from 2017 to 2021. A distribution-lagged nonlinear model was constructed to explore the relationship between meteorological conditions and varicella incidence in each included city. We then used multiple meta-regression to explore sources of heterogeneity using demographic economics indicators, air pollutants, and geographic location as potential modifiers. The cumulative hazard effect plot showed an inverted S-shape for the relationship between temperature and varicella, with the smallest RR (relative risk) (0.533, 95% CI: 0.401–0.708) at temperatures up to 27.2 °C. The maximum RR (1.171, 95% CI: 1.001–1.371) was obtained when the relative humidity was equal to 98.5%. The RR (1.164, 95% CI: 1.002–1.352) was greatest at a diurnal temperature range of 2 °C (1.164, 95% CI: 1.002–1.352) and least (0.913, 95% CI: 0.834–0.999) at a diurnal temperature range of 16.1 °C. The maximum RR (1.214, 95% CI: 1.089–1.354) was obtained at 0 h of sunshine, and the minimum RR (0.808, 95% CI: 0.675–0.968) was obtained at 12.4 h of sunshine. The RR (0.792, 95% CI: 0.633–0.992) was minimum at a wind velocity of 4.8 m/s. Residual heterogeneity ranged from 1 to 42.7%, with PM10 (particles with an aerodynamic diameter less than 10 μm), GDP (gross domestic product), and population density explaining some of this heterogeneity. The temperature has a dual effect on varicella incidence. Varicella cases are negatively correlated with diurnal temperature range, sunshine duration, and wind speed, and positively correlated with relative humidity. GDP and PM10 may have a significant role in altering the association between temperature and varicella, while PM10 and population density may alter the association between wind velocity and varicella. [ABSTRACT FROM AUTHOR]

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being. [ABSTRACT FROM AUTHOR]

This article provides an overview of the main findings of a survey on the effects of ambient temperature and humidity on vehicular emissions of criteria pollutants and greenhouse gases. The present study is focused on the emissions of Carbon Monoxide (CO), Nitrogen Oxides (NOX), Sulfur Oxides (SOX), Particulate Matters (PM), Carbon Dioxide (CO2 ), Nitrous Oxide (N2O), and Methane (CH4 ) from gasoline engine passenger cars. In this analytical research, the International Vehicle Emissions (IVE) model was operated, using long-term meteorological data to determine the effects of various ambient temperature and humidity levels on exhaust pipe pollutants and greenhouse gas emissions. The results of present study indicated that as the ambient temperature increases from -7.5 °C to 20 °C, CO, NOX, and CH4 emissions decrease by 35.8%, 6.46%, and 21.44%, respectively, while SOX, PM, CO2, and N2O emissions remain constant. In contrast, increasing the ambient temperature from 20 °C to 37.5 °C increases the emissions of all the investigated pollutants and greenhouse gases. On the other hand, the findings showed that as the ambient humidity increases from 8% to 98% CO and CH4 emissions increase by 7.3% and 2.13%, respectively; while NOX emissions decrease by 16.84%. However, humidity changes did not have noticeable impact on the emissions of SOX, PM, CO2, and N2O. This study concluded that changes in meteorological parameters over a certain period of time, not only affect global warming, but also the emissions of criteria pollutants. [ABSTRACT FROM AUTHOR]

The present investigation arose from the current problem in the entire territory of the Department of Atlántico in the Republic of Colombia, in which the livestock sector currently lacks a reliable modernization that contributes to the planning and profitability of meat production, translated into weight gain. The main focus of the study gravitated around the ignorance of the real effect exerted by meteorological and nutritional factors on the weight gain of cattle. As a possible solution, it was proposed to carry out a statistical analysis by means of a multiple linear regression model where cattle weight gain was the dependent variable to study under the influence of the following independent variables: accumulated precipitation for two weeks (mm), average daily precipitation for two weeks (mm), average daily forage height consumed for two weeks (cm), percentage daily average of forage consumed during two weeks (%), average protein percentage of forage consumed during two weeks (%), the average maximum temperature recorded during two weeks (°C), the average minimum temperature recorded during two weeks (°C), average daily temperature variation recorded for two weeks (°C) and average relative humidity recorded for two weeks (%). All independent data values were collected in the field. Once the analysis was carried out, it was concluded that there was statistical evidence to affirm that only the independent variables "accumulated precipitation", "average precipitation", "average minimum temperature" and "relative humidity" significantly influenced the changes observed in profit of cattle weight, being formulated a multiple linear regression model that contained only the mentioned variables, the rest were discarded. On the other hand, for the constructed linear regression model, the coefficient of determination R2 = 89.3691% was obtained, that is, for the significance level α = 0.05 (95% confidence level), this determined that the model of Multiple linear regression (A) explained the behavior of the average monthly cattle weight gain by 89.3691%. It was concluded, therefore, that the present work gives veracity to the determination of previous investigations where it is also concluded that the meteorological variables directly affect the changes associated with the weight of cattle for meat production. [ABSTRACT FROM AUTHOR]

Early meteorological observations have been found for the period 1788–1813 in a collection of historical documents entitled "Medical and meteorological observation books" (Livros de observações médicas e meteorológicas) that is preserved nowadays in the Municipal Historical Archive of Almada. Almada is a Portuguese city located on the southern bank of the Tagus River, near the mouth of the river, in front of the capital city of Lisbon, which is located on the northern bank. In this work, more than 5000 meteorological readings for the period 1788–1813 have been recovered and analyzed. Daily values have been preserved for the period 1788–1789. However, only monthly values have stood the test of time for the period 1792–1813. The meteorological variables recovered are temperature, pressure, wind direction and the state of the sky. A quality control was carried out to find possible errors, either in the original data or in the digitization process. Unfortunately, there is no information in the metadata about the instruments or the observational methodology. Pressure and temperature data from modern and reanalysis datasets were used as references to study the agreement between these datasets and the Almada dataset. Daily pressure and temperature values from the Almada dataset were used to study, in particular, the meteorological conditions of the winter of 1788/1789 in Almada because this season was one of the coldest in the last 300 years in Central Europe. The complete dataset of early meteorological observations in Almada is freely available to the scientific community. [ABSTRACT FROM AUTHOR]

Abstract Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. Method A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Results Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low ( 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. Conclusion This study’s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.

• Vertical atmospheric conditions were extracted from the ECMWF 2019. • Eliminating the inputs using multi-collinearity test. • Linear, nonlinear, and hybrid models were developed. • The results of hybrid models showed the best function in predicting surface-air pollution concentrations. Most metropolitan areas experience increasing health and environmental pressure from air pollutants. Air pollutant concentrations are mostly influenced by meteorological factors, pollutant sources, and topography. The vertical atmospheric conditions can affect the surface air pollution concentration. In this paper, to evaluate the relationship between the vertical meteorological characteristics and air pollution concentrations (CO, O 3 , PM 2.5 , PM 10 and SO 2) four different models including multi-linear regression (MLR), multi-linear perceptron (MLP), generalized regression neural network (GRNN), and adaptive Neuro-Fuzzy inference system (ANFIS) were developed. The vertical temperature difference, relative humidity, U and V wind components in different altitudes were extracted from the operational archive of the European Center for Medium Range Weather Forecast (ECMWF 2019) from March 2011 to December 2017. The variance inflation factor (VIF) and tolerance tests were utilized to remove the independent variables, which made multi-collinearity. Results showed that among the studied air pollutant variables, variation of PM 10 concentration was more influenced by vertical meteorological characteristics obtained using the GRNN model with R2 = 0.960, root Mean square error (RMSE) = 0.019 and mean absolute error (MAE) = 0.005 in tearing phase and R2 = 0.940, RMSE = 0.054 and MAE = 0.019 in testing phase. The results of ANFIS model for PM 2.5 concentration were acquired highest among the other air pollutant variables in tearing phase (R2 = 0.819, RMSE = 0.036 and MAE = 0.025) and in testing phase (R2 = 0.960, RMSE = 0.001 and MAE = 0.001). The results illustrated that GRNN and ANFIS models outperform other models for air pollutants prediction using vertical atmospheric conditions in the Sanandaj, Iran. [ABSTRACT FROM AUTHOR]

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment. [ABSTRACT FROM AUTHOR]

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Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy.

This research aimed to determine the potential of the unconfined aquifer in Gorontalo City based on the landform approach. The landforms in Gorontalo City consist of alluvial plains of lake deposits, floodplains of river deposits, alluvial fans of lake deposits, hills of structural fractures of reef limestones, hills of structural fractures of pinogu volcanic rocks, and hills of intrusive bone diorite. The method used consisted of a meteorological approach in the southern hills of Gorontalo City and a dynamic approach on the plains of Gorontalo City. The calculation of groundwater availability using a meteorological approach is 421.561,67 m3/year in the structural limestone reef fractures hill, 1.198.975,33 m3/year in the structural pinogu volcanic fractures hill, and 373.062,86 m3/year in the intrusive bone diorite hill, with a total of groundwater availability using the meteorological approach, is 1.993.599,87 m3/year. Groundwater availability using a dynamic approach was 2.621.535,19 m3/year or a discharge of 49,26 lt/sec (large) in the alluvial plain of lake sediment, discharge of 17,19 lt/sec (large) in the floodplain of river sediment, and discharge of 16,65 lt/sec (large) in the alluvial fan of lake sediment. The potential value of groundwater using the dynamic method is greater than the meteorological approach because of the amount of evapotranspiration, surface runoff, and crop coefficient.

Abstract Background Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. Method Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010–2018, 2010–2019, and 2010–2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. Results The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low ( 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low ( 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low ( 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005–1015 hPa, RHU > 60%, PRE was low, TEM was between 10–20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. Conclusion All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.

Background: This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. Method: A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Results: Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. Conclusion: This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data. [ABSTRACT FROM AUTHOR]

Dogon pseudo-subjects are bare meteorological, temporal-environmental, and partonymic nouns of low referentiality/specificity that occur in fixed noun-verb collocations. The pseudo-subject controls the choice of verb in all cases, but it fails to behave like a true subject in linear position, in a quotative-subject construction, or in pronominal-subject agreement. The pseudo-subject is the sole nominal in these meteorological and temporal collocations, but in partonymic collocations it co-occurs with a true subject denoting the possessor-experiencer. The latter has all of the clear subject properties except controlling the choice of verb. Pseudo-subjects have some similarities with, but are distinct from, a range of typologically familiar phenomena including impersonal subjects, direct objects, possessums stranded by possessor raising, East Asian-style second subjects or post-topic subjects, incorporated nouns, and adverbial adjuncts. They can be classified as pseudo-incorporated nominals if this category is broad enough to include subject-like as well as object-like nominals. The relevant constructions are easily modelled in construction grammars, but not in arboreal syntax. [ABSTRACT FROM AUTHOR]

Understanding the effects of the huge diversity of cropping systems on local biodiversity is challenging but necessary to implement agroecological systems. Through a functional approach, the translation of cropping systems into resource and disturbance gradients is a promising way to decipher the relationship between cropping systems and biodiversity but has never been implemented for arthropods.To investigate the contributions of resource and disturbance gradients arising from cropping systems vs environmental context (regional effect, meteorological conditions and landscape characteristics) on beneficial arthropod communities, we used a dataset collected in 60 crop fields from three French areas over a 5‐year period. It includes all farmer interventions, crop sequences, meteorological data, landscape composition and carabid samplings.We found that environmental context contributed to about 75% of explained carabid variations on average, while resource and disturbance gradients contributed to about 25% of explained carabid variations. The resource and disturbance gradients were particularly important in winter and in the spring preceding the spring–summer period to determine carabid variations.Moreover, we identified thresholds above which resource and disturbance gradients start being beneficial or detrimental for carabids. For example, increased treatment index in spring decreased the total activity density of carabids during the spring–summer period.Synthesis and applications. While implementing for the first time a functional approach to understand the effects of different facets of cropping systems on arthropods, our study also allows us to identify periods and thresholds above which specific practices affect carabids. Finally, avoiding bare soils all along the year, keeping pesticide use intensity below the identified thresholds and adopting a wide diversity of tillage strategies at the landscape scale should all contribute to enhance in‐field carabid occurrences and richness. [ABSTRACT FROM AUTHOR]

IntroductionEnvironmental factors are closely associated with pediatric epistaxis. Whether this association differs according to age has not been previously reported. Therefore, we tried to evaluate the differences in associations between environmental factors and epistaxis in children of different ages.MethodsA total of 20,234 patients with epistaxis who visited the hospital between January 1, 2002, and December 31, 2015, were enrolled in this study. The patients were divided into two groups according to their ages: preschool-aged (10 μm diameter), temperature, humidity, sunshine duration, and sulfur dioxide concentration were associated with epistaxis in school-aged children.ConclusionThis study indicates that the differences in environmental risk factors for epistaxis are associated with the patient’s age.

Large sample datasets of in situ evapotranspiration (ET) measurements with well documented data provenance and quality assurance are critical for water management and many fields of earth science research. We present a post-processed ET oriented dataset at daily and monthly timesteps, from 161 stations, including 148 eddy covariance flux towers, that were chosen based on their data quality from nearly 350 stations across the contiguous United States. In addition to ET, the data includes energy and heat fluxes, meteorological measurements, and reference ET downloaded from gridMET for each flux station. Data processing techniques were conducted in a reproducible manner using open-source software. Most data initially came from the public AmeriFlux network, however, several different networks (e.g., the USDA-Agricultural Research Service) and university partners provided data that was not yet public. Initial half-hourly energy balance data were gap-filled and aggregated to daily frequency, and turbulent fluxes were corrected for energy balance closure error using the FLUXNET2015/ONEFlux energy balance ratio approach. Metadata, diagnostics of energy balance, and interactive graphs of time series data are included for each station. Although the dataset was developed primarily to benchmark satellite-based remote sensing ET models of the OpenET initiative, there are many other potential uses, such as validation for a range of regional hydrologic and atmospheric models.

This study aims to analyze the prospects of salt industries in Maluku Province based on climate information. The method used in this study is qualitative with a descriptive approach that uses meteorological time series data obtained from the BPS-Statistics Indonesia of Maluku Province. The data collected is the amount of rainfall, number of rainy days, and temperature in 2018. The analysis results conclude that Maluku Province has prospects for developing salt industries. Based on the climate information, the most appropriate locations for the industry establishment are Saumlaki, Geser, and Namlea, and the recommended period for production is July to November.

The associations of air pollution and meteorological factors with the outpatient visits of urticaria remain poorly studied. This study aimed to assess the association between air pollution, meteorological factors, and daily outpatient visits for urticaria in Shijiazhuang, China, during 2014–2019. Daily recordings of air pollutant concentrations, meteorological data, and outpatient visits data for urticaria were collected during the 6 years. Descriptive research methods were used to describe the distribution characteristics and demographic features of urticaria. A combination of the generalized linear regression model (GLM) and distribution lag nonlinear model (DLNM) was used to evaluate the lag association between environmental factors and daily outpatient visits for urticaria. Stratified analyses by gender (male; female) and age (< 18 years; 18–39 years; > 39 years) were further conducted. The dose–response relationship between daily urticaria visits and CO, NO2, O3, temperature, and relative humidity was nonlinear. High concentrations of CO, NO2, O3, and high temperatures increased the risk of urticaria outpatient visits. The maximum cumulative association of high concentrations of CO, NO2, and O3 was lag 0–14 days (CO: RR = 1.10, 95%CI: 1.06, 1.31; NO2: RR = 1.09, 95%CI: 1.01, 1.08; O3: RR = 1.16, 95%CI: 1.08, 1.25), and high temperatures was lag 0–7 days (RR = 1.27, 95%CI: 1.14, 1.41). Low concentrations of NO2, O3, and high humidity, on the other hand, act as protective factors for urticaria outpatient. The maximum cumulative association of low concentrations of NO2 was the 0-day lag (RR = 0.97, 95%CI: 0.95, 0.99), O3 was lag 0–5 days (RR = 0.94, 95%CI: 0.88, 0.99), and high humidity was lag 0–10 days (RR = 0.93, 95%CI: 0.89, 0.98). Stratified analyses showed that the risk of urticaria outpatient visits was higher for the males and in the < 18 years age group. In conclusion, we found that the development of urticaria in Shijiazhuang has a distinct seasonal and cyclical nature. Air pollutants and meteorological factors had varying degrees of influence on the risk of urticaria outpatient visits. This study provides indirect evidence for a link between air pollution, meteorological factors, and urticaria outpatient visits. [ABSTRACT FROM AUTHOR]

Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally. [ABSTRACT FROM AUTHOR]

Background: Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. Method: Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010–2018, 2010–2019, and 2010–2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. Results: The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005–1015 hPa, RHU > 60%, PRE was low, TEM was between 10–20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. Conclusion: All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments. [ABSTRACT FROM AUTHOR]

Late frost is one of the most destructive meteorological disasters in the Loess Plateau of China. A great threat can be posed to the sustainable production of apples, leading to great economic losses in the apple industry. Thus, it is a high demand to explore the occurrence of late frost events for the prevention of apple late frost disasters in the decision-making of the local apple industry. In this study, the late frost return periods were investigated on the duration and severity of late frost events using the Coupla functions. The reliability of the model was then verified to analyze the characteristics of apple late frost. The study area was taken as the apple-producing areas of Shaanxi Province in western China. The meteorological datasets were collected from the seven weather stations from 1971-2018. The daily minimum temperature (Tmin) of 0℃ was taken as the critical temperature for the occurrence of apple late frost events, in order to extract the two characteristic variables of duration and severity of late frost events. These characteristic variables of late frost events were first fitted by seven common distribution functions, respectively. Kolmogorov Smirnov (K-S) test was then carried out to verify the model. The joint distributions of late frost characteristic variables were constructed to evaluate the goodness-of-fit using six Copula functions. The occurrence probability and return period of late frost events were analyzed with the optimized Copula functions. The results showed that the severity of late frost risks generally increased from the southeast to northwest in the study area from 1971 to 2018. The optimal marginal distribution of late frost duration was in the log-normal distribution, while there was a great difference in the optimal marginal distributions of late frost severity. A significant positive correlation was found between the duration variables and the severity of late frost at each station. The goodness-of-fit test demonstrated that most of the stations presented the optimal distribution of Normal Copula function for late frost events. The joint cumulative probability increased significantly, as the severity and duration of late frost increased, but the increasing trend was much slower than before. A much more significant increase was observed in the co-occurrence return period under the same increase of univariate value, compared with the joint. The univariate return period was always between the joint and co-occurrence return period. Once the univariate return period was small enough, the optimal range of the actual univariate return period can be estimated, according to the joint and the co-occurrence return period. In general, a low probability was found in the late frost events with long duration and high severity at the weather stations in apple-producing areas in Shaanxi Province of China. However, the stations in the Yan’an area were more susceptible to the late frost events with high severity or long duration, as well as the late frost events with both high severity and long duration. Thus, more attention can be paid to late frost risks in the Yan’an area. This finding can provide a theoretical base to deal with the late frost disaster in apple production. [ABSTRACT FROM AUTHOR]

The variation in refractive index of the atmosphere causes wavefront aberration of optical signal propagating through the random atmosphere. This leads to various effects such as beam spreading, wandering and scintillation (intensity fluctuation) which are responsible for severe signal degrading of the free space optical (FSO) communication system. Incorporation of adaptive optics (AO) technique mitigates the effect of wavefront aberration distortions and reducing the signal fading. Non-conventional adaptive optics is demonstrated using complex search algorithms which requires large number of iterations and computations to obtain the optimum value of the results. In this paper, Convolutional Neural Networks (CNN)-based novel wavefront aberration compensation technique is proposed. The aim of this work is to experience and validate the CNN-based wavefront sensorless adaptive optics (WFSLess AO) technique for 70 m realtime FSOC. The major issues such as time-consuming iterative processes and latency in complex search algorithms and similar algorithms are greatly avoided by detecting the wavefront distortion from the direct images of the optical beam used in the experimentation. LeNet CNN architecture is realized to compensate wavefront distortion for the acquired data sample under different weather condition. Experiments are conducted on Xilinx Spartan-6 FPGA using high level synthesis. The performance of the proposed scheme is compared to existing approaches (T Weyrauch and MA Verontsov in Appl. Opt. 44:6388–6401, 2005; MJ Booth in Opt. Express 14:1339–1352, 2006; H Linhai and R Changhui in Opt. Express 19:371–379, 2011) and it shows an effective Strehl Ratio, Root Mean Square and reduced computation time thereby resolves the latency issue while maintaining accuracy which is a serious problem in AO systems. [ABSTRACT FROM AUTHOR]

Objectives The aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India. Methods Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India. Results In total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p

Abstract Background There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda). Methods Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity. Results Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6–12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0–5 years old (IRR = 2.07 (1.40, 3.07)). Discussion Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models. Conclusion The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health.

Early meteorological observations have been found for the period 1788–1813 in a collection of historical documents entitled “Medical and meteorological observation books” (Livros de observações médicas e meteorológicas) that is preserved nowadays in the Municipal Historical Archive of Almada. Almada is a Portuguese city located on the southern bank of the Tagus River, near the mouth of the river, in front of the capital city of Lisbon, which is located on the northern bank. In this work, more than 5000 meteorological readings for the period 1788–1813 have been recovered and analyzed. Daily values have been preserved for the period 1788–1789. However, only monthly values have stood the test of time for the period 1792–1813. The meteorological variables recovered are temperature, pressure, wind direction and the state of the sky. A quality control was carried out to find possible errors, either in the original data or in the digitization process. Unfortunately, there is no information in the metadata about the instruments or the observational methodology. Pressure and temperature data from modern and reanalysis datasets were used as references to study the agreement between these datasets and the Almada dataset. Daily pressure and temperature values from the Almada dataset were used to study, in particular, the meteorological conditions of the winter of 1788/1789 in Almada because this season was one of the coldest in the last 300 years in Central Europe. The complete dataset of early meteorological observations in Almada is freely available to the scientific community.

The traditional optimization maize nitrogen fertilizer schedule tends to be fixed, even in different meteorological years. Because different meteorological conditions will affect the use efficiency of nitrogen fertilizer, this method of fertilization will limit the yield of maize. Based on the DSSAT crop model, this paper simulated the optimized nitrogen fertilizer schedule of maize in Central Jilin Province of China from 1973 to 1992 on the basis of verified DSSAT model. It was found that the optimized nitrogen fertilizer schedule in different meteorological years had significant differences, and this optimized nitrogen fertilizer schedule changed with different meteorological years and could increase maize yield by 3.9% compared with the fixed optimized nitrogen fertilizer schedule. At the same time, there was a significant positive correlation between amount of nitrogen fertilizer and rainfall in the stages of sowing, VJ and VT. In these stages, the amount of nitrogen fertilizer should be increased with the increase in rainfall. Finally, an optimized agrometeorological prediction method was proposed to provide a theoretical basis for the real-time optimization schedule of maize nitrogen fertilizer in the future. HIGHLIGHTS The optimal fertilization schedule under different meteorological years.; The nitrogen fertilizer amount along rainfall level in different growing stage.; Contribute to the real-time fertilizer schedule.;

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.

ObjectiveClimate and environmental change is a well-known factor causing bronchial asthma in children. After the outbreak of coronavirus disease (COVID-19), climate and environmental changes have occurred. The present study investigated the relationship between climate changes (meteorological and environmental factors) and the number of hospitalizations for pediatric bronchial asthma in Suzhou before and after the COVID-19 pandemic.MethodsFrom 2017 to 2021, data on daily inpatients diagnosed with bronchial asthma at Children's Hospital of Soochow University were collected. Suzhou Meteorological and Environmental Protection Bureau provided daily meteorological and environmental data. To assess the relationship between bronchial asthma-related hospitalizations and meteorological and environmental factors, partial correlation and multiple stepwise regression analyses were used. To estimate the effects of meteorological and environmental variables on the development of bronchial asthma in children, the autoregressive integrated moving average (ARIMA) model was used.ResultsAfter the COVID-19 outbreak, both the rate of acute exacerbation of bronchial asthma and the infection rate of pathogenic respiratory syncytial virus decreased, whereas the proportion of school-aged children and the infection rate of human rhinovirus increased. After the pandemic, the incidence of an acute asthma attack was negatively correlated with monthly mean temperature and positively correlated with PM2.5. Stepwise regression analysis showed that monthly mean temperature and O3 were independent covariates (risk factors) for the rate of acute asthma exacerbations. The ARIMA (1, 0, 0) (0, 0, 0) 12 model can be used to predict temperature changes associated with bronchial asthma.ConclusionMeteorological and environmental factors are related to bronchial asthma development in children. The influence of meteorological and environmental factors on bronchial asthma may be helpful in predicting the incidence and attack rates.

Drought vulnerability is the extent to which an area is susceptible to damage as well as causing a threat to human society. Drought frequently occurs in the Indian state of Rajasthan, and so far, very little attention has been paid towards its vulnerability assessment. Therefore, the present study focuses on a spatial multi-criteria integrated technique for an all-out drought vulnerability assessment and mapping consisting of geographic information systems (GIS) and analytic hierarchy process (AHP) techniques. The data have been acquired from various secondary sources pertaining to a total of 16 indicators under meteorological (rainfall, temperature and evapotranspiration), hydrological (hydrogeology, elevation, groundwater level, groundwater development and surface water bodies), agricultural (available water holding capacity of soils, land use and slope) and socio-economic (density of population, female-to-male ratio, irrigated land, agriculture-dependent population and deep tube wells) drought categories. Further, spatial layers for each category have been developed by various GIS operations followed by the calculation of weights for each drought category and type employing pair-wise comparison matrices by means of AHP criterion. Afterwards, individual drought category and comprehensive drought vulnerability maps have been prepared by employing the weighted overlay technique. The generated maps have effectively displayed the areal spreads and levels of drought vulnerability with respect to normal, mild, moderate, severe and extreme category of droughts. The findings from this study have demonstrated a proneness of severe to extreme drought vulnerability in 25% area of the state. Distinctively, the eastern, western, central and small pockets of south-western parts of the state have witnessed severe to extreme drought vulnerability, while the remaining areas have demonstrated normal to moderate drought vulnerability. The results of the overall drought vulnerability have been validated by employing normalized difference vegetation index and past occurrence of drought disasters, which revealed an accuracy of 81%. The obtained results prove the effectiveness of geoinformatics and AHP techniques in comprehensive drought vulnerability assessment and mapping. Finally, the findings of the present study may be easily applied for designing suitable drought mitigation strategies of the vulnerable areas. [ABSTRACT FROM AUTHOR]

Based on environmental monitoring data and meteorological observation data of the Chinese major energy province, Shanxi, from 2015 to 2020, using the satellite remote sensing data of Atmospheric Infrared Sounder Instrument (AIRS) and Ozone Monitoring Instrument (OMI) in 2017, we analyzed the characteristics of surface ozone (O3) pollution and its correlation with meteorological factors, as well as the vertical distribution of O3 in typical pollution cities in Shanxi Province. The results showed that surface O3 became the primary pollutant in Shanxi. Surface O3 has shown a zonal distribution with a high level in the south and a low level in the north region since 2017. Surface O3 pollution was severe in 2019, and the maximum daily 8 h running average of O3 (MDA8 O3) decreased, but annual mean O3 in northern and central regions showed a slow rising trend in 2020. Comprehensive analyses of the influence of meteorological factors on surface O3 indicated that O3 pollution in Linfen, Yuncheng and Taiyuan was mainly caused by local photochemical reactions, while that in Jincheng, Xinzhou, Lvliang and Yangquan resulted from regional transports. O3 volume mixing ratios (VMR) in the middle and lower troposphere generally increased with altitude, peaking at 120 ppbv at approximately 400 hPa. The positive vertical gradient of O3 in the boundary layer was obvious in Taiyuan in summer and significant in the surface layer in Taiyuan and Linfen during winter and spring, which was associated with greater atmospheric dynamic stability and suppressed vertical mixing. Due to the lack of direct detection of O3 in the lower troposphere in this region, O3 vertical distribution retrieved by satellite observation is critical for the study of vertical mixing and transport of local O3, as well as its regional transport characteristics. [ABSTRACT FROM AUTHOR]

Soil moisture dynamics play an active role in ecological and hydrological processes. Although the variation of the soil water moisture of multiple ecosystems have been well-documented, few studies have focused on soil hydrological properties by using a drying and weighing method in a long time series basis in the Qinghai-Tibet Plateau (QTP). In this study, 13 year (2008–2020) time-series observational soil moisture data and environmental factors were analyzed in a humid alpine Kobresia meadow on the Northern Qinghai–Tibetan Plateau. The results showed no significant upward trend in soil water content during the 2008–2020 period. In the growth season (May–October), the soil water content showed a trend of decreasing firstly, then increasing, and finally, decreasing. Correlation analysis revealed that five meteorology factors (temperature, humidity, net radiation, dew point temperature, and vapor pressure) and a biomass element (above-ground biomass) had a significant effect on the soil moisture, and air temperature impacted the soil water variation negatively in 0–50 cm, indicating that global warming would reduce soil moisture. Humidity and net radiation made a difference on shallow soil (0–10 cm), while dew point temperature and vapor pressure played a role on the deep soil (30–50 cm). Above-ground biomass only effected 30–50 cm soil moisture variation, and underground biomass had little effect on the soil moisture variation. This indirectly indicated that below-ground biomass is not limited by soil moisture. These results provide new insights for the rational allocation of water resources and management of vegetation in alpine meadows, in the context of climate change. [ABSTRACT FROM AUTHOR]

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