Multiple Meteorological Factors Research Articles

Short-term load forecasting method based on multiple meteorological factors and improved TCN-LSTM

Abstract Effective scheduling plans, optimized energy deployment, and grid stability assurance hinge on precisely forecasting short-term power loads. Therefore, this paper introduces an innovative approach to short-term load forecasting that integrates meteorological factors with an enhanced TCN-LSTM methodology. Firstly, the impact of meteorological factors on loads is quantified using Pearson’s phase relationship, facilitating the identification of meteorological input variables for the short-term load forecasting model. Secondly, a novel time-series convolutional neural network (TCN) tailored to power load characteristics is developed. The introduction of a network structure adapted to the characteristics of power loads improves the TCN model, enabling the effective capture of the long-term dependence and short-term fluctuation in load data. Finally, the paper details the construction of the improved TCN-LSTM model for short-term load forecasting. The new vectors, formed based on the load data features extracted by the improved TCN, are used as inputs for LSTM for load forecasting. The proposed short-term load forecasting method is validated using load-meteorological data from a typical period in a specific area, and the results demonstrate a notable enhancement in forecasting.

Qualitative and Quantitative Analyses of Meteorological Impacts on Fine Particle Pollution in Winters of Cold Region in China

Meteorological factors are the key drivers of air pollution. Stable weather conditions, the boundary layer height, and temperature inversion significantly influence the dispersion of particulate matter, which is also associated with the aerodynamic properties of particles. However, limited studies have been conducted on this topic in northeast China. This study investigates the influence of meteorological factors on PM2.5 pollution under cold weather conditions, employing both qualitative and quantitative methods. The key meteorological factors considered include temperature difference, relative humidity, wind speed and direction, the boundary layer height (BLH), and temperature inversion. The stable weather index (SWI) is introduced as a quantitative measure of the stability of weather based on data from the last five winters in a typical megacity of northeast China. The monthly PM2.5 concentrations recorded during the last five Februarys ranged from 59.79 μg/m3 to 85.68 μg/m3, with the highest daily concentration reaching 417 μg/m3. A new parameter, ‘temperature difference (ΔT)’, is defined in this study as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s. The temperature differences were found to have a significantly positive correlation with the differences in PM2.5 concentrations (p < 0.01). The results showed that PM2.5 pollution was associated with increased temperature, higher relative humidity, and lower wind speed, or any combination of these factors. The SWI explained 65% and 64% of the variances in air quality index (AQI) and PM2.5 pollution, respectively. When the predicted SWI exceeds 10, the likelihood of particle pollution increases. A lower BLH, in conjunction with a thicker inversion layer, contributes to the formation of severe particle pollution. In the early stages of a winter pollution episode in Harbin, the temperature inversion layer thickened and intensified, with the inversion top height reaching approximately 200 m. The boundary layer remained below 200 m, resulting in a poor vertical dispersion condition. PM2.5 pollution, therefore, is influenced by the combined effects of multiple meteorological factors. Our study quantitatively analyzed the characteristics of weather conditions and their impacts on air quality, which could provide scientific evidence for air pollution prediction and assist in making specific policy interventions, particularly for the upcoming ninth Asian Winter Games in Harbin in February 2025.

Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region.

Health-Related Parameterization Evaluating Thermal Comfort in Urban Green Spaces: Data Sourced from a Global Database and Mapping at National Scale

Outdoor thermal comfort (OTC) is one of the most important issues for people exposed to urban green space (UGS). It is mainly evaluated by assessing equivalent meteorological factors that may combine and result in human responses of perceptions about comfortable vs. uncomfortable environments. The assessment of these responses is challenged by limits in a database that is powerful enough to support big-data analysis and an essential parameter that can evaluate health-related responses to OTC in urban forests. In this study, a meta-analysis was conducted by synthesizing studies of physiological and psychological health responses of green space (GS) visitors to meteorological factors in host cities on a global scale. A total of 15,000 numerical records about the psychological and physiological responses of UGS visitors were obtained from 105 papers across 68 global cities in 21 countries. A novel parameterization was developed to evaluate OTC by normalizing changes in health-related parameters and detecting their regressions against multiple meteorological factors. It was revealed that OTC resulted in more benefits in psychological health parameters than in physiological ones for UGS visitors worldwide. Globally, health-promoting OTC environments were more found in temperate countries than in countries subjected to other climates. People in Asian countries were indicated to live in environments with higher comfort that benefitted health states in cities at low latitudes. Thermal environments tended to be harsher and more challenging, mostly too chilling, in northern cities than in the south globally. A deep learning model using the ResNet algorithm was found to perform to an expected level with R2 as high as >70% and errors controlled generally lower than 0.01. Taking mainland China for instance, it was predicted that thermal environments in eastern cities of China highly challenged the psychological health of local UGS visitors across all seasons of the year. Northern cities of China were predicted to have satisfied thermal environments that benefited psychological and psychological health responses in spring. In summer, cities in West China were predicted to have comfortable thermal environments, and in winter, environments in the southwestern cities were more comfortable.

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.

Investigating long-term changes in surface water temperature of Dongting Lake using Landsat imagery, China.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash-Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT (R = 0.822, α < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

Comparative Analysis of Machine Learning-Based Predictive Models for Fine Dead Fuel Moisture of Subtropical Forest in China

The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel on the forest surface is a critical challenge in forest fire management. Previous research on fine surface fuel moisture content has been mainly focused on coniferous forests in cold temperate zones, but there has been less attention given to understanding the fuel moisture dynamics in subtropical forests, which limits the development of regional forest fire warning models. Here, we consider the coupled influence of multiple meteorological, terrain, forest stand, and other characteristic factors on the fine dead fuel moisture content within the subtropical evergreen broadleaved forest region of southern China. The ability of five machine learning algorithms to predict the moisture content of fine dead fuel on the forest surface is assessed, and the key factors affecting the model accuracy are identified. Results show that when a single meteorological factor is used as a forecasting model, its forecasting accuracy is less than that of the combined model with multiple characteristic factors. However, the prediction accuracy of the model is improved after the addition of forest stand factors and terrain factors. The model prediction ability is the best for the combination of all feature factors including meteorology, forest stand, and terrain. The overall prediction accuracy of the model is ordered as follows: random forest &gt; extreme gradient boosting &gt; support vector machine &gt; stepwise linear regression &gt; k-nearest neighbor. Canopy density in forest stand factors, slope position and altitude in terrain factors, and average relative air humidity and light intensity in the previous 15 days are the key meteorological factors affecting the prediction accuracy of fuel moisture content. Our results provide scientific guidance and support for understanding the variability of forest surface fuel moisture content and improved regional forest fire warnings.

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China

Accurate soil type maps provide an important basis for agricultural decision-making and land degradation control. In soil classification studies, the various environmental covariates are often selected based on the soil-forming framework. Since the mapping area and available observation data are limited, meteorological and vegetation cover factors have not been fully developed, and their role in soil classification needs to be evaluated. In addition, whether deep learning has out-performance in soil classification remains to be tested. The aim of this paper is to evaluate the accuracy of deep learning modelling techniques in classifying soil type using different combinations of input variables, and evaluate the importance of soil-forming variables in soil type classification. Therefore, we collected commonly used environmental covariates in Northeast China (NEC), including multiple meteorological factors and adopted a satellite-based biophysical model (Boreal Ecosystem Productivity Simulator, BEPS) to enrich vegetation cover factors. Next, four modeling strategies were developed: the soil-forming factors of soil and relief were considered as traditional environmental covariates (T), as well as combined with meteorologic variables (T + C), vegetation cover variables (T + V) and all available environmental covariates (T + C + V). Then, the effectiveness of different modeling strategies for soil classification was explored with convolutional neural network (CNN) model and multi-layer random forest (MRF) model based on soil separability. Finally, a 30 m resolution soil type map was established. The results demonstrated that both MRF and CNN can achieve high accuracy soil classification, while the CNN model performs better. The descending order of classification accuracy based on different modeling strategies of the CNN model is shown as T + C + V: 91.08%, T + V: 88.84%, T + C: 86.82%, and T: 83.96%. Meanwhile, the separability of different soil-forming factors for soil classification is soil properties, vegetation cover, temporal variation, meteorologic and relief in descending order. For Castanozems and Brown soils, MRF has higher classification accuracy, while CNN has better performance in Meadow soils and Fluvo-aquic soils. The methodology proposed in this paper aims to achieve high accuracy soil classification, provide an approach to understand the importance of soil-forming factors for the region, as well as for different soil types, and provide references for facilitating the interpretation of misclassified areas. Our results are accurate in the core areas, and therefore, this work facilitates researchers to be able to focus more on areas where different soil types intersect, thus significantly improves efficiency and saves resources, and promises to be a useful tool for future soil surveys.

The role of meteorological factors on influenza incidence among children in Guangzhou China, 2019-2022.

Analyzing the epidemiological characteristics of influenza cases among children aged 0-17 years in Guangzhou from 2019 to 2022. Assessing the relationships between multiple meteorological factors and influenza, improving the early warning systems for influenza, and providing a scientific basis for influenza prevention and control measures. The influenza data were obtained from the Chinese Center for Disease Control and Prevention. Meteorological data were provided by Guangdong Meteorological Service. Spearman correlation analysis was conducted to examine the relevance between meteorological factors and the number of influenza cases. Distributed lag non-linear models (DLNM) were used to explore the effects of meteorological factors on influenza incidence. The relationship between mean temperature, rainfall, sunshine hours, and influenza cases presented a wavy pattern. The correlation between relative humidity and influenza cases was illustrated by a U-shaped curve. When the temperature dropped below 13°C, Relative risk (RR) increased sharply with decreasing temperature, peaking at 5.7°C with an RR of 83.78 (95% CI: 25.52, 275.09). The RR was increased when the relative humidity was below 66% or above 79%, and the highest RR was 7.50 (95% CI: 22.92, 19.25) at 99%. The RR was increased exponentially when the rainfall exceeded 1,625 mm, reaching a maximum value of 2566.29 (95% CI: 21.85, 3558574.07) at the highest rainfall levels. Both low and high sunshine hours were associated with reduced incidence of influenza, and the lowest RR was 0.20 (95% CI: 20.08, 0.49) at 9.4 h. No significant difference of the meteorological factors on influenza was observed between males and females. The impacts of cumulative extreme low temperature and low relative humidity on influenza among children aged 0-3 presented protective effects and the 0-3 years group had the lowest RRs of cumulative extreme high relative humidity and rainfall. The highest RRs of cumulative extreme effect of all meteorological factors (expect sunshine hours) were observed in the 7-12 years group. Temperature, relative humidity, rainfall, and sunshine hours can be used as important predictors of influenza in children to improve the early warning system of influenza. Extreme weather reduces the risk of influenza in the age group of 0-3 years, but significantly increases the risk for those aged 7-12 years.

Transmission Line Ice Disaster Early Warning Method Based on Ice Thickness Prediction Using GM(1, 6) Model

The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the anti-ice work of transmission line.

Investigation of the impact mechanisms and patterns of meteorological factors on air quality and atmospheric pollutant concentrations during extreme weather events in Zhengzhou city, Henan Province

Quantitative analysis of the complex relationships between meteorological factors and atmospheric pollution under extreme weather conditions is of crucial importance for future climate change assessment and air pollution control policy implementation. In this study, we used a multi-factor generalized additive model (MGAM) to investigate the nonlinear interaction between meteorological factors and atmospheric pollutants during the “7–20″ extreme precipitation event (EP) in 2021 and the heat wave event (EH) in June 2022 in Zhengzhou City. The results showed that atmospheric pollutant concentrations decreased significantly (p < 0.05) relative to the baseline concentrations during the study period. EH events usually have lower humidity than EP events, so the negative effects of precipitation and relative humidity on pollutant concentrations were significantly higher during the EP than during the EH. In contrast, the effect of temperature on O3 concentration was significantly higher than that on other pollutants during the EH. The results of MGAM indicated the changes in pollutant concentrations were significantly nonlinearly influenced by multiple meteorological factors, both during the EP and EH periods. During the EP, O3 concentration peaked when relative humidity was below 70% and temperature was around 26 °C, and PM2.5 concentration was highest when relative humidity was about 80% and hourly precipitation was below 50 mm. During the EH, high temperature (>35 °C) and lower relative humidity (<35%) aggravated atmospheric pollution. Meanwhile, we found that O3 concentration peaked in when relative humidity was below 20% and temperature reached 40 °C. In addition, there was temporal heterogeneity in the interaction effects of different meteorological factors on atmospheric pollutants during the EP and EH. Our findings have implications for the continued implementation of stringent local air quality policies and the adoption of effective air pollution prevention and control measures.

Multivariate Drought Risk Analysis for the Weihe River: Comparison between Parametric and Nonparametric Copula Methods

This study analyzed the multivariate drought risks for the Wei River basin by characterizing the interdependence between the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Both parametric and nonparametric copulas were adopted to quantify the dependence between the SPI and SPEI. The results indicated that the Gaussian copula demonstrated the best fit in most cases, while the nonparametric copula method showed superiority over the parametric models at only one out of eighteen meteorological stations. The joint return periods (TOR, TAND, and TKendall) were computed through copula modeling, providing valuable insights into the co-occurrence of extreme drought events. For the SPI and SPEI with a 50-year return period, the TOR values range from 25.5 to 37.9 years, the TAND values fluctuate between 73.4 and 1233 years, and the TKendall values range from 60.61 to 574.71 years, indicating a high correlation between the SPI and SPEI in the study area. The spatial analysis revealed varying patterns across the basin with some regions more prone to experiencing simultaneous drought conditions characterized by both the SPI and SPEI. Furthermore, our results indicated that the SPEI exhibited more severity in drought characterization than the SPI due to its consideration of temperature effects. The disparities in the spatial features of the SPI and SPEI underscore the importance of incorporating multiple meteorological factors for a comprehensive drought risk analysis. This research contributes to a better understanding of the drought patterns and their joint risks in the Wei River basin, offering valuable information for drought preparedness and water resource management.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

Enhancing Dissolved Oxygen Concentrations Prediction in Water Bodies: A Temporal Transformer Approach with Multi-Site Meteorological Data Graph Embedding

Water ecosystems are highly sensitive to environmental conditions, including meteorological factors, which influence dissolved oxygen (DO) concentrations, a critical indicator of water quality. However, the complex relationships between multiple meteorological factors from various sites and DO concentrations pose a significant challenge for accurate prediction. This study introduces an innovative framework for enhancing DO concentration predictions in water bodies by integrating multi-station meteorological data. We first construct a dynamic meteorological graph with station-specific factors as node features and geographic distances as edge weights. This graph is processed using a Geo-Contextual Graph Embedding Module, leveraging a Graph Convolutional Network (GCN) to distill geographical and meteorological features from multi-station data. Extracted features are encoded and then temporally merged with historical DO values to form time-series data. Finally, a Temporal Transformer module is used for future DO concentration predictions. The proposed model shows superior performance compared to traditional methods, successfully capturing the complex relationships between meteorological factors and DO levels. It provides an effective tool for environmental scientists and policymakers in water quality monitoring and management. This study suggests that the integration of graph-based learning and a Temporal Transformer in environmental modeling is a promising direction for future research.

The Combined Effects of Short-Term Exposure to Multiple Meteorological Factors on Unintentional Drowning Mortality: Large Case-Crossover Study.

Drowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. We aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. Unintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. A total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and -9.5% to the overall effects, respectively. This study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Exposure risk and emergency evacuation modeling of toxic gas leakage in urban areas under the influence of multiple meteorological factors

Frequent toxic gas leakage accidents in urban environments cannot be timely controlled and often cause great harm due to the many factors affecting gas diffusion. In this study, based on the coupling method of the Weather Research and Forecasting Model (WRF) and the open source OpenFOAM software platform, the chlorine gas diffusion process at different temperatures, wind speeds, and wind directions in a chemical laboratory and nearby urban areas in Beijing was studied numerically. A dose-response model was used to calculate chlorine lethality and assess exposure risk at the pedestrian level. To predict the evacuation path, an improved ant colony algorithm, a greedy heuristic search algorithm based on the dose-response model, was applied. The results demonstrated that the combination of WRF and OpenFOAM could consider the effects of various factors such as temperature, wind speed, and wind direction on the diffusion of toxic gases. The direction of chlorine gas diffusion was affected by wind direction, and the range of chlorine gas diffusion was affected by temperature and wind speed. The area of high exposure risk (fatality rate above 40%) at high temperatures was 21.05% larger than that at low temperatures. When the wind direction was opposite the building, the high exposure risk area was 78.95% smaller than that under the building direction. The present work provides a promising approach for exposure risk assessment and evacuation planning for the emergency response to urban toxic gas leakage.

Risk Assessment for Traction Power Supply System Considering the Influence of Multiple Meteorological Factors

Risk Assessment for Traction Power Supply System Considering the Influence of Multiple Meteorological Factors

ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment.

The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model.

Assessing Dynamic Changes, Driving Mechanisms and Predictions of Multisource Vegetation Remote Sensing Products in Chinese Regions

Terrestrial vegetation, a critical component of the Earth’s land surface, directly impacts the planet’s material and energy balance. This study investigated the dynamics of terrestrial vegetation in China from 2000 to 2019 using three remote sensing products (NDVI, EVI, and SIF) and explored the driving mechanisms behind these changes. We considered three meteorological factors, nine land use types, and two socio-economic factors while employing mathematical models to analyze the data. Additionally, we used the CA–Markov model to predict the spatial distribution of vegetation remote sensing products for 2020–2025. Our findings indicate the following: (1) Throughout the study period, the vegetation indices, NDVI, EVI, and SIF, all exhibited increasing trends. The SIF showed a more direct response to vegetation cover changes and was less influenced by other driving factors. The SIF outperforms the NDVI and EVI in detecting vegetation trend changes, particularly regarding sensitivity. (2) Vegetation cover changes are driven by multiple meteorological factors, such as temperature, precipitation, and relative humidity. These factors exhibit a strong spatial correlation with the distribution of vegetation remote sensing products. Among these factors, the SIF shows a higher sensitivity to temperature compared to the NDVI and EVI, while the NDVI and EVI display greater sensitivity to precipitation and relative humidity. (3) Within the study area, land use types reveal a gradient from northwest to southeast, which is consistent with the spatial distribution of the vegetation remote sensing products. For green vegetation types, the three remote sensing products exhibit varying sensitivity levels, with the SIF demonstrating the highest sensitivity to green vegetation types. (4) Overall, the future vegetation outlook in China is promising, especially in the southeastern regions where significant vegetation improvement trends are evident. However, the vegetation conditions in some northwestern areas remain less favorable, necessitating the reinforcement of ecological construction and improvement measures. Additionally, a significant positive correlation exists between population size, GDP, and vegetation remote sensing products. This study highlights the variability in the dynamics and driving mechanisms of terrestrial vegetation remote sensing products in China and employs the CA–Markov model for predicting future vegetation patterns. Our research contributes to the theoretical and technical understanding of remote sensing for terrestrial vegetation in the Chinese context.