Impact Of Weather Research Articles

Explainable deep learning on multi-target time series forecasting: an air pollution use case

Urban air pollution represents a significant threat to public health and the environment, with nitrogen oxides, ozone, and particulate matter being among the most harmful pollutants. These contribute to respiratory and cardiovascular diseases, particularly in urban areas with high traffic and elevated temperatures. Machine learning, especially deep learning, shows promise in enhancing the prediction accuracy of prediction of pollutant's concentrations. However, the “black box” nature of these models often limits their interpretability, which is crucial for informed decision-making. Our study introduces a Temporal Selection Layer technique within deep learning models for time series forecasting to tackle this issue. This technique not only improves prediction accuracy by embedding feature selection directly into the neural network, but also enhances interpretability and reduces computational costs. In particular, we applied this method to hourly concentration data of pollutants, including particulate matter, ozone, and nitrogen oxides, from five urban monitoring sites in Graz, Austria. These concentrations were used as target variables to predict, while identifying the most relevant features and periods that affect prediction accuracy. Comparative analysis with other embedded feature selection methods showed that the Temporal Selection Layer significantly enhances both model effectiveness and transparency. Additionally, we applied explainable techniques to evaluate the impact of weather and time-related factors on air pollution, which also helped assess feature importance. The results show that our approach improves both prediction accuracy and model interpretability, leading finally to more effective pollution management strategies.

Modelling Severe Weather Impact on Traffic Mobility and Evaluating Cross-Regional Applicability to Functionally Distinct Segments in Cold Region Highway Networks

This study in Alberta, Canada, focuses on winter traffic modeling, crucial for transportation agencies in cold regions. By gathering data from six Weigh-in-motion (WIM) sites, models were developed and tested for three vehicle types. Notably, the research assessed the transferability of models between different highway segments, indicating successful transfer of coefficients to varied road functionalities. This suggests that agencies can optimize model structures for specific vehicle classes, enhancing usability without additional monitoring sites. Overall, the study highlights the feasibility of improving winter traffic models and their applicability across diverse road segments, benefiting transportation planning and management in cold climates.

Assessing demographic vulnerability and weather impacts on utility disconnections in California.

When a household is disconnected from their electric utility service the consequences can be severe, including accumulation of debt, the inability to maintain comfortable temperatures, and in the most extreme cases, homelessness or mortality. While the survey-based literature on utility disconnections has yielded important findings about which households are most likely to experience a utility shutoff, only a few existing studies have used data from utility companies themselves. In this analysis, we utilize zip-code level data from four of California's largest utility providers to measure sociodemographic disparities in disconnections, in addition to the impact of adverse weather. We find that zip codes with a higher share of vulnerable households, especially Black and Hispanic households and households with young children, face a higher number and rate of utility disconnections, even after controlling for an extensive set of factors that are commonly thought to explain higher rates of energy insecurity, including income, housing characteristics, and energy costs. Our analysis also suggests that sociodemographic and weather disparities in disconnections differ between utility types. We conclude by discussing the implications of the findings for research and policy, including the impact of regulation, utility provider practices, and more extreme weather driven by climate change.

Impact of Adverse Weather and Image Distortions on Vision-Based UAV Detection: A Performance Evaluation of Deep Learning Models

Unmanned aerial vehicle (UAV) detection in real-time is a challenging task despite the advances in computer vision and deep learning techniques. The increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. Although vision-based UAV detection methods have been proposed in recent years, a standing open challenge and overlooked issue is that of adverse weather. This work is the first, to the best of our knowledge, to investigate the impact of adverse weather conditions and image distortions on vision-based UAV detection methods. To achieve this, a custom training dataset was curated with images containing a variety of UAVs in diverse complex backgrounds. In addition, this work develops a first-of-its-kind dataset, to the best of our knowledge, with UAV-containing images affected by adverse conditions. Based on the proposed datasets, a comprehensive benchmarking study is conducted to evaluate the impact of adverse weather and image distortions on the performance of popular object detection methods such as YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS. The experimental results reveal the weaknesses of the studied models and the performance degradation due to adverse weather, highlighting avenues for future improvement. The results show that even the best UAV detection model’s performance degrades in mean average precision (mAP) by −50.62 points in torrential rain conditions, by −52.40 points in high noise conditions, and by −77.0 points in high motion blur conditions. To increase the selected models’ resilience, we propose and evaluate a strategy to enhance the training of the selected models by introducing weather effects in the training images. For example, the YOLOv5 model with the proposed enhancement strategy gained +35.4, +39.3, and +44.9 points higher mAP in severe rain, noise, and motion blur conditions respectively. The findings presented in this work highlight the advantages of considering adverse weather conditions during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure more reliable UAV detection under extreme weather conditions and image distortions.

Extreme weather impact on carbon-neutral power system operation schemes: A case study of 2060 Sichuan Province

The impact of increasingly frequent and intense climate change events on power supply and consumption has become a concern for many countries. This study evaluated the impact of extreme weather on the power system economy and security by conducting an empirical case study of the Sichuan power network to address uncertainty attributed to different extreme weather events under carbon neutrality scenarios. First, our paper proposed a CNPSSO model of four seasonal typical days by jointly considering the regional hourly disparities of load curves and renewable power output curves. Then, the model creatively introduced the effects of extreme weather on the power supply and demand curves based on various scenario analyses. The most cost-effective output strategy using various generation technologies was provided based on optimization models. Finally, Sichuan Province, a hydropower-dominated province was selected as an empirical case study. The results indicate that extreme weather significantly affects the hourly operational and dispatching schemes of Sichuan's power system. Compared to the "Normal weather scenario”, hydropower's share plummets from 83.3 % to 46.1 %, while pumped storage surges 7.1 billion kWh in summer in extremely hot and dry weather. Imported electricity rises by 24.4 %, 30.5 % and 24.8 % in dry scenario, extremely hot and dry scenario, and extremely cold scenario, respectively. Furthermore, extreme weather increases carbon emissions (mainly from thermal power and imported electricity) and costs. Specifically, when juxtaposed against normal weather, extreme drought, extreme heat drought and extremely cold lead to respective increases in carbon emissions by 32.9 %, 33.5 %, and 12.3 %. Regarding the levelized cost of electricity (LCOE), "Extremely dry scenario”, "Extremely hot and dry scenario”, and "Extremely cold scenario” drive up the LCOE by 7.2 %, 9.2 %, and 4.2 %, respectively.

Resilience assessment and recovery of airport departure flights under severe weather

In order to ensure the overall performance of the airport under severe weather, scientifically evaluate the resilience of the airport's flight operations, improve flight recovery capabilities, and alleviate the impact of severe weather effectively. This article first gives the definition of airport departure flight operation. Starting from the performance of the airport departure flight operation system, it analyzes flight departure delay time, total departure delay time, departure flight normality rate and departure airport flight operation system comprehensive resilience index four indicators to evaluate the resilience changes of the system under severe weather conditions; put forward the concept of the performance recovery strategy of the airport departure flight operation system, and established the corresponding resilience model, and used the genetic algorithm to optimize the sequence of the delayed departure flights; finally, take the "721" heavy rain event in Beijing Capital International Airport in 2012 as an example to analyze the data, obtain the performance index and resilience index of the Capital Airport under the influence of heavy rain, and use the optimized departure flight schedule to compare and analyze the changes in airport departure flight operating system performance and resilience level. The results indicate that: under the influence of heavy rain, the comprehensive resilience index of the airport departure flight operation system decreased from 0.4573 to 0.0628 , and increased to 0.2223 after the rainstorm decreased; after the flight optimization and sequencing was carried out in the recovery stage of the airport departure flight operation performance, the total number of departure flights decreased. The delay time is reduced by 24.85%, the airport performance recovery speed is increased by 13.89%after optimization, and the minimum resilience index of the optimized airport departure flight operation system is increased by 13.38%, and under the same heavy rain, the system performance is given priority to restore to the initial state, indicating that the evaluation Indicators are valid.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

Graph-GIC: A smart and parallelized geomagnetically induced current modelling algorithm based on graph theory for space weather applications

Geomagnetically Induced Current (GIC) refers to the electromagnetic response of the Earth and its conductive modern infrastructures to space weather and would pose a significant threat to high-voltage power grids designed for the alternative current operation. To assess the impact of space weather on the power grid, one needs to calculate the GIC on a national or continental scale. In this study, we developed a smart and parallelized GIC modelling algorithm, Graph GIC. This algorithm deploys a graph representing a power grid in a single-line diagram, in which substations/transformers act as nodes and transmission lines as edges. With these denotations, a power grid and its electric parameters are mathematically represented with an adjacency matrix and an admittance matrix. We used sparse matrix and parallelisation techniques to expedite the intensive computation in cases of large-scale power grids. The Graph GIC was validated with a benchmark grid, applied to the GIC calculation of the 500 kV power grid of Guangdong, China, and conducted preliminary analysis on the grid’s susceptibility to geomagnetic storms. The Graph GIC algorithm has the advantage of an intuitive and highly scalable graph representation of a power grid at any scale. It achieves high-accuracy calculation and a speedup of about 18 times after parallelisation. This algorithm could be applied to assess the impact of space weather on a power grid up to continental scales and could be incorporated into global space weather modelling frameworks.

Research on dangerous flight weather prediction based on machine learning

Abstract With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

Seasonal variation in newborn hip dysplasia: Unraveling the impact of weather on hip development

BackgroundBabies born in winter have greater incidence of developmental dysplasia of the hip (DDH) and related surgeries. How weather conditions impact hip development and how screening program reacts weather issue remain unknown. This study tests a hypothesis that winter born babies have more newborn acetabular dysplasia and laxity that is responsible of later DDH. MethodsRetrospective data from newborns who had hip ultrasonography in the first 3 days of life were analyzed. The Graf type IIc, III, IV (shallow acetabulum) and type D (laxity) were classified as abnormal. The association and risks of an abnormal hip were analyzed with gender, gestational age, fetal presentation, parity and external temperature of birth month and the last 3 months before birth using the Pearson chi-square test and logistic regression. ResultsA total of 10962 newborns participated in hip ultrasound exams voluntarily in nurseries from 2016 to 2022. Distribution of babies with Graf type I, IIa, IIc, D, III/IV hips were 88.8%, 10%, 0.5%, 0.6%, and 0.1%, respectively. Female was the most significant factor for congenital shallow acetabulum (3.8x) and hip laxity (4x) compared to male (p < 0.001). Preterm babies had a borderline lower risk of abnormal hips (0.4x, p = 0.05). Winter season is not associated with newborn abnormal hips (p = 0.36, statistical power = 80%), but a positive correlation was noted between external temperature and incidence of abnormal hips (r = 0.62, p = 0.03). Cold weather does not have a direct internal effect in acetabular dysplasia or hip laxity at birth. ConclusionsBabies who were born in winter were not associated with acetabular dysplasia and hip laxity at birth but had greater risks of late-diagnosed DDH and surgeries. The postnatal effects from weather should be addressed by a public awareness campaign, and hip screening may not be limited on the neonatal stage. Level of evidencelevel III, diagnostic.

Effects of climate change on firm credit risk: evidence from China

ABSTRACT Our study examines the impact of extreme weather on corporate credit risk, identifies the impact pathway, and explores the influences of different heterogeneity contexts. We propose a modified version of the climate risk measure, Modified Regional Wise Climate Risk Index for Company (MRW-CRIc), which considers not only the climate risk on financial loss but also company-specific factors. We find that climate risk significantly affects enterprise credit risk and that the impact of extreme weather varies across three industries. This research contributes to regulatory efforts by enhancing the measurement of climate risk and deepening understanding of its implications for market stability.

Developing correction factors for weather’s influence on the energy efficiency indicators of container ships using model-based machine learning

The International Maritime Organization employs technical and operational indicators to assess ship energy efficiency. Weather conditions significantly impact ship fuel consumption during voyages, necessitating the consideration of this influence in energy efficiency calculations. This study aims to design models for estimating the impact of weather components on fuel consumption and develop correction factors to cope with the weather effect on the fuel consumption of container ships for different sea states. Using model-based machine learning, the study analyzes noon reports and hindcasted weather data from two sister container ships. It quantifies weather-induced fuel consumption across various sea states, ranging from 2% to 20%, with an average of 7%–13% depending on the model used. Correction factors specific to each sea state are derived, and different approaches for their integration into energy efficiency indicators are proposed. This study advocates tailored weather correction factors for energy efficiency metrics tied to specific sea states, emphasizing the need for standardized weather impact assessments. Prior to any formal policy application, future work is needed to address the limitations of the present study and extend this approach to various ship types and sizes and different geographical regions.

The Effect of Weathering Conditions in Combination with Natural Phenomena/Disasters on Microplastics’ Transport from Aquatic Environments to Agricultural Soils

Concern over microplastics (MPs) in the environment is rising. Microplastics are generally known to exist in aquatic settings, but less is known about their occurrence in soil ecosystems. When plastic waste builds up in agricultural areas, it can have a negative impact on the environment and food sources, as well as have an indirect effect on all trophic levels of the food chain. This paper addresses the relationship between microplastics and the management of plastic waste, which contributes to their accumulation, and it describes the sources and the movement processes of microplastics in agricultural soils as a result of natural events and disasters. Evaluating the impact of weather on coastal microplastic contamination is critical, as extreme weather events have become more frequent in recent years. This study sheds light on how weather patterns affect the dispersion of plastic waste in terrestrial habitats, including the impacts of seasonality and extreme weather. According to the results of this review, typhoons, monsoons, rainfall, and floods contribute significantly more microplastics to the surface sediment through surface runoff and wind transport, particle redistribution caused by agitated waves, and fragmentation under intense abrasion forces. Severe weather conditions have the potential to disperse larger and more varied kinds of microplastics.

A cross-sectional study: the impact of extreme weather, sleep quality, and physical activity

This study aims to explore the impact of extreme weather condition on sleep quality and physical activity in Gorontalo using a cross-sectional research design. The data were collected from 220 respondents aged 18-25 using the Pittsburgh Sleep Quality Index (PSQI), International Physical Activity Questionnaire (IPAQ), and weather information from BMKG Gorontalo. The results showed that extreme weather, especially high temperatures, significantly reduced physical activity and affected sleep quality. Respondents with higher physical activity levels showed better sleep quality (lower PSQI scores). Extreme weather also reduces motivation to exercise, increasing the risk of health problems such as dehydration and heat stroke. In conclusion, public health interventions that promote physical activity and improved sleep quality during extreme weather conditions are urgently needed to reduce associated health risks. This study emphasises the importance of understanding the specific mechanisms of how extreme weather affects sleep and physical activity, and developing strategies to maintain healthy routines despite adverse weather conditions. Keywords: extreme weather, sleep quality, physical activity, public health, health interventions.

Impact of the high-speed solar wind stream over the low-latitude ionospheric system – a study combining Indian MOM and InSWIM observations

ABSTRACT In this study, we map the origin, acceleration, and propagation of the high-speed solar wind streams (HSS) and observe their impact on the low-latitude Earth’s ionosphere. Data from radio-sounding experiments conducted by the Indian Mars Orbiter Mission (MOM) from 2015 May 9–19 is analysed to understand the solar wind speed’s evolution at various helio-centric distances. The slope of the turbulence spectrum from 25 to 35 Rs was in the range of 0.2–0.4, indicative of the underdeveloped turbulence corresponding to the high-flow streams. It coincided with the appearance of the earth-facing coronal holes as observed in the coronal EUV images. The particle bulk velocity at L1 showed that the speeds began to rise from 400 km s−1 on May 11th–12th, reaching a peak of around 800 km s−1 on May13th–14th, followed by a gradual decrease to the average slow speeds. Geomagnetic disturbances during the same period manifested as a dip in the DST index values. The GNSS (Global Navigation Satellite System) data from the InSWIM (Indian network for Space Weather Impact Monitoring) network show an appreciable increase in the VTEC (vertical total electron content) of the ionosphere on disturbed days in entire low-latitude ionospheric region in the Indian sector. All these observed parameters correlate well with the HSS arrival. This is a unique study that connects the propagation of the HSS and its impact on near-Earth’s environment from the different vantage points in interplanetary space and proposes the application of Radio beacons to improve space weather forecasting.

Mitigating the effects of extreme weather on crop yields: insights from farm management strategies in the Netherlands

Abstract Weather extremes can drive substantial crop losses. Farm level management strategies play a critical role in mitigating the impacts of and consequences for farmer livelihoods and food security. While the impacts of extreme weather on crop yields are well documented in recent studies, these predominantly focused on expansive geographical scales and commonly overlooked the critical role of management practices in modulating the dynamics of weather-crop sensitivities. In our study, a unique data-set is used that explores the timely relationship between extreme weather and crop yields at farm level in the Netherlands. We cover 10 types of crops and elucidate the role of soil types, irrigation and nutrient application in modulating the relationship between extreme weather and crops. We show substantial impacts from drought during the growing- and harvesting period and excessive precipitation during the planting- and growing period. Severe droughts show significant (p≤0.05) reductions in yield for all crops, and lead to yield reductions up to 24 percent relative to average yields during the growing period. Meanwhile, eight crops show significant reductions in yield due to severe water excess during the planting period, with yield reductions up to 18 percent. Soils such as sand or loess amplify the negative impact of drought on crop yield, while softening the impact of excessive precipitation. Irrigation and to a lesser extent nutrient application are shown to moderately decrease the impact of extreme weather on crop yield. Our findings contribute valuable insights to guide local adaptation priorities which are critical given the projected increase in the intensity and frequency of extreme weather under climate change.

Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility. Doi: 10.28991/CEJ-2024-010-09-019 Full Text: PDF

Observation of Quiet‐Time Mid‐Latitude Joule Heating and Comparisons With the TIEGCM Simulation

AbstractJoule heating is a major energy sink in the solar wind‐magnetosphere‐ionosphere system and modeling it is key to understanding the impact of space weather on the neutral atmosphere. Ion drifts and neutral wind velocities are key parameters when modeling Joule heating, however there is limited validation of the modeled ion and neutral velocities at mid‐latitudes. We use the Blackstone Super Dual Auroral Radar Network radar and the Michigan North American Thermosphere Ionosphere Observing Network Fabry‐Perot interferometer to obtain the local nightside ion and neutral velocities at ∼40° geographic latitude during the nighttime of 16 July 2014. Despite being a geomagnetically quiet period, we observe significant sub‐auroral ion flows in excess of 200 ms−1. We calculate an enhancement to the local Joule heating rate due to these ion flows and find that the neutrals impart a significant increase or decrease to the total Joule heating rate of &gt;75% depending on their direction. We compare our observations to outputs from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM). At such a low geomagnetic activity however, TIEGCM was not able to model significant sub‐auroral ion flows and any resulting Joule heating enhancements equivalent to our observations. We found that the neutral winds were the primary contributor to the Joule heating rates modeled by TIEGCM rather than the ions as suggested by our observations.

Monitoring water quality parameters impacted by Indonesia’s weather using internet of things

Increasing need for food resources, State of Indonesia to strive to maximize the output of food production. Not only in agriculture but also aquaculture results are also trying to be improved. This is also supported by the increase of Indonesia’s national fish consumption rate from 50.69 Kg per capita in 2018 to 55.37 Kg per capita in 2021. Recent aquaculture research only explored topics about monitoring the cultivation environment. But there have been no studies exploring how bad the impact of weather on the process of farming. Hence, this study aims to measure the influence of weather on freshwater aquaculture pond water quality and analyze its impact on fish growth namely Oreochromis Sp., using pH sensors and dissolved oxygen (DO). Then a weather simulation was carried out based on Indonesia’s tropical climate, which majorly consists of sunny and rainy weather. The experimental results indicate the instability of the pH value during the rainy period. DO values tend to decrease at the end of periods of sunny weather. Moreover, fish growth analysis showed that there was a decrease in food conversion ratio (FCR) by 0.956, specific growth rate (SGR) by 2.13% and survival rate (SR) by 5.715% during rainy weather.

Pediatric appendicitis may be co-associated with meteorologic factors: A case-crossover study

BackgroundThe etiopathogenesis of appendicitis is still not fully understood. Few reports have researched the impact of weather as a cofactor for the development of appendicitis. MethodsTo investigate the potential association between the development of appendicitis and exposure to ambient meteorological conditions, a retrospective case-crossover study was conducted. Publicly available meteorologic data were matched with those from 1343 patients aged ≤19 years with histologically confirmed acute (AA; n = 996) or perforated appendicitis (PA; n = 347) treated at our pediatric surgical hospital between October 2001 and October 2018. ResultsA time-stratified conditional logistic regression analysis revealed increased odds ratios (ORs) for AA of 4–5% for each 1 hPa increase in vapor pressure during each 3–7 day cumulative moving averages (CMA) lag preceding appendectomy. This effect was stronger on stratification by age class >10 years (OR 1.05–1.08; 3–7-day CMA) and during spring (OR 1.08–1.09; 4–7-day CMA). Moreover, each 1% increase in relative humidity on the day of appendectomy was associated with a 2% decrease in the OR for PA, which was 4% during autumn under seasonal stratification. ConclusionThe role of meteorologic factors in the development of appendicitis remains largely unclear. In accordance with the literature, our results show that seasonal variations together with exposure to altered levels of humidity and vapor pressure may impact patients at different grades of appendiceal inflammation. Thus, transient fluctuations in meteorologic and seasonal variables may constitute cofactors that potentially influence the occurrence and course of pediatric appendicitis.