Prevention Plan Research Articles

Ocular Manifestations of Pediatric Rhinosinusitis: A Comprehensive Review.

Background: Rhinosinusitis is a common childhood illness that may result in a number of ocular problems. The goal of this thorough analysis is to compile the available data regarding the kinds, prevalence, and treatment of ocular problems related to juvenile rhinosinusitis. Methods: A comprehensive analysis of electronic databases, such as PubMed, Embase, and Cochrane Library, was carried out to pinpoint pertinent research articles written in English and published between the beginning and August of 2023. Included were studies that discuss ocular problems in young children suffering from rhinosinusitis. Study characteristics, ocular complication kinds, occurrence rates, and management approaches were the main topics of data extraction. Results: A significant number of pertinent research on ocular problems related to juvenile rhinosinusitis was found through the literature search. Preliminary searches indicated that orbital cellulitis, subperiosteal abscess, cavernous sinus thrombosis, and optic neuritis are probably the most often reported ocular problems. It is expected that these problems will occur at a wide range of incidence rates, from somewhat unusual to more prevalent. Depending on the severity of the consequences, management techniques are expected to require a combination of surgical and medicinal procedures. Healthcare professionals will benefit from the findings since they will improve knowledge of the clinical presentation, diagnosis, and treatment of these potentially blinding consequences. The evaluation also assists in identifying knowledge gaps and guides future directions for this field of study, both of which are critical to enhancing patient outcomes. Conclusions: The wide range of topics included in this study will help to better understand the burden of ocular consequences related to juvenile rhinosinusitis and will make it easier to build preventative and treatment plans that work better.

An operational discontinuous Galerkin shallow water model for coastal flood assessment

Hydrodynamic modeling for coastal flooding risk assessment is a highly relevant topic. Many operational tools available for this purpose use numerical techniques and implementation paradigms that reach their limits when confronted with modern requirements in terms of resolution and performances. In this work, we present a novel operational tool for coastal hazards predictions, currently employed by the BRGM agency (the French Geological Survey) to carry out its flooding hazard exposure studies and coastal risk prevention plans on International and French territories. The model, called UHAINA (wave in the Basque language), is based on an arbitrary high-order discontinuous Galerkin discretization of the nonlinear shallow water equations with SSP Runge–Kutta time stepping on unstructured triangular grids. It is built upon the finite element library AeroSol, which provides a modern C++ software architecture and high scalability, making it suitable for HPC applications. The paper provides a detailed development of the mathematical and numerical framework of the model, focusing on two key-ingredients : (i) a pragmatic P0 treatment of the solution in partially dry cells which garantees efficiently well-balancedness, positivity and mass conservation at any polynomial order; (ii) an artificial viscosity method based on the physical dissipation of the system of equations providing nonlinear stability for non-smooth solutions. A set of numerical validations on accademic benchmarks is performed to highlight the efficiency of these approaches. Finally, UHAINA is applied on a real operational case of study, demonstrating very satisfactory results.

Personalized Medicine: The Dawn of a New Era in Healthcare

The landscape of healthcare is transforming with the advent of personalized medicine, an approach that alters medical treatment to each individual's specific characteristics. This paradigm shift is driven by advancements in genomics, molecular biology, and bioinformatics, which have developed a deeper understanding of the genetic and molecular basis of diseases. This new healthcare model is based on precision and individualization, aiming to provide customized healthcare with medical decisions and treatments by considering variability in genes, environment and lifestyle of an individual patient and offer accurate diagnosis, better treatments and prevention plans. The accomplishment of the Human Genome Project in 2003 was a significant advancement in genomics that provided blueprints of the human genome [1]. Rapid progress in genomic sequencing technologies makes it feasible and cost effective. Advanced sequencing techniques including next-generation sequencing allow a detailed analysis of genetic makeup to locate mutations and genetic predispositions for better treatment decisions. Cancer is caused by genetic heterogeneity and has benefited greatly from genomic insights. Molecular profiling of tumor cells allows the identification of specific genetic mutations involved in cancer pathogenesis. Targeted therapies can then be designed to inhibit these specific molecular pathways, leading to more effective and less toxic treatments compared to traditional chemotherapy. For example, in breast cancer the identification of Human epidermal growth factor receptor 2 (HER2) mutations has led to the development of HER2-targeted therapies to improve treatment [2]. Besides oncology, personalized medicine is making progress in other fields. In cardiology, genetic testing allows identification of patients who are at risk of getting inherited cardiovascular diseases. In pharmacology, pharmacogenomics, the study of correlation between genes and immune response of an individual to drugs helps in designing most effective drugs with the least side effects for each patient. In infectious diseases, genomic sequencing of pathogenic microbes can lead to development of appropriate antimicrobial drugs to prevent outbreaks. Despite its progress, the implementation of personalized medicine is facing several challenges. The utilization of genomic data in clinical workflows demands significant changes to healthcare setting and training with concerns about data privacy, equal access to diverse populations and the ethical implications of genetic information. Currently, the universal unavailability of sophisticated bioinformatics tools to interpret complex genomic data is also a challenge. Researchers and policymakers must work in to set guidelines and standards for safe use of personalized medicine. Therefore, personalized medicine is a promising tool, which has revolutionized diagnosis, treatment, and prevention in traditional medicine. Using genomics and molecular biology, we can develop a more precise, predictive, and personalized approach to medicine. This exciting frontier must be navigated with the ultimate goal of improving patient outcomes.

Numerical simulation on the prevention and control of landslide-induced wave disasters in Wangjiashan landslide area of Baihetan reservoir

The risk management of landslide surges after water storage in large reservoirs is a major challenge in reservoir management. The water storage in the reservoir area of the Baihetan hydropower station on the Jinsha River brings the potential of Wangjiashan landslide activation. This paper proposes a mitigation measurement based on slope cutting for the Wangjiashan landslide and evaluates the feasibility and effectiveness of the management scheme through numerical simulation. Results indicate that when water storage is in a normal level, after an earthquake, the landslide will impact the Baihetan Reservoir with a maximum velocity of 8.52 m/s, generating waves with a maximum wave height of 15.63 m. By removing the upper part of the landslide, the volume of the landslide is reduced from 611 × 10 4 m 3 to 369 × 10 4 m 3 , it will cut down the maximum wave height in the Xiangbiling residential area to 3.09 m, which significantly reduces the risk of landslide surge waves. It further establishes a disaster prevention and control plan for the landslide-induced wave disaster. Overall, this study provides important theoretical and practical references for the risk management of landslide surge waves and offers valuable insights for addressing similar issues in the future.

Mortality and Place of Residence One Year Post Stroke in an Irish Cohort

Abstract Background Transition from hospital to community post stroke is a critical period in a patient's recovery. Understanding where post-stroke patients are living a year after discharge can provide valuable insights into the effectiveness of discharge planning and rehabilitation strategies. We aimed to identify location of patients one year post stroke and examine the influences on that location. Methods We performed a retrospective cohort study of stroke patients admitted to an acute hospital over a one-year period. Data on demographics, function, length of stay, stroke severity, mortality and discharge location were obtained from the in-hospital stroke registry. Patients were interviewed one year post stroke and follow up data on place of residence obtained. Results 260 strokes occurred during the 1-year period. 11% died as inpatients and 5% were uncontactable leaving 206 stroke patients included in this study. Twenty-five patients (12%) died between discharge and 1 year follow up. 150 (73%) returned home post discharge rising to 157 (76%) one year post stroke. 32 (16%) were discharged to nursing homes falling to 22 (11%) at one year. Patients in nursing homes were older (80 vs 68 years, p = 0.001), had longer inpatient stays (37 vs 9 days, p = 0.000) and had higher Modified Rankin Scores (5 vs 2, p=0.0001). Conclusion Almost one in four stroke patients died within the first year of stroke consistent with international data. Mortality was highest in the acute post stroke period, but half of all deaths occurred post hospital discharge highlighting the importance of secondary vascular prevention and advanced care planning in this population. Older age, higher functional dependence and longer length of hospital stay were all associated with nursing home admission. Post stroke morbidity and mortality remains high after hospital discharge. Further study is needed to understand cause of death in the post discharge period in this population.

Estimating casualties from urban fires: A focus on building and urban environment information

This study developed two prediction models for urban fire occurrence and related casualties via a fire accident dataset from Seoul, South Korea, from 2017 to 2021. Our models exhibit improved predictive performance by incorporating built environment features, such as building characteristics and the urban context, alongside weather and demographic data. This approach showed improved predictive performance suitable for public health implementation. Compared with the weather- and demographic-only models, our models had an 18.1 % greater fire occurrence prediction accuracy and a 10.4 % greater casualty prediction accuracy. Major variables affecting fire occurrence include building characteristics, e.g., the floor area ratio (FAR), building age, and commercial building number. Important features affecting casualty occurrence include demographic aspects, e.g., income level and weather, and network-based features, e.g., road connectivity and fire station proximity. These findings suggest that fire prevention strategies and fire casualty prevention strategies may need to differ. Furthermore, we identify high-risk zones by conducting spatial analysis and fire risk and casualty prediction on all buildings by applying our models to Seoul's Gangnam District. These contributions can promote safe and healthy urban environments by improving fire risk prediction accuracy and providing important insights into urban planning for appropriate urban fire accident response and prevention.

New media's influence on college students' sports injury prevention and treatment skills

Based on the survey of the basic situation and causes of sports injuries among college students, a prevention and treatment plan for sports injuries was formulated in conjunction with new media intervention methods. The study found that: after three months of intervention experiments conducted through three terminals of the internet platform and two concentrated online classes, there was a certain degree of improvement in college students' ability to prevent and treat sports injuries.

Operational excellence in total productive maintenance: statistical reliability as support for planned maintenance pillar

PurposeTotal productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.Design/methodology/approachIn this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.FindingsOur findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.Originality/valueIn terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

Assessing the impact of the 2021 flood event on the archaeological heritage of the Rhineland (Germany)

BackgroundArchaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage.ResultsDuring fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage.ConclusionsThe study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

Variation of surface solar radiation components from 2016 to 2020 in China: Perspective from geostationary satellite observation with a high spatiotemporal resolution

At present, traditional satellite datasets still grapple with inadequacies in terms of capturing solar radiation with fine spatiotemporal granularity. This study utilizes the high spatiotemporal resolution of CARE data, which is developed based on geostationary satellite observations, and employs multivariate analysis techniques to conduct an in-depth investigation into the multidimensional spatiotemporal variations of different types of solar radiation across various regions in China from 2016 to 2020. In addition, the potential of solar energy resources was also assessed using cluster analysis method. The results revealed an upward trend in different components of solar radiation across most of China, with shortwave radiation exhibiting a significantly negative correlation with PM2.5 concentrations (R = −0.91, p < 0.05). This finding suggests that the increase in SWR may be attributed to the effective implementation of China's Air Pollution Prevention and Control Action Plan. It suggests that China's endeavors to mitigate air pollution have not only resulted in improvements in national air quality but have also had an indirect positive effect on enhancing the potential for photovoltaic power generation. The assessment of solar energy resources potential indicated, with 99 % statistical confidence, that western China constitutes the core region for solar energy resources development, whereas northeastern and southeastern regions face certain constraints in solar energy resources utilization. Furthermore, we examined the specific influence of atmospheric circulation patterns on solar energy resources, uncovering that the El Niño phenomenon, by altering cloud distribution and variability, indirectly affects solar radiation intensity in specific regions. This study aids in understanding China's solar radiation variations, crucial for shaping effective energy policies towards carbon neutrality.

Interpretable mortality prediction model for ICU patients with pneumonia: using shapley additive explanation method

BackgroundPneumonia, a leading cause of morbidity and mortality worldwide, often necessitates Intensive Care Unit (ICU) admission. Accurate prediction of pneumonia mortality is crucial for tailored prevention and treatment plans. However, existing mortality prediction models face limited adoption in clinical practice due to their lack of interpretability.ObjectiveThis study aimed to develop an interpretable model for predicting pneumonia mortality in ICUs. Leveraging the Shapley Additive Explanation (SHAP) method, we sought to elucidate the Extreme Gradient Boosting (XGBoost) model and identify prognostic factors for pneumonia.MethodsConducted as a retrospective cohort study, we utilized electronic health records from the eICU-CRD (2014–2015) for all adult pneumonia patients. The first 24 h of each ICU admission records were considered, with 70% of the dataset allocated for model training and 30% for validation. The XGBoost model was employed, and performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHAP method provided insights into the XGBoost model.ResultsAmong 10,962 pneumonia patients, in-hospital mortality was 16.33%. The XGBoost model demonstrated superior predictive performance (AUC: 0.778 ± 0.016)) compared to traditional scoring systems and other machine learning method, which achieved an improvement of 10% points. SHAP analysis identified Aspartate Aminotransferase (AST) as the most crucial predictor.ConclusionsInterpretable predictive models enhance mortality risk assessment for pneumonia patients in the ICU, fostering transparency. AST emerged as the foremost predictor, followed by patient age, albumin, BMI et al. These insights, rooted in strong correlations with mortality, facilitate improved clinical decision-making and resource allocation.

The Impact of Cardiovascular Disease Gene Polymorphism and Interaction with Homocysteine on Deep Vein Thrombosis.

Deep vein thrombosis (DVT) affects vascular health and can even threaten life; however, its pathogenesis remains unclear. Cardiovascular disease (CVD) and DVT share common risk factors, such as dyslipidemia, aging, etc. We aimed to investigate the loci of published CVD susceptibility genes and their association with environmental factors that might be related to DVT. Genotyping by Kompetitive Allele Specific PCR (KASP), collection of lifestyle information, and determination of blood biochemical markers were performed in 165 DVT cases and 164 controls. The impact of six single nucleotide polymorphisms (SNPs) and additional potential variables on DVT morbidity was evaluated using unconditional logistic regression (ULR). To explore the high-order interactions related to genetics and the body's internal environment exposure that affect DVT, ULR, crossover analysis, and multifactor dimensionality reduction/generalized multifactor dimensionality reduction (MDR/GMDR) were employed. Sensitivity analyses were performed using the EpiR package. The polymorphisms of FGB rs1800790 and PLAT rs2020918 were significantly associated with DVT. The optimum GMDR interaction model for gene-gene (G × G) consisted of THBD rs1042579, PLAT rs2020918, and PON1 rs662. The PLAT rs2020918 and MTHFR rs1801133 polymorphisms together eliminated the maximum entropy by the MDR method. The optimum GMDR interaction model for gene-environment (G × E) consisted of MTHFR rs1801133, FGB rs1800790, PLAT rs2020918, PON1 rs662, and total homocysteine (tHcy). Those with high tHcy levels and three risk genotypes significantly increased the DVT risk. In conclusion, certain CVD-related SNPs and their interactions with tHcy may contribute to DVT. These have implications for investigating DVT etiology and developing preventive treatment plans.

Inventory and Spatial Distribution of Landslides on the Eastern Slope of Gongga Mountain, Southwest China

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains.

The Evaluation of Content Relevance and Representativeness of the New Stroke Risk Screening Scales.

Stroke is a leading cause of death and disability worldwide. Early and comprehensive risk identification is essential for identifying individuals at high risk for stroke. This study aimed to evaluate each question in the new Stroke Risk Screening Scales (SRSS) and assess the domains for content relevance and representativeness. Initially, six stroke experts were invited to evaluate the SRSS questions. The content validity index (CVI), including the item-CVI (I-CVI) and the average-CVI (Ave-CVI), was then calculated. In our study, the acceptable standards for I-CVI and Ave-CVI were ≥0.78 and ≥0.9, respectively. The results showed that all invited experts accepted the invitation and evaluated the SRSS questions. The previous version of the SRSS consisted of 33 questions. Of these, 30 questions reached an I-CVI of ≥0.78, indicating good content validity. Three questions had an I-CVI of 0.67 and were considered invalid; thus, they were deleted. The overall instrument achieved an Ave-CVI of 0.95. Comprehensive SRSS are essential for effective stroke prevention planning. By facilitating the early identification of individuals at high risk for stroke, these scales help reduce the incidence and impact of stroke. The high content validity found in this study supports the reliability of the SRSS as a screening tool. In the future, implementing such validated scales in clinical practice can improve early intervention strategies, ultimately enhancing health outcomes and optimizing the use of healthcare resources.

Training Program of Communication Skills of Endocrinologists in the Treatment of Osteoporosis in Age-Related Patients

The study examines how the developed training program improves the communication skills of endocrinologists in the treatment of osteoporosis in age-related patients, which improves the quality of communication between the doctor and the patient, helping to overcome ambivalence to positive changes in behavior and better adhere to a prevention or treatment plan.

Can Oral Traditional Chinese Medicine Be a New Option for Improving Acute Radiation Dermatitis? A Case Report of a Breast Cancer Patient

Over 90% of cancer patients undergoing radiation therapy suffer from the distressing effects of acute radiation dermatitis (ARD), with breast cancer patients being particularly affected. Although there are conventional preventive, care, and treatment plans for ARD, there is still a need for improvement. The efficacy of oral application of traditional Chinese medicine (TCM) in treating ARD is rarely discussed in existing medical literature. Here, we report the case of an 86-year-old female with invasive ductal carcinoma of the left breast, who developed ARD 2 weeks after undergoing radiation therapy following surgery. Despite the use of conventional medications orally and topically, symptoms showed no significant improvement. After 2 months of oral TCM, the symptoms improved from Grade II to normal. This clinical case provides a new perspective that oral TCM can also improve ARD.

Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors’ missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz–Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans.

Trends in human papillomavirus infection and genotype distribution among males in Chongqing, China (2017-2022): an observational study.

This study aimed to investigate Human papillomavirus (HPV) infection and genotyping in men from Chongqing and analyze age-related trends to provide insights for HPV prevention and vaccination plans tailored for men in Chongqing, China. This study is an observational study. We investigated male patients who underwent HPV screening at two hospitals in Chongqing from 2017 to 2022, totaling 4,381 cases, aged 18 to 85. HPV genotyping was conducted using the Hybribio Rapid GenoArray kit. Among the 4,381 men, the overall HPV prevalence was 14.9%, peaking at 19.3% in 2022. Single HPV infection was 11.5%, and multiple infection was 3.4%. The most prevalent genotypes were HPV-52, HPV-16, and HPV-58. Single high-risk HPV infection peaked in the 36-45 age group, while multiple high-risk HPV infection decreased with age (P-trend < 0.01). Prevalence in the ≤ 25 age group was 3.7%. Single low-risk HPV infection also decreased with age (P-trend = 0.03). Genotypes covered by the nonavalent vaccine had the highest prevalence, increasing with age. Co-infection of HPV-16 with HPV-52 and HPV-58 was common. Single HPV infections were prevalent in cases of urethritis (30.8%), anogenital warts (36.4%), and genital warts (26.3%), while multiple HPV infections were significant in anogenital warts (18.2%). Single HPV infection was predominant in Chongqing, with HPV-52, HPV-16, HPV-58, HPV-53, and HPV-39 being the most prevalent genotypes. The prevalence of genotypes covered by the nonavalent vaccine increased with age. This study offers valuable insights for developing male-specific HPV prevention strategies in Chongqing.

Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China

Fine particulate matter (PM2.5) and surface ozone air pollution have severe health consequences. Since China implemented the nationwide Air Pollution Prevention and Control Action Plan (APPCAP) in 2013, annual average PM2.5 concentrations have decreased while O3 concentration have increased in Beijing. It is unclear, however, to the extent that short-term meteorological variability has influenced these trends. To separate the influence of meteorology from long-term emissions-induced changes, we quantify the portion of the long-term PM2.5 and O3 concentration signals associated with short-term meteorological variability. Building off recent literature highlighting the regional nature of pollutant variability, we incorporate both locally observed meteorology and average regional reanalysis. We isolated sub-seasonal variability using the Kolmogorov-Zurbenko filter. Next, we trained and compared the utility of three statistical models of varying complexity (a linear model, a general additive model with splines, and a random forest) in their ability to predict out-of-sample daily concentrations. The random forest model yields the best predictive capability in holdout tests and predicts changing meteorological contributions to PM2.5 and ozone concurrent with changing concentrations across the study period. Results show an overall decrease in meteorology induced PM2.5 concentration variability at daily scales since 2013, but variability from meteorology has increased relative to mean PM2.5 concentrations. In contrast, O3 shows the opposite trend, with increasing meteorology-induced variability (meteorology-induced variability normalized by mean observed O3 has decreased). Our results show the importance of including regional meteorology in explaining local PM2.5 and O3 variability and quantify links between long-term policy implementation and short-term meteorological contributions to pollutant concentrations.

Simulation and prediction of land subsidence in Decheng District under the constraint of InSAR deformation information

Land subsidence, marked by a decline in surface elevation, poses a significant threat to urban infrastructure and safety. Accurate subsidence information and a reliable prediction model are crucial for prevention and control. In this study, we used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to obtain long-term land subsidence data and analyzed subsidence characteristics in Decheng District. By integrating hydrogeological and groundwater data, we developed a three-dimensional groundwater flow and one-dimensional compaction model through numerical simulation. Furthermore, the subsidence data monitored by PS-InSAR were used to further constrain and validate the model. The evolution trend of land subsidence under different groundwater exploitation scenarios was predicted and analyzed. The results showed that from May 2017 to December 2021, the cumulative maximum subsidence in Decheng District reached −173 mm. The subsidence area is mainly concentrated in the northern area, and its subsidence center is near Qiaoyuan Town. According to the Land Subsidence Prevention and Control Plan of Dezhou City, Shandong Province (2018–2025), we set up different groundwater mining scenarios with the goal that the rate of land subsidence in the key prevention and control area is less than 35 mm/yr in 2025.The Fluid-solid coupled model prediction analysis results indicated that a 30% reduction in groundwater exploitation is reasonable.