Analysis Of Distribution Data Research Articles

Aerosol particle number concentration, ultrafine particle number fraction, and new particle formation measurements near the international airports in Berlin, Germany – First results from the BEAR study

Studies revealed airports as a prominent source of ultrafine particles (UFP), which can disperse downwind to residential areas, raising health concerns. To expand our understanding of how air traffic-related emissions influence total particle number concentration (PNC) in the airport’s surrounding areas, we conduct long-term assessment of airborne particulate exposure before and after relocation of air traffic from “Otto Lilienthal” Airport (TXL) to Berlin Brandenburg Airport “Willy Brandt” (BER) in Berlin, Germany. Here, we provide insights into the spatial–temporal variability of PNC measured in 16 schools recruited for Berlin-Brandenburg Air Study (BEAR).The results show that the average PNC in Berlin was 7900 ± 7000 cm−3, consistent with other European cities. The highest median PNC was recorded in spring (6700 cm−3) and the lowest in winter (5100 cm−3). PNC showed a bi-modal increase during morning and evening hours at most measurement sites due to road-traffic emissions. A comparison between measurements at the schools and fixed monitoring sites revealed good agreement at distances up to 5 km. A noticeable decline in this agreement occurred as the distance between measurement sites increased. After TXL was closed, PNC in surrounding areas decreased by 30 %. The opposite trend was not seen after BER was re-opened after the COVID-lock-down, as the air traffic has not reached the full capacity yet. The analysis of particle number size distribution data showed that UFP number fraction exhibit seasonal variations, with higher values in spring and autumn. This can be explained by nucleation events, which notably affected PNC.The presented findings will play a pivotal role in forthcoming source attribution and epidemiological investigations, offering a holistic understanding of airports’ impact on airborne pollutant levels and their health implications. The study calls for further investigations of air-traffic-related physical–chemical pollutant properties in areas found further away (> 10 km) from airports.

Tectonic and eustatic controls on the palaeobiogeographic distribution of Eocene bivalves

ABSTRACT The Eocene epoch experienced important global events—a warming and transgression at the beginning, and a cooling and regression at the end. Crucial tectonic rearrangements were also taking place, such as the northward movement of the Indian plate and closure of the Tethys Sea. Here, the global palaeobiogeography of all Eocene bivalve genera is studied to see the effects of these tectonic and eustatic changes. We have established formal palaeobiogeographic provinces and explored their evolving relationships based on statistical analyses of global distribution data of all bivalve genera for each stage of the Eocene. The late Paleocene (Thanetian) and early Oligocene (Rupelian) stages have also been included to understand potential effects of climate and sea-level changes. Bivalve diversity increased at the beginning and fell at the end of the Eocene, apparently in response to opposite climatic and eustatic changes at these boundaries. The palaeobiogeographic provinces also expanded and shrank, respectively. The tropical and subtropical basins of northwestern Europe and eastern and western America accommodated maximum bivalve diversity and yielded temporally stable provinces. These provinces shared many bivalve genera. However, similarity of the western American fauna with that of eastern America dwindled with time as the Western Interior Seaway disappeared. The distribution sequence of shared diachronous genera between the provinces revealed that (1) the Western American Province later developed affinity to the Japan and Eastern Russian Province facilitated by the Alaska and Oyashio currents, (2) the Northern Equatorial Current and North Atlantic Gyre were primarily responsible for the similarity between the Tethyan and Atlantic provinces, and (3) the Tethys seaway was active until the middle Eocene, which allowed westward migration of the western Indian fauna leading to its similarity with the European and northern African faunas. This link was severed in the late Eocene.

DETERMINANT FACTORS OF THE MENSTRUAL CYCLE IN GRADUATE NURSING STUDENTS

According to WHO, more than 75% of women experience menstrual disorders and on average occur at the age of 17-29 years. One of the factors that influence the occurrence of irregular menstrual cycles is poor sleep quality. This study aims to determine the relationship between sleep quality and menstrual cycle in undergraduate nursing students at Harapan Bangsa University. This type of research is an analytic survey with a cross sectional design. The population in this study were 316 undergraduate nursing students and a sample of 174 students with Proportional random sampling method. Instruments used Pittsburgh Sleep Quality Index (PSQI) and menstrual cycle questionnaire. Data analysis used bivariate analysis for frequency distribution and univariate data analysis with chi square to determine the relationship. The results of this study obtained the frequency distribution of sleep quality obtained the highest results (79.3%) in the bad category and the menstrual cycle obtained results (67.8%) in the normal menstrual cycle category, with a p value = 0.151 (p> 0.05). The conclusion is that there is no significant relationship between sleep quality and menstrual cycle in Nursing Study Program Undergraduate Students at Harapan Bangsa University.

A real-world data analysis of distribution and inconsistency between total serum IgE and allergen-specific IgE results in clinical practice.

The inconsistency between serum total IgE (tIgE) and allergen-specific IgE (sIgE) results is often encountered in clinical practice, but the distribution and influencing factors of the inconsistent results have not been fully understood. The aim of this study was to analyze the distribution and inconsistency between tIgE and sIgE test results. A retrospective study, from the electronic medical records of 2139 patients who underwent both tIgE and sIgE tests, from January to December 2023 was reviewed. The tIgE and sIgE results and their distribution, as well as their inconsistency, were analyzed based on sex, age, and disease subgroups. 36.2% of the patients had a positive sIgE, and 43.7% had an elevated tIgE level. sIgE and tIgE results were discordant in nearly 30% of patients, with no difference between genders, while individuals aged over 60 exhibited a significantly higher inconsistency rate than the other age groups, and the inconsistency rate between tIgE and sIgE results was significantly different among different tIgE levels, sIgE grades, positive allergen count and positive allergen types. In addition, patients with chronic urticaria (CU) had a higher inconsistency rate than those with other allergic diseases, but the difference was not statistically significant. The overall inconsistency rate between tIgE and sIgE results was about 30%. The elderly group older than 60 years old is more likely to have inconsistent results, and tIgE level, sIgE level, the number and type of positive allergens also affected the consistency of tIgE and sIgE results.

Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map

The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I—VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl−), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.

Data-Driven Channel Modeling for End-to-End Visible Light DCO-OFDM Communication System Based on Experimental Data

End-to-end systems have been introduced to address the issue of independent signal processing module design in traditional communication systems, which prevents achieving global system optimization. However, research on indoor end-to-end Visible Light Communication (VLC) systems remains limited, especially regarding the channel modeling of high-speed, high-capacity Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) systems. This paper proposes three channel modeling methods for end-to-end DCO-OFDM VLC systems. The accuracy of the proposed methods is demonstrated through R-Square model fitting performance and data distribution analysis. The effectiveness of the proposed channel modeling methods is further validated by comparing the bit error rate (BER) performance of traditional receivers and existing deep learning (DL)-based receivers. The results show that the proposed methods can effectively mitigate both linear and nonlinear distortions. By employing these channel modeling methods, communication systems can reduce the spectral occupancy of pilot signals, thereby significantly lowering the complexity of traditional channel estimation methods. Thus, these methods are suitable for use in end-to-end VLC communication systems.

Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications

Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lakes, leaving gaps in understanding pollution patterns in specific basins and influencing factors.We compiled comprehensive sediment contamination data from literature and public datasets, including hydro-geomorphological, climatic, soil, landscape, and anthropogenic factors. Using advanced machine learning, we analyzed typical pollution factors to infer potential sources and migration pathways of pollutants and predicted pollution levels in basins with limited data availability. Our analysis of pollutant distribution data revealed that Cd had the most extensive pollution range, with the most severe pollution occurring in the Huaihe and Yangtze River basins. Furthermore, we identified distinct groups of driving factors influencing various heavy metals. Cd, Cr, and Pb were primarily influenced by human activities, while Cu and Ni were affected by both anthropogenic and natural factors, and Zn tended more towards natural sources. Our predictions indicated that, in addition to the typical highly polluted areas, the potential risk of Cd, Cu and Ni is higher in Xinjiang, and in Tibet and Qinghai, the potential risk of Cd, Cr, Cu and Ni is higher. Pb and Zn presented lower risks, except in the Huaihe and Yangtze River Basins. Temperature, wind, precipitation, precipitation rate, and the cation exchange capacity of soil significantly impacted the predictions of heavy metal pollution in sediments, suggesting that particulate migration, rainfall runoff, and soil erosion are likely the main pathways for pollutant migration into sediments. Considering the migration, pathways, and sources of pollutants, we propose strategies such as low-impact development and promoting sustainable transportation to mitigate pollution. This study provides the latest insights into heavy metal pollution in Chinese lake sediments, offering references for policy-making and water resource management.

Knowledge and Attitude to Quality of Life Elderly Complete Denture in New Normal

Background: Knowledge and attitudes of the community, especially older people who wear complete dentures, can influence the quality of life. Quality of life in older people with tooth loss can be treated by making dentures, which are generally complete dentures. Objective: This study determined the relationship between knowledge and attitudes towards the quality of life of elderly complete denture users during the new normal period. This type of research is an analytical survey. Methods: The research method is analytic survey research using a cross-sectional design. The research sample used a purposive sampling technique with a sample size of elderly patients using complete dentures, totaling 36 respondents at the Baiturrahmah General Hospital during the new normal period in January-December 2022. Univariate data analysis processing was presented as a frequency distribution and bivariate data analysis using the Spearman correlation test. Results: Significant (p0.05) in the good knowledge category for 20 (55.6%) respondents, positive attitude category for 27 (75%) respondents, and high quality of life category for 18 (50%) respondents. Conclusion: Knowledge of Quality of life 0.01 (p0.05) and attitudes towards quality of life 0.05 (p0.05).

Geographic conditions impact the relationship between plant phenology and phylogeny

Plant phenology is influenced by a combined effect of phylogeny and climate, although it is yet unclear how these two variables work together to change phenology. We synthesized 107 previously published studies to examine whether phenological changes were impacted by both phylogeny and climate changes in various geographical settings globally. Phenological observation data from 52,463 plant species at 71 sites worldwide revealed that 90 % of phenological records showed phylogenetic conservation. i.e., closely related species exhibited similar phenology. To explore the significant and non-significant phylogenetic conservation between plant phenophases, our dataset comprises 5,47,000 observation records from the four main phenophases (leaf bud, leaf, flower, and fruit). Three-dimensional geographical distribution (altitude, latitude, and longitude) data analysis revealed that plant phenology may exhibit phylogenetic signals at finer special scales (optimal environmental conditions) that vanish in high altitude and latitude regions. Additionally, climatic sensitivity analysis suggested that phylogenetic signals were associated with plant phenophases and were stronger in the regions of ideal temperature (7–18 °C) and photoperiod (10–14 h) and weaker in harsh climatic conditions. These results show that phylogenetic conservation in plant phenological traits is frequently influenced by the interaction of harsh climatic conditions and geographical ranges. This meta-analysis enhances our knowledge of predicting species responses over geographic gradients under varied climatic conditions.

Assessment of the levels of formation and leading components of the patient safety system of the anesthesiology and resuscitation service (message 2)

The objective was to assess the levels of formation and leading components of the patient safety system of the anesthesiology and resuscitation (A&R) service (department) of multidisciplinary hospitals.Materials and methods. Expert assessment of the patient safety system of the anesthesiology and resuscitation service (department) of 235 multidisciplinary hospitals, including the assessment of its level, leading components, and their structure in the overall sample. Expert assessment method. Statistical analysis of data distribution, Student’s t-test for independent samples of different sizes, correlation analysis.Results. It has been established that in the main part (38.3%) of multidisciplinary hospitals, the A&R service is at an average (sufficient) level of ensuring patient safety. 17% are at a high (organized) and highest (system-organized) level in terms of ensuring patient safety. A fairly large part (44.7%) is at a low (insufficient) and minimal (critical, extremely low) level of ensuring patient safety. At the same time, the level of ensuring patient safety in the A&R service of multidisciplinary hospitals is important and has a close but inverse relationship with the level of digitalization. Comparison of the severity of the five leading (main) components (criteria) for ensuring patient safety in the A&R service of multidisciplinary hospitals made it possible to assess the levels of their completeness and their structure and showed that in the general sample, the most pronounced criteria for ensuring patient safety were «Teamwork, staff expertise» and «Availability of medicines».Conclusion. The current state of the patient safety system of the A&R service of multidisciplinary hospitals is characterized by 5 levels and 5 leading (main) components, the expert assessment of which made it possible to distribute the A&R services of hospitals according to the indicated levels, identify the structure of their leading components an determine target areas for improving the patient safety system.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Feasibility of zero emission freight zones: scenario analysis and risk assessment

To further incentivize the adoption of zero-emission trucks (ZETs), Chinese cities would need more proactive policies like zero-emission freight zones (ZEFZs). However, the design of the policy should avoid the disruption of goods supply, increase of logistic costs, and ensure inclusive transition of small carriers and financial sustainability for city governments. To this end, this study employs Beijing as a case: Based on ZEFZ case studies, policy document and literature review, and data analysis of freight demand distribution and truck movements in Beijing, this study constructs four scenarios with different spatial coverages, restricted truck segments, and roll-out years of ZEFZs. The impacts of different ZEFZ scenarios on emissions reduction potentials, goods supply and vehicle fleet replacement, and public and private expenditure are evaluated. The most cost-efficient and easy-to-implement scenario is recommended as the initial phase of the ZEFZ scheme, with risk assessment and risk mitigation measures.

Solvent Extraction of Gold(III) by 2-Ethylhexanol and Modeling of Facilitated Transport across a Supported Liquid Membrane

The solvent extraction of gold(III) by undiluted 2-ethylhexanol or dissolved in toluene from a HCl solution has been investigated in this paper. The numerical analysis of gold distribution data suggests the formation of HAuCl4·L and HAuCl4·2L (L = 2-ethylhexanol) species in the organic phase, with formation constants K11 = 38 and K12 = 309, respectively. The results derived from gold(III) distribution have been implemented in a solid-supported liquid membrane system. The influence of several variables on gold transport has been considered: feed and receiving phases’ stirring speeds, HCl and gold concentrations in the feed phase, and carrier concentration in the membrane phase as well as the presence of base metals (Fe, Cu, Ni) and platinum-group metals (PGMs) in the feed phase. Gold transport is influenced by the stirring speed of the feed phase and the variation in HCl and gold (feed phase) and carrier (membrane phase) concentrations. Also, diffusional resistances to mass transfer are estimated, and the contribution of each resistance to gold transport is estimated. Gold is recovered as zero-valent nanoparticles.

Data-driven determination of zooplankton bioregions and robustness analysis

Identifying biogeographic regions through cluster analysis of species distribution data is a common method for partitioning ecosystems. Selecting the appropriate cluster analysis method requires a comparison of multiple algorithms. In this study, we demonstrate a data-driven process to select a method for bioregionalization based on community data and test its robustness to data variability following these steps:•We aggregated and curated zooplankton community observations from expeditions in the Northeast Pacific.•We determined the best bioregionalization approach by comparing nine cluster analysis methods using ten goodness of clustering indices.•We evaluated the robustness of the bioregionalization to different sources of sampling and taxonomic variability by comparing the bioregionalization of the overall dataset with bioregionalizations of subsets of the data.The K-means clustering of the log-chord transformed abundance was selected as the optimal method for bioregionalization of the zooplankton dataset. This clustering resulted in the emergence of four bioregions along the cross-shelf gradient: the Offshore, Deep Shelf, Nearshore, and Deep Fjord bioregions. The robustness analyses demonstrated that the bioregionalization was consistent despite variability in the spatial and temporal frequency of sampling, sampling methodology, and taxonomic coverage.

Meta Data Analysis of Sex Distribution of Study Samples Reported in Summer Biomechanics, Bioengineering, and Biotransport Annual Conference Abstracts.

The biased use of male subjects in biomedical research has created limitations, underscoring the importance of including women to enhance the outcomes of evidence-based medicine and to promote human health. While federal policies (e.g., the 1993 Revitalization Act and the 2016 Sex as a Biological Variable Act) have aimed to improve sex balance in studies funded by the National Institutes of Health (NIH), data on sex inclusivity in non-NIH funded research remain limited. The objective of this study was to analyze the trend of sex inclusion in abstracts submitted to the Summer Biomechanics, Bioengineering, & Biotransport Conference (SB3C) over 7 years. We scored every abstract accepted to SB3C, and the findings revealed that approximately 20% of total abstracts included sex-related information, and this trend remained stable. Surprisingly, there was no significant increase in abstracts, including both sexes and those with balanced female and male samples. The proportion of abstracts with balanced sexes was notably lower than those including both sexes. Additionally, we examined whether the exclusion of one sex from the corresponding studies was justified by the research questions. Female-only studies had a 50% justification rate, while male-only studies had only 2% justification. Disparity in sex inclusion in SB3C abstracts was apparent, prompting us to encourage scientists to be more mindful of the sex of the research samples. Addressing sex inclusivity in biomechanics and mechanobiology research is essential for advancing medical knowledge and for promoting better healthcare outcomes for everyone.

Can redistribution of vaccine improve global welfare? Lessons from COVID-19.

The disparity in the distribution of COVID-19 vaccine has been recorded with more than 70% vaccination rate for high-income countries as compared to less than 40% for low-income countries. The low affordability of vaccines for the majority of low-income group invites the need for redistribution of vaccines. The disproportionate donation of vaccines across the globe motivates us to explore the incentive for the high-income group to redistribute. An exploratory analysis of cross-country COVID-19 vaccination distribution data shows that the countries which have received vaccines as donation has also contributed to vaccine wastage. This paper intends to provide a theoretical background for this counterintuitive observation using welfare analysis. We find that the market mechanism leads to a negative impact on global welfare due to redistribution. Only an invention with a defined redistribution mechanism may ensure an increase in global welfare. It is found that a critical value of redistribution mechanism reinforced by a minimum threshold level of income is essential to enhance welfare. The reduced form from the theoretical predictions is empirically validated with cross-country data on COVID-19 vaccination for all countries. As identified in theory, the internal support variables like political stability, government effectiveness, and health expenditure at the country level will impact global welfare. Therefore, when global cooperation is essential during a health crisis like COVID-19, improved internal coordination and intentions cannot be ignored.

PERSEPSI KELUARGA TENTANG DUKUNGAN PERAWAT CRITICAL CARE DI RUANG INTENSIVE RSUD ARIFIN ACHMAD PROVINSI RIAU

Intensive care unit is hospital care that has the goal of treating patients with life-threatening conditions due to disease, trauma, or surgery. The purpose of this research is to determine family perceptions about the support of critical care nurses in the intensive care unit at RSUD Arifin Achmad Pekanbaru. The design is Quantitative research with descriptive research design. The sampling technique in this study was accidental sampling technique. The measuring tool used is a family perception questionnaire about nurse support. The analysis used is the analysis of frequency distribution data. The results of this study indicate family perceptions about critical care nurse support from 45 respondents, namely sufficient perceptions (60%), perceptions based on relationships with patients obtained sufficient perceptions (52,4%) and perceptions based on recent education also showed sufficient perceptions (58,3%). Overal it was concluded that the perceptions held by the patient's family in this study were in the sufficient category, the family viewed and assessed treatment in the intensive care unit not in accordance with the needs, expectations and desired circumstances. The result can be used as basic data, information, evidence based to carry out further research and it is hoped that the hospital will provide religious leaders and spiritual support to help families in facing critical conditions.

Optimized Design of Scoring Criteria for Large-Scale Innovation Competitions

The reasonable and reliable evaluation score calculation scheme wins the top priority in large-scale innovation-competitions currently. Due to the disparity among individual experts, and the existing standard score calculation model merely based on the experts' own circumstance, it cannot fully reflect the comprehensive level of players, resulting in a certain degree of error in the evaluation results. From the perspective of individual and decision-making of a group, this paper improves existing models and introduces the concept of modified scores. To verify the feasibility of the model, four schemes were designed based on the analysis of data distribution. Consistency and difference factors were applied to compare. Come to a conclusion: scheme adopted the new standard score calculation model ranked the top, indicating that the new calculation model is more reliable. Next, considering that the volatile correction factor affected by data, there is a possibility of some poor actual results with high correction scores. To rectify this case, a power exponent is added to the correction factor and a sign function is adopted to specify the positive or negative of correction scores. To verify the feasibility of the model, four sets of controlled experiments were designed with the introduction of two factors: power exponent and reconsideration bonus, as well as the ranking consistency test based on the condition that the award order of the expert agreement was consistent. In the end, it was found that the scheme that introduced both power exponent and reconsideration bonus points had a sorting consistency rate of 74%, which increased by 30% compared to the original model, indicating the rationality of the modification. The expert evaluation standard score calculation model established in this paper comprehensively considers individual and group decision-making, and provides reasonable correction for the differential scoring between experts. At the same time, this scheme provides a reference basis for further optimizing large-scale innovation competition plans in the future.

Comprehensive Detection of Security Threats in Wireless Ad Hoc Networks: Bridging Healthcare 4.0

Healthcare 4.0, which is the integration of digital technologies in healthcare, promises to bring about revolutionary advancements but also introduces significant cybersecurity challenges. This research seeks to address the growing concerns by investigating security threats in healthcare 4.0 systems. The study uses a multifaceted methodology that includes a comprehensive literature review and empirical analysis using advanced algorithms such as Random Forest. Using visualization techniques, data distribution analysis, and intrusion detection experiments, the research identifies common vulnerabilities and patterns in healthcare 4.0 environments. The findings highlight the need for proactive measures and strong policies to protect patient data integrity, safeguard medical infrastructure, and ensure continuous provision of health care services. This study calls for a holistic approach to cyber security with an emphasis on collaborative efforts toward strengthening Healthcare 4.0 systems against emerging threats.

Spatio-Temporal Variability of the Aerosol Optical Depth over the Gorky and Cheboksary Reservoirs in 2022–2023

The present study aimed to investigate atmospheric optical characteristics over the Gorky and Cheboksary Reservoirs and their multi-scale temporal variations to obtain the background characteristics and to identify events involving the transfer of absorbing aerosol to the studied region in 2022–2023. The region is located at a considerable distance (500 km) from the nearest AERONET station; therefore, previous atmospheric data were not available. As a solution, the in situ self-measured aerosol optical depth (AOD) and Angström exponent, as well as satellite products (MAIAC and CALIPSO) for MODIS data, were used. This allowed us to set background values of an AOD of 0.11 at a wavelength of 500 nm and an Angström exponent of 1.2, against their maximum values of 0.38 and 2.5, respectively. To explain these variations, the registered conditions and the microstructure of the dust aerosol over the studied region are presented. For days with background values, the analysis of the particle size distribution data did not show a predominance of any particle size. The optical properties of a smoke aerosol in an atmospheric column are described, and an analysis of the dynamics of particle size variability is presented. A comparative analysis of the optical characteristics of atmospheric aerosol over the Gorky and Cheboksary Reservoirs using in situ and MODIS products was carried out.