Linear Trend Coefficient Research Articles

Weather impact on ambient air pollution and its association with land use types/activities over 5,572 municipalities in Brazil

Quantify the impact of meteorological changes on air pollution levels is the aim of numerous recent studies. However, there is still a lack of investigations assessing the influence of land use/activities on the relationship between climate and air quality. In this study, we used a two-stage design to estimate the influence of land use types and activities on the association between weather changes and air pollution (PM2.5, NO2, SO2, O3) over 5572 municipalities in Brazil. To calculate the influence of recent weather change on air pollution concentration for each municipality, we used the “weather penalty” concept. This approach considers differences in linear trend coefficients between two generalized additive models. Then, using quantile regression, we estimated the effect of land use types and activities (8 variables related to transportation, energy generation, and land use) on weather-related increases in ambient air pollution. We found that an increase in PM2.5 was associated to recent weather changes in most municipalities (average increase of 0.07μg/m3per year) and a decrease in NO2 in most municipalities (average decrease of 0.0003 ppb per year). O3 and SO2 had more intense increases associated with weather changes in the North region. Our findings suggest the most robust positive associations between weather penalties on PM2.5 and areas with non-clean energy and oil refineries (average increase of 0.006μg/m3per year and 0.04μg/m3per year, respectively). We also found positive associations between Pasture areas, urban areas, and transportation and the weather penalties of this pollutant. In contrast, forest areas were negatively associated with PM2.5 penalties. We also found that oil refineries, urban areas, and transportation significantly positively influenced weather penalties for SO2 and O3. Overall, the study highlights the importance of considering the influence of land use types and activities on weather-related changes in ambient air pollution.

Regional features of glaze ice events frequency in the north of the European territory of Russia in the current changing climate

Recently, the interest in society in observing and forecasting dangerous meteorological phenomena in the cold period of the year has noticeably grown. In this work, we study the phenomenon of glaze ice in the north of the European territory of Russia in the North-Western Federal District. Here glaze ice (icing) is less frequent than other forms of ice accretion, but its characteristics most often exceed the criteria of dangerous meteorological phenomena. It is a great danger for various areas of the economy, ship navigation, causing an increase in traumatism of the population and damage to infrastructure. In accordance with the aim of the work, the spatial and temporal variability of the frequency of glaze ice accretion on the territory of the North-Western Federal District in the period 1986–2022 is investigated. The results are obtained on the basis of instrumental observations of meteorological stations. Using statistical analysis, the features of mean yearly frequency at each meteorological station were studied. To assess the direction of annual changes in the glaze ice frequency, the linear trend coefficient was calculated for each meteorological station. The least squares method was used for this purpose. It is shown that the maximum number of days with glaze ice occurs in the central part of the North-Western Federal District. This can be attributed to the more frequent movement of Atlantic and southern cyclones to high latitudes and their precipitation, especially at the atmospheric fronts and in the warm sectors of the cyclones, at air temperatures near zero and below. What makes the spatial distribution of glaze ice frequency even more uneven is the presence of uplands in the European territory of Russia. More precipitation falls on their windward slopes, and the temperature decreases with altitude. The findings show that in recent decades there has been an increase in the frequency of the glaze ice phenomenon in the region. At the same time, the interannual variability remains relatively constant. Taking into account the positive trend in the number of glaze ice events and increasing air temperature and precipitation, it is necessary to develop measures to deal with this hazardous phenomenon and minimize damage from it.

Climate change and consequences of climate change in Kazakhstan

The article examines the impact of climate change on the territory of Kazakhstan and its consequences for ecosystems, agriculture, economy and public health. Thecurrent climate change trends in Kazakhstan were analyzed using data from hydrometeorological observations at stations and posts of the state observation network of RSE «Kazhydromet». Climate norms and variable anomalies are considered, and changes in climate characteristics for the period 1976-2022 are estimated using linear trend coefficients and the coefficient of determination. The author focuses on the analysis of current climate change trends in the region and possible strategies for adaptation to them.

Мониторинг гололедно-изморозевых отложений на территории России

A methodology for monitoring glaze-rime deposits (GRD) is developed to be included in the national climate monitoring system. Based on this methodology, in order to assess the current state of GRD characteristics, the standard station characteristics (total number of cases, total duration, and average weight) are calculated for each type of GRD for the period of 1991-2020. The anomalous pattern of GRD throughout Russia is assessed. The trends are analyzed that are characterized by linear trend coefficients based on the weather station data and on the average values for quasihomogeneous regions (1984-2021). The study showed a statistically significant positive trend for the number of cases, duration, and weight of wet snow deposits in several regions. In some quasihomogeneous regions, both positive and negative significant trends in the number of cases, duration, and weight of crystal rime are obtained. In some quasihomogeneous regions, a significant positive trend for the number of cases and duration of glaze ice is also obtained. Keywords: monitoring methodology, glaze-rime deposits, glaze ice, rime, wet snow, anomaly, trend

Study on the process conditions of C4 olefin preparation based on statistical method

Due to the wide application of C4 olefins, it is of profound research significance and important value to explore the process conditions for the preparation of C4 olefins by catalytic coupling of ethanol. This paper calculates and analyzes the data generated in the C4 olefin preparation process by establishing a mathematical model and provides targeted suggestions based on statistical methods. First, one-dimensional linear regression was used to analyze the relationship between ethanol conversion, C4 olefin selectivity and temperature so as to obtain the regression coefficients of each group and test their significance; Secondly, SPSS is used for matrix scatter plot to observe the linear trend and spearman correlation coefficient is used for correlation analysis. Hypothesis test is conducted to draw relevant conclusions; Then the fuzzy synthesis is used to draw the conclusion that the catalyst has a greater impact on the reaction. Finally, the neural network is used to train the data and arrange them to obtain all the combination of catalyst and temperature. It is obtained that at 400°C, the combination of "200mg 2wt% Co/SiO2-200mg HAP ethanol concentration 2.1ml/min" can obtain 45.259% of the maximum C4 olefin yield without limiting the temperature; If the temperature is lower than 350°C, the catalyst is "50mg 5wt% Co/SiO2-50mg HAP ethanol concentration 0.3ml/min" and the temperature is 275°C. The maximum C4 olefin yield is 30.75%.

Regional Effects of Global Climate Changes in the Ural River Basin

The study data obtained on regional climate changes (surface layer temperature and atmospheric precipitation) in the Ural River basin are reported for a 70-year period (1950–2020). Variations in the basic parameters of the regional climate in the basin studied were obtained by standard statistical data analysis, as well as by calculation of the integral climate anomaly indices. Statistically significant linear trend coefficients illustrate a steady increase in the average annual air temperature in the Ural River basin. The largest contribution to the growth of this parameter is made in the first three months of the calendar year (January–March) and the autumn months (October–November). The long-term course of precipitation values is devoid of unidirectional and statistically significant trends. The important macroregional trend is an increase in precipitation during the cold period. In conclusion, it should be noted that the diverse physical and geographical conditions of the regional climate are indicative of a spatially nonuniform response to the global and macroregional changes.

REGIONAL EFFECTS OF GLOBAL CLIMATE CHANGE IN THE URAL RIVER BASIN

The results of the study of regional climate changes (surface layer temperature and precipitation) in the Ural River basin at the current level are presented. Conclusions about the transformation of the main parameters of the regional climate in the studied basin were obtained on the basis of standard methods of statistical data analysis, as well as calculation of integral climate anomaly indices. Statistically significant values of linear trend coefficients illustrate a steady increase in the average annual air temperature in the Ural River basin. The largest contribution to the growth of this parameter is made by the first three months of the calendar year (January–March) and the autumn months (October–November). The long-term course of precipitation values is characterized by the absence of unidirectional and statistically significant trends. An important macro-regional trend in the dynamics of the precipitation regime is an increase in the proportion of precipitation of the cold period. In conclusion, it is noted that the diversity of the physical and geographical conditions of the formation of the regional climate determines the spatial heterogeneity of the response to global and macro-regional changes.

Closed loop simulations on recoverability of climate trends in next generation gravity missions

SUMMARY Gravity field satellite missions are unique observation systems to directly measure mass transport processes on Earth and to gather valuable information for climate research. Next Generation Gravity Missions (NGGMs) are expected to be launched within this decade, setting high anticipation for an enhanced monitoring capability that will improve the spatial and temporal resolutions of gravity observations significantly. They will allow for an evaluation of long-term trends in the Terrestrial Water Storage (TWS) signal. The results of this study are based on a time-series of global changes in soil moisture and snow obtained from future climate projections until the year 2100 of a coupled climate model taking part in the CMIP6 (Coupled Model Intercomparison Project Phase 6). For different mission concepts, namely in-line single-pair missions and a Bender double-pair mission, the recoverability of a time variable mass signal is evaluated, considering realistic noise assumptions, simulated over several decades. The results show that a single-pair mission can fulfill the target requirements for the long-term trend, set by the user community, after 70 yr while a double pair already achieves it after 30 yr of observation. After 100 yr of double-pair constellations the globally averaged RMS (polar areas excluded) improves, compared to a single-pair mission, by a factor of 5 for the linear trend, 2.5 for annual amplitude, and 1.8 for the phase observation. In addition, regional investigations indicate that the simple parameter model consisting of offset, linear trend, and annual signal coefficients, as it was used in this study, in several cases might not be able to capture the whole time-variable signal sufficiently, due to the presence of interannual signals. Hence, advanced, locally more adaptable parameter models need to be considered for a better parametrization of local effects in the future.

СТАТИСТИЧЕСКИЕ АСПЕКТЫ КОЛИЧЕСТВЕННОЙ ОЦЕНКИ ПОЛЯРНОГО УСИЛЕНИЯ. I. ОТНОШЕНИЕ ТРЕНДОВ

Statistical aspects of the quantitative estimation of the climate change polar amplification (PA) are considered. A theoretical study is provided of the distribution density for the PA sample coefficient defined as the ratio of the linear trend coefficients of spatially averaged surface air temperature. It is shown that heavy tails of the distribution lead to instability of the PA sample estimates. A statistically valid quantification of the PA can be obtained from the results of the ensemble modeling of climate change based on the Fieller’s method. The applicability of the method in the case of a non-stationary climate system response to external forcing is considered.

Современные тенденции изменения климата в бассейне реки Урал

The study focuses on the transformation of main climate parameters (surface layer temperature and precipitation) in the Ural River basin at the current level. The study is relevant due to the location of a greater portion of the Ural River basin area within arid and water-deficient regions. Conclusions about the regional climate change in the studied basin are formulated on the basis of standard methods of statistical data analysis. Statistically significant linear trend coefficients illustrate a steady increase in the average annual air temperature in the Ural River basin. The trend component (R2) varies from 12 % (Kuvandyk) to 25–26 % (Aydyrlya, Belyaevka) and 36–42 % (Bredy, Uralsk, Orenburg, Atyrau). The largest contribution to the growth of average annual temperatures is made by the first three months of the calendar year (January-March) and the autumn months (October-November). The long-term precipitation amounts show no directional and statistically significant trends. In annual terms, a statistically significant increase in precipitation was found only in the western part of the Ural River basin (MS Ilek – 10 mm per 10 years; Sharlyk – 13 mm per10 years) and in adjacent areas (MS Buzuluk – 18 mm per 10 years). The key macro-regional trend in changing the precipitation regime is an increase in the proportion of the cold period precipitation. Within the studied basin, a steady trend of seasonal precipitation redistribution was recorded for all sectors of the catchment area, except for the lower reaches of the Ural River. In conclusion, the identified trends in the regional climate transformation indicate that these changes reflect changes in climatic conditions on a larger scale.

Time trends in emergency department use among adults with intellectual and developmental disabilities

BackgroundEmergency Department (ED) visits are common among adults with intellectual and developmental disabilities (IDD). However, little is known about how ED use has varied over time in this population, or how it has been affected by recent Medicaid policy changes. ObjectiveTo examine temporal trends in ED use among adult Medicaid members with IDD in eight states that ranged in the extent to which they had implemented state-level Medicaid policy changes that might affect ED use. MethodsWe conducted repeated cross-sectional analyses of 2010–2016 Medicaid claims data. Quarterly analyses included adults ages 18–64 years with IDD (identified by diagnosis codes) who were continuously enrolled in Medicaid for the past 12 months. We assessed change in number of ED visits per 1000 member months from 2010 to 2016 overall and interacted with state level policy changes such as Medicaid expansion. ResultsStates with no Medicaid expansion experienced an increase in ED visits (linear trend coefficient: 1.13, p < 0.01), while states operating expansion via waiver had a much smaller (non-significant) increase, and states with ACA-governed expansion had a decrease in ED visits (linear trend coefficient: 1.17, p < 0.01). Other policy changes had limited or no association with ED visits. ConclusionsMedicaid expansion was associated with modest reduction or limited increase in ED visits compared to no expansion. We found no consistent decrease in ED visits in association with other Medicaid policy changes.

340-P: Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes Using Combined Supervised and Unsupervised Ensemble Learning

The Aim: To assess the efficacy of combined supervised and unsupervised ensemble machine learning based on continuous glucose monitoring (CGM) data for short-term prediction of nocturnal hypoglycemia (NH) in hospitalized patients with type 1 diabetes (T1D). Materials and Methods: We analyzed CGM records with a minimal duration of 72 hours obtained from 406 adult patients admitted to tertiary referral hospital. An episode of NH was defined as ≥3 consequent glucose values of &amp;lt;3.9 mmol/L recorded between 0 and 6 am. Depending on predictor sequence length (30 min - 1 h) and horizon (H) of forecasting (5-30 min), we obtained 209-256 and near 4∙104 records with and without NH respectively. Twenty nine metrics, including mean, minimal and maximum glucose, range, gradient, linear, quadratic and cubic trend coefficients, dynamic time wrapping similarity-based indices, and a number of glucose variability indices, were evaluated. The ensemble clustering was used for extraction of representative samples. Random Forest ensemble model was applied for NH prediction. The obtained quality metrics were averaged over 100 random partitions of the dataset on training and testing subsets. Results: The quality metrics did not depend significantly on predictor sequence length; we used 30 min as a baseline. Depending on H, values of sensitivity and specificity varied from 98% and 97% (H=5 min) to 85% and 87% (H=30 min). The false alarm ratio values were very high (&amp;gt;90%) due to the unbalanced nature of data. Minimal glucose, low blood glucose index, and increment in the penultimate value of sequence were the most important predictors assessed with Random Forest algorithm. Conclusions: The results demonstrate the usefulness of ensemble machine learning models based on CGM time series data for prediction of NH events in hospitalized patients with T1D. Additional studies are needed for improving false alarm ratio metrics of forecasting quality. Disclosure V. Berikov: None. J. F. Semenova: None. V. Klimontov: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Sanofi, Research Support; Self; Boehringer Ingelheim International GmbH, Speaker’s Bureau; Self; AstraZeneca, Medtronic. Funding Russian Science Foundation (20-15-00057)

Analysis of Online Reviews of Orthopaedic Surgeons and Orthopaedic Practices Using Natural Language Processing.

There is growing interest in measuring and improving patient experience. Machine learning-based natural language processing techniques may help identify instructive themes in online comments written by patients about their healthcare provider. Separating individual surgeon and orthopaedic office reviews, we analyzed themes that are discussed based on the rating category, the association with review length, the number of people posting more than one review for a surgeon or office, the mean number of reviews per rating category, and the difference in review tones. On Yelp.com, we collected 11,614 free-text reviews-together with a one- to five-star rating-of orthopaedic surgeons. Using natural language processing, we identified the most frequently occurring word combinations among rating categories. Themes were derived by categorizing word combinations. Dominant tones (emotional and language styles) were assessed by the IBM Watson Tone Analyzer. We calculated chi-square tests for linear trend and Spearman's rank correlation coefficients to assess differences among rating category. For individual surgeons and orthopaedic offices, themes such as logistics, care and compassion, trust, recommendation, and customer service varied among rating categories. More positive reviews are shorter for individual surgeons and orthopaedic offices, while rating category was comparable among people posting more than one review for both groups. Tones of joy and confidence were associated with higher ratings. Sadness and tentative tones were associated with lower ratings. For individual orthopaedic surgeons and orthopaedic offices, patient experience may be influenced mostly by the patient-clinician relationship. Training in more effective communication strategies may help improve self-reported patient experience.

Changing Patterns of Temperature in Yobe State, North-Eastern Nigeria: An Evidence of Climate Change

Air temperature is one of the fundamental indicators of Climate Change in any place on earth. Understanding the changing patterns of air temperature in Yobe State is instrumental in establishing the manifestation of Climate Change in the area and to enables the policy makers to design the best mechanisms to cope with the vagaries of increasing temperature. This study examined the changing patterns of temperature in Yobe State as an evidence of Climate Change. The study used mean monthly minimum and maximum air temperature data collected from the archives of the Nigerian Meteorological Agency (NIMET) with respect to Nguru and Potiskum weather stations in the State for a period of 1956-2015. The data was analyzed using descriptive statistics, linear regression trend lines and coefficient of variation (C.V). The result showed that April is the hottest month with the highest mean maximum temperatures of 39.6°C and 38.6°C in Nguru and Potiskum respectively while January is the coldest month with mean temperatures of 30.5°C and 30.8°C for Nguru and Potiskum respectively. Further finding revealed that temperatures are highly variable in February in both Nguru and Potiskum with C.V of 8.7% and 5.7% and less variable in October with C.V of 2.0%. Finding showed that in general term, there were upward trends in monthly and annual positive temperature anomalies ranging from 0.2°C to as high as 4.0°C in some months and years in the state. The study concludes that temperatures in Yobe State have witnessed series of changes within the 60 years period. Therefore, it is suggested that stakeholders in the Yobe State Environmental Protection Agency should intensify efforts in reducing deforestation while encouraging people on afforestation with a view to reducing the impact of Greenhouse gases that are contributing to more warming than normal. There should be adequate electricity and water supply for immediate relieve from excessive heat emanating from high temperature.

Тенденции изменения концентрации льда и температуры воздуха в Арктике

The article analyses monthly spatial distributions of linear trend coefficients from 1950 to 2019 in the Arctic region using data from the NCEP/NCAR and ERA5 reanalysis on surface air temperature and the HadISST data array on ice concentration. To assess the reliability of the maximum trend values, the observational data in the areas are used. Additionally, the role of the North Atlantic in the air temperature and ice concentration changes in the Arctic region is considered. As a result of the climatic change analysis in the Arctic using different types of reanalysis and observation data, it is revealed that the temperature increases, whereas the ice cover decreases in this region, but it occurs unevenly over the seasons. The maximum values of the air temperature trends are characteristic of the winter period and consistent with the positive NAO trend. Warming is especially pronounced near Spitsbergen Island, Franz Josef Land and the northern part of New Earth Island. According to the observational data and NCEP/NCAR reanalysis, the trends vary from 2 to 2.8°C/10 years, while according to ERA 5, these values are overestimated by about 30%. Intense warming is also observed in October and November in the Beaufort Sea region, according to both NCEP/NCAR and ERA 5 data. Trend values of the air temperature in October and November in these regions from two reanalyses are from 1,8 to 2,4°C/10 years. Both reanalysis overestimate the values compared to the real station data. The maximum ice melting occurs from August to October. At the same time, in September and October the area of the ice cover formation has almost halved in 70 years. In turn, the decrease in ice cover causes a decrease in reflectivity of the underlying Arctic surface, and this is one of the feedback elements in the warming of this region.

Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

Dynamic Effects of Sustainable Development Activities in the Air Quality for Selected Countries

Purpose: The assessment of the linear trend coefficient plays a key role because it indicates how much the studied phenomenon has changed on average over a unit of time, i.e., five years in this study. On the basis of this coefficient, an analysis of the dynamics of individual parameters determining the degree of air pollution in the examined countries was conducted. Approach/Methodology/Design: For the purpose of the analysis of the dynamics of the effects of sustainable development measures in the area of air quality for selected countries of Central and Eastern Europe, the assessment was made using a linear trend model. The analysis of the dynamics of downward trend of individual indicators was carried out on the basis of the values of parameters b and the graphs of the linear trend models. Findings: The analysis showed that given the level of air pollution by greenhouse gases, it is clear that the worst situation is in the Czech Republic, as shown by the highest chart of the linear trend model with a time series. The smallest air pollution from greenhouse gases is in Hungary. PM 2.5 pollution is highest in Poland and lowest in Slovakia. The same situation occurs in the case of PM 10 dust pollution. Practical Implications: Identify the places where there is the highest level of air pollution in order to propose studies to reduce it. Originality/Value: Originality indicates the use of non-implemented analysis results to precisely identify the most polluting countries and the least polluting ones.

Increasing Number of Amyloidosis Diagnosis in the Veterans Affairs Populations

Introduction Amyloidosis remains underdiagnosed, however newer diagnostic modalities and newer therapeutic agents have increased the awareness for this disease. The prevalence of cardiac amyloidosis in the Veterans Affairs population is unknown and there are now effective treatments for patients with amyloidosis and cardiomyopthy. The objective of this study is to describe the diagnosis of amyloidosis in the Veterans Affairs population. Methods We undertook a retrospective analysis of the Veterans Affairs Department of Veterans Affairs Informatics Computing Infrastructure database from 2000-2014. We queried the diagnosis of amyloidosis using the International Classification of Disease-9th revision -Clinical Modification Diagnosis (ICD) code of 277.3X. We calculated the incidence proportions as cases per 100,000 population per year. Results A total of 5,639 patients with diagnosis codes for amyloidosis were identified during the study period, giving an incidence of 7.4 per 100,000 patients. Of them, 96% were men and 88% were older than 55 years old. The incidence increased over the years (Linear Trend Coefficient = 1.49, p-value = 0.001) and at a faster rate over the last years (Quadratic trend coefficient = 3.04, p-value Conclusions Cases of amyloidosis continues to increase in the US Veterans population. Understanding the epidemiological trends and populations at risk will guide the development of tools to provide early diagnosis and appropriate access to care for patients with amyloidosis.

Temporal Variability of Ground Shaking and Stress Drop in Central Italy: A Hint for Fault Healing?

Ground‐motion prediction equations (GMPEs) are calibrated to predict the intensity of ground shaking at any given location, based on earthquake magnitude, source‐to‐site distance, local soil amplifications, and other parameters. GMPEs are generally assumed to be independent of time; however, evidence is increasing that large earthquakes modify the shallow soil conditions and those of the fault zone for months or years. These changes may affect the intensity of shaking and result in time‐dependent effects that can potentially be resolved by analyzing between‐event residuals (residuals between observed and predicted ground motion for individual earthquakes averaged over all stations). Here, we analyze a data set of about 65,000 recordings for about 1400 earthquakes in the moment magnitude range 2.5–6.5 that occurred in central Italy from 2008 to 2017 to capture the temporal variability of the ground shaking at high frequency. We first compute between‐event residuals for each earthquake in the Fourier domain with respect to a GMPE developed ad hoc for the analyzed data set. The between‐events show large changes after the occurrence of mainshocks such as the 2009 Mw 6.3 L'Aquila, the 2016 Mw 6.2 Amatrice, and Mw 6.5 Norcia earthquakes. Within the time span of a few months after the mainshocks, the between‐event contribution to the ground shaking varies by a factor 7. In particular, we find a large drop in the between‐events in the aftermath of the L'Aquila earthquake, followed by a slow positive trend that leads to a recovery interrupted by a new drop at the beginning of 2014. We also quantify the frequency‐dependent correlation between the Brune stress drop Δσ and the between‐events. We find that the temporal changes of Δσ resemble those of the between‐event residuals; in particular, during the period when the between‐events show the positive trend, the average logarithm of Δσ increases with an annual rate of 0.19 (i.e., the amplification factor for Δσ is 1.56 per year). Breakpoint analysis located a change in the linear trend coefficients of Δσ versus time in February 2014, although no large earthquakes occurred at that time. Finally, the temporal variability of Δσ mirrors the relative seismic‐velocity variations observed in previous studies for the same area and period, suggesting that both crack healing along the main fault system and healing of microcracks distributed at shallow depths throughout the surrounding region might be necessary to explain the wider observations of postearthquake recovery.

ANALYSIS OF INFLUENCE FACTORS ON UKRAINIAN REGIONS DEVELOPMENT OF HOUSING AND MUNICIPAL ECONOMY

It is determined that the specific feature of housing and communal services is the territorial dissociation of housing and communal facilities that require round-the-clock reliable operation, the need for operative, and in some cases - emergency nature of the work of huge volumes and labor intensities, which involve the widespread use of machines, mechanisms and means of small mechanization of various purposes. A number of industrial constraints have been formed, which are the subject of an annual survey of electricity, gas, steam and air conditioners, and water supply, sewerage, waste management companies. In order to find out the trend in the changes in the influence of each factor on the prospects of branch development, the linear trends of the enterprises of industrial production of electricity supply, gas, steam and conditioned air, and water supply enterprises, sewage, waste management were determined. According to the classical definitions, the positive values of the trend line coefficients indicate an increase in the indicators, and negative ones – a decrease; the greater the absolute value of the determined coefficient, the « faster » is the increase or decrease. Similar statistical data processing was carried out for water supply, sewage, waste management companies. Linear trend coefficients for each of the factors of influence were counted. It was revealed that the weight of the influence factors and their ranking are identical in both branches. And the tendency to change their influence over time is excellent. So, despite the greatest impact of financial constraints, over time, « claims » to them gradually decrease. Lack of labor will eventually become more significant. That is, enterprises of industrial production of electricity, gas, steam and air conditioning supply, and enterprises of water supply, sewage, waste management over time depend less on demand, raw materials, materials, equipment. It is noted that the financial results of the utilities enterprises directly depend on the size of capital investments. Financial results of the enterprise industry during the observation time highly depend on financial restrictions, which in the future only increase. Lack of labor has almost no effect on enterprise activity.