The Space and Terrestrial Weather Variations as Possible Factors for Ischemia Events in Saint Petersburg
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methods
- (1)
- The error is to take as an acting factor the variations of some concrete environment characteristic that changed at the same time as the variations of some investigated object characteristic while both these values are only changed at the same time under the action of the third factor, which only is not taken into account in the analysis. This is a classic error that occurs when performing the correlation analysis.
- (2)
- The error is to take the first of the mentioned characteristics (the factor included in the study) as an agent of the third characteristic (factor that is not taken into account in the study), assuming the latter’s effect on the object through the first, whereas there may not be a connection between the variations of the mentioned characteristics, and their in-phase variations are only a manifestation of the same sensitivity to variations of the next unaccounted-for factor (and maybe not even one).
- (3)
- The reverse to describe errors is not to detect the relationship between the variations of a particular factor and the variations of the studied characteristics of the object state, whereas this relationship exists but manifests itself differently in different conditions.
- Reactions to external impacts can manifest themselves peculiarly in people of different genders, different ages and different diagnoses (in the case of the presence of the disease). For example, the deviation from the healthy norms of blood parameters values may have age-related features; those are typical for the age changes but not for the reaction to the external impact.
- People belonging to the different gender groups may respond to different characteristics of variations (daily statistics) of the same environmental parameters. For example, women may respond to a maximum value (or any point statistic) of this parameter but not men in a daily spread of this quantity.
- People of different genders, ages and diagnoses can respond to variations of different environmental parameters.
- (4)
- The next error is to think that changes in the state of the human body do not depend on fluctuations of the environment if the same changes in the human organism occur with variations of quite various environmental parameters. The reason for this may be in the same response of the human body to the action of various factors, e.g., the same human organism reaction to the air temperature and geomagnetic disturbances (the equality and interchangeability of environmental parameters that depend on the temporary conditions can impact human organism).
- (5)
- The next mistake is not to detect the connection between environment variations and variations in the state of the human organism if these connections have features that manifest themselves over a limited period of time (for example, features of terrestrial seasons or features of different phases of the solar activity cycle).
- (6)
- Another error is not to detect the connection between environment variations and variations in the state of the human body if these connections correspond to environmental parameters deviations from a certain level that was established at a given time interval (for example, from the calendar seasonal average level or from the median level characteristic of a particular phase of solar activity). It should be taken into account the fact that different environmental parameters have their own intervals of stable existence of the behavioral features (for example, approximately 27/2 days for the solar parameters, approximately 5–7 days for atmospheric pressure parameters, etc.)
- (7)
- The next mistake is to not detect the connection between environment variations and variations in the state of the human body if there is a time shift in the manifestation of the effect of such a connection (for example, changes in the environmental parameter do not occur on the day of changes in the state of the human body but before that day). Such discrepancies may be explained by the delay in the human reaction to changes in weather conditions (investigated in our study).
- (8)
- The mistake is to not detect the connection between environment variations and variations in the state of the human body, if both of these variations occur with a time shift and are, in both cases, a response to changes in the third not taken into account parameter that is ahead of a weather change. In this case, changes in the studied environmental parameters do not occur on the day of changes in the state of the human body but later that day, although they follow each other regularly. Such discrepancies can occur if the days of medical events fall on the changes in weather processes when these processes have not yet been completed themselves.
- Changes can occur at the level of malfunctions of the functioning of the internal systems of the body, for example, changes in blood characteristics, heart rate variations, etc. In this case, the study leads to the determination of the mechanisms of influence of the external environment on the human body and serves to understand the fundamental foundations of human existence in specific conditions.
- Changes can lead to so-called “clinical outcomes” [43,44,45,46]. We are talking about the occurrence of diseases, inadequate responses to treatment and deaths. In this case, the study leads to conclusions that allow us to determine the risk factors for human life and health and serves as a practical guide for attending physicians and patients in this risk zone.
- The factors to be studied may be the characteristics of variations of such layers of the external environment that are in close contact with a person. For example, the characteristics of the state of the atmosphere. In this case, the studied parameters can claim the role of direct factors affecting the human body or the role of agents transmitting disturbances from areas of the external environment that are remote from humans, for example, intermediaries of solar influences. The study of such parameters serves to search for mechanisms of environmental impacts on the human body.
- The factors to be studied may be the indices of the manifestation of disturbances of the environment external to humans. For example, indices of changes in solar activity. In this case, the studied parameters can claim either the role of initiators of disturbances through their agents transmitting these disturbances to the human body or the role of indicators of possible deterioration of human health. The study of these indicator parameters serves the practical purposes of preventing possible dangerous clinical outcomes.
- (1)
- In order to avoid the errors described in paragraph 3, at the first stage of the study, we conduct a statistical analysis of the input data for the characterizing of the object under study. Actually, we create groups of data that are homogeneous in terms of the largest number of indicators (for example, groups of examined people who are the same in age, gender, diagnosis, etc.).
- (2)
- Within each group, we establish the criteria of “norms” and “anomalies”, agreeing with the opinion of the authors of Clinical Epidemiology [43].
- The concept of norm: “It is customary to consider the norm as the most common, or normal condition” [43]. The definition of the normal state for the concrete human organism is the issue under discussion [45,46]. However, for our study, the “common condition” is important, because it conveniently describes the usual situation in the large people groups under our investigation. We define these concepts through numerical characteristics of statistical distributions of the studied medical parameters. Actually, we consider the values of the studied parameter to be the “norm” when they are within the median deviation of its distribution, even if these values go beyond the boundaries of the medical norm that determines the degree of human health. The reason for this approach is precisely the necessity to determine the “most common or usual state” [44] of the parameter under study. In groups of people of different ages, genders and diagnoses, “frequently occurring values” [43,44] may vary while deviating from the norm of a healthy person without any influence of the external environment.
- (a)
- with a rough estimate, we consider as an “anomaly” the values of the parameters which are lying within the lower and upper quartiles of its distribution.
- (b)
- separately investigated the rare, but indicative values of maxima and minima.
- (c)
- with a more subtle analysis, we investigate the values of the far tails of the distribution (for example, values separated by 10% and 90% critical points) while introducing classes of intermediate values (for example, values within 10–25% of the distribution and 75–90% of the distribution).
- (d)
- some kind of “anomaly” may be presented by unusual events without the consideration of the statistical distribution.
- (3)
- In order to avoid the error of paragraph 5, we investigate the medical data—prepared in the above way—of the conditions of specific terrestrial seasons, taking into account the fact that the time interval of a particular calendar season of a particular year is already inside the time interval of a specific phase of the solar activity (also in the case when this phase is transitional). We consider it suitable to conduct research within each of the investigated groups and permit ourselves to combine only the conclusions. It should be noted that, in this way, the value of small samples increases significantly (and such inevitably occur with a large number of signs of division of a general sample into statistically homogeneous groups). However, the advantage of this procedure is in the implementation of the basic rule of the statistical research—the keeping of the conditions set unchanged during the repetition of the experiment as carefully as possible with the current level of knowledge. This action reduces the degree of uncertainty necessarily present and therefore increases the reliability of the conclusion obtained. We consider it possible to take into account the results obtained when comparing even the minimum number of groups (two to three people), since the formation of typical samples ensures uniformity in the characteristics that form the type, and, consequently, categorization within such a sample if based on the magnitude of the environmental parameter (or the behavior of this parameter) can be of great importance. At the very least, such categorization can direct further research, pointing to the possibility of a so-called subtle effect, the presence of which will be possible to prove with an increase in the sample size in a future study. In other words, if people of the same sex, the same age and the same diagnosis have “changes in the human body”, let us look for the reason for such changes in the state of the environment at the time of registration of the medical indicators. It should be noted that we should not forget about the unaccounted-for (due to their non-manifestation in a particular experiment) individual features of the studied human organisms. These undetected features have a share in the inevitable uncertainty present in the derivation of any result.
- (4)
- At the next step, we collect the environmental parameters—as many as we can get from all the different environmental fields—those characterizing the calendar date of the registration of a specific medical event. Thereby, we form the complexes of environment characteristics (named the “Weather Complex” in this work) presumably affecting the object under study.
- (5)
- Then, we distribute the collected data into the categories of “norms” and “anomalies” (those were described above) by the data of their registration.
- (6)
- In order to avoid the errors described in paragraphs 5 and 6, we investigate the environment parameters variations in terms of the parameter values taken in relative units of deviations from their seasonal (calendar) average. For this purpose, we calculate the relation of the daily characteristics of each parameter to its seasonal characteristics; we carry out the standardization process for the seasonal median.
- To present the daily collection of various environmental parameters as a single holistic sample. It means that the set of heterogeneous parameters translated into the sample of members of the identical conventional units. These units describe the deviation of each parameter from its seasonal distribution center. The previous heterogeneous collection immediately turns into a sample set for which descriptive statistics can be calculated; therefore, it is possible to characterize the whole complex by, for example, its mathematical expectation and variance.
- To estimate the degree of deviation of the entire Weather Complex from its norm for a given calendar season—the proximity of the median of such a sample to zero means the proximity of the entire Weather Complex to its seasonal norm.
- To compare the different Weather Complexes corresponding to the days of different medical events by their descriptive statistical characteristics. In the case of differences in the characteristics of Weather Complexes, we can talk about the difference in the conditions in which different medical events are formed. Actually, the searching for such conditions is the main task of the entire study.
- (7)
- In order to avoid errors in paragraphs 1, 2 and 4, we conduct a study of holistic complexes of environmental parameters (a complete set of all environmental characteristics included in the study—Weather Complexes) corresponding to certain holistic clinical outcomes (that are understood in this work as the result of specific changes in the human body, such as the presence or absence of the ischemia case).
- (8)
- The working scheme of the representation of the natural environment (meteorological and helio-geophysical factors) is focused on the structure of the solar–terrestrial relations, which is based on the position of natural phenomena in space relative to the investigation object on the Earth’s surface—the human person:
- variations of Solar Activity SA (SA global variations and variations of SA flare components)
- variations in the characteristics of processes in the near-Earth space,
- variations in the characteristics of the geomagnetic field,
- variations in the characteristics of the ionosphere and
- variations of meteorological characteristics.
- (9)
- In order to avoid the errors described in paragraphs 7 and 8, we investigate the environmental variations in the interval of +/− 5 days from the date of registration of the medical event. In this work we only considered the half-interval from the (−5) day to the medical event; the investigation of it is useful for the forecast purpose. In this work, we claimed to create the scientific basis for correct environment monitoring and, thereby, for veracious special (medical) weather forecasts.
- (10)
- Keeping in mind the possible time discrepancies of a medical event with a moment of the distinct weather change, we are looking for the day of maximum difference of Weather Complexes (“Maximal Difference Day” in this work) in the mentioned interval.
- We consider the days of registration of the concrete studied categories of medical events as the key days (0 days) of the Folder Epochs Method.
- The sets of environmental parameters, which correspond to the days of registration of different categories of medical events (Middle vs. Nobody), are obviously clusters that are already formed according to a given condition: compliance with a specific medical event under study. Thus, we come to one of the tasks of Cluster Analysis: the determination of inter-cluster distances and the allocation of the maximum of them. The Euclidean inter-cluster distance we used was defined as
- (1)
- The calculations of the distance between 2 points , corresponding to 2 different phenomena under study. This is the Euclidean norm of the difference between the medians (the median was calculated in the sample of days when the concrete medical event occurred) of the observed values of each environmental characteristic:
- (2)
- The calculation of the guiding cosines of each difference in the medians of the observed values of each environmental characteristic:
- (3)
- The determination of the equation of the straight line to which the distance between the points , belongs. The coefficients in the equation of the straight line are the calculated guide cosines.
- (4)
- The determination of the equation of the plane that is perpendicular to the described line. The coefficients in the equation of such a plane, by definition, are the coefficients of a straight line perpendicular to this plane:
- To find the day of the maximal difference of the Weather Complexes, which correspond to the different medical events.
- To find the days when such complexes did not differ (in this case, the Mann-Whitney significance level is quite high).
- To filter out the parameters whose difference is unreliable in terms of the significance of the Mann–Whitney hypothesis test criterion.
- To consider as responsible for the division of medical events into categories only those environmental parameters that would differ by Mann–Whitney criterion when they correspond to the different categories of the medical events.
- (11)
- We investigate—within the interval of the folder epochs—the behavior of some parameters that are different when they correspond to different medical events.
- The day of the Weather Complexes maximal difference when they corresponded to the different medical events: Nobody and Middle—Maximal Difference Day in this work. We have investigated the first part of the folder epochs interval—from the (–5) day to the key day—for the scope of the investigation of forecast perspectives.
- The ratio of Space Weather characteristics to Terrestrial Weather characteristics, which differed when corresponding to the different medical events in the Maximal Difference Days.
- The distribution of the above-mentioned characteristics by Weather Complex blocks. These blocks relate to the different environmental fields.
- The lists of the concrete environmental characteristics that were different when they were registered in the days of the different medical events.
- The plot of the timeline—behaviors of some selected environmental characteristics as an example of such behavior features.
2.2. Materials
2.2.1. Medical Data
2.2.2. Environmental Data
- (1)
- Daily indices of Solar Activity (SA) global variations (the full radio flux on λ = 10.7 cm, Wolf-number, the daily sum of the area of all observed sunspots and the number of the new Active Regions per day) [47]—GlobalSun conditional block in this work.
- (2)
- Daily characteristics of the SA flare-component in various bands of the electromagnetic spectrum (optical-, radio- and X-ray-band) [47]—SolarFlare conditional block in this work.
- (3)
- Daily variations of Interplanetary Space characteristics in the near-Earth space (e–, p+ and α-particle fluxes) [48]—EarthVicinity conditional block in this work.
- (4)
- Daily Geomagnetic Field (GF) variations (the total GF vector in the near-Earth space (GOES orbit), the component of GF that is perpendicular to the Earth’s orbit plane in the near-Earth space (GOES orbit); K–indices on high terrestrial latitudes; K–indices on middle terrestrial latitudes and GF x-,y- and z-components on the latitude of Saint Petersburg) [48,49]—GeoMag conditional block in this work.
- (5)
- Ionosphere phenomena (sudden ionosphere disturbances) [50]—ion conditional block in this work.
- (6)
- Atmosphere parameters (the atmosphere pressure, the low nebulosity, the wind speed, the humidity, the air temperature, the dew point temperature and weight oxygen content in the air) (Saint Petersburg meteorology station, #26063 (59°58′ N 30°18′ E))—respectively, Baric, Humidity, Temperature and Oxy conditional blocks in this work.
3. Results
3.1. The Day of the Weather Complexes Maximal Difference When They Corresponded to the Different Medical Events Nobody and Middle in the First Part of the Folder Epochs Interval—From the (−5) Day to the Key Day
3.2. The Ratio of Space Weather Characteristics to Terrestrial Weather Characteristics Which Differed When Corresponded to the Different Medical Events in the Maximal Difference Days
3.3. The Distribution of the Mentioned above Characteristics by Weather Complexes Blocks. These Blocks Relate to the Different Environmental Fields
3.4. The Lists of the Concrete Environmental Characteristics That Are Different When They Were Registered in the Days of Different Medical Events
3.5. The Plot of the Time–Behaviour of Some Selected Environmental Characteristics as the Example of Such Behaviour Features
4. Discussion
- The classification of the combinations of calendar seasons with solar activity phases cause different weights of Space and Terrestrial types of weather in Weather Complexes of environmental factors, which can impact the human organism. We can see this unbalance from distributions of the weather types by calendar seasons and solar activity phases (Figure 6 and Figure 7).
- The detailed study of the geomagnetic spread characteristics in the autumns of the solar fall phases. The repeating of the spread statistics of the total magnetic field in the Earth vicinity as the factor that is common for both gender groups in the similar seasons and in the same solar phase leads us to such study (Section 3.3 and Section 3.4 in the Results).
- The verification of hypothesis of the equality and interchangeability of environmental parameters that can impact human organism. This hypothesis we assumed when we saw the changing of environmental factors collection from season to season of the concrete years (Section 3.3 in the Results).
- The clarification of the fact of female reaction to large-scale geomagnetic variations when the male reaction was registered on the local-scale geomagnetic variations. This fact was evident as the result of study of the environmental factors specific for each gender group separately (Table 3).
5. Conclusions
- The presented work shows the results of the complex study of the holistic environmental impact (Weather Complex impact) on the human organism.
- The comparing of the Weather Complexes in their relation to the days with different numbers of the ischemia cases in the frames of the study discovered the days of the maximal difference of these complexes (Maximal Difference Days) before the exact days of the registration of the concrete numbers of the ischemia cases. This fact is useful for the forecast purposes.
- The dissimilarity of the Maximal Difference Days for the different ischemic people gender groups led to the necessity of gender-targeted forecasts of the environmental status for the different medical events of the ischemia cases.
- The unprejudiced selection of the environmental parameters for the study shows the primary importance of the geomagnetic parameters as the Space Weather factors and the humidity parameters as the Terrestrial factors for the ischemia outcomes.
- The including of the environmental parameters daily statistics to the study shows the importance of point statistics and spread statistics in different circumstances.
- The investigation of different circumstance that is described by the different combinations of the calendar seasons with the phases of the solar activity cycle leads to the hypothesis of the equality and interchangeability of environmental parameters which can impact the human organism.
- The investigation of different circumstance that is described by the different combinations of the calendar seasons with the phases of the solar activity cycle discovers—in the frames of our work—the different weights of Space and Terrestrial types of weather in Weather Complexes in such combinations.
- The daily spread of the geomagnetic total vector value in near-Earth space turned out—in the frames of our study—to be important for the both ischemia-people gender-groups in the autumns of solar activity cycle falling phase.
- The investigation of the behavior of the daily spread of the total vector value in near-Earth space before the days of different numbers of the ischemia cases discovers—in the frames of our work—the rising of this spread before two days to the absence of the ischemia cases contra falling of it before one day to the normal value of these cases. That fact was noted for the both ischemia-people gender-groups in the autumns of solar activity cycle falling phase.
- The including of the different geomagnetic points to the study discovers—in the frames of our work—the importance of the local geomagnetic daily median for the ischemia-people male group contra the large-scale geomagnetic daily point-statistics (maximum and median) for the female group.
- The including of three geomagnetic field components in the local terrestrial point (Saint Petersburg latitude) to the study discovers—in the frames of our investigation—the importance of y- and z-components for the ischemia-people male group.
- The results of the presented study mostly show the perspective for the expanded investigation. However, the representativity of ischemia cases sample allows us to confide in accuracy of the planned study directions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
The 10.7-cm (2800 MHz) full Sun radio flux was reported by the Dominion Radio Astrophysical Observatory at Penticton, BC, Canada on the date indicated. The measurements were made at approximately 20:00 UTC. Solar flux units (sfu) 10−22 W/(m2Hz) were the unit of measure. |
The daily sunspot number for the indicated date is computed according to the Wolf Sunspot Number R = k (10g + s), where g is the number of sunspot groups (regions), s is the total number of individual spots in all the groups, and k is a variable scaling factor (usually < 1). |
The daily sum of the corrected area of all the observed solar sunspots are in units of millionths of the solar hemisphere (msh). |
The daily sum of the new areas on the solar disk. |
The daily sum of the solar X-ray flares, classes:
|
The daily sum of the solar optical flares in the spectral Hα– line, classes:
|
The daily sums of the solar radio bursts were sorted by various descriptors:
|
The daily number of solar coronal mass ejections. |
The magnitude of the alpha-particles flux with the power of (4–10) MeV (α/(cm2 *s *sr *MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
The magnitude of the alpha-particles flux with the power of (4–21) MeV (α/(cm2*s*sr*MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: mean |
The magnitude of the electrons flux with the power of (>0.8) MeV (e/(cm2*s*sr*MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: sum, mean. |
The magnitude of the electrons flux with the power of (>2) MeV (e/(cm2 *s *sr *MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
The magnitude of the electrons flux with the power of (>4) MeV (e/(cm2 *s *sr *MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: mean. |
The magnitude of the protons flux with the powers of (>1) MeV, (>10) MeV, and (>100) MeV, (e/(cm2*s*sr*MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: sum. |
The magnitude of the protons flux with the power of (>6.5) MeV (p/(cm2 *s *sr *MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: maximum, minimum, median. |
The magnitude of the protons flux with the power of (>11.6) MeV (p/(cm2 *s *sr *MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: median. |
The magnitude of the protons flux with the power of (>30.6) MeV (p/(cm2 *s *sr *MeV)) in the Earth’s vicinity (the satellite GOES orbit) daily statistics: median. |
The magnetic field strength in the Earth’s vicinity (satellite GOES orbit) in the direction that is perpendicular to the Earth’s orbit plan (nT) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
The magnetic field strength in the Earth’s vicinity (satellite GOES orbit), the total vector (nT) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
The background X-ray fluxes in the Earth’s vicinity (the satellite GOES orbit) on the wavelengths: (0.5–4) Angstrom (E < 1*10−3 erg/(cm2 *s)) and (1–8) Angstrom (E < 1*10– 3 erg/(cm2 *s)) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Three-hourly K indices from the College (high-latitude) station monitoring Earth’s magnetic field (nT) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Geomagnetic Field Magnitude of each of 3 components: x-component, y-component, and z-component (SaintPetersburg latitude) (nT) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Three-hourly K indices from the Fredericksburg (middle-latitude) station monitoring Earth’s magnetic field (nT) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
The estimated planetary Three-hourly K indices are derived in real time from a network of Western Hemisphere ground-based magnetometers (nT) daily statistics: daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Atmosphere Pressure (hPa) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Nebulosity (low level), classes (1–10) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Wind speed (m/s) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Relative humidity (%) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Temperature of the dew point (°C) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Humidity deficit (°C) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Air temperature (°C) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
Oxygen part in the air (%) daily statistics: maximum, minimum, median, range, standard deviation, coefficients of oscillation and variation. |
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Calendar Season | Nobody | Male Group Middle | Female Group Middle |
---|---|---|---|
winter 2005–2006 | 11 | 20 | 23 |
spring 2006 | 21 | 19 | 32 |
summer 2006 | 22 | 17 | 23 |
autumn 2006 | 20 | 28 | 28 |
winter 2006–2007 | 19 | 26 | 33 |
spring 2007 | 15 | 15 | 35 |
summer 2007 | 23 | 26 | 31 |
autumn 2007 | 18 | 8 | 29 |
winter 2007–2008 | 13 | 25 | 36 |
spring 2008 | 20 | 31 | 31 |
summer 2008 | 20 | 20 | 23 |
autumn 2008 | 17 | 14 | 34 |
winter 2008–2009 | 16 | 28 | 33 |
spring 2009 | 23 | 26 | 29 |
summer 2009 | 7 | 12 | 22 |
autumn 2009 | 14 | 21 | |
winter 2009–2010 | 7 | 7 |
General Season | Concrete Season | Solar Phase | Maximal Weather Difference Day (Female Group) | Maximal Weather Difference Day (Male Group) |
---|---|---|---|---|
winter | winter 2005–2006 | SolarFall | −4 | −2 |
winter | winter 2006–2007 | SolarFall | −2 | −2 |
winter | winter 2007–2008 | SolarFall | −2 | −2 |
spring | spring 2006 | SolarFall | −1 | −1 |
spring | spring 2007 | SolarFall | 0 | 0 |
summer | summer 2006 | SolarFall | −3 | −4 |
summer | summer 2007 | SolarFall | −5 | −1 |
autumn | autumn 2006 | SolarFall | −2 | −3 |
autumn | autumn 2007 | SolarFall | −1 | −4 |
spring | spring 2008 | CloseToMin | −1 | −4 |
spring | spring 2009 | CloseToMin | 0 | 0 |
summer | summer 2008 | CloseToMin | −4 | −5 |
summer | summer 2009 | CloseToMin | −2 | −4 |
autumn | autumn 2008 | CloseToMin | −4 | −5 |
winter | winter 2008–2009 | SolarMin | −3 | −3 |
Gender Group | Solar Activity Phase | Calendar Season | Environmental Parameter | Weather Type |
---|---|---|---|---|
Female | SolarFall | 2 Winters, Spring | proton flux E = (4–9 MeV)—daily maximum | Space Weather |
Spring, Summer Autumn | total magnetic field in the Earth Vicinity—daily maximum | Space Weather | ||
Summer, Winter, Spring, Autumn | 3-hourly K indices high-latitude station monitoring Earth’s magnetic field—daily maximum | Space Weather | ||
Summer, Winter, Autumn | 3-hourly K indices from high–latitude station monitoring Earth’s magnetic field—daily median | Space Weather | ||
2 Autumns, Winter | 3-hourly K indices from the middle—latitude station monitoring Earth’s magnetic field—daily median | Space Weather | ||
Winter, Spring, Summer | Temperature of Dew Point—standard deviation, coefficient of oscillation | Terrestrial Weather | ||
Male | SolarFall | Winter, Spring, Summer | Geomagnetic Field Magnitude y-component, (Saint Petersburg latitude)—daily median | Space Weather |
2 Summers, Winter | Geomagnetic Field Magnitude z-component, (Saint Petersburg latitude)—daily median | Space Weather | ||
2 Winters, Autumn | Relative Humidity—daily median | Terrestrial Weather | ||
2 Winters, Autumn | Relative Humidity—daily min | Terrestrial Weather | ||
CloseToMin | Spring, Winter, Summer | 3-hourly K indices from the middle-latitude station monitoring Earth’s magnetic field—daily coefficient of oscillation | Space Weather | |
2 Springs, Summer | Temperature of Dew Point—daily minimum | Terrestrial Weather |
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Stupishina, O.M.; Golovina, E.G.; Noskov, S.N.; Eremin, G.B.; Gorbanev, S.A. The Space and Terrestrial Weather Variations as Possible Factors for Ischemia Events in Saint Petersburg. Atmosphere 2022, 13, 8. https://doi.org/10.3390/atmos13010008
Stupishina OM, Golovina EG, Noskov SN, Eremin GB, Gorbanev SA. The Space and Terrestrial Weather Variations as Possible Factors for Ischemia Events in Saint Petersburg. Atmosphere. 2022; 13(1):8. https://doi.org/10.3390/atmos13010008
Chicago/Turabian StyleStupishina, Olga M., Elena G. Golovina, Sergei N. Noskov, Gennady B. Eremin, and Sergei A. Gorbanev. 2022. "The Space and Terrestrial Weather Variations as Possible Factors for Ischemia Events in Saint Petersburg" Atmosphere 13, no. 1: 8. https://doi.org/10.3390/atmos13010008