DOI: 10.31038/GEMS.2023523

Abstract

The patterns and trends of monthly time series of global atmospheric land based surface temperatures and oceanic surface temperatures, and the combination of the two, from 1850 to 2018, are presented and compared to the overall time series of fossil fuel burning which is shown to be highly correlated with atmospheric carbon dioxide concentrations. Considering only monthly temperature data time series dating back to 1850, and climate factor data in-kind, and by employing an empirical mathematical methodology, we decompose the non-linear, non-stationary, global time series and confirm patterns of frequency and amplitude modulated, discreet internal modes of variability. We find periods of warming and cooling on both the surfaces of the ocean and atmosphere over land with prominent seasonal, annual, inter-annual, multi-year, decadal, multi-decadal, and centennial modes, riding atop overall trends. Our calculated overall rates of global warming differ significantly from the estimates of the Intergovernmental Program on Climate Change (IPCC) 2007 findings, and also with the most recent, 2023 IPCC Report (https://www.ipcc.ch/ar6-syr/). Because the ocean has an enormous heat capacity relative to that of the atmosphere, we find the oceanic warming rate to be less than two-thirds of surface air over land, making the ocean a regulator, a heat capacitor, thus the dominant player in determining global surface temperatures. By employing an econometrics-based statistical formula, we establish a causal relationship between fossil fuel burning and global surface temperatures, which causally links the overall trends in planetary surface temperature rise, to the overall upward trend in fossil fuel burning. Our study also found that there is a 1-year phase lag of global temperatures to fossil fuel burning.

Keywords

Patterns of climate variability, Climate change, Global warming, Climate warming, Global surface atmospheric temperature anomalies, Sea and ocean surface temperature anomalies, Fossil fuel burning, Attribution of anthropogenic influence in climate change

Introduction

The Earth system absorbs the Sun’s incoming short-wave radiation and re-radiates, stores or exchanges it at different rates via natural processes. For a planetary condition of thermal equilibrium, the amount of total outgoing, long-wave radiation would equal the total amount of incoming, short-wave radiation. If outgoing radiation does not equal incoming radiation, then the planet’s global body temperature will vary both spatially and temporally; so, “climate variability”. Many studies have shown that the climate has been warming over the 20^th and into the 21^st Centuries, and they collectively attribute the warming climate to fossil fuel burning. Recent studies have suggested links between fossil fuel burning and climate warming [1-7]. Numerical model studies, such as Millar et al. [8] and Ekwurzel et al. [4], employed global energy-balance coupled climate-carbon-cycle models which attempted to assess global surface temperatures with the emissions of global carbon production. The relationships were visually correlated with causality assumed. However visual correlations of one curve to the next does not in and of itself establish true attribution, and thus “causality”. Herein, our study is based entirely data based, and employs only the global temperature time series and the fossil fuel burning time series. Attribution, by definition, links cause to effect.

In this study, we address the global surface temperature anomalies (GSTA) of the global surface of the Earth, in the upper panel in Figure 1, the global atmosphere surface temperature anomalies above land (GLSTA), in the middle panel in Figure 1, and the global ocean surface temperature anomalies (GOSTA), in the lower panel in Figure 1, from 1850 through the first half of 2018. The reason we limit ourselves to 2018 is that the outbreak and spread of the Covid viral infection globally, may have effected fossil fuel consumption and we will only address more “normal” conditions of greenhouse gas loading of the oceans and atmosphere. We employ documented surface-temperature-anomaly-data. While much attention has focused on the global surface atmospheric temperature record, as greenhouse gases have built up in the atmosphere, far less attention has been paid to the global ocean surface temperature record in-kind. We address that fact. Data are from the Climatic Research Unit and the UK Meteorology Office, Hadley Centre: http://www.cru.uea.ac.uk/cru/info/.

Figure 1: Monthly averaged time series of GSTA (top), GLSTA (middle), GOSTA (bottom) 01 01 1850-07 31 2018.

The climate system, as represented by surface temperature anomaly data, consists of non-linear (NL) and non-stationary (NS) processes, so we utilize an empirical, mathematical, data adaptive technique to decompose the data. Our mathematical decomposition methodology, the Ensemble Empirical Mode Decomposition (EEMD) first presented by Wu and Huang (2009) [1-10] produces internal, intrinsic modes of variability buried within the temperature data time series. We account for the patterns revealed by the internal modes of variability, relate them to naturally occurring physical phenomena and also compute the overall data time series “trends” which we find do not have a natural causal basis. However, we employ a statistical hypothesis test for determining whether one time series, such as fossil fuel burning, can be useful in forecasting another, specifically global surface temperatures, and thus to predict climate warming, past, present and future; thus establishing causality.

Background and Methodology

Wu and Huang (2009) developed EEMD based on the earlier work of Huang et al. [11-17], which employed the Hilbert Transform (the HT) [18,19], in the development of the Empirical Mode Decomposition (EMD) methodology. We employ EEMD in the decomposition of global surface temperature anomaly data time series. It is of note that a mode-mixing problem existed in the EMD decomposition in which successive IMFs were discovered to occasionally mix with or contaminate each other. To address this issue, Wu and Huang (2009) [9,10] added white noise to the various time series, created ensembles, and the mean IMFs were found to stay within the natural dyadic filter windows, preserving their dyadic properties, leading to stable decompositions of frequency and amplitude modulated internal modes of variability in the record length data. The decomposition reveals temporal internal modes of variability (referred to as Intrinsic Mode Functions or IMFs) which are frequency and amplitude modulated, and reveal non-linear (NL) and non-stationary (NS) signals in the data. The IMFs stack from higher to lower frequencies. We also produce data time series “trends”. In discussing the trend of any time series, we first need to consider the definitions and the methodologies of computing a trend. As such, no conventional simple averaging process can be utilized to reflect what information is buried in the multiple temperature time series as they are non-linear and non-stationary to the naked eye (see Figure 1, for example). This underscores the importance of clearly defining what constitutes a trend. Granger [20] presented an insightful definition of a trend as “a trend in mean comprises all frequency components whose wavelength exceeds the length of the observed time series”. For NL, NS datasets, none of these definitions is mathematically applicable leading to employment of the definition based on the EEMD methodology. With EEMD, the non-oscillation “residue”, which is left after the higher to lower frequency IMFs are removed from the time varying decomposition, becomes the trend of the time series.

Global Ocean and Land Based Atmospheric Temperature Anomaly Time Series

Figure 2 shows eleven IMF modes in the GSTA (also true of the GLSTA and GOSTA) with the 11^th being the overall trends of the land-based atmosphere, the ocean surface and the combination of the two. The IMF modes have their respective physical bases. Mode 1 is several monthly variability. Mode 2 is seasonal variability. Mode 3 is the motion of the Earth on its axis of rotation. Mode 4 is the annual signal. Mode 5 is inter-annual variability. Mode 6 is the dominant signal of the El Nino Southern Oscillation (ENSO). Mode 7 is the quasi-Solar Cycle (10-12 years), centered about 11 years. Mode 8 reflects both the North Atlantic Oscillation (NAO) and of the 22-year Solar Cycle. Mode 9, 60-70 years is the Atlantic Meridional Overturning Circulation Belt (MOC), described by Cunningham et al. [21]. Mode 10, 105-110 years represents the Global Thermohaline Circulation Conveyor Belt [22,23]. Mode 11 is the gravest mode or overall trend of the 167-year time series of total data (Table 1).

Figure 2: The EEMD Mode decomposition of the 1850-2017 time series of the GSTA (shown in the Top Panel.

Table 1: Intrinsic Mode Functions (IMFs) shown in Figure 2, in Months (M) or Years (Y)

We note in Figure 2, that for the GSTA, the IMF modes 2, 3 and 10 have amplitudes of 0.4°C, modes 4 and 6 have amplitudes of 0.3°C, modes 5, 7 and 9 have amplitudes of 0.2°C and mode 8 amplitude of 1°C. Mode 11 ranges between +/- 0.5°C. Thus, all the IMF modes contribute to the total time series in temperature amplitudes in a nominally equitable manner across the range of 0.1 to 0.4°C. This finding suggests that the Planet’s internal, natural modes of variability all contribute significantly to surface temperatures and thus cannot be ignored. Thus, periods of relative warming and or cooling occur naturally by the positive and negative disposition of the natural variability, all riding atop overall atmospheric and oceanic trends, and presented in the Table 1.

In Figure 3 (left panel), the trend of the GLSTA shows a record length total rise of 1.22°C and the GOSTA displays a rise of 0.67°C. The collective GSTA rises 0.88°C. The ocean surface temperature has risen at a much slower rate than the atmosphere over land. This is an excellent example of the power of the EEMD IMF decomposition. The full time series of the GOSTA displays an overall beginning to end of the series increase of 0.75°C, the GLSTA shows a rise of 2.91°C and the GSTA indicates an overall increase of 1.25°C. If connecting lines from start to end of the three time series had been drawn, the overall trends would have been greatly overestimated, demonstrating the failing of traditional methods (see IPCC, 2007) [24] of computing a trend, which are to create regression lines. Thus, the IPCC rates of rise of GSTA overestimate the actual rises in temperatures over land by 0.72°C and by a combined air-sea overestimate of 0.37°C. The IPCC straight-line, regression slope estimates are 0.05°C/decade over the full GSTA temperature record, 0.07°C/decade over the prior 100 years, 0.13°C/decade over the previous 50 years and 0.18°C/decade over the latter 25 years of the record.

Figure 3: (left panel) The overall trends of the GSTA (blue), GOSTA (red) and GLSTA (yellow) time series. The GOSTA raw data are shown in Figure 2 and represents the GLSTA and the GSTA (both not shown). (center panel) The time rates of change or 1st derivatives, of the trends of the GLSTA, GOSTA and GSTA time series. (right panel) The fossil fuel burning curves. Atmospheric carbon dioxide concentrations also shown in far right panel Confirming sources are: T. Boden and R. Andres, The Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6290, USA; and G. Marland, The Research Institute for Environment, Energy and Economics, Appalachian State University, Boone, North Carolina 28608-2131, USA.

In Figure 3 (left panel), we present the time rates of our trends and find that the rates of warming from 1850 through 2017 are 0.713°C/Century for the GLSTA, 0.427°C/Century for the GOSTA, and 0.529°C/Century for the combined GSTA. In the Figure 3 central panel, the 1^st derivatives of the trend curves are all positive, and curving upward indicating that the rate of warming is accelerating. From 1955 to the present the overall warming trend of the GSTA (Figure 3) has been ~0.09°C/decade. Clearly, the rate of warming of air over land has globally been 167% greater than that of the surface of the global ocean. In the Figure 3 right panel, we present the fossil fuel burning time, Carbon Emissions (CE) series from 1751-2014, provided via <http://cdiac.ornl.gov/trends/emis/tre_glob.html>. From 1751 until 1850, global CO2 production was from 3 MMTs in 1751 to 54 MMTs in 1850. In the latter half of the 19^th century, carbon emissions increased considerably, with a dramatic upsurge from 1949 to 2014, reaching a value of 950 MMTs and this resulted in the nonlinear rise of atmospheric carbon dioxide concentrations. The CE was essentially flat from 1751 through the mid latter half of the 19^th century when the change of carbon burning began to be spiky. One could proceed here with cross correlations between the CE and GLSTA, GOSTA and/or the GSTA. However, although the temperature curves all display strong visual correlation with the fossil fuel burning curve, visual correlation does not prove a cause-and-effect relationship or attribution.

Granger Causality relating Carbon Burning to Global Surface Temperature Anomalies

We next employ the Granger Causality Test (GCT) [25] to obtain evidence for the strength of the causal relationship between the carbon burning and atmospheric carbon dioxide concentrations and atmospheric carbon dioxide concentrations and the globle temperature time series. GCT statistically tests for determining whether one time series is useful in forecasting another. Generally regressions reflect mere correlations, but Granger argued that by measuring the ability to predict the future values of a time series using prior values of another time series, causality in economics could be tested. A time series X is said to “Granger-cause” Y if it can be shown, through a series of t-tests and F-tests on lagged values of X (and with lagged values of Y also included), that those X values provide statistically significant information about future values of Y. The idea is that if series {x_t} helps to cause series {y_t}, then past values of {x_t} should improve predictions of series {y_t}. This type of causality is established by first modeling {y_t} in terms of the past values of {y_t} through an autoregressive (AR) process, then adding past values of series {x_t} to create a second model. If the second model is statistically better than the first, then one has established causality in the Granger sense. Testing for Granger Causality in global temperatures has been considered by Attanasio et al. [26], Pasini et al. [27-29], amongst others. The above papers consider more time series than those considered here so they can for example separate out anthropogenic forcing and they also allow for non-stationarity in the temperature series. Our approach differs in that we consider only anomaly series {y_t} and one emission series {x_t}, and we find a stationary AR model for the anomalies after appropriately differencing the series and testing for white noise of the residuals?, which we justify our modeling by use of a time series goodness-of-fit test (see Fisher and Gallagher, 2012). One advantage of our methodology is that our final fitted model can be used to predict the impact of current (future) carbon emissions on temperature. We will approach this in a systematic process.

We first consider establishing a causal relationship between the carbon burning time series ({x_t}) and the ocean surface temperature time series GOSTA ({y_t}). Here we model on the monthly scale by taking the average temperature anomaly for each year as y and the average monthly carbon emission created by taking the yearly value divided by 12. Our first model relates GOSTA to past values of GOSTA. Using Akiake’s Information Criterion [30] we select the order of the auto regression to be three, meaning that each year’s GOSTA is related to the values from the last 3 years. The model is fitted using Gaussian maximum likelihood [31]. We use the goodness of fit test [32] to verify that the AR model adequately models the autocorrelation in the GOSTA series; the p-value of 0.9464 indicates that we have found an adequately fitting stationary model for GOSTA model given in Equation (1):

The above model explains the dynamics of the observed series solely from past-observed values. However, to establish the (Granger) causal relationship we instead add the previous year CE value to the model. The resulting model is given by Equation (2):

We can test for statistical significance of each fitted coefficient using the asymptotic normality of the Gaussian maximum likelihood estimators [31]. In particular, we conclude with a p-value of 0.00009 that the coefficient of x_t-1 is non-zero. In other words, the predictive model for GOSTA is significantly statistically better if the previous year CE is included. Model (2) explains the changes in temperature through a combination of autocorrelation (AR) and the impact of CE. We have thus established causality in the Granger sense. In this analysis, we selected the data beginning in 1950, since the warming trend in the latter half of the 20^th century is well established (cf. Figure 3, left panel). However, a similar analysis could be conducted starting at any point in the past. For example, the same analyses beginning each decade in the first half of the 20^th, i.e., in years 1900, 1910, 1920, 1930, 1940, and 1950, resulted in p-values, 0.00005, 0.0002, 0.00002, 0.0002, 0.003, 0.009, for the coefficient of x_t-1, respectively. In each case we conclude the model using the previous year CE is statistically better than the AR (3) model alone; the carbon emission series significantly improves predictions for GOSTA over the model based solely on past GOSTA values. Regardless of starting time, in the 20^th century, one finds statistical evidence of a causal relationship between CE and GOSTA. The coefficient for x_t-1 in model (2) is as follows. For each additional one-million metric tons of carbon emissions (CE), we estimate an increase in global ocean temperature (GOSTA) of 0.0007°C. The average increase in carbon emissions per year for years 1950 through 2014 is about 130 metric tons per year. Our model estimates an increase of 0.007+°C for each 10 metric ton increase in carbon emissions. The highly statistically significant coefficients of x_t-1 for land based atmospheric temperatures and total global surface temperatures are 0.0117 and 0.0087, respectively.

We next employ our fitted models to predict the global sea surface anomaly (GOSTA), the land atmosphere (GLSTA) and combined global surface temperature anomalies (GSTA) for 2015. Figure 4 shows the observed anomalies for years 1950 through 2014. The prognostic values are remarkably accurate. In the plots, we mark the predicted value from our fitted model, the upper and lower 95% prediction limits and the observed value for Year 2015. We conducted multiple-year lag experiments between CE and the surface temperature time series, beginning with 10 year down to 1 year lags (not shown except for the latter). We find that the model, which uses both past values of the anomaly series and the previous year’s carbon emission ( a 1-year lag), provides excellent predictions for the observed anomalies of the ocean surface, atmosphere on land and the combination therein for 2015. This provides further empirical evidence of Granger Causality relating previous year carbon emissions to sea surface, atmosphere on land and the combination temperatures on a global scale. We utilized the 2014 and 2015 data because 2014 is the last year for which we were able to obtain Fossil Fuel Burning data from the website provider at the time that this study was conducted. The results presented above provide a pathway and portend to future increases in global surface temperatures given anticipated increases in fossil fuel burning, GOSTA, GLSTA and GSTA as a function of CE. GOSTA is increasing at the rate of 0.0007°C/ Million Metric Tons of CE. GLSTA is increasing at the rate of 0.00117°C/Million Metric Tons of CE. Thus, the GSTA is increasing at the rate of 0.00087°C/Million Metric Tons of CE. Presently we are on a trajectory to reach 10⁴ Million Metric Tons/year of CE by years 2020 to 2022. These numbers are less foreboding than the straight-line estimates of both the IPCC and NIPCC, but are nonetheless very noteworthy.

Figure 4: Global Surface Temperature Anomalies from 1949-2014 with Granger Causality Predicted (X) versus Actual Temperatures (O) for 2015 and upper and lower 95% confidence limits. (left panel) is for the GOSTA. (middle panel) is for the GLSTA. (right panel) is for the GSTA.

Discussion and Conclusions

Mathematical relationships between fossil fuel burning and surface temperatures in the oceans and over land are presented. The statistical relationship curves reveal strongly that there is a one-year phase lag between global carbon loading via fossil fuel burning and planetary surface temperature rise, different from Ricke and Caldeira [33-41] who proposed that planetary temperatures changed about a decade after burning. In fact, robust relationships are presented between the GLSTA, GOSTA and GSTA, and their past time series, and the CE time series. Via Granger Causality, these surface temperatures are predicted very accurately from fossil fuel burning a year earlier. Thus, the conclusion we reach is that we have proven that there is “attribution” between fossil fuel burning and climate warming. In 2007, the IPCC was awarded a Nobel Prize for its comprehensive analyses of global climate change, including a visual-correlative-comparison of fossil fuel burning and temperature rise. However, a visual correlation does not prove “causality”. In 2003 C.W.J Granger was awarded a Nobel Prize for his work in econometrics theory and applications. We invoked “Granger Causality” to attribute the overall trends in global surface warming to fossil fuel burning and carbon emissions (https://en.wikipedia.org/wiki/Clive_Granger).

Acknowledgement

The authors thank Dr. Thomas S. Malone (former member of the U.S. National Academy of Sciences, now deceased), for having encouraged this study. He observed that cause and effect was necessary to prove attribution. The authors thank Coastal Carolina University for supporting this study.

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DOI: 10.31038/GEMS.2023514

Abstract

The human activities have strongly altered size, age, sex ratio, fecundity, feeding nature and biodiversity of fishes from the freshwater ecosystems over the world especially riverine division. During study period 548 specimens of Cyprinus carpio were collected from February 2019 to January 2020 in fish landing centre at Sirsa from the lower stretch of the Tons River at Prayagraj, Uttar Pradesh, India. The size of fishes varied from 97 to 687 mm (total length). The maximum size of fishes indicated that the stock of C. carpio in the Tons River was in healthy condition. The 251-290 mm size group was most dominated (16.42%) compared to 211-250 mm (14.78%) and 291-330 mm (12.96%) in total exploited stock. The lower size group was maximum exploited with 57.83% from the Tons River at Prayagraj. Higher size group contributed minute proportion with 4.74% in the exploited stock. The exploitation pattern was systematic in all size groups. Current exploitation pattern is systematic and healthy form with environmentally friendly sourcing of food.

Keywords

Size composition, Stock, Exploitation pattern, Ecosystem, Tons River, Cyprinus carpio

Introduction

Fish size composition is an essential component of river and stream ecosystem and represents an evident of structure, function, depth and health of stream/river [1-5]. Mostly large size and perennial rivers has large size of fishes in lotic water bodies [6-9]. The fish exploitation is an economic activity governed by social needs, food security and pressures [10-14]. Freshwater fishing is a chief foundation of income and protein for the riverine populations of most tropical regions [15-20]. Cyprinus carpio (Linnaeus, 1758) or Common carp is an omnivorous and bottom feeder fish species. It is distributed throughout countries in Asian countries as like India, Pakistan, Bangladesh, Burma and Nepal and also globally spread [21,22]. It is economically important fish species from the Tons River and also Ganga River system, and supports an important commercial fishery in rivers, reservoirs, lakes and even in culture ponds [23-25]. C. carpio is a non-native fish species for India. Non-native fishes are helping for homogenization of fish faunas, increasing of diversity and create pressure (example food, space, breeding ground, oxygen, infection and survival of organisms) for indigenous species or native species [26-30]. The fish are often key elements in the environmental planning [31-34]. The present study was thus undertaken to estimate size composition and health of the stock of C. carpio from the Tons River at Prayagraj, Uttar Pradesh, Central India. This study will help in formulating the fishery management policies of C. carpio from the Tons River and to update the knowledge in this field.

Material and Methods

Climate and Characteristics of the River

The climate of this region (Tons River basin) is marked by mild cold during winter and intensive heat during summer. The monsoon season is July to September month. Sometimes winter rainfall is also recorded. The Tons River is essentially a hilly stream arising in the Kaimur hills of the Vindhyan range, Madhya Pradesh, India. The Tons River drains the Bundelkhand geographic region of central India. Bundelkhand lies between the Indo-Gangetic Plain to the north and the Vindhya Range to the south. It is a tributary of the Ganga, which forms confluence at Sirsa near Meja in the Prayagraj district. Tons River lies between latitude 24° 0^’ to 25° 16^’ 54^” North and longitude 80° 26^’ 45^” to 82° 04^’ 57^” East. It banks are lined by deep ravines and the bed is rocky. Agriculture and human settlements were the major land use category in its catchment. During study period 548 specimens of Cyprinus carpio were collected from February 2019 to January 2020 in fish landing centre at Sirsa from the lower stretch of the Tons River at Prayagraj, Uttar Pradesh, India. Size composition (total length) varied from 97 to 687 mm. Drag net, cast net, gill net and hook and line were used by fishers/fishermen to catch the fishes in the river. A total of 548 fish samples (male and female) were collected and analyzed. The total length (mm) from the tip of snout to the end of caudal fin rays was measured by measuring scale. The obtained data from the river was classified into a series of size groups of 40 mm intervals. The number of samples calculated according to size group then converted into percentage.

Result and Discussion

Current exploitation pattern is systematic and healthy form with environmentally friendly sourcing of food. The size composition of C. carpio was varied from 97 mm to 687 mm of total length of fishes with majority between 251 to 290 mm from the lower stretch of the Tons River at Prayagraj, Uttar Pradesh, India (Figure 1). The large size of fishes also recorded in the Tons River in respect of river length. The maximum exploitation was recorded in 251 to 290 mm size group with 16.42%. Minimum exploitation was observed with 0.18% in 651-690 mm size group. Exploitation is an economic activity governed by social needs and pressures. Lower size groups 91-130 mm, 131-170 mm 171-210 mm, 211-250 mm and 251-290 mm were shared in exploited with 5.83%, 8.94%, 11.86%, 14.78% and 16.42%, respectively. Middle size groups 291-330 mm, 331-370 mm, 371-410, 411-450 mm and 451-490 mm were shared in exploitation 12.96%, 8.39%, 7.48%, 5.29% and 3.28%, respectively. Higher size groups 491-530 mm, 531-570 mm, 571-610 mm, 611-650 and 651-690 mm were contributed in exploitation with 2.19%, 1.28%, 0.73%, 0.36% and 0.18%, respectively (Figure 1). The lower size group was maximum exploited compared to middle and higher size groups from the lower stretch of the Tons River at Prayagraj. The experienced mature female fish stock was healthy in the river in monsoon season but very high fishing pressure we observed in this season. On the basis of data, it is observed that lower size group was maximum exploited with 57.83% at Prayagraj. Middle size group was exploited with 37.40%. Higher size group shared sizeable proportion with 4.74% in exploited population (Figure 2). The results also indicated that the exploitation was systematic in lower size group to higher size group. If exploitation is systematic then it is indication of healthy and heavy recruitment in near future. The over exploitation and non-targeted fishing is the biggest problem of riverine fishery [35-37]. The fishing pressure, mesh size, size of nets and fishing technique (example degree) are responsible for increasing or decreasing of total size of fishes and recruitment in the lotic ecosystems [14,38-42]. Fishing pressure changes the biodiversity, size composition, growth rate, age composition, sex ratio, income of fisher and maturation [43-45]. Non-native fishes are also changed selectivity of gear due to nature, dwelling behavior and ecological condition [28,46,47]. The growths of fishes are slightly checked by heavy metals accumulation in the body of fishes [48-50] (Insert Fig 1-2).

Figure 1: Size composition and exploitation pattern of C. carpio from the Tons River at Prayagraj, Uttar Pradesh

Figure 2: Exploitation pattern of C. carpio according to major group-wise from the Tons River at Prayagraj, Uttar Pradesh

Conclusion

It may be concluded that the present research work provides an important baseline study of this fish (Cyprinus carpio). Size composition indicated that the stock of C. carpio from the Tons River was in healthy condition and exploitation was also systematic form. Present condition was recorded due to sustainable exploitation of this species in the river. [20,51,52] stated that the when sustainably harvested or farmed, inland fish can be considered part of the green food movement for more environmentally friendly sourcing of food.

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DOI: 10.31038/GEMS.2023513

Abstract

Objectives: Insects arose in the terrestrial environment. Some species eventually evolved forms that could take advantage of freshwater habitats particularly in the larval stage. The numbers of species capable of surviving in saline is rare (about 5% of all known mosquito species). Most plants do not require sodium but animal however require sodium for some physiological function for example nervous activity. Salinity and certain ions such as sodium and sulphate challenges for insects are important. Other somatically active osmolytes occur in the blood of many insect. The evolutionary advantages of flight for mating and dispersal led more species to retain terrestrial or aerial adult stage. This creates substantial physiological problem for freshwater and terrestrial animals. The insects got around this problem by reducing the amount of sodium in the body to an essential minimum. From a water hardness/salinity perspective, arthropods occupy very soft water up to salt lakes twice as concentrated as seawater.

Materials and methods: To provide authentic information about this novel we use reliable data on academic resources such as Google Scholar, Scopus, Web of Science, Springer, Pro Quest, Wiley Online, Science Direct, Research Gate, PubMed, Sage, and SID.

Results: Only a limited number of species thrive in saline lakes, including some fairy shrimp (e.g., Artemia, Parartemia, and Phallocryptus) and brine and shore flies (Diptera, Ephyridae), such as Ephydra hians.

Discussion: Mechanisms of survival of Ephydra hians will provide a guideline for environmental and ecosphere creatures for environmental adaptation.

Keywords

Ephyda hians, Environment adaptation, Osmoregulation habit

Mini Review

Insects capable of surviving in saline water intensively studied are: Culicidae (mosquitoes) and Ephydridae (brine flies). Brine fly is classified in Phylum: Arthropoda, Class: Insecta, Order: Diptera, Family: Ephydridae, Genus: Ephydra, Species: Ephydra hians. Ephydra hians, commonly known as the alkali fly. Both salinity and desiccation lead to dehydration and osmotic stress, which is a critical problem at the cellular level [1-3]. Therefore, salinity and desiccation stress in insects trigger common physiological mechanisms, mainly aimed at increasing water content (e.g. drinking from the medium), avoiding its loss (e.g. control of cuticle permeability) and maintaining ionic homeostasis (e.g. activity of Malpighian tubules and specialized parts) [1,4-6]. Ecology of Brine fly: Among aquatic insects, members of the shore fly family Ephydridae are well-known for their tolerance of severe environmental conditions. High temperatures and salinities, acid and alkaline pH, anoxia and ephemeral waters are among the factors to which a variety of ephydrids have become adapted. Hot springs, tidal splash pools, salt evaporation ponds, hypersaline desert lakes, and even crude petroleum have been described as larval habitats Collection records for species in the genus Ephydra, which are common in saline waters, indicate a wide range in chemical composition and salinity is tolerated by this group, but that any one species tends to be restricted to a particular type of habitat water chemistry [1,7-10]. The adult flies live 3-5 days, during which they eat algae and lay eggs in the hypersaline water; (Figure 1) lays eggs in saline water (Figure 2). The larvae are important for their capacity to survive in highly saline water (Figure 3). Concretions of calcium carbonate in the Malpigian tubules make the larvae more dense and allow them to stay on the beneath of the water. The haemolymph had a total osmotic concentration of about 300 mOsm. The osmotic concentration of the lake was well over 1500 mOsm. It follows that the larvae must lose water across the cuticle. The larvae drink the external medium. The very high concentration sodium, carbonate and sulphate are present in the lake. The lime gland acts like a kidney, by removing carbonate ions from the blood. Inside the glands carbonate is mixed with calcium to form a limestone (Figures 4 and 5). Saline water larvae drink equal to about their body volume every 10 hours. Table 1 shows different ions in the heamolymph of larvae in comparison to Mono lake water. Eggs of the genus Ephydra are attached to an algae mat. The main food sources of adults and larvae are algae, some bacteria, and protozoa.

Figure 1: Adults of brine fly (Ephydra hians)

Figure 2: Egg of brine fly (Ephydra hians)

Figure 3: Larvae of brine fly (Ephydra hians)

Figure 4: Internal organs of the brine fly (Ephydra hians) with limestone

Figure 5: Lime gland of larvae

Table 1: Ionic concentration in the larval haemolymph of Ephydra hians

Larvae change to the pupae (Figure 6). Pupae are non-feeding, and sequestered from the aquatic environment, Pupae then to the adults. Adults emerge from water in a large population (Figure 7). Birds understand the time of adult emergence and reach the lake for catching them as a food. Many migrating birds stop at Lake during their trips to feed on the brine fly larvae in the lake water (Figure 8).

Figure 6: Pupae of brine fly (Ephydra hians)

Figure 7: Emerging of brine fly from saline water lake

Figure 8: Emerging of adult of brine flies as a good food source for immigrant birds

In a research conducted on the mosquitoes, they found that Aedes albopictus was the most tolerant species, followed by Anopheles coluzzii, Ae. aegypti, Culex. quinquefasciatus, and An. gambiae, in decreasing order. Cx. pipiens was the least tolerant species [11].

Conflict of Interest

The author declares that there is no conflict of interest.

Acknowledgments

This research is financially supported by Ministry of Health and Medical Education under code number of NIMAD 995633.

References

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DOI: 10.31038/GEMS.2023522

Abstract

Where did the sea water originally come from? The water must have originated from interstellar mediums. Comets are born from lumps of dusts, mostly at the edge of the solar system. The lump consists of dust particles and ice (H₂O) and moves toward the Sun due to the Sun’s gravitational force. However, in the asteroid belt between Mars and Jupiter exists a snow line. The water collected by comets does not reach the Earth because ice (H₂O) sublimates into vapor around the snow line. It is considered that the sea water of the Earth would have been captured together with interstellar medium before the snow line appeared. The formation of the early Earth must have been in an environment before the nuclear fusion reactions in the Sun. A beginning of clump forms from through point bonds of dusts. The Coulomb force around interatomic distance is approximately 10³⁶ times that of gravitational force. A fixed connection will be formed by short-range forces at the local contact point of solids in cold environment. According to experimental results, when solid CO₂ (dry ice) is mixed with iron powder, the powder turns black. But the iron powder does not change by in a gas state of CO₂. The results indicate that the surface of the fine iron powder is locally oxidized by the solid CO₂ and CO₂ is reduced to carbon. The first step of formation of celestial bodies occurred as a result of chemical characteristics of materials. When the Sun underwent gravitational collapse and nuclear fusion reactions began, the Earth was at the last stage of formation. The Earth’s seawater accumulated as ice with cosmic dust during the Earth’s formation. These initial situations of the solar system led to new scenarios differ from conventional theories. For example, a large planet had formed in the region near the snow line, but one nuclear fusion explosions formed an asteroid belt. The proto-Moon was born in the geostationary orbit of the Earth and once had come into touch slightly with the Earth, resulting in the tilt of the Earth’s rotational axis and the inclination of the Moon’s orbit. The new scenario of formation Moon-Earth system is characterized as “Little touch” instead of “Giant impact”.

Keywords

Accretion, Cosmic dust, Meteorite, Comet, Asteroid, Satellite, Planet

Introduction

Many models have been proposed to explain the origin of the solar system. Among them, the Hayashi (Kyoto)-model [1] is a representative example. Many models have attempted to describe the formations of planets [2-4]. The tradiational models tends to adhere to the viewpoint of physics. The physics is universal, but it is abstracted. The world of logic tends to estrange the real world. The real world includes various aspects. Theoreticians tend to dislike a bird’s-eye view, and sometimes they failed in “idols of the cave” [5]. The first step of traditional models in the formation of the Sun was the accumulation of hydrogen through gravitation. However, the gravitational force of the hydrogen atom is extremely weak. A planetesimal does not form due to the gravitational force. It was formed by local bonds of dusts. Although the London‐Van der Waals attractionis a weak bond, the short-range force is more thirty orders of magnitude stronger than the gravitational force. The short-range forces offsets within a short distance, whereas long range-forces are accumulated very wide range. The accretion models have been discussed [6], while the formation of a solar system from a viewpoint of chemical describes in this paper. When a celestial body becomes sufficiently large to hold hydrogen atoms via gravitation, the hydrogen rich environment increases the body’s growth rate at a fast pace. This will correspond to a “gravitational collapse”. Before nuclear fusion, the proto-Sun contained large amounts of solids. In this context, the iron currently contained in the Sun is 0.014% of the total mass. The mass of the Sun is about 333,000 times that of the Earth where the mass of iron in the Sun is 46.6 times that of the Earth. The large amount of material of core was released as meteorites by the nuclear fusion of the Sun. The meteorite is the most objects in the solar system. The Japanese Hayabusa 2 mission revealed the rubble-pile structure of asteroid 162173 Ryugu [7]. The gravity of this asteroid is extremely weak with an escape speed of 30 cm/sec. Such a small celestial body does not retain its structure by gravitational force, rather the lump of solid matter is formed into the relevant shape by short-range forces at points of attachment. 51 Pegasi b, is the first exoplanet and was discovered by M. Mayor, D. Queloz, in 1995 [8]. It is a massive planet with a mass approximately 149 times that of the Earth and an orbital period of 4.23 days at 7.8 million km from its host star, this is approximately only one-twentieth of the distance between the Sun and the Earth. This suggests that 51 Pegasi b had fairly formed in the environment before fusion reaction began in the host star. The proto-Earth would grow by accumulating solid forms of H₂O (ice) and solid of CO₂ (dry ice) via point contacts. Reconfiguration of solids occurs at the high-pressure and high-temperature environment of the inner celestial body. When the fusion reactions began in the Sun, the protoplanet of the solar system had become considerably large. This paper describes that the first step in the solar system is the accumulation of nebular dust through the intermolecular bonds of short-range forces [A Reference, https://www.youtube.com/watch?v=fiMgXpUz2GQ].

Adhesion among Fine Particles in a Cold Environment

The Model of Formation of the Solar System Based on Material Science

When an isolated H₂O molecule collides with fine particles, the momentum of the gas molecules is low, and it bounces back. Thus, a chemical reaction does not occur in the gas state. Since the kinetic energy of the collision is concentrated at the contact points between ice and fine particles, a localized chemical reaction may take place. Planetesimals were formed by accretion of cosmic dust in a very cold environment. Formation of a celestial body by cold accretion requires lengthy periods of time. The traditional formation theory ignored the importance of non-uniform phenomena. If the celestial body grows to satellite level, the debris scattered by a meteorite impact will fall onto the original celestial body owing to its gravity. When the proto-Sun became able to maintain hydrogen gas by its gravity, its mass rapidly increased via the positive feedback between mass and gravity in the high-hydrogen-density environment. This is corresponded the “gravitational collapse” in the traditional model. The formation of planets began before the nuclear reactions of the Sun, as shown in Figure 1.

Figure 1: Traditional model and proposing model for the formation of the solar system

The Sun exploded by nuclear fusion and released a large amount of core material into space. These materials fell onto planets as meteorites. The radial impacts of meteorites from the Sun had slightly changed its circular orbit of the planet. The traditional model on migration of planet and collision with planets is difficult to explain the nearly circular orbits of the planets. The large number of meteorite impacts caused the hot state of the crust to become a magma ocean, and the solid H₂O and CO₂ molecules entered into gas state. The degassed molecules formed the proto atmosphere of the Earth.

The Cold Accretion due to Chemical Reeactions at the Contact Points between Solids

The Coulomb force that binds atoms together at interatomic distances is equivalent to ~10³⁶ times that of universal gravitation. When fine particles come into contact in the cold vacuum environment of the Universe, atoms near the point of contact may form intermolecular bonds. The interatomic bonds are much stronger forces than the gravitational force. One obstacle in lunar exploration for astronauts is the adhesion of lunar dusts [9]. The adhesion in the environment of the Moon is similar to the adhesion of cosmic dusts. It depends on surface energy, roughness, mechanical properties, and electronic properties. Electronic energy state of fine dust is high compared with that large-size particles. As the size of dust increases, the energy to surface ratio decreases, resulting in a low energy state. According to the Virial theorem, the total energy (E total) becomes minimum at equilibrium for a quantum state (pq=constant). Here, p represents the momentum, and q represents the distance. E total = Ep +Ek, where Ep and Ek denote the potential (Ep=-const/q) and kinetic energy [Ek=p²/(2m)] respectively. A downsized equilibrium state results in lower energy as shown in Figure 2. Therefore, the inner temperature of the planet increases. The compressed electronic state of a substance becomes a low energy state, as shown in Figure 3. As the lump of mass grows, the temperature around the gravitational center increases. The change from a chemical mixture into a chemical compound can be explained by the energy state of the core of a planet.

Figure 2: Variation of energy vs. variation of size. The equilibrium state exists at the minimum energy

Figure 3: Energy splitting inside of a planet. The interactions yield the lower energy level

Surface Oxidation of Iron Fine Particles by Mixing with Dry Ice (CO₂)

The adhesion between solids depends not only on surface forces but also on surface roughness and the degree of ductility of the solids themselves [10]. A chemical reaction at a point on the surface indicates the possibility of localized chemical reactions. The following experiments were carried out to confirm the difference in the oxidation of iron particles with solid-state CO₂ compared with gas-state CO₂.

Mixtures of fine iron powder and dry ice were sealed and stored in a freezer at −18℃ to confirm the oxidation of iron powder by dry ice. Upon increasing in temperature above −79°C, dry ice sublimates. The iron powder used in the present experiments was reduced-iron produced by the Canadian Pharmaceutical Industry. Iron powder and dry ice were placed in a glass jar, mixed well, and the jar was closed with a cap. The jar was removed from the freezer after 29 h. Next, the processed powder was compared with the initial powder. As shown in Figure 4, black-colored ferrous monoxide (FeO) formed inside the glass jar, but brown-colored Fe2O3 on the jar wall. The SiO2 glass wall functions as a catalyst that extracts electrons from FeO. At the left side of Figure 5, iron powder that was not treated with dry ice is gray, whereas the right side of Figure 5 indicates surface oxidation by dry ice. The blackening was attributable to iron monoxide.

Figure 4: Ion powder with dry ice in a bottle

Figure 5: Left, iron powder without dry ice, right, iron powder oxidized after dry ice treatment

Oxidation of Ultrafine Iron Particles by Sprinkling on Dry Ice

Ultrafine Fe particles were obtained from (FeC2O4·2H₂O) via heating at 200°C. Red light emissions were observed when the pyrophoric iron particles were sprinkled on dry ice, as shown in Figures 6 and 7.

Figure 6: Sprinkling of pyrophoric iron on dry ice

Figure 7: CO₂ reacts with pyrophoric iron as an oxidizer

When the dry ice and pyrophoric iron metal were in contact, the iron particles were oxidized by the oxygen in the dry ice. The black-colored particles correspond to FeO, while the red particles indicate the state of oxidation. The relevant video demonstration has been up-loaded online [https://www.youtube.com/watch?v=eyq3qbxFahw].

Distribution of the Interstellar Medium in the Solar System

Distribution of Interstellar Medium Estimated from the Masses of Gas Planets

Figure 8 shows the relationship between the common logarithm of the value of (m planet/L Sun-planet vs. the distance from the Sun on the horizontal plane). Here, the data were obtained from chronological scientific tables [11]. Planet X* represents a hypothetical planet. As explained section 3.2, planet X* disappeared after a single nuclear explosion, leaving asteroids and meteorites in the asteroid belt. The relationship for the distribution of the interstellar medium is estimated from the relationship between the distance of gas planets from the Sun (L Sun-planet) and their mass (m planet). The gravitational potential energy of the planet by the Sun is defined by setting an infinity point for 0 and the change at the current position from the Sun. According to Newton’s mechanics, the gravitational potential energy of a planet is inversely proportional to its distance from the gravitational center (L Sun-planet). Therefore, the value of potential energy (Φ planet) on a planet, obtained by collecting the interstellar medium in the surrounding area is proportional to the mass of the planet (m planet) and inversely proportional to distance (L Sun-planet), as expressed by Eq. (1).

Φ planet = G (m planet)/(LSun-planet)        (1).                       (1). Where, G is the constant of gravitation.

Figure 8: The gravitational potential collected by planets

For exoplanets (Jupiter, Saturn, and Uranus), the value of (m planet/L Sun-planet) decrease exponentially with respect to their distance from the Sun. This indicates that gas planets grew in a state where the interstellar medium was distributed exponentially with respect to the distance from the Sun. Optical data indicate the existence of comet clusters in the main asteroid belt [12]. This refers to the region where material accumulation was active due to the molecule are state of H₂O that changes to the liquid or solid state around the snow line located almost in the center of the planetary belt. However, no large planets exist in this region, as shown in Figure 8. Where did the accumulated substances exist? Although several scenarios attempt to explain the behavior of planets through collisions among celestial bodies, it has been challenging to provide an orbit that has remained the same orbit for a long time at equilibrium.

A scenario of Formation of an Asteroid Belt

Once Existed Planet X* Leaved Debris in the Asteroid Belt by a Nuclear Explosion

The meteorites were created inside of a large planet. The gravitational force cannot shatter a large celestial body. The planet X* increased enormously in the snow line region rapidly collects hydrogen due to gravitational collapse. A scenario that the planet X* has grown to a size of approximately more than 10fold that of Jupiter, and 3.8 billion years ago, it exploded with deuterium nuclear fusion [13] was proposed. Material that scattered as a result of this explosion did not return to the original planet X*, and the explosion ended only once. The author proposed a scenario entitled as “Asteroid belt formed by debris of the planet that failed to become the second Sun”. The relevant video demonstration has been up-loaded online [https://www.youtube.com/watch?v=QY8C7XK6k7I].

[Time series of events for the formation of an asteroid belt]

1. Rapid accumulation of materials takes place around the snow line.
2. Rapid increase of planet X* takes place due to gravitational
3. Nuclear explosion occurs in the rapidly grown planet X*.
4. Planet X*’s core is shattered to pieces by a nuclear
5. Most of the ejected fragments fall onto the
6. The asteroid belt is formed by the debris in the orbit of the exploded planet X*.

The nuclear explosion of planet X* likely explains the late heavy bombardment of meteorites approximately 3.8 billion years ago. The fall of many meteorites on Earth heated the crust, followed by degassing of crustal H₂O and CO₂.

The Rotational Speed of Gas Planets Increases with Increasing Planet’s Mass

A planet grows by accumulating material orbiting around it. When a planet increases in size, its gravitational potential increases. The orbital speed of interstellar material that lands on the planet increases. Therefore, the rotation period shortens as planet grows large. Except for Mercury and Venus, the rotational period of planets is shortened as their mass increase, as shown in Figure 9.

Figure 9: Relationship between the period of rotation and mass of planets [13]

The rotational period of the Sun is 25.38 days, and we consider that the slow rotation speed of the Sun is caused by the large amount of interstellar medium that had been collected without orbiting the Sun at the beginning of the formation.

How Galilean Satellites Formed

The interior structures of the four Galilean moons are shown in the animation at [https://www.esa.int/ESA_Multimedia/Videos/2021/07/Inside_the_Galilean_moons]. The internal materials in the Galilean moons are shown in Figure 10. These facts suggest the formation process.

Figure 10: The internal structure of Galilean moons those suggests the process of satellite formation. The figure was reproduced from https://surrealsciencestuff.files.wordpress.com/2014/10/planets7.jpg.

The average density of the Galilean moons Io, Europa, Ganymede, and Callisto decreases with their distance from Jupiter, as presented in Table 1.

Table 1: Characteristics of Jupiter’s four Galilean moons { *[14], **[11] }

The geostationary orbit is a region in which material accumulation by accretion thrives. The formation of a celestial body at a geostationary orbit shifts the gravitational center towards Jupiter. The geostationary orbit of Jupiter can be determined using Eq.(2). The radius of Jupiter’s geostationary orbit (L geostationary orbit) is short compared with the orbits of the Galilean moons.

L geostationary orbit= {G・m Jupiter (T/2π)²}^1/3 =1.6×10⁸m (2)

Rotational period of all Galilean satellites is synchronized with the revolution. There exists a gravitational coupling between Jupiter and the Galilean moons those had born in the geostationary orbit. The rotational speed of Jupiter will be fast due to the increase in mass, as described in Section 3.2. The coupling due to gravitational force accelerates the orbital speed of the Galilean moons. Even if Jupiter’s rotation speed did not change, the speed at which satellites accelerate their orbit due to gravitational coupling will be faster speed for the moons away from Jupiter. The effect of magnetic couplings among parallel-running charged particles partially collided with solar wind also contributes to the Galilean moons moving away from Jupiter. According to Aharonov-Bohm effect, the conventional consideration that set the geomagnetic field first and describe the behavior of charged particles is incorrect [15]. The magnetic coupling energy (Um) between charged particles must be considered the vector potential (A) instead of the magnetic field (B). Um for a point charged particle (q) with velocity (v) is expressed in Eq. (3). Here, the negative sign is set to be low when v and A are parallel. The relationship between A and magnetic flux density (B) is defined by Eq. (4).

Um= ‐ (qv)・A.                          (3).

B = rot A.                               (4).

Φ=∫Bds= ∮A dx.                             (5).

Here, rot is an operation in which the orbital direction of A in a minute area around a circle is added. Eq. (5) is expressed as the magnetic flux (Φ) of the penetrating area and is given by a line integral of the point field of A around the penetrating area. The chain of magnetic coupling among charged particles forms a donut shape of rotating charged particles. The movement of charged particles above the Arctic and Antarctic regions shown at Aurora are slow owing to the slow rotational speed of charged particles inside planet. Assuming that the Galilean moons were born in the Jupiter’s geostationary orbit and migrated from Jupiter, the order of birth of the Galilean moons will be Callisto, Ganymede, Europa, and Ion.

How Today’s Moon-Earth system Formed

Migration of the Moon due to the Tidal Effect

The rotation period of the Moon is synchronized with the revolution of the Moon. The current distance from the Earth to the Moon is 380,000 km, and the Moon moves away at a rate of 3.8 cm per year. The assumptions that the Moon formed in the geostationary orbit of Earth, and if the velocity of migration due to the acceleration of the orbital speed of the Moon described in section 3.3 has been remained the same, today’s distance will require the time of 10¹⁰ years. Considering that the tidal effect is large when the Moon is close to the Earth, it is estimated that the Moon was born and formed near the Earth’s geostationary orbit. In the Giant Impact Theories, Lagrangian point (Ls) is one of candidate of place where the Moon formed [16]. However, Ls is 395 times of the current orbital radial of the Moon.

The Small Impact Model is Suitable to Explain the Current State of Moon-Earth system

The traditional model considered that the Moon formed by a large off-centered collision nearly at the end of the Earth’s formation [17]. Several simulations have dealt with the giant impact [18]. However, where did impactor originate from and where did it end. The giant impact model must result in a large migration of the orbits. Although far side of the Moon is thick and is composed of light material, shape of the current Moon is nearly spherical, a more accurate description is “slightly pear-shaped”. The highest point on the Moon is +10.75 km and the lowest point is -9.06 km, both at the far side form the Earth. Taking into account the rotation period of Moon is synchronized with the revolution, a scenario of formation of Moon-Earth system was proposed as follows. Proto-Moon was born near the geostationary orbit of the Earth as described in the section 3.3. The proto-Moon includes many voids. As the Moon grew in size, the distributed mass reorganized and the center of mass shifted to the side of the Earth. Thus, the orbital period of the Moon and the rotational period of the Moon coincided. A migration of Moon that caused the Moon was in contact with the Earth was triggered by the fall of materials released from the Sun’s core at the early nuclear explosion of the Sun. The Moon’s orbit was slightly accelerated by the contact with rotating Earth to form an elliptical orbit. After the contact, the distance from the Earth to the Moon has been expanding due to the tidal effect of the sea water of the Earth. Proposing scenario is suitable to explain the current Moon-Earth system. The new scenario is characterized as “Little touch” (Figure 11).

Figure 11: The model describes that proto-Moon touches on the Earth,  marks the position of contact.

The proposed model according to which the proto-Moon lightly touched the Earth is the most suitable candidate to explain following facts. Inclination of the orbit of the Moon is 5.14°. Rotational axis of the Earth is tilted by 23.4° relative to the equatorial plane of the Sun. The tilt of the Earth shifts as a precession in a cycle of 26,000 years, with the direction of the Sun as the axis of the rotation.

Summary

The formation of the solar system was examined from viewpoint of astrophysics and material science. Although orbital motion is explained by Newtonian mechanics, a celestial body is formed by chemical bonds at contact points at first. Traditional studies tend to extrapolate from current facts using serial logics. Even though the correct calculation on the chemical bonds is hard, the assumption or preconditions for a study must be carefully set real facts as the base. This paper describes the protoplanet of the solar system had become considerably large, when the fusion reactions began in the Sun. The verifications based on different viewpoint will reveal new aspects of the world. It led to new scenario that differs from conventional scenarios. For example, the geostationary orbit is a region in which material accumulation by accretion thrives. The formation of a celestial body at a geostationary orbit shifts the gravitational center towards the gravitational center. There exists a gravitational coupling between the planet and satellites those had born in the geostationary orbit. Therefore, a rotation period of all Galilean satellites is synchronized with the revolution. Asteroid belt had formed by debris of the planet that failed to become second Sun. A model of “Little touch” instead of “Giant impact” of the proto-Moon with the Earth resulted in the tilt of Earth’s rotational axis and the inclination for Moon’s orbit. These results will contribute to present the preconditions for the study of or to the understanding of the solar system.

Acknowledgment

I would like to thank Editage (www.editage.com) for English language editing.

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