Characterization of spatial and temporal variations of CO2 concentration on tropical island and analysis of influencing factors

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The results show that from 2011 to 2024, the CO 2 concentration in Hainan Island shows an increasing trend, with a faster growth rate in the early period and a slower growth rate in rencent years with the implementation of the dual-carbon strategy. The spatial distribution of the distribution is affected by human activities, topography, vegetation and solar radiation, and the overall performance is high in the north and low in the south. Human activities are the most important carbon source on Hainan Island, vegetation is the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation also play a certain inhibitory role. It is expected that the CO 2 concentration in Hainan Island will continue to increase at a slower rate and may have a decreasing trend in the future. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Ecology Tropical Island CO2 concentration Influencing factors Variation trend Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Carbon dioxide (CO 2 ) is one of the most dominant greenhouse gases in the global atmosphere, and in just over a hundred years since the Industrial Revolution, human have burned the fossil energy that the Earth has accumulated over billions of years. The burning of fossil fuels has emitted large amounts of carbon dioxide (CO 2 ) into the atmosphere, while at the same time the global vegetation area has been decreasing and the concentration of CO 2 in the atmosphere has been increasing rapidly 1 . From 300 ppm before the start of the first industrial revolution, the monthly average global CO 2 concentration measured by the National Oceanic and Atmospheric Administration (NOAA) in April 2018 reached 410 ppm, a 40% increase in atmospheric CO 2 concentration. The increase in atmospheric CO 2 concentration is now the main cause of global warming, and the 2015 Paris Agreement, which set the goal of achieving net-zero emissions in the second half of the century, is being translated into national strategies by an increasing number of governments, with more than 130 countries and territories now proposing “zero-carbon” or “carbon-neutral” goals 2 . More than 130 countries and regions have proposed “zero carbon” or “carbon neutral” climate targets. China's carbon peak and carbon neutral strategy is not only a major demand for global climate governance, protection of the Earth's homeland and building a community of human destiny, but also an intrinsic demand for China's high-quality development, ecological civilization and comprehensive ecological environment management 3 . The realization of the dual-carbon target has put forward an urgent demand for the monitoring and assessment of the carbon cycle, especially carbon emission accounting, and regional carbon balance has also been a hotspot of global carbon cycle research in recent years 4 – 6 . In order to control carbon emissions, it is necessary to monitor its content first, so the monitoring of atmospheric CO 2 concentration is an important means to study the law of carbon cycle and cope with global warming 7 . At present there are more than 300 greenhouse gas monitoring stations in the world. Ground-based observation has the advantages of high precision, high reliability, real-time access, etc., but the measurement results are only single-point measurements, lacking macro and vertical detection capabilities. Satellite remote sensing detection is an important method to realize the study of large-scale CO 2 concentration changes 8 – 9 . Satellite remote sensing technology is a technology that detects and receives information from target objects through sensors from a high altitude 10 , so as to identify the attributes of the objects and their spatial distributions and other characteristics, and acquires satellite data and analyzes and handles the received information through the remote sensing technology platform. The detection capability of satellite remote sensing mainly depends on the on-board detection instrument and the remote sensing inversion algorithm. The short-wave infrared band, which is more sensitive to the near ground, is more suitable for monitoring the dynamic changes of carbon sources and sinks on the ground. The SCIAMACHY instrument on board the ENVISAT satellite was the first on-board detector to use the short-wave infrared absorption band as its detection band 11 . Subsequently, a number of carbon monitoring satellites, such as the Greenhouse Gas Observing Satellite (GOSAT) launched by Japan 12 , the Orbiting Carbon Observing Satellite-2 (OCO-2) launched by the United States, and China's Chinese Carbon Satellite (TanSat), which was launched in 2017, have followed this detection band, and the detection accuracy has been gradually increased with the continuous improvement of the detector indicators and inversion methods. Satellite remote sensing detection has the advantages of large range, long time series, vertical detection, etc., which can better obtain the spatial and temporal distribution and change characteristics of global CO 2 concentration 13 , and many scholars have also studied the CO 2 concentration using satellite data: Yokota T, el al utilize preliminary GOSAT data to analyze the distribution of global C CO 2 and CH 4 14 ; Heyntann J, et al present the first detailed assessment of the new GOSAT BESD X CO 2 product 15 ; A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS was developed in the current study to assess CO 2 concentrations on a global scale by Guo M, et al 16 . Hainan Island (18°09′~20°11′N, 108°37′~111°03′E) is located in the southernmost part of China and has a typical tropical monsoon climate 17 . The terrain is high in the center and low in the surroundings, with the highest mountain range located in the central Wuzhi Mountain, and with the central Wuzhi Mountain and Parrot Ridge as the core of the uplift, descending step by step to the periphery, and consisting of a circular layered landscape of mountains, hills, plateaus, and plains, with an obvious gradient structure. Hainan Island is an important distribution area for tropical rainforests and monsoon rainforests, and has a variety of natural vegetation such as evergreen broad-leaved forests, mangrove forests and coniferous forests 18 . At present, there are few studies on the distribution and influencing factors of CO 2 concentration on Hainan Island, and there are few monitoring stations for CO 2 concentration on Hainan Island. Satellite data have become an important means to study CO 2 on Hainan Island. In view of this, this study firstly analyzed the changes and distribution characteristics of CO 2 concentration on Hainan Island for many years by using GOSAT satellite data; secondly, selected a variety of natural and anthropogenic driving factors, and investigated the influence of different driving factors on CO 2 concentration on Hainan Island; and then discussed the evolutionary characteristics of CO 2 concentration on Hainan Island. Then the evolution of CO 2 concentration on Hainan Island is discussed, in order to better understand the change rule of atmospheric CO 2 concentration under the special climate background of Hainan Island, which is conducive to a better understanding of the mechanism of controlling atmospheric CO 2 concentration. 2. Results 2.1 Characteristics of CO 2 concentration in Hainan Island from 2011 to 2024 From Fig. 1 (a), it can be seen that the CO 2 concentration in Hainan Island from 2011 to 2024 shows an increasing trend year by year, from 391.39 ppm at the beginning of 2011 to 419.51 ppm at the end of 2024, with an average growth rate of 2.01 ppm/a, which is lower than the average growth rate of the world in the past 10a of 2.06 ppm/a proposed by the World Meteorological Organization. According to the growth rate of CO 2 concentration, it can be divided into three phases. concentration growth rate in general can be divided into three stages, the first stage is 2011–2015, during which the CO 2 concentration growth rate is slower, except for 2012–2013 the growth rate of CO 2 concentration in other years is lower than 2.0 ppm/a, with an average growth rate of 1.92 ppm/a; the second stage is 2015–2021, in which Hainan Island's CO 2 concentration increases rapidly, with an average growth rate of 2.55 ppm/a, of which the largest growth in 2015–2016 reached 4.45 ppm, probably due to the impact of the super-strong El Niño phenomenon in 201520. In the third phase, from 2021 to 2023, with the implementation of the "carbon peak and carbon neutral" strategy, the growth rate of CO 2 concentration slows down significantly in the last two years, with 1.78 and 0.20 ppm/a, respectively. Figure 1 (b) shows the seasonal variation of CO 2 concentration. In winter, the vegetation cover is low and the leaf area of the vegetation is small, so the photosynthesis is limited and the CO 2 concentration is high. In spring, the weather turns warm, vegetation and soil respiration is enhanced, and at the same time, as the temperature rises, soil microbial activity is strengthened to decompose CO 2 in soil biomass, so the CO 2 concentration value reaches the highest, with an average value of 407.47 ppm; on the contrary, in summer, the vegetation has the highest coverage, the leaf area increases, photosynthesis is the strongest, and more CO 2 can be absorbed into the atmosphere, so the CO 2 concentration value in summer is the lowest, with an average value of 402.47 ppm. In the fall, photosynthesis is weakened, and CO 2 concentration gradually rises. Due to the special climatic conditions, the difference in CO 2 concentration between summer and fall on Hainan Island is relatively small. Figure 1 (c) shows that the average monthly CO 2 concentration on Hainan Island was on the rise from January to April, with April being the highest value of the year, reaching 408.04 ppm, and then gradually decreasing from April to August with the enhancement of photosynthesis, and the CO 2 concentration in August was 400.63 ppm, which was the lowest value of the year. From August to December, the leaf area of the vegetation gradually decreased, so the chlorophyll content decreased. The absorption of CO 2 by photosynthes is decreased and the CO 2 concentration gradually increased again. The largest amplitude between seasons was from spring to summer, with an absolute value of 4.81 ppm, followed by 2.62 ppm from fall to winter, and the amplitudes from winter to spring and from summer to spring were closer to each other, with the absolute value of the former (1.22 ppm) being slightly larger than that of the latter (1.00 ppm), and the amplitude in spring and summer was much larger than that in other seasons, which indicates that terrestrial vegetation ecosystems play a significant role as a sink for carbon. The amplitudes in spring and summer were much larger than those in other seasons, indicating that terrestrial vegetation ecosystems play an important role in carbon sinks. 2.2 Characteristics of the spatial distribution of CO 2 concentration on Hainan Island Figure 2 shows the distribution of the average CO 2 concentration on Hainan Island, and the spatial distribution of CO 2 concentration shows obvious differences between the north and the south, which manifests itself as high in the north and low in the south, with the highest value appearing in the capital city of Haikou, reaching 405.19 ppm, and the lowest value in Sanya City, reaching 404.883 ppm. There is a clear demarcation line in the central part of the island, where the concentration gradually rises in the north from Wuzhishan and decreases in the south. In the north, Haikou, Lingao, Wenchang and Chengmai have high CO 2 concentrations and a tendency to spread to the south, and the further south the lower the concentration. The main reason for this is that the highest peaks on Hainan Island are mostly located in the Wuzhishan and Parrot Mountain ranges in the central part of the island, while the terrain in the south-central part of the island is characterized by mountainous and hilly areas, which block the diffusion of CO 2 to the south. The CO 2 concentration in each city and county was in the order of spring > winter > fall > summer, and there was no obvious difference in the spatial distribution of CO 2 concentration in each season, and the overall situation was high in the north and low in the south (Fig. 3 ). The high value area in the north of winter covers the widest range, followed by spring and fall, and the high value area in summer has the smallest range and is concentrated in Haikou and Wenchang in the northeast. The distribution of average CO 2 concentrations on Hainan Island is consistent with the distribution of population and economy. The northern part of the island (Haikou and Wenchang) is the most populated area of the island, with intensive industrial, tourism and international shipping activities, and a high socio-economic level, with the population and GDP accounting for 55% and 60% of the island, respectively, and therefore the CO 2 concentrations due to human activities are on the high side. The south-central part of Hainan Island has the largest tropical rainforest, accounting for 1/7 of the island's area, with a strong carbon sink capacity and relatively sparse population, so the impact of human activities is small. The central part of the island lags behind the southern coastal area in terms of population and economy, but the average CO 2 concentration is higher than that of the southern coastal area, which also indicates that the spatial distribution of CO 2 concentration is not only affected by human activities, but also related to the long-distance transportation of CO 2 caused by atmospheric circulation due to topograph, vegetation and non-uniform distribution of solar radiation on the Earth's surface. 2.3 Analysis of factors affecting CO 2 concentration on Hainan Island CO 2 concentration is affected by various factors such as vegetation photosynthesis, population growth, and industrial emissions, etc. In this study, natural factors such as the enhanced vegetation index, surface temperature, precipitation, solar radiation, and anthropogenic factors such as population, GDP, energy consumption of each city and county of Hainan Island were selected and analyzed at both monthly and yearly scales (of which only yearly averages were available for population, GDP, and energy consumption). 2.3.1 Effects of human activities on CO 2 concentration in Hainan Island On the annual scale, GDP and energy consumption (coal, oil, natural gas, and primary electricity) of Hainan Island cities and counties showed a significant positive correlation with CO2 concentration, with correlation coefficients of 0.9 or more. Population is also significantly positively correlated except for Ding'an, Lingao, Tunchang, and Baisha counties, mainly because the population of these counties has been declining in recent years. Overall, the population, GDP and energy consumption continue to grow and play an important role in promoting the CO 2 concentration on Hainan Island, which is the main source of carbon on the island. 2020 before the population, GDP growth rate is faster, energy consumption is also growing rapidly, resulting in a faster rate of growth of the CO 2 concentration, after 2020, with the slowdown of the population, GDP growth rate, the rate of growth of energy consumption is also showing a downward trend, the CO 2 concentration on Hainan Island is increasing, and the CO 2 concentration in Hainan Island is decreasing. After 2020, as the population and GDP growth rate slow down, the growth rate of energy consumption also shows a downward trend, and the growth trend of CO 2 concentration in Hainan Island is also alleviated. 2.3.2 Influence of natural factors on CO 2 concentration in Hainan Island From Fig. 4 , it can be seen that the correlation between each natural factor and CO 2 concentration on the annual scale is high, and the monthly scale is relatively smooth compared with the annual scale. The greatest inhibition of CO 2 concentration on Hainan Island is moisture, precipitation, soil moisture and relative humidity show negative correlations on both the annual and monthly scales, and the correlation between CO 2 concentration and precipitation is the most significant in each city and county on the annual scale. Surface temperature affects vegetation growth and human activities, and surface temperature is mainly affected by the intensity of solar radiation, which in turn affects ecosystem respiration and photosynthesis. Surface temperature and total solar radiation also showed a significant inhibitory effect on CO 2 concentration. The correlation of surface temperature was second only to that of precipitation on the annual scale, but lower on the monthly scale, which was attributed to the fact that Hainan Island is located in the tropics, and the surface temperature varies less throughout the year. Higher wind speed can accelerate the diffusion of CO 2 in the air and reduce the local concentration, the higher the wind speed, the better the uniform distribution of CO 2 in the atmosphere, which reduces the occurrence of localized areas of high concentration, and strong winds can also drive the vertical transport of CO 2 and change its concentration in different atmospheric layers, but the values of the mean wind speed on both the monthly and yearly scales are low, and so are insensitive to the changes in CO 2 concentration. Enhanced Vegetation Index (EVI) is an important indicator used in remote sensing to monitor vegetation growth and cover, and its sensitivity is better than other vegetation indices for areaswith high vegetation cover. Photosynthetically Active Radiation (PAR) refers to the band of solar radiation that can be used by plants for photosynthesis, which is a key energy source in the light reaction stage of plants. PAR is the solar radiation band that plants can use for photosynthesis and it is a key energy source in the light reaction stage of plants, which directly affects the photosynthetic rate, plant growth and ecosystem productivity. The correlation between CO 2 concentration and EVI and PAR in the cities and counties of Hainan Island was not significant, showing a low positive correlation. On one hand, Hainan Island is in the tropic area, the average annual temperature is high. The pillar industries are tourism and tropical agriculture, less heavy industry, the annual vegetation cover is high and has been on the rise in recent years. The average annual EVI value is higher than the average level in China, compared with the sparsely vegetated areas of CO 2 by the vegetation to improve the role of the more significant, the effect of the vegetation on the concentration of CO 2 on Hainan Island is limited. On the other hand, with the increase of global average temperature, the CO2produced by vegetation respiration may be underestimated, especially in the high temperature and high humidity conditions in Hainan Island, the activity of organic matter in the soil is enhanced to emit more CO 2 . In addition, the long-term high temperature in the tropics will be accompanied by drought stress, and the plants will close the stomatal pores in order to reduce the water transpiration. At the same time, high temperature accelerates the degradation of chlorophyll and reduces the ability of light capture, resulting in the CO 2 . concentration of the vegetation is limited. This leads to a decrease in CO₂ uptake. 2.3.3 Combined impact analysis of multiple drivers CO 2 concentration is affected by a variety of driving factors. Based on multiple regression analysis method, the CO 2 concentration of several driving factors have a greater impact on the comprehensive impact analysis. Due to the large difference in magnitude between the factors, the data were normalized and we obtained the contribution of the different driving factors to the impact of CO 2 concentration in Hainan Island in each season. Table 1 shows that the CO 2 concentration on Hainan Island is significantly affected by the seasonal changes of different driving factors. Although the vegetation is not sensitive to the changes of CO 2 concentration on Hainan Island, it is still the most important carbon sink, especially in the summer when vegetation photosynthesis is the strongest. The control of PAR on CO 2 concentration reaches the maximum value of regression parameter − 0.218, much higher than in other seasons. Relative humidity and surface temperature both showed inhibition, while precipitation would have an inhibitory effect on CO 2 concentration, but showed a certain contribution situation in the regression equation. Energy consumption(E) is the most important carbon source and the regression parameter is higher in all seasons, the highest being 0.894 in winter. The coefficient of determination of the relative regression equation is above 0.75 in all seasons, which is a good fit. Table 1 Seasonal contributions of different driving factors to the variation of CO 2 concentration in Hainan Island. EVI PAR LST PRE RH E R2 springtime 0.081 0.104 -0.006 0.149 -0.228 0.936 0.776 summertime -0.104 -0.218 -0.051 0.077 -0.261 0.741 0.753 fall -0.118 -0.116 -0.086 0.003 -0.16 0.81 0.884 winner 0.217 0.114 -0.072 0.022 0.056 0.906 0.894 2.3.4 Trend analysis of CO 2 concentration on Hainan Island Figure 6 (a) shows the trend of CO 2 concentration on Hainan Island, the value of which is the slope of the regression equation for CO 2 concentration, with a positive value indicating an increasing trend and the larger the value, the more obvious. The slope values of the cities and counties on Hainan Island are small and not much different, between Haikou (0.187) and Sanya (0.182), which means that the CO 2 concentration in the cities and counties will continue to increase at a slower rate. Figure 6 (b) shows the stability analysis of the spatial evolution of CO 2 concentration growth in Hainan Island based on the R/S analysis. The H value (Hurst index) between 0 and 0.5 indicates that the time series of CO 2 concentration has inverse persistence, the past variables are negatively correlated with the future trend, and the series has a sudden jump, and the closer the H is to 0, the stronger the inverse persistence is; when H < 1, the sequence of CO 2 concentration has long-term correlation, and the process has persistence. The sequence of CO 2 concentration at 0.5 < H < 1 has long-term correlation and the process has continuity. From the figure, it can be seen that the H value of all cities and counties in Hainan Island is less than 0.5, which indicates that the time series of CO 2 concentration has strong inverse persistence, and the CO 2 concentration may have a decreasing trend in the future. 2.3.5 Future CO 2 Concentration Forecast for Hainan Island The gray model is suitable for scenarios with little data and obvious trends, while the SARIMA model is suitable for scenarios with obvious seasonality. Therefore, the interannualchange and monthly change of CO 2 concentration in Hainan Island from 2025 to 2030 were predicted using the gray model and the SARIMA model, respectively. The results show that the interannual (Fig. 6 a) and monthly (Fig. 6 b) changes in CO 2 concentrations on Hainan Island in the future show an increasing trend, but the growth rate may be lower than the global average, which is consistent with the conclusion of 3.3.4. 3. Conclusions (1) Hainan Island 2011–2024 CO 2 concentration showed a rising trend year by year, the average growth rate of 2.01ppm/a, 2015–2021 faster growth rate, the average growth rate of 2.55ppm/a, the largest increase in 2015–2016, reached 4.45ppm, the smallest increase of 0.20% for 2022–2023. ppm. The CO 2 concentration in Hainan Island has obvious seasonal variations, as shown in spring > winter > fall > summer, with April being the highest value in a year, reaching 408.04 ppm. The CO 2 concentration in August being the lowest in a year, at 401.55 ppm. The largest amplitude between seasons was from spring to summer with an absolute value of 4.81 ppm. (2) The spatial distribution of CO 2 concentration on Hainan Island is affected by human activities, topography, vegetation and solar radiation, and the overall performance is high in the north and low in the south, and there is no obvious difference in the spatial distribution of CO 2 concentration in all seasons. Human activities are the most important carbon source in Hainan Island. Vegetation is insensitive to changes in CO 2 concentration on Hainan Island, but remains the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation also play a role in suppressing CO 2 concentration. (3) The trend of CO 2 concentration show that the slope values of each city and county of Hainan Island are small and do not differ much, and the CO 2 concentration will continue to increase at a slower rate. The H-value (Hurst index) of each city and county is less than 0.5, which indicates that the time series of CO 2 concentration has strong inverse persistence, and the CO 2 concentration may have a decreasing trend in the future. The gray model and SARIMA model predicted the CO 2 concentration in Hainan Island from 2025 to 2030, the results also showed that the CO 2 concentration in Hainan Island will continue to grow at a slower rate. 4. Discussion At the background of a global increase in CO 2 concentrations of 2–3 ppm/a. Despite its slower growth rate, as part of the global atmospheric cycle, Hainan island still finds it difficult to escape the trend of increasing CO 2 concentrations. In order to achieve the "zero carbon" and "carbon neutral" goal at an early date, the following recommendations are proposed: (1) Promoting the green transformation of industries. Promote energy-saving transformation of petrochemical and cement industries. Carry out low-carbon transformation of tropical agriculture and tourism to reduce total carbon emissions. (2) Increase the utilization rate of renewable energy. Promote the use of new energy vehicles. Leverage the geographical advantages of the South China Sea to accelerate the large-scale development of offshore wind power and distributed photovoltaics. (3) Strengthening the ecological protection of the central tropical rainforest, expanding the scale of restoration of mangrove forests, seagrass beds and coral reefs, enhancing ecological carbon sinks. (4) Deploying greenhouse gas observation systems in key areas of Hainan Island and the South China Sea, and carrying out monitoring and assessment of the capacity of land and ocean carbon sinks. 5. Methods 5.1 Research data: GOSAT is a greenhouse gas observation satellite launched by Japan in 2009, and it is also the world's first satellite dedicated to the observation of greenhouse gases. the observation performance of GOSAT has made a great leap compared with the early SCIAMACHY, the revisit period of GOSAT is only 3 days, the spatial resolution has been greatly improved from the original 30 * 60 to 10.5 * 10.5. GOSAT is a satellite with a short-wave near-infrared (NIR) band, which is sensitive to the change of CO 2 content in the near-surface layer, and it can effectively obtain the whole layer of CO 2 information, including the bottom atmosphere. This study uses the L3-level data of GOSAT satellite, which mainly includes the monthly average CO 2 concentration with a spatial resolution of 2.5°*2.5°. It is verified that the GOSAT data can better capture the characteristics of the variation of CO 2 concentration in the near-surface observation data, and the comparison study with the observation data from the atmospheric background station at Wariguan shows that the accuracy and stability of the GOSAT data are high. The Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP), Photosynthetically Active Radiation (PAR) and (LST) data were obtained from MODIS (Moderate-Resolution Imaging Spectroradiometer), a large-scale space-based remote sensing instrument developed by NASA. The MODIS (Moderate Resolution Imaging Spectroradiometer) is a large spaceborne remote sensing instrument developed by NASA (National Aeronautics and Space Administration), which is characterized by free access, a wide spectral range (36 bands, from visible to thermal infrared), and a high updating frequency (at least twice a day with global coverage), and the raw raster data were averaged according to the administrative boundaries of the prefectures and municipalities. Total solar radiation, Relative humidity, precipitation and wind speed data were obtained from the ERA5-Land reanalysis dataset from the European Center. ERA5-Land uses the laws of physics to combine modeled data with observations from around the world to form a globally complete and consistent dataset, with a spatial and temporal resolution of 0.1°*0.1° per month. Population, GDP, and energy data were obtained from the Statistical Yearbook of Hainan Province. 5.2 Kriging Interpolation Kriging interpolation (Kriging) is a statistically based spatial interpolation method, which is capable of generating a more accurate spatial prediction model by considering spatial autocorrelation and the weights of the data points.Kriging interpolation not only takes into account the positional relationship between the observation points and the estimation points, but also integrally takes into account the relative positional relationship between the observation points, and it has been widely used in spatial statistical analysis 18 . In this study, the monthly data of spatially resolved CO 2 concentration at 0.1°*0.1° on Hainan Island from 2011 to 2023 are obtained by kriging interpolation using GOSAT L3 2.5°*2.5° data. 5.3 Pearson correlation coefficient Pearson correlation coefficient is a statistical index used to measure the degree of linear correlation between two groups of variables, the formula is as follows: $$\:{r}_{xy}=\frac{{\sum\:}_{i=1}^{n}({x}_{i}-\overline{x}\left)\right({y}_{i}-\overline{y})}{\sqrt{{{\sum\:}_{i=1}^{n}({x}_{i}-\overline{x})}^{2}{\left({y}_{i}-\overline{y}\right)}^{2}}}$$ The value of r is between − 1 and 1, the absolute value of the magnitude of the x and y variables between the degree of correlation. r is a positive value of the two groups of variables is positively correlated, that is, x increases (decreases) when y increases (decreases), r is a negative value of the negative correlation, y with the increase (decrease) with the increase (decrease) of x and decrease (increase). 5.4 R/S Analysis R/S analysis, also known as Rescaled Range Analysis, is a statistical analysis method first proposed by hydrologist H.E. Hurst in 1951, which is mainly used to study the long-range correlation of time series data.R/S analysis evaluates the statistical characteristics of a time series by calculating the R/S ratio of the time series, i.e., the ratio of the range to the standard deviation 19 . R/S analysis assesses the statistical properties of time series, especially by analyzing the H-value (Hurst's index) to determine the trend persistence or anticontinuity of the time series, and the method is also applied to geography, climatology, etc. to study the long-term changes and forecasts of natural phenomena. 5.5 Gray and SARIMA models Grey forecasting and Grey Model (GM) are the core forecasting methods in Grey Systems Theory, which are applicable to the forecasting of small samples and uncertain systems. The most commonly used is the GM(1,1) model (first-order univariate gray model), which is suitable for short-term forecasting of time series data.The SARIMA model (Seasonal Autoregressive Integral Sliding Average Model) is an extension of the ARIMA model, which is specifically used to deal with the time series data (such as monthly and quarterly data) with a seasonal cycle, and the core idea of which is to add seasonal difference and seasonal autoregressive/moving average terms to ARIMA 21 . Declarations Funding This study is supported by the Natural science foundation of China(Grant No.42465006, Grant No. U21A6001) Author Contribution Luo wrote the main manuscript text and prepared the figures. Han dowload the satellite and reanlysis data. Liu provided technical guidance and revised the article .All authors reviewed the manuscript. Data Availability The GOSAT satellite data and the ERA5 reanalysis data used in this study can be dowload from website“https://data2.gosat.nies.go.jp/” and “https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview”. References The state of. greenhouse gases in the atmosphere based on global observations through 2010. WMO Greenh. gas Bull. 7 (21) (2011). Zhang, S. et al. Policy recommendations for the zero energy building promotion towards carbon neutral in Asia-Pacific Region. Energy Policy . 159 10.1016/j.enpol.2021.112661 (2021). Gregg, J. S., Andres, R. J., Marland, G. & China Emissions pattern of the world leader in CO 2 emissions from fossil fuel consumption and cement production. Geophys. Res. Lett. 35 (8), 135–157 (2008). Etheridge, D. M. et al. Natural and anthropogenic changes in atmospheric CO 2 over the last 1000 years froin air in Antarctic ice and firn. J. Geophys. Research: Atmos. 101 (D2) (1996). Li, C., Li, H. & Qin, X. Spatial heterogeneity of carbon emissions and its influencing factors in China: Evidence from 286 prefecture-level cities. Int. J. Environ. Res. Public Health . 19 (3), 1226 (2022). Zhang, M. N. et al. Elevated CO 2 moderates the impact of climate change on future bamboo distribution in Madagascar. Sci. Total Environ. 810 , 152235 (2022). Jung, M. et al. Compensatory water effects link yearly global land CO 2 sink changes to temperature. Nature 541 (7638), 516–520 (2017). Humphrey, V. et al. Sensitivity of atmospheric CO 2 growth rate to observed changes in terrestrial water storage. Nature 560 (7720), 628–631 (2018). Yi, L. I. U. et al. Advances in Technologies and Methods for Satellite Remote Sensing of Atmospheric CO 2 . Remote Sens. Technol. Application . 26 (2), 247–254 (2011). Wang, Q., Chiu, Y. H. & Chiu, C. R. Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis. Energy Econ. 51 , 252–260 (2015). Heyntann, J. et al. Consistent satellite X CO 2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm. Atmos. Meas. Tech. 8 (2), 2961–2980 (2015). Takagi, H. et al. On the Benefit of GOSAT Observationsto the Estimation of Regional CO 2 Fluxes. Sola 7 , 161–164 (2011). Yizhen, J. I. A. et al. Spatial and temporal distribution of XCO 2 and XCH 4 in China based on satellite remote sensing. J. Atmospheric Environ. Opt. 17 (6), 679692 (2022). Yokota, T. et al. Global concentrations of CO 2 and CH4 retireved from GOSAT: First preliminary results. Sola 5 , 160:163 (2009). Guo, M. et al. Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data. Sensors 12 (12). 10.3390/s121216368 (2012). Da-zhang, H. E. ZHANG Sheng-ling. The reginal climate division of Hainan Island. ACTA Geogr. SINACA . 40 (2), 169–178 (1985). Bo-Sun, W. A. N. G. et al. Diversity of tropical forest landscape-type in Hainan Island, China. Acta Ecol. Sin. 27 (5), 1690–1695 (2007). Goovaerts, P. K. & Interpolation Geographic Inform. Sci. Technol. Body Knowl. DOI: 10.22224/gistbok/2019.4.4 (2019). YU Yan-sheng, C. H. E. N. & Xing-wei Analysis of future trend characteristics of hydrological time series based on R/S and Mann-Kendall methods. J. Water Resour. Water Enigineering . 19 (3), 4144 (2008). Zhang, C., Li, S. & Wan, J. H. The warmest year 2015 in 1he instrumental record and its comparison with year 1998. Atmospheric Ocean. Sci. Lett. 9 (6), 487–494 (2016). Wang Li, Z. & Yuan Yang xian-ming, et al. Prediction of air quality in Lanzhou using time series model and residual control chart. Planteau Meteorology, (1):7. 10.7522/j.issn.1000-0534.2013.00150(2015 ). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviews received at journal 09 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor invited by journal 29 May, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 20 May, 2025 First submitted to journal 14 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6668361","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472750510,"identity":"5094578c-9d2a-4331-8ea2-124b0e6160f9","order_by":0,"name":"Qi Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACPhDBw8CQwM/A2ECcFjaYFskGkrUYHCDWYWzs6Y9fvPlTm2d8/nDbgx8MdnK6hCxj43ljZjm37Xix2Y3EdsMehmRjM0LWsUnksBnzNhxL3HaDsU2Ch+FA4jbCWtKfGfP8OZa4uf9gm+Qf4rQkGD/mYatJ3MCQ2CZNnC1AvzDObTuQOOMGUIuMARF+4QeG2Ic3f+oS+/uPP5N8U2EnR1ALMEbYJBgYDkM5BgSVg7Uwf2BgqCNK6SgYBaNgFIxQAABaaUQXQpyp3wAAAABJRU5ErkJggg==","orcid":"","institution":"Hainan Institute of Meteorological Sciences","correspondingAuthor":true,"prefix":"","firstName":"Qi","middleName":"","lastName":"Luo","suffix":""},{"id":472750511,"identity":"d06fb5e6-84d6-427b-903f-a3d8de9d1a17","order_by":1,"name":"Jing Han","email":"","orcid":"","institution":"Hainan Institute of Meteorological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Han","suffix":""},{"id":472750512,"identity":"b6d2fb41-141b-4c3e-9f1e-96713989cf74","order_by":2,"name":"Shaojun Liu","email":"","orcid":"","institution":"Hainan Institute of Meteorological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shaojun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-05-15 03:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6668361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6668361/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-32647-x","type":"published","date":"2025-12-13T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84909799,"identity":"aff7fdff-9e86-4c9f-b98d-be23da6c8c81","added_by":"auto","created_at":"2025-06-18 16:43:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":213814,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual variation (a), seasonal variation (b) and monthly mean (c) of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island, 2011-2024\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/554a67c1ab958c2aad275398.png"},{"id":84909803,"identity":"50a2bb13-26f5-4b26-bcf6-33c3b69ff70f","added_by":"auto","created_at":"2025-06-18 16:43:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172171,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-year average distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island, 2011-2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/9f3b515509bd3239e62c28ad.png"},{"id":84910184,"identity":"db07511e-55ff-49b1-9849-62b06825c262","added_by":"auto","created_at":"2025-06-18 16:51:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":427351,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island in different seasons. (a)\u0026nbsp;Spring (b) Summer (c) Fall (d) Winter\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/5a71695888c710133b1b222f.png"},{"id":84909804,"identity":"2a6aa95b-3b54-4093-b0f0-19a8ab5207c2","added_by":"auto","created_at":"2025-06-18 16:43:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209031,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between CO\u003csub\u003e2 \u003c/sub\u003econcentration and driving factors in cities and counties of Hainan Island\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/9e550a65a9c08fef3f2af1f0.png"},{"id":84909805,"identity":"ca4b0de9-2246-4d1d-995e-9405ebe81d21","added_by":"auto","created_at":"2025-06-18 16:43:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":203494,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial evolution of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island\u003c/p\u003e\n\u003cp\u003e(a)\u0026nbsp;Trend analysis (b) R/S analysis (Hurst index)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/7c19b23366cc42bd1d69ae1d.png"},{"id":84909806,"identity":"eb323da1-9faa-4d0e-a03e-65c338672d29","added_by":"auto","created_at":"2025-06-18 16:43:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":98490,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of CO\u003csub\u003e2 \u003c/sub\u003econcentration in Hainan Island from 2025 to 2030 (a) SARIMA model (b) Gray model\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/9bdac6d1ba7d9253d022e8d6.png"},{"id":98244233,"identity":"41d028fd-94ef-46b6-910b-9da5abbaec43","added_by":"auto","created_at":"2025-12-15 16:13:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1962061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6668361/v1/ea9aec58-5e62-4754-854e-0cd88bdf8b27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCharacterization of spatial and temporal variations of CO\u003csub\u003e2\u003c/sub\u003e concentration on tropical island and analysis of influencing factors\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) is one of the most dominant greenhouse gases in the global atmosphere, and in just over a hundred years since the Industrial Revolution, human have burned the fossil energy that the Earth has accumulated over billions of years. The burning of fossil fuels has emitted large amounts of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) into the atmosphere, while at the same time the global vegetation area has been decreasing and the concentration of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere has been increasing rapidly\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. From 300 ppm before the start of the first industrial revolution, the monthly average global CO\u003csub\u003e2\u003c/sub\u003e concentration measured by the National Oceanic and Atmospheric Administration (NOAA) in April 2018 reached 410 ppm, a 40% increase in atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration. The increase in atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration is now the main cause of global warming, and the 2015 Paris Agreement, which set the goal of achieving net-zero emissions in the second half of the century, is being translated into national strategies by an increasing number of governments, with more than 130 countries and territories now proposing \u0026ldquo;zero-carbon\u0026rdquo; or \u0026ldquo;carbon-neutral\u0026rdquo; goals\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. More than 130 countries and regions have proposed \u0026ldquo;zero carbon\u0026rdquo; or \u0026ldquo;carbon neutral\u0026rdquo; climate targets. China's carbon peak and carbon neutral strategy is not only a major demand for global climate governance, protection of the Earth's homeland and building a community of human destiny, but also an intrinsic demand for China's high-quality development, ecological civilization and comprehensive ecological environment management\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The realization of the dual-carbon target has put forward an urgent demand for the monitoring and assessment of the carbon cycle, especially carbon emission accounting, and regional carbon balance has also been a hotspot of global carbon cycle research in recent years\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn order to control carbon emissions, it is necessary to monitor its content first, so the monitoring of atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration is an important means to study the law of carbon cycle and cope with global warming\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. At present there are more than 300 greenhouse gas monitoring stations in the world. Ground-based observation has the advantages of high precision, high reliability, real-time access, etc., but the measurement results are only single-point measurements, lacking macro and vertical detection capabilities. Satellite remote sensing detection is an important method to realize the study of large-scale CO\u003csub\u003e2\u003c/sub\u003e concentration changes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Satellite remote sensing technology is a technology that detects and receives information from target objects through sensors from a high altitude\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, so as to identify the attributes of the objects and their spatial distributions and other characteristics, and acquires satellite data and analyzes and handles the received information through the remote sensing technology platform. The detection capability of satellite remote sensing mainly depends on the on-board detection instrument and the remote sensing inversion algorithm. The short-wave infrared band, which is more sensitive to the near ground, is more suitable for monitoring the dynamic changes of carbon sources and sinks on the ground. The SCIAMACHY instrument on board the ENVISAT satellite was the first on-board detector to use the short-wave infrared absorption band as its detection band\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Subsequently, a number of carbon monitoring satellites, such as the Greenhouse Gas Observing Satellite (GOSAT) launched by Japan\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, the Orbiting Carbon Observing Satellite-2 (OCO-2) launched by the United States, and China's Chinese Carbon Satellite (TanSat), which was launched in 2017, have followed this detection band, and the detection accuracy has been gradually increased with the continuous improvement of the detector indicators and inversion methods. Satellite remote sensing detection has the advantages of large range, long time series, vertical detection, etc., which can better obtain the spatial and temporal distribution and change characteristics of global CO\u003csub\u003e2\u003c/sub\u003e concentration\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and many scholars have also studied the CO\u003csub\u003e2\u003c/sub\u003e concentration using satellite data: Yokota T, el al utilize preliminary GOSAT data to analyze the distribution of global C CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e14\u003c/sup\u003e; Heyntann J, et al present the first detailed assessment of the new GOSAT BESD X CO\u003csub\u003e2\u003c/sub\u003e product\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e; A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS was developed in the current study to assess CO\u003csub\u003e2\u003c/sub\u003e concentrations on a global scale by Guo M, et al\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHainan Island (18\u0026deg;09\u0026prime;~20\u0026deg;11\u0026prime;N, 108\u0026deg;37\u0026prime;~111\u0026deg;03\u0026prime;E) is located in the southernmost part of China and has a typical tropical monsoon climate \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The terrain is high in the center and low in the surroundings, with the highest mountain range located in the central Wuzhi Mountain, and with the central Wuzhi Mountain and Parrot Ridge as the core of the uplift, descending step by step to the periphery, and consisting of a circular layered landscape of mountains, hills, plateaus, and plains, with an obvious gradient structure. Hainan Island is an important distribution area for tropical rainforests and monsoon rainforests, and has a variety of natural vegetation such as evergreen broad-leaved forests, mangrove forests and coniferous forests\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. At present, there are few studies on the distribution and influencing factors of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island, and there are few monitoring stations for CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island. Satellite data have become an important means to study CO\u003csub\u003e2\u003c/sub\u003e on Hainan Island. In view of this, this study firstly analyzed the changes and distribution characteristics of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island for many years by using GOSAT satellite data; secondly, selected a variety of natural and anthropogenic driving factors, and investigated the influence of different driving factors on CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island; and then discussed the evolutionary characteristics of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island. Then the evolution of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island is discussed, in order to better understand the change rule of atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration under the special climate background of Hainan Island, which is conducive to a better understanding of the mechanism of controlling atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Characteristics of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island from 2011 to 2024\u003c/h2\u003e\n \u003cp\u003eFrom Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(a), it can be seen that the CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island from 2011 to 2024 shows an increasing trend year by year, from 391.39 ppm at the beginning of 2011 to 419.51 ppm at the end of 2024, with an average growth rate of 2.01 ppm/a, which is lower than the average growth rate of the world in the past 10a of 2.06 ppm/a proposed by the World Meteorological Organization. According to the growth rate of CO\u003csub\u003e2\u003c/sub\u003e concentration, it can be divided into three phases. concentration growth rate in general can be divided into three stages, the first stage is 2011\u0026ndash;2015, during which the CO\u003csub\u003e2\u003c/sub\u003e concentration growth rate is slower, except for 2012\u0026ndash;2013 the growth rate of CO\u003csub\u003e2\u003c/sub\u003e concentration in other years is lower than 2.0 ppm/a, with an average growth rate of 1.92 ppm/a; the second stage is 2015\u0026ndash;2021, in which Hainan Island\u0026apos;s CO\u003csub\u003e2\u003c/sub\u003e concentration increases rapidly, with an average growth rate of 2.55 ppm/a, of which the largest growth in 2015\u0026ndash;2016 reached 4.45 ppm, probably due to the impact of the super-strong El Ni\u0026ntilde;o phenomenon in 201520. In the third phase, from 2021 to 2023, with the implementation of the \u0026quot;carbon peak and carbon neutral\u0026quot; strategy, the growth rate of CO\u003csub\u003e2\u003c/sub\u003e concentration slows down significantly in the last two years, with 1.78 and 0.20 ppm/a, respectively.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(b) shows the seasonal variation of CO\u003csub\u003e2\u003c/sub\u003e concentration. In winter, the vegetation cover is low and the leaf area of the vegetation is small, so the photosynthesis is limited and the CO\u003csub\u003e2\u003c/sub\u003e concentration is high. In spring, the weather turns warm, vegetation and soil respiration is enhanced, and at the same time, as the temperature rises, soil microbial activity is strengthened to decompose CO\u003csub\u003e2\u003c/sub\u003e in soil biomass, so the CO\u003csub\u003e2\u003c/sub\u003e concentration value reaches the highest, with an average value of 407.47 ppm; on the contrary, in summer, the vegetation has the highest coverage, the leaf area increases, photosynthesis is the strongest, and more CO\u003csub\u003e2\u003c/sub\u003e can be absorbed into the atmosphere, so the CO\u003csub\u003e2\u003c/sub\u003e concentration value in summer is the lowest, with an average value of 402.47 ppm. In the fall, photosynthesis is weakened, and CO\u003csub\u003e2\u003c/sub\u003e concentration gradually rises. Due to the special climatic conditions, the difference in CO\u003csub\u003e2\u003c/sub\u003e concentration between summer and fall on Hainan Island is relatively small. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(c) shows that the average monthly CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island was on the rise from January to April, with April being the highest value of the year, reaching 408.04 ppm, and then gradually decreasing from April to August with the enhancement of photosynthesis, and the CO\u003csub\u003e2\u003c/sub\u003e concentration in August was 400.63 ppm, which was the lowest value of the year. From August to December, the leaf area of the vegetation gradually decreased, so the chlorophyll content decreased. The absorption of CO\u003csub\u003e2\u003c/sub\u003e by photosynthes is decreased and the CO\u003csub\u003e2\u003c/sub\u003e concentration gradually increased again. The largest amplitude between seasons was from spring to summer, with an absolute value of 4.81 ppm, followed by 2.62 ppm from fall to winter, and the amplitudes from winter to spring and from summer to spring were closer to each other, with the absolute value of the former (1.22 ppm) being slightly larger than that of the latter (1.00 ppm), and the amplitude in spring and summer was much larger than that in other seasons, which indicates that terrestrial vegetation ecosystems play a significant role as a sink for carbon. The amplitudes in spring and summer were much larger than those in other seasons, indicating that terrestrial vegetation ecosystems play an important role in carbon sinks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Characteristics of the spatial distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the distribution of the average CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island, and the spatial distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration shows obvious differences between the north and the south, which manifests itself as high in the north and low in the south, with the highest value appearing in the capital city of Haikou, reaching 405.19 ppm, and the lowest value in Sanya City, reaching 404.883 ppm. There is a clear demarcation line in the central part of the island, where the concentration gradually rises in the north from Wuzhishan and decreases in the south. In the north, Haikou, Lingao, Wenchang and Chengmai have high CO\u003csub\u003e2\u003c/sub\u003e concentrations and a tendency to spread to the south, and the further south the lower the concentration. The main reason for this is that the highest peaks on Hainan Island are mostly located in the Wuzhishan and Parrot Mountain ranges in the central part of the island, while the terrain in the south-central part of the island is characterized by mountainous and hilly areas, which block the diffusion of CO\u003csub\u003e2\u003c/sub\u003e to the south. The CO\u003csub\u003e2\u003c/sub\u003e concentration in each city and county was in the order of spring\u0026thinsp;\u0026gt;\u0026thinsp;winter\u0026thinsp;\u0026gt;\u0026thinsp;fall\u0026thinsp;\u0026gt;\u0026thinsp;summer, and there was no obvious difference in the spatial distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration in each season, and the overall situation was high in the north and low in the south (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The high value area in the north of winter covers the widest range, followed by spring and fall, and the high value area in summer has the smallest range and is concentrated in Haikou and Wenchang in the northeast.\u003c/p\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003eThe distribution of average CO\u003csub\u003e2\u003c/sub\u003e concentrations on Hainan Island is consistent with the distribution of population and economy. The northern part of the island (Haikou and Wenchang) is the most populated area of the island, with intensive industrial, tourism and international shipping activities, and a high socio-economic level, with the population and GDP accounting for 55% and 60% of the island, respectively, and therefore the CO\u003csub\u003e2\u003c/sub\u003e concentrations due to human activities are on the high side. The south-central part of Hainan Island has the largest tropical rainforest, accounting for 1/7 of the island\u0026apos;s area, with a strong carbon sink capacity and relatively sparse population, so the impact of human activities is small. The central part of the island lags behind the southern coastal area in terms of population and economy, but the average CO\u003csub\u003e2\u003c/sub\u003e concentration is higher than that of the southern coastal area, which also indicates that the spatial distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration is not only affected by human activities, but also related to the long-distance transportation of CO\u003csub\u003e2\u003c/sub\u003e caused by atmospheric circulation due to topograph, vegetation and non-uniform distribution of solar radiation on the Earth\u0026apos;s surface.\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Analysis of factors affecting CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island\u003c/h2\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e concentration is affected by various factors such as vegetation photosynthesis, population growth, and industrial emissions, etc. In this study, natural factors such as the enhanced vegetation index, surface temperature, precipitation, solar radiation, and anthropogenic factors such as population, GDP, energy consumption of each city and county of Hainan Island were selected and analyzed at both monthly and yearly scales (of which only yearly averages were available for population, GDP, and energy consumption).\u003c/p\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 Effects of human activities on CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island\u003c/h2\u003e\n \u003cp\u003eOn the annual scale, GDP and energy consumption (coal, oil, natural gas, and primary electricity) of Hainan Island cities and counties showed a significant positive correlation with CO2 concentration, with correlation coefficients of 0.9 or more. Population is also significantly positively correlated except for Ding\u0026apos;an, Lingao, Tunchang, and Baisha counties, mainly because the population of these counties has been declining in recent years. Overall, the population, GDP and energy consumption continue to grow and play an important role in promoting the CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island, which is the main source of carbon on the island. 2020 before the population, GDP growth rate is faster, energy consumption is also growing rapidly, resulting in a faster rate of growth of the CO\u003csub\u003e2\u003c/sub\u003e concentration, after 2020, with the slowdown of the population, GDP growth rate, the rate of growth of energy consumption is also showing a downward trend, the CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island is increasing, and the CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island is decreasing. After 2020, as the population and GDP growth rate slow down, the growth rate of energy consumption also shows a downward trend, and the growth trend of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island is also alleviated.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 Influence of natural factors on CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island\u003c/h2\u003e\n \u003cp\u003eFrom Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be seen that the correlation between each natural factor and CO\u003csub\u003e2\u003c/sub\u003e concentration on the annual scale is high, and the monthly scale is relatively smooth compared with the annual scale. The greatest inhibition of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island is moisture, precipitation, soil moisture and relative humidity show negative correlations on both the annual and monthly scales, and the correlation between CO\u003csub\u003e2\u003c/sub\u003e concentration and precipitation is the most significant in each city and county on the annual scale. Surface temperature affects vegetation growth and human activities, and surface temperature is mainly affected by the intensity of solar radiation, which in turn affects ecosystem respiration and photosynthesis. Surface temperature and total solar radiation also showed a significant inhibitory effect on CO\u003csub\u003e2\u003c/sub\u003e concentration. The correlation of surface temperature was second only to that of precipitation on the annual scale, but lower on the monthly scale, which was attributed to the fact that Hainan Island is located in the tropics, and the surface temperature varies less throughout the year. Higher wind speed can accelerate the diffusion of CO\u003csub\u003e2\u003c/sub\u003e in the air and reduce the local concentration, the higher the wind speed, the better the uniform distribution of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere, which reduces the occurrence of localized areas of high concentration, and strong winds can also drive the vertical transport of CO\u003csub\u003e2\u003c/sub\u003e and change its concentration in different atmospheric layers, but the values of the mean wind speed on both the monthly and yearly scales are low, and so are insensitive to the changes in CO\u003csub\u003e2\u003c/sub\u003e concentration.\u003c/p\u003e\n \u003cp\u003eEnhanced Vegetation Index (EVI) is an important indicator used in remote sensing to monitor vegetation growth and cover, and its sensitivity is better than other vegetation indices for areaswith high vegetation cover. Photosynthetically Active Radiation (PAR) refers to the band of solar radiation that can be used by plants for photosynthesis, which is a key energy source in the light reaction stage of plants. PAR is the solar radiation band that plants can use for photosynthesis and it is a key energy source in the light reaction stage of plants, which directly affects the photosynthetic rate, plant growth and ecosystem productivity. The correlation between CO\u003csub\u003e2\u003c/sub\u003e concentration and EVI and PAR in the cities and counties of Hainan Island was not significant, showing a low positive correlation. On one hand, Hainan Island is in the tropic area, the average annual temperature is high. The pillar industries are tourism and tropical agriculture, less heavy industry, the annual vegetation cover is high and has been on the rise in recent years. The average annual EVI value is higher than the average level in China, compared with the sparsely vegetated areas of CO\u003csub\u003e2\u003c/sub\u003e by the vegetation to improve the role of the more significant, the effect of the vegetation on the concentration of CO\u003csub\u003e2\u003c/sub\u003e on Hainan Island is limited. On the other hand, with the increase of global average temperature, the CO2produced by vegetation respiration may be underestimated, especially in the high temperature and high humidity conditions in Hainan Island, the activity of organic matter in the soil is enhanced to emit more CO\u003csub\u003e2\u003c/sub\u003e. In addition, the long-term high temperature in the tropics will be accompanied by drought stress, and the plants will close the stomatal pores in order to reduce the water transpiration. At the same time, high temperature accelerates the degradation of chlorophyll and reduces the ability of light capture, resulting in the CO\u003csub\u003e2\u003c/sub\u003e. concentration of the vegetation is limited. This leads to a decrease in CO₂ uptake.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3 Combined impact analysis of multiple drivers\u003c/h2\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e concentration is affected by a variety of driving factors. Based on multiple regression analysis method, the CO\u003csub\u003e2\u003c/sub\u003e concentration of several driving factors have a greater impact on the comprehensive impact analysis. Due to the large difference in magnitude between the factors, the data were normalized and we obtained the contribution of the different driving factors to the impact of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island in each season. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island is significantly affected by the seasonal changes of different driving factors. Although the vegetation is not sensitive to the changes of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island, it is still the most important carbon sink, especially in the summer when vegetation photosynthesis is the strongest. The control of PAR on CO\u003csub\u003e2\u003c/sub\u003e concentration reaches the maximum value of regression parameter \u0026minus;\u0026thinsp;0.218, much higher than in other seasons. Relative humidity and surface temperature both showed inhibition, while precipitation would have an inhibitory effect on CO\u003csub\u003e2\u003c/sub\u003e concentration, but showed a certain contribution situation in the regression equation. Energy consumption(E) is the most important carbon source and the regression parameter is higher in all seasons, the highest being 0.894 in winter. The coefficient of determination of the relative regression equation is above 0.75 in all seasons, which is a good fit.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSeasonal contributions of different driving factors to the variation of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEVI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLST\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePRE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003espringtime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esummertime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewinner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.4 Trend analysis of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e(a) shows the trend of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island, the value of which is the slope of the regression equation for CO\u003csub\u003e2\u003c/sub\u003e concentration, with a positive value indicating an increasing trend and the larger the value, the more obvious. The slope values of the cities and counties on Hainan Island are small and not much different, between Haikou (0.187) and Sanya (0.182), which means that the CO\u003csub\u003e2\u003c/sub\u003e concentration in the cities and counties will continue to increase at a slower rate.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e(b) shows the stability analysis of the spatial evolution of CO\u003csub\u003e2\u003c/sub\u003e concentration growth in Hainan Island based on the R/S analysis. The H value (Hurst index) between 0 and 0.5 indicates that the time series of CO\u003csub\u003e2\u003c/sub\u003e concentration has inverse persistence, the past variables are negatively correlated with the future trend, and the series has a sudden jump, and the closer the H is to 0, the stronger the inverse persistence is; when H\u0026thinsp;\u0026lt;\u0026thinsp;1, the sequence of CO\u003csub\u003e2\u003c/sub\u003e concentration has long-term correlation, and the process has persistence. The sequence of CO\u003csub\u003e2\u003c/sub\u003e concentration at 0.5\u0026thinsp;\u0026lt;\u0026thinsp;H\u0026thinsp;\u0026lt;\u0026thinsp;1 has long-term correlation and the process has continuity. From the figure, it can be seen that the H value of all cities and counties in Hainan Island is less than 0.5, which indicates that the time series of CO\u003csub\u003e2\u003c/sub\u003e concentration has strong inverse persistence, and the CO\u003csub\u003e2\u003c/sub\u003e concentration may have a decreasing trend in the future.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.5 Future CO\u003csub\u003e2\u003c/sub\u003e Concentration Forecast for Hainan Island\u003c/h2\u003e\n \u003cp\u003eThe gray model is suitable for scenarios with little data and obvious trends, while the SARIMA model is suitable for scenarios with obvious seasonality. Therefore, the interannualchange and monthly change of CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island from 2025 to 2030 were predicted using the gray model and the SARIMA model, respectively. The results show that the interannual (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea) and monthly (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb) changes in CO\u003csub\u003e2\u003c/sub\u003e concentrations on Hainan Island in the future show an increasing trend, but the growth rate may be lower than the global average, which is consistent with the conclusion of 3.3.4.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Conclusions","content":"\u003cp\u003e(1) Hainan Island 2011\u0026ndash;2024 CO\u003csub\u003e2\u003c/sub\u003e concentration showed a rising trend year by year, the average growth rate of 2.01ppm/a, 2015\u0026ndash;2021 faster growth rate, the average growth rate of 2.55ppm/a, the largest increase in 2015\u0026ndash;2016, reached 4.45ppm, the smallest increase of 0.20% for 2022\u0026ndash;2023. ppm. The CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island has obvious seasonal variations, as shown in spring\u0026thinsp;\u0026gt;\u0026thinsp;winter\u0026thinsp;\u0026gt;\u0026thinsp;fall\u0026thinsp;\u0026gt;\u0026thinsp;summer, with April being the highest value in a year, reaching 408.04 ppm. The CO\u003csub\u003e2\u003c/sub\u003e concentration in August being the lowest in a year, at 401.55 ppm. The largest amplitude between seasons was from spring to summer with an absolute value of 4.81 ppm.\u003c/p\u003e \u003cp\u003e(2) The spatial distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island is affected by human activities, topography, vegetation and solar radiation, and the overall performance is high in the north and low in the south, and there is no obvious difference in the spatial distribution of CO\u003csub\u003e2\u003c/sub\u003e concentration in all seasons. Human activities are the most important carbon source in Hainan Island. Vegetation is insensitive to changes in CO\u003csub\u003e2\u003c/sub\u003e concentration on Hainan Island, but remains the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation also play a role in suppressing CO\u003csub\u003e2\u003c/sub\u003e concentration.\u003c/p\u003e \u003cp\u003e(3) The trend of CO\u003csub\u003e2\u003c/sub\u003e concentration show that the slope values of each city and county of Hainan Island are small and do not differ much, and the CO\u003csub\u003e2\u003c/sub\u003e concentration will continue to increase at a slower rate. The H-value (Hurst index) of each city and county is less than 0.5, which indicates that the time series of CO\u003csub\u003e2\u003c/sub\u003e concentration has strong inverse persistence, and the CO\u003csub\u003e2\u003c/sub\u003e concentration may have a decreasing trend in the future. The gray model and SARIMA model predicted the CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island from 2025 to 2030, the results also showed that the CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island will continue to grow at a slower rate.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAt the background of a global increase in CO\u003csub\u003e2\u003c/sub\u003e concentrations of 2\u0026ndash;3 ppm/a. Despite its slower growth rate, as part of the global atmospheric cycle, Hainan island still finds it difficult to escape the trend of increasing CO\u003csub\u003e2\u003c/sub\u003e concentrations. In order to achieve the \"zero carbon\" and \"carbon neutral\" goal at an early date, the following recommendations are proposed:\u003c/p\u003e \u003cp\u003e(1) Promoting the green transformation of industries. Promote energy-saving transformation of petrochemical and cement industries. Carry out low-carbon transformation of tropical agriculture and tourism to reduce total carbon emissions.\u003c/p\u003e \u003cp\u003e(2) Increase the utilization rate of renewable energy. Promote the use of new energy vehicles. Leverage the geographical advantages of the South China Sea to accelerate the large-scale development of offshore wind power and distributed photovoltaics.\u003c/p\u003e \u003cp\u003e(3) Strengthening the ecological protection of the central tropical rainforest, expanding the scale of restoration of mangrove forests, seagrass beds and coral reefs, enhancing ecological carbon sinks.\u003c/p\u003e \u003cp\u003e(4) Deploying greenhouse gas observation systems in key areas of Hainan Island and the South China Sea, and carrying out monitoring and assessment of the capacity of land and ocean carbon sinks.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cp\u003e5.1 Research data: GOSAT is a greenhouse gas observation satellite launched by Japan in 2009, and it is also the world's first satellite dedicated to the observation of greenhouse gases. the observation performance of GOSAT has made a great leap compared with the early SCIAMACHY, the revisit period of GOSAT is only 3 days, the spatial resolution has been greatly improved from the original 30 * 60 to 10.5 * 10.5. GOSAT is a satellite with a short-wave near-infrared (NIR) band, which is sensitive to the change of CO\u003csub\u003e2\u003c/sub\u003e content in the near-surface layer, and it can effectively obtain the whole layer of CO\u003csub\u003e2\u003c/sub\u003e information, including the bottom atmosphere. This study uses the L3-level data of GOSAT satellite, which mainly includes the monthly average CO\u003csub\u003e2\u003c/sub\u003e concentration with a spatial resolution of 2.5\u0026deg;*2.5\u0026deg;. It is verified that the GOSAT data can better capture the characteristics of the variation of CO\u003csub\u003e2\u003c/sub\u003e concentration in the near-surface observation data, and the comparison study with the observation data from the atmospheric background station at Wariguan shows that the accuracy and stability of the GOSAT data are high. The Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP), Photosynthetically Active Radiation (PAR) and (LST) data were obtained from MODIS (Moderate-Resolution Imaging Spectroradiometer), a large-scale space-based remote sensing instrument developed by NASA. The MODIS (Moderate Resolution Imaging Spectroradiometer) is a large spaceborne remote sensing instrument developed by NASA (National Aeronautics and Space Administration), which is characterized by free access, a wide spectral range (36 bands, from visible to thermal infrared), and a high updating frequency (at least twice a day with global coverage), and the raw raster data were averaged according to the administrative boundaries of the prefectures and municipalities. Total solar radiation, Relative humidity, precipitation and wind speed data were obtained from the ERA5-Land reanalysis dataset from the European Center. ERA5-Land uses the laws of physics to combine modeled data with observations from around the world to form a globally complete and consistent dataset, with a spatial and temporal resolution of 0.1\u0026deg;*0.1\u0026deg; per month. Population, GDP, and energy data were obtained from the Statistical Yearbook of Hainan Province.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Kriging Interpolation\u003c/h2\u003e \u003cp\u003eKriging interpolation (Kriging) is a statistically based spatial interpolation method, which is capable of generating a more accurate spatial prediction model by considering spatial autocorrelation and the weights of the data points.Kriging interpolation not only takes into account the positional relationship between the observation points and the estimation points, but also integrally takes into account the relative positional relationship between the observation points, and it has been widely used in spatial statistical analysis\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In this study, the monthly data of spatially resolved CO\u003csub\u003e2\u003c/sub\u003e concentration at 0.1\u0026deg;*0.1\u0026deg; on Hainan Island from 2011 to 2023 are obtained by kriging interpolation using GOSAT L3 2.5\u0026deg;*2.5\u0026deg; data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Pearson correlation coefficient\u003c/h2\u003e \u003cp\u003ePearson correlation coefficient is a statistical index used to measure the degree of linear correlation between two groups of variables, the formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{r}_{xy}=\\frac{{\\sum\\:}_{i=1}^{n}({x}_{i}-\\overline{x}\\left)\\right({y}_{i}-\\overline{y})}{\\sqrt{{{\\sum\\:}_{i=1}^{n}({x}_{i}-\\overline{x})}^{2}{\\left({y}_{i}-\\overline{y}\\right)}^{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe value of r is between \u0026minus;\u0026thinsp;1 and 1, the absolute value of the magnitude of the x and y variables between the degree of correlation. r is a positive value of the two groups of variables is positively correlated, that is, x increases (decreases) when y increases (decreases), r is a negative value of the negative correlation, y with the increase (decrease) with the increase (decrease) of x and decrease (increase).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.4 R/S Analysis\u003c/h2\u003e \u003cp\u003eR/S analysis, also known as Rescaled Range Analysis, is a statistical analysis method first proposed by hydrologist H.E. Hurst in 1951, which is mainly used to study the long-range correlation of time series data.R/S analysis evaluates the statistical characteristics of a time series by calculating the R/S ratio of the time series, i.e., the ratio of the range to the standard deviation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. R/S analysis assesses the statistical properties of time series, especially by analyzing the H-value (Hurst's index) to determine the trend persistence or anticontinuity of the time series, and the method is also applied to geography, climatology, etc. to study the long-term changes and forecasts of natural phenomena.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Gray and SARIMA models\u003c/h2\u003e \u003cp\u003eGrey forecasting and Grey Model (GM) are the core forecasting methods in Grey Systems Theory, which are applicable to the forecasting of small samples and uncertain systems. The most commonly used is the GM(1,1) model (first-order univariate gray model), which is suitable for short-term forecasting of time series data.The SARIMA model (Seasonal Autoregressive Integral Sliding Average Model) is an extension of the ARIMA model, which is specifically used to deal with the time series data (such as monthly and quarterly data) with a seasonal cycle, and the core\u003c/p\u003e \u003cp\u003eidea of which is to add seasonal difference and seasonal autoregressive/moving average terms to ARIMA\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study is supported by the Natural science foundation of China(Grant No.42465006, Grant No. U21A6001)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLuo wrote the main manuscript text and prepared the figures. Han dowload the satellite and reanlysis data. Liu provided technical guidance and revised the article .All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe GOSAT satellite data and the ERA5 reanalysis data used in this study can be dowload from website\u0026ldquo;https://data2.gosat.nies.go.jp/\u0026rdquo; and \u0026ldquo;https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview\u0026rdquo;.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe state of. greenhouse gases in the atmosphere based on global observations through 2010. \u003cem\u003eWMO Greenh. gas Bull.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e(21) (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, S. et al. 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Lett.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (6), 487\u0026ndash;494 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Li, Z. \u0026amp; Yuan Yang xian-ming, et al. Prediction of air quality in Lanzhou using time series model and residual control chart. Planteau Meteorology, (1):7.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7522/j.issn.1000-0534.2013.00150(2015\u003c/span\u003e\u003cspan address=\"10.7522/j.issn.1000-0534.2013.00150(2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tropical Island, CO2 concentration, Influencing factors, Variation trend","lastPublishedDoi":"10.21203/rs.3.rs-6668361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6668361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe spatial and temporal variations and distribution characteristics of CO\u003csub\u003e2 \u003c/sub\u003econcentration on Hainan Island are analyzed using GOSAT L3 data from 2011 to 2024, and the impacts of various driving factors on CO\u003csub\u003e2 \u003c/sub\u003econcentration on Hainan Island are discussed. The results show that from 2011 to 2024, the CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island shows an increasing trend, with a faster growth rate in the early period and a slower growth rate in rencent years with the implementation of the dual-carbon strategy. The spatial distribution of the distribution is affected by human activities, topography, vegetation and solar radiation, and the overall performance is high in the north and low in the south. Human activities are the most important carbon source on Hainan Island, vegetation is the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation also play a certain inhibitory role. It is expected that the CO\u003csub\u003e2\u003c/sub\u003e concentration in Hainan Island will continue to increase at a slower rate and may have a decreasing trend in the future.\u003c/p\u003e","manuscriptTitle":"Characterization of spatial and temporal variations of CO2 concentration on tropical island and analysis of influencing factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 16:43:26","doi":"10.21203/rs.3.rs-6668361/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-25T17:49:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T09:14:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-09T18:40:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256447778776174767082133512115826978278","date":"2025-07-01T06:10:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-26T17:21:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165553288366614949317899442495763804002","date":"2025-06-17T21:24:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48469270495217613884533556235884614129","date":"2025-06-17T08:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T06:37:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-29T18:56:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-21T08:34:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-20T11:43:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-15T03:19:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb1a6dca-6566-4835-a9e4-c71cc71f9c26","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50255753,"name":"Earth and environmental sciences/Climate sciences"},{"id":50255754,"name":"Earth and environmental sciences/Ecology"}],"tags":[],"updatedAt":"2025-12-15T16:07:26+00:00","versionOfRecord":{"articleIdentity":"rs-6668361","link":"https://doi.org/10.1038/s41598-025-32647-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-13 15:58:22","publishedOnDateReadable":"December 13th, 2025"},"versionCreatedAt":"2025-06-18 16:43:26","video":"","vorDoi":"10.1038/s41598-025-32647-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-32647-x","workflowStages":[]},"version":"v1","identity":"rs-6668361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6668361","identity":"rs-6668361","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}