Spatio-temporal response and projection of CO2 capture rates by different rock weathering to climate change in subtropics in China

Spatio-temporal response and projection of CO2 capture rates by different rock weathering to climate change in subtropics in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatio-temporal response and projection of CO2 capture rates by different rock weathering to climate change in subtropics in China Wenpu Liu, Yinxian Song, Xianqiang Men, Zhong Chen, He Chang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3961192/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The capture of CO 2 has become a global research focus. Rock weathering in the natural environment makes significant contributions to the stable carbon capture at both long and short time scales. However, traditional methods of estimating carbon capture potential are still uncertain due to the solely instantaneous carbon capture rates, dependence of measured data, and difficulty in predicting future carbon sink potential. Here, the estimated carbon capture potential of rock weathering using conventional methods and the PROFILE weathering model were compared for the various rocks in subtropics in China. The results showed that the carbon capture rates estimated by the GEM-CO 2 model vary from 1.64 to 27.40 mmol·m − 2 ·d − 1 , while 2.63 ~ 13.46 mmol·m − 2 ·d − 1 by traditional the water chemistry method. Similarly, carbon capture rates calculated by the PROFILE model based on chemical weathering rate of individual specific mineral, ranging from 0.03 to 19.03 mmol·m − 2 ·d − 1 . The results of the PROFILE calculation showed that, the carbon capture rate was 1.30 to 1.99 times in summer than in winter due to the higher temperature and precipitation. In extreme climates, high temperatures (≥ 30°C) and heavy precipitation (≥ 25mm) have increased the capture rate of carbon dioxide by approximately 21.33% and 66.23%, respectively. On the interdecadal time scale, the carbon capture rate increased by 6.1% from 1970 to 2020, due to temperature rising by 1.4°C, precipitation increasing by 2.8%, and partial pressure of atmospheric carbon dioxide ( pco 2 ) increasing by 28.4%. Further, we predict an increase in carbon capture rates will change approximately from 4.7 to 5.1% in the period of 2020–2100 under four Representative Concentration Pathway (RCP) modes. The findings of this study will offer novel scientific recommendations and methods for future research and policy making on global carbon neutrality. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences/Environmental chemistry Rocks weathering PROFILE model Carbon capture rate Spatial-temporal prediction Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Human activities have contributed to a significant rise in atmospheric CO 2 concentration over last the hundred years, garnering widespread concern over the greenhouse effect and global climate disasters (Hughen et al., 2004). Consequently, studying carbon cycle processes haves emerged as a prominent subject to retard atmospheric CO 2 rise (Qiu et al., 2004 ). Presently, a major concern in global carbon cycle is focused on how to capture CO 2 by both artificial and natural processes to offset anthropogenic CO 2 emissions, achieving carbon neutrality (Kennedy, 2001 ). Growing studies have demonstrated the utilization of carbon capture and storage (CCS) technologies, including both artificial and natural methods, to mitigate the ongoing enhancement of the greenhouse effect(Dziejarski et al., 2023 ; Shen et al., 2022 ). However, artificial CCS processes are expensive and possess limited capacity to capture carbon. In contrast, the atmosphere, ocean, and terrestrial ecosystems in natural processes, which represent three significant reservoirs of carbon, have a large potential to sequester carbon (Li et al., 2005 ). The stability of ecosystems' carbon capture is compromised by the reduction in global forest coverage and the impact of natural disasters. Moreover, these processes are an unstable carbon sink, which has a risk of re-releasing carbon. Conversely, the rock-soil system in natural processes serves as an exceptionally stable carbon sink. The rock-soil system constitutes one of Earth's primary natural carbon reservoirs, with carbon content approximately ten thousand times greater than that found in the exogenous system, encompassing the atmosphere, ocean, biosphere, and shallow sediments (Lee et al., 2019 ). Nevertheless, chemical weathering and organic carbon burial serve as significant carbon captures within the global carbon cycle (Gaillardet et al., 1999 ; Li et al., 2014 ).Unveiling the influence of chemical weathering on the carbon cycle and accurately quantifying the CO 2 absorption flux via mineral weathering in rocks and soils are crucial undertakings in elucidating long-term carbon cycling and climate change mechanisms (Zhang et al., 2021 ). The rock-soil sphere is an open system that continuously interacts with the atmosphere, hydrosphere, and biosphere through exchange processes. Previous studies have employed various methods, including kinetic methods, dissolution measurement methods, and water chemistry methods, to assess the carbon sequestration capacity of rock-soil weathering (Qiu et al., 2004 ). The water chemistry method has been used to determine the total concentration of ions in rivers by measuring the solute concentration at both the source and outlet of the watershed, along with considering the flow rate and discharge. By taking into account the distribution of rocks within the watershed, researchers can deduce the weathering rates of different rock types and estimate their capacities (Jiang et al., 2020 ; Zhang et al., 2021 ). The GEM-CO 2 model, utilizes water flow or runoff as a benchmark to deduce the correlation between weathering rates of various rock types and water flow rates (Amiotte Suchet and Probst, 1993 ). Several studies have reported that global rock weathering plays a significant role in the overall understanding of this process, contributing to approximately 87% of carbon dioxide consumption (Gaillardet et al., 1999 ). When investigating rock weathering s, researchers estimate the amount of carbon dioxide absorbed through rock weathering using either established empirical equations from previous studies or by measuring the ion flux in watershed rivers (Dreybrodt et al., 1992 ; Jiang et al., 2020 ; Liu et al., 2008 ; Noh et al., 2009 ; Qiu et al., 2004 ; Zhang et al., 2021 ). In summary, existing methods for calculating weathering rates and carbon capture rates in watersheds are limited. For example, the GEM-CO 2 model uses empirical methods to estimate the carbon capture rates in different watersheds based on years of average precipitation data. However, it cannot assess the carbon capture capacity under seasonal and extreme climatic conditions, nor can it provide accurate estimates. The most significant issue with the ion balance approach used in the water chemistry method is that during the process of rock and/or soil weathering, the released ions are retained to some extent in the soil and do not entirely follow the rainfall or groundwater into rivers. This makes it difficult to estimate carbon capture capacity in areas with limited river distribution. To achieve carbon neutrality in the mid-21st century, predicting the future carbon capture capacity of the rock-soil system is very important, especially in the context of future global changes. The Intergovernmental Panel on Climate Change (IPCC) projects a global temperature increase of 1.5°C, as well as a 30% rise in precipitation in China (Allen et al., 2018 ; Collins and Knutti, 2013 ). With forecasted changes in future climate, factors such as temperature and precipitation are expected to increase. These changes will strongly impact the natural process of rock weathering, intensifying the ability of rock chemical weathering to capture atmospheric carbon dioxide. Hence, evaluating the variations in rock carbon capture rates on a long timescale becomes particularly important. This study quantitatively estimates the carbon capture rate in the subtropical watershed of southern Anhui, China based on the PROFILE model (Huang et al., 2016 ; Sverdrup and Warfvinge, 1993 ; Warfvinge and Sverdrup, 1992 ). In order to understand the global trend of rising CO 2 levels, the impact of climate factors on the ability of rock chemical weathering to capture CO 2 , and to provide effective recommendations for future global greenhouse effect management. The main objectives of this paper are as follows: 1. Quantitatively estimate the carbon capture rates of various types of rocks based on the PROFILE model. 2. Compare this method with traditional methods for estimating carbon capture rates to verify its reliability. 3. Analyze the capturing of atmospheric carbon dioxide by rock chemical weathering under different seasonal and extreme climates. 4. Based on climate data from NOAA and IPCC between 1970–2020, and the Hurst exponent and Representative Concentration Pathway (RCP) datasets between 2021 ~ 2100, estimate the changes in carbon capture caused by rock chemical weathering under past and future climate change conditions. 5. Investigate the relationship between carbon capture rates and the Gibbs free energy and equilibrium constant (logK) during the carbon capture reaction process in rock weathering. 2. Materials and Methods 2.1 Study Area This study was carried out in the southern Anhui region of subtropical China, approximately between 118°30′~119°40′E longitude and 29°70′~31°30′N latitude, characterized by a landscape dominated by hills and mountains, gradually increasing in elevation from north to south (Fig. 1 ). The annual average temperature in the study area is approximately 15.5℃, with an average annual precipitation of 1498mm. (Huang et al., 2012 ). The southern Anhui region lies in the transitional zone between the northern subtropical and central subtropical monsoon climates. It is a climate-sensitive area and the degree of weathering activity is comparable to the global average level, encompassing a diverse range of rock types and intricate geological formations. Consequently, this study collected a range of rock samples from southern Anhui, encompassing igneous rocks, sedimentary rocks, and metamorphic rocks. Additionally, water samples were obtained from the Qingyi River and Huishui River. 2.2 Samples collection and chemical analysis In the natural environment of southern Anhui province, a total of 347 rock samples in varying degrees of weathering were collected. These samples were obtained from the entire region of Xuancheng and the eastern part of Huangshan. The collected samples comprised 127 igneous rock samples (including igneous clastic rocks and granite), 187 sedimentary rock samples (including sandstone, mudstone, siliceous rock, and conglomerate), and 32 metamorphic rock samples (including slate and phyllite) (Fig. 1 ). To mitigate the impact of external contamination during field sampling, the surface gravel was initially cleared using a shovel prior to collecting the partially weathered rock samples from deeper layers. Subsequently, these samples were carefully packed in geologic sample bags and appropriately labeled and numbered. Additionally, a total of 16 water samples were gathered from the Qingyi River and its tributary, the Huishui River, located in southern Anhui province. Upon collection, the partially weathered rock samples underwent initial crushing to eliminate any remaining debris. Subsequently, the samples were dried in an oven at a temperature of 105°C. Next, the samples were ground and sifted through a 200-mesh nylon screen. The major elements (Si, Ti, Al, Fe, Mg, Ca, Na, K) and trace elements in each sample were quantified using a PANalytical Axios Max X-ray fluorescence spectrometer (XRF) and a high-resolution inductively coupled plasma mass spectrometer (HR-ICP-MS, ELEMENT XR). The mineral composition of the rocks was examined using an ARL9800-XP X-ray diffractometer. X-ray diffraction (XRD) analysis of minerals is often conducted based on the characteristics of diffraction peaks, which include position, intensity, shape, and width. The relative standard deviation (RSD) for the analysis is maintained below 5%. The river water samples were filtered using a 0.22 µm cellulose acetate membrane and subsequently stored in clean polyethylene containers. Water pH and conductivity were measured on-site using a portable water quality parameter meter. The water alkalinity data were determined within 24 hours through acid titration using hydrochloric acid. Cation and dissolved SiO 2 concentrations in the water were analyzed using an inductively coupled plasma spectrometer (ICAP 6300 DUO), while anion concentrations were measured using an ion chromatograph (Dionex ICS-1100) To ensure result reliability, quality assurance and quality control (QA/QC) procedures were rigorously followed during sample processing and testing. The testing procedures adhered to national material standards (SARM-3, SARM-23HE, SARM-45), with an analysis accuracy surpassing 0.5% ~1.0%, with SD < 10%. 2.3 Data collection Considering the similarity of elements and minerals composition between rocks and parent material, the estimation of rock weathering was conducted using parameters from the soil parent material. The parameters for parent material, such as pH, bulk density, particle size distribution etc., were collected from the Chinese Soil Database ( http://vdb3.soil.csdb.cn/ ). The soil moisture content was estimated using local precipitation data. The climate data (temperature and precipitation) from 1970 to 2020 is sourced from the National Oceanic and Atmospheric Administration (NOAA) ( https://ncei.noaa.gov/maps/daily/ ), while the RCP data (temperature and precipitation) from 2021 to 2100 is sourced from the World Meteorological Organization (WMO) ( https://climexp.knmi.nl/start.cgi ). Atmospheric CO 2 partial pressure ( pCO 2 ) data for 1970 to 2100 was acquired from the Intergovernmental Panel on Climate Change (IPCC) ( https://ipcc-data.org/observ/ddc_CO 2 .html).Th e range of these parameters can be found in Table S2. 2.4 Data model description The correlation between the weathering process of primary minerals and the carbon storage capacity in soil has not been extensively examined (Slessarev et al., 2022 ). In this study, the PROFILE model was employed to estimate the weathering processes of diverse parent rocks and the rates of carbon capture in the southern Anhui region. The PROFILE model calculates the cumulative weathering rates of different minerals in their natural soil composition (Sverdrup and Warfvinge, 1993 ). The rates at which minerals in the soil parent material weather are utilized to assess carbon capture rates. The PROFILE model can be described as follows: $$\varvec{R}\varvec{w}=\sum _{\varvec{i}=1}^{\varvec{m}\varvec{i}\varvec{n}\varvec{e}\varvec{r}\varvec{a}\varvec{l}\varvec{s}}{\varvec{r}}_{\varvec{i}}\bullet {\varvec{x}}_{\varvec{i}}\bullet {\varvec{A}}_{\varvec{w}}\bullet \varvec{Z}\bullet \varvec{\theta }$$ 1 The total weathering rate of rocks is represented by Rw (mmol·m − 2 ·a − 1 ), while the rate of release of alkaline cations by minerals is represented by r i (kmol·m − 2 ·s − 1 ) (Sverdrup and Warfvinge, 1993 ); x i represents the relative amount of minerals, while Aw denotes the surface area of minerals (m 2 ·m − 3 ) (Stendahl et al., 2013 ); The variable Z represents the thickness of the soil layer (m), while θ represents the percentage of soil water saturation (Sverdrup and Warfvinge, 1993 ; Warfvinge and Sverdrup, 1992 ) (Table S2). The carbon capture rate generated during the weathering process of parent material can be derived by considering the mechanisms in carbonate and silicate mineral weathering processes, as well as the chemical weathering rate Rw, which is calculated using the PROFILE model: Fw = Rw·y i (2) The variable Fw represents the overall rate at which rocks consume CO 2 (mmol·m − 2 ·a − 1 ); The coefficient y i represents the efficiency of mineral weathering in absorbing CO 2 (Table 4 ). When calculating the rate of mineral decomposition (r i ), the reaction rate constant is standardized using the Arrhenius relationship derived from laboratory studies at 8℃, and then adjusted according to the ambient temperature (Sverdrup and Warfvinge, 1993 ): $$\varvec{ln}\left(\frac{{\varvec{k}}_{\varvec{T}}}{{\varvec{k}}_{8\varvec{℃}}}\right)=\frac{{\varvec{E}}_{\varvec{A}}}{\varvec{R}}\left(\frac{1}{281}-\frac{1}{\varvec{T}}\right)$$ 3 The environmental temperature is denoted by T (K), the rate constant of the reaction is represented by k, the activation energy is denoted by E A (kJ·kmol − 1 ), and the universal gas constant is denoted by R (kJ·kmol − 1 ·K − 1 ). 2.5 Data analysis Basic data processing and analysis were performed using the SPSS software package (SPSS Inc. Version 26, 2019). Cartography and spatial analysis were performed using Origin (Origin Pro Inc. Version 9.1, 2020) and SURFER software (Golden Software, Inc. Version 21, 2021). 3. Results and Discussion 3.1 Geochemical characteristics of rocks 3.1.1 Major elements distribution in various rocks The average compositions of major elements in various types of weathered rocks in the southern Anhui region are presented in Table 1 . When compared to the upper continental crust (UCC) and the Australian post-Archean shale (PASS). The abundances of Ca, Na, and Mg in these rocks are significantly lower, indicating severe feldspar weathering. Additionally, siliceous rocks have higher Si content (Table 1 ). The elemental content of slate and quartzite in the metamorphic rocks is similar. The Ca element is significantly lower than the UCC and PAAS values. The presence of plagioclase and carbonate minerals in the rocks suggests a moderate degree of weathering, possibly due to weathering. Igneous rocks have lower Mg and Ca contents compared to PAAS and UCC, likely because of the low content of white-colored minerals in these rocks, which is similar to the geochemical composition of granites in the southern Anhui region (Weng et al., 2011 ). The leaching and enrichment of most elements between rock types reflect their respective degrees of weathering (Table 1 ). Table 1 The Major elements and weathering indicators of different weathered rocks from southern Anhui. Lithology samples Altitude (m) Major elements (wt%) Weathering indicators SiO 2 TiO 2 Al 2 O 3 TFe 2 O 3 MgO CaO Na 2 O K 2 O Total CIA PIA Pyroclastic rock 22 116 ~ 461 69.62 0.54 14.44 4.13 1.22 1.12 2.53 3.55 97.14 54.65 56.78 Granite 105 71 ~ 681 71.03 0.35 13.79 2.54 0.71 1.39 3.20 4.21 97.22 48.39 47.80 Sandstone 119 51 ~ 470 75.10 0.53 10.98 4.72 1.24 0.71 1.09 2.30 96.68 69.48 79.07 Mudstone 45 65 ~ 478 69.77 0.67 13.79 5.40 1.58 0.84 0.55 3.02 95.64 74.50 87.58 Siliceous rock 16 174 ~ 691 86.73 0.14 3.08 1.24 0.37 1.78 0.16 0.58 94.09 75.93 83.54 Conglomerate 7 41 ~ 181 71.85 0.43 11.56 3.76 0.57 3.45 0.50 2.08 94.20 74.90 85.74 Slate 15 293 ~ 604 65.68 0.80 16.20 6.12 1.98 0.53 2.00 3.63 96.92 61.87 69.15 Phyllite 18 109 ~ 228 65.07 0.75 16.53 6.40 1.75 0.41 1.98 3.69 96.56 61.44 67.02 UCC 66.60 0.64 15.40 5.04 2.48 3.59 3.27 2.80 99.82 52.74 53.47 PAAS 62.80 1.00 18.90 7.22 2.20 1.30 1.20 3.70 98.32 70.36 79.04 The elements contents of each type of weathered rock above are displayed as mean values; UCC data were collected from(Rudnick, 2003 );PAAS data were collected from(Taylor and McLennan, 1985 ). According to the spatial distribution of whole rock major elements (Figure S1 ), SiO 2 predominates the rock composition across the study area. Regions with higher Si content are characterized by the presence of conglomerates, siliceous rocks, and sandstones. The spatial distribution of TiO 2 , Fe 2 O 3 , and MgO exhibits similar patterns, and the variation in element values aligns with the transformation rules of different rock types, revealing the diagenetic processes in the study area, with regions of high content primarily influenced by igneous rocks. Al 2 O 3 and K 2 O demonstrate a highly similar spatial distribution, and the highest value of Na 2 O also corresponds to that of Al 2 O 3 . Based on the geological map of Anhui Province and sample points, areas with high values of these elements mainly consist of igneous rocks with abundant silicate minerals (such as feldspar and mica), low weathering degree, and notable element leaching. The content of CaO may be attributed to the presence of calcite, dolomite, and some feldspars during the diagenetic process. Due to the complex geological conditions in southern Anhui, some sandstones contain a significant amount of limestone components, resulting in a relatively higher calcite content in the central region. 3.1.2 Mineral composition in different rocks To examine the mineral compositions of various weathered rock types, 27 representative samples covering most of the rock types were selected from a total of 367 samples for XRD analysis. The XRD results indicate that the primary minerals in different rock types in the southern Anhui region are quartz, feldspar, mica, calcite, and dolomite, along with secondary minerals like illite, chlorite, and kaolinite (Fig. 2 and Table S3). Pyroclastic rock exhibit higher illite content due to the presence of igneous ashes, which typically transform into clay minerals (such as illite) during depositional diagenesis (Zhou et al., 2021 ). The mineral compositions of igneouslastic rocks in the Xiashaxi Formation in Yunyang, Chongqing, primarily consist of quartz, feldspar, and illite (Zhou et al., 2021 ), similar to this study. The lower quartz content in granite can be attributed to the prevalence of syenite, which contains higher levels of feldspar minerals (Nesbitt et al., 1996 ). The content of illite and feldspar is relatively high in igneous rocks, consistent with the spatial distributions of Al and K elements (Figure S1 ). In the region where igneous rocks are distributed, the Al and K elemental content is higher. In Table S3, the sandstone samples SA-1 and SA-2 have a higher content of calcite. This is due to the complex stratigraphy in the southern Anhui region, where the sandstones in the central part of the study area are intermixed with limestone bodies, resulting in an increased content of calcite. Therefore, the Ca element in the central area of the study region will be partially elevated, as depicted in Figure S1 . In sedimentary environments, mudstones undergo significant weathering, resulting in quartz and clay minerals (illite, kaolinite, etc.) being the predominant components, and clay mineral content exceeding 40%, similar to mudstones in the southern North China region (Li et al., 2021 ). Siliceous rocks showcase a SiO 2 content of approximately 90%, primarily composed of quartz mineral (Table 1 ). Conglomerate consists primarily of crystalline quartz as large clastic particles, accompanied by some feldspar and clay minerals. The sandstone, siliceous rock and conglomerate contain a high amount of quartz, mainly distributed in the central area of the study region. As a result, the Si element content is high, consistent with the variation pattern of SiO 2 in Table 1 and Figure S1 . The high content of illite and dolomite in the shale leads to a high Mg element in the central part of the study area (Figure S1 ). The mineral composition of slate and phyllite is similar, both are formed through metamorphism and are mainly composed of quartz, feldspar, and mica, with similar contents. Dolomite minerals represent the carbonate component. Slate in the Barahulish area of Scotland exhibits a feldspar content of approximately 5%, whereas the slate in this study area demonstrates a higher feldspar content (Walsh, 2007 ). The mineral composition of phyllite in this study is akin to that of phyllite in the Opawskie Mountains in southwestern Poland, primarily comprised of quartz, feldspar, and ferromagnesian mica, with the presence of clay minerals and dolomite minerals, potentially related to calcite veins (Sawicka et al., 2018 ). 3.2 Chemical weathering intensity and CO 2 capture rate 3.2.1 Chemical weathering index The Chemical Index of Alteration (CIA) and Plagioclase Index of Alteration (PIA) serve as indicators of the chemical weathering degree in watersheds, determining the chemical weathering degree of various rocks based on the degree of feldspar weathering, with their respective formulas provided in appendix equations 1 and 2(Fedo et al., 1996 ; Nesbitt and Young, 1982 ).Through the application of mass balance principles, thermodynamic calculations of mineral stability, and experiments involving feldspar leaching, the process of chemical weathering in the upper crustal rocks was inferred. This led to the development of ternary diagrams, specifically the A-CN-K and (A-K)-C-N diagrams (Nesbitt and Young, 1984 ; Selvaraj and Chen, 2006 ). Table 1 presents the average CIA and PIA values for different types of partially weathered rocks, demonstrating an overall CIA range of 36.3 to 91.3 and a PIA range of 54.13 to 97.92. These wide ranges are influenced by the varying lithologies. Different rocks and minerals exhibit diverse CIA and PIA values. For instance, feldspar is more susceptible to weathering, resulting in lower CIA and PIA values around 50. Conversely, clay minerals tend to possess higher values, indicating more pronounced weathering potential. The CIA and PIA values were calculated to assess the spatial distribution of the degree of weathering in the southern Anhui region (Figure S2) and were depicted in ternary diagrams (Figure S3). The spatial distribution patterns of CIA and PIA exhibit a high degree of similarity, with higher indices observed in the central and southwestern regions. These areas are characterized by extensive distribution of sedimentary rocks, primarily composed of clay minerals according to the mineral percentage content provided in Table S3. This finding suggests a greater degree of weathering in these regions. Additionally, higher CIA and PIA values indicated a more significant loss of active elements such as K, Na, and Ca from silicates (Nesbitt and Young, 1984 ; Nesbitt and Young, 1989 ). The diversity of weathering degrees in the study area is evident in the variation of weathering indices across different rock and mineral types. Based on the ternary diagrams (Figure S3), igneous rocks exhibit weak chemical weathering, characterized by slight leaching of Ca and Na. Sedimentary rocks, on the other hand, primarily demonstrate moderate to strong chemical weathering, evidenced by the increasing prevalence of clay minerals. Metamorphic rocks generally display a moderate level of chemical weathering, following the conventional continental weathering trend from the upper continental crust (UCC) towards the Proterozoic Australian shale standard (PASS) (Wu et al., 2016 ). The study area exhibits varying weathering indices due to the higher content of feldspar in igneous rocks, lower indices in sedimentary rocks, and intermediate rankings in metamorphic rocks. These findings aligned with previous studies that have examined weathering indices across different rock types (Cheng et al., 2014 ; Dang et al., 2022 ; Selvaraj and Chen, 2006 ; Singh et al., 2021 ). 3.2.2 CO 2 capture through weathering estimated by GEM-CO 2 model and water chemistry method. The chemical weathering of soil or rocks has been assumed to play a pivotal role as a significant component in the geological carbon cycle (Torres et al., 2014 ). The majority of carbon within the terrestrial biosphere is stored below the surface, specifically in the form of soil organic carbon (Jobbágy and Jackson, 2000 ). When primary minerals in rocks undergo weathering to form soil, they react with atmospheric carbon dioxide, resulting in the formation of poorly crystalline minerals and storing them in the form of organic carbon (Slessarev et al., 2022 ). There exist several estimation methods for quantifying the rate of carbon capture resulting from rock weathering (Qiu et al., 2004 ). The absorption of carbon dioxide in the atmosphere through rock weathering processes can primarily be divided into two reaction types: the reaction of carbonate minerals and the reaction of silicate minerals with carbon dioxide (Berner, 1991 ; Berner, 1992 ). Amiotte and Probst suggest that the rate at which CO 2 is consumed through rock weathering is primarily influenced by the flow rate of surface water, the temperature, and the types of rock. To estimate the amount of atmospheric CO 2 consumed by rock weathering, they have developed the GEM-CO 2 model (see appendix equations 3 ~ 6) (Amiotte Suchet and Probst, 1993 ). The CO 2 capture rates of various rock types in the southern Anhui region calculated using the GEM-CO 2 model range from 1.64 to 27.40 mmol·m − 2 ·d − 1 (Table 2 ). Among them, metamorphic and plutonic rocks have the lowest rates, while carbonate rocks have the highest rates, as carbonate rocks are naturally more prone to weathering processes (Sverdrup and Warfvinge, 1993 ). Qiu et al. ( 2004 ) estimated the average CO 2 capture rates of various rock types in China to be between 0.09 and 1.71 mmol·m − 2 ·d − 1 . A comparison between the two studies reveals that the CO 2 capture rates in the southern Anhui region are relatively high. This can be attributed to the influence of precipitation and evaporation on the GEM-CO 2 model, with the study area experiencing high annual precipitation and low evaporation rates, resulting in higher estimated CO 2 capture rates. Table 2 Comparison of CO 2 capture rates by various rock/minerals types from different regions and methods. Region Manner Rock/mineral CO 2 capture (mmol·m − 2 ·d − 1 ) Source Bishuiyan underground basin, Guangxi, China Water chemistry Carbonate minerals 1.30 (Jiang et al., 2020 ) Silicate minerals 0.53 Xijiang River Basin, China Water chemistry Carbonate minerals 1.51 (Zhang et al., 2021 ) Silicate minerals 0.10 Yalong River draining the eastern Tibetan Plateau, China Water chemistry Carbonate minerals 0.76 (Li et al., 2014 ) Silicate minerals 0.25 Qingyi River Basin in southern Anhui, China Water chemistry Carbonate minerals 2.56 This study Silicate minerals 1.70 Southern Anhui region, China GEM-CO 2 model Metamorphic rocks and plutonic rocks 1.64 This study Acidic igneous rock 3.84 basalt 8.28 Sand and sandstone 2.63 Shale class 10.83 Acidic carbonate rocks 27.40 Evaporative rock salt 5.06 Previous studies have estimated the rates of weathering and carbon uptake in the basins by analyzing the dissolved solutes in rivers and applying the principle of ion balance. This is because the atmospheric CO 2 consumed through rock-soil weathering ultimately enters the ocean via river transport (Jiang et al., 2020 ; Liu et al., 2008 ; Zhang et al., 2021 ). The soluble components found in rivers primarily originate from atmospheric deposition, mineral dissolution, and anthropogenic inputs. Mineral contributions typically consist of carbonate, silicate, and evaporite minerals (Li et al., 2014 ; Noh et al., 2009 ). Based on the distribution of rocks, atmospheric deposition, and anthropogenic inputs in the southern Anhui region, the sources of dissolved components in river water have been identified, along with the corresponding weathering rates and carbon uptake rates for carbonate and silicate minerals (appendix equations 7 ~ 11). The carbon capture rates calculated through water chemistry methods in the study area range from 2.62 to 13.46 mmol·m − 2 ·d − 1 , with an average of 4.26 mmol·m − 2 ·d − 1 (Table 2 ). Compared to the Bi Shuiyan underground basin in Guangxi, the Xi River Basin in western China, and the Yalong River Basin in the eastern part of the Qinghai-Tibet Plateau, the values are slightly higher(Jiang et al., 2020 ; Li et al., 2014 ; Zhang et al., 2021 ). This is due to the mountainous nature of the study area and the influence of higher annual precipitation and temperatures, which result in a greater capture of carbon dioxide through rock weathering. 3.3 Estimating the rates of CO 2 capture using the PROFILE model 3.3.1 Chemical weathering rate of parent rock When using the PROFILE model to calculate the CO 2 capture process, we assume that the CO 2 capture rate was estimated in the scenario with unchanged base cations during the weathering process. The chemical weathering rate, CO 2 capture rate, and base cation leaching rate in southern Anhui Province in 2020 were calculated using the PROFILE model, based on the mineral percentages of various-type weathered rock in Table S3, as well as the various parameters of parent material in the PROFILE in Table S2. For different parent rock in southern Anhui Province in 2020, the average chemical weathering rates ranged from 0.03 to 14.43 mmol·m -2 ·d -1 , the CO 2 capture rates from 0.03 to 19.03 mmol·m -2 ·d -1 , and the amounts from 0.03 to 1415.50 t·d -1 (Table 3 ). The distribution areas of different types of parent rocks in the study area vary according to the geological map of Anhui Province (Table 3 ), leading to significant variations in the amounts among different parent material. Different minerals in the same natural environment have varying chemical weathering rate coefficients, resulting in significant differences in the chemical weathering rates and carbon capture rates of soil parent materials with different types (Sverdrup and Warfvinge, 1993 ). Table 3 Parent rock weathering rates (Rw), CO 2 capture rate (Fw) by rock weathering, base cation releasing rates (R BC ) and CO 2 capture amount every day in 2020. Parent rock Area Rw Fw R BC F (km 2 ) mmol·m − 2 ·d − 1 t·d − 1 Pyroclastic rock 218.65 2.96 ~ 11.30 4.19 ~ 11.30 2.95 ~ 11.30 40.29 ~ 108.72 Granite 1182.05 0.99 ~ 8.37 1.98 ~ 14.23 1.83 ~ 14.18 103.18 ~ 740.21 Sandstone 2229.94 0.53 ~ 14.43 0.63 ~ 14.43 0.56 ~ 14.43 61.62 ~ 1415.50 Mudstone 56.9 3.69 ~ 9.58 7.44 ~ 19.03 7.29 ~ 19.05 18.64 ~ 47.63 Siliceous rock 29.86 0.03 ~ 0.06 0.03 ~ 0.08 0.03 ~ 0.05 0.03 ~ 0.10 Conglomerate 193.65 0.53 ~ 1.11 0.98 ~ 2.18 0.98 ~ 1.81 8.32 ~ 18.56 Slate 366.81 3.53 ~ 6.32 7.06 ~ 12.78 6.96 ~ 12.54 113.98 ~ 206.21 Phyllite 70.32 2.93 ~ 3.86 6.07 ~ 7.96 5.95 ~ 7.70 18.77 ~ 24.63 Table 4 The Gibbs free energy of weathering processes of partially silicate minerals and carbonate minerals at 25.0°C and 1 atm. Phase Equation ∆ \({\text{G}}_{\text{f}}^{0}\) (KJ·mol − 1 ) Reacting equation with carbon dioxide y i ∆G 0 (KJ·mol − 1 ) logK Albite NaAlSi 3 O 8 -3694.84 2NaAlSi 3 O 8 + 2H + +9H 2 O→2Na + +Al 2 Si 2 O 5 (OH) 4(s) + 4H 4 SiO 4(aq) 1.00 -46.79 8.20 k-feldspar KAlSi 3 O 8 -3737.24 2KAlSi 3 O 8 + 2H + +9H 2 O→2K + + Al 2 Si 2 O 5 (OH) 4(s) + 4H 4 SiO 4(aq) 1.00 -5.21 0.91 Anorthite CaAl 2 Si 2 O 8 -4000.15 CaAl 2 Si 2 O 8 + 2H + +H 2 O→Ca 2+ +Al 2 Si 2 O 5 (OH) 4(s) 2.00 -95.17 16.67 Muscovite KAl 3 Si 3 O 10 (OH) 2 -5567.68 2KAl 3 Si 3 O 10 (OH) 2 +2H + +3H 2 O→2K + +3Al 2 Si 2 O 5 (OH) 4(s) 1.00 -57.51 10.07 Biotite K 0.9 Mg 1.9 Fe 1.1 AlNa 0.1 Si 3 O 10 (OH) 2 -5303.96 K 0.9 Mg 1.9 Fe 1.1 AlNa 0.1 Si 3 O 10 (OH) 2 +4.8H + +0.5H 2 O→ 0.9K + +1.9Mg 2+ +0.1Na + +0.5Al 2 Si 2 O 5 (OH) 4(s) + 0.55Fe 2 SiO 4(s) + 1.45H 4 SiO 4(aq) 3.80 -282.92 49.56 Chlorite Mg 2 Fe 2 Al 2 Si 3.5 O 10 (OH) 8 -7206.03 Mg 2 Fe 2 Al 2 Si 3.5 O 10 (OH) 8 +4H + →2Mg 2+ +Al 2 Si 2 O 5 (OH) 4(s) + Fe 2 SiO 4(s) + 0.5H 4 SiO 4(aq) + 3H 2 0 4.00 -234.49 41.07 Illite KMg 0.25 Fe 0.3 Al 2.5 Si 3.1 O 10 (OH) 2 -5105.89 KMg 0.25 Fe 0.3 Al 2.5 Si 3.1 O 10 (OH) 2 +1.5H + +1.65H 2 O→ K + +0.25Mg 2+ +1.25Al 2 Si 4 O 10 (OH) 2(s) + 0.15Fe 2 SiO 4(s) + 0.45H 4 SiO 4(aq) 1.50 -449.30 78.70 Calcite CaCO 3 -1129.81 CaCO 3 + H + →Ca 2+ + \({\text{H}\text{C}\text{O}}_{3}^{-}\) 1.00 -10.59 1.85 Dolomite CaMg(CO 3 ) 2 -2171.37 CaMg(CO 3 ) 2 +2H + →Ca 2+ + Mg 2+ +2 \({\text{H}\text{C}\text{O}}_{3}^{-}\) 2.00 -11.98 2.10 Al 2 Si 2 O 5 (OH) 4(s) -3779.32 Fe 2 SiO 4(s) . -1379.00 H 4 SiO 4(aq) -1317.30 H 2 O (l) -237.29 \({\text{H}\text{C}\text{O}}_{3}^{-}\) -587.11 Na + -261.78 K + -283.39 Ca 2+ -553.29 Mg 2+ -455.84 Data from(Dean, 1999 ; Robie and Waldbaum, 1968 ). Table 3 shows that siliceous rock and gravel exhibit relatively low annual chemical weathering rates and CO 2 capture rates. This can be attributed to the mineral compositions of siliceous rock and gravel, which primarily consist of quartz and contain minimal amounts of other minerals, as supported by Table S3. Quartz serves as a protective mineral during chemical weathering, resulting in its low susceptibility to chemical weathering (Sverdrup and Warfvinge, 1988 ). The parent materials derived from igneous debris, granite, shale, and sandstone exhibit higher rates of chemical weathering and carbon capture due to their higher content of silicate and carbonate minerals. Carbonate minerals, in particular, have much higher rates of chemical weathering and CO 2 capture compared to silicate minerals. This explains why soil parent materials containing carbonate minerals exhibit higher rates of chemical weathering and carbon capture compared to other parent materials (Pokrovsky et al., 2005 ). Compared to the rates of weathering observed in surface soils of other regions, the findings of this study demonstrate significantly higher levels (Liang et al., 2023 ; Phelan et al., 2014 ). This disparity arises from the fact that prior studies tend to concentrate on surface soils, which experience more extensive weathering. In contrast, our study focuses on the parent rock, which could have relatively high weathering potential. In comparison with results obtained from estimating the CO 2 capture rate using the GEM-CO 2 model and water chemistry methods in Table 2 . This study yielded similar rates and fell into the same order of magnitude as other research. Nevertheless, several distinctions exist between our study and previous ones. CO 2 capture rates were calculated from a mineral perspective, considering factors such as climate conditions and parent materials properties. In c ontrast, GEM-CO 2 relies on an empirical equation derived from numerous experiments. Additionally, a comparison between the carbon capture rates of our basin and other basins using previously established water chemistry methods (Jiang et al., 2020 ; Li et al., 2014 ; Zhang et al., 2021 ). The CO 2 capture rate in this study was higher because some of the cations produced by mineral weathering using river water chemistry could be retained in the soil. In this study, the carbon capture rate is estimated directly from the mineral source, thereby reducing cations loss during the transfer process. Consequently, the estimated CO 2 capture rate in this study was higher. Comparing the results of CO 2 capture estimated using the PROFILE model with those estimated using alternative methods illustrates the viability of the PROFILE model for estimating carbon capture rates. 3.3.2 The influence of climate factors on CO 2 capture capacity of the parent rock weathering In 2020, weathering rates and CO 2 capture rates obtained through the PROFILE model and its derived model varied with variations in temperature, soil moisture, and p CO2 (Table S4 and Fig. 3 ). According to the temperature data provided by NOAA in 2020, the average temperature was 17.77°C in spring, 27.29°C in summer, 17.93°C in autumn, and 6.79°C in winter (January and February), with the lowest temperature 5.25°C in December. Precipitation also varied significantly across different seasons in the study area. Summer had the highest amount of rainfall, while spring and autumn remained at a moderate level of precipitation. Winter receives less rainfall and is characterized by a dry climate. At the seasonal scale, the highest weathering rates and carbon capture rates of parent rock occurred in summer, followed by spring and autumn, and lowest in winter. The weathering rates and carbon capture rate in the warmest month were about 1.7 times higher than those in the coldest month (Table S4). Temperature plays a significant role in influencing chemical reactions during rock weathering, as it affects the kinetics. A 4°C change could lead to a 30% change in weathering rates (Warfvinge and Sverdrup, 1992 ). Chemical weathering reactions exclusively take place on moist surfaces, with the level of surface moisture being directly related to soil or weathering crust saturation. Adequate soil solution is necessary for chemical weathering reactions to occur, and dry conditions hinder weathering processes (Sverdrup and Warfvinge, 1993 ). The data presented in Fig. 3 demonstrates that weathering rates and CO 2 capture rates in parent rocks are higher during the hot and humid summer climate, than in winter. Similarly, a comparison of climate change's effect on chemical weathering in forested areas of southern Sweden revealed that weathering rates were relatively higher during the hot and dry conditions of summer (Kronnäs et al., 2019 ). According to Figure S4, which showing that the weathering rate in summer is significantly higher than in winter. Areas of high value could be found in rock formations characterized by elevated levels of igneous rocks, feldspar, and carbonate minerals. The combination of the spatial distribution map of weathering indices provided in Figure S2 demonstrates a negative correlation, where regions with higher weathering rates correspond to lower weathering indices. The indices CIA and PIA primarily indicate the degree of transformation from feldspar to clay minerals, with higher values indicating lower content of feldspar and silicate minerals (Fedo et al., 1995 ; Nesbitt and Young, 1984 ). By using the PROFILE model for calculation, it can be concluded that when the content of feldspar minerals is higher in the samples, their weathering rates are also higher (Sverdrup and Warfvinge, 1993 ; Warfvinge and Sverdrup, 1992 ). Combining the spatial variations depicted in Figure S4 and Supplementary Figure S2 illustrates the distribution of chemical weathering processes in southern Anhui Province, indirectly reflecting the carbon sequestration capacity in different regions. Summarizing the above content, taking the subtropical climate of southern Anhui as an example. The high summer temperatures and abundant rainfall lead to enhanced chemical weathering of rocks, increasing the CO 2 capture capacity in the atmosphere, the opposite occurs in winter. Therefore, the summer climate in the subtropical region is more conducive to reducing the concentration of CO 2 in the atmosphere. 3.3.3 The influence of climate extremes on CO 2 capture capacity of the parent rock weathering The Intergovernmental Panel on Climate Change (IPCC) pointed out that extreme precipitation events (EPEs) and heatwaves (HWs) will have serious impacts on ecosystems and human societies in the future, raising particular concern for some continental countries (such as China) (Chen and Sun, 2015 ). Over the past few decades, China has experienced an increase in the frequency of EPEs and HWs (Bao et al., 2017 ; Luo and Lau, 2017 ; Yao et al., 2023 ). Among them, Huang-Huai-Hai river basin (including the Yellow River basin, Huaihe River basin and Haihe River basin) is one of the most vulnerable regions in China, highly sensitive to climate change (Wu et al., 2021 ). The study area is located in the subtropical region south of the Yangtze River in China, where sustained high temperatures and heavy rainfall are common during the summer. This study selected the months of June to August in 2020 to compare the rates of rock weathering and carbon capture under two scenarios of high temperatures and heavy precipitation, relative to the annual carbon capture rate (Fig. 4 ). Under scenario A, with high temperature (≥ 30°C) and no rain, the rates of chemical weathering of various types of rocks, carbon capture, and release of alkaline cations are approximately 18.25%, 21.33% and 20.40% higher than the annual average, respectively. Under scenario B, with heavy rainfall (≥ 25mm·day − 1 ) and a temperature of around 25°C, the rates of chemical weathering of various types of rocks, carbon capture, and release of alkaline cations are approximately 64.20%, 66.23% and 65.70% higher than the annual average, respectively. This shows that under extreme weather conditions, both high temperatures and heavy rainfall can accelerate the chemical weathering of rocks, allowing carbon dioxide in the atmosphere to be captured more rapidly, promoting the global carbon cycle process. Comparing the changes in carbon capture rates between scenarios A and B, it is found that the increase in rate under scenario A is much lower than that under scenario B. This indicates that temperature and precipitation play a decisive role in the rock weathering process, with acceleration of chemical weathering of rocks occurring only under conditions of high temperatures and sufficient rainfall, which effectively captures carbon dioxide from the atmosphere and reduces the impact of the greenhouse effect. Conversely, in cold and arid regions, carbon dioxide capture is less effective. Compared to traditional methods of predicting the capture of atmospheric carbon dioxide by rock chemical weathering under climate change, which rely solely on long-term average temperatures and precipitation for prediction (Xi et al., 2021 ). There hasn't been much research on the capture of atmospheric carbon dioxide by rock weathering under extreme climatic conditions. In the contemporary context of frequent extreme weather events, more attention should be given to predicting the carbon capture capacity of rock chemical weathering under these conditions. This will have a significant impact on controlling the carbon cycle in the atmosphere and mitigating the greenhouse effect. 3.3.4 Changes estimation of parent rock weathering and CO 2 capture from 1970 to 2100 Soil and rock weathering are crucial for global carbon sequestration. Climate change significantly impacts the process of chemical weathering, particularly through temperature variations (Kronnäs et al., 2019 ). In this study, climate data from the periods 1970–2020 and 2020–2100 were used to calculate chemical weathering rates, carbon sequestration rates, and cation leaching rates at different time intervals using weathering and carbon sink models (Fig. 5 ). Specifically, the climate change in study area between 1970 and 2020 was analyzed using data from NOAA and the IPCC. During this period, the temperature increased by1.35℃, the precipitation increased by 2.80%, and the pco 2 increased by 28.37%. From Figs. 5 a, b, and c, it can be seen that during the period from 1970 to 2020, the rates of rock weathering increased by 6.14%, carbon capture increased by 6.11%, and the rate of alkaline cation leaching increased by 6.07%. Over the past 50 years, the overall carbon capture capacity of rock chemical weathering has shown a gradual increasing trend, due to factors such as human activities leading to the gradual rise in atmospheric temperature, precipitation, and carbon dioxide concentration, enhancing the process of rock chemical weathering and gradually increasing the carbon capture rate. The climate (temperature, precipitation) and pco 2 changes under the four RCP models are shown in Table S3. Figures 5 d, e, and f provide predictions of the change in rock chemical weathering intensity from 2020 to 2100 under four RCP scenarios. In the RCP2.6 scenario, the carbon capture rate is projected to increase by approximately 4.68% by 2100, while in the RCP4.5 scenario, it is expected to grow by around 4.78%, in the RCP6.0 scenario by approximately 4.87%, and in the RCP8.0 scenario by roughly 5.09%. Under the RCP2.6 and RCP4.5 scenarios, the growth rate of carbon capture is projected to remain nearly constant after 2080, and there may even be a decreasing trend. In the RCP6.0 scenario, the carbon capture rate is expected to slowly increase after 2070, and then stabilize. Under the RCP8.0 scenario, the carbon capture rate is in a continuously increasing state. The trends of the estimated changes in rock carbon capture rates in this study are consistent with the climate change trends caused by different types of carbon emissions under various RCP scenarios(Hurtt et al., 2011 ; Masui et al., 2011 ; Riahi et al., 2011 ; Thomson et al., 2011 ; van Vuuren et al., 2011 ). Referring to the changing trends of carbon capture rates over the past 50 years and the next 80 years, it is evident that climate has a significant positive correlation with the rock weathering process. According to the RCP4.5 and RCP8.5 scenarios, the annual global carbon capture from rock weathering calculated using the GEM-CO 2 model is projected to increase by 25% and 31.25% respectively from 2050 to 2100(Xi et al., 2021 ). A study conducted by Akselsson utilized the PROFILE model to simulate the increase in weathering rate in Sweden during the 21st century due to climate change(Akselsson et al., 2016 ). The predicted median increase in weathering rates for alkaline cations by the ECHAM and HADLEY models was 20% and 33%, respectively. These values are approximately equivalent to 10%·℃ −1 , which aligns with the findings in this study. Based on the PROFILE model, the daily weathering rates and carbon capture rates were estimated for the periods 1970 ~ 2020 and 2020 ~ 2100 under the RCP4.5 scenario. Under the RCP4.5 scenario, it represents a climate change scenario with government intervention, which closely aligns with the future trend of climate change(Thomson et al., 2011 ). The Kriging interpolation method was used to establish the spatial variations of both factors (Fig. 6 ). It is evident from the figure that high values of weathering rate and carbon capture rate are predominantly found in the central and western parts of the study area. The formation of the high-value region in the northwest corner of the spatial variation map may be partially attributed to errors resulting from the use of kriging spatial interpolation, considering the distribution of sampling points shown in Fig. 1 . Additionally, other high-value areas are primarily influenced by the presence of igneous debris rock, granite, shale, and high-calcium sandstone. These areas have a high content of silicate minerals (feldspar, mica) and carbonate minerals (calcite, dolomite), which are considered active minerals according to the PROFILE model, indicating a strong weathering process in the region (Sverdrup and Warfvinge, 1993 ). The contour lines depicted in Fig. 6 a illustrate the average weathering rates for each year in the study area. These rates were calculated relative to the baseline of the average weathering rate in 2020, with abnormaly of -0.06, -0.03, 0.04, and 0.06 for the years 1970, 1990, 2060, and 2100, respectively. In conjunction with Fig. 5 , the weathering rate for different parent rock exhibits a gradual increase from 1970 to 2100, indicating an intensified weathering process at the same rock location within the region. Moreover, contour lines were generated to examine the spatial variations in carbon capture rates. These lines are based on the average value in 2020, facilitating analysis of area changes across different years (Fig. 6 b). The abnormal changes for the years 1970, 1990, 2060, and 2100 were − 0.08, -0.03, 0.07, and 0.08, respectively. Considering area abnormaly, CO 2 capture within the study area progressively will strengthen over the period of 130 years. Thus, the utilization of the PROFILE model can characterize the spatial-temporal change of weathering rates and CO 2 capture rates within the local area. Furthermore, the trends can be predicted in response to climate change and be applied in regions with limited river distribution, bypassing the constraints associated with chemical methods. Through the past and future changes in global climate, the concentration of carbon dioxide in the atmosphere is continuously increasing, leading to a continuous rise in global overall temperature, precipitation, and the enhanced chemical weathering process in the ecosystem. The ability to capture carbon dioxide is also increasing. Although it still cannot achieve the role of carbon cycle balance, the amount of carbon captured by chemical weathering is also very large, contributing to carbon neutrality. 3.3.5 The alterations in Gibbs free energy during CO 2 capture by weathering The chemical weathering reactions of rock can be understood as acid-base reactions, in which the primary mineral components in the parent rock react with acidity to produce secondary minerals. Sources of acidity primarily include CO 2 hydration and organic matter decomposition. The acid consumed during the weathering process of the parent rock mainly originates from CO 2 . The release of hydrogen ions from carbonic acid (formed by the reaction of CO 2 and water) can interact with silicate minerals or carbonate minerals, leading to the exchange of hydrogen ions with metal cations in the minerals. Consequently, primary minerals undergo further transformation into secondary minerals, typically clay minerals, with an increase in hydroxyl or water content. To investigate the thermodynamic processes, direction, and limitations of solid-liquid interactions in soil weathering at 25°C and standard atmospheric pressure, we evaluated the Gibbs free energy increment and chemical equilibrium constants (see appendix equations 12 and 13). Table 4 presents the carbon capture reaction equations, Gibbs free energy changes, and equilibrium constants of various silicate and carbonate minerals formed through weathering under standard conditions (25°C, 1 atm). For some silicate minerals and carbonate minerals, the Gibbs free energy changed during the reaction with hydrogen ions supplied by CO 2 in the carbon capture process ranges from − 5.21 to 449.30 kJ·mol − 1 and − 1059 to -11.98 kJ·mol − 1 , respectively. The equilibrium constants (logK) for these reactions range from 0.91 to 78.70 and 1.85 to 2.10, respectively. Table 4 displays negative reaction Gibbs free energy changes (∆G 0 < 0), indicating the occurrence of spontaneous and irreversible reactions in the carbon capture process under standard conditions. Most minerals exhibited a high conversion degree in the carbon capture process based on the equilibrium constant (logK). However, certain minerals show lower conversion degrees. In the weathering zone of potassic feldspar minerals, their higher stability results in a less autonomous carbon capture process. The ability of reactants to transition towards products varies depending on the stability of different minerals, which also implies discrepancies in the weathering rates of these minerals. According to the mineral content of various partially weathered rock types listed in Table S1 and the Gibbs free energy of different mineral types involved in the carbon capture process outlined in Table 4 , calculations were made to determine the Gibbs free energy changes of different soil parent materials during the carbon capture process (see Table S5). Under standard conditions, the average range of Gibbs free energy for the carbon capture process in different rock types is between − 7.62 and − 161.73 kJ·mol − 1 , with an average range of equilibrium constants (logK) from 1.33 to 28.33. The differences in mineral composition among rock types resulted in significant variations in the Gibbs free energy and equilibrium constants during the carbon capture process. Under standard conditions, the overall reaction tends to proceed spontaneously. The reaction processes of rocks containing high amounts of mica, chlorite, and illite, such as volcanic debris rocks and claystones, exhibit greater completeness. By comparing the carbon capture rates and equilibrium constants (logK) values of different rock types as shown in Figure S5. For example, mudstones and pyroclastic rocks have higher carbon capture rates, corresponding to logK averages of 23.33 and 28.33, while siliceous rocks and conglomerates have lower carbon capture rates with low logK of 1.33 and 2.27. The results demonstrate that a higher equilibrium constant (logK) corresponds to a higher carbon capture rate. This indicates that the weathering of rocks for CO 2 capture reactions is more thorough, highlighting the enhanced chemical weathering capacity of these rocks for CO 2 capture. From the perspective of thermodynamics, on the one hand, it shows that the process of capturing carbon dioxide by chemical weathering of various rocks in the standard state is carried out spontaneously. On the other, the carbon capture capacity by different rocks weatherng is positively correlated with the equilibrium constant (logK). 4. Conclusion The rocks in the southern Anhui primarily consist of quartz, silicate minerals, and carbonate minerals, with variations in mineral composition and proportions depending on the specific rock type. The chemical weathering indices, including CIA and PIA, range from 36.35 to 91.35 and from 54.13 to 97.92, respectively. These values indicate that the intensity of chemical weathering follows the order of sedimentary rocks > metamorphic rocks > igneous rock. The CO 2 capture rate of rock weathering calculated using the PROFILE model ranged from 0.03 to 19.03 mmol·m − 2 ·d − 1 in this study, which was comparable to results from the GEM-CO 2 model and water Chemistry methods (1.64 to 27.40 mmol·m − 2 ·d − 1 and 2.63 ~ 13.46 mmol·m − 2 ·d − 1 ). Climate factors exert a substantial influence on the capacity of rock weathering to sequester CO 2 . During the summer of 2020, the average temperature in the southern Anhui region was 21.02℃, accompanied by a 5-fold increase in precipitation than winter. These conditions led to a corresponding CO 2 capture rate in summer was 1.30 to 1.99 times higher than in winter approximately. Considering the frequent occurrence of extreme weather, the capturing rate of carbon dioxide by rock weathering under the condition of high temperatures (≥ 30°C) and heavy rainfall (≥ 25mm) has increased by about 21.33% and 66.23% respectively. From 1970 to 2020, the temperature increased by approximately 1.35°C, precipitation increased by approximately 2.80%, the partial pressure of pco 2 increased by approximately 28.37%, resulting in a rise in the carbon capture rate of about 6.11%. Nevertheless, the projected results under the four RCP models for the period from 2020 to 2100 show that the carbon capture rate increase due to rock weathering is approximately 4.68 to 5.09%. As a result, the projected rise in temperatures, precipitation levels, and atmospheric pco 2 will continue to amplify the role of rock weathering in sequestering CO 2 from the atmosphere. Through the examination of the thermodynamic properties of CO 2 capture reactions in various rock weathering processes, it was discovered that, under standard conditions, these reactions occur spontaneously. The average range of equilibrium constants (logK) fell between 1.33 and 28.33. Rocks that possess higher quantities of minerals like mica, chlorite, and illite exhibit superior potential for complete reactions, with higher values of logK could have high carbon capture potential. Revealed the thermodynamic mechanism of rock weathering carbon capture. Clearly, rock weathering makes significant contribution to the capture of CO 2 in the atmosphere. The exacerbation of global climate change has resulted in enhanced chemical weathering of rocks. The results indicate that climatic factors contribute to the increasing carbon capture capacity from natural rock weathering, further highlighting the important role of rock weathering in global carbon cycling. With the global trend of rising CO 2 levels, the greenhouse effect intensifies, leading to frequent extreme weather events such as high temperatures and heavy rainfall. These factors exacerbate the natural chemical weathering of rocks, enhancing CO 2 capture. This provides a baseline reference for predicting future global CO 2 concentration changes and carbon cycling. Furthermore, it supplements reference values for natural effects in the formulation of global energy-saving and emission reduction policies. Description The total word count of this research manuscript, including the main text and references, is approximately 9840 words. The main text is about 8124 words, and the references are about 1718 words. Declarations Ethics declarations: Consent to participate: All subjects gave their consent to participate in the study. Consent for publication: All subjects gave their consent to the publication of the data. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Clarification: No human participants or human data were involved during the study Author contributions: Wenpu Liu: Samples collection, Experiment, Data analysis, Writing - original draft, Formal analysis, Investigation. Yinxian Song: Experiment, Investigation, Conceptualization, Supervision, Methodology, Validation, Writing- Reviewing and Editing. Xianqiang Meng: Methodology, Investigation, Validation, Writing-Editing. Zhong Chen: Methodology, Investigation, Validation. He Chang: Validation, Software. Shitao Zhang: Methodology, Investigation, Validation, Software. Chunjun Tao: Samples collection, Experiment, Investigation, Conceptualization, Supervision. Junfeng Ji: Investigation, Conceptualization, Supervision, Project administration. Shilei Li: Investigation, Conceptualization, Writing-Reviewing. Quan Chen: Methodology, Investigation, Data analysis, Validation, Software. Godwin A. Ayoko: Methodology, Writing, Language editing. Ray L. Frost: Data analysis, Language editing. Funding: This work was supported by grants from Yunnan Fundamental Research Projects (grant NO. 202301AT070428); the National Key R&D Program of China (grant number 2023YFC3709100); Key Research and Development Project of the Ningxia Hui Autonomous Region, China (Grant No. 2022BEG03054). Availability of data and materials: The climate (temperature, precipitation) data for the Wannan region of subtropical China from 1970 to 2020 is sourced from the National Oceanic and Atmospheric Administration (NOAA) (https://ncei.noaa.gov/maps/daily/). The climate (temperature, precipitation) data for the period 2020 to 2100 under four RCP models is downloaded from the World Meteorological Organization (WMO) (https://climexp.knmi.nl/start.cgi). The data on the increase in carbon dioxide partial pressure from 1970 to 2100 is obtained from the Intergovernmental Panel on Climate Change (IPCC) (https://ipcc-data.org/observ/ddc_co 2 .html). The PROFILE model used in this study is based on the publications by Sverdrup et al. (1993) and Warfvinge et al. (1992). The Gibbs free energy data for various minerals at standard conditions (at 25.0°C and 1 atm) are obtained from the publications by Dvonch et al. (1999) and Robie et al. (1968). Samples were collected from the Wannan region of subtropical China, and the sample test data were obtained through testing in a standard laboratory according to prescribed procedures. The ThinkPad notebook used for processing and evaluating the data and for creating the charts for this study can be obtained at (https://www.thinkpad.com/). References Akselsson, C., Olsson, J., Belyazid, S., Capell, R., 2016. Can increased weathering rates due to future warming compensate for base cation losses following whole-tree harvesting in spruce forests? Biogeochemistry, 128: 89–105. Allen, M., Mustafa, B., Shukla, P., 2018. 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Newly Discovered Tuff in the Lower Shaximiao Formation in Yunyang, Chongqing, Southwestern China and Its Constraint on the Burial Age of the Yunyang Dinosaur Fauna. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3961192","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275546332,"identity":"fd2eb581-82cb-45b8-9567-d6df89446f11","order_by":0,"name":"Wenpu Liu","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenpu","middleName":"","lastName":"Liu","suffix":""},{"id":275546333,"identity":"77515484-2c84-4754-bafc-dfbf7ee79171","order_by":1,"name":"Yinxian Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYNACAwYGNgYGxgdAJg8fKVqYDUBa2Eixi00CTBJSJt/ee/g1T8GdxD72s8cqv+bYybAxMD98dAOfk86cS7PmMXhmzMaTl3Zbdlsy0GFsxsY5+LRI5JgZ8xgclmNjyDG7LbmNGaiFh00anxb5GRAtPGz8b8yKJbfVE9bCcCPH+DHYFqB1jB+3HSasxeDMGTPGOQaHjdkk3hhLM247zsPGTMAv8u09xh/e/DmcOL8/x/Djz23V9vzszQ8f43UYMCKkeKAsZjCDGb9ysJKPP6Asxh94FY6CUTAKRsFIBQBUhj/D4QLFKwAAAABJRU5ErkJggg==","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yinxian","middleName":"","lastName":"Song","suffix":""},{"id":275546334,"identity":"72bcbfaa-c4b2-4674-86b4-4822566a981c","order_by":2,"name":"Xianqiang Men","email":"","orcid":"","institution":"Nanjing Institute of Geography and Limnology","correspondingAuthor":false,"prefix":"","firstName":"Xianqiang","middleName":"","lastName":"Men","suffix":""},{"id":275546335,"identity":"568e3a59-0900-44c3-b952-162e2280ec55","order_by":3,"name":"Zhong Chen","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Chen","suffix":""},{"id":275546336,"identity":"87d5f8ac-32ed-4837-8528-f77edda4d1a2","order_by":4,"name":"He Chang","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Chang","suffix":""},{"id":275546337,"identity":"3a9ebae7-f134-4514-92f1-0d2c6cf8b1e9","order_by":5,"name":"Shitao Zhang","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shitao","middleName":"","lastName":"Zhang","suffix":""},{"id":275546338,"identity":"332d1b43-9a02-48f5-a517-fd8d45c99b96","order_by":6,"name":"Chunjun Tao","email":"","orcid":"","institution":"Geological Survey Institute of Anhui Province","correspondingAuthor":false,"prefix":"","firstName":"Chunjun","middleName":"","lastName":"Tao","suffix":""},{"id":275546339,"identity":"78aa0544-d4d6-4301-a31a-3f3b761a818e","order_by":7,"name":"Junfeng Ji","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Ji","suffix":""},{"id":275546340,"identity":"e5185206-fcbd-4774-8b71-96b2f7ca04bc","order_by":8,"name":"Shilei Li","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Shilei","middleName":"","lastName":"Li","suffix":""},{"id":275546341,"identity":"7c379de0-9941-46a6-80c0-7fd0e4460376","order_by":9,"name":"Quan Chen","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Chen","suffix":""},{"id":275546342,"identity":"e9425b93-17aa-400e-aa07-da5fb23d700b","order_by":10,"name":"Godwin A. Ayoko","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Godwin","middleName":"A.","lastName":"Ayoko","suffix":""},{"id":275546343,"identity":"9fae9639-c419-46e2-b231-2f655b743d7c","order_by":11,"name":"Ray L. Frost","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ray","middleName":"L.","lastName":"Frost","suffix":""}],"badges":[],"createdAt":"2024-02-16 12:20:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3961192/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3961192/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51973766,"identity":"f5a5300a-1f14-4f72-8c2e-0996d622e370","added_by":"auto","created_at":"2024-03-04 19:03:23","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331982,"visible":true,"origin":"","legend":"\u003cp\u003eThe sampling locations in the study area in southern Anhui. Provided by the National Platform for Common Geospatial Information Services(https://www.tianditu.gov.cn/).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/08793a205f70a36fce167077.jpeg"},{"id":51973769,"identity":"95da68b6-047f-43c4-a1c9-ec21f796415e","added_by":"auto","created_at":"2024-03-04 19:03:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":421237,"visible":true,"origin":"","legend":"\u003cp\u003eXRD pattern analysis from representative rock samples in the study area.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/9ed3cfd5080c15d59ff72178.jpeg"},{"id":51973770,"identity":"0f76af96-5dc5-417f-b0d1-46c74117565f","added_by":"auto","created_at":"2024-03-04 19:03:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":722593,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in chemical weathering rate, CO\u003csub\u003e2\u003c/sub\u003e capture rate and base cations (BC) releasing rates of various rocks in 2020, where the ordinate, R\u003csub\u003ew\u003c/sub\u003e, represents the rate of chemical weathering; F\u003csub\u003ew\u003c/sub\u003e represents the rate of carbon capture from rock weathering; R\u003csub\u003eBC\u003c/sub\u003e indicates the rate of release of salt-based cations, and the unit is (mmol·m\u003csup\u003e-2\u003c/sup\u003e·day\u003csup\u003e-1\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/0d684acec2a7503021f189e6.jpeg"},{"id":51973768,"identity":"9f60464a-7e4a-4a80-a801-b75a9eca8403","added_by":"auto","created_at":"2024-03-04 19:03:23","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":253010,"visible":true,"origin":"","legend":"\u003cp\u003eBased on the percentage of chemical weathering rate (R\u003csub\u003ew\u003c/sub\u003e), carbon capture rate (F\u003csub\u003ew\u003c/sub\u003e) and salt-based cation release rate (R\u003csub\u003eBC\u003c/sub\u003e) of various types of rocks relative to the annual average rate under the both extreme climate (high temperature and heavy precipitation) modes in southern Anhui in June to August 2020. Scenario A is high temperature, and scenario B is heavy precipitation, where pr-pyroclastic rock, gr=granite, sa=sandstone, mu=mudstone, sr=siliceous rock, co=conglomerate, sl=slate, ph=phyllite.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/f6932bbae1269defc78b00d7.jpeg"},{"id":51973772,"identity":"3d4dc349-a136-42fa-94ec-b06ac812c469","added_by":"auto","created_at":"2024-03-04 19:03:24","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":478519,"visible":true,"origin":"","legend":"\u003cp\u003eEstimate the yearly variations in the weathering rate, CO\u003csub\u003e2\u003c/sub\u003e uptake rate, and cation release rate of rocks in the southern Anhui region between 1970 and 2020 and between 2020 and 2100. Variables a, b, and c are based on IPCC and NOAA meteorological data, and variables d, e, and f are based on various types of RCP data and IPCC meteorological data.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/c28d106fb05875a0a13c6ddf.jpeg"},{"id":51973771,"identity":"3cb42588-7db0-4013-b663-99ce29d303f9","added_by":"auto","created_at":"2024-03-04 19:03:24","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":512408,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution and anomaly of rock weathering rate (mmol·m\u003csup\u003e-2\u003c/sup\u003e·d\u003csup\u003e-1\u003c/sup\u003e) and CO\u003csub\u003e2\u003c/sub\u003e capture rate (mmol·m\u003csup\u003e-2\u003c/sup\u003e·d\u003csup\u003e-1\u003c/sup\u003e) from 1970 to 2100. From left to right, the spatial variation distribution of chemical weathering rate (R\u003csub\u003ew\u003c/sub\u003e) and carbon capture rate (F\u003csub\u003ew\u003c/sub\u003e) in 1970, 1990, 2020, 2060 and 2100 is shown in legend color to indicate the range of weathering rate and carbon capture rate value in (mmol·m\u003csup\u003e-2\u003c/sup\u003e·day\u003csup\u003e-1\u003c/sup\u003e), a and b in the figure represent anomaly based on RW and FW changes in 2020, respectively\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/cc6b391613a8aa518f0ce596.jpeg"},{"id":69057846,"identity":"773e7162-c72a-4558-be3d-c73c90e49b32","added_by":"auto","created_at":"2024-11-15 06:53:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3968948,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/fa0fd65a-7b5c-4ff6-89c2-5a209de28476.pdf"},{"id":51973767,"identity":"ce01196c-7c86-42eb-bbce-873dc5d5c07f","added_by":"auto","created_at":"2024-03-04 19:03:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2313295,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3961192/v1/6cad2a39bda0da0f515849a0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatio-temporal response and projection of CO2 capture rates by different rock weathering to climate change in subtropics in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHuman activities have contributed to a significant rise in atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration over last the hundred years, garnering widespread concern over the greenhouse effect and global climate disasters (Hughen et al., 2004). Consequently, studying carbon cycle processes haves emerged as a prominent subject to retard atmospheric CO\u003csub\u003e2\u003c/sub\u003e rise (Qiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Presently, a major concern in global carbon cycle is focused on how to capture CO\u003csub\u003e2\u003c/sub\u003e by both artificial and natural processes to offset anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions, achieving carbon neutrality (Kennedy, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrowing studies have demonstrated the utilization of carbon capture and storage (CCS) technologies, including both artificial and natural methods, to mitigate the ongoing enhancement of the greenhouse effect(Dziejarski et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, artificial CCS processes are expensive and possess limited capacity to capture carbon. In contrast, the atmosphere, ocean, and terrestrial ecosystems in natural processes, which represent three significant reservoirs of carbon, have a large potential to sequester carbon (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The stability of ecosystems' carbon capture is compromised by the reduction in global forest coverage and the impact of natural disasters. Moreover, these processes are an unstable carbon sink, which has a risk of re-releasing carbon.\u003c/p\u003e \u003cp\u003eConversely, the rock-soil system in natural processes serves as an exceptionally stable carbon sink. The rock-soil system constitutes one of Earth's primary natural carbon reservoirs, with carbon content approximately ten thousand times greater than that found in the exogenous system, encompassing the atmosphere, ocean, biosphere, and shallow sediments (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, chemical weathering and organic carbon burial serve as significant carbon captures within the global carbon cycle (Gaillardet et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).Unveiling the influence of chemical weathering on the carbon cycle and accurately quantifying the CO\u003csub\u003e2\u003c/sub\u003e absorption flux via mineral weathering in rocks and soils are crucial undertakings in elucidating long-term carbon cycling and climate change mechanisms (Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rock-soil sphere is an open system that continuously interacts with the atmosphere, hydrosphere, and biosphere through exchange processes. Previous studies have employed various methods, including kinetic methods, dissolution measurement methods, and water chemistry methods, to assess the carbon sequestration capacity of rock-soil weathering (Qiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The water chemistry method has been used to determine the total concentration of ions in rivers by measuring the solute concentration at both the source and outlet of the watershed, along with considering the flow rate and discharge. By taking into account the distribution of rocks within the watershed, researchers can deduce the weathering rates of different rock types and estimate their capacities (Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The GEM-CO\u003csub\u003e2\u003c/sub\u003e model, utilizes water flow or runoff as a benchmark to deduce the correlation between weathering rates of various rock types and water flow rates (Amiotte Suchet and Probst, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Several studies have reported that global rock weathering plays a significant role in the overall understanding of this process, contributing to approximately 87% of carbon dioxide consumption (Gaillardet et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). When investigating rock weathering s, researchers estimate the amount of carbon dioxide absorbed through rock weathering using either established empirical equations from previous studies or by measuring the ion flux in watershed rivers (Dreybrodt et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Noh et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Qiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, existing methods for calculating weathering rates and carbon capture rates in watersheds are limited. For example, the GEM-CO\u003csub\u003e2\u003c/sub\u003e model uses empirical methods to estimate the carbon capture rates in different watersheds based on years of average precipitation data. However, it cannot assess the carbon capture capacity under seasonal and extreme climatic conditions, nor can it provide accurate estimates. The most significant issue with the ion balance approach used in the water chemistry method is that during the process of rock and/or soil weathering, the released ions are retained to some extent in the soil and do not entirely follow the rainfall or groundwater into rivers. This makes it difficult to estimate carbon capture capacity in areas with limited river distribution. To achieve carbon neutrality in the mid-21st century, predicting the future carbon capture capacity of the rock-soil system is very important, especially in the context of future global changes. The Intergovernmental Panel on Climate Change (IPCC) projects a global temperature increase of 1.5\u0026deg;C, as well as a 30% rise in precipitation in China (Allen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Collins and Knutti, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). With forecasted changes in future climate, factors such as temperature and precipitation are expected to increase. These changes will strongly impact the natural process of rock weathering, intensifying the ability of rock chemical weathering to capture atmospheric carbon dioxide. Hence, evaluating the variations in rock carbon capture rates on a long timescale becomes particularly important.\u003c/p\u003e \u003cp\u003eThis study quantitatively estimates the carbon capture rate in the subtropical watershed of southern Anhui, China based on the PROFILE model (Huang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Warfvinge and Sverdrup, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). In order to understand the global trend of rising CO\u003csub\u003e2\u003c/sub\u003e levels, the impact of climate factors on the ability of rock chemical weathering to capture CO\u003csub\u003e2\u003c/sub\u003e, and to provide effective recommendations for future global greenhouse effect management. The main objectives of this paper are as follows: 1. Quantitatively estimate the carbon capture rates of various types of rocks based on the PROFILE model. 2. Compare this method with traditional methods for estimating carbon capture rates to verify its reliability. 3. Analyze the capturing of atmospheric carbon dioxide by rock chemical weathering under different seasonal and extreme climates. 4. Based on climate data from NOAA and IPCC between 1970\u0026ndash;2020, and the Hurst exponent and Representative Concentration Pathway (RCP) datasets between 2021\u0026thinsp;~\u0026thinsp;2100, estimate the changes in carbon capture caused by rock chemical weathering under past and future climate change conditions. 5. Investigate the relationship between carbon capture rates and the Gibbs free energy and equilibrium constant (logK) during the carbon capture reaction process in rock weathering.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThis study was carried out in the southern Anhui region of subtropical China, approximately between 118\u0026deg;30\u0026prime;~119\u0026deg;40\u0026prime;E longitude and 29\u0026deg;70\u0026prime;~31\u0026deg;30\u0026prime;N latitude, characterized by a landscape dominated by hills and mountains, gradually increasing in elevation from north to south (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The annual average temperature in the study area is approximately 15.5℃, with an average annual precipitation of 1498mm. (Huang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The southern Anhui region lies in the transitional zone between the northern subtropical and central subtropical monsoon climates. It is a climate-sensitive area and the degree of weathering activity is comparable to the global average level, encompassing a diverse range of rock types and intricate geological formations. Consequently, this study collected a range of rock samples from southern Anhui, encompassing igneous rocks, sedimentary rocks, and metamorphic rocks. Additionally, water samples were obtained from the Qingyi River and Huishui River.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Samples collection and chemical analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the natural environment of southern Anhui province, a total of 347 rock samples in varying degrees of weathering were collected. These samples were obtained from the entire region of Xuancheng and the eastern part of Huangshan. The collected samples comprised 127 igneous rock samples (including igneous clastic rocks and granite), 187 sedimentary rock samples (including sandstone, mudstone, siliceous rock, and conglomerate), and 32 metamorphic rock samples (including slate and phyllite) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To mitigate the impact of external contamination during field sampling, the surface gravel was initially cleared using a shovel prior to collecting the partially weathered rock samples from deeper layers. Subsequently, these samples were carefully packed in geologic sample bags and appropriately labeled and numbered. Additionally, a total of 16 water samples were gathered from the Qingyi River and its tributary, the Huishui River, located in southern Anhui province.\u003c/p\u003e \u003cp\u003eUpon collection, the partially weathered rock samples underwent initial crushing to eliminate any remaining debris. Subsequently, the samples were dried in an oven at a temperature of 105\u0026deg;C. Next, the samples were ground and sifted through a 200-mesh nylon screen. The major elements (Si, Ti, Al, Fe, Mg, Ca, Na, K) and trace elements in each sample were quantified using a PANalytical Axios Max X-ray fluorescence spectrometer (XRF) and a high-resolution inductively coupled plasma mass spectrometer (HR-ICP-MS, ELEMENT XR). The mineral composition of the rocks was examined using an ARL9800-XP X-ray diffractometer. X-ray diffraction (XRD) analysis of minerals is often conducted based on the characteristics of diffraction peaks, which include position, intensity, shape, and width. The relative standard deviation (RSD) for the analysis is maintained below 5%.\u003c/p\u003e \u003cp\u003eThe river water samples were filtered using a 0.22 \u0026micro;m cellulose acetate membrane and subsequently stored in clean polyethylene containers. Water pH and conductivity were measured on-site using a portable water quality parameter meter. The water alkalinity data were determined within 24 hours through acid titration using hydrochloric acid. Cation and dissolved SiO\u003csub\u003e2\u003c/sub\u003e concentrations in the water were analyzed using an inductively coupled plasma spectrometer (ICAP 6300 DUO), while anion concentrations were measured using an ion chromatograph (Dionex ICS-1100)\u003c/p\u003e \u003cp\u003eTo ensure result reliability, quality assurance and quality control (QA/QC) procedures were rigorously followed during sample processing and testing. The testing procedures adhered to national material standards (SARM-3, SARM-23HE, SARM-45), with an analysis accuracy surpassing 0.5% ~1.0%, with SD\u0026thinsp;\u0026lt;\u0026thinsp;10%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eConsidering the similarity of elements and minerals composition between rocks and parent material, the estimation of rock weathering was conducted using parameters from the soil parent material. The parameters for parent material, such as pH, bulk density, particle size distribution etc., were collected from the Chinese Soil Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vdb3.soil.csdb.cn/\u003c/span\u003e\u003cspan address=\"http://vdb3.soil.csdb.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The soil moisture content was estimated using local precipitation data. The climate data (temperature and precipitation) from 1970 to 2020 is sourced from the National Oceanic and Atmospheric Administration (NOAA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ncei.noaa.gov/maps/daily/\u003c/span\u003e\u003cspan address=\"https://ncei.noaa.gov/maps/daily/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the RCP data (temperature and precipitation) from 2021 to 2100 is sourced from the World Meteorological Organization (WMO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climexp.knmi.nl/start.cgi\u003c/span\u003e\u003cspan address=\"https://climexp.knmi.nl/start.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Atmospheric CO\u003csub\u003e2\u003c/sub\u003e partial pressure (\u003cem\u003epCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) data for 1970 to 2100 was acquired from the Intergovernmental Panel on Climate Change (IPCC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ipcc-data.org/observ/ddc_CO\u003c/span\u003e\u003cspan address=\"https://ipcc-data.org/observ/ddc_CO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csub\u003e2\u003c/sub\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.html).Th\u003c/span\u003e\u003cspan address=\"http://.html).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee range of these parameters can be found in Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data model description\u003c/h2\u003e \u003cp\u003eThe correlation between the weathering process of primary minerals and the carbon storage capacity in soil has not been extensively examined (Slessarev et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, the PROFILE model was employed to estimate the weathering processes of diverse parent rocks and the rates of carbon capture in the southern Anhui region. The PROFILE model calculates the cumulative weathering rates of different minerals in their natural soil composition (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The rates at which minerals in the soil parent material weather are utilized to assess carbon capture rates. The PROFILE model can be described as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\varvec{R}\\varvec{w}=\\sum _{\\varvec{i}=1}^{\\varvec{m}\\varvec{i}\\varvec{n}\\varvec{e}\\varvec{r}\\varvec{a}\\varvec{l}\\varvec{s}}{\\varvec{r}}_{\\varvec{i}}\\bullet {\\varvec{x}}_{\\varvec{i}}\\bullet {\\varvec{A}}_{\\varvec{w}}\\bullet \\varvec{Z}\\bullet \\varvec{\\theta }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe total weathering rate of rocks is represented by Rw (mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while the rate of release of alkaline cations by minerals is represented by r\u003csub\u003ei\u003c/sub\u003e (kmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e); x\u003csub\u003ei\u003c/sub\u003e represents the relative amount of minerals, while Aw denotes the surface area of minerals (m\u003csup\u003e2\u003c/sup\u003e\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Stendahl et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); The variable Z represents the thickness of the soil layer (m), while θ represents the percentage of soil water saturation (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Warfvinge and Sverdrup, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) (Table S2).\u003c/p\u003e \u003cp\u003eThe carbon capture rate generated during the weathering process of parent material can be derived by considering the mechanisms in carbonate and silicate mineral weathering processes, as well as the chemical weathering rate Rw, which is calculated using the PROFILE model:\u003c/p\u003e \u003cp\u003e \u003cb\u003eFw\u0026thinsp;=\u0026thinsp;Rw\u0026middot;y\u003c/b\u003e \u003csub\u003e \u003cb\u003ei\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e(2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe variable Fw represents the overall rate at which rocks consume CO\u003csub\u003e2\u003c/sub\u003e (mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;a\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); The coefficient y\u003csub\u003ei\u003c/sub\u003e represents the efficiency of mineral weathering in absorbing CO\u003csub\u003e2\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen calculating the rate of mineral decomposition (r\u003csub\u003ei\u003c/sub\u003e), the reaction rate constant is standardized using the Arrhenius relationship derived from laboratory studies at 8℃, and then adjusted according to the ambient temperature (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\varvec{ln}\\left(\\frac{{\\varvec{k}}_{\\varvec{T}}}{{\\varvec{k}}_{8\\varvec{℃}}}\\right)=\\frac{{\\varvec{E}}_{\\varvec{A}}}{\\varvec{R}}\\left(\\frac{1}{281}-\\frac{1}{\\varvec{T}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe environmental temperature is denoted by T (K), the rate constant of the reaction is represented by k, the activation energy is denoted by E\u003csub\u003eA\u003c/sub\u003e (kJ\u0026middot;kmol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and the universal gas constant is denoted by R (kJ\u0026middot;kmol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cp\u003eBasic data processing and analysis were performed using the SPSS software package (SPSS Inc. Version 26, 2019). Cartography and spatial analysis were performed using Origin (Origin Pro Inc. Version 9.1, 2020) and SURFER software (Golden Software, Inc. Version 21, 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":" \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Geochemical characteristics of rocks\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Major elements distribution in various rocks\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe average compositions of major elements in various types of weathered rocks in the southern Anhui region are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. When compared to the upper continental crust (UCC) and the Australian post-Archean shale (PASS). The abundances of Ca, Na, and Mg in these rocks are significantly lower, indicating severe feldspar weathering. Additionally, siliceous rocks have higher Si content (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The elemental content of slate and quartzite in the metamorphic rocks is similar. The Ca element is significantly lower than the UCC and PAAS values. The presence of plagioclase and carbonate minerals in the rocks suggests a moderate degree of weathering, possibly due to weathering. Igneous rocks have lower Mg and Ca contents compared to PAAS and UCC, likely because of the low content of white-colored minerals in these rocks, which is similar to the geochemical composition of granites in the southern Anhui region (Weng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The leaching and enrichment of most elements between rock types reflect their respective degrees of weathering (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Major elements and weathering indicators of different weathered rocks from southern Anhui.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLithology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c13\" namest=\"c4\"\u003e \u003cp\u003eMajor elements (wt%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eWeathering indicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAl\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTFe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMgO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCaO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNa\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eK\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eCIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePIA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyroclastic rock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u0026thinsp;~\u0026thinsp;461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e54.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e56.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u0026thinsp;~\u0026thinsp;681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e97.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e48.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e47.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u0026thinsp;~\u0026thinsp;470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e96.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e69.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e79.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMudstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u0026thinsp;~\u0026thinsp;478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e74.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e87.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiliceous rock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174\u0026thinsp;~\u0026thinsp;691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e94.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e75.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e83.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConglomerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u0026thinsp;~\u0026thinsp;181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e94.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e74.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e85.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293\u0026thinsp;~\u0026thinsp;604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e96.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e61.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e69.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhyllite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u0026thinsp;~\u0026thinsp;228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e96.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e61.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e67.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e99.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e52.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e53.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e98.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e70.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e79.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eThe elements contents of each type of weathered rock above are displayed as mean values; UCC data were collected from(Rudnick, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e);PAAS data were collected from(Taylor and McLennan, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the spatial distribution of whole rock major elements (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), SiO\u003csub\u003e2\u003c/sub\u003e predominates the rock composition across the study area. Regions with higher Si content are characterized by the presence of conglomerates, siliceous rocks, and sandstones. The spatial distribution of TiO\u003csub\u003e2\u003c/sub\u003e, Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, and MgO exhibits similar patterns, and the variation in element values aligns with the transformation rules of different rock types, revealing the diagenetic processes in the study area, with regions of high content primarily influenced by igneous rocks. Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e and K\u003csub\u003e2\u003c/sub\u003eO demonstrate a highly similar spatial distribution, and the highest value of Na\u003csub\u003e2\u003c/sub\u003eO also corresponds to that of Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e. Based on the geological map of Anhui Province and sample points, areas with high values of these elements mainly consist of igneous rocks with abundant silicate minerals (such as feldspar and mica), low weathering degree, and notable element leaching. The content of CaO may be attributed to the presence of calcite, dolomite, and some feldspars during the diagenetic process. Due to the complex geological conditions in southern Anhui, some sandstones contain a significant amount of limestone components, resulting in a relatively higher calcite content in the central region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Mineral composition in different rocks\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo examine the mineral compositions of various weathered rock types, 27 representative samples covering most of the rock types were selected from a total of 367 samples for XRD analysis. The XRD results indicate that the primary minerals in different rock types in the southern Anhui region are quartz, feldspar, mica, calcite, and dolomite, along with secondary minerals like illite, chlorite, and kaolinite (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S3). Pyroclastic rock exhibit higher illite content due to the presence of igneous ashes, which typically transform into clay minerals (such as illite) during depositional diagenesis (Zhou et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The mineral compositions of igneouslastic rocks in the Xiashaxi Formation in Yunyang, Chongqing, primarily consist of quartz, feldspar, and illite (Zhou et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), similar to this study. The lower quartz content in granite can be attributed to the prevalence of syenite, which contains higher levels of feldspar minerals (Nesbitt et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The content of illite and feldspar is relatively high in igneous rocks, consistent with the spatial distributions of Al and K elements (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the region where igneous rocks are distributed, the Al and K elemental content is higher.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Table S3, the sandstone samples SA-1 and SA-2 have a higher content of calcite. This is due to the complex stratigraphy in the southern Anhui region, where the sandstones in the central part of the study area are intermixed with limestone bodies, resulting in an increased content of calcite. Therefore, the Ca element in the central area of the study region will be partially elevated, as depicted in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In sedimentary environments, mudstones undergo significant weathering, resulting in quartz and clay minerals (illite, kaolinite, etc.) being the predominant components, and clay mineral content exceeding 40%, similar to mudstones in the southern North China region (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Siliceous rocks showcase a SiO\u003csub\u003e2\u003c/sub\u003e content of approximately 90%, primarily composed of quartz mineral (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conglomerate consists primarily of crystalline quartz as large clastic particles, accompanied by some feldspar and clay minerals. The sandstone, siliceous rock and conglomerate contain a high amount of quartz, mainly distributed in the central area of the study region. As a result, the Si element content is high, consistent with the variation pattern of SiO\u003csub\u003e2\u003c/sub\u003e in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The high content of illite and dolomite in the shale leads to a high Mg element in the central part of the study area (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mineral composition of slate and phyllite is similar, both are formed through metamorphism and are mainly composed of quartz, feldspar, and mica, with similar contents. Dolomite minerals represent the carbonate component. Slate in the Barahulish area of Scotland exhibits a feldspar content of approximately 5%, whereas the slate in this study area demonstrates a higher feldspar content (Walsh, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The mineral composition of phyllite in this study is akin to that of phyllite in the Opawskie Mountains in southwestern Poland, primarily comprised of quartz, feldspar, and ferromagnesian mica, with the presence of clay minerals and dolomite minerals, potentially related to calcite veins (Sawicka et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Chemical weathering intensity and CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e capture rate\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Chemical weathering index\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe Chemical Index of Alteration (CIA) and Plagioclase Index of Alteration (PIA) serve as indicators of the chemical weathering degree in watersheds, determining the chemical weathering degree of various rocks based on the degree of feldspar weathering, with their respective formulas provided in appendix equations \u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2(Fedo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Nesbitt and Young, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).Through the application of mass balance principles, thermodynamic calculations of mineral stability, and experiments involving feldspar leaching, the process of chemical weathering in the upper crustal rocks was inferred. This led to the development of ternary diagrams, specifically the A-CN-K and (A-K)-C-N diagrams (Nesbitt and Young, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Selvaraj and Chen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the average CIA and PIA values for different types of partially weathered rocks, demonstrating an overall CIA range of 36.3 to 91.3 and a PIA range of 54.13 to 97.92. These wide ranges are influenced by the varying lithologies. Different rocks and minerals exhibit diverse CIA and PIA values. For instance, feldspar is more susceptible to weathering, resulting in lower CIA and PIA values around 50. Conversely, clay minerals tend to possess higher values, indicating more pronounced weathering potential. The CIA and PIA values were calculated to assess the spatial distribution of the degree of weathering in the southern Anhui region (Figure S2) and were depicted in ternary diagrams (Figure S3). The spatial distribution patterns of CIA and PIA exhibit a high degree of similarity, with higher indices observed in the central and southwestern regions. These areas are characterized by extensive distribution of sedimentary rocks, primarily composed of clay minerals according to the mineral percentage content provided in Table S3. This finding suggests a greater degree of weathering in these regions. Additionally, higher CIA and PIA values indicated a more significant loss of active elements such as K, Na, and Ca from silicates (Nesbitt and Young, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Nesbitt and Young, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The diversity of weathering degrees in the study area is evident in the variation of weathering indices across different rock and mineral types. Based on the ternary diagrams (Figure S3), igneous rocks exhibit weak chemical weathering, characterized by slight leaching of Ca and Na. Sedimentary rocks, on the other hand, primarily demonstrate moderate to strong chemical weathering, evidenced by the increasing prevalence of clay minerals. Metamorphic rocks generally display a moderate level of chemical weathering, following the conventional continental weathering trend from the upper continental crust (UCC) towards the Proterozoic Australian shale standard (PASS) (Wu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study area exhibits varying weathering indices due to the higher content of feldspar in igneous rocks, lower indices in sedimentary rocks, and intermediate rankings in metamorphic rocks. These findings aligned with previous studies that have examined weathering indices across different rock types (Cheng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Selvaraj and Chen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 CO\u003c/b\u003e\u003csub\u003e2\u003c/b\u003e\u003c/sub\u003ecapture through weathering estimated by GEM-CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e model and water chemistry method.\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe chemical weathering of soil or rocks has been assumed to play a pivotal role as a significant component in the geological carbon cycle (Torres et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The majority of carbon within the terrestrial biosphere is stored below the surface, specifically in the form of soil organic carbon (Jobb\u0026aacute;gy and Jackson, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). When primary minerals in rocks undergo weathering to form soil, they react with atmospheric carbon dioxide, resulting in the formation of poorly crystalline minerals and storing them in the form of organic carbon (Slessarev et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There exist several estimation methods for quantifying the rate of carbon capture resulting from rock weathering (Qiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The absorption of carbon dioxide in the atmosphere through rock weathering processes can primarily be divided into two reaction types: the reaction of carbonate minerals and the reaction of silicate minerals with carbon dioxide (Berner, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Berner, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmiotte and Probst suggest that the rate at which CO\u003csub\u003e2\u003c/sub\u003e is consumed through rock weathering is primarily influenced by the flow rate of surface water, the temperature, and the types of rock. To estimate the amount of atmospheric CO\u003csub\u003e2\u003c/sub\u003e consumed by rock weathering, they have developed the GEM-CO\u003csub\u003e2\u003c/sub\u003e model (see appendix equations \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026thinsp;~\u0026thinsp;6) (Amiotte Suchet and Probst, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The CO\u003csub\u003e2\u003c/sub\u003e capture rates of various rock types in the southern Anhui region calculated using the GEM-CO\u003csub\u003e2\u003c/sub\u003e model range from 1.64 to 27.40 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, metamorphic and plutonic rocks have the lowest rates, while carbonate rocks have the highest rates, as carbonate rocks are naturally more prone to weathering processes (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Qiu et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) estimated the average CO\u003csub\u003e2\u003c/sub\u003e capture rates of various rock types in China to be between 0.09 and 1.71 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A comparison between the two studies reveals that the CO\u003csub\u003e2\u003c/sub\u003e capture rates in the southern Anhui region are relatively high. This can be attributed to the influence of precipitation and evaporation on the GEM-CO\u003csub\u003e2\u003c/sub\u003e model, with the study area experiencing high annual precipitation and low evaporation rates, resulting in higher estimated CO\u003csub\u003e2\u003c/sub\u003e capture rates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CO\u003csub\u003e2\u003c/sub\u003e capture rates by various rock/minerals types from different regions and methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManner\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRock/mineral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e capture\u003c/p\u003e \u003cp\u003e(mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBishuiyan underground basin, Guangxi, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbonate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilicate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eXijiang River Basin, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbonate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilicate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYalong River draining the eastern Tibetan Plateau, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbonate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilicate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQingyi River Basin in southern Anhui, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWater chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbonate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilicate minerals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eSouthern Anhui region, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eGEM-CO\u003csub\u003e2\u003c/sub\u003e model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetamorphic rocks and plutonic rocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcidic igneous rock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebasalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSand and sandstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShale class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcidic carbonate rocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaporative rock salt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have estimated the rates of weathering and carbon uptake in the basins by analyzing the dissolved solutes in rivers and applying the principle of ion balance. This is because the atmospheric CO\u003csub\u003e2\u003c/sub\u003e consumed through rock-soil weathering ultimately enters the ocean via river transport (Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The soluble components found in rivers primarily originate from atmospheric deposition, mineral dissolution, and anthropogenic inputs. Mineral contributions typically consist of carbonate, silicate, and evaporite minerals (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Noh et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Based on the distribution of rocks, atmospheric deposition, and anthropogenic inputs in the southern Anhui region, the sources of dissolved components in river water have been identified, along with the corresponding weathering rates and carbon uptake rates for carbonate and silicate minerals (appendix equations 7\u0026thinsp;~\u0026thinsp;11). The carbon capture rates calculated through water chemistry methods in the study area range from 2.62 to 13.46 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 4.26 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to the Bi Shuiyan underground basin in Guangxi, the Xi River Basin in western China, and the Yalong River Basin in the eastern part of the Qinghai-Tibet Plateau, the values are slightly higher(Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is due to the mountainous nature of the study area and the influence of higher annual precipitation and temperatures, which result in a greater capture of carbon dioxide through rock weathering.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Estimating the rates of CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e capture using the PROFILE model\u003c/b\u003e\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Chemical weathering rate of parent rock\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWhen using the PROFILE model to calculate the CO\u003csub\u003e2\u003c/sub\u003e capture process, we assume that the CO\u003csub\u003e2\u003c/sub\u003e capture rate was estimated in the scenario with unchanged base cations during the weathering process. The chemical weathering rate, CO\u003csub\u003e2\u003c/sub\u003e capture rate, and base cation leaching rate in southern Anhui Province in 2020 were calculated using the PROFILE model, based on the mineral percentages of various-type weathered rock in Table S3, as well as the various parameters of parent material in the PROFILE in Table S2. For different parent rock in southern Anhui Province in 2020, the average chemical weathering rates ranged from 0.03 to 14.43 mmol\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e, the CO\u003csub\u003e2\u003c/sub\u003e capture rates from 0.03 to 19.03 mmol\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e, and the amounts from 0.03 to 1415.50 t\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The distribution areas of different types of parent rocks in the study area vary according to the geological map of Anhui Province (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), leading to significant variations in the amounts among different parent material. Different minerals in the same natural environment have varying chemical weathering rate coefficients, resulting in significant differences in the chemical weathering rates and carbon capture rates of soil parent materials with different types (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParent rock weathering rates (Rw), CO\u003csub\u003e2\u003c/sub\u003e capture rate (Fw) by rock weathering, base cation releasing rates (R\u003csub\u003eBC\u003c/sub\u003e) and CO\u003csub\u003e2\u003c/sub\u003e capture amount every day in 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent rock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csub\u003eBC\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003emmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyroclastic rock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96\u0026thinsp;~\u0026thinsp;11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19\u0026thinsp;~\u0026thinsp;11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95\u0026thinsp;~\u0026thinsp;11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.29\u0026thinsp;~\u0026thinsp;108.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGranite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1182.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026thinsp;~\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98\u0026thinsp;~\u0026thinsp;14.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83\u0026thinsp;~\u0026thinsp;14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.18\u0026thinsp;~\u0026thinsp;740.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSandstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2229.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026thinsp;~\u0026thinsp;14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u0026thinsp;~\u0026thinsp;14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u0026thinsp;~\u0026thinsp;14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.62\u0026thinsp;~\u0026thinsp;1415.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMudstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.69\u0026thinsp;~\u0026thinsp;9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.44\u0026thinsp;~\u0026thinsp;19.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.29\u0026thinsp;~\u0026thinsp;19.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.64\u0026thinsp;~\u0026thinsp;47.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiliceous rock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConglomerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026thinsp;~\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;~\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u0026thinsp;~\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.32\u0026thinsp;~\u0026thinsp;18.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.53\u0026thinsp;~\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06\u0026thinsp;~\u0026thinsp;12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.96\u0026thinsp;~\u0026thinsp;12.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113.98\u0026thinsp;~\u0026thinsp;206.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhyllite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.93\u0026thinsp;~\u0026thinsp;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.07\u0026thinsp;~\u0026thinsp;7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.95\u0026thinsp;~\u0026thinsp;7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.77\u0026thinsp;~\u0026thinsp;24.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cbr\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Gibbs free energy of weathering processes of partially silicate minerals and carbonate minerals at 25.0\u0026deg;C and 1 atm.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e∆\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{G}}_{\\text{f}}^{0}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e(KJ\u0026middot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReacting equation with carbon dioxide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e∆G\u003csup\u003e0\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(KJ\u0026middot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003elogK\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaAlSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3694.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2NaAlSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2H\u003csup\u003e+\u003c/sup\u003e+9H\u003csub\u003e2\u003c/sub\u003eO\u0026rarr;2Na\u003csup\u003e+\u003c/sup\u003e+Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;4H\u003csub\u003e4\u003c/sub\u003eSiO\u003csub\u003e4(aq)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-46.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ek-feldspar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKAlSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3737.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2KAlSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2H\u003csup\u003e+\u003c/sup\u003e+9H\u003csub\u003e2\u003c/sub\u003eO\u0026rarr;2K\u003csup\u003e+\u003c/sup\u003e+ Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;4H\u003csub\u003e4\u003c/sub\u003eSiO\u003csub\u003e4(aq)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnorthite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaAl\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4000.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaAl\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2H\u003csup\u003e+\u003c/sup\u003e+H\u003csub\u003e2\u003c/sub\u003eO\u0026rarr;Ca\u003csup\u003e2+\u003c/sup\u003e+Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-95.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscovite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKAl\u003csub\u003e3\u003c/sub\u003eSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5567.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2KAl\u003csub\u003e3\u003c/sub\u003eSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2\u003c/sub\u003e+2H\u003csup\u003e+\u003c/sup\u003e+3H\u003csub\u003e2\u003c/sub\u003eO\u0026rarr;2K\u003csup\u003e+\u003c/sup\u003e+3Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-57.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiotite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003e0.9\u003c/sub\u003eMg\u003csub\u003e1.9\u003c/sub\u003eFe\u003csub\u003e1.1\u003c/sub\u003eAlNa\u003csub\u003e0.1\u003c/sub\u003eSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5303.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003csub\u003e0.9\u003c/sub\u003eMg\u003csub\u003e1.9\u003c/sub\u003eFe\u003csub\u003e1.1\u003c/sub\u003eAlNa\u003csub\u003e0.1\u003c/sub\u003eSi\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2\u003c/sub\u003e+4.8H\u003csup\u003e+\u003c/sup\u003e+0.5H\u003csub\u003e2\u003c/sub\u003eO\u0026rarr;\u003c/p\u003e \u003cp\u003e0.9K\u003csup\u003e+\u003c/sup\u003e+1.9Mg\u003csup\u003e2+\u003c/sup\u003e+0.1Na\u003csup\u003e+\u003c/sup\u003e+0.5Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.55Fe\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e4(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;1.45H\u003csub\u003e4\u003c/sub\u003eSiO\u003csub\u003e4(aq)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-282.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg\u003csub\u003e2\u003c/sub\u003eFe\u003csub\u003e2\u003c/sub\u003eAl\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e3.5\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7206.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMg\u003csub\u003e2\u003c/sub\u003eFe\u003csub\u003e2\u003c/sub\u003eAl\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e3.5\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e8\u003c/sub\u003e+4H\u003csup\u003e+\u003c/sup\u003e\u0026rarr;2Mg\u003csup\u003e2+\u003c/sup\u003e+Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e+\u003c/p\u003e \u003cp\u003eFe\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e4(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.5H\u003csub\u003e4\u003c/sub\u003eSiO\u003csub\u003e4(aq)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;3H\u003csub\u003e2\u003c/sub\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-234.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKMg\u003csub\u003e0.25\u003c/sub\u003eFe\u003csub\u003e0.3\u003c/sub\u003eAl\u003csub\u003e2.5\u003c/sub\u003eSi\u003csub\u003e3.1\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5105.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKMg\u003csub\u003e0.25\u003c/sub\u003eFe\u003csub\u003e0.3\u003c/sub\u003eAl\u003csub\u003e2.5\u003c/sub\u003eSi\u003csub\u003e3.1\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2\u003c/sub\u003e+1.5H\u003csup\u003e+\u003c/sup\u003e+1.65H\u003csub\u003e2\u003c/sub\u003eO\u0026rarr;\u003c/p\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e+0.25Mg\u003csup\u003e2+\u003c/sup\u003e+1.25Al\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e(OH)\u003csub\u003e2(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.15Fe\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e4(s)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.45H\u003csub\u003e4\u003c/sub\u003eSiO\u003csub\u003e4(aq)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-449.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1129.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;H\u003csup\u003e+\u003c/sup\u003e\u0026rarr;Ca\u003csup\u003e2+\u003c/sup\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{H}\\text{C}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDolomite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaMg(CO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2171.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaMg(CO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e+2H\u003csup\u003e+\u003c/sup\u003e\u0026rarr;Ca\u003csup\u003e2+\u003c/sup\u003e+ Mg\u003csup\u003e2+\u003c/sup\u003e+2\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{H}\\text{C}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e(OH)\u003csub\u003e4(s)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3779.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e4(s)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1379.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e4\u003c/sub\u003eSiO\u003csub\u003e4(aq)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1317.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e(l)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-237.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{H}\\text{C}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-587.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-261.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-283.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-553.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-455.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eData from(Dean, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Robie and Waldbaum, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1968\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that siliceous rock and gravel exhibit relatively low annual chemical weathering rates and CO\u003csub\u003e2\u003c/sub\u003e capture rates. This can be attributed to the mineral compositions of siliceous rock and gravel, which primarily consist of quartz and contain minimal amounts of other minerals, as supported by Table S3. Quartz serves as a protective mineral during chemical weathering, resulting in its low susceptibility to chemical weathering (Sverdrup and Warfvinge, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The parent materials derived from igneous debris, granite, shale, and sandstone exhibit higher rates of chemical weathering and carbon capture due to their higher content of silicate and carbonate minerals. Carbonate minerals, in particular, have much higher rates of chemical weathering and CO\u003csub\u003e2\u003c/sub\u003e capture compared to silicate minerals. This explains why soil parent materials containing carbonate minerals exhibit higher rates of chemical weathering and carbon capture compared to other parent materials (Pokrovsky et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Compared to the rates of weathering observed in surface soils of other regions, the findings of this study demonstrate significantly higher levels (Liang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Phelan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This disparity arises from the fact that prior studies tend to concentrate on surface soils, which experience more extensive weathering. In contrast, our study focuses on the parent rock, which could have relatively high weathering potential.\u003c/p\u003e \u003cp\u003eIn comparison with results obtained from estimating the CO\u003csub\u003e2\u003c/sub\u003e capture rate using the GEM-CO\u003csub\u003e2\u003c/sub\u003e model and water chemistry methods in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This study yielded similar rates and fell into the same order of magnitude as other research. Nevertheless, several distinctions exist between our study and previous ones. CO\u003csub\u003e2\u003c/sub\u003e capture rates were calculated from a mineral perspective, considering factors such as climate conditions and parent materials properties. In c\u003c/p\u003e \u003cp\u003eontrast, GEM-CO\u003csub\u003e2\u003c/sub\u003e relies on an empirical equation derived from numerous experiments. Additionally, a comparison between the carbon capture rates of our basin and other basins using previously established water chemistry methods (Jiang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The CO\u003csub\u003e2\u003c/sub\u003e capture rate in this study was higher because some of the cations produced by mineral weathering using river water chemistry could be retained in the soil. In this study, the carbon capture rate is estimated directly from the mineral source, thereby reducing cations loss during the transfer process. Consequently, the estimated CO\u003csub\u003e2\u003c/sub\u003e capture rate in this study was higher. Comparing the results of CO\u003csub\u003e2\u003c/sub\u003e capture estimated using the PROFILE model with those estimated using alternative methods illustrates the viability of the PROFILE model for estimating carbon capture rates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 The influence of climate factors on CO\u003c/b\u003e\u003csub\u003e2\u003c/b\u003e\u003c/sub\u003e capture capacity of the parent rock weathering\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn 2020, weathering rates and CO\u003csub\u003e2\u003c/sub\u003e capture rates obtained through the PROFILE model and its derived model varied with variations in temperature, soil moisture, and \u003cem\u003ep\u003c/em\u003e\u003csub\u003eCO2\u003c/sub\u003e (Table S4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the temperature data provided by NOAA in 2020, the average temperature was 17.77\u0026deg;C in spring, 27.29\u0026deg;C in summer, 17.93\u0026deg;C in autumn, and 6.79\u0026deg;C in winter (January and February), with the lowest temperature 5.25\u0026deg;C in December. Precipitation also varied significantly across different seasons in the study area. Summer had the highest amount of rainfall, while spring and autumn remained at a moderate level of precipitation. Winter receives less rainfall and is characterized by a dry climate. At the seasonal scale, the highest weathering rates and carbon capture rates of parent rock occurred in summer, followed by spring and autumn, and lowest in winter. The weathering rates and carbon capture rate in the warmest month were about 1.7 times higher than those in the coldest month (Table S4). Temperature plays a significant role in influencing chemical reactions during rock weathering, as it affects the kinetics. A 4\u0026deg;C change could lead to a 30% change in weathering rates (Warfvinge and Sverdrup, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Chemical weathering reactions exclusively take place on moist surfaces, with the level of surface moisture being directly related to soil or weathering crust saturation. Adequate soil solution is necessary for chemical weathering reactions to occur, and dry conditions hinder weathering processes (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The data presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates that weathering rates and CO\u003csub\u003e2\u003c/sub\u003e capture rates in parent rocks are higher during the hot and humid summer climate, than in winter. Similarly, a comparison of climate change's effect on chemical weathering in forested areas of southern Sweden revealed that weathering rates were relatively higher during the hot and dry conditions of summer (Kronn\u0026auml;s et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to Figure S4, which showing that the weathering rate in summer is significantly higher than in winter. Areas of high value could be found in rock formations characterized by elevated levels of igneous rocks, feldspar, and carbonate minerals. The combination of the spatial distribution map of weathering indices provided in Figure S2 demonstrates a negative correlation, where regions with higher weathering rates correspond to lower weathering indices. The indices CIA and PIA primarily indicate the degree of transformation from feldspar to clay minerals, with higher values indicating lower content of feldspar and silicate minerals (Fedo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Nesbitt and Young, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). By using the PROFILE model for calculation, it can be concluded that when the content of feldspar minerals is higher in the samples, their weathering rates are also higher (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Warfvinge and Sverdrup, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Combining the spatial variations depicted in Figure S4 and Supplementary Figure S2 illustrates the distribution of chemical weathering processes in southern Anhui Province, indirectly reflecting the carbon sequestration capacity in different regions.\u003c/p\u003e \u003cp\u003eSummarizing the above content, taking the subtropical climate of southern Anhui as an example. The high summer temperatures and abundant rainfall lead to enhanced chemical weathering of rocks, increasing the CO\u003csub\u003e2\u003c/sub\u003e capture capacity in the atmosphere, the opposite occurs in winter. Therefore, the summer climate in the subtropical region is more conducive to reducing the concentration of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 The influence of climate extremes on CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e capture capacity of the parent rock weathering\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe Intergovernmental Panel on Climate Change (IPCC) pointed out that extreme precipitation events (EPEs) and heatwaves (HWs) will have serious impacts on ecosystems and human societies in the future, raising particular concern for some continental countries (such as China) (Chen and Sun, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Over the past few decades, China has experienced an increase in the frequency of EPEs and HWs (Bao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Luo and Lau, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among them, Huang-Huai-Hai river basin (including the Yellow River basin, Huaihe River basin and Haihe River basin) is one of the most vulnerable regions in China, highly sensitive to climate change (Wu et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The study area is located in the subtropical region south of the Yangtze River in China, where sustained high temperatures and heavy rainfall are common during the summer.\u003c/p\u003e \u003cp\u003eThis study selected the months of June to August in 2020 to compare the rates of rock weathering and carbon capture under two scenarios of high temperatures and heavy precipitation, relative to the annual carbon capture rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Under scenario A, with high temperature (\u0026ge;\u0026thinsp;30\u0026deg;C) and no rain, the rates of chemical weathering of various types of rocks, carbon capture, and release of alkaline cations are approximately 18.25%, 21.33% and 20.40% higher than the annual average, respectively. Under scenario B, with heavy rainfall (\u0026ge;\u0026thinsp;25mm\u0026middot;day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and a temperature of around 25\u0026deg;C, the rates of chemical weathering of various types of rocks, carbon capture, and release of alkaline cations are approximately 64.20%, 66.23% and 65.70% higher than the annual average, respectively. This shows that under extreme weather conditions, both high temperatures and heavy rainfall can accelerate the chemical weathering of rocks, allowing carbon dioxide in the atmosphere to be captured more rapidly, promoting the global carbon cycle process. Comparing the changes in carbon capture rates between scenarios A and B, it is found that the increase in rate under scenario A is much lower than that under scenario B. This indicates that temperature and precipitation play a decisive role in the rock weathering process, with acceleration of chemical weathering of rocks occurring only under conditions of high temperatures and sufficient rainfall, which effectively captures carbon dioxide from the atmosphere and reduces the impact of the greenhouse effect. Conversely, in cold and arid regions, carbon dioxide capture is less effective.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared to traditional methods of predicting the capture of atmospheric carbon dioxide by rock chemical weathering under climate change, which rely solely on long-term average temperatures and precipitation for prediction (Xi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There hasn't been much research on the capture of atmospheric carbon dioxide by rock weathering under extreme climatic conditions. In the contemporary context of frequent extreme weather events, more attention should be given to predicting the carbon capture capacity of rock chemical weathering under these conditions. This will have a significant impact on controlling the carbon cycle in the atmosphere and mitigating the greenhouse effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Changes estimation of parent rock weathering and CO\u003csub\u003e2\u003c/sub\u003e capture from 1970 to 2100\u003c/h2\u003e \u003cp\u003eSoil and rock weathering are crucial for global carbon sequestration. Climate change significantly impacts the process of chemical weathering, particularly through temperature variations (Kronn\u0026auml;s et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, climate data from the periods 1970\u0026ndash;2020 and 2020\u0026ndash;2100 were used to calculate chemical weathering rates, carbon sequestration rates, and cation leaching rates at different time intervals using weathering and carbon sink models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Specifically, the climate change in study area between 1970 and 2020 was analyzed using data from NOAA and the IPCC. During this period, the temperature increased by1.35℃, the precipitation increased by 2.80%, and the \u003cem\u003epco\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e increased by 28.37%. From Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b, and c, it can be seen that during the period from 1970 to 2020, the rates of rock weathering increased by 6.14%, carbon capture increased by 6.11%, and the rate of alkaline cation leaching increased by 6.07%. Over the past 50 years, the overall carbon capture capacity of rock chemical weathering has shown a gradual increasing trend, due to factors such as human activities leading to the gradual rise in atmospheric temperature, precipitation, and carbon dioxide concentration, enhancing the process of rock chemical weathering and gradually increasing the carbon capture rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe climate (temperature, precipitation) and \u003cem\u003epco\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e changes under the four RCP models are shown in Table S3. Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, e, and f provide predictions of the change in rock chemical weathering intensity from 2020 to 2100 under four RCP scenarios. In the RCP2.6 scenario, the carbon capture rate is projected to increase by approximately 4.68% by 2100, while in the RCP4.5 scenario, it is expected to grow by around 4.78%, in the RCP6.0 scenario by approximately 4.87%, and in the RCP8.0 scenario by roughly 5.09%. Under the RCP2.6 and RCP4.5 scenarios, the growth rate of carbon capture is projected to remain nearly constant after 2080, and there may even be a decreasing trend. In the RCP6.0 scenario, the carbon capture rate is expected to slowly increase after 2070, and then stabilize. Under the RCP8.0 scenario, the carbon capture rate is in a continuously increasing state. The trends of the estimated changes in rock carbon capture rates in this study are consistent with the climate change trends caused by different types of carbon emissions under various RCP scenarios(Hurtt et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Masui et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Riahi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thomson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; van Vuuren et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReferring to the changing trends of carbon capture rates over the past 50 years and the next 80 years, it is evident that climate has a significant positive correlation with the rock weathering process. According to the RCP4.5 and RCP8.5 scenarios, the annual global carbon capture from rock weathering calculated using the GEM-CO\u003csub\u003e2\u003c/sub\u003e model is projected to increase by 25% and 31.25% respectively from 2050 to 2100(Xi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study conducted by Akselsson utilized the PROFILE model to simulate the increase in weathering rate in Sweden during the 21st century due to climate change(Akselsson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The predicted median increase in weathering rates for alkaline cations by the ECHAM and HADLEY models was 20% and 33%, respectively. These values are approximately equivalent to 10%\u0026middot;℃\u003csup\u003e\u0026minus;1\u003c/sup\u003e, which aligns with the findings in this study.\u003c/p\u003e \u003cp\u003eBased on the PROFILE model, the daily weathering rates and carbon capture rates were estimated for the periods 1970\u0026thinsp;~\u0026thinsp;2020 and 2020\u0026thinsp;~\u0026thinsp;2100 under the RCP4.5 scenario. Under the RCP4.5 scenario, it represents a climate change scenario with government intervention, which closely aligns with the future trend of climate change(Thomson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The Kriging interpolation method was used to establish the spatial variations of both factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). It is evident from the figure that high values of weathering rate and carbon capture rate are predominantly found in the central and western parts of the study area. The formation of the high-value region in the northwest corner of the spatial variation map may be partially attributed to errors resulting from the use of kriging spatial interpolation, considering the distribution of sampling points shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, other high-value areas are primarily influenced by the presence of igneous debris rock, granite, shale, and high-calcium sandstone. These areas have a high content of silicate minerals (feldspar, mica) and carbonate minerals (calcite, dolomite), which are considered active minerals according to the PROFILE model, indicating a strong weathering process in the region (Sverdrup and Warfvinge, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The contour lines depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea illustrate the average weathering rates for each year in the study area. These rates were calculated relative to the baseline of the average weathering rate in 2020, with abnormaly of -0.06, -0.03, 0.04, and 0.06 for the years 1970, 1990, 2060, and 2100, respectively. In conjunction with Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the weathering rate for different parent rock exhibits a gradual increase from 1970 to 2100, indicating an intensified weathering process at the same rock location within the region. Moreover, contour lines were generated to examine the spatial variations in carbon capture rates. These lines are based on the average value in 2020, facilitating analysis of area changes across different years (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The abnormal changes for the years 1970, 1990, 2060, and 2100 were \u0026minus;\u0026thinsp;0.08, -0.03, 0.07, and 0.08, respectively. Considering area abnormaly, CO\u003csub\u003e2\u003c/sub\u003e capture within the study area progressively will strengthen over the period of 130 years. Thus, the utilization of the PROFILE model can characterize the spatial-temporal change of weathering rates and CO\u003csub\u003e2\u003c/sub\u003e capture rates within the local area. Furthermore, the trends can be predicted in response to climate change and be applied in regions with limited river distribution, bypassing the constraints associated with chemical methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough the past and future changes in global climate, the concentration of carbon dioxide in the atmosphere is continuously increasing, leading to a continuous rise in global overall temperature, precipitation, and the enhanced chemical weathering process in the ecosystem. The ability to capture carbon dioxide is also increasing. Although it still cannot achieve the role of carbon cycle balance, the amount of carbon captured by chemical weathering is also very large, contributing to carbon neutrality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.5 The alterations in Gibbs free energy during CO\u003csub\u003e2\u003c/sub\u003e capture by weathering\u003c/h2\u003e \u003cp\u003eThe chemical weathering reactions of rock can be understood as acid-base reactions, in which the primary mineral components in the parent rock react with acidity to produce secondary minerals. Sources of acidity primarily include CO\u003csub\u003e2\u003c/sub\u003e hydration and organic matter decomposition. The acid consumed during the weathering process of the parent rock mainly originates from CO\u003csub\u003e2\u003c/sub\u003e. The release of hydrogen ions from carbonic acid (formed by the reaction of CO\u003csub\u003e2\u003c/sub\u003e and water) can interact with silicate minerals or carbonate minerals, leading to the exchange of hydrogen ions with metal cations in the minerals. Consequently, primary minerals undergo further transformation into secondary minerals, typically clay minerals, with an increase in hydroxyl or water content. To investigate the thermodynamic processes, direction, and limitations of solid-liquid interactions in soil weathering at 25\u0026deg;C and standard atmospheric pressure, we evaluated the Gibbs free energy increment and chemical equilibrium constants (see appendix equations 12 and 13).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the carbon capture reaction equations, Gibbs free energy changes, and equilibrium constants of various silicate and carbonate minerals formed through weathering under standard conditions (25\u0026deg;C, 1 atm). For some silicate minerals and carbonate minerals, the Gibbs free energy changed during the reaction with hydrogen ions supplied by CO\u003csub\u003e2\u003c/sub\u003e in the carbon capture process ranges from \u0026minus;\u0026thinsp;5.21 to 449.30 kJ\u0026middot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and \u0026minus;\u0026thinsp;1059 to -11.98 kJ\u0026middot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The equilibrium constants (logK) for these reactions range from 0.91 to 78.70 and 1.85 to 2.10, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays negative reaction Gibbs free energy changes (∆G\u003csup\u003e0\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0), indicating the occurrence of spontaneous and irreversible reactions in the carbon capture process under standard conditions. Most minerals exhibited a high conversion degree in the carbon capture process based on the equilibrium constant (logK). However, certain minerals show lower conversion degrees. In the weathering zone of potassic feldspar minerals, their higher stability results in a less autonomous carbon capture process. The ability of reactants to transition towards products varies depending on the stability of different minerals, which also implies discrepancies in the weathering rates of these minerals.\u003c/p\u003e \u003cp\u003eAccording to the mineral content of various partially weathered rock types listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and the Gibbs free energy of different mineral types involved in the carbon capture process outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e, calculations were made to determine the Gibbs free energy changes of different soil parent materials during the carbon capture process (see Table S5). Under standard conditions, the average range of Gibbs free energy for the carbon capture process in different rock types is between \u0026minus;\u0026thinsp;7.62 and \u0026minus;\u0026thinsp;161.73 kJ\u0026middot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average range of equilibrium constants (logK) from 1.33 to 28.33. The differences in mineral composition among rock types resulted in significant variations in the Gibbs free energy and equilibrium constants during the carbon capture process. Under standard conditions, the overall reaction tends to proceed spontaneously. The reaction processes of rocks containing high amounts of mica, chlorite, and illite, such as volcanic debris rocks and claystones, exhibit greater completeness. By comparing the carbon capture rates and equilibrium constants (logK) values of different rock types as shown in Figure S5. For example, mudstones and pyroclastic rocks have higher carbon capture rates, corresponding to logK averages of 23.33 and 28.33, while siliceous rocks and conglomerates have lower carbon capture rates with low logK of 1.33 and 2.27. The results demonstrate that a higher equilibrium constant (logK) corresponds to a higher carbon capture rate. This indicates that the weathering of rocks for CO\u003csub\u003e2\u003c/sub\u003e capture reactions is more thorough, highlighting the enhanced chemical weathering capacity of these rocks for CO\u003csub\u003e2\u003c/sub\u003e capture. From the perspective of thermodynamics, on the one hand, it shows that the process of capturing carbon dioxide by chemical weathering of various rocks in the standard state is carried out spontaneously. On the other, the carbon capture capacity by different rocks weatherng is positively correlated with the equilibrium constant (logK).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe rocks in the southern Anhui primarily consist of quartz, silicate minerals, and carbonate minerals, with variations in mineral composition and proportions depending on the specific rock type. The chemical weathering indices, including CIA and PIA, range from 36.35 to 91.35 and from 54.13 to 97.92, respectively. These values indicate that the intensity of chemical weathering follows the order of sedimentary rocks\u0026thinsp;\u0026gt;\u0026thinsp;metamorphic rocks\u0026thinsp;\u0026gt;\u0026thinsp;igneous rock. The CO\u003csub\u003e2\u003c/sub\u003e capture rate of rock weathering calculated using the PROFILE model ranged from 0.03 to 19.03 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in this study, which was comparable to results from the GEM-CO\u003csub\u003e2\u003c/sub\u003e model and water Chemistry methods (1.64 to 27.40 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2.63\u0026thinsp;~\u0026thinsp;13.46 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eClimate factors exert a substantial influence on the capacity of rock weathering to sequester CO\u003csub\u003e2\u003c/sub\u003e. During the summer of 2020, the average temperature in the southern Anhui region was 21.02℃, accompanied by a 5-fold increase in precipitation than winter. These conditions led to a corresponding CO\u003csub\u003e2\u003c/sub\u003e capture rate in summer was 1.30 to 1.99 times higher than in winter approximately. Considering the frequent occurrence of extreme weather, the capturing rate of carbon dioxide by rock weathering under the condition of high temperatures (\u0026ge;\u0026thinsp;30\u0026deg;C) and heavy rainfall (\u0026ge;\u0026thinsp;25mm) has increased by about 21.33% and 66.23% respectively. From 1970 to 2020, the temperature increased by approximately 1.35\u0026deg;C, precipitation increased by approximately 2.80%, the partial pressure of \u003cem\u003epco\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e increased by approximately 28.37%, resulting in a rise in the carbon capture rate of about 6.11%. Nevertheless, the projected results under the four RCP models for the period from 2020 to 2100 show that the carbon capture rate increase due to rock weathering is approximately 4.68 to 5.09%. As a result, the projected rise in temperatures, precipitation levels, and atmospheric \u003cem\u003epco\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e will continue to amplify the role of rock weathering in sequestering CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere.\u003c/p\u003e \u003cp\u003eThrough the examination of the thermodynamic properties of CO\u003csub\u003e2\u003c/sub\u003e capture reactions in various rock weathering processes, it was discovered that, under standard conditions, these reactions occur spontaneously. The average range of equilibrium constants (logK) fell between 1.33 and 28.33. Rocks that possess higher quantities of minerals like mica, chlorite, and illite exhibit superior potential for complete reactions, with higher values of logK could have high carbon capture potential. Revealed the thermodynamic mechanism of rock weathering carbon capture.\u003c/p\u003e \u003cp\u003eClearly, rock weathering makes significant contribution to the capture of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere. The exacerbation of global climate change has resulted in enhanced chemical weathering of rocks. The results indicate that climatic factors contribute to the increasing carbon capture capacity from natural rock weathering, further highlighting the important role of rock weathering in global carbon cycling. With the global trend of rising CO\u003csub\u003e2\u003c/sub\u003e levels, the greenhouse effect intensifies, leading to frequent extreme weather events such as high temperatures and heavy rainfall. These factors exacerbate the natural chemical weathering of rocks, enhancing CO\u003csub\u003e2\u003c/sub\u003e capture. This provides a baseline reference for predicting future global CO\u003csub\u003e2\u003c/sub\u003e concentration changes and carbon cycling. Furthermore, it supplements reference values for natural effects in the formulation of global energy-saving and emission reduction policies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDescription\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe total word count of this research manuscript, including the main text and references, is approximately 9840 words. The main text is about 8124 words, and the references are about 1718 words.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects gave their consent to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects gave their consent to the publication of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClarification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human participants or human data were involved during the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWenpu Liu:\u0026nbsp;\u003c/strong\u003eSamples collection, Experiment, Data analysis, Writing - original draft, Formal analysis, Investigation. \u003cstrong\u003eYinxian Song:\u0026nbsp;\u003c/strong\u003eExperiment, Investigation, Conceptualization, Supervision, Methodology, Validation, Writing- Reviewing and Editing.\u003cstrong\u003e\u0026nbsp;Xianqiang Meng:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation, Validation, Writing-Editing. \u003cstrong\u003eZhong Chen:\u003c/strong\u003e Methodology, Investigation, Validation. \u003cstrong\u003eHe Chang:\u003c/strong\u003e Validation, Software.\u003cstrong\u003e\u0026nbsp;Shitao Zhang:\u003c/strong\u003e Methodology, Investigation, Validation, Software. \u003cstrong\u003eChunjun Tao:\u003c/strong\u003e Samples collection, Experiment, Investigation, Conceptualization, Supervision. \u003cstrong\u003eJunfeng Ji:\u0026nbsp;\u003c/strong\u003eInvestigation, Conceptualization, Supervision, Project administration.\u003cstrong\u003e\u0026nbsp;Shilei Li:\u0026nbsp;\u003c/strong\u003eInvestigation, Conceptualization, Writing-Reviewing. \u003cstrong\u003eQuan Chen:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation, Data analysis, Validation, Software.\u003cstrong\u003e\u0026nbsp;Godwin A. Ayoko:\u0026nbsp;\u003c/strong\u003eMethodology, Writing, Language editing. \u003cstrong\u003eRay L. Frost:\u003c/strong\u003e Data analysis, Language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Yunnan Fundamental Research Projects (grant NO. 202301AT070428); the National Key R\u0026amp;D Program of China (grant number 2023YFC3709100); Key Research and Development Project of the Ningxia Hui Autonomous Region, China (Grant No. 2022BEG03054).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe climate (temperature, precipitation) data for the Wannan region of subtropical China from 1970 to 2020 is sourced from the National Oceanic and Atmospheric Administration (NOAA) (https://ncei.noaa.gov/maps/daily/). The climate (temperature, precipitation) data for the period 2020 to 2100 under four RCP models is downloaded from the World Meteorological Organization (WMO) (https://climexp.knmi.nl/start.cgi). The data on the increase in carbon dioxide partial pressure from 1970 to 2100 is obtained from the Intergovernmental Panel on Climate Change (IPCC) (https://ipcc-data.org/observ/ddc_co\u003csub\u003e2\u003c/sub\u003e.html). The PROFILE model used in this study is based on the publications by Sverdrup et al. (1993) and Warfvinge et al. (1992). The Gibbs free energy data for various minerals at standard conditions (at 25.0\u0026deg;C and 1 atm) are obtained from the publications by Dvonch et al. (1999) and Robie et al. (1968). Samples were collected from the Wannan region of subtropical China, and the sample test data were obtained through testing in a standard laboratory according to prescribed procedures. The ThinkPad notebook used for processing and evaluating the data and for creating the charts for this study can be obtained at (https://www.thinkpad.com/).\u003cu\u003e\u003cbr\u003e\u003c/u\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkselsson, C., Olsson, J., Belyazid, S., Capell, R., 2016. Can increased weathering rates due to future warming compensate for base cation losses following whole-tree harvesting in spruce forests? Biogeochemistry, 128: 89\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen, M., Mustafa, B., Shukla, P., 2018. 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The responses of weathering carbon sink to eco-hydrological processes in global rocks. Science of The Total Environment, 788: 147706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao, Y., Zhang, W., Kirtman, B., 2023. Increasing impacts of summer extreme precipitation and heatwaves in eastern China. Climatic Change, 176(10): 131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. et al., 2021. New estimate of chemical weathering rate in Xijiang River Basin based on multi-model. Scientific Reports, 11(1): 5728.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y. et al., 2021. Newly Discovered Tuff in the Lower Shaximiao Formation in Yunyang, Chongqing, Southwestern China and Its Constraint on the Burial Age of the Yunyang Dinosaur Fauna.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rocks weathering, PROFILE model, Carbon capture rate, Spatial-temporal prediction, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-3961192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3961192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe capture of CO\u003csub\u003e2\u003c/sub\u003e has become a global research focus. Rock weathering in the natural environment makes significant contributions to the stable carbon capture at both long and short time scales. However, traditional methods of estimating carbon capture potential are still uncertain due to the solely instantaneous carbon capture rates, dependence of measured data, and difficulty in predicting future carbon sink potential. Here, the estimated carbon capture potential of rock weathering using conventional methods and the PROFILE weathering model were compared for the various rocks in subtropics in China. The results showed that the carbon capture rates estimated by the GEM-CO\u003csub\u003e2\u003c/sub\u003e model vary from 1.64 to 27.40 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while 2.63\u0026thinsp;~\u0026thinsp;13.46 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e by traditional the water chemistry method. Similarly, carbon capture rates calculated by the PROFILE model based on chemical weathering rate of individual specific mineral, ranging from 0.03 to 19.03 mmol\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The results of the PROFILE calculation showed that, the carbon capture rate was 1.30 to 1.99 times in summer than in winter due to the higher temperature and precipitation. In extreme climates, high temperatures (\u0026ge;\u0026thinsp;30\u0026deg;C) and heavy precipitation (\u0026ge;\u0026thinsp;25mm) have increased the capture rate of carbon dioxide by approximately 21.33% and 66.23%, respectively. On the interdecadal time scale, the carbon capture rate increased by 6.1% from 1970 to 2020, due to temperature rising by 1.4\u0026deg;C, precipitation increasing by 2.8%, and partial pressure of atmospheric carbon dioxide (\u003cem\u003epco\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) increasing by 28.4%. Further, we predict an increase in carbon capture rates will change approximately from 4.7 to 5.1% in the period of 2020\u0026ndash;2100 under four Representative Concentration Pathway (RCP) modes. The findings of this study will offer novel scientific recommendations and methods for future research and policy making on global carbon neutrality.\u003c/p\u003e","manuscriptTitle":"Spatio-temporal response and projection of CO2 capture rates by different rock weathering to climate change in subtropics in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 19:03:18","doi":"10.21203/rs.3.rs-3961192/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67085412-6311-42e6-bf3e-5ecaf0bad17f","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29046189,"name":"Earth and environmental sciences/Climate sciences"},{"id":29046190,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry"}],"tags":[],"updatedAt":"2024-11-15T06:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-04 19:03:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3961192","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3961192","identity":"rs-3961192","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}