喀斯特小流域地表水碳酸盐系统化学平衡对酸性矿山废水的缓冲作用

黄江浔; 李清光; 安丽; 杜双雪; 郭兴强

doi:10.11932/karst2022y20

喀斯特小流域地表水碳酸盐系统化学平衡对酸性矿山废水的缓冲作用

doi: 10.11932/karst2022y20

黄江浔^1,,
李清光^{1, 2, ,},
安丽¹,
杜双雪¹,
郭兴强¹

1.
贵州大学资源与环境工程学院喀斯特地质资源与环境教育部重点实验室, 贵州贵阳 550025
2.
中国科学院地球化学研究所环境地球化学国家重点实验室, 贵州贵阳 550081

基金项目: 国家自然科学基金项目(41867050)；国家重点研发计划 (2019YFC1805300)；贵州省基础研究计划([2019]1096)；贵州省人才基地项目 (RCJD2018-21)

详细信息

作者简介:
黄江浔（1998−），男，硕士研究生，从事矿山环境过程研究。E-mail：2431033709@qq.com

通讯作者:
李清光（1984−），男，副教授，从事环境地球化学研究。E-mail：leeqg12@163.com

中图分类号: P342；X751
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出版历程
- 收稿日期: 2021-06-13
- 网络出版日期: 2023-02-14
- 刊出日期: 2023-02-25

Buffering effect of chemical equilibrium of surface water carbonate system on acid mine drainage in small karst watershed

1.
Key Laboratory of Karst Geological Resources and Environment of Ministry of Education, College of Resources and Environmental Engineering, Guizhou University, Guiyang, Guizhou 550025, China
2.
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, Guizhou 550081, China

摘要

摘要: Revelle因子不仅能反映弱碱性水体对吸收大气CO₂的缓冲能力，还能体现水体酸化过程中CO₂去气对H⁺的缓冲作用。本研究通过对多个缓冲因子的分析，探讨喀斯特中高硫煤矿区地表水碳酸盐系统对酸性矿山废水的缓冲作用，有助于进一步理解喀斯特地区流域水体中DIC循环过程和CO₂源汇关系特征。结果表明，地表水碳酸盐系统内车田河流域Revelle因子变化区间在1.00~51.96之间，能有效揭示地表水碳酸盐系统内CO₂去气对H⁺的缓冲过程，其敏感区间为pH=7.0~8.38的弱碱性水体。γ_DIC、β_DIC、ω_DIC、γ_Alk、β_Alk、ω_Alk等缓冲因子是基于pH和DIC浓度的二元方程。这些因子进一步细化了CO₂(aq)、H⁺和${\rm{CO}}_3^{2-}$等组分对DIC浓度和碱度的相对变化关系。在pH＞7.0的DIC碳酸盐体系内，6个缓冲因子对水体酸化过程中碳酸盐组分的动态转化具有很好的响应。当pH＜7.0以后，水−气界面和水−岩界面的碳传输过程增强，当CO₂去气过程占主导，则缓冲因子绝对值变大；当H⁺对碳酸盐岩溶蚀过程占主导，则缓冲因子绝对值变小。
- 地表水碳酸盐系统 /
- Revelle因子 /
- 缓冲作用 /
- CO₂去气 /
- 碳酸盐岩溶蚀
Abstract: The dynamic changes of different components in water carbonate system (CO₂+HCO$_3^{-}$+CO$_3^{2-}$) can be characterized by Revelle factor which can not only reflect the buffering capacity of weak-basicity water to absorb atmospheric CO₂, but also reflect the buffering effect of CO₂ degassing on H⁺ during water acidification. Compared with the marine system, the Revelle factor in the surface water carbonate system has a larger variation range. However, the study on the variation of buffering factors in the dynamic transformation of carbonate components in freshwater system is still very limited. This study selected the Chetian river located in Eastern Jinsha county, Guizhou Province as the research area. Through the analysis of multiple buffering factors, the buffering effect of the surface water carbonate system on AMD input was discussed. The results will help to further understand the DIC cycle process and the CO₂ source-sink relationship in surface water in the karst area of medium-high sulfur coal mine. Based on the 13-month sampling analysis from November 2020 to November 2021, the equations of Revelle factor—γ_DIC, β_DIC, ω_DIC, γ_Alk, β_Alk and ω_Alk—were established to characterize the relationship between acid-base chemical balance of water and the dynamic variation of carbonate components. Results show that when the Revelle factor is at the maximum, the buffering capacity of the water carbonate system is the weakest. In the marine system, the maximum value of Revelle factor appears at pH 7.50, and the seawater sample data are mainly distributed on the right side of this factor, reflecting the absorption and buffering capacity of the ocean to atmospheric CO₂. When the pH is in the range of 6.35-8.38, the carbonate balance in surface water mainly reflects the conversion between CO₂(aq) and HCO$_3^{-}$, and CO$_3^{2}$⁻ is almost negligible. Due to the influence of AMD input, all data in the Chetian river fall on the left side of the maximum value, with a variation range of 1.00-51.96, which is shown as the buffering of CO₂ degassing on H⁺. In acidic water with pH<6.35, DIC is gradually dominated by CO₂(aq), and the sensitivity of Revelle factor is reduced. The buffering factors such as γ_DIC, β_DIC, ω_DIC, γ_Alk, β_Alk and ω_Alk, based on the binary equations of pH and DIC concentration, can be used to further elaborate the relative variation of CO₂(aq), H⁺ and CO$_3^{2}$⁻ on DIC concentration and alkalinity. It can be found that the six buffering factors show a good response to the dynamic transformation of carbonate components during water acidification. When pH is equal to 8.38, the six factors have extreme values, reflecting the low buffering capacity of water carbonate system. At pH>6.35, β_Alk is linearly related to the concentration of CO₂(aq). With the improvement of acidification degree, β_DIC can respond well to the buffering of chemical equilibrium of carbonate system to H⁺. When pH is less than 6.35, with the gradual increase of the proportion of CO₂(aq), the water carbonate system is no longer in a closed environment, and the carbon transport at the water-gas interface and water-rock interface is enhanced. When the CO₂ degassing is dominant, the absolute value of these buffering factors becomes larger; when the erosion of carbonate rocks by H⁺ is the dominate process, the absolute value of these buffering factors become smaller.
- surface water carbonate system /
- Revelle factor /
- buffering effect /
- CO₂ degassing /
- carbonate erosion

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图 1 车田河流域区域地质和采样点分布图

Figure 1. Regional geology and distribution of sampling point of the Chetian river basin

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图 2 海水碳酸盐系统和喀斯特河流碳酸盐系统Revelle因子对pH的响应

Figure 2. Responses of Revelle factor to pH in marine and karst water carbonate system

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图 3 Revelle因子对CO₂(aq)、${\rm{HCO}}_3^{-}$和碳酸盐饱和度的响应

Figure 3. Response of Revelle factor to CO₂(aq), ${\rm{HCO}}_3^{-}$ and carbonate saturation

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图 4 缓冲因子(γ_DIC、β_DIC、ω_DIC、γ_Alk、β_Alk、ω_Alk)相对于pH的变化特征

Figure 4. Variation characteristics of buffering factors (γ_DIC,β_DIC,ω_DIC,γ_Alk,β_Alk and ω_Alk) relative to pH

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图 5 车田河流域缓冲因子(γ_DIC、β_DIC、ω_DIC、γ_Alk、β_Alk、ω_Alk)对pH的响应关系

Figure 5. Response of buffering factors (γ_DIC, β_DIC, ω_DIC, γ_Alk, β_Alk and ω_Alk) of the Chetian river basin to pH

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图 6 车田河流域β_DIC和β_Alk分别对CO₂(aq)、${\rm{HCO}}_3^{-}$、碳酸盐饱和度的响应

Figure 6. Responses of β_DIC and β_Alk to CO₂(aq), ${\rm{HCO}}_3^{-}$ and carbonate saturation in the Chetian river basin

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表 1 缓冲因子计算方法

Table 1. Calculation method of buffering factors

缓冲因子对DIC变化的响应	缓冲因子对Alk变化的响应
$ {\mathrm{\gamma }}_{\mathrm{D}\mathrm{I}\mathrm{C}}={\left(\dfrac{\partial \mathrm{l}\mathrm{n}\left[{\mathrm{C}\mathrm{O}}_{2}\right]}{\partial \mathrm{D}\mathrm{I}\mathrm{C}}\right)}^{-1}=\mathrm{D}\mathrm{I}\mathrm{C}-\dfrac{{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}^{2}}{\mathrm{S}} $	$ {\mathrm{\gamma }}_{\mathrm{A}\mathrm{l}\mathrm{k}}={\left(\dfrac{\partial \mathrm{l}\mathrm{n}\left[{\mathrm{C}\mathrm{O}}_{2}\right]}{\partial \mathrm{A}\mathrm{l}\mathrm{k}}\right)}^{-1}=\dfrac{{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}^{2}-\mathrm{D}\mathrm{I}\mathrm{C}\times \mathrm{S}}{{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}} $
$ {\mathrm{\beta }}_{\mathrm{D}\mathrm{I}\mathrm{C}}={\left(\dfrac{\partial \mathrm{l}\mathrm{n}\left[{\mathrm{H}}^{+}\right]}{\partial \mathrm{D}\mathrm{I}\mathrm{C}}\right)}^{-1}=\dfrac{\mathrm{D}\mathrm{I}\mathrm{C}\times \mathrm{S}-{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}^{2}}{{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}} $	$ {\mathrm{\beta }}_{\mathrm{A}\mathrm{l}\mathrm{k}}={\left(\dfrac{\partial \mathrm{l}\mathrm{n}\left[{\mathrm{H}}^{+}\right]}{\partial \mathrm{A}\mathrm{l}\mathrm{k}}\right)}^{-1}=\dfrac{{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}^{2}}{\mathrm{D}\mathrm{I}\mathrm{C}}-\mathrm{S} $
$ {\mathrm{\omega }}_{\mathrm{D}\mathrm{I}\mathrm{C}}={\left(\dfrac{\partial \mathrm{l}\mathrm{n}\mathrm{\Omega }}{\partial \mathrm{D}\mathrm{I}\mathrm{C}}\right)}^{-1}=\mathrm{D}\mathrm{I}\mathrm{C}-\dfrac{{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}\times \mathrm{P}}{\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_{3}^{-}\right]} $	$ {\mathrm{\omega }}_{\mathrm{A}\mathrm{l}\mathrm{k}}={\left(\dfrac{\partial \mathrm{l}\mathrm{n}\mathrm{\Omega }}{\partial \mathrm{A}\mathrm{l}\mathrm{k}}\right)}^{-1}={\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}-\dfrac{\mathrm{D}\mathrm{I}\mathrm{C}\times \left[{\mathrm{H}\mathrm{C}\mathrm{O}}_{3}^{-}\right]}{\mathrm{P}} $
注：$ \mathrm{S}=\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_{3}^{-}\right]+4\left[{\mathrm{C}\mathrm{O}}_{3}^{2-}\right]+\left[{\mathrm{H}}^{+}\right]-\left[{\mathrm{O}\mathrm{H}}^{-}\right],{\mathrm{A}\mathrm{l}\mathrm{k}}_{\mathrm{C}}=\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_{3}^{-}\right]+2\left[{\mathrm{C}\mathrm{O}}_{3}^{2-}\right], $$ \mathrm{D}\mathrm{I}\mathrm{C}=\left[{\mathrm{C}\mathrm{O}}_{2}\right]+\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_{3}^{-}\right]+\left[{\mathrm{C}\mathrm{O}}_{3}^{2-}\right],A\mathrm{l}\mathrm{k}=\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_{3}^{-}\right]+2\left[{\mathrm{C}\mathrm{O}}_{3}^{2-}\right]-\left[{\mathrm{H}}^{+}\right]+\left[{\mathrm{O}\mathrm{H}}^{-}\right], $$\mathrm{P}=2\left[\mathrm{C}{\mathrm{O} }_{2}\right]+\left[\mathrm{H}\mathrm{C}{\mathrm{O} }_{3}^{-}\right]。$

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