______http://www.paper.edu.cn
Geoderma 110 (2002) 205–225 www.elsevier.com/locate/geoderma
Determining weathering rates of soils in China
Lei Duan a,*, Jiming Hao a, Shaodong Xie b, Zhongping Zhou a, Xuemei Ye a
aDepartment of Environmental Science and Engineering, Tsinghua University, Beijing 100084, PR China bCenter of Environmental Sciences, Peking University, Beijing 100871, PR China Received 10 October 2001; received in revised form 14 May 2002; accepted 12 July 2002
Abstract
As an important parameter for critical load calculation and soil acidification simulation, weathering rates of soils in China were studied using different methods of calculation. The approaches used were the mass balance approach, the soil mineralogical classification, the total analysis correlation, the PROFILE model, the MAGIC model and a simulated leaching experiment. Since chemical weathering of secondary minerals usually plays a much more important role in neutralizing the long-term acidification of soils in China than that of parent material, soil mineralogy rather than parent rock/material type, which is regarded as the most suitable factor representing weathering rates in Europe, should be adopted as the basis for soil classification. The weathering rate assigned to each soil should also be corrected when the effect of temperature is considered. Due to the variation in experimental conditions, the weathering rates of soils from laboratory experiment may be difficult to compare with field determined rates, and should be adjusted by pH and percolation rate. The comparison of various methods in this study shows that the weathering rates of soils estimated by the PROFILE model coincide well with those from other independent methods such as the dynamic modeling by MAGIC and the modified leaching experiment. The weathering rates were very low (usually lower than 1.0 kEq ha 1 year 1) for Allites (including Latosol, Lateritic Red Earth, Red Earth, Yellow Earth and Yellow-Brown Earth) in south China and Silalsols (consisting of Dark Brown Forest Soil, Black Soil and Podzolic Soil) in northeast China, and very high for Alpine Soils, Desert Soils and Pedocals in west China. The content of weatherable minerals in soil is the most important factor in determining the spatial distribution of weathering rate in China, while the difference in temperature may be the reason why the weathering rate of soil in northeast China was lower than that in southeast China. D 2002 Elsevier Science B.V. All rights reserved.
Keywords: Weathering rate; Critical load; Soil acidification; China
* Corresponding author. Tel.: +86-10-62782030; fax: +86-10-62773650. E-mail address: [email protected] (L. Duan).
0016-7061/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S0016-7061(02)00231-8 中国科技论文在线______http://www.paper.edu.cn ______http://www.paper.edu.cn
206 L. Duan et al. / Geoderma 110 (2002) 205–225
1. Introduction
The supply of base cations from chemical weathering of soil minerals is an important geochemical process determining the long-term availability of plant nutrients and the chemical status of soils. In particular, the weathering of soil provides the major long-term source of alkalinity and, thus, determines the susceptibility of a soil to acidification. If the rate of weathering cannot compensate for the depletion of base cations by biomass uptake and drainage, it is inevitable that acidification occurs (Langan et al., 1996; Sverdrup and Warfvinge, 1988; White and Brantley, 1995). The role of SO2 and NOx emissions (the precursors of acid deposition) in enhancing the rate of soil and freshwater acidification is widely recognized. Critical load, which is defined as the highest deposition of acidifying compounds that will not cause long-term harmful effect to the ecosystem (Nilsson and Grennfelt, 1988), has come into wide use for planning emission abatement of sulphur and nitrogen compounds. For example, the second Sulphur Protocol signed in 1994 by the member countries in Europe prescribed that a reduction of the excess of sulphur deposition over critical loads by 60% (‘‘60% gap closure,’’ 1990 as basic year) should be fulfilled before 2000 (Hettelingh et al., 1995). The Chinese govern- ment has also adopted critical load as a guide to formulating strategies of acid deposition control. In 1998, the Acid Rain Control Zone and Sulphur Dioxide Pollution Control Zone (called the two Control Zones for short) were designated in China for those areas which are, or could become, affected by acid deposition or ambient sulphur dioxide concentrations (Hao et al., 1998). Critical loads derived through a semiquantitative method (Duan et al., 2000) and critical load exceedances were adopted as the most important scientific basis for designation. Critical loads updated by the Steady State Mass Balance (SSMB) method (Duan et al., 2001) should also be applied in formulating national/regional acid deposition control strategies in China (Hao et al., 2001). Central to critical load calculation is the determination of the weathering rates of soils. Different measuring methods have been used, which ranges from field based studies, such as the watershed budget studies (Paces, 1983, 1986; Velbel, 1985; Drever and Clow, 1995) and the use of chronosequences (Bain et al., 1993), to laboratory studies involving Sr isotopic ratios (A˚ berg et al., 1989) or long-term leaching experiments (Hodson and Langan, 1999; Zulla and Billett, 1994). Many acidification models such as PROFILE (Warfvinge and Sverdrup, 1992) and MAGIC (Cosby et al., 1985) may also be applied to calculate weathering rate both for soils and cachments (Langan et al., 1995, 1996).To provide the necessary input data for critical load calculation and acidification simulation, weathering rates of soils in China were studied in 1990s. For instance, long-term leaching experiments (Liao et al., 1997; Liu et al., 1999; Qiu and Yang, 1998; Rong et al., 1997; Wu et al., 1998) and PROFILE model (Larssen et al., 2000; Xie et al., 1995, 1997; Zhao et al., 1995) was applied in determining weathering rates of soils in south and southwest China. However, these studies were confined to several soils and a few areas heavily polluted by acid deposition and cannot meet the requirement of critical load mapping for the whole country. Further studies on weathering rate are needed. Moreover, since the methods mentioned above were developed for European ecosystems, uncertainty may occur when they are applied in China, where the characteristics of soils are quite different. The object of this paper is to compare the weathering rates for spatially extensive and acid 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 207 sensitive soils estimated through different methods, and to test the applicability of these methods in China.
2. Methods and materials
The methods used to calculate weathering rate of soil in this study are briefly described below.
2.1. Mass balance approach
The supply of base cations from chemical weathering of selected soil horizons can be calculated by comparing the chemical composition of each soil horizon with that of the C horizon, which is assumed to represent the unweathered parent material (Bain et al., 1993; White, 1995). It is considered that titanium (Ti), zirconium (Zr) or quartz is stable and immobile through the pedogenlic process and, therefore, provides a reference against which the loss of base cations can be measured (White, 1995). The amount of base cations lost divided by the age of the soil profile produces the historical weathering rate, i.e., the long-term average of weathering rate in the past: X qSz 1 xS;R RW ¼ xP;i xS;i ; i ¼ Ca; Mg; K; Na ð1Þ Dt i Mi xP;R where xS,R is the fraction of reference component in soil, xP,R is the fraction of reference component in parent material, xS,i is the fraction of element i in soil, xP,i is the fraction of element i in parent material, Mi is the equivalent mass of element i, qS is the bulk density of soil, z is the soil depth and Dt is the soil age. For this study, Ti was used as the conservative element because of its relatively high content in the soil samples. The soils were assumed with large uncertainty to be formed during the last period of glaciation, which ended 12,000 years ago.
2.2. Soil classification
At a workshop on critical loads held at Skokloster, Sweden, in 1988, five critical load (assumed to be equal to the weathering rate) classes for forest soils were proposed based on the dominant mineralogy of the soils’ parent materials (as shown in Table 1; Nilsson and Grennfelt, 1988). Soils in Class 1 (with lowest weathering rates) are derived from highly siliceous parent rocks such as quartzite and K-feldspar-rich granite, and soils in Class 5 (with the highest weathering rates) are from parent materials with free carbonates (Marls, limestone and lime-rich proluviun, sediment and aeolian deposit, etc.). Between these extremes are soils derived from plagioclase-rich granite, gneiss, etc. (Class 2), granodiorite, schist, etc. (Class 3), and gabbro and basalt, etc. (Class 4). As compared with soil mineralogy, parent rock/material, the geological origin of Skokloster mineral classes, is less suitable for assessing weathering rates of soils in China, although it is regarded as the most suitable variable representing weathering rates in 中国科技论文在线______http://www.paper.edu.cn
208 L. Duan et al. / Geoderma 110 (2002) 205–225
Table 1 Mineralogical and petrological classifications of soil material and the corresponding weathering rates for forest soils (Nilsson and Grennfelt, 1988; Langan and Wilson, 1994) Sensitivity Minerals controlling Usual parent rock Weathering rates class weathering (kEq ha 1 year 1) 1 Quartz, K-feldspar Granite, Quartzite < 0.2 2 Muscovite, Plagioclase, Granite, Gneiss 0.2–0.5 Biotite ( < 5%) 3 Biotite Amphibole ( < 5%) Granodiorite, Greywakee, 0.5–1.0 Schist, Gabbro, Shale 4 Pyroxene, Epidote, Gabbro, Basalt 1.0–2.0 Olivine ( < 5%) 5 Carbonate Limestone, Marlstone >2.0
Europe. Since the surface is always covered by till in temperate regions and large amount of parent materials remains in the upper 0.5 m of soils, the weathering of parent rock plays a much more important role in neutralizing the long-term acid input than the buffering capacity of the soil itself. However, in tropical or subtropical regions, parent materials in the upper soil layer have almost been consumed by chemical weathering during the pedogenic processes, with the chemical weathering of parent materials only occurring in the soil layer several meters deep. As a result, the weathering rate of soils in these regions depends on secondary minerals rather than parent materials. In addition, the rate of weathering reaction in soil is kinetically limited, hence, temperature dependent. The weathering rates in Table 1 should be corrected when the effect of temperature is considered (Duan et al., 2000). The temperature effect can be quantified through the Arrhenius equation (Sverdrup, 1990): À Á A A T T RW ¼ RW0e 0 ð2Þ where T is the actual temperature (K), RW0 is the weathering rate at the reference 1 1 temperature T0 (kEq ha year ) and A is the Arrhenius preexponential factor with an approximate value of 3600 K (Sverdrup, 1990).
2.3. Total analysis correlation
Olsson and Melkerud (1990) derived a series of equations that calculate weathering rates of soils using the elemental composition of the soil together with the soil temperature and depth. These empirical equations are only applicable to soils of granitic origin. Another equation, which was used in this study, was given in kEq ha 1 year 1 by Sverdrup (1990):
4 RW ¼ 10 zTSUMð%CaO þ 2:12 %MgO þ 0:37 %Na2O þ 0:21 %K2O
þ 2:7 %P2O5 0:17Þð3Þ where TSUM is the temperature sum of all temperatures above 5 jC. 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 209
2.4. PROFILE model
The PROFILE model has been developed to derive critical loads for soils and terrestrial ecosystem (Warfvinge and Sverdrup, 1992). It is a widely used steady-state mass balance model, which can also be used to calculate weathering rates for soils from independently measured soils properties such as mineralogy and texture. For dissolution in natural soil environment, the rate equation is (Sverdrup and Warfvinge, 1988): ! horizonsX mineralsX RW ¼ rjAWxjHz ð4Þ i j where RW is the total rate of base cation production by chemical weathering (kEq 2 1 2 1 m s ), rj is the release rate of base cation from mineral j (kEq m s ), z is the soil layer thickness (m), H is the soil moisture saturation, xj is the fraction of soil mineral j 2 3 in the soil horizon and AW is the exposed surface area of soil minerals (m m ). The rate is proportional to the total exposed surface area of the minerals, which can be estimated from a particle size distribution of the soil (Sverdrup and Warfvinge, 1995):
q AW ¼ð8:0xclay þ 2:2xsilt þ 0:3xsandÞ ð5Þ q0
where xclay is the fractions of soil particles < 2 Am, xsilt the fraction of 2–60 Am and xsand the 3 fraction of 60–200 Am. q is the bulk density of the soil (kg m ) and q0 the reference density of 1000 kg m 3. Based on the Transition State Theory and experimental results, Sverdrup and Warfvinge (1988, 1995) derived a kinetic expression of the release rate as the + sum of the reaction rates of the mineral with H , water, OH ,CO2 and organic acids (Sverdrup and Warfvinge, 1988):
þ n Pm 0:5 ½H kH2O CO2 ½R þ rj ¼ kH þ þ kCO2 þ korg ð6Þ fH fH2O fCO2 forg where m and n are apparent reaction orders, k is the lumped reaction rate coefficient for the each reaction and f is rate reduction factor for product inhibition. Numerical values for all these coefficients of the minerals considered in the PROFILE model are given by Sverdrup and Warfvinge (1988, 1995).
2.5. MAGIC model
The MAGIC model is a dynamic conceptualized catchment model and has been widely used for reconstructing acidification history and predicting future acidification over time periods of decades to centuries across a range of catchments in different pollution climates (Cosby et al., 1985). The model consists of a series of soil–soil solution equilibrium equations in which the chemical composition of the soil solution is governed by simulta- neous reactions involving sulphate adsorption, cation exchange, solubility and mobilization 中国科技论文在线______http://www.paper.edu.cn
210 L. Duan et al. / Geoderma 110 (2002) 205–225 of aluminum and dissociation of carbonic acid. For each of the base cations, a dynamic mass balance equation can be written:
dx T ¼ F þ W Q½x n ð7Þ dt x x
2 where xT is the total amount of cation x in the catchment (Eq m ), Fx is the atmospheric 2 1 2 deposition into the watershed (Eq m year ), Wx is the weathering rate (Eq m year 1), [x] is the total concentration of the cation in streamwater (mol m 3), Q is the volume flow of the stream (m 2 year 1) and n is the charge of the cation. The same equation is for anions. During the model calibration, the weathering rate for each individual base cation is determined by a trail and adjustment procedure, choosing different values until the model successfully predicted currently observed surface-water and soil chemistry (target variables including surface-water concentrations of Ca, Mg, Na, K and soil-exchangeable fractions of Ca, Mg, Na and K) (Cosby et al., 1985; Jenkins et al., 1997). The obtained rate is interpreted as a weathering rate but is really a residual term for all sinks or sources not included in the model. As a tool to determine the weathering rate, the MAGIC model makes no connection to any geochemical property of the soil, and can be seen as an enhanced budget study (Sverdrup and Warfvinge, 1995). It should be noted that while the MAGIC model was applied in calculating critical loads of acid deposition or simulating the acidification of soils and surface waters, weathering rates calculated by the PROFILE model were sometimes used directly as an input parameter (Xie et al., 1995; Zhao et al., 1995) or indirectly to decide the initial range of weathering rate for model calibration (Larssen et al., 2000). In order to show how much the results of the two models are different, the weathering rates calculated by the PROFILE model were not used in this study for running the MAGIC model.
Fig. 1. Measured weathering rates under different percolation rates (for Red Earth from Jiujiang, Jiangxi province). 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 211
2.6. Simulated leaching experiment
Weathering rates can also be estimated using a range of laboratory based methods. However, the variations in experimental conditions and design make these results difficult to compare with field determined rates. Swoboda-Colberg and Drever (1993) suggest that laboratory-based determinations are several orders of magnitude greater than field based rates. They attribute this to physical/hydrological factors such as percolation rate rather than chemical factors such as pH. In this study, chemical weathering rates were calculated from a 4-month simulated weathering experiment in which soils were slowly leached with deionized water adjusted to a constant pH of 4.5 with HCl. The pH of leaching solution was approximately the same as the lowest pH of the soils treated in the experiment. Air-dried soil samples within a soil profile were placed in a 30-mm diameter leaching tube. The depth of each layer was set to one-fourth of the actual depth and the sum is 250 mm. Each soil was leached with an amount of simulated acid rain equivalent to approximately 2.5 times of the actual annual precipitation rate, which was necessary to generate sufficient volume of leachate to analyze
Table 2 Characteristics of the study sites No. Site Soil Parent Landuse Temperature Rainfall Wet deposition material (jC) (m) (kEq ha 1 year 1) SNBC 1 Suixi, Guangdong Latosol Basalt Masson pine 22 1.8 1.9 1.5 1.1 2 Wuchuan, Latosol Granite Masson pine 23 1.8 1.6 1.2 0.9 Guangdong 3 Zhanjiang, Latosol Sediment Masson pine 23 1.8 1.5 1.2 0.8 Guangdong 4 Jiujiang, Jiangxi Red Earth Phyllite Masson pine 17 1.6 1.2 1.1 0.9 5 Jiujiang, Jiangxi Red Earth Sandstone Masson pine 17 1.6 1.2 1.2 1.0 6 Xiangtan, Hunan Red Earth Granite Masson pine 17 1.4 1.5 1.5 1.2 7 Jingxian, Anhui Yellow-Red Granite Masson pine 14 0.9 1.7 0.7 1.4 Earth 8 Conghua, Lateritic Red Granite Masson pine 22 1.7 2.0 1.6 1.1 Guangdong Earth 9 Shenzhen, Lateritic Red Sandstone Masson pine 22 1.9 1.8 1.4 1.0 Guangdong Earth 10 Guiyang, Guizhou Yellow Earth Granite Masson pine 15 1.2 1.5 0.6 1.1 11 Jinzhai, Anhui Yellow- Granite Masson pine 15 1.0 1.5 1.0 1.3 Brown Earth 12 Zhouxian, Shandong Brown Forest Granite Chinese pine 13 0.7 1.0 0.7 1.1 Soil 13 Shenyang, Liaoning Cinnamon Soil Loess Chinese pine 8 0.7 1.9 0.8 1.5 14 Leshan, Sichuan Purplish Soil Schist Masson pine 16 1.0 1.5 0.4 1.3 15 Acheng, Heilongjiang Black Soil Proluvium Larch 2 0.5 0.3 0.4 0.8 16 Hulin, Heilongjiang Albic Granite Larch 3 0.5 0.3 0.5 0.9 Bleached Soil 17 Huma, Heilongjiang Dark Brown Granite Larch 3 0.4 0.4 0.6 1.2 Soil 18 Xinlin, Heilongjiang Podzolic Soil Quartzite Larch 4 0.4 0.3 0.4 0.8 中国科技论文在线______http://www.paper.edu.cn
212 L. Duan et al. / Geoderma 110 (2002) 205–225 for the various chemical components. Leaching solution was applied automatically during timed events lasting 5–10 min at a rate of 1 ml/min via a peristaltic pump. Leachates were collected weekly during the first 2 weeks and once every 2 weeks thereafter, and analyzed for Ca, Mg, K and Na using ion chromatograph (IC). The weathering rate was calculated in kEq ha 1 year 1 by linear regression between the measured amount of released cations and time during the phase with constant release rate, which can be visually selected from the plot of cumulative cation release versus time. Considering the difference between experimental condition and field, the weathering rates of soil should be adjusted by pH and percolation rate. In this study, the possible effect of percolation rate on weathering rate was studied. The results show that weathering rate increases linearly with an increase in percolation rate (as an example, see Fig. 1) while the percolation rate was not high (around the actual percolation rate in soil). It is similar with the result of Van der Salm et al. (1996), which indicates that weathering rate increases exponentially (1.2 as the exponent) with the increase of percolation rate. In addition, the influence of pH on the weathering rate was incorporated by multiplying the weathering rate by [H]0.5 (Sverdrup and Warfvinge, 1988). Therefore, it is assume that weathering rate under field conditions can be calculated by H 0:5 Q RW ¼ RW0 ð8Þ H0 Q0 where RW and RW0 is the calculated weathering rate and the measured weathering rate, + respectively, H and H0 is the concentration of H in soil solution and in simulated acid rain and Q and Q0 is the field percolation rate and the laboratory percolation rate.
Fig. 2. Sampling sites in China. 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 213
2.7. Soils sampling and data
For the present study, soil groups are particularly appropriate units for calculating weathering rates because the soil series within a group generally, but not necessarily, have similar properties. Weathering rates of several major soils, including Latosol, Lateritic Red Earth, Red Earth, Yellow Earth, Yellow-Brown Earth, Brown Forest Soil, Dark Brown Forest Soil, Podzolic Soil, Cinnamon Soil, Black Soil, Albic Bleached Soil and Purplish Soil in east China were studied. Table 2 presents brief description of the 16 study sites, the geographical distribution of which is schematically shown in Fig. 2. The sites
Table 3 Physical and chemical characteristics of the soils
No. Horizon Depth Bulk density pHH2O CEC ECa EMg EK ENa BS (cm) (103 kg/m3) (mEq/kg) (mEq/kg) (mEq/kg) (mEq/kg) (mEq/kg) (%) 1 A 0–30 1.07 5.0 130.4 6.9 3.4 0.7 0.6 9.0 B 30–50 1.14 5.2 149.4 4.9 3.9 0.7 1.2 7.1 2 A 0–20 1.25 4.9 61.6 3.2 1.6 0.6 0.9 10.1 B 20–45 1.45 4.8 31.9 2.2 0.0 0.6 0.7 11.0 3 A 0–25 1.11 4.6 102.8 3.5 1.3 0.4 2.6 7.5 B 25–50 1.28 4.7 101.6 4.2 0.5 0.5 0.6 5.7 4 AB 0–20 1.01 4.8 72.0 5.6 4.0 2.8 0.1 17.3 BC 20–50 1.30 5.0 68.4 6.6 5.4 2.3 0.2 21.2 5 A 0–22 1.00 4.7 48.0 1.0 0.5 1.2 0.2 6.0 B 22–60 1.25 5.0 38.7 1.0 0.4 0.9 0.3 6.5 6 A 0–10 0.90 4.5 49.3 1.4 0.4 1.2 0.5 6.8 B 10–40 0.93 4.7 54.1 2.2 0.3 0.9 0.7 7.5 7 A 0–20 0.60 4.8 79.7 3.9 1.7 2.3 0.2 10.1 AB 20–50 0.73 4.6 68.1 0.5 0.9 1.9 0.2 5.0 8 A 0–15 0.90 4.9 34.1 1.0 1.7 1.1 1.0 14.0 B1 15–50 1.00 5.0 14.5 0.6 1.1 0.5 0.8 20.5 9 A 0–10 0.91 4.7 55.1 2.4 0.5 0.9 0.7 8.3 B1 10–28 1.20 4.6 44.0 1.4 0.5 0.5 0.2 5.9 10 A 0–23 0.78 4.8 65.3 2.1 0.6 1.3 1.2 7.9 B 23–43 0.99 4.9 53.1 2.8 0.5 1.4 0.5 9.5 11 A 0–20 0.99 4.6 84.7 4.4 0.5 4.8 5.5 17.9 AB 20–39 1.25 5.1 28.4 1.4 1.5 3.1 1.4 26.0 12 A 0–13 1.23 5.5 158.6 32.4 9.3 1.1 1.9 28.1 B 13–35 1.31 5.4 107.7 13.9 2.4 1.3 1.6 17.8 13 A 0–30 1.32 6.5 135.3 113.7 15.9 1.8 0.5 97.5 B 30–60 1.47 7.0 103.4 73.4 20.3 1.5 0.6 92.7 14 A 0–20 1.20 5.1 190.8 22.3 15.8 3.8 0.7 22.3 B 20–50 1.36 5.0 239.4 40.1 23.7 0.8 2.0 27.8 15 A 0–20 1.14 6.4 214.2 143.1 39.4 2.9 7.4 90.0 B1 20–50 1.49 5.8 212.4 112.5 28.5 2.9 5.7 70.4 16 A 0–25 1.37 5.6 155.1 60.8 19.3 3.2 4.9 56.8 B1 25–45 1.68 5.9 245.7 68.4 39.6 4.1 7.1 48.5 17 A 0–20 1.18 5.2 146.6 30.6 26.7 2.9 7.8 46.4 B 20–45 1.36 5.1 184.0 26.9 30.6 3.8 8.8 38.1 18 A 0–13 1.22 4.0 199.5 3.0 1.7 2.1 1.9 4.3 B 13–30 1.38 4.6 281.4 1.8 2.2 3.1 2.0 3.2 中国科技论文在线______http://www.paper.edu.cn 214 .Da ta./Goem 1 20)205–225 (2002) 110 Geoderma / al. et Duan L.
Table 4 Soil texture and mineralogy No. Soil Depth Texture (%) Mineralogical composition (%) (cm) Sand Silt Clay K-feldspar Plagioclase Albite Hornblende Muscovite Chlorite Vermiculite Kaolinite Quartz 1 Latosol 0–30 30.6 13.7 55.7 4 2 70 6 30–50 26.9 27.4 45.7 5 2 73 7 2 Latosol 0–20 13.7 19.7 13.7 1 2 3 31 62 20–45 13.1 27.8 21.5 1 1 2 32 61 3 Latosol 0–25 18.9 4.3 7.9 2 6 37 47 25–50 17.2 7.7 10.6 2 6 4 38 47 4 Red Earth 20–50 31.4 31.6 14.8 2 18 24 37 29 5 Red Earth 22–60 24.1 32.3 18.5 2 17 21 41 34 6 Red Earth 0–10 22.3 28.4 15.2 4 23 3 93 10–40 15.5 27.2 14.0 8 5 17 2 84 7 Yellow- 0–20 15.7 49.6 20.1 17 1 11 77 Red Earth 20–50 15.7 62.8 35.0 19 1 1 13 76 8 Lateritic 0–15 21.1 22.5 7.7 8 3 2 23 54 Red Earth 15–50 19.8 25.4 8.9 7 2 2 26 55 9 Lateritic 0–10 10.2 14.8 27.8 11 4 2 32 43 Red Earth 10–28 9.1 16.7 30.1 8 3 2 40 39 中国科技论文在线______http://www.paper.edu.cn
10 Yellow 0–23 21.7 43.6 14.7 7 5 63 88 Earth 23–43 18.3 39.3 11.5 10 10 50 80 11 Yellow- 0–20 14.8 32.3 15.3 17 10 22 2 70 Brown Soil 20–39 20.1 30.5 13.6 6 12 33 3 79 12 Brown 13–35 22.3 22.2 13.3 7 12 45 8 73 Forest Soil 13 Cinnamon 0–30 15.0 24.2 6.6 20 6 26 24 41 Soil .Da ta./Goem 1 20)205–225 (2002) 110 Geoderma / al. et Duan L. 30–60 15.5 23.2 6.8 38 4 12 11 31 14 Purplish 0–20 31.5 22.3 9.8 22 9 20 1 67 Soil 20–50 31.6 19.0 11.5 23 11 21 1 65 15 Black Soil 0–20 4.0 69.5 14.7 21 14 3 47 20–50 13.4 58.5 17.0 9 12 8 47 18 Podzolic 0–13 11.6 15.3 7.4 7 1 2 1 12 Soil 13–30 9.3 23.8 13.8 2 1 1 5 215 中国科技论文在线______http://www.paper.edu.cn
216 L. Duan et al. / Geoderma 110 (2002) 205–225 selected are typical of those areas of China in which ecosystem may be sensitive to acid deposition (Duan et al., 2000). All of them but four in Heilongjiang province are in the Acid Rain Control Zone in China. At each site, soil pits were dug and each soil profile was sampled as necessary. Each sample was analyzed for exchangeable cations, pH, acidity, particle size distribution, soil moisture and bulk density. In addition, the soils were analyzed using X-ray diffraction and X-ray fluorescence techniques to determine the soil mineralogy and chemistry respectively. Site conditions such as temperature, precipitation (and its chemistry) and runoff (and water chemistry) were gathered from local meteoro- logical station, hydrological station and environmental monitoring station. Atmospheric inputs were derived from the measured wet depositions and dry deposition factors (Duan et al., 2001).
3. Results and discussion
3.1. Comparison of different methods
Based on the physical and chemical characteristics of the soils (see Table 3), together with atmospheric input (Table 2) and surface water chemistry, weathering rates of the soils were calculated by the MAGIC model. The weathering rates of these soils were also calculated through the PROFILE model (based on the mineralogical composition listed in Table 4) and the mass balance approach (based on the element content in Table 5). The eight estimates of weathering rate by different methods for each of the soils used in the study were summarized in Table 6. An empty cell indicates those sites at which a particular method was not applied. Due to the variation in experimental conditions such as the soil solution concentration, pH, temperature and water content, the results of weathering rates obtained from different simulated leaching experiments (see Table 7) showed a large variation, even for the same soil collected in the same area. In this study, the weathering rates obtained from the laboratory experiment (the second column in Table 6) are one or two orders of magnitude higher than those from model calculation. Taking account of the influence of percolation and pH, the ‘measured’ weathering rates (the third column of Table 6) under field conditions (lower percolation rate, higher pH) were close to calculated rates. However, the measured weath- ering rates were still higher than the calculated ones. Although the PROFILE model is generally acknowledged as the most reliable tool for calculating weathering rate and the most widely used acidification model, the applicability of the model, especially its parameters in China had not been tested before. During 1991– 1995, as a part of the National Key Project in the Eighth Five-Year Plan, weathering rates of soils in Liuzhou area, Guangxi province in south China were estimated through the PROFILE model (Xie et al., 1995, 1997). Zhao et al. (1995) also calculated weathering rates of soils in southwest China. Some of these results are shown in Table 8. As can be seen from Table 6 (the eighth column) and Table 8, although the weathering rates of soils within a group calculated by the PROFILE model varied from site to site, they were approximately of the same order of magnitude. The comparison of various methods in this study shows that the results of the PROFILE model (the eighth column of Table 6) coincide well with those from other independent methods such as the dynamic modeling by MAGIC (the ninth 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 217
Table 5 Total element contents of the soils and parent materials No. Horizon Element content (%)
TiO2 CaO MgO K2ONa2O 1 Soil 2.32 0.05 0.46 0.08 0.08 Parent material 1.98 0.62 2.47 4.57 1.59 2 Soil 0.31 0.05 0.36 0.21 0.21 Parent material 0.27 0.18 0.77 3.97 1.28 4 Soil 1.31 0.10 0.02 0.06 0.67 Parent material 1.25 0.39 0.34 2.85 0.71 6 Soil 1.18 0.05 0.15 0.49 0.18 Parent material 1.07 0.24 0.54 4.86 0.71 10 Soil 1.23 0.02 0.46 0.55 0.10 Parent material 1.02 0.02 0.52 2.77 0.20 11 Soil 0.51 1.51 0.71 3.12 2.54 Parent material 0.48 2.15 0.97 4.07 3.28 12 Soil 0.48 1.27 1.98 2.26 1.57 Parent material 0.46 2.63 2.28 2.27 1.71 13 Soil 0.78 1.12 2.20 2.52 1.75 Parent material 0.63 1.93 2.76 2.97 2.13 14 Soil 0.78 1.40 2.66 3.27 1.60 Parent material 0.75 1.48 2.79 3.54 1.51 15 Soil 0.38 1.18 1.54 2.73 2.42 Parent material 0.31 1.73 1.83 2.9 2.54 17 Soil 0.74 1.00 1.07 3.04 2.65 Parent material 0.6 1.61 1.38 3.68 3.83 18 Soil 0.42 0.78 2.68 2.00 0.96 Parent material 0.36 1.01 2.95 2.91 1.38 column) and the modified leaching experiment (the third column). These methods may, therefore, be applied in determining the weathering rates of Chinese soils. As can be seen from Table 4, the weathering rates estimated on the basis of the soil elemental composition (the seventh column of Table 6) were very close to those calculated by the PROFILE model. It indicated that the method of total analysis correlation was a very good approximation of the PROFILE model, and the key step of the method is to take the effect of temperature into account. By comparison, the variation between the soil classification and the PROFILE model was higher. Although there is large difference in parent material, the soil series within a soil group in tropical or subtropical regions (i.e., Red Earth, Latosol and Lateritic Red Earth) usually show similar mineralogical character- istics and hence weathering rates. For example, Latosol, the dominant soil south of the tropic of cancer, is derived from different parent materials such as basalt and granite. Latosol from granite, which contains a large quantity of quartz and kaolinite, is obviously in Skokloster Class 1. However, the identification of Latosol derived from basalt as insensitive to acid deposition (Class 4) based on the parent rock is misleading because the dominant minerals of this soil association are kaolinite and the soil is actually very sensitive to acid deposition. Consequently, it seems that the type of parent material might not be the most important factor in determining weathering rate of soil in China. In addition, the weathering rates estimated based on the soil classification are generally lower 中国科技论文在线______http://www.paper.edu.cn
218 L. Duan et al. / Geoderma 110 (2002) 205–225
Table 6 Weathering rates of Chinese soils derived by different methods No. Weathering rates (kEq ha 1 year 1) (1) Simulated leaching Mass Soil classification Total PROFILE MAGIC Without Adjusted balance Without Corrected analysis (8) (9) adjustment (2) (3) (4) correction (5) (6) (7) 1 10.9 1.1 4.68 1.0–2.0 0.2–0.6 0.44 0.50 0.80 2 6.0 0.6 2.52 < 0.2 0.2–0.6 0.40 0.28 0.24 3 < 0.2 0.2–0.6 0.32 0.16 0.16 4 1.18 0.5–1.0 0.4–1.0 0.73 1.08 5 11.6 1.2 0.5–1.0 0.4–1.0 0.68 0.44 6 6.8 0.9 1.80 0.2–0.5 0.4–1.0 0.19 0.23 0.29 7 0.2–0.5 0.4–1.0 0.76 0.31 0.40 8 10.4 0.8 < 0.2 0.4–0.8 0.70 0.50 0.80 9 7.5 0.7 0.5–1.0 0.4–0.8 0.98 0.37 0.80 10 4.1 0.4 0.78 0.2–0.5 0.4–0.8 0.30 0.25 0.54 11 5.5 0.7 1.30 0.2–0.5 0.3–0.7 0.38 0.39 0.49 12 1.26 0.5–1.0 0.5–1.0 0.24 0.25 0.32 13 4.63 1.0–2.0 1.0–2.0 0.50 0.60 0.56 14 12.1 0.9 0.46 0.5–1.0 0.5–1.0 0.57 0.59 0.63 15 1.40 0.5–1.0 0.4–0.9 0.70 0.79 1.11 16 0.2–0.5 0.1–0.3 0.18 0.10 17 2.35 0.2–0.5 0.1–0.3 0.24 0.23 18 1.43 < 0.2 < 0.2 0.20 0.11 0.29 than those calculated by models in south China and higher in north China, which shows that the influence of temperature on weathering rate should not be neglected. In this study, two weathering rates have been estimated and presented using the soil classification method. One was assigned based on the parent material without any adjustment, the other based on the minerals controlling weathering and adjusted by the temperature. Obviously, the results of modified method (the sixth column) compared well with the results obtained by other methods such ad PROFILE, MAGIC and total analysis, while the result without adjustment (the fifth volume) was not. This suggests that the temperature correction, as well as the mineralogy of soil instead of the parent material type as the basis of soil classification, was necessary. The classification of soils by weathering rate has formed the underlying structure of the Chinese critical load maps (Duan et al., 2000). In essence, the weathering rates calculated from the MAGIC model represent an overall weathering rate for a catchment rather than for the soil. In terms of ecosystem sensitivity, these catchment-based calculations of weathering rate by the MAGIC model will result in a higher critical load (less sensitive) than the soil-based calculation by the PROFILE model because the later is based on the most sensitive soil within the catchment. As compared with the current weathering rate that was calculated from the PROFILE model and the updated soil classification, the long-term average weathering rates in the past estimated by the mass balance approach (the fourth column in Table 4) were always higher because of the higher content of weatherable minerals in soils in the past than at present. In contrast, the historical weathering rates were generally lower than the current ones for European soils (Langan et al., 1995, 1996) as a result of the environmental changes such 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 219
Table 7 Weathering rates of soils experimentally determined in previous studies Soil Sampling site Simulated acid rain Weathering ratea Refs. 1 1 Composition pH (kEq ha year )
Cinnamon Soil Jinan, H2SO4/HNO3 =5 3.0 58.9 Liu et al., 1999 Shandong (mole ratio) 4.0 42.8 5.0 40.6
Yellow Earth Chongqing HCL + NH4CL 3.5 30.6 Qiu and Yang, 1998 4.5 31.8 5.6 29.7
Lateritic Red Guangzhou, HCL + NH4CL 3.5 54.1 Earth Guangdong 4.5 17.0 5.6 18.3
Red Earth Qinxian, H2SO4/HNO3 =5 3.0 11.5 Rong et al., 1997 Zhejiang (mole ratio)
Red Earth Shangyu, H2SO4/HNO3 =5 3.5 4.20 Zhejiang (mole ratio) 5.6 2.56
Red Earth Shaoguan, H2SO4/HNO3 =9 3.0 236 Wu et al., 1998 Guangdong (mole ratio) 3.5 135 4.5 88.1 5.6 90.1
Lateritic Red Guangzhou, H2SO4/HNO3 =9 3.0 197 Earth Guangdong (mole ratio) 3.5 131 4.5 91.8 5.6 86.4
Latosol Qionghai, H2SO4/HNO3 =9 3.0 200 Hainan (mole ratio) 3.5 122 4.5 98.9 5.6 118
Yellow Earth Chongqin H2SO4/HNO3 =9 3.0 134 (mole ratio) 3.5 75.2 4.5 56.5 5.6 74.4
Purple Soil Chongqin H2SO4/HNO3 =9 3.0 124 (mole ratio) 3.5 45.7 4.5 25.8 5.6 20.1
Yellow Earth Guiyang, H2SO4/HNO3 c 8 3.5 34.3 Liao et al., 1997 Guizhou 4.3 29.7
Red Earth Nanchang, H2SO4/HNO3 c 8 3.5 12.4 Jiangxi 4.3 8.42 a Recalculated on the basis of the results in millimole per kilogram. 中国科技论文在线______http://www.paper.edu.cn
220 L. Duan et al. / Geoderma 110 (2002) 205–225
Table 8 Weathering rates of soils estimated by the PROFILE model in previous studies Soil Sampling site Weathering rate Refs. (kEq ha 1 year 1) Yellow Earth 0.59 Zhao et al., 1995 Red Earth 0.50 Lateritic Red Earth 0.39 Latosol 0.35 Red Earth Liuzhou, Guangxi 0.20–2.3 Xie et al., 1997 Sandy 0.20–0.69 Loamy 0.95–2.3 Yellow Earth Liuzhou, Guangxi 0.54 Xie et al., 1997 Lateritic Red Earth Liuzhou, Guangxi 0.80 Purple Soil Liuzhou, Guangxi 0.56 as climate, landuse and atmospheric deposition. It can be seen from Table 6 that the results from different methods were approximately of the same order of magnitude although variations of up to 100 times were observed at some sites. However, the historical weathering rates were even underestimated for Chinese soils because some of the soils investigated are too deep to reach the parent material, and the horizon at which the soils were actually sampled may not be deep enough for C layer sampling. In the fourth column of Table 6, there are several empty cells. This was because of chemical composition of the
Table 9 Weathering rates of major soils in China estimated by the PROFILE model Soil group Weathering rate Soil group Weathering rate (kEq ha 1 year 1) (kEq ha 1 year 1) Yellow Eartha 0.25 Albic Bleached Soilb 0.18 Torrid Red Earthb 0.35 Chernozem >5.0 Yellow-Brown Eartha 0.39 Chestnut Soil >7.0 Brown Forest Soila 0.25 Brown Soil >6.0 Dark Brown Forest Soilb 0.24 Sierozem >11.0 Podzolic Soila 0.11 Grey Desert Soil >7.0 Cinnamon Soila 0.59 Grey-Brown Desert Soil >3.5 Old Manured Loessial Soil >3.5 Brown Desert Soil >6.0 Yellow Cultivated Loessial Soil >9.0 Volcanic Soil >6.0 Dark Loessial Soil >7.0 Fluva-Aquic Soil >3.5 Gray-Cinnamon Soilb 1.52 Warped irrigated Soil >7.0 Grey Forest Soilb 0.51 Oasis Soil >7.0 Black Soila 0.79 Paddy Soilb 1.98 Meadow Soilb 1.57 Aeolian Soil >2.0 Bog Soilb 0.50 Alpine Frozen Soil >3.0 Solonchak >10.0 Alpine Meadow Soilb 1.17 Solonetz >3.0 Subalpine Meadow Soilb 1.09 Limestone Soilb 1.47 Alpine Steppe Soil >5.0 Phospho-Calcic Soil >3.0 Subalpine Steppe Soil >5.0 Purplish Soila 0.59 Alpine Desert Soil >5.0 Soil groups not marked are based on lime content. a Based on measured mineralogy. b Based on normative mineralogy. 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 221 upper layers was not different with that of the C layer, and, therefore, the estimated weathering rates were negative.
3.2. Mapping weathering rates of soils in China
Table 6 only lists the weathering rates of some soil groups in China. The weathering rates of other soil groups, such as Torrid Red Earth, Dark Brown Forest Soil, Gray- Cinnamon Soil, Gray Forest Soil, Paddy Soil, Meadow Soil, Bog Soil, Limestone Soil and Mountain Meadow Soil, were also derived by the PROFILE model. For these soils, there was only a limited amount of data available (Xiong and Li, 1987). Since the soil samples within a group may have different physicochemical and mineralogical properties, hence, different weathering rates, the smallest weathering rate was assigned to the group. The results, which provided the basic data for mapping critical load of acid deposition in China through the SSMB method (Duan et al., 2001), are shown in Table 9. Based on the weathering rates of every soil group, a map of weathering rate for Chinese soils was finally compiled, shown in Fig. 3. As can be seen from the figure, the weathering rate was very low (usually lower than 1.0 kEq ha 1 year 1) for Allites (including Latosol, Lateritic Red Earth, Red Earth, Yellow Earth and Yellow-Brown Earth) in south China and Silalsols (consisting of Dark Brown Forest Soil, Black Soil and Podzolic Soil) in northeast China, and very high for Alpine Soils, Desert Soils and Pedocals in west China. The content of weatherable minerals in soil is the most important factor in determining the spatial distribution of weathering rate. In east China, especially in southeast China, where intensive chemical weathering occurred because of the high temperature and abundant rainfall, weatherable materials in soil have almost been consumed during the pedogenic processes.
Fig. 3. Approximate distribution of weathering rates in China. 中国科技论文在线______http://www.paper.edu.cn
222 L. Duan et al. / Geoderma 110 (2002) 205–225
On the contrary, since chemical weathering was quite weak in the history in west China, there remain plenty of weatherable minerals, even limestone in soil. In addition to the soil mineralogy, the distribution of weathering rates in China depends on several other variables such as soil texture, water content and temperature. For example, the reason why the weathering rate of soil in northeast China is lower than that in southeast China may be attributed to the difference of temperature.
4. Conclusion
The recent application of the critical load approach in acid deposition control in China has emphasized the requirement for accurate and reliable estimates of weathering rates. Although various methods have been developed to determine weathering rates of soils, the applicability of these methods in China should be critically tested. Practically, not all of these methods can be used for mapping purpose due to the long experimental time, significant uncertainties or high costs of analysis which are not accountable. Although the rates from field measurements and laboratory determinations are too few to be of any use for mapping purposes in China, they may serve as checkpoints for the regional estimates derive by the PROFILE model and the MAGIC model. At present, it seems that where the data requirement can be met, the PROFILE model can well predict weathering rates of Chinese soils, so can the MAGIC model at a catchment scale. It shows that the updated soil classification may represent a reasonable approximation of soil weathering rates, by which a synthesis of regional weathering rates can be provided. Future work should aim to increase the number of sites at which multiple methods of weathering rate determinations are applied, and above all, to collect data representing the characteristics of each soil.
Acknowledgements
The authors are grateful to the National Natural Science Foundation of China for its financial support to carry out this study. Gratitude should also go to Harald Sverdrup and Alan Jenkins for giving us the running program of the PROFILE model and the MAGIC model. We gratefully acknowledge the contributions made to this paper by an anonymous reviewer and Simon J. Langan. This project was supported by the National Natural Science Foundation of China.
Appendix A. Approximate correspondence of several soil taxonomy system
Chinese ST (1988) USA ST (1996) FAO/Unesco (1988) Latosol Hapludox Rhodic Ferralsol Lateritic Red Earth Hapludox Haplic Ferralsol Red Earth Kandiudult, Paleudult, Orthic Acrisol Kanhapludult Yellow Earth Kandihumult, Othic Acrisol, Kanhaplohumult Humic Acrisol 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 223
Appendix A. (continued) Chinese ST (1988) USA ST (1996) FAO/Unesco (1988) Torrid Red Earth Ustochrept Ferralic Cambisol Yellow-Brown Earth Hapludalf Haplic Luvisol Brown Forest Soil Haplustalf Haplic Luvisol Dark Brown Forest Soil Eutroboralf Haplic Luvisol Podzolic Soil Haplorthod Haplic Podzol Cinnamon Soil Haplustalf Haplic Luvisol Old Manured Loessial Soil Cumulic Haplustalf Cumulic Anthrosol Yellow Cultivated Loessial Soil Ustorthent Calcaric Regosol Dark Loessial Soil Cumulic Haplustoll Grey Forest Soil Cryumbrept Humic Cambisol Black Soil Pachic Udic Argiboroll Luvic Phaeozem Meadow Soil Typic Hapludert Eutric Vertisol Bog Soil Humic Cryaquept Umbric Gleysol Solonchak Salorthids Solonchak Solonetz Natrargids Solonetz Limestone Soil Ustorthent Eutric Leptosol Phospho-Calcic Soil Eutropeptic Rendoll Purplish Soil Udorthent Eutric Regosol Albic Bleached Soil Glossoboralf Albic Luvisol Chernozem Pachic Haploboroll Haplic Chernozem Chestnut Soil Typic Haploboroll Haplic Kastanozem Brown Soil Haplocalcid Haplic Calcisols Sierozem Haplocalcid Calcaric Cambisol Grey Desert Soil Haplocalcid Yermosol Grey Brown Desert Soil Haplocalcid Yermosol Brown Desert Soil Haplosalid Gypsic Soloncnak Volcanic Soil Ando Andosol Fluva-Aquic Soil Dystrochrept Dystric Fluvisol Warped Irrigated Soil Endoaquept Calcaric Fluvisol Oasis Soil Anthracambid Anthrosol Paddy Soil Typical Haplaquept Hydrgric Anthrosol Aeolian Soil Psamment Arenosol Alpine Frozen Soil Cryorthent Gelic Regosol Alpine Meadow Soil Calciudoll Subalpine Meadow Soil Cryboroll Alpine Steppe Soil Calcicryid Subalpine Steppe Soil Cryboroll Alpine Desert Soil Gypsicryid Reference: Gong Zitong, Chen Zhicheng, Shi Xuezheng, et al., 1999. Chinese Soil Taxonomy. Beijing: Science Press (in Chinese).
References
A˚ berg, G., Jacks, G., Hamilton, P.J., 1989. Weathering rates and 87Sr/86Sr ratios: an isotopic approach. Journal of Hydrology 109, 65–78. Bain, D.C., Mellor, A., Roberson-Rintoul, M.S.E., Buckland, S.T., 1993. Variations in weathering processes and rates with time in a chronosequence of soils from Glen Feshie, Scotland. Geoderma 57, 275–293. Cosby, B.J., Wright, R.F., Hornberger, G.M., Galloway, J.N., 1985. Modeling the effect of acid deposition: 中国科技论文在线______http://www.paper.edu.cn
224 L. Duan et al. / Geoderma 110 (2002) 205–225
estimation of long-term water quality responses in a small forested catchment. Water Resources Research 21, 1591–1601. Drever, J.I., Clow, D.W., 1995. Weathering rates in catchments. Reviews in Mineralogy 31, 463–483. Duan, L., Xie, S., Zhou, Z., Hao, J., 2000. Critical loads of acid deposition on soil in China. Water, Air and Soil Pollution 118, 35–51. Duan, L., Xie, S., Zhou, Z., Ye, X., Hao, J., 2001. Calculation and mapping critical loads for S, N and acidity in China. Water, Air and Soil Pollution 130, 1199–1204. Hao, J., Xie, S., Duan, L., Ye, X., 1998. Acid deposition and ecosystem sensitivity in China. In: Bashkin, V., Park, S. (Eds.), Acid Deposition and Ecosystem Sensitivity in East Asia. Nova Science Publishers, New York, pp. 267–311. Hao, J., Duan, L., Zhou, X., Fu, L., 2001. Application of a LRT model to acid rain control in China. Enviromental Science and Technology 35 (17), 3407–3415. Hettelingh, J.-P., Posch, M., De Smet, P.A.M., Downing, R.J., 1995. The use of critical loads in emission control agreement in Europe. Water, Air and Soil Pollution 85, 2381–2388. Hodson, M.E., Langan, S.J., 1999. A long-term soil leaching column experiment investigating the effect of variable sulphate loads on soil solution and soil drainage chemistry. Environmental Pollution 104, 11–19. Jenkins, A., Renshaw, M., Helliwell, R.C., Sefton, C., Ferrier, R.C., Swingewood, P., 1997. Modeling Surface Water Acidification in the UK: Application of the MAGIC Model to Sites in the UK Acid Waters Monitoring Network. IH Report, vol. 131. Institute of Hydrology, Wallingford, UK. Langan, S.J., Wilson, M.J., 1994. Critical loads of acid deposition on Scottish soils. Water, Air, and Soil Pollution 75, 177–191. Langan, S.J., Hodson, M.E., Bain, D.C., Skeffington, R.A., Wilson, M.J., 1995. A preliminary review of weath- ering rates in relation to their method of calculation for acid sensitive soil parent materials. Water, Air and Soil Pollution 85, 1075–1081. Langan, S.J., Reynolds, B., Bain, D.C., 1996. The calculation of base cation release from mineral weathering in soil from Paleozoic greywackes and shales in upland UK. Geoderma 69, 275–285. Larssen, T., Schnoor, J.L., Seip, H.M., Zhao, D., 2000. Evaluation of different approaches for modeling effects of acid rain on soil in China. The Science of Total Environment 246 (2–3), 175–193. Liao, B., Seip, H.M., Larssen, T., 1997. Response of two Chinese forest soils to acidic inputs: leaching experi- ment. Geoderma 75, 53–73. Liu, C., Yang, S., Song, G., Shi, Y., Ma, Y., Xiao, Y., 1999. The effect of simulated acid rain on base cation leaching in Cinnamon Soil. Chinese Journal of Soil Science 30 (1), 42–43 (in Chinese). Nilsson, J., Grennfelt, P. (Eds.), 1988. Critical Loads for Sulphur and Nitrogen. Report from a Workshop Head at Skokloster, Sweden, March 1988. Nordic Council of Ministers, Copenhagen. Olsson, M., Melkerud, P.A., 1990. Determination of weathering rates based on geochemical properties of the soil. In: Pulkine, E. (Ed.), Proceedings of the Conference on Environmental Geochemistry in Northern Europe. Volume Symposium Series, vol. 34. Geological Survey of Finland, Espoo, Finland, pp. 45–61. Paces, T., 1983. Rate constants of dissolution derived from the measurement of mass balance in hydrologic catchments. Geochim et Cosmochimical Acta 47, 1855–1863. Paces, T., 1986. Weathering rates of gneiss and depletion of exchangeable cations in soils under environmental acidification. Journal of the Geological Society, London 143, 673–677. Qiu, R., Yang, P., 1998. Study of sensitivity of soil to acid deposition in south china: weathering characteristics of soil minerals under simulated acid rain. Acta Scientiarum Naturalium Universitatis Sunyiseni 37 (4), 89–93 (in Chinese). Rong, Q., Yang, C., Xu, W., 1997. Leaching of base cations and aluminum in red soils from Zhejiang province by simulated acid rain. Acta Sciebtiae Circumstantiae 17 (1), 32–38 (in Chinese). Sverdrup, H., Warfvinge, P., 1988. Weathering of primary silicate minerals in the natural soil environment in relation to a chemical weathering model. Water, Air and Soil Pollution 38, 387–408. Sverdrup, H., 1990. The Kinetics of Base Cation Release Due to Chemical Weathering. Lund University Press, Lund, Sweden. Sverdrup, H., Warfvinge, P., 1995. Estimating field weathering rates using laboratory kinetics. Review in Min- eralogy 31, 485–541. 中国科技论文在线______http://www.paper.edu.cn
L. Duan et al. / Geoderma 110 (2002) 205–225 225
Swoboda-Colberg, N.G., Drever, J.I., 1993. Mineral dissolution rates in plot-scale filed and laboratory experi- ments. Chemical Geology 105, 51–69. Van der Salm, C., Verstraten, J.M., Tiktak, A., 1996. The influence if percolation rate on the weathering of silicates in an E horizon of an Umbric Albaqualf. Geoderma 73, 83–106. Velbel, M.A., 1985. Geochemical mass balance and weathering rate in forested watersheds in Southern Blue Ridge. American Journal of Science 285, 904–930. Warfvinge, P., Sverdrup, H., 1992. Calculating critical loads of acid deposition with PROFILE—a steady-state soil chemistry model. Water, Air and Soil Pollution 63, 119–143. White, A.F., 1995. Chemical weathering rates of silicate minerals in soil. Reviews in Mineralogy 31, 407–461. White, A.F., Brantley, S.R., 1995. Chemical weathering rates of silicate minerals: an overview. Reviews in Mineralogy 31, 1–22. Wu, Q., Qiu, R., Liu, Y., 1998. Effects of simulated acid rain on cation releasing in soils of south China. Journal of Environmental Sciences 10 (3), 309–315. Xie, S., Hao, J., Zhou, Z., Qi, L., Yin, H., 1995. Assessment of critical loads in Liuzhou, China using static and dynamic models. Water, Air and Soil Pollution 85, 2401–2406. Xie, S., Hao, J., Zhou, Z., 1997. Calculation of critical loads for acid deposition with the simple mass balance method. Chinese Journal of Environmental Sciences 18 (6), 5–8 (in Chinese). Xiong, Y., Li, Q., 1987. Chinese Soils, 2nd ed. Science Press, Beijing (in Chinese). Zhao, D., Mao, J., Xiong, J., Yang, J., 1995. Critical load of sulfur deposition for ecosystem and its application in China. Journal of Environmental Sciences 7, 325–337. Zulla, Y., Billett, M.F., 1994. Long-term changes in chemical weathering rates between 1949–50 and 1987 in forest soils from North-east Scotland. European Journal of Soil Science 45, 327–335.