Abstract
Purpose
Acid rain is a problem of increasing agricultural, environmental, and ecological concerns worldwide. In recent years, coupled studies have been conducted to evaluate impacts of simulated acid rain (SAR) on Latosol in China. However, no efforts have been devoted to investigating which soil parameters are most sensitive to the influences of SAR. In this study, we addressed the issue using the principal component analysis (PCA) and principal factor analysis (PFA) techniques.
Materials and methods
A plastic cylinder with a 10-cm inner diameter was used to contain a 20-cm-long Latosol (acidic red soil) column. A total of 24 column spray treatments, namely 6 pH levels (i.e., 2.5, 3.0, 4.0, 4.5, 5.0, and 7.0) × 4 spray periods (i.e., 5, 10, 15, and 21 days) with triplicates, were carried out in this study. The SAR solution was slowly sprayed at a rate of 6 cm3 min−1 to the top of the column every 24 h. The effluent and soil samples from the columns were collected and analyzed for soil quality parameter contents. These parameters were then analyzed by the PCA and PFA techniques.
Results and discussion
Changes in Ca2+ content in the Latosol due to the influences of SAR did not have discernable impacts on soil parameters. In contrast, Mg+2 had fairly good positive correlations with Fe3+ (0.642) and negative correlation with humic acid (−0.644); K+ had good positive correlations with Mn2+ (0.724) and negative correlation with Fe3+ (−0.703); Fe3+ had good negative correlations with \( {\hbox{PO}}_4^{3 - } \)(−0.755); Na+ had fairly good positive correlations with Cu2+ (0.656); Cu2+ had fairly good positive correlations with humic acid (0.638) and good negative correlations with phosphatase (−0.766); Zn2+ had good positive correlations with \( {\hbox{PO}}_4^{3 - } \) (0.722); and OM had fairly good negative correlations with \( {\hbox{PO}}_4^{3 - } \) (−0.696). Soil cations (i.e., Mg2+, K+, Na+, Cu2+, and Zn2+) and enzymes (i.e., peroxidase, urease, and amylase) were positively influenced by the SAR, while the most susceptible soil parameters under the SAR stress were in the following order: Mg2+ (91%) > Na+ (89.61%) > urease (89.6%) > amylase (89%) > Cu2+ (85.2%).
Conclusions
Impacts of the SAR on soil cations (i.e., Mg2+, K+, Na+, Cu2+, and Zn2+) and enzymes (i.e., peroxidase, urease, and amylase) were larger than those of OM and \( {\hbox{PO}}_4^{3 - } \) in the Latosol. The most susceptible soil parameters under the influences of the SAR were in the following order: Mg2+ > Na+ > urease > amylase > Cu2+.
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References
ACSSSC (Agricultural Committee of Soil Science Society of China) (1983) Standard analysis method of soil agricultural chemicals. Science and Technology Publisher, Beijing, pp 179–190, in Chinese
Bengraine K, Marhaba TF (2003) Using principal component analysis to monitor spatial and temporal changes in water quality. J Hazard Mater B100:179–195
Brady NC (1984) The natural and properties of soils, 9th edn. New York, London
Calace N, Fiorentini F, Petronio BM, Pietroletti M (2001) Effects of acid rain on soil humic compounds. Talanta 54:837–846
Cattell RB, Jaspers J (1976) A general plasmode (No. 30-15-5-2) for factor analytic exercises and research. Mult Behave Res Monogr 67:1–212
Cole MA (1977) Lead inhibition of enzyme synthesis in soil. Appl Environ Microbiol 33:262–268
Davis JC (1986) Statistical and data analysis in geology, 2nd edn. Wiley, New York
Gangopadhyay S, Gupta AD, Nachabe MH (2001) Evaluation of ground water monitoring network by principal component analysis. Ground Water 39:181–191
Islam KR, Weil RR (2000) Soil quality indicator properties in mid-Atlantic soils as influenced by conservation management. J Soil Water Conserv 55:69–78
Ivring PM (1983) Acidic precipitation effects on crops. A review and analysis of research. J Environ Qual 12:442–453
Johnson JL, Temple KL (1964) Some variables affecting the measurement of “catalase activity” in soil. Soil Sci Soc Am Proc 28:207–209
Kandeler E, Gerber H (1988) Short-term assay of soil urease activity using colorimetric determination of ammonium. Biol Fert Soils 6:68–72
Karlen DL, Mausbach MJ, Doran JW, Cline RG, Harris RF, Schuman GE (1997) Soil quality: a concept, definition, and framework for evaluation. Soil Sci Soc Am J 61:4–10
Liu KH, Mansell RS, Rhue RD (1990) Cation removal during application of acid solution into air dry soil columns. Soil Sci Soc Am S4:1747–1753
Liu JX, Zhou GY, Zhang DQ (2007a) Simulated effects of acidic solutions on element dynamics in monsoon evergreen broad-leaved forest at Dinghushan, China. Part 1: dynamics of K, Na, Ca, Mg and P. Environ Sci Pollut Res 14:123–129
Liu JX, Zhou GY, Zhang DQ (2007b) Simulated effects of acidic solutions on element dynamics in monsoon evergreen broad-leaved forest at Dinghushan, China. Part 2: dynamics of Fe, Cu, Mn and Al. Environ Sci Pollut Res 14:215–218
Liu L, Song CY, Li FS (2007c) Release of Si, Al and Fe in red soil under simulated acid rain. Huan Jing Ke Xue 28:2376–2382 (in Chinese)
Ling DJ, Zhang JE, Ouyang Y, Huang QC (2007) Role of simulated acid rain on cations, phosphorus and organic matter dynamics in Latosol. Arch Environ Contam Toxicol 52:16–21
Manly BFJ (1986) Multivariate statistical methods: a primer. Chapman & Hall, London
Menz FC, Seip HM (2004) Acid rain in Europe and the United States: an update. Environ Sci Policy 7:253–265
NSICSA (Nanjing Soil Institute of China Science Academy) (1978) Soil physical and chemical analysis. Science and Technology Publisher, Shanghai, pp 105–136, in Chinese
Ouyang Y (2005) Application of principal component and factor analysis to evaluate surface water quality monitoring network. Water Res 39:2621–2635
Ouyang Y, Mansell RS, Ou LT (2005) Application of principal component and factor analysis to evaluate groundwater quality. Soil Crop Sci Soc Fla Proc 64:35–44
Ouyang Y, Nkedi-Kizza P, Wu QT, Shinde D, Huang CH (2006) Evaluation of seasonal changes in river water quality. Water Res 40:3800–3810
Perkins RG, Underwood GJC (2000) Gradients of chlorophyll a and water chemistry along an eutrophic reservoir with determination of the limiting nutrient by in situ nutrient addition. Water Res 34:713–724
SAS Institute Inc (1999) SAS proprietary software release 8.2. SAS Institute, Cary, NC
Shine JP, Ika RV, Ford TE (1995) Multivariate statistical examination of spatial and temporal patterns of heavy metal contamination in New Bedford Harbor marine sediments. Environ Sci Technol 29:1781–1788
Sneddon IR, Orueetxebarria M, Hodson ME, Schofield PF, Valsami-Jonesa E (2006) Use of bone meal amendments to immobilise Pb, Zn and Cd in soil: a leaching column study. Environ Pollut 144:816–825
Tabachnick BG, Fidell LS (2001) Using multivariate statistics. Allyn and Bacon, Boston
Tabatabai MA, Bremner JM (1969) Use of p-nitrophenyl phosphatase activity. Soil Biol Biochem 1:30–307
Tauler R, Barcelo D, Thurman EM (2000) Multivariate correlation between concentrations of selected herbicides and derivatives in outflows from selected U.S. Midwestern reservoirs. Environ Sci Technol 34:3307–3314
Vega M, Pardo R, Barrado E, Deban L (1998) Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res 32:3581–3592
Voegelin A, Barmettler K, Kretzschmar R (2003) Heavy metal release from contaminated soils: comparison of column leaching and batch extraction results. J Environ Qual 32:865–875
Voutsa D, Manoli E, Samara C, Sofoniou M, Stratis I (2001) A study of surface water quality in Macedonia, Greece: speciation of nitrogen and phosphorus. Water Air Soil Pollut 129:13–32
Wackernagel H (1995) Multivariate geostatistics. An introduction with applications. Springer, New York
Winter TC, Mallory SE, Allen TR, Rosenberry DO (2000) The use of principal component analysis for interpreting ground water hydrographs. Ground Water 38:234–246
Yu JC, Quinn JT, Dufournaud CM, Harrington JJ, Roger PP, Lohani BN (1998) Effective dimensionality of environmental indicators: a principal component analysis with bootstrap confidence intervals. J Environ Manage 53:101–119
Zhang JE, Ouyang Y, Ling DJ (2007) Impacts of simulative acid rain on cation leaching from the Latosol in South China. Chemosphere 67:2131–2137
Acknowledgments
The study was supported by the Foundation of Guangdong Ocean University, China (No. 0812075).
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Responsible editor: Gilbert Sigua
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Ling, DJ., Huang, QC. & Ouyang, Y. Identification of most susceptible soil parameters for Latosol under simulated acid rain stress using principal component analysis. J Soils Sediments 10, 1211–1218 (2010). https://doi.org/10.1007/s11368-010-0231-5
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DOI: https://doi.org/10.1007/s11368-010-0231-5