全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Estimation of River Pollution Index in a Tidal Stream Using Kriging Analysis

DOI: 10.3390/ijerph9093085

Keywords: geostatistics, kriging, river pollution index, tidal stream, water quality

Full-Text   Cite this paper   Add to My Lib

Abstract:

Tidal streams are complex watercourses that represent a transitional zone between riverine and marine systems; they occur where fresh and marine waters converge. Because tidal circulation processes cause substantial turbulence in these highly dynamic zones, tidal streams are the most productive of water bodies. Their rich biological diversity, combined with the convenience of land and water transports, provide sites for concentrated populations that evolve into large cities. Domestic wastewater is generally discharged directly into tidal streams in Taiwan, necessitating regular evaluation of the water quality of these streams. Given the complex flow dynamics of tidal streams, only a few models can effectively evaluate and identify pollution levels. This study evaluates the river pollution index (RPI) in tidal streams by using kriging analysis. This is a geostatistical method for interpolating random spatial variation to estimate linear grid points in two or three dimensions. A kriging-based method is developed to evaluate RPI in tidal streams, which is typically considered as 1D in hydraulic engineering. The proposed method efficiently evaluates RPI in tidal streams with the minimum amount of water quality data. Data of the Tanshui River downstream reach available from an estuarine area validate the accuracy and reliability of the proposed method. Results of this study demonstrate that this simple yet reliable method can effectively estimate RPI in tidal streams.

References

[1]  Kjerfve, B.; Magill, K.E. Geographic and hydrodynamic characteristics of shallow coastal lagoons. Mar. Geol. 1989, 88, 187–199, doi:10.1016/0025-3227(89)90097-2.
[2]  Dronkers, J.; Leussen, W.V. Physical Processes in Estuaries; Springer-Verlag: Berlin, Germany, 1988.
[3]  Dyer, K.R. Estuaries—A Physical Introduction; Wiley: New York, NY, USA, 2000.
[4]  Chen, C.-H.; Lung, W.-S.; Li, S.-W.; Lin, C.-C. Technical challenges with BOD/DO modeling of rivers in Taiwan. J. Environ. Res. 2012, 6, 3–8.
[5]  Ekdal, A.; Gürel, M.; Guzel, C.; Erturk, A.; Tanik, A.; Gonenc, I.E. Application of WASP and SWAT models for a Mediterranean coastal lagoon with limited seawater exchange. J. Coast. Res. 2011, 64, 1023–1027.
[6]  Chen, W.-B.; Liu, W.-C.; Hsu, M.-H. Water quality modeling in a tidal estuarine system using a three-dimensional model. Environ. Eng. Sci. 2011, 28, 443–459.
[7]  Chen, Y.-C.; Wei, C.; Yeh, H.-C. Rainfall network design using kriging and entropy. Hydrol. Process. 2008, 22, 340–346, doi:10.1002/hyp.6292.
[8]  Lo, S.-L.; Kuo, J.-T.; Wang, S.-M. Water quality network design of Keelung river, northern Taiwan. Water Sci. Technol. 1996, 34, 49–57.
[9]  Mohammad, K.; Kerachian, R.; Akhbari, M.; Hafez, B. Design of river water quality monitoring networks: A case study. Environ. Model. Assess. 2009, 14, 705–714.
[10]  Yang, X.; Jin, W. GIS-based spatial regression and prediction of water quality in river networks: A case study in IOWA. J. Environ. Manage 2010, 91, 1943–1951, doi:10.1016/j.jenvman.2010.04.011.
[11]  Polus, E.; Flipo, N.; de Fouquet, C.; Poulin, M. Geostatistics for assessing the efficiency of a distributed physically-based water quality model: Application to nitrate in the Seine River. Hydrol. Process. 2011, 25, 217–233.
[12]  Liu, W.-C.; Yu, H.-L.; Chung, C.-E. Assessment of water quality in a subtropical alpine lake using multivariate statistical techniques and geostatistical mapping: A case study. Int. J. Environ. Res. Public Health 2011, 8, 1126–1140, doi:10.3390/ijerph8041126.
[13]  Militino, A.F.; Ugarte, M.D.; Ibá?ez, B. Longitudinal analysis of spatially correlated data. Stoch. Env. Res. Risk A. 2008, 22, S49–S57, doi:10.1007/s00477-007-0158-6.
[14]  Garreta, V.; Monestiez, P.; Ver Hoef, J.M. Spatial modeling and prediction on river networks: Up model, down model or hybrid? Environmetics 2010, 21, 439–456.
[15]  Velasco-Forero, C.A.; Sempere-Torres, D.; Cassiraga, E.F.; Gómez-Hernández, J.J. A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data. Adv. Water Resour. 2009, 32, 986–1002.
[16]  Lin, Y.P.; Wang, C.L.; Chang, C.R.; Yu, H.H. Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods. Environ. Monit. Assess. 2011, 178, 1–18.
[17]  Darrouzet-Nardi, A.; Erbland, J.; Bowman, W.D.; Savarino, J.; Williams, M.W. Landscape-level nitrogen import and export in an ecosystem with complex terrain, Colorado Front Range. Biogeochemistry 2012, 109, 271–285, doi:10.1007/s10533-011-9625-8.
[18]  R?ty, M.; Kangas, A. Comparison of k-MSN and kriging in local prediction. Forest Ecol. Manag. 2012, 263, 47–56.
[19]  Matheron, G. Principles of geostatistics. Econ. Geol. 1963, 58, 1246–1266.
[20]  Matheron, G. Theory of R egionalized Variables and Its Applications; Ecole National Superieure des Mines: Paris, France, 1971.
[21]  Journel, A.G.; Huijbregts, C.J. Mining Geostatistics; Academic Press: New York, NY, USA, 1978.
[22]  Liou, S.-M.; Lo, S.-L.; Wang, S.-H. A generalized water quality index for Taiwan. Environ. Monit. Assess. 2004, 96, 35–52, doi:10.1023/B:EMAS.0000031715.83752.a1.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133