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2016 | 25 | 3 |

Tytuł artykułu

Estimation of rainwater quality using GPS-derived atmospheric propagation delay and meteorological data

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
We investigated GPS-derived atmospheric propagation delays and meteorological data for estimating rainwater quality by creating a regression model using the combinatorial group method of the datahandling algorithm (COMBI GMDH). The dependent variable was the daily wet deposition, while the independent variables were zenith hydrostatic delay, zenith wet delay, daily rainfall, and daily average wind speed. The model had a coefficient of determination (R²) of 0.70 and a correlation coefficient of 0.84. The mean absolute error (MAE) was 30.20, μmol/m²-day and the root mean square error (RMSE) was 40.94 μmol/m²-day. Accuracy testing to validate the model revealed an R² of 0.95 with a correlation coefficient of 0.98. The MAE was 12.14 μmol/m²-day and the RMSE 15.35 μmol/m²-day. Rainwater quality could be estimated using GPS-derived atmospheric propagation delay (APD) and meteorological data.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

25

Numer

3

Opis fizyczny

p.1271-1278,fig.,ref.

Twórcy

autor
  • Department of Environmental Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand
  • Faculty of Science and Technology, Sakon Nakhon Rajabhat University, Sakon Nakhon, Thailand
  • Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand
autor
  • Department of Environmental Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand

Bibliografia

  • 1. NOYES P.D., MCELWEE M.K., MILLER H.D., CLARK B.W., VAN TIEM L.A., WALCOTT K.C., ERWIN Fig. 5. Plot of daily wet deposition model. K.N., LEVIN E.D. The toxicology of climate change: Environmental contaminants in a warming world, Environ. Int., 35, 971, 2009.
  • 2. SUDALMA S., PURWANTO P., SANTOSO L.W. The Effect of SO2 and NO2 from Transportation and Stationary Emissions Sources to SO42- and NO3- in Rain Water in Semarang, P. Environ. Sci., 23, 247, 2015.
  • 3. SINGH A., AGRAWAL M. Acid rain and its ecological consequences, J Environ Biol., 29, 15, 2008.
  • 4. SCHNEIDER T., O’GORMAN P.A., LEVINE X.J. Water Vapor and The Dynamics of Climate Changes, Rev. Geophys., 48, 1, 2010.
  • 5. WANG X., LIU Z., NIU L., FU B. Long-term effects of simulated acid rain stress on a staple forest plant, Pinus massoniana Lamb: a proteomic analysis, Trees, 27, 297, 2012.
  • 6. LI J., JIA C., LU Y., TANG S., SHIM H. Multivariate analysis of heavy metal leaching from urban soils following simulated acid rain, Microchem. J., 122, 89, 2015.
  • 7. ERKAL A., D’AYALA D., SEQUEIRA L. Assessment of wind-driven rain impact, related surface erosion and surface strength reduction of historic building materials, Build. Environ., 57, 336, 2012.
  • 8. CAHOON L.B. Coal Use as a Cause of Water Quality Impairment; in S. Ahuja., eds, Food, Energy, and Water, Elsevier, Boston, 261, 2015.
  • 9. PRIHATIN J., COREBIMA A.D., ARIFFIN GOFUR A. The Effect of Exposure of Mulberry to Acid Rain on the Defects Cocoon of Bombyxmori L, P. Environ. Sci., 23, 186, 2015.
  • 10. RICE E.W. American Public Health Association, Eds., Standard methods for the examination of water and wastewater, 22th Edn., American Public Health Association, Washington, 2012.
  • 11. HERRING T.A., KING R.W., MCKLUSKY S.C., Gamit Reference Manual GPS Analysis at MIT, Massachusetts Institute of Technology, Boston, 2010.
  • 12. BENNITT G.V., JUPP A. Operational Assimilation of GPS Zenith Total Delay Observations into the Met Office Numerical Weather Prediction Models, Mon. Wea. Rev., 140, 2706, 2012.
  • 13. BOSY J., ROHM W., BORKOWSKI A., KROSZCZYNSKI K., FIGURSKI M. Integration and verifi cation of meteorological observations and NWP model data for the local GNSS tomography, Atmos. Res., 96, 522, 2010.
  • 14. DORN M., BRAGA A.L.S., LLANOS C.H., COELHO L.S. A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides, Expert. Syst. Appl., 39, 12268, 2012.
  • 15. ROEBUCK K. Data Mining: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors, Emereo Publishing, Brisbane, 2012.
  • 16. PARK C.C. Acid Rain (Routledge Revivals): Rhetoric and Reality, Routledge, 2013.
  • 17. PANYAKAPO M., ONCHANG R. A four-year investigation on wet deposition in western Thailand, J. Environ. Sci., 20, 441, 2008.
  • 18. SATOMURA M. et al., On the precipitable water vapor obtained by using GPS observations in Thailand (2001- 2006), Geosci. R. Shizuoka U., 1, 1, 2010.
  • 19. SHI JUNBO, XU CHAOQIAN, GUO JIMING, GAO YANG, Real-Time GPS Precise Point Positioning-Based Precipitable Water Vapor Estimation for Rainfall Monitoring and Forecasting, IEEE. T. Geosci. R emote, 53, 3452, 2015.
  • 20. BLOCKEN B., CARMELIET J. Overview of three state-ofthe-art wind-driven rain assessment models and comparison based on model theory, Build. Environ., 45, 691, 2010.
  • 21. GERIVANI H., LASHKARIPOUR G.R., GHAFOORI M. The Source of Dust Storm in Iran: A case study based on geological information and rainfall data, Carpath. J. Earth. Env., 6, 297, 2011.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-1b0b1779-5a4d-4e78-9964-96e1eded674b
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