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Using EPA's Computerized Data Base (STORET) to Analyze for Agricultural Water Pollution
Author(s) -
Reardon John C.,
Hanson Lowell D.,
Randolph John
Publication year - 1982
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq1982.00472425001100030022x
Subject(s) - environmental science , watershed , hydrology (agriculture) , fecal coliform , sampling (signal processing) , agriculture , water quality , pollution , linear regression , environmental engineering , regression analysis , water pollution , nonpoint source pollution , statistics , mathematics , geography , engineering , ecology , geotechnical engineering , electrical engineering , archaeology , filter (signal processing) , machine learning , computer science , biology
The U.S. Environmental Protection Agency's computer data base (STORET) was employed to retrieve stream water quality data for a 9‐year period on an agricultural watershed in Maryland, 40 km northwest of Washington, D.C. Most of the farms in the area are large dairy operations that have waste management and storage facilities. During the study period, farmers increased their use of no‐till and minimum‐tillage corn ( Zea mays L.) practices by as much as 90%, and several operators also installed animal waste‐control facilities. Total PO 4 , fecal coliform, and NO 3 + NO 2 ‐N data from four sampling stations in the watershed were retrieved from STORET and statistically analyzed using linear regression to relate concentration and time. The regressions indicated that concentrations of total PO 4 and fecal coliform decreased with time. Total PO 4 data from all four sampling stations showed a significant inverse relationship between time and concentration (5% probability or less). Of the four fecal coliform regressions, the furthest‐upstream sampling station was significant while the next one downstream approached significance (6% level). No statistical significance was evident in the NO 3 + NO 2‐ N regression equations at any of the four stations during the 9‐year period. Results indicated that the time and costs for this type of analysis were reduced substantially by using the computer‐stored data base rather than direct sampling and laboratory analysis. A study similar to this one, but with in‐house data collection and analysis, would cost approximately 10 times more than the study reported herein.