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STATISTICAL METHODS FOR SOURCE APPORTIONMENT OF RIVERINE LOADS OF POLLUTANTS
Author(s) -
GRIMVALL ANDERS,
STÅLNACKE PER
Publication year - 1996
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/(sici)1099-095x(199603)7:2<201::aid-env205>3.0.co;2-r
Subject(s) - apportionment , upstream (networking) , environmental science , pollutant , drainage basin , matrix (chemical analysis) , structural basin , hydrology (agriculture) , disjoint sets , soil science , mathematics , geology , computer science , geography , geotechnical engineering , computer network , paleontology , chemistry , materials science , cartography , organic chemistry , combinatorics , political science , law , composite material
This article shows how information about the riverine load of pollutants at different sites in a study area can be combined with data on the spatial distribution of sources and selected river basin characteristics to carry out a statistical source apportionment of the load observed at the mouth of a river or the outlet of an arbitrary sub‐basin. The suggested approach is based on the following: (i) a partitioning of the study area into disjoint subbasins partially ordered by their upstream–downstream relations; (ii) matrix formulas for the transport and retention of an arbitrary substance in such a system of sub‐basins; (iii) matrix formulas for the source apportionment of the output from an arbitrary sub‐basin; (iv) general principles for the parameterization of losses of substances from soil and retention of substances in lakes and watercourses. As compared to conventional source inventories, our approach offers several advantages: data from sub‐basins with arbitrary upstream–downstream relationships can be combined in the same study; retention and source‐strength parameters can be estimated simultaneously; matrix forms of all models and source apportionment formulas enable parameter estimation by the use of standard methods and software for non‐linear regression analysis. Some of the formulas derived are based on the theory of Markov chains. To illustrate the different steps of the procedure, the time‐averaged output of nitrogen from a Swedish river basin during a five year period was apportioned to various diffuse and point sources.