Statistical analyses of regional surface water quality in southeastern Ontario

Historical records from Ontario's Provincial Water Quality Monitoring Network (PWQMN) for rivers and streams were analyzed to assess the feasibility of mapping regional water quality patterns in southeastern Ontario which spans two major geologic zones, the Precambrian Shield and the St. Lawrence Lowlands, thus serving as a paradigm for much of Ontario. Despite biases toward the populated Lowlands and associated pollution problems, general spatial trends are evident via the analysis of intervariable relations, individual parameter maps and multivariate analysis. Using a robust algorithm designed to identify outliers and abstract underlying bivariate relations, a linear regional hardness-alkalinity relation was derived, where most anomalies proved to be mine drainage impacted sites. Regionally, multivariate ordination reveals that central tendencies of common indicators of ionic strength and nutrient richness correlate positively and that site clusters broadly reflect the transition from oligotrophic Shield waters to eutrophic conditions of heavily agricultural Lowland streams. Results suggest that on the Shield more precisely delineated aquatic regions may be realized by applying GIS to integrate river and synoptic lake survey data. Further synthesis with bedrock and surficial geology, physiography, pedology and other temporally invariant spatial attributes should yield regional patterns of background quality from which locally attainable water objectives might be defined. Additional synoptic surveys of sedimentary Lowland streams may be required as most available sites relflect anthropogenic pollution.     

Robust graphical methods for diagnosing trend in irregularly spaced water quality time series

Graphical methods can play an important role in the reliable assessment of trends in typically ill behaved river quality data series both as diagnostic tools and as visual corroborative evidence when assumptions required for formal statistical tests are not met. Robust, graphically-oriented trend diagnosis procedures are presented for data series characterized by nonnormal populations, uneven time spacing, nonmonotonic trend and other factors which can create serious problems for standard parametric time series methods. Cleveland's robust locally weighted regression (RLWR) developed for investigating nonlinearity in x-y scatterplots is adapted as a robust/resistant smoothing filter for the analysis of irregular time series comprising quantitative observations. Low powered RLWR trend lines reveal temporally local phenomena, e.g. abrupt jumps (often associated with point source impacts) and periodicities, while higher powered RLWR yields smooth lines characterizing medium and longer term trends. Simple variants of Tukey smoothing concepts are developed for series with censored observations. Applications to Ontario river quality series reveal that graphical evidence is frequently sufficient to obviate the need for formal trend testing. The methods are generally applicable to most time series.