Hydrochemical constituents in streams may originate from currently active sources at the surface and/or legacy sources from earlier surface inputs, waste deposits and land contamination. Distinction and quantification of these source contributions are needed for improved interpretation of tracer data and effective reduction of waterborne environmental pollutants. This article develops a methodology that recognizes and quantifies some general mechanistic differences in stream concentration and load behavior versus discharge between such source contributions. The methodology is applied to comparative analysis of stream concentration data for chloride (Cl-), copper (Cu), lead (Pb), and zinc (Zn), and corresponding data for water discharge, measured over the period 1990-2018 in multiple hydrological catchments (19 for Cl-, 11 for Cu and Zn, 10 for Pb) around the major Lake Malaren in Sweden. For Cl-, the average load fraction of active sources is quantified to be 19%, and the average active and legacy concentration contributions as 2.9 and 11 mg/L, respectively. For the metals, the average active load fractions at outlets are 1%-3% over all catchments and 9%-14% in the relatively few catchments with mixed metal sources. Average active and legacy concentration contributions are 0.14 and 3.2 mu g/L for Cu, 0.05 and 1.5 mu g/L for Pb, and 1.4 and 12 mu g/L for Zn, respectively. This multi-catchment analysis thus indicates a widespread prevalence of legacy sources, with greater legacy than active concentration contributions for both Cl- and the metals, and active contributions playing a greater role for chloride than for the metals. The relatively simple first-order methodology developed and applied in the study can be used to screen commonly available stream monitoring data for possible distinction of active and legacy contributions of any hydrochemical constituent in and across various hydrological catchment settings.

Legacy pollutants are increasingly proposed as possible reasons for widespread failures to improve water quality, despite the implementation of stricter regulations and mitigation measures. This study investigates this possibility, using multi-catchment data and relatively simple, yet mechanistically-based, source distinction relationships between water discharges and chemical concentrations and loads. The relationships are tested and supported by the available catchment data. They show dominant legacy contributions for total nitrogen (TN), total phosphorus (TP) and total organic carbon (TOC) across catchment locations and scales, from local to country-wide around Sweden. Consistently across the study catchments, close relationships are found between the legacy concentrations of TN and TOC and the land shares of agriculture and of the sum of agriculture and forests, respectively. The legacy distinction and quantification capabilities provided by the data-driven approach of this study could guide more effective pollution mitigation and should be tested in further research for other chemicals and various sites around the world.

Water is a fundamental resource and, as such, the object of multiple environmental policies requiring systematic monitoring of its quality as a main management component. Automatic sensors, allowing for continuous monitoring of various water quality variables at high temporal resolution, offer new opportunities for enhancement of essential water quality data. This study investigates the potential of sensor-measured data to improve understanding and management of water quality at watershed level. Self-organizing data maps, non-linear canonical correlation analysis, and linear regressions are used to assess the relationships between multiple water quality and hydroclimatic variables for the case study of Lake Malaren in Sweden, and its total catchment and various watersheds. The results indicate water discharge from dominant watersheds into a lake, and lake water temperature as possible proxies for some key water quality variables in the lake, such as blue-green algae; the latter is, in turn, identified as a potential good proxy for lake concentration of total nitrogen. The relationships between water discharges into the lake and lake water quality dynamics identify the dominant contributing watersheds for different water quality variables. Seasonality also plays an important role in determining some possible proxy relationships and their usefulness for different parts of the year.