Mineralogical, geospatial, and statistical methods combined to estimate geochemical background of arsenic in soils for an area impacted by legacy mining pollution.

The estimation of geochemical background is complex in areas impacted by point sources of atmospheric emissions due to unknowns about pollutant dispersion, persistence of pollutants on the landscape, and natural concentrations of elements associated with parent material. This study combined mineralogical analysis with conventional statistical and geospatial methods to separate anthropogenically impacted soils from unimpacted soils in the Yellowknife area, Northwest Territories, Canada, a region that was exposed to 60 years of arsenic (As)-rich atmospheric mining emissions (1938-1999) and that hosts natural enrichments of As. High concentrations of As (up to 4700 mg kg) were measured in publicly accessible soils near decommissioned roaster stacks in the region and strong relationships between As and distance from the main emission sources persisted in surface soils and soils at depth in the soil profile more than 60 years after the bulk of mining emissions were released.

Intra-lake response of Arcellinida (testate lobose amoebae) to gold mining-derived arsenic contamination in northern Canada: Implications for environmental monitoring.

Arcellinida (testate lobose amoebae) were examined from 40 near-surface sediment samples (top 0.5 cm) from two lakes impacted by arsenic (As) contamination associated with legacy gold mining in subarctic Canada. The objectives of the study are two folds: quantify the response of Arcellinida to intra-lake variability of As and other physicochemical controls, and evaluate whether the impact of As contamination derived from two former gold mines, Giant Mine (1938-2004) and Tundra Mine (1964-1968 and 1983-1986), on the Arcellinida distribution in both lakes is comparable or different.

Diatom ecological response to deposition of the 833-850 CE White River Ash (east lobe) ashfall in a small subarctic Canadian lake.

A <5 mm thick volcanic ashfall layer associated with the White River Ash (east lobe [WRAe]) originating from the eruption of Mount Churchill, Alaska (833-850 CE; 1,117-1,100 cal BP) was observed in two freeze cores obtained from Pocket Lake (62.5090°N, -114.3719°W), a small subarctic lake located within the city limits of Yellowknife, Northwest Territories, Canada.

Late Holocene climatic variability in Subarctic Canada: Insights from a high-resolution lake record from the central Northwest Territories.

We examined late Holocene (ca. 3300 yr BP to present-day) climate variability in the central Northwest Territories (Canadian Subarctic) using a diatom and sedimentological record from Danny's Lake (63.48ºN, 112.

Organic matter control on the distribution of arsenic in lake sediments impacted by ~65years of gold ore processing in subarctic Canada.

Climate change is profoundly affecting seasonality, biological productivity, and hydrology in high northern latitudes. In sensitive subarctic environments exploitation of mineral resources led to contamination and it is not known how cumulative effects of resource extraction and climate warming will impact ecosystems. Gold mines near Yellowknife, Northwest Territories, subarctic Canada, operated from 1938 to 2004 and released >20,000t of arsenic trioxide (AsO) to the environment through stack emissions.

Lacustrine Arcellinina (Testate Amoebae) as Bioindicators of Arsenic Contamination.

Arcellininids (testate amoebae) were examined from 61 surface sediment samples collected from 59 lakes in the vicinity of former gold mines, notably Giant Mine, near Yellowknife, Northwest Territories, Canada to determine their utility as bioindicators of arsenic (As), which occurs both as a byproduct of gold extraction at mines in the area and ore-bearing outcrops. Cluster analysis (Q-R-mode) and detrended correspondence analysis (DCA) reveal five arcellininid assemblages, three of which are related to varying As concentrations in the sediment samples. Redundancy analysis (RDA) showed that 14 statistically significant environmental parameters explained 57 % of the variation in faunal distribution, while partial RDA indicated that As had the greatest influence on assemblage variance (10.