Air emissions from oil sands development can come from a number of sources including industrial smokestacks, tailings ponds, transportation, and dust from mining operations. Air quality monitoring under the Joint Canada-Alberta Implementation Plan for the Oil Sands is designed to determine the contribution of emissions from oil sands activities to local and regional air quality and atmospheric deposition both now and in the future. Ambient air quality data include: - Filter Pack (24-hour integrated concentrations of particle-bound SO2-4, NO-3, Cl-, NH+4, Ca2+, Mg2+, Na+, K+ and gaseous SO2 and HNO3 collected daily by the Canadian Air and Precipitation Monitoring Network) - Total Gaseous Mercury (hourly mixing ratios measured by the Canadian Air and Precipitation Monitoring Network and Prairie and Northern Region) - Atmospheric speciated mercury (Hg) (2-hour average concentrations of gaseous elemental Hg (GEM), reactive gaseous Hg (RGM), and Hg on PM2.5 (total particulate Hg - TPM) - Comprehensive set of measurements collected from an aircraft (various time resolutions) covering an area of 140,000 km2 over the oil sands region - Comprehensive set of measurements collected from the Fort McKay Oski-ôtin monitoring site - Ozone (hourly mixing ratios measured by the Canadian Air and Precipitation Monitoring Network) - Ozone Vertical Profiles (ozone mixing ratios as a function of height) measured by the Canadian Ozone Sonde Network - Aerosol Optical Depth (measure of the degree to which the presence of aerosols in the atmosphere prevents the transmission of light, from the ground to the top of the atmosphere) measured as part of the AErosol RObotic CANadian (AEROCAN) network - Satellite overpass data have a relatively high spatial resolution over the Oil Sands region to produce images and geo-referenced data of nitrogen dioxide (NO2) and sulphur dioxide (SO2) “vertical column density” (which correlates with surface concentration)

Gridded monthly, seasonal and annual mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year.

Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record.

Annual and five-year (5YA) average wet deposition maps for the non-sea-salt sulfate ion are available. The file formats include geodatabase files (*.gdb) compatible with geospatial software (e.g. ESRI ArcGIS) and KMZ files compatible with virtual globe software (e.g. Google Earth™). Maps can also be viewed online via Open Maps and the ArcGIS online viewer. Annual deposition from each site was screened for completeness using the following criteria: (1) precipitation amounts were recorded for >90% of the year and >60% of each quarter, and (2) sulfate concentrations were reported for >70% of the precipitation measured over the year and for >60% of each quarter. Five-year average wet deposition values are averaged annual deposition values with a completeness criterion >60% for the five-year period. Units for wet deposition fluxes are in kg of xSO4 per hectare per year (kg ha-1 y-1). Sources of measurement data and spatial interpolation method are described here: https://doi.org/10.18164/e8896575-1fb8-4e53-8acd-8579c3c055c2. Recommended citation: Environment and Climate Change Canada, [year published]. xSO4 Wet Deposition Maps. Air Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada. [URL/DOI], accessed [date]. Recommended acknowledgement: The author(s) acknowledge Environment and Climate Change Canada for the provision of Canada-U.S. wet deposition kriging maps accessed from the Government of Canada Open Government Portal at open.canada.ca, and the data providers referenced therein.

The Great Lakes Migrant Waterfowl Surveys provide periodic data on waterfowl abundance, spatial and temporal distributions, and use along the shorelines of major water bodies and river systems in Ontario during mostly during spring and fall, and to a lesser extent during summer and winter, seasons. The primary survey area covers the shoreline and nearshore (~1km) waters of the Lower Great Lakes region of Ontario, specifically including the St. Lawrence River, Lake Ontario, Niagara River, Lake Erie, Detroit River and Lake St. Clair and associated major marshes and embayments. Aerial surveys, typically flown several times within spring (March –May: 1969, 1971, 1972, 1975 –1979, 1981, 1982, 1984 –1988, 1991 –1996, 1998 –2003 & 2009 –2011) and fall (September –December: 1968, 1970, 1971, 1974 –2003 & 2009 –2011) survey periods, have been conducted periodically on a relatively regular basis (approx. 5-10 years) along the Lower Great Lakes shorelines between 1968 and 2011. Smaller-scale surveys also have been conducted periodically during summer (June –August: 1968 –1970, 1972, 1974, 1975, 1977, 1982, 1984, 1986, 1989, 1999 & 2002) in this region. This survey often has been conducted in conjunction with the Midwinter Survey, so its data (up to 2004) also are included in the CWS Migrant Waterfowl Surveys database (Year ≥2004 & Month = January & February).Data from several aerial surveys conducted periodically during the non-breeding period outside the Lower Great Lakes region also are included in this database. Spring and fall surveys have been conducted along the shorelines and nearshore waters of the Upper Great Lakes region of Ontario, specifically at St. Clair River (Fall 2012 & 2013), Lake Huron (Fall 1973, 1996; Spring 1974) / Georgian Bay (Fall 1973, 1996, 2012 & 2013) & Lake Superior (Fall 2000). Aerial surveys also have been conducted inland in southeastern Ontario along the Rideau River (Fall 1998 & Spring 1999).

Plant health assessments and vegetation surveys are undertaken at both terrestrial and wetland sites in the oil sands region and in reference areas. Plant monitoring is being conducted for biodiversity and contaminants, and because plants are important both as wildlife habitat and as traditional-use species. Plant and soil samples are collected at monitoring sites near and at varying distances from oil sands operations. Plant tissues are being examined for levels of naphthenic acids (NAs), polycyclic aromatic hydrocarbons (PAHs) and heavy metals. Plant indicator species include Vaccinium spp. (blueberry), Ledum groenlandicum (Labrador tea), Arctostaphyllos uva-ursi (common bearberry), and Cornus canadensis (bunchberry). Soil samples from riparian banks and boreal forest locations are also collected for greenhouse studies. These experiments evaluate the uptake, distribution, and toxicity of the contaminants in plant tissue.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in maximum temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected change in maximum temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected maximum temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of maximum temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled maximum temperature (°C) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Eelgrass (Zostera marina) is important to waterfowl such as Atlantic Brant (Branta bernicla hrota), Canada Goose (Branta canadensis), American Black Duck (Anas rubripes), Common Goldeneye (Bucephala clangula) and Barrow's Goldeneye (Bucephala islandica). In New Brunswick eelgrass can be found along the Gulf of St. Lawrence, in protected harbours. Within this dataset are the results of eelgrass land-cover classifications using either satellite or aerial photography for seven harbours: Bouctouche (46 30’N, 64 39’W); Miscou (47.90 N, -64.55 W); Neguac (47.25 N, -65.03 W); Richibucto (46.70 N, -64.80 W); Saint-Simon (47.77 N, -64.76 W); Tracadie (47.55 N, -64.88 W); and Cocagne (46.370 N, -64.600 W). Information on each dataset is provided: 1. Bouctouche This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 83.7% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837]. 2. Miscou True colour aerial photography at 57 centimetre resolution was collected on August 20th and 24th, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 103 sites. From these sites 70% were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 96.7% of the time (30/31 = 0.967). 3. Neguac This dataset contains results from an eelgrass classification for Neguac Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 126 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 81% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 39 sites that were within the classification area, 27 were correct, 9 were "one-off", and 3 were incorrect [(27 + (9/2))/ 39 = 0.81]. 4. Richibucto Eelgrass classification in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 5. Saint-Simon An eelgrass distribution map was classified from remotely sensed imagery in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 6. Tracadie This dataset contains results from an eelgrass classification for Tracadie Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 101 sites. Approximately two-thirds of these sites were used to assist in image classification, while the remainder was used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 79.3% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 29 sites that were within the classification area, 18 were correct, 10 were "one-off", and 1 was incorrect [(18 + (10/2))/ 29 = 0.793]. 7. Cocagne Visible orthorectified aerial photography was used to classify polygons containing eelgrass in Cocagne Harbour. Field data for image training and validation were collected along transects in summer 2008 using a dGPS positioned towfish holding sidescan sonar and a video camera that was later transcribed as XY geographic points to describe eelgrass presence and a qualitative description of density. The area was flown for photography on September 24, 2008. eCognition Developer 8 software was used to segment the imagery, essentially polygons. Polygons were then classified manually for the presence of eelgrass. Using field data revealed eelgrass presence to be mapped correctly 87.2% of the time.

Water level and discharge data are available from Water Survey of Canada’s Hydrometric Network. The Water Survey of Canada (WSC) is the national authority responsible for the collection, interpretation and dissemination of standardized water resource data and information in Canada. In partnership with the provinces, territories and other agencies, WSC operates over 2500 active hydrometric gauges across the country, maintains an archive of historical information for over 7600 stations and provides access to near real-time (water level and stream flow) provisional data at over 1700 locations in Canada.

Patterns of wet deposition of the nitrate (NO3), non-sea-salt sulfate (xSO4) and ammonium (NH4) ions across areas of Canada and the United States are based on measurements of precipitation depth and ion concentrations in precipitation samples. xSO4 refers to the wet deposition of sulfate with the sea-salt sulfate contribution removed at coastal sites. These measurements were collected and quality controlled by their respective networks: in Canada, the federal Canadian Air and Precipitation Monitoring Network (CAPMoN) and provincial or territorial networks in Alberta, New Brunswick, the Northwest Territories, Nova Scotia, Ontario and Quebec. In the United States, wet deposition measurements were made by two coordinated networks: the National Atmospheric Deposition Program (NADP) / National Trends Network (NTN) and the NADP/Atmospheric Integrated Research Monitoring Network (AIRMoN). Only data from sites that were designated as regionally representative were used in the mapping. Wet deposition amounts were interpolated by ordinary kriging using ArcMap Geostatistical Analyst. The map is limited to the contiguous U.S. and southeastern or southern Canada because outside that region, the interpolation error exceeds 30% due to the larger distances between stations. Links to annual and five-year average maps are available in the associated resources.