X77e.^i THE ACIDIFICATION OF ONTARIO LAKES: AN ASSESSMENT OF THEIR SENSITIVITY AND CURRENT STATUS WITH RESPECT TO BIOLOGICAL DAMAGE JANUARY 1990 Environment Environnement Ontario Jlm Sradley, Minister/ministre ISBN:0-7729-6550-l THE ACIDIFICATION OF ONTARIO LAKES: AN ASSESSMENT OF THEIR SENSITIVITY AND CURRENT STATUS WITH RESPECT TO BIOLOGICAL DAMAGE Prepared By; B.P. Neary P.J. Dillon J.R. Munro B.J. Clark Limnology Section DORSET RESEARCH CENTRE JANUARY 1990 o Copyright: Queen's Printer for Ontario, 1990 This publication may be reproduced for non-commercial purposes with appropriate attribution. Table of Contents Summary xi Sommaire xii Acknowledgements xiii 1. Introduction 1 2. Data Sources 2 2.1 Lake Surveys in the Data Base 2 2.1.1 Ministry of the Environment Studies 2 2.1.2 Ministry of Natural Resources Studies 10 2.1.3 University Studies 11 2.1.4 Federal Government Studies 11 2.2 Data Available 12 2.3 Representativeness of the Sample 12 2.3.1 Geographic Extent of the Sample 12 2.3.2 Size Distribution of Lakes in the Sample 14 2.3.3 Sample Date 15 2.3.4 Sample l^pe 16 3. Measured Lake Chemistry 19 3.1 Cations 19 3.1.1 pH 19 3.1.2 Calcium 21 3.1.3 Magnesium 21 3.1.4 Sodium 25 3.1.5 Potassium 25 32 Anions 25 32.1 Alkalinity 25 3.2.2 Sulphate 31 32.3 Chloride 31 32.4 Nitrate 34 3.3 Metals 34 3.3.1 Total Aluminum 34 3.32 Iron 37 3.3.3 Manganese 37 3.4 Other Data 40 3.4.1 Conductivity 40 3.42 Apparent Colour 40 3.4.3 True Colour 40 3.4.4 Dissolved Organic Carbon 44 4. Calculated Parameters 46 4.1 Estimated DOC 46 4.1.1 DOC and Apparent Colour 46 4.12 Dissolved Organic Carbon and True Colour 47 42 Organic Anions 48 4.3 Estimated Chloride 51 5. Data Validation 55 5.1 Ion Balancing 55 52 Calculated Conductivity 58 53 Other Data Relationships 60 5.4 Data Quality Code 60 6. Sulphur Deposition Zones in Ontario 64 6.1 Sources of Sulphur Deposition 64 6.2 Sulphur Deposition Data 65 6.3 Sulphur Deposition Mapping 68 6.4 Water Chemistry Mapping 68 6.5 Defining the Sulphur Deposition Zones 72 7. Chemical Characteristics of Lakes by Sulphur Deposition Zone 77 7.1 Estimate of Lake Numbers and Area by Deposition Zone 77 12 Lake Size Distribution in the Database 78 7.3 Lake Water Chemistry Stratified by Deposition Zone 80 7.3.1 Data Exclusions 80 7.3.2 Cations 81 7.3.3 Anions 88 7.3.4 Conductivity 90 7.4 Lake Water Chemistry Stratified by Conductivity Class and Sulphur Deposition 2^ne 95 1A2 Cations 98 7.4.3 Anions 104 7.4.4 Conductivity _ 116 7.5 Lake Water Chemistry Stratified by Lake Size, Conductivity Class and Sulphur Deposition Zone 118 7.5.1 Cations 118 7.5^ Anions 125 8. Estimates of Lake Resources Affected by Acid Deposition 135 8.1 Critical pH and Alkalinity Levels at the Deposition Zone Level 135 8.2: Estimation of Critical pH and Alkalinity Levels for Well Sampled Watersheds 141 References 143 111 List of Tables Table 2.1 Table 22: Table 2.3: Table 2.4: Table 3.1: Table 32: Table 3.3: Table 4.1: Table 4.2: Table 4.3: Table 4.4: Table 4.5: Table 5.1: Table 6.1: Table 62: Table 7.1: Table 72: Table 7J: Table 7.4: Summary of data sources, and criteria for, and known biases in, lake selection. 3 Number of lakes with measurements made for each parameter of interest. The total number of individual lakes included in the OASD is 6000. 13 Size distribution of lakes Ontario and of those sampled in this study 14 Sample type and sampling season of lakes in database 18 Statistical summary for pH and base cations (^ieq L"'^) 21 Statistical summary for alkalinity and major anions in solution (/xeq L-') 31 Statistical suimnary for aluminum, iron, and manganese (ixg L'^) 37 Relationship between DOC and apparent colour. DOC is treated as the dependent variable. 47 Relationship between DOC and true colour. DOC is treated as the dependent variable. 48 Statistics for DOC data including measured and estimated (from apparent and true colour) values (mg C L'^) 49 Statistics for estimates of organic anion concentration (/xeq L"^) 49 Statistics for CI data, including measured (n = 996) and estimated (n = 2067) values 54 Simimary of % difference between cations and anions 58 Sulphur deposition data 65 Sulphur deposition zones in Ontario 76 Number of lakes in each of four size classes by sulphur deposition zone 77 Total area of lakes in each of four size classes by sulphur deposition zone 78 Lake size distribution by deposition zone 80 Sunmiary of pH and base cation statistics stratified by deposition zone 81 Table 7.5: Summary of anion statistics stratified by deposition zone (alkalinity is treated as an anion since it is assumed to approximate HCO3 + CO3' ). A"is estimated organic anion concentration. Table 7.6: Summary of statistics for lake conductivity stratified by sulphur deposition zone Table 7.7: Lake size statistics Table 7.8: Summary of cation statistics for low conductivity (< 50 iiS) lakes Table 7.9: Summary of cation statistics for medium conductivity (50-100 /xS) lakes Table 7.10: Summary of cation statistics for high conductivity (> 100 /xS) lakes Table 7.11: Summary of anion statistics for low conductivity (< 50 iiS) lakes Table 7.12: Summary of anion statistics for medium conductivity (50-100 mS) lakes Table 7.13: Summary of anion statistics for high conductivity (> 100 /iS) lakes Table 7.14: Conductivity statistics for lakes in the three conductivity classes Table 8.1: Estimates of numbers of small lakes (1-9.9 ha) with pH less than 5.0, 5.5 and 6.0 by deposition zone. Definitions as on p. 135. Table 8.2: Estimates of numbers of medium-sized lakes (10-99 ha) with pH less than 5.0, 5.5 and 6.0 by deposition zone. Definitions as on p. 135. Table 8.3: Estimates of numbers of large lakes (100-999 ha) with pH less than 5.0, 5.5 and 6.0 by deposition zone. Definitions as on p. 135. Table 8.4: Estimates of number of small lakes (1-9.9 ha) with alkalinity less than 0, 20 and 40 neq L'^ by deposition zone. Definitions as on p. 135. Table 8.5: Estimates of number of medium lakes (10-99 ha) with alkalinity less than 0, 20 and 40 ^eq L"^ by deposition zone. Definitions as on p. 135. Table 8.6: Estimates of number of large lakes (100-999 ha) with alkalinity less than 0, 20 and 40 /xeq L'^ by deposition zone. Definitions as on p. 135. Table 8.7: Estimates of lake acidification estimated by pH for well sampled watersheds Table 8.8: Estimates of lake acidification estimated by alkalinity for well sampled watersheds 95 97 101 102 103 109 110 111 116 137 137 138 138 139 139 142 142 List of Figures Figure 2.1: Percent of lakes sampled in different parts of Ontario. Data are grouped by tertiary watershed. 15 Figure 22: Distribution of lakes by year (top) and by month of year (bottom) sampled. There are 57 lakes included where the sampling time is not known. 17 Figure 3.1: Distribution of pH in Ontario lakes. 20 Figure 3.2: Distribution of calcium in Ontario lakes. 22 Figure 3.3: The relationship between calcium and magnesium in Ontario lakes. 23 Figure 3.4: Distribution of magnesium in Ontario lakes. 24 Figure 3.5: The relationship between sodium and chloride in Ontario lakes. 26 Figure 3.6: Distribution of sodium in Ontario lakes. 27 Figure 3.7: Distribution of potassium in Ontario lakes. 28 Figure 3.8: The relationship between calcium and alkalinity in Ontario lakes. 29 Figure 3.9: Distribution of alkalinity in Ontario lakes. 30 Figure 3.10: Distribution of sulphate in Ontario lakes. 32 Figure 3.11: Distribution of chloride in Ontario lakes. 33 Figure 3.12: Distribution of nitrate in Ontario lakes. 35 Figure 3.13: Distribution of aluminum in lakes. 36 Figure 3.14: Distribution of iron in lakes. 38 Figure 3.15: Distribution of manganese in lakes. 39 Figure 3.16: Distribution of conductivity in lakes. 41 Figure 3.17: Distribution of apparent colour in lakes. 42 Figure 3.18: Distribution of true colour in lakes. 43 Figure 3.19: Distribution of DOC in Ontario lakes. 45 Figure 4.1: Distribution of organic anions in Ontario lakes. 50 Figure 42: The relationship between sodium and chloride in Ontario lakes. 52 Figure 4.3: Distribution of estimated chloride in Ontario lakes. 53 Figure 5.1: Observed percent ion difference for lakes in the database where calculation was possible. 57 Figure 5.2: Measured vs calculated conductivity for all lakes. 59 Figure 5.3: Measured vs calculated conductivity for all lakes below 100 mS. 61 Figure 5.4: pH and alkalinity for lakes < 200 Meq.L'\ Rejected lakes were based on charge balance, theoretical vs measured conductivity or by this relationship. 62 Figure 6.1: Wet vs dry sulphur deposition at 20 stations in Ontario. 67 Figure 6.2: Sulphur deposition (g S.m"^.yr"^) in 1983. 69 Figure 6.3: Location of lakes with SO^. and alkalinity data between latitude 45°00-50°00 and longitude 77°00-86°00. 70 Figure 6.4: Distribution of sulphate concentration (/jeq.L"^) in lakes shown in Figure 6.3. 71 Figure 6.5: Areas where lakes shown in Figure 6.3 have a ratio of SO^ to (SO^ + alkalinity) greater than 0.7. 73 Figure 6.6: Delineation of total sulphur deposition zones in Ontario, including zone 7, believed to be affected by SO2 point sources at Sudbury. 75 Figure 7.1: Lake area distribution shown as percent of lakes sampled for each sulphur deposition zone. 79 Figure 12: pH distribution shown as percent of lakes sampled for each sulphur deposition zone. 83 Figure 7.3: Calcium distribution shown as percent of lakes sampled for each sulphur deposition zone. 84 Figure 7.3: Magnesium distribution shown as percent of lakes sampled for each sulphur deposition zone. 85 Figure 7.5: Sodium distribution shown as percent of lakes sampled for each sulphur deposition zone. 86 Figure 7.6: Potassium distribution shown as percent of lakes sampled for each sulphur deposition zone. 87 Figure 7.7: Sulphate distribution shown as percent of lakes sampled for each sulphur deposition zone. 91 Figure 7.8: Alkalinity distribution shown as percent of lakes sampled for each sulphur deposition zone. 92 Figure 7.9: Organic anion distribution shown as percent of lakes sampled for each sulphur deposition zone. 93 Figure 7.10: Chloride distribution shown as percent of lakes sampled for each sulphur deposition zone. 94 Figure 7.11: Conductivity distribution shown as percent of lakes sampled for each sulphur deposition zone. 96 Figure 7.12: pH by conductivity class and deposition zone. 99 Figure 7.13: Calcium by conductivity class and deposition zone. 100 Figure 7.14: Magnesium by conductivity class and deposition zone. 105 Figure 7.15: Sodium by conductivity class and deposition zone. 106 Figure 7.16: Potassium by conductivity class and deposition zone. 107 Figure 7.17: Sulphate by conductivity class and deposition zone. 108 Figure 7.18: Alkalinity by conductivity class and deposition zone. 1 13 Figure 7.19: Organic anions by conductivity class and deposition zone. 1 14 Figure 7.20: Chloride by conductivity class and deposition zone. 1 15 Figure 7.21: Conductivity by conductivity class and deposition zone. 117 Figure 7.22: Relationship between S deposition (expressed at S deposition zone number) and mean pH for lakes with conductivity < 50 ^S bylake size class. 119 Figure 7.23: Relationship between S deposition (expressed at S deposition zone number) and mean pH for lakes with conductivity 50-100 ^iS bylake size class. 120 Figure 7.24: Relationship between S deposition (expressed at S deposition zone number) and mean pH for lakes with conductivity > 100 mS bylake size class. 121 Figure 7J5: Relationship between S deposition (expressed at S deposition zone number) and mean base cations for lakes with conductivity < 50 mS bylake size class. 122 Figure 7.26: Relationship between S deposition (expressed at S deposition zone number) and mean base cations for lakes with conductivity 50-100 nS bylake size class. 123 Figure 121: Relationship between S deposition (expressed at S deposition zone number) and mean base cations for lakes with conductivity > 100 /iS bylake size class. 124 Figure 7^8: Relationship between S deposition (expressed at S deposition zone number) and mean sulphate for lakes with conductivity < 50 nS bylake size class. 126 Figure 729: Relationship between S deposition (expressed at S deposition zone number) and mean sulphate for lakes with conductivity 50-100 mS bylake size class. 127 Figure 7.30: Relationship between S deposition (expressed at S deposition zone number) and mean sulphate for lakes with conductivity > 100 mS bylake size class. 128 Figure 7.31: Relationship between S deposition (expressed at S deposition zone number) and mean alkalinity for lakes with conductivity < 50 ixS bylake size class. 129 Figure 7.32: Relationship between S deposition (expressed at S deposition zone number) and mean alkalinity for lakes with conductivity 50-100 mS bylake size class. 130 Figure 7.33: Relationship between S deposition (expressed at S deposition zone number) and mean alkalinity for lakes with conductivity > 100 mS bylake size class. 131 Figure 7.34: Relationship between S deposition (expressed at S deposition zone number) and mean organic anions for lakes with conductivity < 50 /xS bylake size class. 132 Figure 7.35: Relationship between S deposition (expressed at S deposition zone number) and mean organic anions for lakes with conductivity 50-100 /LiS bylake size class. 133 Figure 7.36: Relationship between S deposition (expressed at S deposition zone number) and mean organic anions for lakes with conductivity > 100 mS bylake size class. 134 Figure 8.8: Map showing areas of estimates by lake size. 140 Page List of Appendices Appendix A: Number of Lakes Sampled by Watershed 147 Appendix B: Lakes Sampled by Watershed and Lake Size 153 Appendix C: Surmnary of analytical procedures for water chemistry parameters 157 Appendix D: Watersheds included in each Deposition Zone 159 Summary This report describes the water chemistry of 6,000 lakes in Ontario. Trends in some water chemistry parameters are strongly correlated with acid deposition rate. Although this sample represents a small fraction of the estimated 262,000 lakes in Ontario, there are regions of the province where the lake sample is sufficiently large to allow estimates of the number of lakes at various stages of acidification to be made. Major conclusions of the report are: - Atmospheric deposition of sulphate is the major source of sulphate in Ontario lakes, and is strongly correlated with significant reduction of pH and acid neutralizing capacity in low-ionic strength (conductivity < 50 ^S) lakes. - There are at least 19,000 lakes in Ontario estimated to have been acidified to the point (pH < 6.0) where adverse biological effects have occurred. An additional number of lakes have undoubtedly been acidified, but the subsample of smaller lakes in much of Ontario was too small to permit accurate estimation of the numbers. - The majority of the acidified lakes (over 11,000) are in central Ontario, where total annual sulphur deposition exceeds 0.75 gS m'^ yr"\ This deposition region includes Haliburton, Muskoka, Parry Sound, Nipissing, Timiskaming, Sudbury and Algoma. - A large number of the acidified (7,300) lakes are in the Sudbury area, and have been acidified primarily by past sulphur emissions from the Sudbury smelters. - There are at least 7,250 lakes in Ontario estimated to be very acidic, with all of their acid neutralizing capacity eliminated (alkalinity < 0). - For a small subsample of watersheds in Ontario, enough data were available to permit very accurate estimations of all sizes of lakes in various stages of acidification. In these watersheds, an estimated 7,232 lakes (standard error of 308) have pH < 6.0. Of these lakes, 1978 (standard error of 37) are estimated to have alkalinity < 0. - Acidification due to natural organic acids is not a major factor in explaining the acidification of lakes. Sommaire Ce rapport presente les caracteristiques chimiques de 6000 lacs en Ontario. Les tendances dans quelques parametres chimiques sont fortement en correlation avec le taux du dep8t acide. Puisqu'on estime qu'il y a 262000 lacs en Ontario, les 6000 lacs analyses ne representent qu'une petite portion de ce total. Tout de meme, il y a des regions dans la province ou le nombre de lacs echantillonnes suffise pour estimer le nombre de lacs aux differents niveaux d'acidification. Ce rapport a conclu les points suivants: - Le dep6t atmospherique de sulfate contribue la plupart du sulfate aux lacs Ontariens, et est fortement en correlation avec la reduction significative de pH et du potentiel de neutralisation de 1 'acide dans les lacs de basse teneur ionique (ou la conductivite < 50 mS). - On estime qu'il y a au moins 19000 lacs en Ontario qui sont si acidifies qu'ils ont subi des effets biologiques adverses (pH < 6.0). Sans doute, il y a plus des lacs qui soni acidifies mais le sous-echantillon de plus petits lacs n'etait pas suffisant pour estimer ce nombre plus exactement. - Le plus grand nombre des lacs acidifies (plus que 11000) sont dans la region centrale de i'Ontario, ou le depot total annuel de sulfate surpasse 0.75 g S m'^ yr"\ Cette region de depot comprend Haliburton, Muskoka, Parry Sound, Nipissing, Timiskaming, Sudbury et Algoma. - Un grand nombre des lacs acidifies (7300) sont dans la region de Sudbury, et sont acidifies principalement par les emissions historiques d 'anhydride sulfureux des fonderies de metaux non-ferreux a Sudbury. - On estime qu'il y a au moins 7250 lacs en Ontario qui sont tres acidifies et qui ont perdu tout leur potentiel de neutralisation de I'acide (alcalinite < 0). - Pour un petit nombre des bassins hydrographiques en Ontario, les donnees suffisent a faire des approximations tres exactes des lacs de toutes grandeurs aux differents niveaux d'acidification. Pour ces bassins, on estime que 7232 lacs (ecart-type de 308) ont un pH < 6.0. De ces lacs, on estime que 1978 (ecart-type de 37) ont une alcalinite < 0. - Le r81e des acides organiques naturels n'estpas important pour I'acidification des lacs. Acknowledgements The acquisition and organization of data in this report are the result of the efforts of many people. The authors would like to gratefully acknowledge the contributions of Donna Wales and Doug Dodge of the Ministry of Natural Resources, Bill Keller, Len Maki, Dave Hollinger, Reg Genge, Jan Beaver, Ron Reid, and Bob Girard of the Ministry of Environment. Data collected from surveys conducted by Harold Harvey of the University of Toronto, Jim Kramer of McMaster University, and John Kelso of the Department of Fisheries and Oceans are included in this report. The assistance of Sheryl Gleave in typing (and re-typing) this report is gratefully acknowledged. 1. Introduction The Ontario Acid Sensitivity Database (OASD) is a compilation of chemical data collected on about 6000 lakes in Ontario. These data were derived from a number of studies, most of them conducted as part of the Acid Precipitation in Ontario Study (APIOS). Prior to these surveys, little was known about the geographic distribution of water chemical parameters in Ontario. Some rudimentary information was available as part of data collected by the Ontario Ministry of Natural Resources' (MNR) aquatic habitat surveys, but the chemical data were usually obtained using field analytical kits by personnel unskilled in analytical chemistry, and were considered to be semi-quantitative. Some fundamentals were known: lakes on the Canadian Shield were primarily softwater, lakes in the Clay Belt and in areas of calcareous sedimentary rock were hardwater, and there were acidic lakes in the vicinity of smelting and sintering operations in Sudbury and Wawa (Beamish and Harvey, 1972; Somers and Harvey, 1984). Other than these general observations, data were only available on specific lakes from detailed studies conducted by university or Ministry of the Environment (MOE) researchers, and most of these studies were related to lake eutrophication or metal contamination. Exceptions were several surveys conducted by the Ministry of the Environment's northeastern region. These surveys were designed to delineate the extent of the Sudbury acidification zone (Conroy et al. 1978; Pitblado et al. 1980; Keller et al 1980). The first evaluation of the extent and magnitude of the acidification problem in Ontario was conducted in 1980 (MOE 1980). Several other compilations of these and other data were made (MOE 1981,1982,1983, MNR 1987) and parts of these data have been published elsewhere (Jeffries 1986, Neary and Dillon 1988). This report provides an update of the acid sensitivity data on lakes in Ontario, documents the analytical methods and data sources, and provides estimates for some areas of the province of the total number of lakes in various pH and alkahnity classes, which are interpreted in terms of the aquatic resources at risk in the province. Finally, the number of lakes in which the aquatic biota have been deleteriously affected is estimated. 2. Data Sources Most of the data were collected as part of the Aquatic Effects Programme of APIOS, but data from universities and other government agencies were included provided that similar analytical methods were used. It was a prerequisite that Gran titration alkalinity (equivalence-point titration) rather than a fixed-endpoint titration alkalinity be measured. This effectively eliminated all but a few data sets collected before 1979. A brief description of each survey with a discussion of known lake sampling biases is included in Table 1.1. 2.1 Lake Surveys in the Data Base 2.1.1 Ministry of the Environment Studies Data on 2912 of the lakes are derived from studies carried out by various groups within MOE. These include: Northwestern Region - Data for 649 lakes from annual surveys conducted between 1980 and 1988. Chemical analyses were conducted at MOE laboratories at Thunder Bay and Toronto. The two most recent surveys (1987 and 1988; each with 100 lakes) covered large areas of the province with no previously measured lake water chemistry. These lakes were selected in a randomized design within lake size strata. Other surveys were focused on the vicinity of the Atikokan generating station, and in the Pukaskwa Park area. Data on 589 of the lakes include major anions and cations. Overall, however, the sample from the northwest is biased strongly to large lakes (median size = 188 ha). Northeastern Region - Data are available on 928 lakes in the Northeast. Most of the lakes are south of latitude 50°, and many of the surveys were conducted within the Sudbury acidification zone (defined in Section 6.5). Annual 'random lake' surveys were conducted within the region from 1979 to 1984, v^dth periodic reassessments of over 200 lakes ongoing. Samples were analyzed at the MOE labs in Rexdale and Thunder Bay, and complete anion and cation data are available for 414 of the lakes. Overall, the sample is biased toward large lakes, with the median lake size being 70 ha. Over 200 of the lakes ^■^ == c EM crt > ' > o V o CQ u t« N ra Vi £ C >^ ^ o l> c •^C t/5 o c on o > C c/3 2 £:' 5 ^' CO CO I o c •a c CO -a c CQ E 6 s e2 u 3 O z u z o > z 0-1 o Z o o '5 u o Z gB' ra » O X3 ro V! t; "D -3 CO 5 - - c > X) 5 u "O O- o >< o o ra C/! V5 CO E O ^ < CO c 9. 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D. o c^ 2 :: an O u, *-* ca "O o c x: c S 5 E iS ^ o O •^ 00 OC 00 oc Qs On On gB gl ^ E o L ■=: D 3 hJ CO c/O o •a o o 60 O (U '5b ^ 3 c ^ c < •a ■*- C '-^ OJ o "^ >- .X oj re ^ CO 3 Jj >, J,| c re < ty^ C- bt u CO oa u "O u CO D. u trt j: c •^- o o V >,'C J4 U CO so — "a CO o c u- c u. 'ob C a c 03 t_) ^ CO O < p 03 c H O 1^ H .5 o CO c crag o U 3 c>5 u O 5 w a: on E O H Z H < UJ Q C/D m o rvi Q 00 00 0\ OS C >- 00 __ e:^ 3 -^ o Di CO c £rc): UU c o CO z c > c 1? 3 CO GD 0- > z E ^ El Et3 were sampled as part of a research project to determine the feasibihty of using remote sensing to determine the acidification status of lakes (Pitblado,1988). The lakes were selected from two discrete areas, one in Algoma and the other in Sudbury, and they represented all lakes visible to LANDSAT (> 1 ha) within those areas. These two surveys contain most of the smaller lakes in the data set from the northeast region. Central Region - Data on 123 lakes in central Ontario from surveys completed in 1980, 1981, and 1982. 68 of the lakes have complete anion and cation data, while 55 have pH, alkalinity, and conductivity only. All analyses were performed at the MOE Rexdale laboratories. Lake selection was biased towards those used for recreation, so the median lake size is a little large (39 ha) to be representative of the distribution of lake sizes in this area of the province. Southeastern Region - Data on 627 lakes in southeastern Ontario, both on and off the Shield. Most of the data comes from surveys conducted in 1981, 1983 and 1988. Most of the lakes (433) have pH, alkalinity, and conductivity data only. Samples were analyzed in the MOE labs at Kingston and Rexdale. These surveys were random, and included both large and very small lakes. The median lake size of 19.5 ha makes these data the most representative of any of the regional surveys. Surveys in 1987 and 1988 were specifically designed to obtain complete water chemistry from smaller lakes in the region. Dorset Research Centre - 520 lakes in central Ontario were sampled by staff of the Limnology Section, Dorset Research Centre. Some data are from lakes studied much more intensively as part of the chemical studies of APIOS, and some data are from lakes being studied under other programmes. Most samples were taken in surveys in 1980, 1984, and 1988. Almost all (491) of the lakes have complete anion and cation data. Samples were all analyzed at the MOE labs in Dorset or Rexdale. The median size of the lakes is 26 ha, which is slightly skewed toward larger lakes. MOE/MNR Combined studies - There are two joint studies with lake information represented in the data base. One is a set of 35 lakes in the Sudbury area sampled in 1980. The median lake size is 55 ha. Since all of these lakes either support or used to support lake trout populations, the lakes tend to be larger and deeper than most lakes. The other is a set of 18 larger lakes from central Ontario (median of 80 ha) which were evaluated as part of a selection process for lakes to be studied more intensively for fisheries. All of these lakes have relatively complete water chemistry, and the samples were analyzed at the MOE laboratories in Rexdale. 2.12 Ministry of Natural Resources Studies A total of 2540 lakes in the data base have been sampled by the Ministry of Natural Resources. These surveys include: Regional Surveys - Various sampling programmes conducted by MNR Regions and Districts between 1979 and 1981 yielded data for 1058 lakes. Lake selection was biased towards lakes with known or suspected sports fisheries and was strongly skewed to larger lakes (median size 86 ha). Many lakes were drawn from the OFIS (Ontario Fisheries Information System) data base, which includes fish presence/absence information. The chemical data include only pH, alkalinity, and conductivity with the pH and conductivity determined in the field with portable meters. Fisheries Branch Acidification Programme - Data are included for 1467 lakes surveyed between 1982 and 1985 as part of the acidification programme of the Ministry of Natural Resources. These surveys included specific studies where all lakes greater than 5 hectares within a tertiary watershed were analyzed. Samples were mostly taken through the ice as a 5 m composite, and were analyzed at the MOE labs in Dorset or Rexdale. Data include pH, alkalinity, conductivity, colour, DOC, metals, and all major cations and anions except chloride. Overall, the median lake size of 34 ha makes the collection slightly skewed toward larger lakes, although data are available for many small lakes. Algoma Fisheries Assessment Unit - Data on 5 large lakes (median size 111 ha) was obtained from the MNR Algoma Fisheries assessment unit. Data on major anions and cations are available. Conductivity and pH were determined with field meters. 10 2.1.3 University Studies University of Toronto (Zimmerman and Harvey 1979) - Data were obtained in 1978 from 298 lakes in both low and high acid deposition areas. Conductivity and pH were determined by portable meters (Sargent-Welch PBL and Barnstead PMC-51, respectively), and Gran alkalinity was measured by titration in a field laboratory. Although a wide range of lake sizes were sampled, the sample is skewed toward larger lakes (median 68 ha). The lakes were selected based on the data in the Ontario Fisheries Information System . From that data base, lakes with low pH and total dissolved solids were selected, with an emphasis on any headwater lakes. Areas of Ontario known to have mostly hardwater lakes were avoided, introducing a bias toward softwater lakes. Since the original OFIS data base is strongly biased toward large lakes supporting important fisheries, this bias was also carried over to the subset selected by Zimmerman and Harvey. McMaster University (Kramer, 1979) - Study lakes were located in the Quetico- Atikokan region. This region was identified as having low alkalinity in a 1977 survey. Tube composite (6 m depth) or 1 m "grab" samples from 55 lakes were taken in the summer of 1978. The survey is strongly biased toward large lakes (median size 96 ha). Conductivity and pH were determined by field measurements, with the pH verified on a subset of the lakes by laboratory analysis. All alkalinities were determined in a field laboratory by Gran titration. 2.1.4 Federal Government Studies Department of Fisheries and Oceans (DFO) National Inventory - data from a survey of 176 headwater lakes across Ontario. Chemical analyses were conducted by Enviroimient Canada. The lakes were all headwater lakes, and the median lake size was small (4.5 ha). Lake selection methodology and details of analytical methods are described in Kelso et al. (1986). Major cation and anion data, with the exception of potassium, are available for all of the lakes. Sulphate was analyzed colorimetrically (methylthymol blue), atypical to the other surveys represented here. 11 DFO Ontario Survey - 41 lakes in the Sudbury area of Ontario were sampled in 1982. Cation and anion analyses were performed by Environment Canada. The median lake size was 11 ha, and the sample represented all of the lakes greater than 1 ha in one river system (the Mahzenazing River). 2.2 Data Available As described above, there is not always consistency in the number of chemical parameters for each lake. In some of the earlier surveys, only pH, alkalinity, and conductivity were measured. Later surveys have usually measured all of the major cations and anions, as well as nutrients and selected metals. The number of lakes with data for each parameter is shown in Table 2.2. 2.3 Representativeness of the Sample 2.3.1 Geographic Extent of the Sample The exact number of lakes in Ontario is unknown. Further, there is no consensus about what constitutes a lake. For the purposes of this report, the term 'lake' is taken to be a non-ephemeral water body greater than or equal to 1 ha in size. A very small number of water bodies less than 1 ha are represented in the OASD. The best estimate of the number of lakes in Ontario can be found in Cox (1978), who attempted to measure and enumerate all of the lakes. Although Cox enumerated water bodies less than 1 ha in size in some areas of the province, the results excerpted here are only for water bodies greater than or equal to 1 ha in size. Even at that size, lakes could not be directly enumerated from available topographic maps because much of northern Ontario had been mapped at a minimum scale of 1:250,000. At that scale, lakes less than 10 ha are not mapped. 12 Table 22: Number of lakes with measurements made for each parameter of interest. The total number of individual lakes included in the OASD is 6000. Parameter Number of Lakes Physical Data: Lake Area 6000 Chemical Data: pH 5982 Gran Alkalinity 5911 Cations: Calcium 3702 Magnesium 3591 Sodium 3304 Potassium 3145 Anions: Sulphate 3599 Chloride 996 Nitrate 377 Other: Conductivity 5617 Colour (apparent) 2066 Colour (true) 605 Dissolved Organic Carbon 2581 Metals: Aluminum 3264 Iron 1267 Manganese 2952 Cox developed 2 methods for estimating the number of lakes in the 1-9.9 ha range, based on the distribution and numbers of larger water bodies. To estimate the number of lakes in the 1-9.9 ha size range, we have taken as the population of lakes in the northern watersheds the most conservative of the estimates made by Cox. The location of lakes in this sample are unevenly distributed throughout the province (Figure 2.1). This is in part due to the biases in the various surveys discussed in Section 2.1, and partly due to the remote nature of many of the northern lakes. 13 2.3.2 Size Distribution of Lakes in the Sample Cox estimated that there were 262,762 lakes in Ontario greater than 1 ha in size (excluding the Great Lakes). The size distribution of the lakes in the province is shown in Table 2.3, along with the number of lakes in the OASD in each size category. The lake size distribution represented in the OASD is skewed strongly to larger lakes, ranging from less than 1 percent of lakes in the 1-9.9 ha range to 37 percent of the lakes greater than 1000 ha. This is primarily due to bias built in to the sampling design, where lakes with known sport fisheries being more likely to be sampled than lakes with unknown fishery status. In Ontario, the lakes that have been assessed for fisheries tend to be the larger lakes. There were, however, several surveys which attempted to collect data representative of all lake sizes. As a result, some individual watersheds have good representation in all size ranges. Based on these numbers, pH data are available for 2.29% of the estimated number of lakes greater than 1 ha in the province (see Appendix A). Table 2.3: Size distribution of lakes Ontario and of those sampled in this study Lake Size Range Ontario Total Number Sampled % 39 ? 1011 0.59 2913 3.59 1701 17.53 375 37.68 Total 262762 6000 2.29 < 1 ha ? 1-9.9 ha 170668' 10-99 ha 81426 100-999 ha 9771 > 1000 ha 897 * estimated by Cox (1978) 14 Figure 2.1: Percent of lakes sampled in different parts of Ontario. Data are grouped by tertiary watershed. 2.3.3 Sample Date The lakes in this data base were sampled between 1976 and 1989 with the most lakes sampled in 1980 and 1981. The number of lakes sampled in each year is shown in Fig. 2.2. February is the month with the most lakes sampled; a histogram with the month of sampling is shown in Figure 2.2. The influence of sample collection time can be quite marked in smaller lakes, particularly for pH and alkalinity (Jeffries et al, 1979, Stoddard, 1987). Because only a very small number of lakes were sampled more than once, this effect is not considered here. 15 2.3.4 Sample T^Tpe Because this database results from a compilation of data collected as part of many studies, there were differem sampling methods used. The vast majority of the samples were composite (unweighted) samples collected to either 5 m or a lesser depth in the case 16 1200 ^ 1000 CL E D cn 800 w 0) O 600 E :3 400 200 ^^ f^ K\1 K\1 KX1 K 'Nl ro3 K" n=5943 :^ ix^ i. 78 79 80 81 82 83 84 85 86 87 88 89 Year 1200 h 0) Q. 1000 E D 800 - (0 6.00 and pH < 6.50. 3.1 Cations 3.1.1 pH As discussed in Appendbc C, measured pH was either laboratory determined on a non-degassed sample, or determined in the field with a pre-calibrated portable meter. Lake pH ranged from slightly over 3.0 in an acid mine drainage lake to a maximum of 9.8 in a productive hardwater lake sampled in July. The mean lake pH was 6.69 (median = 6.68). Expressed as a cation concentration, hydrogen ions are a minor water constituent (less than 1% of the total cation concentration on average). The distribution of pH values is shown in Figure 3.1. Biological damage appears to begin at approximately pH 6.0 (RMCC 1989). Of the total number of lakes sampled, 1150 had pH less than or equal to 6.0 (19.2%). This is discussed in detail in Section 8.1. There are several factors which influence the pH of a lake, and these factors will be discussed further in Section 6. 19 inoinoiootnoinoinqmo I I I I I I I I I I I I I ■ oinoiooinomoinomo'M m ro in m (O 00 00 o> pH Figure 3.1: Distribution of pH in Ontario lakes. 20 Table 3.1: Statistical summary for pH and base cations (/xeq L"^) pH Ca Mg Na K n 5982 3702 3591 3304 3153 Mean 6.69 373 49 48.7 13.1 Minimum 3.03 5 44.4 4.4 1.0 Maximum 9.80 3523 1956 1956 76.2 First Quartile 6.16 127 26 26.4 9.0 Median 6.68 175 35 35.2 11.8 Third Quartile 7.25 376 46 46.1 15.6 Std. Deviation 0.83 462 89 89.3 6.9 Skewness 0.18 2.52 12.3 12.3 2.24 Kurtosis 0.13 6.73 190 189 9.7 3.1.2 Calcium Calcium is the dominant cation in Ontario lakes, constituting on average 59.2 % of the total cations in solution. The concentration of calcium in lake water is highly variable, ranging from 5 to over 3500 microequivalents. The distribution is highly skewed to lower values (see Figure 3.2), with 80 % of the lakes having calcium concentrations less than 500 ueq.L'\ There are data from 153 lakes with calcium concentrations between 1500 and 3523 /Lteq.L"^ not shown on Figure 3.2. 3.1.3 Magnesium Magnesium is the second most important cation in Ontario lakes, constituting an average of 24.7 % of the total cations in solution. Its concentration is highly correlated to that of calcium (r^ = 0.81), with an average molar ratio of 2.68:1 Ca:Mg (Figure 3.3). Like calcium, the sample of Ontario lakes represented in this data base is strongly skewed to lower values (see Figure 3.4). There are data from 20 lakes between 1000 and 1595 ^eq.L'-^ not shown on Figure 3.4. 21 1750 - 1500 - en ^ 1250 - D 1000 _g 750 - E =3 500 250 0 n = 3702 R^ f^ R^ F^ r^ c^ r;^ o o o o ■^ a I I o o o o o in o o (O o o o o oo o o o o o in 8 8 o o 00 o o o o o o o CM o g o 1 8 in 1 o o 1 o 8 1 o o 1 o o CM 1 o o 1 8 -1 Calcium (/xeq.L ) Figure 32: Distribution of calcium in Ontario lakes. 22 1800 1600 1400 Y = 17.77 + 0.328*X r^ = 0.809 n = 3591 500 1000 1500 2000 2500 3000 3500 -1 Calcium (y^eq.L ) Figure 3.3: The relationship between calcium and magnesium in Ontario lakes. 23 1600 - 1400 - 0? 1200 h D -" 1000 h o 800 "I 600 13 ^ 400 h 200 0 n = 3591 B^R^^ Ks>3 cvn wrra ktt^ O O O in CS M K) I I o o in o o o o m ro »- «- CM CM »o ro o o in o m in o o (O o m o o o in 1^ o o 00 o m o o in o m in o o o in § s r^ Magnesium (/xeq.L ) Figure 3.4: Distribution of magnesium in Ontario lakes. 2k 3.1.4 Sodium There are apparently two sources of sodium in Ontario lakes: natural and anthropogenic. The vast majority of the lakes have low sodium levels (median = 35 Meq.L"^), but there is a number of lakes where sodium values exceed this significantly. All of these lakes are in the vicinity of roads which receive road salt for de-icing during the winter. In these lakes, the sodium values are highly correlated with chloride concentrations (Figure 3.5). On average, sodium comprises 11.6% of the cations in solution, but it can be the dominant cation if there is a road salt influence. The distribution of sodium concentrations in Ontario lakes is shown in Figure 3.6, with data from 8 lakes with sodium concentrations between 1000 and 1956 /xeq.L'^ not shown. 3.1.5 Potassium Potassium is present in low levels in all of the lakes, but is a minor cation in solution (average 3.6 %). The concentration of potassium is not correlated to any of the other cations in solution, and its distribution is less skewed than the other cations (Figure 3.7). 3.2 Anions 3.2.1 Alkalinity Alkalinity is a measure of the total amount of acid neutralizing substances in water. Over most of the pH range of lakes in this database, it is predominantly a measure of the bicarbonate anion in solution. In calcareous areas, the bicarbonate anion is produced during natural weathering or congruent dissolution. In this database, the Gran alkalinity is correlated with calcium (r^= 0.963, Figure 3.8). Unlike the other chemistry parameters. Gran alkalinity can be negative. When less than zero, it is a measure of the mineral acidity of the solution. 4.6 % of the lakes had negative Gran alkalinities. On average, bicarbonate constitutes 41.5% of the anions in solution. The distribution of alkalinity is shown in Figure 3.9, except for data from 34 lakes with alkalinities between 3000 and 5850 neq.L'\ 25 2200 Y = -25.252 + 1.112*X 1^ = 0.969 n = 958 200 400 600 800 1000 1200 1400 1600 1800 2000 -1 Sodium {fieq.L ) Figure 3.5: The relationship between sodium and chloride in Ontario lakes. 26 2500 - ^ o S5 o in o S I »- ^ CM CN K) n o I I I I I I o o o o o o lO o in o in p »- •- CNJ CM n KJ § § in m o m § s 1^ o o oo o o in ■<*■ o o ■n o in in o o o in o o o m -1 Sodium (/xeq.L ) Figure 3.6: Distribution of sodium in Ontario lakes. 27 D CD E 3 1200 - 1000 - 800 - 600 400 200 0 n = 3145 -t^S-SSSL o xn o CM in CM o in to o in o in m in o in (O o in o GO m o in o CM m CM o m ro § in g in in o in o 15 Potassium (/xeq.L ) Figure 3.7: Distribution of potassium in Ontario lakes. 28 500 1000 1500 2000 2500 3000 3500 4000 -1 Calcium (yaeq.L ) Figure 3.8: The relationship between calcium and alkalinity in Ontario lakes. 29 3500 h 3000 w -g 2500 h ^ 2000 _g 1500 E ^ 1000 500 0 n = 5911 t^^^ F:n^ K^^ rcr:^ O O o in O CM T T o o in o t^ o o o o o o o o o in o m o in o in t^ o CM m r^ o »- »- CM CN tM (N fO I I I I I I I o o o o o o o in o in o in o m CM in r^ o CM m r^ ^^^»-CMCMCMCM -1 Alkalinity (/xeq.L ) Figure 3.9: Distribution of alkalinity in Ontario lakes. 30 Table 32: Statistical summary for alkalinity and major anions in solution (^eq L'^) Alkalinity Sulphate Chloride Nitrate n 5911 3599 996 377 Mean 380 135 27.2 0.86 Minimum -216 6.5 <0.3 0.02 Maximum 5851 720 2,158 6.86 First Quartile 49 85.3 4.9 0.40 Median 127 137 8.5 0.65 Third Quartile 400 171 17 1.13 Std. Deviation 608 68.8 115 0.77 Skewness 2.75 1.41 12.5 2.96 Kurtosis 9.16 6.86 189 15.33 3.2.2 Sulphate Sulphate is a relatively minor anionic constituent of lakes in northern Ontario. However, in southern Ontario, and particularly in the vicinity of non-ferrous smelters such as Sudbury, Rouyn-Noranda, or the sintering operations in Wawa, sulphate can be the major anion in solution. On average, sulphate comprises 42.0 % of the anions in lakes in this sample. The geographic distribution of sulphate concentrations will be discussed in Section 6. The distribution of sulphate concentrations is shown in Figure 3.10, except for 9 lakes with sulphate concentrations between 500 and 720 fieq.U^. 3.2.3 Chloride Chloride is usually a relatively minor anion in solution. In many of the studies from which these data are compiled, chloride was not measured, however, chloride data are available for 996 lakes. The distribution of chloride concentrations for those lakes where the ion was directly measured is shown in Figure 3.11 except for data from 52 lakes with chloride concentrations over 200 /ieq.L'\ 31 en CD D E 13 1000 - 800 600 - 400 - 200 - o o O o o O o o o o lO o in o in O in o in o 1 CM N ro ro •* ■* in o 1 1 1 1 1 1 1 1 1 o o o o o o o o o ID o in o in o in o in CM CM tn to •* •* -1 Sulphate (/i^eq.L ) Figure 3.10: Distribution of sulphate in Ontario lakes. 32 800 700 - 0) 600 - 3 500 - ° 400 -^ 300 Z5 ^ 200 100 0 n = 996 _QsssL-S^a. Chloride (/xeq.L ) Figure 3.11: Distribution of chloride in Ontario lakes. 33 3.2.4 Nitrate Nitrate data are not available for the majority of lakes in the data base. However, there are 377 lakes which have analyses for all of the anions (chloride, sulphate, bicarbonate, organic anions, and nitrate). In this subset, nitrate constitutes an average of 0.28% of the total anions in solution, with a maximum of 2.15%; thus, nitrate is a minor anion in all of the lakes for which nitrate was measured. There is no indication that the nitrate anion plays a significant role in the acid-base status of Ontario lakes at this time. The distribution of nitrate values for those lakes where it was measured is shown in Figure 3.12. 3.3 Metals 3.3.1 Total Aluminum Aluminum was measured on many of the lakes because of its implication in exacerbating the toxic effects of low pH on biological organisms. As with many of the chemical parameters, total aluminum concentration was strongly skewed to lower values, but ranged as high as 840 Mg-L^- There was one value of 3200 Mg-L^ for Lake Abitibi which was not included. This is an extremely large lake on the Ontario-Quebec border, and is the receiving water for a number of rivers which drain the Clay Belt. The lake is frequently turbid with suspended clays, and it was felt that the extraordinarily high aluminum value represented the aluminum content of colloidal clays rather than aluminum dissolved or complexed in solution. For the purposes of the statistical analysis and other calculations, aluminum values less than the detection limit of 1.0 Mg-L'^ were set to 0.5 Mg-L'\ The distribution of aluminum concentrations in Ontario lakes is shown in Figure 3.13, with the exception of 20 lakes with aluminum concentrations ranging up to 840 Mg-L ^ 34 inoinqiooinoinoinoino I I I I I I I I I I I I I I oinqinqinqmqinoinqin C)'-^»-^c4c>Jroro'**'*inif)«d0.99 in both cases). The lakes represented in this sample range from very soft (<10 mS) to very hard (>1000 mS). The majority of the lakes are softwater, with the median conductivity being 39 /xS. The distribution of conductivity values is shown in Figure 3.16, with the exception of 13 lakes with conductivity between 400 and 1320 ^S. 3.4.2 Apparent Colour Apparent colour includes the colour due to dissolved humic and fulvic substances as well as the additional colour contributed by suspended matter since the samples were not filtered prior to measurement in a colorimeter. Analyses for apparent colour were not done after 1983, with the test being abandoned in favour of "true colour" measurements. Negative apparent colours were reported by the laboratory for some extremely clear waters indicating colours less than that of the analytical blank. The distribution of observed values is shown in Figure 3.17. There are data from 6 lakes with apparent colour between 200 and 352 Hazen units not shown on Figure 3.17. 3.4.3 True Colour True colour is theoretically the same as apparent colour with the exception that samples used for true colour measurement were filtered prior to analysis. However, in some comparison samples, true colour was higher than apparent colour. The results of the intercomparison were such that the two methods of measuring colour are not considered to be comparable. The distribution of true colour values is shown in Figure 3.18, except for data from 14 lakes with true colour values between 200 and 337 Hazen units. 40 3000 CO CD D O 2500 2000 1500 0) E 1000 Z5 500 561 [^r^r^FT^r^^ o CM 't CD CO o CN o oo o o o CN O o to o 00 CN CN CSJ OJ I I I CNCNCMCNICsirOnrrJt-On Conductivity (/^Siemens Figure 3.16: Distribution of conductivity in Ontario lakes. U^ CD o 500 400 h 300 ^ 200 100 n = 2066 ^F^r^r^r^txsr^ ooooooooooooooooooo I ^cMnTj-in«or^ooo)0'-c\itO'*ini » o°o S°g° S e oloB^L t •« 1°°: ocfe* I o ° o . « o .^^J. 10 20 30 40 Sodium (/i,eq.L~^) 50 Figure 42: The relationship between sodium and chloride in Ontario lakes. 52 en CD D O 1200 1000 800 600 CD E 400 Z3 200 - IP Ilii 11 3063 r^ FX^ rv^ nr-r, T^ r^^ O O O O >- CM ro I I o o IT) la o o o o o o o o n o o a CM n "t U) (D r^ oo en o CM o o o o o o O o o o o Ol o "" CM to 'J- in CO r-- oo en Estimated Chloride (ytxeq.L Figure 4.3: Distribution of estimated chloride in Ontario lakes. 53 Table 4.5: Statistics for CI data, including measured (n = 996) and estimated (n = 2067) values n = 3063 Minimum 0.1 Mean 31.9 First Quartile 6.8 Median 13.9 Std. Dev. 104.0 Skewness 11.9 Maximum 2157.8 Third Quartile 26.1 Kurtosis 174.6 54 5. Data Validation 5.1 Ion Balancing One method of assessing the quality of the data is to compare the concentration of all of the cations and anions in equivalence units. These concentrations should, of course, balance, and those lakes whose anions and cations are seriously mismatched have either uimieasured ions in solution, or reflect analytical problems with one or more of the measurements. The ion balancing procedure used here makes use of estimates for missing data according to methods outlined in Section 4. The specific assumptions, by parameter, were: Potassium - assumed to be zero if value is missing Chloride - estimated from sodium if value is missing Organic Anions - estimated from DOC and pH with the Lazerte and Dillon (1984) equation. For some lakes, DOC was estimated from colour according to methods outlined in Section 4. A- = (K*CJ/(K + HO where H^ = lO'^'' K = 10"P^ pK = 0.96 + 0.90*pH - 0.039*pH' and Ct = 10.9*DOC - 13.7 55 Aluminum added as a cation assuming oxidation states listed in below. pH < 5.0 3' 5.0 < pH < 5.5 2.5* 5.5 < pH < 6.0 2* pH > 6.0 r If aluminum was missing, it was assumed to be zero. Manganese added as a divalent cation, but assumed to be zero if the value was missing Iron added as a divalent cation, but assumed to be zero if the value was missing Nitrate added as an anion, but assumed to be zero if the value was missing Bicarbonate - estimated from alkalinity, since DIC was not measured With these assumptions, the minimum data needed to calculate an ion balance included: pH, alkalinity, colour or DOC, calcium, magnesium, sodium, and sulphate. Out of the 6000 lakes in the data base, there were 2987 with enough data to calculate an ion balance; the results are presented in Table 5.1 and Figure 5.1. The statistics are shown for the percent difference between cations and anions, where the percent difference is [abs(z + -E-)/{0.5*(s+ +2-)}]*100. 56 1400 1200 CD 1000 D 800 0) 600 - _Q E 3 400 h 200 h 0 n = 2987 ^^'^'^'^'< m CM o o o in o in CM o u? § ^ % Ion Difference Figure 5.1: Observed percent ion difference for lakes in the database where calculation was possible. 57 Table 5.1: Summary of % difference between cations and anions n = 2987 Minimum 0.0 Mean 7.4 Maximum 123.4 First Quartile 2.7 Median 5.7 Third Quartile 9.7 The ion balance results are very encouraging considering the diverse data sources and the assumptions made in attempting the ion balance. Of the 2987 lakes with enough data to attempt an ion balance, 2291 or 77 % agreed to within 10 %, and 2843 or 95 % agreed to within 20 %. One problem with using 'percentage difference' as a criterion for comparison arises with very dilute lakes. In lakes with low ionic strength, (conductivity < 15 mS), small analytical errors can result in large ionic imbalances. For 16 of these dilute lakes, the mean percent difference was 17%. Observations regarding data quality can be derived from the results of the ion balancing. Analyses prior to 1982 from the Northwest region were done in the MOE Thunder Bay laboratory. The colorimetric method for calcium and magnesium reported these cations to the nearest 1 mg.L'\ The magnesium results, in particular, appear to be high for several lakes. This is evidenced by the fact that of 37 lakes with a ratio of cations to anions greater than 1.3, 33 were from the Northwest and analyzed in the Thunder Bay laboratory prior to 1982. 5.2 Calculated Conductivity Another less stringent data 'filter' permits a check based on conductivity data. Using the concentrations of major ions, the theoretical conductivity of the lake can be calculated (Amer. Soc. Test. Mat. 1974). The overall relationship between observed and calculated conductivity is very good (r^>0.99, Fig 5.2). 58 600 3 500 >^ -4-' > 400 o 5 300 o o "S 200 Y = -2.913 + r^ = 0.995 n = 3047 1.013*X 100 200 300 400 500 Measured Conductivity (/xS) 600 Figure 5.2: Measured vs calculated conductivity for all lakes 59 This approach reveals a problem with one of the surveys. Lakes in the DFO National Inventory survey of 1980 have calculated conductivities that are consistently greater than the measured conductivities (Figure 5.3). In addition, ion balancing on the same data shows that the cations were overestimated (mean Z + /E- of 1.06), even with the absence of potassium data. Presumably, if potassium data were present, both the ion balancing and the conductivity checks would be even worse. The problems associated with that survey are almost certainly, in part, associated with the colourimetric (methylthymol blue) method for sulphate. Problems with the data quality of this survey have been identified earlier (Kelso et al. 1986). 5.3 Other Data Relationships The ion balance and conductivity checks are satisfactory data filters for those lakes with sufficient data to estimate ions. However, there are 3013 lakes with insufficient data to attempt an ion balance or estimate conductivity. For the purposes of making reliable lake population estimates for individual parameters, the pH - alkalinity relationship was examined, with outliers from the relationship flagged as data of dubious quality. Because the pH was frequently a field pH, and because the degree of CO2 saturation was not controlled, considerable spread in the pH-alkalinity relationship was expected. The pH- alkalinity relationship for lakes with alkalinity values between -100 and 200 ^eq-L^ is shown in Fig. 5.4. There were 138 lakes where the pH-alkalinity data were sufficiently incongruous so as to exclude the data from further analysis. Lakes which were excluded from subsequent analysis because of gross inconsistencies between pH and alkalinity, because of disagreement between calculated and measured conductivity, or because of ion imbalance are identified. 5.4 Data Quality Code Based on the completeness of the data and the degree that the data complied with the various validation procedures described above, a data quality code was assigned to each lake. The codes are: 60 100 00 o 13 ■D C o o o o o 10 20 30 40 50 60 70 80 90 100 Measured Conductivity (yuS) Figure 5.3: Measured vs. calculated conductivity for lakes below 100 ^iS. 61 9.0 8.5 8.0 7.5 7.0 6.5 h 6.0 5.5 5.0 4.5 4.0 -100 -50 0 50 100 150 Alkalinity (/xeq.L"^) ♦ Rejected data o Accepted data • — ♦ - ■ « * •*° '°j^^^^^^^^^^^y^^^°°'^° °'^"'' • *^»!^^^^^^S^^?"°V.'*°° •'" °'«°° ° ^ ♦ « o *^^^^K^^TO|^^^ ° * '^° o * ° ° ° ♦ *^^^^^^nMp*°°°° °/ . °o * • • * S^E^^S*^1» *= * •* ° ' ° ^ ♦ ^JM^B^ T^Jc^n O «i o ♦« .•»^'/.". •• °° ^ _*i6^^ " ^^ ' ° y«° *««"° » . \ 1- — ^ 1 i 1 200 Figure 5.4: pH and alkalinity for lakes < 200 neq.L'\ Rejected lakes were based on charge balance, theoretical vs measured conductivity or by this relationship. 62 1 - All major cations and anions were analyzed and there was less than 10% difference between the ions. There are 639 lakes in this category. The lack of chloride data for many of the lakes is a common reason for otherwise good data being placed in another category. 2 - There is agreement to within 10% between the cations and anions. However, one or more of the ionic consituents has been estimated. The most common estimates were chloride (estimated from sodium), organic anions (estimated from colour), or a minor ionic constituent (for example, potassium = 0 if the potassium datum was missing). The specific assumptions for ion balancing are listed in Section 6.1. There are 1633 lakes in this category. 3 - There is agreement to within 20% between the cations and anions. Many of the very dilute lakes ended up in this category, since relatively small differences in analytical measurement can yield significant ion imbalances. There are 687 lakes in this category. 4 - No ion balancing possible due to missing data. However, other data checks indicate that at least the pH and alkalinity data are reasonable. There are 2802 lakes in this category, 5 - Poor data quality is suspected. Either the charge balance deviates by greater than 20%, or the theoretical conductivity differs significantly from the estimated conductivity, or there is a pH-alkalinity inconsistency. There are 143 lakes in this category. 6 - Ion balancing agrees to within 20%, but there is a discrepancy between observed and calculated conductivity. Given ion concentrations, the field conductivity is suspected to be wrong, and the calculated conductivity will be used for lake classification. There are 120 lakes in this category, mostly from the 1980 DFO inventory survey. 63 6. Sulphur Deposition Zones in Ontario 6.1 Sources of Sulphur Deposition Lakes in Ontario have been acidified both by local sources of sulphur emissions and by the long range transport of sulphur compounds. Sulphur dioxide emissions from the smelter complex in Sudbury have long been recognized as the cause of local lake acidification (Gorham and Gordon 1960, Beamish and Harvey 1972). Excluding the Sudbury area, lake acidification attributable to longer range transport of sulphur has also been documented (Neary and Dillon 1988). There have been several attempts to delineate the area in which lake acidification is mostly attributable to the Sudbury emissions rather than long range transport. There are several reasons for this. First, in part of the 'Sudbury zone', biotic effects associated with lake acidification can be exacerbated by elevated levels of metals, particularly copper and nickel, so observations of aquatic effects on fish or other biota may not be generally applicable to other areas. Secondly, the prediction of lake water chemistry response associated with provincial and regional SO2 emission reduction plans require a distinction between lakes affected primarily by local sources and lakes affected by long range transport. Finally, the sulphur emissions from the smelters in the Sudbury area have dropped by about 80% from the late 1960's, causing a change in local lake water chemistry (Dillon et al. 1986, Keller et al. 1986), so it is difficult to attribute lake chemistry to a specific sulphur deposition rate. Monitoring of sulphur deposition in the Sudbury area began in the mid-1970's, so the deposition rate of sulphur causing the widespread lake acidification in the area is unknown (Dillon 1984, Jeffries 1984). In 1974-76, an extensive survey of 209 lakes within 250 km of Sudbury was conducted to determine the spatial extent of water quality impacts related to the Sudbury smelters (Conroy et al. 1978). That study, and subsequent surveys (Keller et al. 1980, and Pitblado et al 1980) attempted to delineate the 'Sudbury Zone' according to various individual or combinations of lake water chemistry parameters. Other authors have excluded large areas in the vicinity of the smelters to isolate effects associated with long range transport from the smelter effects (Beggs et al. 1985, Neary and Dillon 1988). 64 6.2 Sulphur Deposition Data Wet sulphur deposition data (associated with rain or snow) in 1983 was obtained from the APIOS deposition monitoring network (Tang et al. 1986a) and from the federal APN network (Table 6.1). Total sulphur deposition was calculated by adding wet sulphate, dry sulphate, and dry sulphur dioxide data (all converted to sulphur). Several Table 6.1: Sulphur deposition data (from Tang et. al. 1986a) for 1983 from monitoring stations in Ontario Wet SO, Dry SO, Dry SO2 Total S Latitude Longitude Station mg.m'^.yr'^ mg.m"^.yr'^ mg.m'^yr'^ g.m'^yr"^ 41°59'15" 82°55'41" Colchester 3356 479 1280 1.918 42°14'47" 82°13'30" Merlin - 3669 NA NA 1.849* 42°40'22" 81°09'55" Pt. Stanley 4040 425 962 1.969 42°42'11" 82°21'13" Wilkesport 3941 493 1750 2.353 42°49'36" 81°50'04" Alvinston 3722 NA NA 1.893* 44°34'54" 81°05'24" Shallow Lk. 3260 444 602 1.536 43°48'19" 80°54'12" Palmerston 2597 351 588 1.277 43°17'28" 81°30'03" Huron Park 3527 NA NA 1.734* 43°28'39" 80°35'09" Waterloo 3156 NA NA 1.467* 45°13'26" 78°55'52" Dorset 2374 394 211 1.028 43°31'05" 79°55'54" Milton 3607 388 838 1.750 44°12'46" 79°12'38" Uxbridge 2798 300 415 1.240 45°00'54" 78°12'58" Wilberforce 2651 NA NA 1.161* 44°17'28" 77°47'33" Campbellford 3053 375 377 1.331 44°37'31" 79°32'08" Colwater 2292 NA NA 0.972* 44°56'41" 75°57'48" Smith's Falls 1941 223 213 0.828 45°19'00" 74°28'13" Dalhousie Mills 2632 269 287 1.111 45°36'48" 77°12'03" Golden Lake 2168 325 119 0.891 45°30'57" 79°55'19" McKellar 3136 349 219 1.271 (cont'd) 65 Table 6.1: (Cont'd) Wet SO, Dry SO, Dry SO2 Total S Latitude Longitude Station mg.m'^yr"^ mg.m"^.yr"^ mg.m'^.yr'^ g.m"V"^ 45°59'26" 81°29'18" Killaraey 2918 410 NA 1.316* 46°16'45" 78°49'19" Mattawa 2510 314 108 0.995 46°58'22" 80°04'40" Bear Island 892 NA NA 0.365* 47°26'33" 82°20'14" Ramsey 659 NA NA 0.278* 47039-04" 80°46'32" Gowganda 1193 293 201 0.596 49°19'16" 82°08'46" Moonbeam 1375 206 102 0.578 45°32'21" 78°15'35" Whitney 2268 NA NA 0.960* 47°03'15" 84°24'00" Turkey Lake NA NA NA 1.210** 48°50'33" 88°36'45" Dorion 1139 98 31 0.427 50°10'38" 86°42'90" Nakina 565 104 23 0.235 50°38'31" 93°13'13" Ear Falls 507 94 18 0.209 51°27'41" 90°12'04" Pickle Lake 543 123 20 0.232 48°21'14" 92°12'32" Lac La Croix 573 NA NA 0.244* 48°44'24" 91°12'08" Quetico Centre 1074 134 NA 0.436* 49039-22" 93°43'28" ELA 574 NA NA 0.352* 45°54'08" 77°17'30" Chalk River NA NA NA 1.217** 42°36'03" 80°27'09" Long Point NA NA NA 2.274** • calculated from wet SO, (see text) •• 1979-1982 average, obtained from Barrie and Sirois (1986) of the stations had no dry sulphur deposition measurements. To provide an estimate of dry sulphur deposition, the dry component (SO2 + SO4) was regressed on the wet sulphur deposition measurement. There was a good correlation between wet sulphur deposition and dry sulphur deposition for the twenty stations for which both data were available (Figure 6.1). Those stations for which no dry deposition data were available had estimated dry sulphur deposition calculated from the regression formula, and the total 66 1250 en 1000 c o S 750 CL (D Q b 500 ^ 250 Y = 33.23*e(°-°°24*^) r^ = 0.866 250 500 750 1000 1250 Wet Sulphur Deposition (mg) 1500 Figure 6.1: Wet vs dry sulphur deposition at 20 stations in Ontario. 67 sulphur deposition estimate was then made by adding the observed wet sulphur deposition to the estimated dry sulphur deposition. 6.3 Sulphur Deposition Mapping Mapping was done on a geographic information system called SPANS (SPatial ANalysis System). One of the utilities in this system POTMAP, (or POTential MAPping) permits the construction of areal maps from point data. Sulphur deposition data were imported onto a base map of Ontario obtained from the Environmental Information Systems Division of the Canadian Lands Directorate. The assumptions behind potential mapping are that the attribute (in this case sulphur deposition) of a given point (the deposition monitoring station) is related to the attribute values of the points around it, and that this effect is lessened with increasing distance between the points. The weighting function used permitted an outer radius of influence of 300 km for each of the deposition monitoring stations. The weighting function was constructed so that the weighting of adjacent points declined exponentially with distance and was less than 0.5 if the point was greater than 50 km (TYDAC, 1988). The resulting map (Figure 6.2) has assigned the northern portion of the province (outside the 300 km radius associated with the most northerly deposition monitoring stations) an arbitrary deposition of <0.25 g.S.m"^.yr"\ This assumption was felt to be valid in light of the declining sulphur deposition gradient from south to north. 6.4 Water Chemistry Mapping Data from 2236 lakes were selected between 45°00' to 50°00 latitude and 77°00' to 86°00' longitude. All lakes with alkalinity and sulphate data were used. The location of the lakes is shown in Figure 6.3. The water chemistry mapping was done with the POTMAP utility of SPANS, as described above. Each lake's radius of influence was 30 km, with a decay function such that its influence on nearby points was less than 0.5 at a distance greater than 5 km. 68 Figure 6.2: Sulphur deposition (g S.m'^yr'^)in 1973. 69 .^ Lake Superior North Chonne ... A • ■■■?2- • •; • ./AX •■V ••••,v.'. ">■•■. .- Figure 6.3: Location of lakes with SO^ and alkalinity data between latitude 45°00- 50°00 and longitude 77°00-86°00. 70 Rouyn— Noranda contou Figure 6.4: Distribution of sulphate concentration (^eq.L ^) in lakes shown in Figure 6.3. 71 To determine the 'Sudbury zone', two maps were generated, one of the sulphate concentration of the water, and the other of the ratio of sulphate to (sulphate + alkalinity). Figure 6.4 identifies zones where the concentration of sulphate was higher than that expected considering an overall trend of increasing sulphate in lake water in Ontario from low values in the Northwest to higher values in the South (Neary and Dillon, 1988; also see section 8, below). The parameter [SOJ/([SOJ + [Alk]) was used as an estimate of degree of lake acidification. As described in section 4, these are the two major anions in solution in Ontario lakes, and the ratio [SOJ/([SOJ + [Alk]) can be used as an estimate of the amount of alkalinity which has been replaced by sulphate resulting from the atmospheric input of sulphur. Where this ratio is close to one, the lakes are acidified, whereas a ratio approaching zero indicates a lake with significant amounts of bicarbonate buffering capacity. Lakes with the [SOJ/([SOJ + [Alk]) ratio greater than 0.7 have had most of their alkalinity replaced by sulphate. The zone around Sudbury where this occurred corresponds closely with the area where 'Sudbury effects' had been previously documented (Pitblado et al. 1980). There are several other areas where this ratio exceeds 0.7 (Figure 6.5). Since these areas do not correspond with the zone where lake water sulphate has been increased by sulphur deposition from the Sudbury smelters, the acidification in these areas is more likely to be attributable to long range transport of sulphur compounds. 6.5 Defining the Sulphur Deposition Zones The potential mapping of the 1983 sulphur deposition data shows a clear gradient in total sulphur deposition from low deposition rates in the northern part of the province to higher rates in the south. This has been documented previously for wet sulphate deposition, and since the total sulphur deposition is correlated to wet sulphur deposition, it is not surprising to see this trend for total sulphur deposition. At the resolution of the monitoring stations, the effects from the Sudbury smelters are not evident. This is consistent with both previously published deposition maps, and with modelling studies conducted on the effects of the Sudbury smelters (Tang et al. 1986b). 72 Figure 6.5: Areas where lakes shown in Figure 6.3 have a ratio of SO^ to (SO4 + alkalinity) greater than 0.7. 73 The results of mapping lakewater sulphate (Figure 6.4) showed that there is an area of elevated sulphate extending in a band from the southwest of Sudbury to the northeast, where it connects with an area influenced by the sulphur deposition from the smelters in the Rouyn-Noranda area of Quebec. Small areas with high lakewater sulphate are also evident in the vicinity of the Algoma Steel sintering plant in Wawa, and around the uranium mining and refining activities in Elliot Lake. The area with [SO4]/([SOJ + [Alk])>0.7 around Sudbury is somewhat smaller. This is due to the presence of extensive areas of carbonate-rich sedimentary bedrock in parts of the Sudbury basin which extend to the northeast of the region (Cowell 1986). These areas have high lake water sulphate concentrations, but as the buffering capacity of the lakes is high, there has been no significant acidification. The 'Sudbury zone' was defined as the intersection of the area with lake water sulphate concentration >200 /ieq.L'^ and the ratio [SOJ/([SOJ + [Alk])>0.7. The area is shown in Figure 6.6 as 'Zone 7' and covers 17022 km^. The 'Sudbury zone' extends from the southwest (Killamey) to the north and northeast of Sudbury. There was also a small zone around Elliot Lake which met these criteria, but this area of acidified lakes with high sulphate concentration is due to acid mine drainage rather than the atmospheric input of sulphate (W. Keller, pers. comm.). For the purposes of stratifying lakes, the 'Sudbury zone' was superimposed on the map of total sulphur deposition (see Figure 6.6). The sulphur deposition associated with each zone is given in Table 6.2. 74 Figure 6.6: Delineation of total sulphur deposition zones in Ontario, including zone 7, believed to be affected by SO2 point sources at Sudbury. 75 Table 6.2: Sulphur deposition zones in Ontario Zone Sulphur Deposition (g S.m'^.yr"^) 1 <0.25 2 0.25-0.50 3 0.50-0.75 4 0.75-1.00 5 1.00-1.25 6 > 1.25 7 (Sudbury effects) unkown, but > 1.25 76 7. Chemical Characteristics of Lakes by Sulphur Deposition Zone 7.1 Estimate of Lake Numbers and Area by Deposition Zone Ideally, all of the lakes could be grouped according to deposition zone, and the lake water chemistry extrapolated to the total number of lakes in each zone. However, the only available estimates of total lake populations are organized according to watershed. Therefore, to make lake population estimates for each sulphur deposition zone, it was necessary to fit each quaternary-level watershed into the nearest deposition zone boundary. This was done by assigning each lake to a deposition zone (shown in Figure 7.6) according to it's latitude and longitude using the SPANS 'classify' utility. In most cases, entire watersheds fell into one deposition zone. However, there were several cases where a quaternary watershed was divided between two (or more) deposition zones. In these cases, the watershed was assigned to the deposition zone in which more of the lakes occurred. The exception was Zone 7, the 'Sudbury zone'. If any portion of a quaternary watershed fell into Zone 7, the entire watershed was placed in that zone. The estimated numbers of lakes in each of the deposition zones are listed in Table 7.1, stratified by lake size range. The details of which watersheds fell into which deposition zones are included in Appendix D. Table 7.1: Number of lakes in each of four size classes by sulphur deposition zone S Deposition Size (ha) Zone >1000 100-999 10-99 1-9.9 Total 1 403 5663 48703 84024 138793 2 296 2507 17992 40605 61400 3 35 383 4155 12862 17435 4 34 253 3423 11725 15435 5 88 704 4353 12672 17817 6 1 33 206 1072 1312 7 37 304 2594 7708 10643 Total 894 9847 81426 170668 262835 77 A similar breakdown of the area of lakes in each deposition zone, stratified by lake size range, is presented in Table 7.2. Table 72: Total area of lakes in each of four size classes by sulphur deposition zone S Deposition /u«\ Zone >1000 100-999 10-99 1-9.9 Total 1 1816998 1312006 1225330 377725 4732259 2 1623113 621774 504164 155507 2904558 3 100925 94124 109496 38554 343096 4 114505 61790 85074 45735 307104 5 408489 191044 120211 50968 770712 6 1242 9763 5371 3209 19586 7 111254 69445 68424 32389 281513 Total 4176527 2359947 2118269 704085 9358828 7.2 Lake Size Distribution in the Database There are differences in the distribution of sampled lake sizes in the various zones. Smaller lakes were more heavily sampled in the more southerly deposition zones than in the north. This is partly due to the differences in sampling study design, and partly due to logistical considerations. Many of the northern lakes were sampled from an aircraft, which precludes lakes smaller than 10 ha. Additionally, when helicopters were used to specifically sample lakes in the winter, some of the smaller northern lakes were frozen to the bottom. The lake size distribution by deposition zone is shown in Figure 7.1 and summarized in Table 7.3. 78 CD C o Q. E D in D C U Q_ 50 40 30|- 20 10 Zone 1 n=243 ^3_ Zone 3 n=559 Zone 5 n=2519 Zone 2 n=1320 Zone 4 n=692 1^^^^ .^^. Zone 7 n=588 Figure 7.1: Area (ha) Lake area distribution shown as percent of lakes sampled for each sulphur deposition zone. 79 Table 7.3: Lake size distribution by deposition zone S Deposition Zone — 1 2 3 4 5 7 Lake Size Median 319 115 52 40 30 51 Ql 108 33 15 13 12 16 Q3 875 332 136 102 94 148 N 231 1223 520 656 2443 558 7.3 Lake Water Chemistry Stratified by Deposition Zone 7.3.1 Data Exclusions The data description in Section 4 included lakes which were influenced by point sources, and data which could not be validated. For the data analysis below, all data with data quality codes 5 and 6 were excluded. As described earlier the 'Sudbury' zone was treated seperately. In addition, data from lakes close to point sources of either airborne sulphur or acid mine drainage were also excluded. These latter lakes were identified by their proximity to either Wawa, Elliot Lake, or Rouyn-Noranda. They included all lakes within the 150 ^J,eq.L'^ sulphate isopleths around Elliot Lake and in the Rouyn-Noranda area, and all lakes within the 100 iieq.L'^ isopleth around Wawa (see Figure 6.3). There were a total of 85 'point source' lakes excluded and a further 260 lakes omitted because of questionable data quality. An additional 24 lakes were excluded because their size was greater than 10000 ha. These lakes ranged up to 400,000 ha in size (Lake Nipigon), and were felt to be non-representative of the overall lake population, primarily because their enormous water volume would make their response time to atmospheric inputs much longer than that for smaller lakes. 80 132 Cations The medians and quartiles of pH, Ca, Mg, Na, and K stratified by sulphur deposition zone are presented in Table 7.4. Medians and quartiles are used because of the highly skewed nature of the distribution of all of the parameters except pH (see Section 3). All of the differences in cations between deposition zones are significant (p< 0.001). Table 7.4: Summary of pH and base cation statistics stratified by deposition zone S Deposition Zone — 1 2 3 4 5 7 pH Median 7.10 7.30 7.40 6.58 6.36 5.98 Ql 6.89 6.78 7.00 6.12 6.02 5.17 Q3 7.39 7.75 7.74 7.03 6.78 6.67 N 231 1213 517 656 2441 556 Ca Median 274 409 514 165 149 140 01 200 172 302 97 120 105 Q3 499 1103 818 250 182 197 N 175 716 298 322 1552 311 Mg Median 99 164 177 49 66 66 Ql 82 82 99 33 51 52 Q3 197 414 268 72 89 87 N 175 716 297 321 1443 311 Na Median 44 38 35 26 34 31 Ql 36 29 29 18 28 26 Q3 53 48 44 35 46 40 N 175 690 255 277 1311 285 (cont'd) 81 Table 7.4: (Cont'd) S Deposition Zone 1 2 3 4 5 7 K Median 15 13 10 7 12 10 Ql 11 10 7 4 10 9 Q3 19 17 14 10 15 13 N 175 702 252 229 1311 285 Histograms of the pH distribution of lakes in each of the deposition zones are shown in Figure 7.2. There is a shift in the distribution of pH to lower pH values in deposition zones 5 and 7 relative to those found in zones 1, 2, and 3. Zone 4 reappears to be transitional. The calcium statistics show that zone 2 and to a lesser degree, zone 3 have much higher calcium levels than the other zones. This trend to higher calcium levels is also evident in the calcium histograms displayed in Figure 7.3. This is mostly due to the the fact that some of the lakes are located in the Clay Belt, and in areas of sedimentary rock (see Cowell, 1986) within these zones. The pattern of magnesium concentrations is closely related to that of calcium, a result that is not surprising given the strong correlation between the variables. Zone 2 has the highest magnesium concentration. Zone 1 and 3 also have a significant proportion of lakes with magnesium concentration in excess of 500 Meq.L'\ The distribution of magnesium concentrations is shown in Figure 7.4. The distribution of sodium shows less variability between deposition zones (Figure 7.5). Despite a relative lack of hardwater lakes and a lower density of roads, zone 1 has the highest median sodium concentration. This may be due to the relatively 82 40 - 30 20 10 Zone 1 n=231 J iii ill ill la Zone 2 n=1213 III j^ i I 11 ill 40 30 20 10 Zone 3 ri=517 M. ill l Zone 4 n=656 .^ ll« ■i i .^. 40 - 30 - 20 10 Zone 5 n=2441 I i li Zone 7 n=556 ii lii ^IL mpinpinoiooir^in o m o o in o m m «o «D h. r^ (0 inonoinomoioiq oinoinompinq pH Figure 7.2: pH distribution shown as percent of lakes sampled for each suphur deposition zone. 83 C o 0) D 00 en CD O c CD U 0 CL 30 20 10 40 30 20 10 0 70 h 60 50 40 30 20 10 0 Zone 1 n=175 L ■ 11 i*^ — t^^ ^one 3 n=298 ill I I Zone 5 n=1552 Zone 2 n=716 i Illlli^^^i ^ Zone 4 n=322 ii ■ Lsi^j .M-. LcSj Zone 7 n=311 J 'ii. Calcium (/xeq.L ) Figure 7.3: Calcium distribution shown as percent of lakes sampled for each sulphur deposition zone. 8A 60|- 50 40 30 20 10 0 60 50 40 30 20 10 0 60 50 40 30 20 10 0 Zone 1 n=175 „1 l^Mj fs?^!^ ^ Zone 2 n=716 L_j Zone 3 n=297 i B8| M iiii s ^ ■^t^g^ Zone 4 n=321 L i ■ la^ ^1 Zone 5 n=1443 J S^rr,, i I Zone 7 n=311 If^t^ O Q O Q Q in o tn o in 8 S ? !? I I I I I t I I I A QOQOQOQO ||lig§gi§§§ I I I I I 8 S S S S »- •- CM fi ^ a Magnesium Figure 7.4: Magnesium distribution shown as for each suphur deposition zone. (Meq.L-^) percent of lakes sampled C o M 0) D CO CO CD u c CD O CD Q. 30 - 20 10 30 20 10 30 20 10 Zone 1 n=175 ^ III ^ il ill Zone 3 n=255 ^ ii ^ ill II i Zone 5 n=1311 Zone 2 n=690 Zone 4 n=277 J J I L 11 I 1^1 if LSJ Zone 7 n=285 J ill ■ III J momoinoiQQiOQo - - ' - - s ^ ii •n b in o in »- »- CM CM K) inoinoinoinQioQo i»-«-cMCM^to*^iSin I I I I I I I I I A moinoinbiQQin Sodium (/xeq.L ^) Figure 7.5: Sodium distribution shown as percent of lakes sampled for each suphur deposition zone. 86 30 - 20 - 10 > [ i Zone 1 n=175 ■I 1 (T^ rm i Ji Zone 2 n=702 1 1^^ — 40 30 20 10 I Zone 3 n=252 4 $^t$^r^P^l^ ll Zone 4 n=229 ^Mjss. 50h 40 30 20 10 0 I 1 ill iii IL Zone 5 n=1311 Zone 7 n=285 I i^ I La I Jii 11 I oinoinQiQO^OO inoinoinQiQoio m o in o in •- •- N CM in p Potassium (//.eq.L ) Figure 7.6: Potassium distribution shown as percent of lakes sampled for each suphur deposition zone. 87 enriched sodium concentrations in the bedrock in areas of northwestern Ontario (Brunskill et al. 1971). Potassium also shows little variability between zones. The median potassium value ranges from 7 to 15 Meq.L"\ and the third quartiles of the distributions are consistently under 20 Meq.L"\ The potassium concentration in wet deposition is higher in northwestern Ontario than in most other regions of the province (Tang et al. 1986). The source of potassium is probably associated with fertilizer application in the prairie provinces. Some of this material is undoubtedly transported with dust in the prevailing west to east air flows in this area, and may account for the slightly higher potassium concentrations in lakes in this zone. 7.3.3 Anions The relative anionic composition of lake water shows much more variability from zone to zone. This is apparent from the data presented in Table 7.5. There are strong trends for several of the anions from deposition zone to deposition zone which will be discussed separately. Differences in all anion concentrations between deposition zones are significant (p< 0.001). Table 7.5: Summary of anion statistics stratified by deposition zone (alkalinity is treated as an anion since it is assumed to approximate HCO3 + CO3"). A"is estimated organic anion concentration. S Deposition Zone 3 4 SO, Median 34 69 117 102 157 222 01 24 47 98 86 139 198 03 65 88 152 131 177 260 N 146 693 297 321 1500 313 (cont'd) 88 Table 7.5: (Cont'd) — S Deposition Zone 1 2 3 4 5 7 Alk Median 335 467 495 94 76 22 Ql 191 171 244 40 39 -4 Q3 672 1205 876 206 149 98 N 231 1210 513 651 2401 545 CI Median 3 8 17 4 11 8 Ql 3 6 17 2 8 5 Q3 8 25 37 8 23 14 N 94 112 15 187 297 136 A- Median 89 65 63 44 42 26 Ql 41 41 39 32 32 17 Q3 137 103 85 61 55 40 N 173 709 294 265 1424 314 Since it appears that most of the sulphate in Ontario lakes is derived from atmospheric input (with the exception of acid mine drainage lakes), a difference in the sulphate concentration of lakes is expected. Histograms of the sulphate concentrations found in each of the deposition zones are shown in Figure 7.7. There is a clear gradient in lake sulphate concentration from the lowest to the highest sulphur deposition zone. The trends in alkalinity are the inverse of those observed for sulphate. The low sulphur deposition zones have higher alkalinity than those in the high sulphur deposition zones. This is partly due to the greater proportion of hardwater lakes in these zones, but is also a result of the replacement of alkalinity by sulphate. The distribution of lake alkalinity for each of the sulphur deposition zones is shown in Figure 7.8. There is also a trend towards lower organic anion concentrations in the higher sulphur deposition zones, shown in Figure 7.9. Part of this trend may be geographic. 89 Eilers et al. (1988) reported lakes in the Upper Midwest United States had significantly higher organic anion concentrations than those in the Northeast. The lakes in deposition zone 1 are at approximately the same longitude range as those in the Upper Midwest subpopulation of the Eastern Lake Survey. However, it should be noted that there was a long-term trend toward lower DOC concentration in an acidifying lake in Central Ontario (Dillon et aL 1987). The possibility that lower organic anion concentrations in higher sulphur deposition zones result from acidification cannot be excluded. Chloride distributions within sulphur deposition zones showed evidence of bimodality. As discussed above, the likely cause of higher chloride concentrations in a few Ontario lakes is runoff from road salting operations. It should also be noted that although the sodium concentration in deposition zone 1 is higher than that in the other zones, there is no corresponding increase in the chloride concentration, indicating that the higher sodium is probably associated with geological influences. The distributions of chloride concentrations by zone are shown in Figure 7.10. 7.3.4 Conductivity As reflected in the discussion on the distribution of cation and anion concentrations, there are significant (p< 0.001) differences in ionic strength among lakes in different sulphur deposition zones. These are reflected in the conductivity distributions shown in Figure 7.11 and summarized in Table 7.6. 90 40 - 30 - 20 10 40 30 20 10 i Zone 1 n=146 ■II Zone 2 n=693 i Jil ill Sja Zone 3 n=297 Zone 4 n=321 ^ § II ill I 11^ |li ;$ i^t^ .^ i. ■ ill p^f^ 70 - 60 - 50 - 40 30 20 10 Zone 5 n=1500 Zone 7 n=313 1^1^ tS^ Ji i I Sulphate (/^eq.L ) Figure 7.7: Sulphate distribution shown as percent of lakes sampled for each suphur deposition zone. 91 0) c o M T5 Q) Q_ E D 00 C/) 0) D O c CD O (D CL 30 - 20 10 40 30 20 - 10 Zone 1 n=231 S L [M Zone 3 ri=513 III s iiiii^ii^i Zone 2 n=1210 1^^^^ I i. .^i ii i Zone 5 n=2401 If^,^^^^^^^ ^ Zone 4 § n=651 k P^FT^fra Zone 7 n=545 ■ §1 § If33fra, O O Q p I o o o Q »- CM P) ©III T "^ 8 8 ♦- CM S s s OOP - - ^ o o s « h<- A o> o o I I 1 I T - Alkalinity (/xeq.L ) Figure 7.8: Alkalinity distribution shown as percent of lakes sampled for each suphur deposition zone. 92 I Zone 1 n=173 u. u. ¥ ll. il ill 1 Zone 2 n=709 ^ R III ii iiiiii^^ Zone 3 n=294 i ii iiii u. ■ r 11 . i" f^fT-TI ^ Zone 4 n=265 i M jQ_ ii Zone 5 n=1424 I Ii 1 i| 1 Zone 7 n=314 (O oo I I § s 100 iiS. For convenience, these strata will be referred to as low, medium, and high conductivity lakes, respectively. 95 0) c o M D 00 (/) CD D O CD U v_ 0) Q- 60 - 50 - 40 30|- 20 10 0 'i . I 80 60 40 20 80 60 40 20 Zone 1 n=205 11 i^ il IS ^^C^rrq Zone 3 n=444 1 Ihf^ Zone 2 n=1148 1^ iiii^t^ ^Sj Zone 5 n=2330 iK^rrif Zone 4 n=608 ' '''^^ — -~ -~- Zone 7 n=533 \ .Qu S 8 S »- CM CM I I s I I o m 1 1 I I A c o o o o ^ S § !? Conductivity (y^S) o o o mom •- CM tM I I I I o m S m »- •- CM CM Figure 7.11: Conductivity distribution shown as percent of lakes sampled for each suphur deposition zone. 96 7.4.1 Lake Size Variations in lake size within conductivity strata and between deposition zones are significant (p< 0.001). In all of the conductivity strata, the lakes sampled in zone 1 are much larger than in the other zones. The effect that this has on the observations presented is examined in Section 7.5, where the lakes are stratified by size category. Table 7.7: Lake size statistics by conductivity class and deposition zone. S Deposition Zone — 1 2 3 4 5 7 Low Conductivity ( < 50 /xS) Median 324.1 145.0 29.8 34.9 25.4 48.1 Ql 139.0 43.0 9.0 11.5 11.4 12.8 Q3 891.0 386.2 29.8 91.3 79.6 300.3 N 111 441 103 498 1897 385 Medium Conductivity (50-100 ^lS) Median 393.1 143.9 81.5 76.9 42.8 87.8 Ql 96.0 40.3 26.5 38.8 17.2 35.4 Q3 988.6 483.2 173.0 179.0 140.3 300.3 N 63 262 186 76 289 114 High Conductivity (> 100 /xS) Median 257.0 79.2 62.9 57.4 . 41.4 51.3 Ql 160.0 23.0 17.0 6.9 13.3 19.9 Q3 1307.0 220.1 151.0 624.4 153.2 243.5 N 31 445 155 34 144 34 97 7.4.2 Cations The statistics for cations in the three conductivity classes are presented in Tables 7.8 to 7.10 in the low conductivity class, there is a trend to lower pH with higher sulphur deposition (Figure 7.12). In the other conductivity classes, the highest pH is in deposition zone 3, with declines in pH in zones 4, 5, and 7. Since pH can be strongly influenced by the degree of CO^ saturation at the time of sampling, alkalinity is a more useful measure of the extent of lake acidification than pH. 98 7^ 7.0 6.5 6.0 5.5 5.0 4.3 8.0 - 7.5 X Q. 7.0 6.5 - 6.0 8.5 8.0 7.5 7.0 6.5 6.0 I'" 0440 Conductivity < 50 /xS O103 9498 I 1896 -r 9385 J L Conductivity 60-100 >iS Ie3 |258 il84 J 1 6288 kuA ConductMly > 100 /iS p h I 154 A34 0144 034 J L 2 3 4 5 7 Deposition Zone Figure 7.12: pH by conductivity class and deposition zone. 99 300 250 200 150 100 - 50 697 0259 976 Conductivity < 50 ^ 0288 01316 1 ^258 0 Y 600 _l Q-500 0) 300 E .5 200 _o D 100 O 1500 1000 53 6l54 6ll0 Conductivity 50-100 fjS 619 9153 041 625 0299 <^13 6100 Conductivity > 100 ^ 679 500 ^12 2 3 4 5 Deposition Zone Figure 7.13: Calcium by conductivity class and deposition zone. 100 Calcium also shows a trend to lower values in higher deposition zones in low conductivity lakes. In medium conductivity lakes, zones 5 and 7 have lower calcium values than the other zones, while in the high conductivity class, only zone 7 is significantly different from the other zones (Figure 7.13). Part of these trends can be explained by the effect of differing conductivity depending on the anionic composition of the lake water in the different zones. Table 7.8: Siunmary of cation statistics for low conductivity (< 50 /iS) lakes — S Deposition Zone 1 2 3 4 5 7 pH Median 6.97 6.76 6.62 6.40 6.25 5.68 Ql 6.77 6.49 6.27 5.91 5.94 4.95 Q3 7.16 7.00 7.00 6.75 6.54 6.27 N 111 400 103 498 1896 385 Ca Median 199.6 149.7 199.6 144.7 139.7 126.0 Ql 149.7 99.8 124.8 83.8 119.8 99.8 Q3 249.5 214.6 254.5 209.6 164.7 159.7 N 97 259 76 288 1316 258 Mg Median 82.2 82.2 64.1 42.4 61.7 61.7 Ql 82.2 61.7 46.1 30.4 49.3 49.3 Q3 90.5 82.2 94.6 60.0 77.3 76.9 N 97 258 75 288 1225 258 Na Median 483 35.6 26.8 23.3 33.0 30.8 Ql 36.5 26.8 22.0 16.7 26.4 26.4 Q3 52.7 43.9 35.2 30.8 40.4 35.6 N 97 251 63 249 1129 245 K Median 14.6 11.0 6.1 5.9 11.5 10.2 Ql 11.3 8.4 4.3 4.1 9.7 8.2 Q3 16.9 14.1 9.0 8.2 14.3 12.8 N 97 255 62 203 1129 245 101 Table 7.9: Summary of cation statistics for medium conductivity (50-100 mS) lakes S Deposition Zone — 1 2 3 4 5 7 pH Median 7.30 7.35 7.36 7.13 6.95 6.93 Ql 7.10 7.19 7.14 6.89 6.62 6.38 Q3 7.48 7.59 7.58 7.35 7.16 7.13 N 63 258 184 76 288 114 Ca Median 464.1 474.0 469.1 509.0 279.4 309.4 Ql 384.2 386.7 374.3 389.2 219.6 244.5 Q3 548.9 598.8 588.8 598.8 399.2 349.3 N 53 154 110 19 153 41 Mg Median 164.5 164.5 176.4 139.8 129.9 135.7 Ql 131.6 94.6 135.7 64.1 88.4 116.0 Q3 222.0 227.8 208.1 156.3 162.0 153.0 N 53 156 110 19 138 41 Na Median 43.5 39.6 35.2 42.2 70.3 50.1 Ql 33.8 30.3 28.8 30.8 50.5 39.3 Q3 57.1 52.7 42.4 44.4 126.6 76.0 N 53 149 100 16 111 32 K Median 14.6 13.3 9.2 13.2 17.9 14.7 Ql 10.7 9.5 7.4 7.8 13.8 10.2 Q3 18.9 16.9 10.7 14.1 22.8 17.9 N 53 155 98 14 111 32 102 Table 7.10: Summary of cation statistics for high conductivity (> 100 nS) lakes — S Deposition Zone 1 2 3 4 5 7 pH Median 7.58 7.85 7.93 7.90 7.60 7.04 Ql 7.30 7.60 7.60 7.33 7.15 6.69 Q3 7.88 8.10 8.09 8.15 7.98 7.50 N 31 440 154 34 144 34 Ca Median 1047.9 1362.3 1000.5 1392.2 1222.6 613.8 Ql 698.6 993.0 825.8 923.2 813.4 447.9 Q3 1472.1 1781.4 1265.0 1876.2 1671.6 703.6 N 25 299 100 13 79 12 Mg Median 370.1 495.9 335.5 296.1 254.5 242.6 Ql 304.3 371.3 272.2 242.6 163.7 193.3 Q3 483.6 656.3 388.2 433.4 408.3 312.1 N 25 298 100 12 78 12 Na Median 37.4 39.6 41.8 50.1 54.9 290.1 Ql 30.8 29.0 33.2 44.4 33.0 121.7 Q3 70.3 59.3 46.1 54.1 189.0 1399.7 N 25 287 92 12 69 8 K Median 20.7 15.3 12.3 22.8 25.8 26.3 Ql 12.8 10.7 9.7 18.2 19.4 13.8 Q3 24.8 21.5 15.9 33.0 32.0 42.2 N 25 289 92 12 69 8 103 Magnesium shows trends toward lower values as sulphur deposition increases in all conductivity classes (see Figure 7.14). In the low conductivity class, zone 4 has the lowest magnesium values. In low conductivity lakes, zone 1 has the highest median sodium levels (Figure 7.15). This may be a reflection of higher sodium concentrations in the bedrock in northwestern Ontario, as discussed above. Otherwise, zone 5 generally has the highest sodium values across the different conductivity classes. This is probably a reflection of higher densities of population and roads in this zone. The exception is the high conductivity lakes in zone 7. Because these lakes also have very high chloride (Figure 7.20) they are undoubtedly road salt lakes and are misclassified through artiflcially high conductivity contributed by the road salt. Potassium appears to be consistently lowest in zone 3, in all of the conductivity classes (Figure 7.16). This may be attributed to differences in the potassium content of the bedrock in this area, but this cannot be confirmed with available data. 7.4.3 Anions The deposition zone statistics for the anions in the three conductivity strata are presented in Tables 7.11, 7.12, and 7.13. In general, the anionic composition of water in Ontario lakes appears to be much more affected by sulphur deposition than the cations. The sulphate content of Ontario lake water, regardless of conductivity class, increases with sulphur deposition (Figure 7.17). This is entirely consistent with the hypothesis that atmospheric sulphate deposition is the primary source of sulphate in Ontario lakes. In the northwest, sulphate is a trace anion, usually less than 50 Meq.L'\ In the higher deposition zones, it represents the dominant anion. 104 150 Conductivity < 50 fiS 100 50 ig? 0258 075 0288 01225 0258 J L . 200 ::! O150 D 100 'to D 0 600 500 400 300 200 100 0 Conductivity 50-100 fiS 653 6156 6110 619 6138 J. J L 625 Conductivity > 100 /iS 6298 0100 612 678 012 -I 1 I I I L. 12 3 4 5 7 Deposition Zone Figure 7.14: Magnesium by conductivity class and deposition zone. 105 120 100 80 60 40 20 0 150 100 097 ConductJvtty < 50 ftS 0251 063 9249 V^29 „2^ cr E ■3 '-0 50 O CO 250 200 150 100 50 653 Conducttvfty 50-100 ^S 0149 0100 die 0111 032 025 Conductivity > ^ 00 ftS 62B7 092 012 0 69 2 3 4 5 Deposition Zone Figure 7.15: Sodium by conductivity class and deposition zone. 106 20 IS 10 697 i>255 ConductMfy < 50 ^ 01129 062 ^203 {■ 0245 ■ ' 0) 20 I" w D a. 40 30 20 053 025 I' 10 - 6289 092 ConduotMty 50-100 pS 0155 Ot4 98 r 0111 032 J U Conductivtty > 100 fiS 11 69 <>8 J 1. 2 3 4 5 7 Deposition Zone Figure 7.16: Potassium by conductivity class and deposition zone. 107 250 200 - 150 - 100 - 50 *- 300 ^250 ^200 Q) 150 •4-' D -C 100 -^ 50 500 400 300 200 100 0 244 C-^nductMly }„ ^ 574 J -^ 6288 < 50 MS T I il278 ConductMty fiO-100 fiS j J14. i'" I" 6l41 042 ConductMly > 100 /iS T (MS 7292 ^100 ^12 h 1 2 3 4 5 7 Deposition Zone Figure 7.17: Sulphate by conductivity class and deposition zone. 108 Table 7.11: Summary of anion statistics for low conductivity (< 50 ^S) lakes S Deposi tion ^one 1 2 3 4 5 7 SO, Median 45.7 69.1 113.3 97.4 154.9 218.6 Ql 29.1 48.7 98.4 83.5 137.6 194.9 Q3 70.8 83.3 124.0 116.2 169.7 242.5 N 78 251 74 288 1278 258 Alk Median 220.0 153.4 128.0 70.6 61.1 8.0 Ql 164.0 94.2 62.6 27.8 33.0 -9.8 Q3 275.6 236.0 206.6 134.0 101.3 42.6 N 111 441 101 495 1878 375 CI Median 2.8 5.6 7.9 3.1 10.2 6.5 Ql 0.1 2.8 5.4 1.7 8.2 4.5 Q3 2.8 8.5 . - 14.4 8.2 13.5 10.2 N 39 47 3 179 248 121 A' Median 52.8 57.8 51.4 44.0 40.6 6.5 Ql 34.4 41.9 38.5 31.9 31.1 4.5 Q3 109.2 84.6 68.4 60.8 53.6 10.2 N 96 257 74 236 1186 121 109 Table 7.12: Summary of anion statistics for medium conductivity (50-100 iiS) lakes — S Deposition Zone 1 2 3 4 5 7 SO, Median 24.5 58.2 119.1 112.6 178.0 272.7 Ql 18.9 37.6 93.5 96.4 155.5 211.3 Q3 34.2 87.4 140.1 154.0 199.8 322.6 N 44 146 111 19 141 42 Alk Median 572.0 546.0 454.7 365.4 259.0 166.0 Ql 474.0 428.0 318.4 223.4 162.0 88.0 Q3 734.0 716.0 618.1 513.5 371.4 251.0 N 63 257 182 76 281 113 C!' Median 5.6 8.5 33.8 11.3 70.5 15.5 Ql 2.8 5.6 25.4 8.5 39.5 7.1 Q3 8.5 8.5 67.7 59.2 170.6 163.6 N 38 41 11 3 23 11 A' Median 119.5 68.3 78.1 49.0 46.6 34.6 Ql 69.7 39.3 60.3 38.4 34.3 23.7 Q3 158.3 112.6 101.2 64.9 57.8 52.8 N 543 153 109 17 123 40 no Table 7.13: Summary of anion statistics for high conductivity (> 100 iiS) lakes C* Ti^T^/^Cl o ucpubi iiuii z_(jne 1 2 3 4 5 7 soT Median 24.6 68.1 107.1 131.7 199.8 430.9 Ql 15.6 48.4 83.6 108.3 176.3 194.6 Q3 44.3 90.2 132.5 197.8 238.6 603.7 N 24 292 100 12 77 13 Alk Median 1240.0 1595.0 1213.7 1552.6 1134.5 329.3 Ql 930.0 1179.6 916.2 1075.2 625.6 120.8 Q3 1836.0 2296.0 1530.4 2188.0 1832.0 525.4 N 31 438 154 33 142 34 cr Median 5.6 36.7 36.7 16.9 53.6 782.7 Ql 2.8 9.9 36.7 14.1 14.1 90.4 Q3 14.1 57.8 .- 36.7 19.7 155.1 1775.6 N 17 24 1 5 26 4 A' Median 118.2 76.5 56.8 42.8 48.6 36.4 Ql 84.6 44.9 32.9 20.4 37.8 29.4 Q3 161.6 112.7 83.5 73.7 62.1 42.4 N 24 295 99 12 70 14 111 Alkalinity follows consistent trends opposite to that of sulphate (Figure 7.18). Again, these observations are entirely consistent with a simple alkalinity titration model of lake acidification resulting from atmospheric sulphate deposition. A portion of these trends within the individual conductivity strata can be attributed to the use of conductivity as a classification variable, but these effects are minimal compared to the alkaUnity depletion associated with sulphate input. In the high conductivity class, the trend is not as clear as the lower classes because this strata is open ended, and includes some extremely bicarbonate dominated high conductivity lakes in zones 2 and 4. The organic anion content of lakes shows a consistent trend toward lower concentrations in the higher sulphur deposition zones (Figure 7.19). A temporal trend toward lower organic anion concentration has been observed during the acidification of an intensively studied lake in Central Ontario (Dillon et al. 1987), but the consistency of the drop in A' in each of the conductivity classes argues for, at least in part, a geographic explanation for the observation. Chloride shows no significant trends (Figure 7.20). The anomalously high values for chloride in the high conductivity class in zone 7, coupled with the high sodium and low calcium and magnesium indicates that these lakes are road salt lakes. The slightly higher chloride concentrations in zone 5 are also a road salt effect. 112 280 230 180 130 80 30 I- -20 ,^r^7oo I —J 600 O" Q^ 500 ^""^400 .-ti 300 c *0 200 < 100 0 2250 2000 1750 1500 1250 1000 750 500 250 0 6111 663 931 Conductivity < 50 ^S 6441 6101 6495 11878 ^375 Conductivity 50-100 >iS 6257 6182 676 6281 |l13 6438 ► 154 033 Conductivity > 100 |*S 0142 }- 2 3 4 5 Deposition Zone Figure 7.18: Alkalinity by conductivity class and deposition zone. 113 150 100 - 50 ^150 100 - (0 c o < O 50 - 'c D cn o 150 - 100 - 50 - Conductivity < 50 ;iS 9257 996 T ( :'* J236 Jii86 1 1 6260 ConductMty 50-100 ^S ( >53 P '- < H53 • 109 ^17 Il23 040 1 Conductivfty > ^ 00 fiS ( •24 - ' >295 r - < >99 { h 514 1 1 i 1 12 3 4 5 7 Deposition Zone Figure 7.19: Organic anions by conductivity class and deposition zone. 1U zu Conductivity < 50 fiS 15 - r _ r 10 . ^246 -^ _ - r 63 J L 5 - ('47 - ^121 c • 39 - L 0179 0 , ' 1 Conductivity 50-100 fiS 7 —J 150 . & ^"-^100 - Chloride 8 D38 V 1 ['' (123 :'' Conductivity > 100 fiS 150 - ■ ■ 100 - . 50 0 ?,7 i 1 24 01 ( n5 1 26 2 3 4 5 Deposition Zone Figure 7.20: Chloride by conductivity class and deposition zone. 115 7.4.4 Conductivity Table 7.14: Conductivity statistics for laices in the three conductivity classes S Deposition Zone — 1 2 3 4 5 7 Lx)w conductivity (< 50 fiS) Median 35.0 30.0 35.0 30.0 32.1 37.0 Ql 30.0 25.0 24.0 22.0 28.0 33.0 Q3 42.0 38.0 43.0 37.0 38.0 41.0 N 111 441 103 498 1897 385 Medium Conductivity (50-100 /xS) Median 68.0 71.8 72.9 65.0 61.0 60.0 Ql 60.0 60.0 60.2 56.0 54.0 54.0 Q3 86.0 83.0 85.7 80.0 74.0 72.0 N 63 262 186 76 289 114 High Conductivity (> 100 nS) Median 127.0 167.0 133.0 185.5 158.0 167.5 Ql 112.0 125.0 114.0 142.5 126.4 120.5 Q3 182.0 228.0 168.0 230.0 212.0 315.0 N 31 445 155 34 144 34 Even within conductivity strata, there are significant differences in conductivity between the deposition zones (Tables 7.14, Figure 7.21). In the low conductivity stratum, zones 2 and 4 have the most dilute lakes, while zones 1, 3 and 7 have higher conductivity. These differences are statistically significant (p<0.01). In the medium conductivity stratum, the four lower sulphur deposition zones tend to have slightly higher conductivity. Again, these are significantly different (p<0.05). In the high conductivity stratum, zones 1 and 3 are significantly lower than the other zones. 116 50 AO - 30 - 20 - 10 - 6111 ConductMfy < 50 fjS A 441 6103 6498 1 1897 6385 3" O 40 13 ■D C O 20 O 663 i262 1 188 ConductMty 50-100 ftS 676 6289 iiu 300 200 - 100 - T H I ^31 1 ^155 Conductivify > 100 ^S I" I- 634 2 3 4 5 Deposition Zone Figure 721: Conductivity by conductivity class and deposition zone. 1 17 7.5 Lake Water Chemistry Stratifled by Lake Size, Conductivity Class and Sulphur Deposition Zone Lakes of different size may respond at different rates to acid inputs. Typically, larger lakes have longer hydraulic retention times and on a simple hydrologic basis would be expected to respond more slowly than smaller lakes to an increase or decrease in acid loading. In most cases, larger lakes are not headwaters, and they derive much of their water from upstream lakes rather than the immediate watershed. The impacts of acid input are mediated by in lake processes in the upstream lakes and their watersheds (Schindler et al. 1987), resulting in a delayed response to acidification. The effect of lake size on acid sensitivity was examined by stratifying the sample into four size categories: 1000-999 ha, 100-999 ha, 10-99 ha, and 1-9.9 ha. These categories were chosen because there are population estimates for the total number of lakes in the province based on these size strata. 7.5.1 Cations Measured pH within the sulphur deposition, conductivity, and lake size strata is shown in Figures 7.22 to 7.24. There is a consistent trend toward lower pH in smaller lakes in the low conductivity class, regardless of deposition zone. In the low deposition zones, the lower pH in the smaller lakes appears to be a function of higher organic anion concentrations (see 7.5.2). In the high sulphur deposition zones, there is no consistent trend to higher organic anion concentrations, and the pH trend indicates that small lakes have been more strongly acidified by sulphate inputs. It is likely that these smaller lakes had higher organic anion concentrations and lower alkalinity before the onset of anthropogenic acidification, and so are more strongly acidified because of lower initial acid buffering capacity. The differences in pH between size classes within deposition zone are significant (p<0.01) for low conductivity lakes. The data for calcium, magnesium, sodium, and potassium have been combined and show that the smaller lakes tend to have significantly (p<0.01) lower total base cation 118 X Cl 7.5 7.0 6.5 6.0 5.5 5.0 -O- <10 ha -5^- 10-99 ••••0-- 100-999 >1000 Conductivity <50 /llS 2 3 4 5 Deposition Zone Figure 7.22: Relationship between S deposition (expressed at S deposition zone number) and mean pH for lakes with conductivity < 50 nS by lake size class. 1 19 8.0 7.5 ^ 7.0 6.5 6.0 - 5.5 - 5.0 -O— <10 ha -2^-- 10-99 ...<>.... 100-999 >1000 Conductivity 50-100 fiS 12 3 4 5 Deposition Zone Figure 723: Relationship between S deposition (expressed at S deposition zone number) and mean pH for lakes with conductivity 50-100 mS by lake size class. 120 8.5 8.0 7.5 7.0 6.5 -O— <10 ha -^- 10-99 •••0-- 100-999 >1000 Conductivity >100 /xS _i I I 2 5 4 5 Deposition Zone 7 Figure 7.24: Relationship between S deposition (expressed at S deposition zone number) and mean pH for lakes with conductivity > 100 iiS by lake size class. 121 cr CD =1 CO c o D O 500 400 300 200 - CO o 100 DQ -O- <10 ha -2^- 10-99 O • 100-999 >1000 Conductivity <50 /jlS I I L 12 3 4 5 Deposition Zone Figure 7.25: Relationship between S deposition (expressed at S deposition zone number) and mean base cations for lakes with conductivity < 50 by lake size class. 122 1000 900 800 cr 700 CD ::l c o D O 600 500 400 - 300 - (/} D 200 - CD 100 - 0 Conductivity 50-100 fj,S -O— <10 ha -2^-- 10-99 ...<>.... 100-999 >1000 12 3 4 5 Deposition Zone Figure 726: Relationship between S deposition (expressed at S deposition zone number) and mean base cations for lakes with conductivity 50-100 by lake size class. 123 3000 2500 - u- (D 3 2000 (/) C o n 1500 CJ .... 100-999 \ — D— >1000 / ...0.. VA /^--^^ ,/ /' ■%><-''---\ 0 Conductivity >100 /xS 2 3 4 5 Deposition Zone Figure 7.27: Relationship between S deposition (expressed at S deposition zone number) and mean base cations for lakes with conductivity > 100 by lake size class. 12A concentrations (Figures 7.25 to 7.27), regardless of conductivity stratum. This may be due to short retention times and to the fact that a greater proportion of water in the smaller lakes is derived from the immediate watershed. This is also reflected in lower conductivity, even within conductivity strata. 7.5.2 Anions There is a significant (p<0.01) trend toward lower sulphate concentrations in smaller lakes (Figures 7.28 to 7.30) in the low conductivity stratum. Again, this is probably in keeping with the above discussion on rapid flushing of smaller bodies of water. Lake alkalinity generally follows the same trend as lake pH. In the low conductivity stratum, smaller lakes have significantly (p<0.05) lower conductivity than the larger lakes with the exception of zone 5. In medium conductivity lakes, alkalinity differences between lake sizes are only significant (p<0.05) in the high deposition zones (5 and 7). In the high conductivity lakes, size is generally not a factor. As noted above, organic anions tend to be higher in smaller lakes in the low sulphur deposition zones (Fig. 7.34 to 7.36). These differences are significant (p<0.05) for low conductivity lakes in all but zone 4. In medium conductivity lakes, significant (p<0.05) differences between lakes of different size appear only in zones 1 and 3 (where smaller lakes have higher A") and in zone 7, where smaller lakes have lower A". In high conductivity lakes, lakes size is not important. 125 300 250 H 200 cr Q) "^ 150 (D D ;§_ 100 50 - -O— <10 ha •2^-- 10-99 •0-- 100-999 -D— >1000 Conductivity <50 fiS 2 3 4 5 Deposition Zone 7 Figure 7.28: Relationship between S deposition (expressed at S deposition zone number) and mean sulphate for lakes with conductivity < 50 mS by lake size class. 126 CD D 500 250 200 150 Q. 100 13 00 50 - -O— <10 ha ■2^- 10-99 •0-- 100-999 -D-- >1000 ^^^^^ Conductivity 50-100 fiS 2 3 4 5 Deposition Zone 7 Figure 7.29: Relationship between S deposition (expressed at S deposition zone number) and mean sulphate for lakes with conductivity 50-100 nS by lake size class. 127 650 600 550 ^ 500 ^ 450 - ^400 - 3 350 - (D 300 _^ 250 — 200 ^ 150 100 50 - 0 -O- <10 ha ■^- 10-99 •<>•••• 100-999 -D— >1000 Conductivity >100 /xS I \ 2 3 4 5 Deposition Zone 7 Figure 7.30: Relationship between S deposition (expressed at S deposition zone number) and mean sulphate for lakes with conductivity > 100 /iS by lake size class. 128 550 300 250 cr 200 0) ::! ""^ 150 >^ !e 100 D < 50 0 - -50 -O— <^0 ha -2^-- 10-99 ...<>,... 100-999 -D— >1000 Conductivity <50yU,S I I 12 3 4 5 Deposition Zone Figure 7.31: Relationship between S deposition (expressed at S deposition zone number) and mean alkalinity for lakes with conductivity < 50 iiS by lake size class. 129 700 600 500 cr 400 CD ^"^ 300 !| 200 D < 100 0 h -100 -O— <10 ha -£y-- 10-99 •••0-- 100-999 >1000 Conductivity 50-100 /jlS 12 3 4 5 Deposition Zone Figure 7.32: Relationship between S deposition (expressed at S deposition zone number) and mean alkalinity for lakes with conductivity 50-100 /xS by lake size class. 130 2500 cr 2000 CD >N D 1500 < 1000 500 O— <10 ha 2^- 10-99 O ■■ 100-999 ■D— >1000 Conductivity >100 jj,S I \ i_ 12 3 4 5 Deposition Zone Figure 7.33: Relationship between S deposition (expressed at S deposition zone number) and mean alkalinity for lakes with conductivity > 100 mS by lake size class. 131 200 180 - _, 160 - S" 140 ^ 120 en o 100 c < 80 .| 60 D ^ 40 O 20 0 Conductivity <50 /xS — O— <10 ha -^- 10-99 '"0" 100-999 — D— >1000 2 3 4 5 Deposition Zone Figure 734: Relationship between S deposition (expressed at S deposition zone number) and mean organic anions for lakes with conductivity < 50 mS by lake size class. 132 Conductivity 50-100 /^S — O— <10 ha -^.. 10-99 ••••O— 100-999 —a— >1000 2 3 4 5 Deposition Zone Figure 735: Relationship between S deposition (expressed at S deposition zone number) and mean organic anions for lakes with conductivity 50-100 /iS by lake size class. 133 CO c o < o D cn O 160 140 - 120 - 100 80 60 40 20 Conductivity >100 fzS — O- <10 ha -^- 10-99 ....<>.... 100-999 — D— >1000 2 3 4 5 Deposition Zone 7 Figure 136: Relationship between S deposition (expressed at S deposition zone number) and mean organic anions for lakes with conductivity > 100 mS by lake size class. 13A 8. Estimates of Lake Resources Affected by Acid Deposition The data described in this report may be used to make estimates of the total number of lakes affected by acid deposition. The first level of estimate may be made at the deposition zone level. In this exercise, the pH and alkalinity distributions of sampled lakes within each size stratum and deposition zone were assumed to be representative of the total population of lakes within the zone. Most of the known biases in sampling the lakes were associated with lake size. As discussed in Section 7 and shown in Table 2.3, much of the sampling was of lakes greater than 10 ha in size, and in many cases, of lakes greater than 100 ha in size. The variance of the estimates was calculated using the equation 8.1 (Cochran, 1977) : V(n) =[N,(N,-n3)/(N,-l)][n>3][(ns-nJ/n3] (Equ. 8.1) where n = the estimated number of lakes lower than a particular value N, =the total number of lakes in the subpopulation (watershed or deposition zone) n^ = the number of sampled lakes with value less than x. and n^ = the number of lakes sampled in the subpopulation. The standard error (se) of estimate was the square root of the variance. 8.1 Critical pH and Alkalinity Levels at the Deposition Zone Level Estimates were made of numbers of lakes with pH and alkalinity below several specific levels. A pH of 6.0 was selected as the threshold where noticeable biological damage occurs. Pronounced biological effects were observed in experimentally-acidified Lake 223 (ELA) (Schindler et al. 1987) and in Plastic Lake, Ontario (Mierle et al. 1987) at pH's just below 6.0. In a review of fish species richness in 2931 Ontario lakes, Matuszek and Beggs (1988) found that after adjusting for lake size, lakes with pH <6.0 had fewer species than would be expected. 135 A pH of 6.0 corresponds to an alkalinity of approximately 40 ^eq L'^ (Figure 5.4, and Dillon, unpublished studies). While an alkalinity of 20 ueq.L"^ (very approximately equivalent to a pH of 5.6) is indicative of lakes with significant biological damage, an alkalinity of 0 (very approximately equivalent to a pH of 5.2) indicates severe damage. The results of these estimations at the deposition zone level are presented in Tables 8.1 through 8.6 for lakes in the 1-9.9, 10-99, and 100-999 ha size ranges. Extrapolations were not performed unless there were data for at least a 1% subsample of the total number of lakes within the size range. These estimates should be viewed with some caution. One of the assumptions behind the stratification is that there is an unbiased subsample of lakes within the deposition zone strata. The most obvious source of bias in this data base is an orientation toward large lakes. By making separate estimates for each of the size ranges, some of this bias can be overcome. However, there may be other sources of bias within some deposition zones which are not accounted for, e.g. the influence of geology. For example, large portions of deposition zones 1 and 2 lie within the James and Hudson Bay lowlands, an area dominated by organic deposits. There are no lakes in our subsample from that portion of the region, although the number of lakes that they contain is relatively small. Additionally, zones 2 and 3 both contain portions of extensive clay deposits. It is not knowTi whether the lakes in our data base proportionally represent these areas. On the other hand, our best estimates are those made for zones 4, 5 and 7 which are also those receiving the highest level of acid deposition. As a result, our estimates of the number of lakes affected are probably fairly reliable. Our best estimate of lakes whose biota have been affected by acid deposition is approximately 19,000, the average of the number of lakes with pH < 6.0 (19,293) and the number with alkalinity < 40 Mcq L'^ (18,408). These figures do not include estimates for small lakes in zones 1-3 or medium lakes in zone 1, so must be considered lower limits. The number with significant biological damage is estimated as about 12,000, while those with severe damage (pH < 5.0) is about 5,500. Again, zones 1-3 are under- represented in these calculations, but it is expected that there will not be a large number 136 of lakes in these regions (with the possible exception of zone 3) that have extensive biological damage. Exclusion of zone 7 from the calculations results in an estimate of 11,400 lakes biologically damaged by acid deposition. Table 8.1: Estimates of numbers of small lakes (1-9.9 ha) with pH less than 5.0, 5.5 and 6.0 by deposition zone. Definitions as on p. 135. Zone Nt n^ n<5.o ''' of the Environment Report APIOS-011-86. (ISBN 0-7729-1664-0). 33p. TYDAC, 1988. 'SPANS 4.0 Manual', vol.1, pp TlO-1 to TlO-2. TYDAC Technologies, Inc. Ste 310, 1600 Carling Ave., Ottawa, Ontario, Canada KIZ 8R7. Zimmerman A.P., and H.H. Harvey. 1979. 'Final Report on Sensitivity to Acidification of Waters of Ontario and Neighbouring States'. Ontario Hydro Report. 136p. 146 Appendix A: Number of Lakes Sampled by Watershed Number of Lakes % Lakes' Sampled Sampled 2A Western Lake Superior Tributaries 2AA Eight Superior Tributaries 2AB Five Superior Tributaries 2AC Twenty- three Superior Tributaries 2AD Nipigon River 2AE Twenty- two Superior Tributaries 2B Eastern Lake Superior Tributaries 2BA Twenty Superior Tributaries 2BB Pic River and Two Other Superior Tributaries 2BC Thirty Superior Tributaries 2BD Thirty-one Superior Tributaries 2BE Twenty-one Superior Tributaries 2BF Twenty- two Superior Tributaries 2C North Channel Tributaries and Manitoulin Island 274 20 7.30 1875 83 4.43 1243 62 4.99 5276 66 1.25 1084 24 2.21 2450 45 1.84 1910 37 1.94 3163 131 4.14 4900 198 4.04 3073 145 4.72 2726 226 8.29 2CA Fourteen St. Marys R.- North Channel Tributaries 2CB Upper Mississagi River 2CC Lower Mississagi River 2CD Four North Channel Tributaries 2CE Spanish River and One North Channel Tributary 2CF Seven North Channel Tribs. and Trib. to Spanish R. 2CG Manitoulin Island 1245 65 5.22 2943 48 1.63 1891 103 5.45 1008 100 9.92 4271 108 2.53 2846 189 6.64 305 1 0.33 2D French River and Islands 2DA Upper Wanapitei River 2DB Lower Wanapitei River 2DC Sturgeon River 2DD French River and Pickerel River 1209 39 3.23 207 15 7.25 2622 209 7.97 1289 90 6.98 2E Eastern Georgian Bay Tributaries 2EA Nineteen Georgian Bay Tributaries 2EB Moon River and Go Home River 2EC Severn River 2ED Nottawasaga River and Thirteen Other Tributaries 1441 308 21 .37 1809 479 26 .48 937 123 13 .13 157 0 0 ,00 (cont'd) 147 Appendix A: (Cont'd) Number of Lakes % Lakes' Sampled Sampled 2F Western Georgian Bay and Eastern Lake Huron Tributaries 2FA Bruce Peninsula Streams 2FB Fourteen Georgian Bay Tributaries 2H Lake Ontario Tributaries 119 31 0.00 0.00 2FC Saugeen River 2FD Twenty- three Lake Huron Tributaries 2FE Maitland River and Two Other Tributaries 2FF Eighteen Lake Huron Tributaries 2G Tributaries of the St. Clair River and Lake Erie 136 0 0.00 16 0 0.00 21 0 0.00 13 0 0.00 2GA Upper Grand River 2GB Lower Grand River 2GC Fifteen Lake Eire Tributaries 2GD Upper Thames River 2GE Lower Thames River 2GF Nine Lake Eire Tributaries 2GG St. Clair River and Lake St. Clair Tributaries 2GH Essex County 2HA Niagra River - Western Lake Ontario Tributaries 2HB Lake Ontario Tributaries 2HC Lake Ontario Tributaries 2HD Lake Ontario Tributaries 2HE Prince Edward County 2HF Cameron Lake Drainage 2HG Scugog River 2HH Kawartha Lakes Drainage 2HJ Otonabee River - Rice Lake 2HK Trent River and Crowe River 2HL Moira River 2HM Lake Ontario Tributaries 117 0 0.00 74 0 0.00 140 0 0.00 64 0 0.00 20 0 0.00 2 0 0.00 79 0 0.00 52 0 0.00 67 0 0.00 128 0 0.00 187 0 0.00 37 0 0.00 22 0 0.00 945 175 18.52 35 0 0.00 747 36 4.82 52 0 0.00 452 23 5.09 369 7 1.90 253 8 3.16 2J Northern Ottawa River 2JC Blanche River 2JD Montreal River 2JE Lake Temiskaming Ottawa River 551 17 3.09 2592 109 4.21 1996 163 8.17 (cont'd) 148 Appendix A: (Cont'd) Number of Lakes °L Lakes' Sampled Sampled 2K Central Ottawa River 2KA Holden Lake - Ottawa River 2KB Petawawa River 2KC Allumette Lake - Lac Des Chats - Ottawa River 2KD Upper Madawaska River 2KE Lower Madawaska River 2KF Mississipi River - Lac Deschenes 401 132 32, .92 1998 224 11, .21 693 127 18, ,33 1782 466 26 .15 516 24 4 .65 658 10 1 .52 2L Lower Ottawa River 2LA 2 LB Rideau River Lower Ottawa River 173 33 12 0 6.94 0.00 2M Western St. Lawrence River 2MA Western St. Lawrence Tributaries 2MB West St. Lawrence River 2MC Lake St. Lawrence - Lake St. Frances 225 15 6.67 41 0 0.00 14 0 0.00 4A Hayes River 4AC Upper Gods River 4AD Lower Gods River 4AE Echoing River 941 0 0.00 1602 0 0.00 2669 0 0.00 4B Hudson Bay Tributaries Between Nelson and Severn Rivers 4BA 4BB Twelve Hudson Bay Tributaries Five Hudson Bay Tributaries 4129 887 0.00 0.00 4C Severn River 4CA Upper Severn River 4CB Windigo River - Shade River 4CC Lower Severn River 4CD Sachigo River 4CE Fawn River 4CF Beaver River 8035 22 0.27 3812 15 0.39 4383 0 0.00 5343 0 0.00 4226 0 0.00 1864 0 0.00 (cont'd) 149 Appendix A: (Cont'd) Number of Lakes % Lakes' Sampled Sampled AD Winisk River and Hudson Bay Tributaries between Severn and Winisk Rivers 4DA Upper Winisk River 4DB Middle Winisk River 4DC Lower Winisk River 4DD Twenty- four Hudson Bay Tributaries 4E Hudson and James Bays Tributaries between Winisk and Attawapiskat Rivers 4EA Upper Ekwan River 4EB Lower Ekwan River 4EC Five Hudson Bay Tribs. - 13 James Bay Tributaries 4ED Sixteen Hudson Bay Tributaries 4F Attawapiskat River 4FA Upper Attawapiskat River 4FB Middle Attawapiskat River 4FC Lower Attawapiskat River AG Upper Albany River 4GA Upper Albany River 4GB Ogoki Diversion 4GC West Middle Albany River 4GD Middle Albany River 4GE Ogoki River 4GF East Middle Albany River 4H Lower Albany River and James Bay Tributaries between Attawapiskat Rivers and Moose Rivers 4HA Lower Albany River 4HB Ten James Bay Tributaries 4HC Upper Kapiskau River 4HD Kapiskau R. and Six Other James Bay Tributaries 9133 11 0.12 10213 1 0.01 9435 0 0.00 7062 0 0.00 3578 0 0.00 390 0 0.00 5117 0 0.00 7201 0 0.00 4761 12 0.25 2811 5 0.18 5351 0 0.00 8108 5290 3934 2720 1055 573 31 0.38 65 1.23 5 0.13 5 0.18 1 0.09 2 0.35 5215 0 0.00 2782 0 0.00 273 0 0.00 3923 0 0.00 (cont'd) 150 Appendix A: (Cont'd) Number of Lakes % Lakes' Sampled Sampled 4J Upper Missinalbi River 4JA Upper Kabinakagami River 4JB Lower Kabinakagami River 4JC Nagagami River 4JD Upper Kenogami River 4JE Drowning River 4JF Little Current River 4JG Lower Kenogami River 4K Kwataboahegan River 4KA Kwataboahegan River AL Moose River 1024 18 1.76 422 33 7.82 1984 123 6.20 2996 48 1.60 402 5 1.24 2360 13 0.55 108 0 0.00 865 0.00 4LA Upper Mattagami River 4LB Middle Mattagami River 4LC Upper Groundhog River 4LD Lower Groundhog River — ALE Upper Kapuskasing River 4LF Lower Kapuskasing River 4LG Cheepash River 4LH Upper Missinaibi River 4LJ South Middle Missinaibi River 4LK North Middle Missinaibi River 4LL Opasatika River 4LM Lower Missinabi River 2311 141 6.10 74 23 31.08 3102 54 1.74 271 17 6.27 1234 34 2.76 396 24 6.06 380 9 2.37 1053 21 1.99 357 4 1.12 638 17 2.66 697 16 2.30 87 0 0.00 AM Abitibi and North French River 4MA Upper Abitibi River 4MB Black River 4MC Middle Abitibi River 4MD Fredrickhouse River 4ME Lower Abitibi River AMF French River 294 7 2.38 566 11 1.94 480 6 1.25 926 27 2.92 1490 14 0.94 1713 4 0.23 AN Southern James Bay Tributaries 4NB Upper River Turgeon 4NC Seven James Bay Tributaries 199 2269 1.01 0.31 (cont'd) 151 Appendix A: (Cont'd) Number of Lakes % Lakes' Sampled Sampled 5P Winnipeg River SPA Upper Rainy River 5PB Middle Rainy River 5PC Lower Rainy River 5PD Lake of the Woods and Drainage 5PE Upper Winnipeg River 5PF Lower Winnipeg River SPG Whiteshell River SPJ Oiseau River 3543 165 4.66 5692 193 3.39 30 1 3.33 1998 31 1.55 955 21 2.20 49 1 2.04 35 0 0.00 593 2 0.34 5Q English River SQA Upper English River SQB Lac Seul Drainage 5QC Pakwash River 5QD Wabigoon River 5QE Lower English River 3559 53 1.49 1763 30 1.70 1307 9 0.69 2074 44 2.12 2412 32 1.33 5R Lake Winnipeg Tributaries 5RA Ten Lake Winnipeg Tributaries SRB Four Lake Winnipeg Tributaries SRC Upper Berens River 5RD Lower Berens River and Others 5RE Poplar River and Others 312 3 0.96 1889 4 0.21 3610 16 0.44 232 1 0.43 456 0 0.00 Estimated Number of Lakes Between 1 and 9999 ha 152 Appendix B: Lakes Sampled by Watershed and Lake Size 1, -9.9 ha 10 -99 ha ---IOC 1-999 ha--- --1000 -999S ) ha-- Wshe d y/ Samp % // Samp % // Samp % # Samp % 2AA 197 2 1.0 57 6 10.5 17 9 52.9 3 3 100.0 2AB 1247 15 1.2 554 33 6.0 68 27 39.7 6 8 133.3 2AC 886 7 0.8 314 29 9.2 42 24 57.1 1 2 200.0 2AD 3552 7 0.2 1501 12 0.8 202 34 16.8 21 13 61.9 2AE 884 11 1.2 180 9 5.0 20 4 20.0 0 0 - 2BA 1998 3 0.2 406 11 2.7 42 30 71.4 4 1 25.0 2BB 1340 1 0.1 522 14 2.7 46 21 45.7 2 1 50.0 2BC 2470 20 0.8 640 66 10.3 49 41 83.7 4 4 100.0 2BD 3695 30 0.8 1104 101 9.1 88 60 68.2 13 7 53.8 2BE 2434 28 1.2 608 90 14.8 31 27 87.1 0 0 - 2BF 2157 84 3.9 536 117 21.8 33 25 75.8 0 0 - 2CA 967 11 1.1 248 29 11.7 27 22 81.5 3 3 100.0 2CB 2107 3 0.1 784 20 2.6 48 22 45.8 4 3 75.0 2CC 1409 3 0.2 442 64 14.5 36 32 88.9 4 4 100.0 2CD 651 13 2.0 293 43 14.7 59 40 67.8 5 3 60.0 2CE 3136 9 0.3 1044 61 5.8 79 31 39.2 12 7 58.3 2CF 2063 32 1.6 670 96 14.3 108 55 50.9 5 6 120.0 2CG 194 0 0.0 82 0 0.0 26 0 0.0 3 1 33.3 2 DA 931 8 0.9 255 20 7.8 21 10 47.6 2 1 50.0 2DB 146 1 0.7 53 9 17.0 8 5 62.5 0 0 - 2DC 1899 49 2.6 637 96 15.1 74 54 73.0 12 10 83.3 2DD 892 10 1.1 352 40 11.4 42 37 88.1 3 3 100.0 2EA 960 61 6.4 407 169 41.5 69 74 107.2 5 4 80.0 2EB 1169 92 7.9 556 302 54.3 77 78 101.3 7 7 100.0 2EC 716 15 2.1 193 81 42.0 22 22 100.0 6 5 83.3 2 ED 110 0 0.0 40 0 0.0 7 0 0.0 0 0 - 2 FA 59 0 0.0 43 0 0.0 17 0 0.0 0 0 . 2FB 17 0 0.0 13 0 0.0 1 0 0.0 0 0 - 2FC 102 0 0.0 34 0 0.0 0 0 - 0 0 - 2FD 13 0 0.0 3 0 0.0 0 0 - 0 0 - 2FE 15 0 0.0 6 0 0.0 0 0 - 0 0 - 2FF 12 0 0.0 0 0 - 1 0 0.0 0 0 - 2GA 101 0 0.0 12 0 0.0 3 0 0.0 1 0 0.0 2GB 62 0 0.0 12 0 0.0 0 0 - 0 0 - 2GC 121 0 0.0 19 0 0.0 0 0 - 0 0 - 2GD 56 0 0.0 5 0 0.0 3 0 0.0 0 0 - (cont'd) 153 Appendix B: (Cont'd) 1, -9.9 hi a 10- -99 ha ---100 1-999 ha--- --1000 -9999 ha-- Wshe d // Samp % # Samp 1 // Samp % # : Samp % 2GE 18 0 0.0 2 0 0.0 0 0 0 0 2GF 2 0 0.0 0 0 - 0 0 - 0 0 - 2GG 70 0 0.0 8 0 0.0 1 0 0.0 0 0 - 2GH 38 0 0.0 13 0 0.0 1 0 0.0 0 0 - 2HA 55 0 0.0 10 0 0.0 2 0 0.0 0 0 . 2HB 118 0 0.0 7 0 0.0 3 0 0.0 0 0 - 2HC 174 0 0.0 13 0 0.0 0 0 - 0 0 - 2HD 35 0 0.0 2 0 0.0 0 0 - 0 0 - 2HE 13 0 0.0 3 0 0.0 4 0 0.0 2 0 0.0 2HF 630 13 2.1 257 108 42.0 53 51 96.2 5 3 60.0 2HG 33 0 0.0 1 0 0.0 0 0 - 1 0 0.0 2HH 548 4 0.7 169 19 11.2 22 11 50.0 8 2 25.0 2HJ 47 0 0.0 5 0 0.0 0 0 - 0 0 - 2HK 297 2 0.7 126 13 10.3 28 7 25.0 1 1 100.0 2HL 295 0 0.0 64 2 3.1 9 4 44.4 1 1 100.0 2HM 166 0 0.0 69 1 1.4 18 7 38.9 0 0 - 2JC 379 2 0.5 146 5 3.4 24 10 41.7 2 0 0.0 2JD 1848 16 0.9 669 46 6.9 67 42 62.7 8 5 62.5 2JE 1422 15 1.1 511 104 20.4 55 38 69.1 8 6 75.0 2KA 273 51 18.7 117 73 62.4 9 8 88.9 2 0 0.0 2KB 1623 35 2.2 322 142 44.1 48 43 89.6 5 4 80.0 2 KG 544 39 7.2 127 74 58.3 13 8 61.5 9 6 66.7 2KD 1311 152 11.6 402 247 61.4 63 59 93.7 6 8 133.3 2KE 400 13 3.3 101 8 7.9 12 1 8.3 3 2 66.7 2KF 468 0 0.0 153 2 1.3 32 5 15.6 5 3 60.0 2LA 95 0 0.0 56 3 5.4 19 6 31.6 3 3 100.0 2LB 27 0 0.0 5 0 0.0 0 0 1 0 0.0 2MA 94 0 0.0 86 2 2.3 41 10 24.4 4 3 75.0 2MB 39 0 0.0 2 0 0.0 0 0 - 0 0 - 2MC 10 0 0.0 3 0 0.0 1 0 0.0 0 0 - 4AC 277 0 0.0 567 0 0.0 92 0 0.0 5 0 0.0 4AD 940 0 0.0 605 0 0.0 52 0 0.0 5 0 0.0 4AE 1754 0 0.0 859 0 0.0 52 0 0.0 4 0 0.0 4BA 2904 0 0.0 1205 0 0.0 20 0 0.0 0 0 . ABB 384 0 0.0 478 0 0.0 25 0 0.0 0 0 ■ (cont'd) 154 Appendix B: (Cont'd) 1, -9.9 ha 10 -99 ha ---IOC 1-999 ha--- --1000 1-9999 ha-- Wshe d y/ Samp % # Samp % y/ Samp % # Samp 1 4CA 5344 0 0.0 2398 9 0.4 261 8 3.1 32 5 15.6 4CB 1600 0 0.0 1833 2 0.1 356 10 2.8 23 3 13.0 4CC 2322 0 0.0 1792 0 0.0 264 0 0.0 5 0 0.0 4CD 2675 0 0.0 2332 0 0.0 328 0 0.0 8 0 0.0 4CE 2236 0 0.0 1744 0 0.0 237 0 0.0 9 0 0.0 4CF 1210 0 0.0 637 0 0.0 17 0 0.0 0 0 - 4DA 5246 0 0.0 3369 5 0.1 487 5 1.0 31 1 3.2 4DB 5610 0 0.0 3998 0 0.0 589 1 0.2 16 0 0.0 4DC 6176 0 0.0 2999 0 0.0 258 0 0.0 2 0 0.0 4DD 4828 0 0.0 2123 0 0.0 108 0 0.0 3 0 0.0 4EA 1950 0 0.0 1429 0 0.0 193 0 0.0 6 0 0.0 4EB 179 0 0.0 197 0 0.0 13 0 0.0 1 0 0.0 4EC 3482 0 0.0 1521 0 0.0 109 0 0.0 5 0 0.0 4 ED 5026 0 0.0 2079 0 0.0 94 0 0.0 2 0 0.0 4 FA 2705 1 0.0 1729 8 0.5 299 3 1.0 28 0 0.0 4FB 1533 0 0.0 1096 0 0.0 173 3 1.7 9 2 22.2 4FC 3652 0 0.0 1548 0 0.0 143 0 0.0 8 0 0.0 4GA 5384 3 0.1 2391 10 0.4 297 13 4.4 36 5 13.9 4GB 3285 1 0.0 1739 16 0.9 239 40 16.7 27 8 29.6 4GC 2688 0 0.0 1079 1 0.1 151 3 2.0 16 1 6.3 4GD 1636 0 0.0 941 0 0.0 133 4 3.0 10 1 10.0 4GE 677 0 0.0 322 0 0.0 51 1 2.0 5 0 0.0 4GF 288 0 0.0 243 0 0.0 37 1 2.7 5 1 20.0 4HA 3805 0 0.0 1328 0 0.0 75 0 0.0 7 0 0.0 4HB 2061 0 0.0 714 0 0.0 7 0 0.0 0 0 - 4HC 91 0 0.0 163 0 0.0 19 0 0.0 0 0 - 4HD 2916 0 0.0 983 0 0.0 24 0 0.0 0 0 - 4JA 662 1 0.2 342 11 3.2 16 4 25.0 4 2 50.0 4JB 326 22 6.7 87 8 9.2 7 3 42.9 2 0 0.0 4JC 1447 26 1.8 479 60 12.5 53 33 62.3 5 4 80.0 4JD 2215 1 0.0 696 17 2.4 76 25 32.9 9 5 55.6 4JE 276 0 0.0 111 1 0.9 12 3 25.0 3 1 33.3 4JF 1685 0 0.0 579 2 0.3 84 8 9.5 12 3 25.0 4JG 78 0 0.0 23 0 0.0 6 0 0.0 1 0 0.0 4KA 576 0.0 280 0 0.0 0.0 (cont'd) 155 Appendix B: (Cont'd) 1, -9.9 ha 10 -99 ha ---100 1-999 ha--- --1000 1-9999 ha-- Wshe d # Samp % // Samp 1 y/ Samp % # Samp % 4LA 1622 20 1.2 612 65 10.6 72 52 72.2 5 4 80.0 4LB 509 1 0.2 191 16 8.4 14 6 42.9 0 0 - 4LC 2225 8 0.4 792 30 3.8 80 13 16.3 5 3 60.0 4LD 178 1 0.6 86 15 17.4 7 1 14.3 0 0 - 4LE 907 10 1.1 289 17 5.9 33 3 9.1 5 4 80.0 4LF 277 3 1.1 104 17 16.3 13 3 23.1 2 1 50.0 4LG 339 3 0.9 38 5 13.2 1 1 100.0 2 0 0.0 4LH 788 11 1.4 241 5 2.1 22 4 18.2 2 1 50.0 4U 252 0 0.0 89 3 3.4 15 0 0.0 1 1 100.0 4LK 496 0 0.0 125 12 9.6 14 3 21.4 3 2 66.7 4LL 547 1 0.2 130 6 4.6 19 9 47.4 1 0 0.0 4MA 220 0 0.0 66 4 6.1 8 3 37.5 0 0 . 4MB 406 1 0.2 146 5 3.4 13 4 30.8 1 1 100.0 4MC 351 0 0.0 123 2 1.6 6 4 66.7 0 0 - 4MD 701 14 2.0 210 6 2.9 13 6 46.2 2 1 50.0 4ME 1081 3 0.3 365 1 0.3 40 9 22.5 4 1 25.0 4MF 1083 0 0.0 570 0 0.0 56 4 7.1 4 0 0.0 4NB 20 0 0.0 156 0 0.0 23 2 8.7 0 0 - 4NC 1433 1 0.1 736 1 0.1 96 4 4.2 4 1 25.0 5 PA 2371 12 0.5 958 51 5.3 192 80 41.7 22 22 100.0 5PB 3479 31 0.9 1843 67 3.6 323 72 22.3 47 23 48.9 5PC 22 0 0.0 8 1 12.5 0 0 - 0 0 - 5PD 1149 2 0.2 723 1 0.1 115 27 23.5 11 1 9.1 5PE 493 0 0.0 380 6 1.6 71 10 14.1 11 5 45.5 5PF 26 0 0.0 21 0 0.0 1 1 100.0 1 0 0.0 5PG 14 0 0.0 16 0 0.0 5 0 0.0 0 0 - 5PJ 363 0 0.0 193 0 0.0 35 1 2.9 2 1 50.0 5QA 1997 2 0.1 1279 10 0.8 248 25 10.1 35 16 45.7 5QB 830 3 0.4 756 10 1.3 159 11 6.9 18 6 33.3 5QC 614 0 0.0 571 3 0.5 107 4 3.7 15 2 13.3 5QD 1194 1 0.1 736 9 1.2 126 24 19.0 18 10 55.6 5QE 1375 0 0.0 840 1 0.1 167 19 11.4 30 12 40.0 5RA 163 0 0.0 133 0 0.0 15 3 20.0 1 0 0.0 5RB 1004 0 0.0 781 0 0.0 90 3 3.3 14 1 7.1 SRC 1684 0 0.0 1665 2 0.1 240 12 5.0 21 2 9.5 5RD 101 0 0.0 115 0 0.0 16 1 6.3 0 0 - 5RE 218 0 0.0 208 0 0.0 29 0 0.0 1 0 0.0 156 Appendix C: Summary of analytical procedures for water chemistry parameters (Ontario Ministry of the Environment (1) and Environment Canada, Water Quality Branch (2).) There are four Ontario Ministry of the Environment laboratories involved: Rexdale, Dorset, Thunder Bay, and Kingston. Parameter Analytical Procedure pH Alkalinity Combination glass and reference electrode pH meter. Potentiometrically determined end point using Gran analysis of titration data. Conductivity Colour Conductivity meter, temperature corrected. Apparent Colour - Comparator disc technique, includes dissolved and suspended substances. True Colour - Colourimetric measurement after correction for residual turbidity. Dissolved Organic Carbon Colourimetric measurement after removal of inorganic carbon species. Calcium Atomic absorption spectrophotometry. Magnesium Sodium Atomic absorption spectrophotometry. Atomic absorption spectrophotometry. (1) Automated flame photometry. (2) Potassium Atomic absorption spectrophotometry. (1) Automated flame photometry. (2) (cont'd) 157 Appendix C: (Cont'd) Parameter Analytical Procedure Sulphate Methyl-thymol blue (MTB) colourimetric method. (2) Before June 1980, methyl-thymol blue (MTB) colourimetric method. After June 1980, automated suppressed ion chromatography. (1) Aluminum Graphite furnace atomic absorption spectrophotometry. Manganese Atomic absorption spectrophotometry. (2) Before June 1985, manganese-formaldoxime colourimetric determination. After June 1985, atomic absorption spectrophotometry after preconcentration. (1) Iron Atomic absorption spectrophotometry. (2) Before June 1985, digestion and analysis colourimetrically by TPTZ method. After June 1985, atomic absorption spectrophotometry after preconcentration. (1) Chloride Mercuric thiocyanate colourimetry. (2) Before June 1980, mercuric thiocyanate colourimetry. After June 1980, automated suppressed ion chromatography. (1) Nitrate 4- Nitrite Hydrazine reduction method, automated deazotization colourimetry. (1) 158 Appendix D: Watersheds included in each Deposition Zone Zone Wshed 1000- ■9999 ha 100 -999 ha 10- ■99 ha 1-9.9 ha N Area N Area N Area N Area 4AD** 5 22909 52 13891 605 14346 940 3739 4AE** 4 14253 52 10279 859 18990 1754 7792 4BA** - - 20 3136 1205 23269 2904 12480 4BB** - - 25 5848 478 10785 384 1797 4CA** 32 78698 261 71247 2398 59600 5344 20910 4CB** 23 72068 356 75332 1833 54563 1600 9411 4CC** 5 15742 264 55826 1792 45416 2322 11734 4CD** 8 23917 328 57506 2332 64659 2675 16143 4CE** 9 16288 237 45143 1744 47621 2236 12165 4CF** - - 17 3288 637 11928 1210 5242 4DA** 31 83470 487 115021 3369 87007 5246 23289 4DB** 16 43747 589 130228 3998 112513 5610 26974 4DC** 2 3642 258 57728 2999 71174 6176 27113 4DD** 3 5504 108 22642 2123 42057 4828 21015 4EA** 6 11344 193 36003 1429 37292 1950 10520 4EB** 1 2428 13 3581 197 4634 179 616 4EC** 5 13207 109 22652 1521 34807 3482 15283 4ED** 2 5200 94 19941 2079 44101 5026 21616 4 FA** 28 50563 299 75284 1729 44323 2705 11035 4FB** 9 28813 173 39072 1096 31798 1533 7127 4FC** 8 22662 143 33639 1548 34064 3652 15401 4GA01 5 8861 34 9075 307 7001 924 4039 4GA02 3 15338 30 5999 294 6404 301 1316 4GA03 - - 4 971 35 658 74 324 4GA04 1 1012 10 2023 51 1194 84 367 4GA05 - - 2 223 29 870 44 194 4GA06 1 1093 7 1840 74 1599 104 453 4GA07 5 17235 28 6111 200 3885 327 1429 4GA08 - - 3 1335 22 658 39 173 4GA09 - - 8 1831 13 273 52 226 4GA10 4 17240 25 5682 223 5311 409 1790 4GA11 - - 21 4391 110 2792 143 625 4GA12 3 9955 20 4452 172 3966 301 1316 4GA13 - - 5 856 42 1032 123 539 4GC01 - - 7 1892 28 506 56 244 4GC02 1 1735 18 3885 79 1831 243 1063 4GC03 2 4411 20 5109 111 2661 146 639 4GC04 4 12586 12 2469 77 1953 203 887 4GC05 1 5787 3 2469 46 911 82 359 4GC06 1 1312 5 324 23 617 42 183 4GC07 - - 3 1366 16 445 25 108 4GD01 - - - - 5 162 50 228 (cont'd) 159 Appendix D: (Cont'd) Zone Wshed 1000- ■9999 ha 100- ■999 ha 10- ■99 ha 1-9 .9 ha N Area N Area N Area N Area 4GD02 1 243 7 202 43 194 4GD03 2 23512 13 2752 111 3369 331 1502 4GD04 - - - - 1 30 20 90 4GD05 - - 4 627 75 2428 158 714 4GD06 - - 9 1538 65 2034 119 540 4GD07 - - 2 243 17 546 37 169 4GD08 - - 2 405 24 921 27 121 4GD15 3 8658 21 4219 89 2448 150 681 4GE01 - - - - 2 69 8 23 4GE02 - - 2 330 7 213 17 57 4GE03 1 1700 - - 2 64 9 22 4GE04 - - 6 1291 30 643 60 235 4GE05 - - 4 963 5 105 8 38 4GE06 - - - - 9 253 9 37 4GE11 - - 3 1381 32 876 66 227 4GF** 5 15829 37 9462 243 6646 288 1064 4HA** 7 31525 75 18150 1328 28379 3805 15665 4HB** - - 7 1649 714 10927 2061 8451 4HC** - - 19 3804 163 3966 91 465 4HD** - - 24 5099 983 15904 2916 12102 4JE01 3 4780 9 2346 92 3099 206 836 4JE02 - - - - 5 214 12 34 4JF01 1 1221 1 113 12 293 16 62 4JF02 - - 3 934 9 282 8 57 4JF03 - - 1 254 8 138 5 25 4JF04 - - - - 4 99 - 0 4JF14 1 4293 1 625 37 987 92 333 4JF15 2 2863 7 1752 30 836 69 222 4JG** 1 3116 6 1740 23 654 78 236 5PD01 - - 21 4694 148 4372 176 746 5PD07 2 8400 25 7362 171 5059 179 863 5PD08 1 2478 7 2988 74 2001 93 481 5PD09 - - 1 649 1 51 2 10 5PD10 - - - - 1 40 2 5 5PD11 - - - - 9 159 10 53 5PD12 - - - - - - 1 3 5PE** 11 25214 71 17325 380 11879 493 2260 5PF** 1 1934 1 196 21 698 26 126 5PG** - - 5 798 16 509 14 76 5PJ** 2 2809 35 7465 193 5902 363 1635 5QB04 1 1514 9 1681 54 1477 81 316 5QB05 - - 4 1185 26 859 21 100 (cont'd) 160 Appendix D: (Cont'd) Zone Wshed 1000 -9999 ha 100 -999 ha 10 -99 ha 1- 9.9 ha N Area N Area N Area N Area 5QB06 1 2366 5 1769 49 1623 39 170 5QB07 - 12480 6 2759 9 253 23 82 5QB08 8 - 34 10339 167 5102 176 775 5QB09 - - 8 2384 23 718 31 146 5QC** 15 51563 107 28084 571 17783 614 2868 5QD02 3 4981 47 11973 267 7805 437 1809 5QD04 - - 7 2170 47 1630 42 192 5QD05 4 6846 21 6786 112 3327 165 722 5QE** 30 78299 167 46501 840 26619 1375 5561 5RA** 1 1334 15 4320 133 3844 163 871 5RB** 14 25817 90 24118 781 23617 1004 5085 5RC** 21 48141 240 72098 1665 48780 1684 7930 5RD** - - 16 2957 115 3653 101 518 5RE** 1 1614 29 9180 208 6317 218 1057 Zone 1 Totals 370 984307 5571 1289291 48136 1210378 83770 376566 2 2AA** 3 7853 17 4643 57 1388 197 607 2 2AB** 6 18468 68 14395 554 15184 1247 4934 2 2AC** 1 4766 42 13183 314 8401 886 3549 2 2AD** 21 50489 202 51704 1501 42055 3552 14275 2 2AE** - - 20 3493 180 4588 884 3328 2 2BA** 4 6012 42 9353 406 11175 1998 7371 2 2BB** 2 4303 46 9439 522 13935 1340 5600 2 2BC01 2 7418 22 5876 187 5749 321 1422 2 2BC02 - - - - 7 101 52 178 2 2BC03 - - 4 540 45 1218 198 790 2 2BC05 - - - - 6 90 85 309 2 2BC11 2 2111 16 4632 180 4825 413 1685 2 4GA14 2 8206 15 2378 94 2206 196 857 2 4GA15 5 14605 21 6076 225 6080 1454 6360 2 4GA16 6 14207 32 8468 254 6435 412 1801 2 4GA17 1 1255 10 2752 102 2610 148 647 2 4GA18 - - 22 4937 144 3723 249 1089 2 4GB** 27 66479 239 60511 1739 49849 3285 13294 2 4GC08 - - 2 996 30 698 908 3970 2 4GC09 - - 3 364 25 728 42 183 2 4GC10 5 10416 19 1538 232 5504 389 1703 2 4GC11 - - 21 4965 159 3784 162 711 2 4GC12 1 4168 8 4694 52 1072 84 366 (cont'd) 161 Appendix D: (Cont'd) Zone Wshed 1000- -9999 ha 100- ■999 ha 10- ■99 ha 1-9 .9 ha N Area N Area N Area N Area 2 4GC13 6 1143 59 1335 80 348 2 4GC14 1 2954 24 1295 142 3086 226 991 2 4GD09 1 1255 18 4269 58 1862 73 329 2 4GD10 - - 4 607 31 840 112 506 2 4GD11 - - 4 809 39 1629 60 270 2 4GD12 1 1044 7 2428 69 2246 74 337 2 4GD13 1 7042 10 2590 75 2034 121 548 2 4GD14 1 1093 10 2639 59 1781 66 298 2 4GD16 - - 7 1140 40 945 27 124 2 4GD17 - - 4 885 56 1750 48 217 2 4GD18 - - 12 2894 82 2276 78 352 2 4GD19 1 1295 5 1109 38 1032 42 188 2 4GE07 1 1497 6 1312 31 1030 45 149 2 4GE08 - - 4 499 15 395 24 89 2 4GE09 - - 2 235 18 536 37 134 2 4GE10 - - 1 102 11 318 17 76 2 4GE12 . . 2 745 15 451 53 199 2 4GE13 - - 3 1022 18 493 53 187 2 4GE14 - - 13 2935 56 1709 127 462 2 4GE15 3 9853 5 1242 71 1944 144 544 2 4JA01 1 1252 8 2392 157 4079 321 1200 2 4JB** 2 3035 7 1729 87 2541 326 1136 2 4JC** 5 13503 53 11005 479 13057 1447 5632 2 4JD** 9 21529 76 21797 696 19654 2215 9100 2 4JE03 - - 1 241 1 17 9 20 2 4JE04 - - 1 108 3 54 21 90 2 4JE05 - - 1 107 10 244 28 85 2 4JF05 - - 2 800 24 775 13 55 2 4JF06 - - 1 304 9 312 17 67 2 4JF07 - - 1 129 3 47 11 40 2 4JF08 - . 1 251 10 297 12 66 2 4JF09 - - - - 14 392 18 79 2 4JF10 - - 2 290 9 307 40 116 2 4JF11 - . 11 4249 51 1427 111 432 2 4JF12 1 7252 9 2189 79 2263 134 461 2 4JF13 - - 3 863 20 478 59 237 2 4JF16 - - 3 507 18 642 40 142 2 4JF17 2 8009 1 550 12 315 36 150 2 4JF18 1 1243 2 549 1 83 8 30 2 4JF19 - - 7 1137 56 1671 187 643 2 4JF20 1 1936 6 1502 10 359 52 236 (cont'd) 162 Appendix D: (Cont'd) Zone Wshed 1000 -9999 ha 100- ■999 ha 10- •99 ha 1-9 .9 ha N Area N Area N Area N Area 2 4JF21 1 231 4 97 26 84 2 4JF22 1 3526 1 797 40 1159 245 812 2 4JF23 1 1295 3 949 11 337 58 200 2 4JF24 - - - - 14 304 80 278 2 4JF25 1 1808 8 3365 39 1054 189 594 2 4JF26 - - 9 3589 55 1458 159 543 2 4KA** - - 9 1255 280 5301 576 2481 2 4LB01 - - - - 22 539 76 246 2 4LB02 - - - - 15 322 41 151 2 4LB03 - - 1 146 5 120 20 72 2 4LB04 - - 2 349 37 930 97 335 2 4LB05 - - 2 525 18 425 44 163 2 4LD** - - 7 1991 86 2237 178 720 2 4LF** 2 4495 13 2747 104 3229 277 987 2 4LG** 2 8377 1 151 38 1124 339 1078 2 4LJ01 - - 3 704 24 454 59 201 2 4LJ05 1 3828 2 321 13 344 42 133 2 4LK** 3 4465 14 4821 125 3492 496 1749 2 4LL** 1 1930 19 6688 130 3813 547 1726 2 4LM** 1 3351 6 1113 19 574 61 193 2 4MA01 - - 1 219 26 722 125 425 2 4MA02 - - 1 108 14 419 28 119 2 4MC** . . 6 1258 123 3385 351 1258 2 4MD02 - - 1 174 13 380 27 109 2 4MD03 - - - - 18 378 40 176 2 4ME** 4 14581 40 8901 365 9624 1081 3836 2 4MF** 4 6070 56 8695 570 15591 1083 449 2 4NB** - - 23 4532 156 4047 20 229 2 4NC** 4 7770 96 17847 736 19233 1433 6955 2 5 PA** 22 54524 192 55668 958 29511 2371 7545 2 5PB** 47 108124 323 80216 1843 54444 3479 13537 2 5PC** - - - - 8 179 22 83 2 5PD02 - - - - 4 168 6 20 2 5PD03 - - - - - - 1 6 2 5PD04 2 2141 11 3212 49 1203 176 569 2 5PD05 3 10567 25 5804 142 4469 282 1034 2 5PD06 3 8666 25 7272 124 3491 221 914 2 5QA** 35 92129 248 65476 1279 38256 1997 8140 2 5QB01 4 7096 63 17660 293 9566 321 1327 2 5QB02 1 1102 12 2320 36 1368 39 155 2 5QB03 3 3873 18 4025 99 2780 99 471 (cont'd) 163 Appendix D: (Cont'd) Zone Wshed 1000-9999 ha N Area 100-999 ha N Area 10-99 ha N Area 1-9.9 ha N Area 2 5QD01 2 5QD03 10 1 27144 2343 44 10731 7 2378 302 9190 8 342 518 32 2038 131 Zone 2 Totals 276 683753 2507 621774 17992 504163 42476 163666 3 2BC04 3 2BC06 3 2BC07 3 2BC08 3 2BC09 3 2BC10 3 2BC12 3 2BD01 3 2BD02 3 2BD03 3 2BD04 3 2BD05 3 2BD06 3 2BD10 3 2CE10 3 2JC** 3 2JD06 3 2JD08 3 2JD09 3 2JD10 3 4JA02 3 4JA03 3 4JA04 3 4LA** 3 4LB06 3 4LB07 3 4LB08 3 4LB09 3 4LB10 3 4LC** 3 4LE01 3 4LE04 3 4LE06 3 4LE07 3 4LE08 2932 2 4 1 5365 14005 1226 2 4915 2 2627 1 2 1131 12648 1 5 1295 10994 12896 1828 1263 1890 18 72 392 197 351 4509 1 146 16 2723 24 6709 6 1779 2 312 24 7655 4 803 15 4615 3 700 2 479 7 1149 1 113 20039 2 928 7 1778 80 18867 6 1940 6 1238 1 210 6 1293 4 606 20 321 154 556 18 344 142 403 9 155 105 314 81 1538 605 2057 16 246 121 370 59 1219 238 847 12 281 36 141 28 479 252 0 152 3796 564 0 7 168 114 0 16 312 154 0 342 8788 714 0 281 7734 946 0 56 1397 175 0 76 2413 167 724 146 4492 379 1352 36 1003 143 552 177 4410 516 2085 29 735 59 274 25 671 85 338 135 3851 200 948 26 789 59 240 24 599 82 278 612 16045 1622 6131 2 33 9 38 2 44 3 13 4 60 32 118 32 1019 63 255 54 1569 124 446 792 21095 2225 9036 44 1129 117 438 44 1305 176 682 6 222 25 87 46 1031 150 582 31 776 89 389 (cont'd) 164 Appendix D: (Cont'd) Zone Wshed 1000 -9999 ha 100- ■999 ha 10- ■99 ha 1-9 .9 ha N Area N Area N Area N Area 3 4LE09 . . 4 1601 50 1387 104 405 3 4LE10 - - 2 297 18 382 60 235 3 4LE11 - - - - 3 91 7 42 3 4LH** 2 10037 22 4544 241 6579 788 3234 3 4U02 - - - - 3 66 16 47 3 4LJ03 - - 1 108 5 108 13 58 3 4LJ04 - - 9 1494 44 1201 122 541 3 4MA03 - - 1 142 4 150 11 32 3 4MA04 - - - - 5 118 23 69 3 4MA05 - - 5 1301 17 705 33 118 3 4MB** 1 2165 13 2413 146 3794 406 1544 3 4MD01 2 13708 1 158 50 1520 215 888 3 4MD04 - - 2 446 10 342 18 69 3 4MD05 - - 2 360 27 709 107 427 3 4HD06 - - 3 957 53 1399 187 779 3 4MD07 - - 2 315 11 206 43 149 3 4MD08 - - 2 456 28 673 64 218 Zone 3 Totals 35 100925 383 94123 4155 109499 12862 38599 4 2BD07 1 1127 5 1368 36 1027 164 629 4 2BD08 - - 8 1751 71 2350 358 1326 4 2BD09 3 6840 10 2477 115 2870 254 1048 4 2BE** - - 31 6196 608 14719 2434 9396 4 2BF** - - 33 5580 536 12721 2157 8721 4 2CA01 - - 3 1081 16 425 42 138 4 2CA02 1 2254 5 1616 115 2492 471 1860 4 2CA03 1 1149 4 803 29 683 151 695 4 2CA04 - - 3 652 8 208 14 55 4 2CB** 4 11540 48 12648 784 19679 2107 8799 4 2CC02 - - 4 957 87 1881 362 1466 4 2CC04 1 2707 1 127 18 363 13 62 4 2CC05 - - 9 2359 107 2788 308 1160 4 2CC06 - - 6 1798 53 1347 74 732 4 2CC07 - - 2 681 47 1018 187 985 4 2CC08 1 2469 3 906 40 981 111 451 4 2CC09 1 1098 6 1102 69 1875 251 1916 4 2CC10 - - 1 125 18 397 97 316 4 2CE03 1 1162 8 1654 131 3217 425 1873 (cont'd) 165 Appendix D: (Cont'd) Zone Wshed 1000- ■9999 ha 100- ■999 ha 10^ ■99 ha 1-9 .9 ha N Area N Area N Area N Area 4 2CE06 7 1344 172 3532 668 2941 4 2CE09 - - 5 665 64 1617 192 853 4 2JE15 1 2106 17 4780 125 3534 321 1255 4 2JE16 - - 2 325 8 295 19 65 4 2JE27 - - - 4 100 6 13 4 2KC01 4 9105 1 761 6 125 16 81 4 2KC05 1 1202 1 110 4 143 6 21 4 2KC07 1 1467 1 531 7 178 13 66 4 2KE01 2 4755 3 1030 41 1233 166 620 4 2KE02 1 2356 - - 7 246 27 107 4 2LA01 - - - - - - 2 11 4 2LA02 - - - - 4 138 8 12 4 2LA03 - - - - 1 24 5 13 4 2LA05 - - 2 376 2 36 4 16 4 2LA06 - - 3 837 1 19 6 23 4 2LA07 2 7841 4 1810 20 658 23 87 4 2LB** 1 1068 - - 5 146 27 87 4 2MA08 - - 7 2512 7 225 3 15 4 2MA09 1 2517 5 919 5 189 5 24 4 2MB** - - - - 2 143 39 90 4 2MC** - - 1 346 3 95 10 32 4 4LE02 1 1041 2 1275 16 517 70 273 4 4LE03 . - - - 11 329 32 123 4 4LE05 1 1599 2 288 20 512 77 278 Zone 4 Totals 30 65403 253 61790 3423 85075 11725 48734 5 2CA05 - - 2 571 3 100 5 11 5 2CA06 - - - - 2 40 4 11 5 2CA07 - - - - - - 4 11 5 2CA08 - - - - 2 31 7 23 5 2CA09 - - - - 3 94 5 19 5 2CA10 - - - - - 1 3 5 2CA11 1 1029 10 2639 70 1927 260 1325 5 2CA12 - - - - - - 3 11 5 2CC01 - - - - 2 83 6 18 5 2CC03 1 1189 4 685 1 19 - 0 5 2CD01 - - - - 6 182 10 48 5 2CD02 2 3156 25 7120 126 3254 351 1405 5 2CD03 - - 7 1365 17 675 13 56 (cont'd) 166 Appendix D: (Cont'd) Zone Wshed 1000- ■9999 ha 100- -999 ha 10- ■99 ha 1-9, .9 ha N Area N Area N Area N Area 5 2CD04 2 259 16 374 23 84 5 2CD05 1 2242 2 215 11 258 21 104 5 2CD06 - - 4 1159 4 175 23 149 5 2CD08 2 14701 13 3847 76 2355 120 541 5 2CD09 - - 1 225 9 357 18 80 5 2CD10 - - - - 1 20 - 0 5 2CE01 1 1144 4 747 19 576 34 127 5 2CE11 - - - - - - 2 11 5 2CF04 - - 2 1184 16 522 52 226 5 2CF06 - - 5 750 8 223 19 78 5 2CF17 - - - - 1 13 6 27 5 2CG** 3 11693 26 6320 82 2125 194 656 5 2DC03 - - - - 1 32 1 4 5 2DC05 - - 1 147 6 160 18 62 5 2DD** 3 3526 42 11359 352 9330 892 3687 5 2EA** 5 7780 69 18245 407 11869 960 4082 5 2EB** 7 25397 77 19343 556 14854 1169 5418 5 2EC** 6 11256 22 5583 193 5213 716 2681 5 2ED01 - - - - 2 142 - 0 5 2ED02 - .- _. 2 255 4 99 1 4 5 2ED03 - - 2 482 - - 2 7 5 2ED04 - - - - - - 1 10 5 2ED05 - - - - - - 3 5 5 2ED06 - - 1 542 - - 2 2 5 2ED07 - - 1 159 28 712 58 294 5 2ED08 - - - - - - 3 5 5 2ED16 - . - - - - 1 2 5 2HD** . - - - 2 22 35 83 5 2HE** 2 3108 4 1124 3 244 13 35 5 2HF** 5 9532 53 15685 257 7195 630 2566 5. 2HG** 1 8262 - - 1 23 33 78 5 2HH** 8 21655 22 7552 169 4226 548 2299 5 2HJ** - - - - 5 178 47 144 5 2HK** 1 1387 28 9774 126 3392 297 1115 5 2HL** 1 1225 9 3164 64 1692 295 1088 5 2HM** - - 18 5509 69 1909 166 646 5 2JE01 1 1653 3 364 53 1751 218 841 5 2JE02 2 4569 3 515 29 834 55 225 5 2JE03 - - 2 319 12 457 36 154 5 2JE04 3 3914 10 2940 120 3339 318 1489 5 2JE05 - - 2 359 8 212 22 95 5 2JE06 - - - - 5 83 14 69 (cont'd) 167 Appendix D: (Cont'd) Zone Wshed 1000- ■9999 ha 100- ■999 ha 10^ ■99 ha 1-9, .9 ha N Area N Area N Area N Area 5 2JE07 1 1708 8 246 13 41 5 2JE08 - - - - 26 642 81 312 5 2JE09 - - - - 4 125 20 81 5 2JE12 - - 8 1294 62 1789 126 571 5 2JE13 - - 6 881 19 392 52 219 5 2JE14 - - 2 234 28 799 121 465 5 2KA** 2 11230 9 1612 117 2839 273 1176 5 2KB** 5 8441 48 14389 322 8532 1623 5872 5 2KC02 2 6449 6 1200 67 1688 392 1376 5 2KC06 - - 1 151 28 647 66 267 5 2KC08 1 1730 - - 5 99 12 44 5 2KC09 - - 1 146 3 109 14 47 5 2KC10 - 2 814 7 234 25 93 5 2KD** 6 17486 63 16616 402 11617 1311 4837 5 2KE03 - - 1 611 12 280 46 180 5 2KE04 - - 2 252 11 334 43 168 5 2KE05 - - 6 1459 16 468 40 151 5 2KE06 - - - - 10 233 49 196 5 2KE07 - - - - 4 179 29 129 5 2KF** 5 13326 32 9021 153 4436 468 1930 5 2LA08 - - 1 625 7 163 3 10 5 2LA09 - - 2 585 2 79 4 12 5 2LA10 1 2449 7 2172 19 580 40 163 5 2MA01 - - - - 1 16 0 5 2MA06 2 2678 28 8342 67 2111 77 360 5 2MA07 1 1803 1 136 6 203 9 53 Zone 5 Totals 55 20571^ 704 191046 4353 120211 12672 50967 6 2ED09 6 2ED10 6 2ED11 6 2ED12 6 2ED13 6 2ED14 6 2ED15 6 2 FA** 6 2FB** 6 2FC** 6 2FD** - - 2 110 12 34 - - 1 12 4 8 - - 2 36 6 15 . . . - 8 21 - . 1 48 4 12 1 188 - - 1 2 - - - - 4 16 17 3983 43 1529 59 240 1 723 13 348 17 54 . . 34 887 102 376 - - 3 38 13 39 (cont'd) 168 Appendix D: (Cont'd) Zone Wshed 1000- ■9999 ha 100- •999 ha 10- -99 ha 1-9 .9 ha N Area N Area N Area N Area 6 2FE** 6 128 15 41 6 2FF** - - 1 217 - - 12 37 6 2GA** 1 1242 3 1441 12 208 101 347 6 2GB** - - - - 12 178 62 170 6 2GC** - - - - 19 444 121 378 6 2GD** - - 3 1700 5 71 56 118 6 2GE** - - - - 2 23 18 66 6 2GF** - - - - - - 2 14 6 2GG** - - 1 421 8 223 70 188 6 2GH** - - 1 145 13 298 38 141 6 2HA** - - 2 439 10 338 55 136 6 2HB** - - 3 507 7 205 118 362 6 2HC** - - - - 13 246 174 394 Zone 6 Totals 1242 33 9764 206 5370 1072 3209 7 2CD07 7 2CE02 7 2CE04 7 2CE05 7 2CE07 7 2CE08 7 2CF01 7 2CF02 7 2CF03 7 2CF05 7 2CF07 7 2CF08 7 2CF09 7 2CF10 7 2CF11 7 2CF12 7 2CF13 7 2CF14 7 2CF15 7 2CF16 7 2 DA** 7 2DB** 7 2DC01 7 2DC02 7 2DC04 22835 1117 2471 1142 8337 1078 2 2 5846 2053 4 1 6071 1819 5 1529 36 8755 3 854 3 937 11 1922 1 250 12 2757 27 6849 10 3159 11 2810 2 806 3 939 2 570 1 316 1 184 5 849 26 5969 21 3577 8 1180 23 519 10 2645 6 2238 27 557 72 284 426 10585 1126 5087 19 415 30 113 30 655 142 638 37 744 125 591 70 1736 225 976 8 168 17 57 1 10 4 14 56 1319 133 580 87 2558 105 543 108 2996 347 1492 16 410 24 104 5 182 12 63 22 667 102 428 4 77 4 29 11 233 81 361 15 362 126 533 11 239 55 241 13 280 123 507 288 7665 853 3697 255 6386 931 3593 53 1447 146 582 172 4589 595 2361 93 2440 195 832 26 745 87 338 (cont'd) 169 Appendix D: (Cont'd) Zone Wshed 1000- •9999 ha 100- ■999 ha 10- ■99 ha 1-9 .9 ha N Area N Area N Area N Area 7 2DC06 4 6987 16 4495 181 5317 583 2391 7 2DC07 2 2321 7 1315 57 1512 105 425 7 2DC08 1 3189 7 1754 50 1235 206 884 7 2DC09 - - 4 728 51 1328 109 431 7 2JD01 1 1455 15 3046 123 3528 330 1326 7 2JD02 1 6263 7 1862 50 1543 129 547 7 2JD03 1 1006 8 2346 108 3025 279 1088 7 2JD04 1 2129 3 1024 35 912 85 386 7 2JD05 . - - - 19 646 54 187 7 2JD07 1 1046 10 3261 67 1915 168 683 Zone 7 Totals 34 77165 304 69445 2594 68426 7708 32392 170 I