Source and Distribution of Heavy Metals in Sediment Samples from Select Creeks of the Highly Urbanized Metro Vancouver Watersheds, Canada

doi:10.21203/rs.3.rs-3964375/v1

Forty-seven samples collected from 23 creeks in Metro Vancouver, BC, Canada were analyzed to assess how different land use activities affect physicochemical properties in surface water and metal concentrations in sediments. The pH, total dissolved solids (TDS) and electrical conductivity (EC) in surface water samples ranged from 5.50 to 8.27, 14.0 to 410.0 ppm, and 19.0 to 903.0 µS/cm, respectively, with no discernible relationship between the surrounding land use and the parameters. However, land use influenced metal concentrations in the sediments with As, Cd, Cr, Cu, Pb, Ag, and Zn at some locations exceeding the Sediment Quality Guidelines (SQGs). The enrichment factor (EF) value, geo-accumulation index (Igeo), pollution load index (PLI) and concentration factor (CF) indicates most of the sediments are highly contaminated. The geo-accumulation index showed the study area contained extremely contaminated sediment and >64-fold increase due to high levels of Ba, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn. The calculated contamination factors are found to fall in the following sequences: Cd>Ag>As>Pb>Zn>Mn>Cu>Ba>Cr>Ni>V>Fe>Hg in the study area. Creeks closer to highways with heavy vehicular activities reported significantly higher concentrations of metals. Principal component and cluster analyses revealed anthropogenic and natural processes enrich the sediments with metals. It is therefore important to put measures in place to curb increase in metal concentrations in creek sediments.

Heavy metals

Metro Vancouver

Principal component analysis

Index of geo-accumulation

Pollution load index

Sediment quality issues are an important focus in the assessment, protection, and management of aquatic ecosystems. Since sediments influence the fate of many chemicals, concern exists about the potential impact on organisms that are exposed to sediments with elevated chemical concentrations. To address this concern, environmental managers require practical, scientific tools with which to evaluate the potential impacts of sediment-associated chemicals on various resource uses (e.g., aquatic life or wildlife consumers of aquatic life). Although much is still to be understood with respect to the behavior, bioavailability, and toxicity of chemicals in sediments, the use of a variety of sediment quality assessment tools and the consideration of all available information help environmental managers make decisions that are based on the best scientific information [1]. Quantitative environmental quality standards (EQSs) derived from sediment quality guidelines (SQGs) can be useful to control and assess the quality of sediment because EQSs synthesize information regarding the relationships between the sediment concentrations of chemicals and adverse biological effects on benthic communities resulting from exposure to these chemicals. Hence, the use of SQGs has become an important part of broadly protective tools to support the functioning of healthy aquatic ecosystems [2] and is recognized as an effective component of a tiered risk assessment or weight of evidence approach [3].

There is a growing concern about surrounding land use and its effects on the flora and fauna of the ecosystem in the Metro Vancouver watershed. Creeks in Metro Vancouver are located and flow through most of the industrialized areas known for automobile works, cement production, power generation, construction, steel production, and other related activities. Automobile shops, vehicular activities, and the construction of numerous high-rise buildings leads to the inflow of metals into the creeks. The high population in Metro Vancouver associated with domestic and commercial activities has also contributed to the high level of certain heavy metals in creek sediments. Also, stormwater runoff and soil erosion are likely to collect heavy metals that may impact the creek’s ecological system. Pollution caused by heavy metals in creeks is adverse and has health implications for sediment quality and aquatic life. Despite the economic, social, and ecological importance of creeks in the Metro Vancouver watershed, there is little data available, and rarely has a wide and broader survey been studied to investigate the influence of human and natural activities on heavy metal loading in sediments. Previous studies from Li et al. [4] focused only on the Brunette watershed with studies merely on Cu, Fe, Mn, Pb, and Zn which is within the broader Metro Vancouver watershed. Furthermore, the study [4] lacks a comparison to other creeks within the Metro Vancouver area which will give an insight into how different municipalities and their land uses affect sediment and aquatic quality. Also, the analysis of various pollution indices used in the present study for sediment quality addresses the gaps that exist in the literature in the study area. According to Harikumar et al., [5] as cited by Nkinda et al., [34], pollution indices are powerful tools for evaluating, developing, and conveying raw environmental data into more meaningful information for politicians and decision-makers.

The objective of the study included; (1) determination of land use activity (urban/residential, commercial, industrial, agricultural) and how it affects metal distribution in sediments and physicochemical properties in water in selected creeks in Metro Vancouver; (2) investigate the distribution of heavy metals in sediments and physicochemical parameters in surface water in selected creeks in Metro Vancouver and their impact on sediment and aquatic quality; (3) Initiate a wide survey focused on determining the current levels of heavy metals in the sediments of some creeks in Metro Vancouver, BC.

Metro Vancouver is a metropolitan area in British Columbia, Canada at latitude 49°14′24.00″ North, longitude 123°03′00.00″ West. Metro Vancouver is home to the City of Vancouver and 20 other municipalities and hundreds of parks and industries. Municipalities within Metro Vancouver include the City of Vancouver, North Vancouver, West Vancouver, Burnaby, Richmond, Surrey and White Rock [6]. According to Statistic Canada, [7], Metro Vancouver with dense traffic has the highest population density in Canada, with over 5,700 people per square kilometer.

According to Earle, [8], the oldest rocks in British Columbia are the strongly metamorphosed sedimentary, volcanic, and intrusive rocks of the Monashee Complex, situated to the west of the Columbia River and further stated that there are much more extensive Precambrian rocks within the Columbia and Rocky Mountains of southeastern B.C. and the southwestern corner of Alberta. Brendan, [9], further stated that there are five major rock types in the Vancouver area. The most extensive are granitic and metamorphic rocks of the Coast and Cascade Mountains. Overlying these within the Fraser Valley is a thick sequence of sedimentary rock (sandstone and shale). Volcanic intrusions fill fractures within granitic, metamorphic, and sedimentary rocks. Younger volcanic rocks make up volcanoes built on older granitic and metamorphic rocks.

According to Metro 2050, [10], regional land use designations in Metro Vancouver includes general urban, industrial, employment, rural, agricultural, conservation and recreation (Fig. 1). Metro Vancouver manages three water supply areas - Capilano, Seymour, and Coquitlam – which together provide 2.8 million residents with a clean, reliable, and affordable supply of drinking water [11]. Industrial emissions, vehicular activities, used water from toilets, showers, baths, kitchen sinks, laundries, general domestic and industrial processes are known to introduce wastewater into the environment and into various creeks, thereby polluting them. Vancouver is well-known for its rain. On average, it rains over 160 days and between 1200–1600 mm a year. Rainwater that flows off roofs, streets, parking lots and other surfaces, picks up pollutants, and is conveyed through pipes, either to the treatment plant or directly into local waterbodies polluting them [12].

3.1 Sampling Locations

Forty-seven sediment samples were collected from 23 creeks in the highly urbanized Metro Vancouver area. The creeks were chosen in residential, urban, recreational, commercial and a few industrial urban areas. The sampling stations are summarized in Table 1 and a map of their locations is in Fig. 2.

Table 1

Summary of sampling locations
Creek	Location/Surrounding Land Use	Sample #	N Coordinates	W Coordinates
Bolivar Creek	13591 Crestview Drive, Surrey, BC. General Urban	MVC 1	49.199327	-122.853252
Bolivar Creek	13591 Crestview Drive, Surrey, BC. General Urban	MVC 2	49.199909	-122.853445
Bon Accord Creek	Hawthorne Park, 10513–144 Street, Surrey, BC, Residential, schools. Recreation and Conservation, General Urban	MVC 3	49.20768	-122.81961
		MVC 4	49.210260	-122.818895
		MVC 5	49.20086	-122.82393
King Creek	Green Timbers Park, 14600 Block of 100 Avenue, Surrey, BC Recreation and Conservation, General Urban	MVC 6	49.1784	-122.82096
		MVC 7	49.17976	-122.82004
		MVC 8	49.17825	-122.82106
		MVC 9	49.16656	-122.83454
Bear Creek	Bear Creek Park 13750 88 Ave, Surrey, BC Recreation and Conservation	MVC 10	49.1552	-122.84220
		MVC 11	49.15893	-122.84283
		MVC 12	49.16257	-122.84467
Quibble Creek	Surrey Memorial Hospital 13750 96 Ave, Surrey, BC General Urban	MVC 13	49.182826	-122.845475
Quibble Creek		MVC 14	49.174881	-122.844765
Healy Creek	8738 132nd St, Surrey, BC General Urban	MVC 15	49.16190	-122.85661
Healy Creek	8738 132nd St, Surrey, BC General Urban	MVC 16	49.160314	-122.854598
Hyland Creek	13665 68 Ave, Surrey, BC General Urban, Industrial	MVC 17	49.127019	-122.842973
		MVC 18	49.121928	-122.823884
		MVC 19	49.120961	-122.815158
Stoney Creek	6438 King George Boulevard, Surrey, BC General Urban	MVC 20	49.12256	-122.84267
Stoney Creek	6438 King George Boulevard, Surrey, BC General Urban	MVC 21	49.118011	-122.844009
Surrey Lake	7500 152 St, Surrey, BC Recreation and Conservation, Agricultural	MVC 22	49.137475	-122.798123
Bear Creek	152 St @ 76 Ave, Surrey, BC General Urban	MVC 23	49.139819	-122.799853
Bear Creek	152 St @ 76 Ave, Surrey, BC General Urban	MVC 24	49.144708	-122.800862
Bolivar Creek	King George Blvd @ 132 St, Surrey BC General Urban	MVC 25	49.21078	-122.85354
Robson Creek	9949 127A St Surrey, BC General Urban, Recreation and Conservation	MVC 26	49.183416	-122.870739
Cougar Creek	122 Street and 75th Ave Surrey, BC General Urban, Recreation and Conservation	MVC 27	49.131628	-122.881688
		MVC 28	49.129294	-122.884997
		MVC 29	49.132446	-122.884977
First Beach Creek	5435 Sperling Ave, Burnaby, BC Recreation and Conservation, General Urban	MVC 30	49.235091	-122.965096
Buckingham Creek	5435 Sperling Ave, Burnaby, BC Recreation and Conservation, General Urban	MVC 31	49.236180	-122.965352
Dear Lake Brook	5435 Sperling Ave, Burnaby, BC Recreation and Conservation, General Urban	MVC 32	49.234538	-122.990419
Beaver Creek	Royal Oak Ave@ Deer Lake Pkwy Burnaby, BC General Urban, Recreation and Conservation	MVC 33	49.238403	-122.988416
Eagle Creek	4084 McConnell Ct #100, Burnaby, BC, 7966 Winston St, Burnaby, BC General Urban, Recreation and Conservation, Industrial	MVC 34	49.248211	-122.936679
Eagle Creek		MVC 35	49.246462	-122.935000
Guichon Creek	4399 Wayburne Dr, Burnaby, BC General Urban	MVC 36	49.245601	-122.999796
Guichon Creek	4399 Wayburne Dr, Burnaby, BC General Urban	MVC 37	49.248362	-122.99701
Chubb Creek	3989 Henning Dr #122, Burnaby, BC General Urban	MVC 38	49.264926	-123.017379
Chubb Creek	3989 Henning Dr #122, Burnaby, BC General Urban	MVC 39	49.263032	-123.012772
Hoy Creek	Johnson St @ Glen Dr Coquitlam, BC General Urban, Recreation and Conservation	MVC 40	49.282582	-122.802728
Hoy Creek		MVC 41	49.283052	-122.801921
Scott Creek	524 Rambler Way, Coquitlam, BC General Urban, Recreation and Conservation	MVC 42	49.286263	-122.810566
Scott Creek		MVC 43	49.280958	-122.812430
Noons Creek	1159 Eagleridge Dr, Coquitlam, BC General Urban	MVC 44	49.301350	-122.820828
Noons Creek	1159 Eagleridge Dr, Coquitlam, BC General Urban	MVC 45	49.299025	-122.821556
Hyde Creek	1379 Laurier Ave, Port Coquitlam, BC General Urban	MVC 46	49.275617	-122.751851
Cedar Creek	1930 Oxford Connector, Port Coquitlam, BC. General Urban	MVC 47	49.264288	-122.756005
General Urban: high population density, high commercial, industrial, transportation and residential activities, high infrastructure

3.2 Sampling and Field Analysis

The sediment samples were collected using the BCFSM, [14] standard protocol for sediment sampling. Sediment samples were collected with stainless-steel scoops and placed in Ziploc bags. The scoop was washed with laboratory detergent, rinsed with deionized water, and wiped with a clean paper towel to ensure no residue is transferred between samples. The samples were stored in a cooler and transported to the analytical laboratory for analysis. The pH, TDS, conductivity of surface water in the creeks were measured in situ using an Oakton® PCTSTestetr™ 50 meter.

3.3 Laboratory Analysis

The sediments were air dried in polyethylene weigh boats in the laboratory at ambient temperatures and sieved to < 2 mm prior. The sieve samples were analyzed by X-ray fluorescence spectroscopy (XRF) using United States Environmental Protection Agency (USEPA) Method 6200 [15]. X-ray fluorescence spectroscopy is fast, simple, and non-destructive. The X-ray beam ionizes the atoms in the measurement sample. When XRF peaks with varying intensities are created and present in the spectrum, a graphical representation of the X-ray intensity serves as a function of the energy peaks. The peak energy identifies the element, and the peak height and intensity is indicative of its concentration. The precision of the XRF measurement is expressed in terms of the standard deviation (SD, sigma, σ or repeatability) of a number of measurements and indicates the spread of the data about the mean result. Two sigma is twice the standard deviation and indicates that 95% of readings will fall within this range. The detector detects the occurring fluorescence radiation and developed software processes the signals to yield concentrations in mg/kg. The elements of interest which are Ag, As, Ba, Cr, Cd, Cu, Fe, Hg, Mn, Ni, Pb, V, Zn were then determined.

3.4 pH of Sediments

Ten grams of sediment were diluted with 20 mL of distilled water in a 50 mL beaker and stirred for five minutes. The mixture was allowed to stand for 15 min to allow most of the suspended sediment to settle out from the suspension and the pH was measured using a Denver Instrument Model UB-10 pH meter in the laboratory.

3.5 Quality Assurance/Quality Control

The XRF method of analysis was validated using NIST 2711a standard reference material (Montana II Soil), supplied by the National Institute of Standards and Technology (NIST), USA. The accuracy was checked by analyzing the certified reference material under the same conditions at the beginning of the analysis and after every 10th sample. The percentage recovery was then calculated. Analytical precision was assessed by analyzing every 10th sample in duplicate and calculating the relative percent difference (RPD).

3.6 Data Analysis

In this study, SPSS 16.0 for Windows statistical software was utilized for the descriptive and multivariate analysis to support the observed data. All the data were log transformed and subsequently standardized to their standard scores (z-cores) as outlined by Brown [16], so that each variable measured with different units was rescaled to a range of 0–1 to have equal weight for the multivariate statistical analysis. In those samples in which an element was not detected (ND), its concentration was assumed to be one-half of the detection limit (ND = 1/2LOD). Statistical significance of the differences was determined by one-way analysis of variance (ANOVA) for variables with normal distribution, and the Kruskal-Wallis test for those with non-normal distribution. A p value of < 0.05 was considered significant.

Hierarchical cluster analysis (HCA) or squared Euclidean distance scheme was chosen as a similarity/dissimilarity measure among parameters and the linking distances among initial clusters was determined using the Ward’s linkage algorithm. A combination of the Euclidean or squared Euclidean distance as a similarity measure and the Ward’s agglomeration scheme to link clusters was determined to yield optimal results in HCA. The R-mode HCA was done to classify the parameters into groups based on their similarity with each other, whereas the Q-mode HCA was done to highlight spatial relationships among the sample points. Dendrograms produced was used to illustrate the relationships revealed by the HCA.

With regards to factor analysis, principal components (PCA) were used with Kaiser criterion [17] and varimax rotation. The Kaiser criterion allows only factors that form unique sources of variance in the observation to be included in the final factor model. In the Kaiser criterion, factors which represent unique sources of variance in the observation are defined as those with an eigenvector sum of at least 1.0. Naturally, it is possible to obtain as many factors as there are variables, but most of these factors cannot be relied upon to correctly interpret the processes taking place from the observational point of view. Therefore, they will not be included in the factor model. Varimax rotation is one of the most used rotation schemes in factor analysis. This is because it maximizes the differences among dissimilar parameters and results in factors, each of which represents a process in the observation at different sampling sites. The factor loadings of each variable in the factor model were used to calculate the factor scores for each location where data was taken. This way, it is possible to explain the impact of each process at each location.

The coefficient of correlation was worked out to understand the relationship between the various parameters and to test the significance of the model’s example HCA. High positive or negative correlation observed among parameters will indicate their interdependence. A positive correlation is a relationship between two variables that move in tandem. A positive correlation exists when one variable decreases as the other variable decreases, or one variable increases while the other increases. A negative, or inverse correlation, between two variables, indicates that one variable increases, while the other decreases, and vice-versa. This relationship may or may not represent causation between the two variables, but it describes an observable pattern.

3.7 Contamination Indices

Different contamination indices were used to evaluate the findings. These included contamination factor (CF), the geo-accumulation index (Igeo), pollution load index (PLI) and enrichment Factor (EF).

3.8 Contamination Factor (CF)

Contamination Factor (CF) was used as a simple and effective tool in monitoring the level of heavy metal contamination at each site using Eq. (1) [18].

CF = Cm/Bm ……………. Eq. (1)

Where; Cm is the mean concentration, while Bm is the background concentration of metal from literature (average crustal abundance). The ratio of the measured concentration to the natural abundance of a given metal had been proposed as the index CF being classified into four grades for monitoring the pollution of a single metal over a period of time: low degree (CF < 1), moderate degree (1 ≤ CF < 3), considerable degree (3 ≤ CF < 6), and very high degree (CF > 6). Thus, the CF values can monitor the enrichment of a given metal in sediments over time.

3.9 Geo-Accumulation Index (Igeo)

To characterize the level of heavy metal contamination in sediment samples, geo-accumulation index (Igeo) was calculated using the equation:

lgeo = log2 (Cn/1.5Bn) ……………… Eq. (2)

Where Cn is the content of measured metal ‘‘n’’ in the samples, Bn is the crustal shale background content of the metal ‘‘n’’ [19], (Table 4), the constant of 1.5 is introduced to minimize the variation of background values due to lithogenic origins, and Igeo is a quantitative index of metal enrichment or contamination levels. Geo-accumulation index (Igeo) values will be interpreted as Igeo ≤ 0 uncontaminated; 0 ≤ Igeo ≤ 1—uncontaminated to moderately contaminated; 1 ≤ Igeo ≤ 2—moderately contaminated; 2 ≤ Igeo ≤ 3—moderately to heavily contaminated; 3 ≤ Igeo ≤ 4— heavily contaminated; 4 ≤ Igeo ≤ 5—heavily to extremely contaminated; and Igeo—5 ≥ extremely contaminated [20].

3.10 Pollution Load Index (PLI)

PLI was used to evaluate sediment quality and defined as the nth root of the multiplications of the contamination factor of the target heavy metals (CF).

PLI = (CF1 × CF2 × CF3 × ... × CFn)1/n……………… Eq. (3)

Where CF1 is the concentration of the first metal, CF2 is the concentration of the second metal, CF3 is the concentration of the third metal and CFn is the concentration of metal nth, and n = the total number of studied heavy metals in the sample. PLI = 0 indicates excellence; PLI < 1 suggests the presence of only a baseline level of pollutants and PLI > 1 indicates progressive deterioration of the site and estuarine quality [21]. The PLI evaluated the overall toxicity status of the sample and its contribution to the contribution of the metals.

3.11 Enrichment Factor (EF)

Enrichment Factor (EF) has been employed for the assessment of contamination in various environmental media [22].

EF = (Metal/RE) soil/(Metal/RE) background…………. Eq. (4)

Where, RE is the concentration of metal (Fe), adopted as Reference Element. The numerical results are indicative of different pollution level. Values of 0.5 ≤ EF ≤ 1.5 suggest that the trace metal concentration may come from natural weathering processes [22]. However, an EF > 1.5 indicates that a significant portion of the trace metals is delivered from non-crustal materials, so these trace metals are delivered by other sources, like point and non-point pollution and biota. Furthermore, the contamination categories are recognized on the basis of the enrichment factor: EF < 2 states deficiency to minimal enrichment, EF = 2–5 moderate enrichment; EF = 5–20 severe enrichment; EF = 20–40 very high enrichment and EF > 40 extremely high enrichment [23].

4.1 Quality Assurance/Quality Control

The accuracy of the analyses was determined using the percentage recovery for the NIST 2711a while precision was accessed with the relative percent difference (RPD) for the duplicate samples. The percentage recovery for NIST 2711a varied from 73.6–127% (Table 2). Apart from Ag, the recovery obtained were within the range of ± 30% for Cr and ± 20% for all other elements as specified in USEPA Method 6200 [15]. The RPD values obtained (Table 2) were less than 20% which showed acceptable results for duplicate samples [15].

Table 2

Mean Recovery for NIST 2711a and Relative Percent Differences for Duplicate Samples
Element	Mean Recovery for NIST 2711a (%)	Relative Percent Difference for Duplicate Samples (%)
Ag	73.6	n/a
As	104	n/a
Ba	90.4	5.1
Cd	84.8	n/a
Cr	127	13.8
Cu	95.9	1.6
Fe	101	0.3
Hg	87.9	n/a
Mn	89.1	1.5
Ni	102	18.8
Pb	108	16.7
V	104	8.2
Zn	100	2.9

n/a = RPD was not calculated since concentrations were below the limit of detection

4.2 Physiochemical Parameters

Physiochemical parameters determined for the 23 creeks in Metro Vancouver are provided in Table 3. The minimum and maximum pH levels of the water samples were 5.50 and 8.27 at Noons Creek (MVC 44) and Quibble Creek (MVC 13), respectively (Table 3). The pH for the sediments ranged from 5.77 at Beaver Creek (MVC 33) to 8.16 at King Lake (MVC 7). The pH affects most chemical and biological processes in water. It is one of the most important environmental factors limiting species distributions in aquatic habitats [24]. According to CCME [2], the pH of fresh waters should fall within the range of 6.5–9.0 units unless it can be demonstrated that such a pH is a result of natural processes. Two water samples at Noons Creek (MVC44 and MVC45) did not have their pH within the standard range of the Canadian water quality guidelines for the protection of aquatic life [2]. The low pH in those sites may be attributed to serpentine geology [24], since those sites were rocky and hilly in nature [25].

Total dissolved Solids (TDS) concentrations in the sampled creeks ranged from 14.0–633 ppm with the minimum at Noons Creek (MVC44) and the maximum of 633 ppm at Quibble Creek (MVC13). According to Garrison Investigative Board [26], in areas of Precambrian rock, TDS concentrations in water are generally less than 65 ppm. The two samples from Bon Accord Creek (MVC3 and MVC4) and the two samples from Noons Creek (MVC44 and MVC45) had their concentrations less than 65 ppm. According to the geology of the study area, rock formation in those sampled sites could predominantly be Precambrian. The highest TDS concentration at MVC13 may suggest a higher residential (urban) runoff as the site was closer to the very busy Surrey Memorial Hospital. The highest EC value (903 uS/cm) was also recorded at MVC13. The lowest EC value was recorded at MVC44 which is a sparsely populated area hence less chemicals such as chloride and phosphate from businesses and household products which might cause an increase in EC in the surface water.

Table 3

Physicochemical parameters for sediment and surface samples collected from Metro Vancouver Creeks
Creeks Sample sites		Sediment		Surface Water
		pH		pH	EC (uS/cm)	TDS (ppm)
exc	Bolivar		7.71	7.49	310	340
MVC 2	Bolivar		7.72	7.62	314	224
MVC 3	Bon Accord		7.58	6.89	82	58
MVC 4	Bon Accord		7.22	6.26	198	14
MVC 5	Bon Accord		6.25	7.05	176	124
MVC 6	King		6.66	7.22	391	270
MVC 7	King (Lake)		8.16	6.99	148	104
MVC 8	King		7.08	6.71	225	157
MVC 9	King (Kiyo)		7.01	7.34	279	198
MVC 10	Bear		7.03	7.74	348	244
MVC 11	Bear		7.27	7.37	429	305
MVC 12	Bear		6.82	7.81	429	306
MVC 13	Quibble		6.87	8.27	903	633
MVC 14	Quibble		7.41	7.41	403	288
MVC 15	Healy		6.80	7.32	155	111
MVC 16	Healy		7.15	6.80	436	325
MVC 17	Hyland		7.10	7.24	436	310
MVC 18	Hyland		7.18	7.42	158	113
MVC 19	Hyland (Industrial)		6.97	7.24	532	373
MVC 20	Stoney		6.61	7.32	457	328
MVC 21	Stoney		6.84	7.43	411	293
MVC 22	Surrey Lake		6.62	7.39	463	325
MVC 23	Bear		6.27	7.02	486	347
MVC 24	Bear		6.17	7.46	502	356
MVC 25	Bolivar		6.25	6.85	468	335
MVC 26	Robson		6.12	6.75	149	106
MVC 27	Cougar Lake		6.57	6.58	232	166
MVC 28	Cougar		6.59	7.40	119	85
MVC 29	Cougar		6.76	7.13	111	79
MVC 30	First Beach		6.22	7.46	386	276
MVC 31	Buckingham		6.56	7.55	344	243
MVC 32	Dear Lake Brook		6.55	7.13	336	109
MVC 33	Beaver		5.77	6.83	368	262
MVC 34	Eagle (Industrial)		6.81	7.82	306	219
MVC 35	Eagle		6.59	7.77	311	222
MVC 36	Guichon		6.68	7.59	388	276
MVC 37	Guichon		6.97	7.59	380	270
MVC 38	Chubb		6.46	7.07	563	400
MVC 39	Chubb		6.14	6.83	580	410
MVC 40	Hoy		6.72	7.02	605	430
MVC 41	Hoy		6.79	7.11	249	178
MVC 42	Scott		6.79	7.54	218	155
MVC 43	Scott		7.20	7.35	227	161
MVC 44	Noons		6.10	5.50	19	14
MVC 45	Noons		6.41	6.48	44	31
MVC 46	Hyde		6.73	-	-	-
MVC 47	Cedar		6.35	6.61	283	201

4.3 Heavy Metals in Sediments

Heavy metal concentrations in sediments collected from the 23 creeks are presented in Table 4. The concentrations of Ag, As and Cd representing 96.6%, 94.4% and 94.4%, respectively were below the detection limit while all Hg concentrations were below the detection limit. Ag (2.53–4.11 mg/kg), As (7.51–16.27 mg/kg) and Cd (2.72–9.10 mg/kg) concentrations detected at Bon Accord (MVC5), King Creek (MVC8, MVC9), Bolivar Creek (MVC25) and Beaver Creek (MVC33) exceeded the LEL and SEL. The concentration of Cr ranged from 22.83–136.68 mg/kg in the study area. The lowest and highest concentrations were recorded at Hoy Creek (MVC40) and Hyland Creek (MVC19), respectively. The higher concentration of Cr at MVC19 may be due to effluent from an iron fabrication industry close to the sampled site and treated stormwater runoff from a concrete ready-mix operation at the site.

The highest Cu concentration (146 mg/kg) was recorded at Beaver Creek (MVC33) and the lowest (6.70 mg/kg) at King Lake. In the study area, 21.2% of sediment samples had Cu concentrations above the TEL. On the other hand, all Cu concentrations were below the PEL. For Ni concentration, 21.27% of sediment samples were below the detection limit while 32 sampling sites representing 68.0% had concentrations above the LEL. King Creek (MVC9) with a concentration of 75.9 mg/kg and Cedar Creek with a concentration of 102.12 mg/kg are the only sites with concentrations above the SEL (Table 4), hence 10.6% of the sampling sites was within the LEL and SEL. The major sources of Cu in the study area include the use of pesticides and herbicides especially on farmlands close to Bear Creek (MVC23 and MVC24), and wood preservatives such as chromated copper arsenate (CCA) used in the construction industry in the Metro Vancouver area. Corrosion of household plumbing, faucets, and water fixtures may also introduce Cu in the study area [27]. The potential sources of Ni are commercial effluents and leachates, as well as through atmospheric deposition after release from anthropogenic sources [28].

The Fe concentrations in the sediment ranged from 14,516.76–62,755.68 mg/kg with the lowest at Bon Accord Creek (MVC4) and highest at Cedar Creek (MVC47). The sediment at MVC47 had a reddish nature which was attributed to the high concentration of Fe. According to the United States Geological Survey (USGS) [29], Fe tends to add a rusty, reddish-brown color to water and sediments. With the exception of Bon Accord Creek (MVC4) with a Fe concentration of 14,516.76 mg/kg which is below the LEL, all the sampling sites in the study area had Fe concentration above the LEL representing 97.8% of the sampling sites. According to the standard for SEL, King Creek (MVC9), Bolivar Creek (MVC25), Scott Creek (MVC42, MVC43), Noons Creek (MVC45) and Cedar Creek (MVC47) had concentrations of Fe above the required limit representing 12.7% of the sampling sites in the study area. The rest of the sampling sites were below the SEL. For Mn, Bon Accord Creek (MVC4) had the lowest concentration of 385.16 mg/kg and King Lake (MVC7) had the highest concentration of 5,126.12 mg/kg. Forty-three sediment samples out of the 47 samples representing 91.4% had Mn concentrations above the LEL limit. Bon Accord Creeks (MVC4, MVC5), Stoney Creek (MVC20) and Cougar Lake (MVC27) had Mn concentrations below the LEL limit. Twelve sampling sites (25.5%) had Mn concentrations above the SEL. The concentration of vanadium (V) ranged from 50.93 mg/kg at Hyde Creek (MVC46) which was the lowest and 151.63 mg/kg at Cedar Creek (MVC47) which was the highest. The increased concentration of Fe, Mn and V in the study area may be attributed to geogenic sources naturally occurring from weathering of Fe, Mn and V bearing minerals and rocks [30].

The elevated concentrations of Pb above the TEL (Table 4) in Bon Accord Creek (MVC5-64.78 mg/kg), Quibble Creek (MVC14, 56.66 mg/kg), Bear Creek (MVC23, 68.53 mg/kg), Chubb Creek (MVC39, 38.44 mg/kg) and Beaver Creek (MVC33, 161.83 mg/kg) may be due to the sites being under heavy vehicular traffic. MVC33 with a Pb concentration of 161.83 mg/kg was the only site in the study area above the PEL. The concentration for Zn ranged from 21.40 mg/kg at Bon Accord Creek (MVC3) to 660.87 mg/kg at Beaver Creek (MVC33). Twenty-three sediment samples representing 48.9% were above the TEL. On the other hand, Beaver Creek (MVC33) and Chubb creek (MVC39) had concentrations above the PEL. The lowest Ba concentration was at Beaver Creek (MVC33; 98.06 mg/kg) and highest at Hyde Creek (MVC46; 674.21 mg/kg). According to Bonnevie et al. [31], Kennedy & Gadd [32] and Al-Masri et al. [33] as cited by Giri et al. [30], vehicular anthropogenic activities are known to be associated with augmentation of Pb, Zn and Ba.

The concentrations of heavy metals in the sampling area were also compared to background values [19], Table 4. It was observed that apart from Bon Accord Creek (MVC4), all the sampling sites in the study area had at least one heavy metal above the background level (Table 4), suggesting that there were probably some anthropogenic inputs at the sites [34]. According to results in Table 4, Bolivar Creek (MVC25) and Beaver Creek (MVC33) had at least nine heavy metals at each site above the TEL/LEL hence these sites were considered the most polluted. The high levels of these heavy metals may be due to the closeness of these sites to major highways with heavy vehicular activities. According to Awonaike et al. [35], chemicals commonly used in vehicle fluids, tires and paints were much higher in rivers next to roads with heavy traffic due to road surface runoffs thus suggesting a strong relationship between traffic and the concentration of these chemicals. Sediment contamination in this regard could pose a risk and cause adverse impacts to aquatic life.

Table 4

Concentrations of metals (mg/kg) in sediment samples collected from creeks in Metro Vancouver
Sample	Ag	As	Ba	Cd	Cr	Cu	Fe	Hg	Mn	Ni	Pb	V	Zn
MVC-1	< 1.91	< 1.63	599.20	< 3.10	62.05	34.18	33006	< 3.89	693.99	29.76	14.20	89.10	163.50
MVC-2	< 2.02	< 2.47	533.44	< 3.22	63.69	28.87	39157	< 3.63	1265.82	53.58	16.39	98.56	163.30
MVC-3	< 1.93	< 1.80	552.71	< 3.09	58.56	23.70	29042	< 3.45	815.05	51.95	21.80	90.24	140.30
MVC-4	< 1.60	< 1.33	221.82	< 2.63	52.31	13.84	14516	< 3.18	385.16	< 13.85	9.20	51.40	21.40
MVC-5	< 2.57	16.27	176.42	3.13	65.61	85.79	23754	< 3.43	398.79	< 13.50	64.78	71.42	425.90
MVC-6	< 1.88	< 1.41	460.89	< 3.03	46.68	15.01	28801	< 3.591	505.86	11.89	7.77	80.16	57.23
MVC-7	< 1.91	< 1.39	518.04	< 3.06	116.45	6.70	31141	< 4.82	5126.12	43.92	5.02	86.24	87.22
MVC-8	2.53	< 1.49	211.03	2.72	55.94	30.01	31904	< 3.08	708.16	35.25	13.45	91.82	134.80
MVC-9	< 1.97	10.26	530.23	< 3.15	56.31	24.33	53432	< 4.91	2600.33	75.95	13.11	105.48	218.80
MVC-10	< 1.90	< 1.63	527.16	< 3.05	50.37	15.85	31213	< 3.62	1192.16	38.27	9.44	89.82	107.30
MVC-11	< 2.03	< 1.68	607.90	< 3.23	47.70	28.68	34936	< 4.68	744.58	25.30	12.61	96.68	135.80
MVC-12	< 1.84	< 1.84	498.79	< 3.01	54.34	21.09	27010	< 3.41	449.58	22.02	27.34	86.12	123.60
MVC-13	< 1.91	< 1.76	429.99	< 3.04	59.01	68.74	33579	< 3.91	717.88	21.77	24.03	97.73	288.30
MVC-14	< 1.97	< 2.31	464.47	< 3.04	104.33	26.16	31786	< 5.28	546.85	< 14.85	56.66	100.83	124.70
MVC-15	< 1.86	< 1.64	464.20	< 2.96	104.27	37.10	28640	< 3.65	479.25	38.41	15.75	90.71	174.40
MVC-16	< 1.87	< 1.90	467.74	< 3.00	50.69	30.75	28546	< 3.46	564.52	< 10.23	30.41	82.71	166.70
MVC-17	< 1.91	< 1.86	510.62	< 3.08	53.39	55.45	32765	< 3.96	1097.41	44.73	26.67	92.52	303.10
MVC-18	< 1.86	< 1.59	433.10	< 2.95	61.82	11.76	32090	< 3.46	697.84	14.85	10.82	99.06	77.75
MVC-19	< 1.82	< 1.54	509.80	< 2.98	136.68	11.77	28446	< 3.59	651.98	31.72	9.72	77.48	101.00
MVC-20	< 1.79	< 1.76	380.08	< 2.88	53.10	13.10	25275	< 3.73	453.92	< 15.01	29.57	79.90	89.11
MVC-21	< 1.87	< 1.54	515.51	< 3.05	61.91	22.77	33177	< 3.42	1271.15	47.03	11.09	93.99	134.70
MVC-22	< 1.86	< 1.54	472.74	< 2.99	46.07	7.77	25100	< 3.55	479.73	17.75	11.48	69.97	78.59
MVC-23	< 2.44	5.82	410.51	< 2.96	76.58	37.89	36285	< 3.35	661.77	43.90	68.53	109.48	150.40
MVC-24	< 1.90	< 1.73	461.41	< 3.07	41.77	15.24	34715	< 3.55	993.76	39.06	17.28	81.98	186.30
MVC-25	3.31	7.51	139.46	6.57	44.37	44.05	51343	< 3.39	607.70	41.57	15.56	86.10	208.80
MVC-26	< 1.83	< 1.89	448.18	< 2.94	65.04	35.01	28504	< 3.47	524.46	12.54	33.55	91.91	152.80
MVC-27	< 1.83	< 1.41	505.23	< 2.95	61.56	27.96	26350	< 3.43	405.01	< 10.17	8.51	115.03	57.60
MVC-28	< 1.89	< 1.56	507.66	< 3.03	48.62	20.25	28638	< 3.54	625.88	25.31	10.61	89.83	146.30
MVC-29	< 1.87	< 1.51	499.78	< 3.06	66.93	25.33	33789	< 3.51	673.26	31.52	10.33	99.73	122.80
MVC-30	< 1.82	< 1.66	485.34	< 2.93	47.78	21.80	25163	< 3.41	602.25	13.90	16.07	82.21	94.11
MVC-31	< 1.88	< 1.60	545.91	< 3.06	44.60	18.24	32662	< 3.51	601.40	26.92	10.97	89.44	86.85
MVC-32	< 1.93	< 1.94	522.03	< 3.09	45.28	48.04	40056	< 3.60	1620.03	48.95	27.68	106.63	174.10
MVC-33	4.11	15.41	98.06	9.10	39.02	146.02	41751	< 3.65	1123.22	< 15.56	161.83	87.62	660.80
MVC-34	< 2.06	< 1.61	608.36	< 3.26	35.31	16.48	42896	< 3.65	1537.73	34.36	10.40	104.38	108.00
MVC-35	< 1.86	< 1.49	537.81	< 2.97	43.73	11.44	26907	< 3.52	777.22	14.53	9.510	70.41	72.22
MVC-36	< 1.98	< 1.46	601.95	< 3.14	37.45	19.25	33047	< 3.39	831.35	26.82	10.55	95.25	120.60
MVC-37	< 1.87	< 1.71	510.29	< 2.96	36.44	19.02	27221	< 3.47	868.41	< 10.73	19.40	70.00	103.50
MVC-38	< 1.95	< 1.85	600.38	< 3.14	47.37	40.24	36137	< 3.56	606.32	30.56	24.90	88.49	128.90
MVC-39	< 1.97	< 3.26	483.37	< 3.16	42.52	82.67	43252	< 4.34	675.20	33.29	38.44	75.02	359.50
MVC-40	< 1.95	< 1.39	565.89	< 3.13	22.83	18.13	36024	< 3.78	743.81	17.09	5.95	94.9	58.73
MVC-41	< 1.88	< 1.52	490.26	< 3.02	29.17	19.59	30084	< 3.48	744.19	< 12.87	8.90	71.27	89.92
MVC-42	< 2.10	< 1.48	574	< 3.35	32.46	28.96	51067	< 4.98	1240.94	39.57	5.88	125.05	118.6
MVC-43	< 2.05	< 1.51	626.75	< 3.24	40.35	24.01	45407	< 3.65	1279.39	38.00	5.35	106.37	90.28
MVC-44	< 1.98	< 1.59	612.79	< 3.19	30.40	11.46	42975	< 3.59	896.90	22.34	11.44	127.60	54.89
MVC-45	< 2.10	2.22	584.51	< 3.33	32.87	15.01	53484	< 4.54	1924.81	64.82	15.90	140.39	102.36
MVC-46	< 1.89	< 1.45	674.21	< 2.99	24.15	10.58	22071	< 3.38	620.50	< 9.28	11.19	50.93	52.30
MVC-47	< 2.11	< 1.72	541.59	< 3.36	34.35	25.25	62755	< 4.01	742.67	102.12	12.72	151.63	76.30
	Ag	As	Ba	Cd	Cr	Cu	Fe	Hg	Mn	Ni	Pb	V	Zn
ISQG/TEL^a		5.9		0.6	37.3	35.7		0.17			35		123
PEL^a		17		3.5	90	190		0.486			91.3		315
LEL, BC^b	0.5	5.9		0.6	37.3	37.7	21200	0.17	460	16	35		123
SEL, BC^b	0.5	17		3.5	90	190	43766	0.486	1100	75	91.3		315
Background^c	0.06	1.8	400	0.1	100	55	56300	0.07	900	75	14	135	70
Notes

Bold values are above Threshold effect levels (TEL)

Interim freshwater sediment quality guidelines (ISQGs (CCME, [2]^a

BC MOE Working Guidelines for Sediments [36]^b

Kabata-Pendias & Mukherjee [19]^c

Threshold effect levels (TELs), Probable effect levels (PEL), Lowest Effect Level (LEL), Severe Effect Level (SEL)

4.4 Contamination and Toxicity Assessment of Metals in Sediments

4.4.1 Enrichment Factor (EF)

The enrichment factors are presented in Table 5. According to the data, enrichment factors indicate natural weathering to moderate contamination with a range for Ag (0.06–0.09), Ba (0.22 to 2.45), Cr (0.30 to 2.63), Ni (0.44 to 1.92) and V (1.03 to 2.27) in the study area. EF for As at Bon Accord Creek (MVC5, EF = 7.19) shows severe contamination. King Creek (MVC9, EF = 2.02) and Beaver Creek (MVC33, EF = 3.87) showed moderate contamination whereas Bear Creek (MVC23, EF = 1.68) and Bolivar Creek (MVC25, EF = 1.54) show minimal contamination. The EF value for Cu showed severe contamination at Bon Accord Creek (MVC5, EF = 6.50) and Beaver Creek (MVC33, EF = 6.30). The EF for Mn was 8.30 at King Lake (MVC7) which indicates severe contamination. The EF for Pb recorded severe contamination at Bon Accord Creek (MVC5, EF = 8.08), Quibble Creek (MVC14, EF = 5.28) and Beaver Creek (MVC33, EF = 11.49). The rest of the sampling sites in terms of Cu, Mn and Pb, in the study area showed natural weathering to moderate contamination. The EF for Zn was the highest at Bon Accord Creek (MVC5, EF = 13.49) indicating severe contamination. Quibble Creek (MVC13, EF = 6.46), Hyland Creek (MVC17, EF = 6.96), Beaver Creek (MVC33, EF = 11.91) and Chubb Creek (MVC39, EF = 6.25) also recorded severe contamination. The EF shows Zn at the other sampling sites in the study area to be minimal to moderately contaminated. For Cd, EF shows extremely high contamination for Beaver Creek (MVC33) with a value of 122.00. Subsequently, Bon Accord Creek (MVC5), Bolivar Creek (MVC25) and King Creek (MVC8) had EF values of 73.79, 71.62 and 47.66 respectively and shows high level of contamination in the study area. EF values exceeding 20 in sediments are considered significantly enriched with metals and such high EF values are usually associated with anthropogenic inputs according to Silva Y.J, et al., [37] as cited by Nkinda et al., [34].

4.4.2 Pollution Load Index (PLI)

To evaluate the sediment pollution severity, PLI was used to find out the mutual pollution effect at different stations by different metals (Table 5). A PLI value close to 1 indicates metal loads near the background level, while values > 1 indicate pollution according to Cabrera et al., [38] as cited by Giri et al., [30]. According to Table 5, PLI values range from 0.50 to 1.09 in the study area. The joint pollution effect of different metals at different sampling locations in the study area is depicted in Fig. 3. King Creek (MVC9), Hyland Creek (MVC17), Bear Creek (MVC23), Dear Lake Brook (MVC32) and Chubb Creek (MVC39) had PLI values above 1 and indicate pollution. The rest of the sampling sites had PLI values < 1 indicating baseline level of pollution.

4.4.3 Contamination Factor

To quantify the magnitude of contamination by different metals, contamination factor (CF) was used (Table 6). The calculated CF values applied in this study were based on the four categories: CF values less than 1 (low degree of contamination); CF values starting from 1 but less than 3 (moderate degree of contamination); CF values ranging from 3 but less than 6 (considerable degree of contamination); and CF values greater than 6 (very high degree of contamination) [34]. With respect to the CF values for Cd and Ag, high degree of contamination in the sediments persist and may be indicative of anthropogenic influence in elemental loadings in the study area as stipulated by Gomez-Alvarez et al., [39]; Gajere & Okegye, [40] as cited by Nkinda., et al., [34].

Contamination factors for Ba, Cr, Cu, Fe, Ni and Mn show low to moderate contamination in the study area with the exception of Mn at King Lake (MVC7, CF = 5.7) which indicates considerable degree of contamination (Table 6). The CF for Zn at Bon Accord Creek (MVC5, CF = 6.09) and Beaver Creek (MVC33, CF = 9.44) were above the threshold limit of 6 and show high degree of contamination. King Creek (MVC9, CF = 3.13), Hyland Creek (MVC17, CF = 4.33) and Chubb Creek (MVC39, CF = 5.14) for Zn shows considerable degree of contamination in the study area (Table 6). The highest CF values for As were determined at Bon Accord Creek (MVC5, CF = 9.04) and Beaver Creek (MVC33, CF = 8.56). The CF value for Pb was also highest at Beaver Creek (MVC33, CF = 11.56), with a high degree of contamination. For As, CF values for King Creek (MVC9, CF = 5.70), Bear Creek (MVC23, CF = 3.23) and Bolivar Creek (CF = 4.17) shows moderate degree of contamination. CF values for Pb in the study area on the other hand shows moderate contamination at Bon Accord Creek (MVC5, CF = 4.63), Quibble Creek (MVC14, EF = 4.05) and Bear Creek (MVC23, EF = 4.9). The CF value for Cd (CF = 90.96) and Ag (68.45) was the highest at Beaver Creek (MVC33) in the study area. Bolivar Creek (MVC25) recorded CF values of 65.66 and 55.17 for Cd and Ag, respectively. King Creek (MVC8) recorded CF value of 27.15 for Cd and 42.12 for Ag in the present study. The CF for Cd at Bon Accord Creek (MVC5) was 31.3 and above the threshold limit of 6. The calculated contamination factors (Table 6) for the heavy metals in sediments in the study area are found to fall in the following sequences: Cd > Ag > As > Pb > Zn > Mn > Cu > Ba > Cr > Ni > V > Fe > Hg.

4.4.4 Geo-Accumulation Index (Igeo)

The sediment Igeo values for Ag and Cd in the study area were negative and indicate no contamination or enrichment above background value (Table 7). For As, Igeo values at Bon Accord Creek (MVC5, Igeo = 4.29) and Beaver Creek (MVC33, Igeo = 4.21) indicate high to highly contaminated sediment. At King Creek (MVC9, Igeo = 3.62) and Bolivar Creek (MVC25, Igeo = 3.17), Igeo values for As indicates highly contaminated sediment. At Bear Creek (MVC23, Igeo = 2.80) As indicates moderately to heavily contaminated level while Noons Creek (MVC45, Igeo = 1.41) indicates moderately contaminated level in the study area. The Igeo values for Ba, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn represents extremely contaminated sediment and > 64-fold increase [20]. Hence these metals pose an environmental risk to sediment quality in the study area.

Table 5

Enrichment Factor of Metals in Creeks Sediments in Metro Vancouver
Sample	Ag	As	Ba	Cd	Cr	Cu	Fe	Mn	Ni	Pb	V	Zn	PLI
MVC-1	0.00	0.00	1.46	0.00	1.03	1.86	1.00	1.06	0.97	1.28	1.4	3.73	0.82
MVC-2	0.00	0.00	1.09	0.00	0.89	1.33	1.00	1.63	1.47	1.24	1.31	3.14	0.95
MVC-3	0.00	0.00	1.53	0.00	1.10	1.47	1.00	1.41	1.92	2.23	1.61	3.64	0.85
MVC-4	0.00	0.00	1.23	0.00	1.97	1.72	1.00	1.34	0.00	1.88	1.84	1.11	0.00
MVC-5	0.00	7.19	0.60	73.79	1.51	6.50	1.00	0.85	0.00	8.08	1.56	13.49	0.00
MVC-6	0.00	0.00	1.28	0.00	0.89	0.94	1.00	0.89	0.44	0.8	1.45	1.49	0.50
MVC-7	0.00	0.00	1.34	0.00	2.05	0.39	1.00	8.30	1.51	0.48	1.44	2.11	0.77
MVC-8	0.08	0.00	0.53	47.66	0.96	1.69	1.00	1.12	1.18	1.25	1.5	3.18	0.70
MVC-9	0.00	2.02	0.80	0.00	0.58	0.82	1.00	2.45	1.52	0.73	1.03	3.08	1.09
MVC-10	0.00	0.00	1.36	0.00	0.88	0.91	1.00	1.92	1.31	0.9	1.5	2.59	0.72
MVC-11	0.00	0.00	1.40	0.00	0.75	1.48	1.00	1.07	0.78	1.07	1.44	2.92	0.76
MVC-12	0.00	0.00	1.48	0.00	1.10	1.41	1.00	0.84	0.87	3.00	1.66	3.44	0.70
MVC-13	0.00	0.00	1.03	0.00	0.96	3.68	1.00	1.08	0.70	2.12	1.51	6.46	0.94
MVC-14	0.00	0.00	1.17	0.00	1.80	1.48	1.00	0.87	0.00	5.28	1.65	2.95	0.00
MVC-15	0.00	0.00	1.30	0.00	1.99	2.33	1.00	0.84	1.44	1.63	1.65	4.58	0.84
MVC-16	0.00	0.00	1.32	0.00	0.97	1.94	1.00	1.00	0.00	3.16	1.51	4.39	0.00
MVC-17	0.00	0.00	1.25	0.00	0.89	3.05	1.00	1.69	1.46	2.41	1.47	6.96	1.06
MVC-18	0.00	0.00	1.08	0.00	1.06	0.66	1.00	1.00	0.50	1.00	1.60	1.82	0.58
MVC-19	0.00	0.00	1.44	0.00	2.63	0.75	1.00	1.16	1.20	1.01	1.42	2.67	0.68
MVC-20	0.00	0.00	1.21	0.00	1.15	0.93	1.00	0.91	0.00	3.47	1.64	2.65	0.00
MVC-21	0.00	0.00	1.25	0.00	1.02	1.24	1.00	1.93	1.52	0.99	1.47	3.06	0.83
MVC-22	0.00	0.00	1.51	0.00	1.01	0.56	1.00	0.96	0.76	1.36	1.45	2.36	0.50
MVC-23	0.00	1.68	0.91	0.00	1.16	1.88	1.00	0.92	1.30	5.60	1.57	3.12	1.03
MVC-24	0.00	0.00	1.07	0.00	0.66	0.79	1.00	1.44	1.21	1.48	1.23	4.04	0.77
MVC-25	0.06	1.54	0.22	71.62	0.47	1.54	1.00	0.60	0.87	0.90	0.87	3.06	0.76
MVC-26	0.00	0.00	1.26	0.00	1.25	2.21	1.00	0.93	0.47	3.49	1.68	4.03	0.76
MVC-27	0.00	0.00	1.54	0.00	1.28	1.91	1.00	0.77	0.00	0.96	2.27	1.64	0.00
MVC-28	0.00	0.00	1.42	0.00	0.93	1.27	1.00	1.10	0.95	1.10	1.63	3.84	0.67
MVC-29	0.00	0.00	1.19	0.00	1.09	1.35	1.00	1.00	1.00	0.91	1.53	2.73	0.74
MVC-30	0.00	0.00	1.55	0.00	1.04	1.56	1.00	1.21	0.59	1.89	1.70	2.81	0.61
MVC-31	0.00	0.00	1.34	0.00	0.75	1.01	1.00	0.93	0.88	1.00	1.42	2.00	0.64
MVC-32	0.00	0.00	1.05	0.00	0.62	2.16	1.00	2.04	1.31	2.05	1.38	3.27	1.06
MVC-33	0.09	3.87	0.19	122	0.51	6.30	1.00	1.36	0.00	11.49	1.09	11.91	0.00
MVC-34	0.00	0.00	1.14	0.00	0.45	0.69	1.00	1.81	0.86	0.72	1.26	1.90	0.76
MVC-35	0.00	0.00	1.60	0.00	0.89	0.77	1.00	1.46	0.58	1.05	1.36	2.02	0.53
MVC-36	0.00	0.00	1.46	0.00	0.62	1.05	1.00	1.27	0.87	0.95	1.50	2.75	0.69
MVC-37	0.00	0.00	1.50	0.00	0.73	1.26	1.00	1.61	0.00	2.11	1.34	2.86	0.00
MVC-38	0.00	0.00	1.33	0.00	0.72	2.00	1.00	0.85	0.91	2.04	1.27	2.69	0.83
MVC-39	0.00	0.00	0.90	0.00	0.54	3.44	1.00	0.79	0.83	2.64	0.90	6.25	1.05
MVC-40	0.00	0.00	1.26	0.00	0.35	0.91	1.00	1.04	0.51	0.49	1.37	1.23	0.53
MVC-41	0.00	0.00	1.31	0.00	0.53	1.17	1.00	1.25	0.00	0.88	1.23	2.25	0.00
MVC-42	0.00	0.00	0.90	0.00	0.35	1.02	1.00	1.22	0.83	0.34	1.27	1.75	0.78
MVC-43	0.00	0.00	1.11	0.00	0.49	0.95	1.00	1.42	0.90	0.35	1.22	1.50	0.73
MVC-44	0.00	0.00	1.14	0.00	0.39	0.48	1.00	1.05	0.56	0.79	1.54	0.96	0.62
MVC-45	0.00	0.00	0.88	0.00	0.34	0.51	1.00	1.81	1.30	0.88	1.36	1.44	0.90
MVC-46	0.00	0.00	2.45	0.00	0.60	0.86	1.00	1.42	0.00	1.50	1.20	1.79	0.00
MVC-47	0.00	0.00	0.69	0.00	0.30	0.72	1.00	0.60	1.74	0.60	1.26	0.91	0.87

Table 6

Contamination Factor of Metals in Creeks Sediments in Metro Vancouver
Sample	Ag	As	Ba	Cd	Cr	Cu	Fe	Mn	Ni	Pb	V	Zn
MVC-1	0.00	0.00	1.50	0.00	0.62	0.62	0.59	0.77	0.40	1.01	0.66	2.34
MVC-2	0.00	0.00	1.33	0.00	0.64	0.52	0.70	1.41	0.71	1.17	0.73	2.33
MVC-3	0.00	0.00	1.38	0.00	0.59	0.43	0.52	0.91	0.69	1.56	0.67	2.01
MVC-4	0.00	0.00	0.55	0.00	0.52	0.25	0.26	0.43	0.00	0.66	0.38	0.31
MVC-5	0.00	9.04	0.44	31.3	0.66	1.56	0.42	0.44	0.00	4.63	0.53	6.09
MVC-6	0.00	0.00	1.15	0.00	0.47	0.27	0.51	0.56	0.16	0.55	0.59	0.82
MVC-7	0.00	0.00	1.30	0.00	1.16	0.12	0.55	5.70	0.59	0.36	0.64	1.25
MVC-8	42.12	0.00	0.53	27.15	0.56	0.55	0.57	0.79	0.47	0.96	0.68	1.93
MVC-9	0.00	5.70	1.33	0.00	0.56	0.44	0.95	2.89	1.01	0.94	0.78	3.13
MVC-10	0.00	0.00	1.32	0.00	0.50	0.29	0.55	1.32	0.51	0.67	0.67	1.53
MVC-11	0.00	0.00	1.52	0.00	0.48	0.52	0.62	0.83	0.34	0.90	0.72	1.94
MVC-12	0.00	0.00	1.25	0.00	0.54	0.38	0.48	0.50	0.29	1.95	0.64	1.77
MVC-13	0.00	0.00	1.07	0.00	0.59	1.25	0.60	0.80	0.29	1.72	0.72	4.12
MVC-14	0.00	0.00	1.16	0.00	1.04	0.48	0.56	0.61	0.00	4.05	0.75	1.78
MVC-15	0.00	0.00	1.16	0.00	1.04	0.67	0.51	0.53	0.51	1.13	0.67	2.49
MVC-16	0.00	0.00	1.17	0.00	0.51	0.56	0.51	0.63	0.00	2.17	0.61	2.38
MVC-17	0.00	0.00	1.28	0.00	0.53	1.01	0.58	1.22	0.60	1.90	0.69	4.33
MVC-18	0.00	0.00	1.08	0.00	0.62	0.21	0.57	0.78	0.20	0.77	0.73	1.11
MVC-19	0.00	0.00	1.27	0.00	1.37	0.21	0.51	0.72	0.42	0.69	0.57	1.44
MVC-20	0.00	0.00	0.95	0.00	0.53	0.24	0.45	0.50	0.00	2.11	0.59	1.27
MVC-21	0.00	0.00	1.29	0.00	0.62	0.41	0.59	1.41	0.63	0.79	0.70	1.92
MVC-22	0.00	0.00	1.18	0.00	0.46	0.14	0.45	0.53	0.24	0.82	0.52	1.12
MVC-23	0.00	3.23	1.03	0.00	0.77	0.69	0.64	0.74	0.59	4.90	0.81	2.15
MVC-24	0.00	0.00	1.15	0.00	0.42	0.28	0.62	1.10	0.52	1.23	0.61	2.66
MVC-25	55.17	4.17	0.35	65.66	0.44	0.80	0.91	0.68	0.55	1.11	0.64	2.98
MVC-26	0.00	0.00	1.12	0.00	0.65	0.64	0.51	0.58	0.17	2.40	0.68	2.18
MVC-27	0.00	0.00	1.26	0.00	0.62	0.51	0.47	0.45	0.00	0.61	0.85	0.82
MVC-28	0.00	0.00	1.27	0.00	0.49	0.37	0.51	0.70	0.34	0.76	0.67	2.09
MVC-29	0.00	0.00	1.25	0.00	0.67	0.46	0.60	0.75	0.42	0.74	0.74	1.75
MVC-30	0.00	0.00	1.21	0.00	0.48	0.4	0.45	0.67	0.19	1.15	0.61	1.34
MVC-31	0.00	0.00	1.36	0.00	0.45	0.33	0.58	0.67	0.36	0.78	0.66	1.24
MVC-32	0.00	0.00	1.31	0.00	0.45	0.87	0.71	1.80	0.65	1.98	0.79	2.49
MVC-33	68.45	8.56	0.25	90.96	0.39	2.65	0.74	1.25	0.00	11.56	0.65	9.44
MVC-34	0.00	0.00	1.52	0.00	0.35	0.3	0.76	1.71	0.46	0.74	0.77	1.54
MVC-35	0.00	0.00	1.34	0.00	0.44	0.21	0.48	0.86	0.19	0.68	0.52	1.03
MVC-36	0.00	0.00	1.50	0.00	0.37	0.35	0.59	0.92	0.36	0.75	0.71	1.72
MVC-37	0.00	0.00	1.28	0.00	0.36	0.35	0.48	0.96	0.00	1.39	0.52	1.48
MVC-38	0.00	0.00	1.50	0.00	0.47	0.73	0.64	0.67	0.41	1.78	0.66	1.84
MVC-39	0.00	0.00	1.21	0.00	0.43	1.50	0.77	0.75	0.44	2.75	0.56	5.14
MVC-40	0.00	0.00	1.41	0.00	0.23	0.33	0.64	0.83	0.23	0.43	0.70	0.84
MVC-41	0.00	0.00	1.23	0.00	0.29	0.36	0.53	0.83	0.00	0.64	0.53	1.28
MVC-42	0.00	0.00	1.43	0.00	0.32	0.53	0.91	1.38	0.53	0.42	0.93	1.69
MVC-43	0.00	0.00	1.57	0.00	0.40	0.44	0.81	1.42	0.51	0.38	0.79	1.29
MVC-44	0.00	0.00	1.53	0.00	0.30	0.21	0.76	1.00	0.30	0.82	0.95	0.78
MVC-45	0.00	1.23	1.46	0.00	0.33	0.27	0.95	2.14	0.86	1.14	1.04	1.46
MVC-46	0.00	0.00	1.69	0.00	0.24	0.19	0.39	0.69	0.00	0.80	0.38	0.75
MVC-47	0.00	0.00	1.35	0.00	0.34	0.46	1.11	0.83	1.36	0.91	1.12	1.09

Table 7

Geo-accumulation of Metals in Creeks Sediments in Metro Vancouver
Sample	Ag	As	Ba	Cd	Cr	Cu	Fe	Hg	Mn	Ni	Pb	V	Zn
MVC-1	0.00	0.00	17.29	0.00	12.01	10.29	30.21	0.00	18.67	10.54	7.05	12.97	12.90
MVC-2	0.00	0.00	17.12	0.00	12.05	10.05	30.45	0.00	19.53	11.39	7.26	13.11	12.90
MVC-3	0.00	0.00	17.17	0.00	11.93	9.76	30.02	0.00	18.90	11.34	7.67	12.99	12.68
MVC-4	0.00	0.00	15.85	0.00	11.77	8.99	29.02	0.00	17.82	0.00	6.42	12.18	9.96
MVC-5	0.00	4.29	15.52	-2.26	12.09	11.62	29.73	0.00	17.87	0.00	9.24	12.65	14.28
MVC-6	0.00	0.00	16.91	0.00	11.60	9.10	30.01	0.00	18.21	9.22	6.18	12.82	11.38
MVC-7	0.00	0.00	17.08	0.00	12.92	7.94	30.12	0.00	21.55	11.10	5.55	12.92	11.99
MVC-8	-3.31	0.00	15.78	-2.47	11.86	10.10	30.16	0.00	18.70	10.78	6.97	13.01	12.62
MVC-9	0.00	3.62	17.11	0.00	11.87	9.80	30.90	0.00	20.57	11.89	6.94	13.21	13.32
MVC-10	0.00	0.00	17.1	0.00	11.71	9.18	30.13	0.00	19.45	10.90	6.46	12.98	12.29
MVC-11	0.00	0.00	17.31	0.00	11.63	10.04	30.29	0.00	18.77	10.30	6.88	13.09	12.63
MVC-12	0.00	0.00	17.02	0.00	11.82	9.59	29.92	0.00	18.04	10.10	8.00	12.92	12.49
MVC-13	0.00	0.00	16.81	0.00	11.94	11.30	30.23	0.00	18.72	10.09	7.81	13.10	13.72
MVC-14	0.00	0.00	16.92	0.00	12.76	9.91	30.15	0.00	18.32	0.00	9.05	13.15	12.51
MVC-15 MVC-16	0.00 0.00	0.00 0.00	16.92 16.93	0.00 0.00	12.76 11.72	10.41 10.14	30.00 30.00	0.00 0.00	18.13 18.37	10.91 0.00	7.20 8.15	13.00 12.86	12.99 12.93
MVC-17	0.00	0.00	17.06	0.00	11.80	10.99	30.20	0.00	19.33	11.13	7.96	13.02	13.79
MVC-18	0.00	0.00	16.82	0.00	12.01	8.75	30.17	0.00	18.68	9.54	6.66	13.12	11.83
MVC-19	0.00	0.00	17.05	0.00	13.15	8.75	29.99	0.00	18.58	10.63	6.50	12.77	12.20
MVC-20	0.00	0.00	16.63	0.00	11.79	8.91	29.82	0.00	18.06	0.00	8.11	12.81	12.02
MVC-21	0.00	0.00	17.07	0.00	12.01	9.71	30.21	0.00	19.54	11.20	6.69	13.05	12.62
MVC-22	0.00	0.00	16.94	0.00	11.58	8.15	29.81	0.00	18.13	9.79	6.74	12.62	11.84
MVC-23	0.00	2.80	16.74	0.00	12.32	10.44	30.34	0.00	18.60	11.10	9.32	13.27	12.78
MVC-24	0.00	0.00	16.91	0.00	11.44	9.13	30.28	0.00	19.19	10.93	7.33	12.85	13.09
MVC-25 MVC-26 MVC-27	-2.92 0.00 0.00	3.17 0.00 0.00	15.18 16.87 17.04	-1.19 0.00 0.00	11.53 12.08 12.00	10.66 10.33 10.00	30.84 29.99 29.88	0.00 0.00 0.00	18.48 18.26 17.89	11.02 9.29 0.00	7.18 8.29 6.31	12.92 13.01 13.34	13.25 12.80 11.39
MVC-28	0.00	0.00	17.05	0.00	11.66	9.54	30.00	0.00	18.52	10.31	6.63	12.98	12.74
MVC-29	0.00	0.00	17.02	0.00	12.12	9.86	30.24	0.00	18.62	10.62	6.59	13.13	12.48
MVC-30	0.00	0.00	16.98	0.00	11.64	9.64	29.81	0.00	18.46	9.44	7.23	12.85	12.10
MVC-31	0.00	0.00	17.15	0.00	11.54	9.39	30.19	0.00	18.46	10.39	6.68	12.97	11.98
MVC-32	0.00	0.00	17.09	0.00	11.56	10.78	30.49	0.00	19.89	11.26	8.01	13.23	12.99
MVC-33	-2.61	4.21	14.67	-0.72	11.35	12.39	30.55	0.00	19.36	0.00	10.56	12.95	14.91
MVC-34	0.00	0.00	17.31	0.00	11.20	9.24	30.58	0.00	19.82	10.75	6.60	13.20	12.30
MVC-35	0.00	0.00	17.13	0.00	11.51	8.71	29.91	0.00	18.83	9.51	6.47	12.63	11.72
MVC-36	0.00	0.00	17.29	0.00	11.29	9.46	30.21	0.00	18.93	10.39	6.62	13.07	12.46
MVC-37	0.00	0.00	17.05	0.00	11.25	9.45	29.93	0.00	18.99	0.00	7.50	12.62	12.24
MVC-38	0.00	0.00	17.29	0.00	11.62	10.53	30.34	0.00	18.47	10.58	7.86	12.96	12.56
MVC-39	0.00	0.00	16.98	0.00	11.47	11.57	30.60	0.00	18.63	10.7	8.49	12.72	14.03
MVC-40	0.00	0.00	17.20	0.00	10.57	9.38	30.33	0.00	18.77	9.74	5.80	13.06	11.42
MVC-41	0.00	0.00	17.00	0.00	10.93	9.49	30.07	0.00	18.77	0.00	6.38	12.65	12.03
MVC-42	0.00	0.00	17.22	0.00	11.08	10.05	30.84	0.00	19.51	10.95	5.78	13.46	12.43
MVC-43	0.00	0.00	17.35	0.00	11.39	9.78	30.67	0.00	19.55	10.89	5.64	13.22	12.04
MVC-44	0.00	0.00	17.32	0.00	10.98	8.72	30.59	0.00	19.04	10.13	6.74	13.49	11.32
MVC-45	0.00	1.41	17.25	0.00	11.10	9.10	30.90	0.00	20.14	11.66	7.21	13.63	12.22
MVC-46	0.00	0.00	17.46	0.00	10.65	8.60	29.63	0.00	18.51	0.00	6.71	12.16	11.26
MVC-47	0.00	0.00	17.14	0.00	11.16	9.85	31.13	0.00	18.77	12.32	6.89	13.74	11.80

4.5 Principal Component Analysis (PCA)/Factor Analysis (FA)

Correlation among the heavy metals in the sediments and the results of PCA are provided in Tables 8, 9 and 10. PCA was adopted to assist in the interpretation of elemental data. This powerful method allows identifying the different groups of metals that correlate and thus can be considered as having a similar behavior and common origin [41]. The number of significant principal components is selected on the basis of Kaiser criterion with eigenvalue higher than 1 [17].

Four factors were discriminated based on the Kaiser criterion and explain 76.61% of the total variance in the study area (Table 7). The first factor has high loadings for Fe, Ni and V. PC1 may be associated with the earth’s crust and the geological formation of the study area [30]. These variables were demonstrated to be correlated (Table 8). PC2 was negatively loaded with Ba and positively loaded with Cu, Pb and Zn that explained 22.20% of the total variance. The negative loading for Ba may suggest a different origin and its loading in the sediment samples in the study area was influenced by different factors compared to Cu, Pb and Zn. From Table 8, it could be observed that Cu, Pb and Zn were correlated. This component appears to be from anthropogenic sources such as industrial wastes and vehicular pollution. Barium is reported to be associated with many industries like manufacture of alloys, paints, soaps, paper, rubber, glass, ceramics, television picture tubes, brick and tile refractories. Barium is also present in fuel additives and in fillers [32, 42]. According to Cheng & Hu [43] as cited by Giri et al., [30], Pb is known to be introduced into the environment by anthropogenic activities like industrial uses and inadequate or inappropriate municipal water treatment. Lead is also used for batteries, fuel tanks, solder, seals, bearings, and wheel weights according to Sander et al., [44] as cited by Giri et al., [30]. Zinc is known to be used in galvanizing on iron products, in alloys, rubber, glazes, enamels, paper and glass [45]. Zinc, typically as zinc oxide (ZnO) particles, is also added to tire rubber to aid in the vulcanization process [46, 47]. Copper on the other hand is used in electrical wiring, wiring in cars roofing, plumbing, and industrial machinery [48].

The third component which is PC3 represents 12.55% of the total variance with high loadings on pH, TDS and conductivity. Moderately high positive correlation can be observed between pH, EC and TDS and these variables were demonstrated to be correlated (Table 8). PC3 thus can be interpreted as water mineralization of the creeks, physical, chemical and biological activities [49, 50]. PC4 explains 11.02% of the total variance and has high loadings for Cr and Mn. PC4 represents domestic effluent discharges which may contain high levels of Cr and Mn that contaminate aquatic environments.

Table 8

Pearson correlation matrix between different metals in creek sediment in Metro Vancouver (n = 47) collected in 2023. The bold values indicate a statistically significant correlation
	Ba	Cr	Cu	Fe	Mn	Ni	Pb	V	Zn	pH	TDS	Con
Ba	1.000
Cr	− .179	1.000
Cu	− .255	− .052	1.000
Fe	.079	− .392	.178	1.000
Mn	.181	.277	− .210	.226	1.000
Ni	.067	.005	.091	.722	.375	1.000
Pb	− .232	.096	.571	− .036	− .212	.060	1.000
V	.275	− .316	− .041	.765	.161	.621	− .002	1.000
Zn	− .276	.062	.870	.120	− .036	.171	.487	− .176	1.000
pH	.083	.085	.037	− .352	− .070	− .236	− .118	− .416	.144	1.000
TDS	− .146	− .015	.430	− .134	− .287	− .219	.247	− .392	.397	.460	1.000
Con	− .170	− .031	.462	− .120	− .262	− .197	.279	− .375	.409	.450	.975	1.000

Table 9

Extraction values of the total variance using PCA for 12 parameters between different metals in creek sediment and physicochemical parameters in surface water in Metro Vancouver
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.699	30.826	30.826	3.699	30.826	30.826
2	2.665	22.208	53.034	2.665	22.208	53.034
3	1.506	12.552	65.586	1.506	12.552	65.586
4	1.323	11.026	76.612	1.323	11.026	76.612

Table 10

**Loadings of 12 experimental variables on four significant principal components in creek sediment and physicochemical parameters in surface water in Metro Vancouver**
	Component
	1	2	3	4
Ba	.294	− .601	.140	− .034
Cr	− .371	.177	− .067	.781
Cu	.200	.815	.345	− .102
Fe	.925	.058	− .091	− .112
Mn	.348	− .238	− .066	.766
Ni	.820	.109	− .121	.315
Pb	.003	.775	.017	− .080
V	.816	− .113	− .348	− .162
Zn	.172	.772	.387	.138
pH	− .262	− .215	.746	.143
TDS	− .138	.279	.864	− .165
Con	− .115	.309	.858	− .160
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. The bold values indicate a statistically significant correlation.

4.6 Cluster Analysis

Cluster analysis grouped variables into clusters on the basis of similarities or dissimilarities such that each cluster represents a specific process in the system. Unlike PCA that normally uses only two or three PCs for display purposes, cluster analysis uses all the variance or information contained in the original data set. In this study, the hierarchical cluster analysis (HCA) was applied to the raw data. Two distinct clusters are visible from the results of the HCA (Fig. 4). These groups, respectively, represent the effects of two main factors in the study area.: anthropogenic and natural factors. Group 1 comprises Pb, pH, Cu, Ni, Cr, V, Zn and TDS which represents anthropogenic activities affecting the sediment quality in the study area. Sediment particles can carry toxic industrial and domestic effluents in the form of compounds and may have negative impact on aquatic organisms. Cluster 2 comprises EC, Ba, Mn and Fe and reflects the natural weathering of rocks enriching the study area with these elements.

The purpose of this work was to study how urban areas under different land uses influence heavy metals concentrations in select creeks in the Metro Vancouver area. This evaluation was accomplished by determining the concentrations of heavy metals deposited in sediments and then applying various pollution indices. The study sought to examine the associations between land uses and heavy metal concentration, to distinguish heavy metal concentrations resulting from anthropogenic activities different from background surface rock accumulation and to suggest the implications for the sustainable management of the Metro Vancouver area. All the identified land uses generated varying proportions of heavy metals thus confirming the influence of anthropogenic factors. The concentration of heavy metals, enrichment factor, contamination factor, pollution load index and geo-accumulation index differed among land uses. Most of the land uses reported contaminated to minimal contaminated indices and long-term accumulation could result in significant impacts in sediment quality, which is of concern in the study area. Creeks close to highways with heavy vehicular activities reported significantly higher concentrations of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in sediments. Industrial urban land use especially the large number of auto-repair shops and poor waste disposal practices by shop owners in the industrial area are believed to be the greatest contributors of Cr, Mn, Ni, Zn in the study area. Residential areas equally recorded elevated contamination of some heavy metals.

The findings from the research suggest there was an uneven distribution of heavy metal concentrations and the sediment samples from creeks close to highways were extremely higher. The calculated contamination factors are found to fall in the following sequences: Cd > Ag > As > Pb > Zn > Mn > Cu > Ba > Cr > Ni > V > Fe > Hg in the study area. The geo-accumulation index showed that the study area contained extremely contaminated sediment and > 64-fold increase due to high levels of Ba, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn. In terms of the physicochemical parameters (TDS, EC) in selected creeks in Metro Vancouver area, land use did not have an impact on water quality. The pH of some sampled sediments were however lower and correlated with sites with high level of heavy metals.

The data generated may provide useful information for pollution control boards and urban environmental planning agencies for further designing corrective strategies to minimize the adverse impact of heavy metals to sediment quality in the creeks from domestic and industrial effluent.

Author Contribution

EKG was involved in the study’s conceptualization and design, sampling, sample analysis, data acquisition, and manuscript drafting. MD contributed to the study’s conception and design, sampling, sample analysis, and manuscript drafting. MVD contributed to the study’s conception and design, and manuscript drafting. GD contributed to the study’s conception and design, sample analysis, and manuscript drafting.

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No competing interests reported.

Appendix.docx

Source and Distribution of Heavy Metals in Sediment Samples from Select Creeks of the Highly Urbanized Metro Vancouver Watersheds, Canada

Status:

Version 1

Abstract

Figures

1 Introduction

2 Study Area

3 Methodology