Journal of SustainabilityE.K. Quagraine, Volume / Journal 8, Issue of 1, MarchWater 2018,Sustainability 1-24 1 (2018) 1-24 1

© University of Technology Sydney & Xi’an University of Architecture and Technology

Two Decades Experience in Treating Municipal Effluent for Power Plant Cooling at the Shand , SaskPower Part V: The Effect of Seasonal Changes in , Rainfall and Influent Concentration on Removal

Emmanuel K. Quagraine Saskatchewan Power Corporation, Shand Power Station, Estevan, Saskatchewan, S4A 2K9, Canada

ABSTRACT The paper is part of publication series on 2-decade constructed wetland (CW) operation at SaskPower’s Shand Power 3- Station. It highlights influence of some climatological factors and PO4 -P load on its removal efficiency. Influent 3- PO4 -P was influenced by temperature and rainfall in concentration-dependent manner. The respective effects were 3- o estimated as ~3.3% PO4 -P reduction of the (at 0 C) background concentrations per degree rise and 0.7% NO3-N 3- reduction of (at zero rainfall) background concentrations per mm depth of rainfall. Rainfall effect in reducing PO4 - P is attributed to dilution and was typically noticed after one-month lag period. Its immediate impact was usually 3- adverse leading to ~2.3% increase of the WWTP output PO4 -P (at zero rainfall) and attributed to releases from 3- sediment perturbation. Seasonal variation of influent PO4 -P load subsequently affected effectiveness in its removal 3- by the CW. Regression analysis was used to estimate influent PO4 -P, temperature and rainfall effects on monthly 3- removal efficiency of PO4 -P. Temperature was the most consistent and statistically significant influencing factor (at 3- least, at 80% confidence level) across the years, causing release of PO4 -P. Its magnitude of effect was shown in 3- either of two main ways: primarily as 3.4 ± 0.9% or otherwise as 10.9 ± 0.3% PO4 -P release (over the inlet concen- tration) per degree rise in temperature. Rainfall effect was erratic both in direction (positive or negative) and in magnitude (extent) of influence. The influent concentration effect was consistent in direction (net removal) but variable in magnitude suggesting co-dependence on other variables. On the whole, except the first spring season 3- where effluent PO4 -P of only 0.09 mg/L was displayed with ~99% removal, the CW was incapable of producing 3- effluent PO4 -P of the ≤0.33 mg/L required to prevent Ca3(PO4)2 scale formation during condenser cooling process.

Keywords: Wetlands; ; temperature; rainfall; phosphate; cooling; scaling; regression

1. INTRODUCTION (MWW) (Cooper, 2012; USEPA, 2004; Veil, 2007; Vidic et al., 2009). However, the reuse of Steam electric power plants require vast treated MWW (usually to secondary standard) amount of water for cooling and hence is for industrial cooling raises three main currently one of the major beneficial sectors operational concerns: scaling, corrosion and taking advantage to reuse the abundant bio- (Barcelo and Petrovic, 2011; resource of treated municipal wastewater Puckorious, 2015; Rebhun and Engel, 1988;

*Corresponding to: [email protected] DOI: 10.11912/jws.2018.8.1.1-24 2 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

Selby et al., 1996; Veil, 2007; Vidic et al., influent concentration on performance of the 2009). CW, but the focus was specifically on total - The relatively higher nutrients (nitrogen (N) ammonia-nitrogen (TAN) and nitrate (NO3 )- and phosphorous (P)) and organic matter con- nitrogen (N). In this present paper, we continue tents in MWW sources as compared to fresh the discussion on seasonal influence on the CW water sources such as surface and groundwater performance, but this time with phosphate 3- is one of the major reasons for these conse- (PO4 )-P as the specific nutrient focus. The 3- quence in industrial cooling applications. Con- paper also discusses seasonal PO4 -P varia- structed wetlands (CWs) have demonstrated tions in the effluent quality and the potential potential to further reduce these constituents in impact on reuse applications in thermoelectric treated MWW of various standards (Greenway, power plant condenser cooling. As earlier 2005; Kadlec and Wallace, 2009; Quagraine, discussed (Quagraine and Duncan, 2017), a 2017; Vymazal, 2010), and there is current good understanding of seasonal variations of interest to take advantage of various other nutrient levels in the CW effluent used for benefits inherent in CW technologies (includ- power plant cooling application is so critical in ing cooling, water harvesting, electricity making necessary adjustments in power plants’ harvesting, etc.) to further process municipal operation to minimize the potential risks wastewater (MWW) for power plant cooling associated with seasonal effluent quality. Why 3- (Apfelbaum et al., 2013; Bengston, 2010; Duke should we be concerned with PO4 ? First, as Energy, 2012; Quagraine, 2017). outlined in the third of the series (Quagraine et 3- al., 2017b), PO4 predominates the P fractions SaskPower’s Shand Power Station (SHPS) in Saskatchewan, Canada seems to be a pioneer in the MWW going into the SaskPower CW; in employing a CW on a commercial scale to constituting an annual average of 90% TP with 3- polish secondary treated MWW effluent for standard deviation of only 6.6%. PO4 occurs condenser cooling since 1994. Experience in relatively high but variable levels in treated gained over 2-decades is expected to help in MWW effluents (e.g. 0.6-51.0 mg/L in bridging knowledge gaps towards current and secondary treated MWW (SMWW) effluents future efforts in using CW technology as key from different USA locations (Vidic et al., component to address challenges around the 2009)) and is a critical constituent in dictating water-energy nexus. The present manuscript is scale formation and bio-fouling tendencies in the fifth in series of publications in sharing industrial cooling applications; whilst in con- such experience. The first reviewed the trast inhibiting corrosion due to the protective rationale to consider CW as polishing unit for layer of scales it forms on the metal surfaces. 3- power plant cooling and outlined several PO 4 content is indeed of concern in cooling benefits inherent in such reuse application water systems; it can form the tenacious nature (Quagraine, 2017). The second (Quagraine et of phosphate (Ca3(PO4)2), and its al., 2017a) and third (Quagraine et al., 2017b) presence has the potential to nucleate or “seed” focused on the annual performances of the other mineral scales. For this reason, even SaskPower CW in removing various when polyphosphates (condensed ) are added for corrosion protection in make-up contaminants for condenser cooling purpose. 3- Recognizing differences in annual and seasonal pipe-lines, total PO4 concentration in a cool- treatment performance data, the fourth paper ing tower (CT) make-up water from potable (Quagraine and Duncan, 2017) was dedicated water systems is advised to be kept below 0.5 to the effect of seasonal changes in parameters mg/L (Tierney, 2002); as also recommended by (Schimmoller, 2012) in for such as rainfall, temperature, plant growth and E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 3

CT-make up. Others however offer less strict limiting for cyanobacteria and algae growth, recommendations. For example, McNicholas not just in the environment but also in cooling 3- 3- (2002) recommends a maximum total PO4 of systems. Higher PO4 levels may result in 1 mg/L in CT make-up water. Odell (2015) excessive algae growth on CT fill material suggests (Ca3(PO4)2) scale formation to occur surfaces and other components within cooling in power plant cooling systems with reclaimed systems resulting in flow restrictions, high 3- make-up water of P ≥0.6 mg/L (i.e. PO4 demand, and high potential in equivalent of 1.84 mg/L). Such differences are transfer surfaces (Post et al., however rational, considering the different 2014; Veil, 2007). TP maximum limit of 1 3- cycles of concentrations (COCs) various CTs mg/L (PO4 equivalent of 3.1 mg/L) in operate. (Ca3(PO4)2) is reported to likely form reclaimed water for environmental reuse 3- when PO4 concentration in CTs exceeds 10 applications has been stipulated by North mg/L (Harfst, 2015). In an earlier EPRI Carolina, a state in USA (USEPA, 2012); thus, 3- guideline (1982), a PO4 limit of 5 mg/L was a stricter guideline is anticipated for industrial recommended for power plant CTs. However, cooling purposes considering the COCs some latter reports suggest a much higher limit expected in CTs and the favourable conditions 3- of 50 mg/L PO4 for refinery CTs (Eble and for biological growth in such systems. Feathers, 1993; EPRI, 2012; EPRI and CEC, Various types and sizes of CWs have 3- 3- 2003). Even so, PO4 concentration not demonstrated capability to remove PO4 (or exceeding 8 mg/L in recirculating CW of a CT total P, TP) from treated MWW effluents of in a Refinery Plant is of more recent different grades (i.e. primary, secondary-both recommendation (IOCL, 2016). With expected conventional and lagoon/stabilizing ponds, and make-up calcium (Ca) of <40 mg/L as CaCO3 tertiary) (Quagraine, 2017). In the third of the and an operating CT Ca maximum of 1000 series, the annual performance of the 3- mg/L as CaCO3 (i.e. COC up to 25) for this SaskPower CW in removing PO4 -P was refinery plant, the CT make-up water is discussed (Quagraine et al., 2017b). However, 3- expected to contain PO4 ≤0.32 mg/L. From nutrient removal by CWs commonly follows this brief review of the literature, it is fair to seasonal patterns, which may not necessarily expect CT make-up water from various sources reflect annual patterns. Whilst on annual basis, (including MWW sources) to contain less than it is only the net removal to permanent storage 3- 3- 1 mg/L PO4 (or 0.33 mg/L PO4 -P) to avoid which is of concern, seasonal patterns of vege- (Ca3(PO4)2) scale deposition. In terms of tation growth and P storage involve complex 3- biofouling, PO4 (again) is the preferred P pattern of biomass allocation and stoichiometry source of uptake by both algae and bacteria among plant parts-both living and dead 3- (Cotner and Wetzel, 1992). Initial PO4 uptake (Kadlec and Wallace, 2009). Equilibrium exists is reported to be by bacteria especially at low to regulate the storage and release of P; concentrations, but their uptake becomes more however, climatological factors (e.g. tempera- readily saturated (Cotner and Wetzel, 1992; ture and rainfall) and influent variability Paerl and Lean, 1976). Therefore in high prevent a stationary (or rather create a dy- 3- concentrations, PO4 is more taken by namic) equilibrium (Kadlec, 1989). Therefore, 3- phytoplankton than bacterioplankton. PO4 -P during the course of a year, uptake and return levels when greater than 0.1 mg/L can stimu- may occur at different times, thus influencing late plant growth above natural rates and may removal differently in different seasons result in excessive algal growth (Johnson et al., (Kadlec and Wallace, 2009). Plant uptake can 3- 2007). In fact, P (likely as PO4 ) is generally be a very large part of seasonal P removal or 4 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 release; where plant storage follows typically marsh. The designed feed flow is 86.8 L/s at the pattern of a growing season (spring and water depth of 0.2-0.3 m and hydraulic early summer) increase to a maximum, retention time of 5-9 days. The site (49.12o N, followed by a senescence-season decrease to a 103.04o W) is located in a temperate region minimum, with the cycle repeating each year where monthly temperature and rainfall (Kadlec and Wallace, 2009). Such seasonal averages from April to November are 5.0, 12.1, variations ultimately impact the effluent 16.8, 19.5, 18.6, 12.4, 5.6 and -4.3oC, and 17.2, quality exiting the CW and subsequently the 52.1, 76.2, 65.0, 49.5, 43.0, 18.6 and 3.2 mm, condenser cooling process they serve as respectively (El Dorado Weather, 2016). make-up; but to what extent? Identification and The feed to the CW comes from storage quantification of the main seasonal factors of ponds of treated MWW from the City of Es- 3- influence on PO4 removal or release by the tevan. Pumping of the treated MWW effluent CW are therefore crucial in wastewater reuse through the CW typically begins in early May application. The present paper therefore has 3 and cease in late October, although various main objectives: (a) to assess seasonality of logistical factors sometimes allow inclusion of 3- inlet PO4 -P into the CW and determine the April and November. Thus, outside these CW individual contributions of temperature and operating periods (i.e. for the cold storage rainfall on such seasonality (i.e. assess influ- months from November to April of average 3- ence of these parameters on PO4 -P removal ambient air temperature of 7.4 ± 7.4oC), the due to the pre-storage); (b) to assess seasonality city’s treated MWW is fully stored in the 3- of the removal efficiency of PO4 -P and ponds). At the start of the CW operation in determine the dependence on the load input, 1994, the MWW effluent from the city’s temperature, rainfall and other responsible plant (WWTP) was of a factors in the annual cycles of processing (FL) quality, which was (removing) this substrate; and (c) establish the upgraded to a conventional secondary (CS) seasonal performance and effluent quality of standard in early 1996. A flood incident oc- 3- PO4 -P and how that relate to the influent qual- curred in the area in the spring of 2011 which ity and vegetative conditions. prevented the CW operation and subsequently no supply from this source to SHPS for the remainder of 2011 operational year as well as 2. MATERIALS AND METHODS the full 2012 operational year. The CW operation was resumed later in 2013 and 2.1 CW site, design and operation continues to date, although this report extends In the earlier papers (Quagraine, 2017; only to the end of 2014 CW operating period. Quagraine and Duncan, 2017; Quagraine et al., 2017a, b), details of the CW features were 2.2 Sampling and testing outlined. For brevity, the reader is referred to Typically, sampling for CW influent and efflu- these articles for further details. Yet, some key ent quality was done once or twice in a week. features of the CW system are outlined here. It The samples were then sent to an accredited is a free-water surface (FWS) CW of total third party laboratory for analyses of various surface area 235,000 m2 consisting of two parameters as discussed previously (Quagraine major identical cells with each divided into et al., 2017a, b). Despite using different three sub-cells and of vegetation consisting laboratories in the course of the study period, mostly of planted bulrush and cattails and 3- reported PO4 -P was generally measured as emergence of a variety of natural sedges and E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 5

3- PO4 by colorimetric means either by the independent variables, namely inlet concentra- ascorbic /molybdo blue or molybdovana- tion (x1), temperature (x2), and rainfall (x3) to 3- date-phosphoric acid/yellow method. It is the percentage PO4 -P removal (y) for the 3- noteworthy however that reported PO4 -P by various months in any particular year or as either method, commonly called “orthophos- monthly averages for the entire study period or phate phosphorous” or “reactive phospho- within any period of years was calculated using rous”, is actually a composite measure of four MLR approach (i.e. the regression tool of inorganic monomeric phosphate species of Excel®) to optimize the “fit” of acquired n- 3- general formula (H(3-n)PO4 ; n = 0-3), which (observed) percentage PO4 -P removal to a are interchangeable simply by pH variations. calculated model (Eq. 1):

Furthermore, polyphosphates and phospho- Y = k1x1 + k2x2 + k3x3 + C* (1) nates may be hydrolyzed to some extent by where k1, k2, and k3 are the regression either analytical preparative process and coefficients whose product with the respective thereby be partially measured by these methods independent variables: i.e. k1x1, k2x2, and k3x3 too. represent their individual contribution to the 3- 3- A total of 1,739 individual PO4 -P data overall PO4 -P percentage removal (y). C* is (including ~670 inflow/outflow storage pond another constant representing the remaining 3- pairs of samples and 336 samples taken directly contributing sources to PO4 -P removal; thus, from the outlets of the six individual sub-cells) of more convoluted origin. Previously in the span of three seasons (spring to fall) per (Quagraine and Duncan, 2017), we had used year were collected within the twenty year least squares approach for the analysis of the - period of investigation and analyzed. These TAN or NO3 -N data in the investigation, which individual data were further analyzed as is more laborious as compared to the MLR weekly, monthly, or seasonal averages within approach used here. individual years for the study period. For brev- Due to the numerous potential factors of ity, details on the performance of the individual 3- influence on PO4 -P removal or release within sub-cells are omitted in this manuscript. the CW (most of which are not accounted for in the MLR model) and the general complexity 2.3 Statistical and mathematical analysis of interactions between the factors (some with of data overlapping effects), a less stringent statistical 2.3.1 Correlation analysis significant p value criteria than the commonly used 0.05 to determine statistical significance Correlation analyses were conducted with the of the model predictions was adopted. Further, 3- removal efficiency of PO4 -P as the dependent the reader should note that estimated p values and season-related parameters such as temper- are themselves influenced by various factors: ature, rainfall and influent concentration as in- e.g. sample size (N), effect size (ki) and spread dependent parameters as previously described of data (measurable by standard deviation or (Quagraine and Duncan, 2017). coefficient of variation) (Dahiru, 2008). With the approach adopted here as previously done - 2.3.2 Multi-linear regression (MLR) model- for TAN and NO3 -N (Quagraine and Duncan, 3- ing of seasonal influences on PO4 -P 2017) to determine best model fits in monthly 3- removal PO4 -P removal for individual years or period The individual contributions of three main of years, N was commonly small and hence larger p values were generally expected. The smaller the sample size, the more likely a 6 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

3- difference may go undetected (i.e. the higher As shown in Fig. 1, the inlet PO4 -P the p estimate) (Dahiru, 2008). concentration to the CW generally followed Based on initial assessments, only model fits seasonal/ monthly cycles. Highest concentra- with an arbitrary p value <0.2 for significance tions occurred typically in June, followed by of F in the analysis of variance (ANOVA) for declining trend to minimum concentrations in an overall MLR model and for at least two August, which was then followed by reversal independent variables, and involving at least 5 in trend to higher concentrations in September monthly data sets per year or period (i.e. N ≥5) or later in October. This trend, especially for were deemed acceptable; outside this criterion, the first 5 months of the CW operation (i.e. the data fit was rejected. A p value of <0.2 for May-September) is quite similar to the trend - a MLR model here thus connotes a statistically for TAN and NO3 -N (Quagraine and Duncan, significant relation between the dependent 2017). With respect to temperature, highest variable and the various independent variables concentrations of the substrates seemed to and suggests at least 80% chance that there is a occur during the lower temperature operating true relationship between the variables in the seasons and the lowest during the warmer population. summer months. These reflect the temperature- Following the estimation of the regression dependent performance expected of biological and biochemical processes associated with the coefficients (k1, k2, and k3) from the MLR analysis as described above, their annual or pre-storage pond system. Higher temperature periodic variability were evaluated to increases specific growth rate of bacteria (McMahan, 2006). Yet, the generally lower determine if they were indeed constants or 3- - depended on other variables. The coefficient PO4 -P concentration (as also for NO3 -N (Quagraine and Duncan, 2017) observed in k1, representing the principal effect associated 3- May compared to June and July does suggest with inlet PO4 -P concentration dependent 3- other factors, apart from temperature, to also removal of PO4 -P, was especially found to be play some role in the cyclical pattern of these very variable. The annual k1 values determined from the statistically significant regression substrates in the influent to the CW. The only 3- models were matched with corresponding unique year of higher May than June PO4 -P 3- - concentration was 1995 when the city’s WWTP annual average data sets of inlet PO4 -P, NO3 -N, BOD and pH. Simple linear regression was operating FL treatment. (correlation) analyses (Section 2.3.1) were then In fact, despite the general seasonal trend, performed for k1 and these individual notable differences were observed for some parameters to assess their significance as years - especially during spring. For instance, predictor variables of k1 based primarily on the the typical higher initial concentrations in June r or R2 and the p values. From these initial seem to delay in some cases to July (e.g. 1997, assessments k1 was found to be dependent on 2008 and 2009 (not shown)). So, apart from 3- - PO4 -P, BOD and pH but not NO3 -N. See variations in temperature (Section 3.1.1), other Section 3.2.1.3 for details. factors may also account for the apparent distinctions in cyclic pattern variations

amongst the various years. Rainfall was earlier 3. RESULTS AND DISCUSSION implicated is one of such factors exhibiting - 3- influence on both the inlet TAN and NO3 -N 3.1 Seasonality in the influent PO4 -P concentration (Quagraine and Duncan, 2017), concentration to the CW 3- and its influence on inlet PO4 -P is also

evaluated in Section 3.1.2. E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 7

3- PO4 -P concentration dependence, i.e. m/C in 3.1.1 Temperature effect Table 1, was fairly constant (i.e. 0.033 ± 0.007 3- mg/L PO4 -P reduction per degree rise in tem- 3- As mostly shown by moderate to strong inverse perature per background PO4 -P concentration 2 correlations (R ) between the two parameters (i.e. at T = 0oC)). This was irrespective of 3- (Table 1), the CW influent PO4 -P concentra- whether the temperature effect was immediate 3- tion was generally influenced by temperature. or lagged and translates to ~3.3% PO4 -P - Similar to TAN and NO3 -N, despite apparent reduction of the (at 0 oC) background concen- year-to-year variations in the temperature- trations per degree rise in temperature. In fact, dependent relation with inlet concentration but for 1997 and 2008 which deviated (depicted by m in Table 1), such variations did completely from the norm, this magnitude of correspond also to variations in the substrate temperature influence showed in most cases load (shown by m/C in the Table). both immediately and in a month later or in rare With some minor exceptions, the magnitude cases, only as immediate effect (2014) or only of temperature effect, when accounting for the in a month later (2007 and 2009).

3- Figure 1 Inlet mean monthly total PO4 -P concentration to the SaskPower CW

8 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

Table 1 Results from linear regression analysis (m = slope; c = intercept; R2 = coefficient of 3- determination as expression of correlation strength) for inlet PO4 -P (dependent parameter) versus temperature (independent parameter). N is the total number of months used per year or period in the analysis; monthly data for better fits are noted in parenthesis Period Response m C m/C R2 Comments 1995 Immediate -0.326 10.200 -0.032 0.60 N = 5 (May-Sep., but not Oct.) 1-month lag -0.140 7.677 -0.018 0.70 N =4 (May-Sep., but not June & Oct.) 1996 Immediate -0.527 13.981 -0.038 0.97 N = 3 (June-Aug., but not Sep. & Oct.) 1-month lag -0.176 6.694 -0.026 0.56 N = 4 (June- Sep., but not Oct.) 1997 Immediate 0.059 1.363 0.043 0.36 N =7 (May-Nov.) 1-month lag 0.047 1.466 0.032 0.15 N = 7 (May-Nov.) 1998 Immediate -0.116 3.630 -0.032 0.46 N = 5 (May- Sep., but not Oct. & Nov.) 1-month lag -0.058 2.483 -0.023 0.19 N = 6 (May-Oct.; Nov excluded) 1999 Immediate -0.087 2.502 -0.035 0.90 N = 5 (May-Oct., but not June) 1-month lag -0.149 3.435 -0.043 0.90 N = 4 (June-Oct., but not May and Sep.) 2000 Immediate -0.095 3.307 -0.029 0.23 N = 4 (June-Sep.; Oct. excluded) 1-month lag -0.133 3.801 -0.035 0.65 N = 5 (June-Oct.) 2001 Immediate -0.080 2.400 -0.033 0.25 N = 5 (May-Sep.; Oct. & Nov. excluded) 1-month lag -0.055 1.926 -0.029 0.37 N = 5 (May-Sep.; Oct. & Nov. excluded) 2002 Immediate -0.080 2.556 -0.031 0.99 N = 3 (June, July, Oct. & Nov. excluded) 1-month lag -0.098 3.306 -0.030 0.37 N = 5 (June-Oct., May & Nov. excluded) 2003 Immediate -0.162 4.070 -0.040 0.78 N = 5 (May-Sep.; Oct. excluded) 1-month lag -0.109 2.718 -0.040 0.70 N = 5 (May-Oct.; Sep. excluded) 2004 Immediate -0.086 2.631 -0.033 0.69 N = 4 (May-Aug.) 1-month lag -0.074 2.296 -0.032 0.97 N = 4 (May-Aug.) 2005 Immediate -0.084 2.546 -0.033 0.64 N = 3 (July-Sep.; June, Oct. & Nov. excluded) 1-month lag -0.117 3.175 -0.037 0.69 N = 5 (June-Oct.; Nov. excluded) 2006 Immediate -0.206 5.166 -0.040 0.99 N = 3 (June-Aug.; May, Sep. & Oct. excluded) 1-month lag -0.028 1.648 -0.017 0.23 N = 5 (May-Sep.; Oct. excluded) 2007 Immediate 0.092 -0.087 -1.057 0.38 N = 6 (May-Oct.) 1-month lag -0.163 4.196 -0.039 0.55 N = 4 (June-Sep.; May & Oct. excluded) 2008 Immediate 0.068 0.687 0.099 0.65 N =7 (May-Nov.) 1-month lag 0.070 0.605 0.116 0.42 N =7 (May-Nov.) 2009 Immediate 0.078 0.987 0.079 0.29 N = 7(May-Nov.) 1-month lag -0.280 6.279 -0.045 0.58 N = 5 (June-Oct.; May & Nov. excluded) 2010 Immediate -0.187 4.720 -0.040 0.46 N = 4 (May-Aug.; Sep. & Oct. excluded) 1-month lag -0.169 3.794 -0.045 0.72 N = 6 (May-Oct.) 2014 Immediate -0.151 5.610 -0.027 0.41 N = 4 (June-Sep.; Oct. & Nov. excluded) 1-month lag 0.112 1.170 0.095 0.83 N = 5 (June-Nov.; no data for Sep.)

E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 9

3- Comparing the results here on PO4 -P to that in the table; this was irrespective of whether the - earlier on TAN and NO3 -N (Quagraine and influence was immediate or delayed by a Duncan, 2017), it can be concluded that the month. On average, the magnitude of rainfall 3- magnitude of temperature effect on removing dilution effect when accounting for the PO4 -P various substrates in pre-stored WWTP concentration dependence (i.e. m/C in Table 2) 3- effluent to the CW are fairly similar; ~3.3% was fairly constant (0.007 ± 0.002 mg/L PO4 - 3- - PO4 -P, 4.1% NO3 -N, and 4.6% TAN reduc- P per mm depth of rainfall per at no rain 3- tion of the background substrate concentration WWTP PO4 -P output (i.e. concentration at (i.e. at T = 0oC) per degree rise in temperature. rainfall = 0 mm)) irrespective of whether the 3- The lower temperature effect on PO4 -P rainfall effect was immediate or lagged by a reduction may possibly be due to counteractive month. This translates to approximately 0.7% 3- 3- effect from the relatively higher tendency also PO4 -P reduction of (at no rain) WWTP PO4 - 3- for PO4 -P release into and persistence in the P output per mm depth of rainfall. Once again water column from microbial decay of detritus (as in the case of temperature), the magnitude 3- materials high in P. in reduction of PO4 -P is relatively smaller - than for TAN and NO3 -N. We earlier attributed 3.1.2 Rainfall effect this difference possibly to counteractive effect 3- from a higher tendency of PO4 -P (over TAN Rainfall can favorably or adversely affect pond - and NO3 -N) release into and persistence in the performance in several ways: Favorably, it can water column from temperature-induced do so by dilution, by influencing aerobic activ- microbial decay of detritus material, but with ities via increase in content and distribution of reference to rainfall influence, it is more likely , and by creating temperature inversion due to sediment perturbation. via pond turbulence (Rodrigues, 1993). Adversely, rainfall may pick up nutrients in the As shown in Table 2 by the many positive process and as it passes through soils (Nigai, m/C values, sediment perturbation (and/or 3- 2014). Larger rains can cause rapid mixing and P-rich runoff input) to cause PO4 -P release stronger disturbance when the runoffs enter the into water column was more likely the common stored water (Mangangka et al., 2016), and phenomenon on monthly basis; being may stir up nutrients buried in the sediment. displayed immediately with rainfall for all the - years except 1999 and sustaining such effect Unlike TAN and NO3 -N for which the influ- even a month later for some exclusive years ence of rainfall was expressed more commonly (1996, 1997, 2003, 2005, 2007, 2008 and in dilution (Quagraine and Duncan, 2017), 3- 2014). These results are consistent with a report rainfall effect on PO4 -P was not as obvious on a lagoon system where rain disturbance was (Table 2). Nonetheless, rainfall dilution effect 3- noted to induce PO4 -P increase, which did not seemed to have been displayed for about half return to pre-rainfall scenario 11 days after a the time of investigation (1995, 1998-2002, rainstorm (Mendoza-Salgado et al., 2005). It 2004, 2006, 2009 and 2010); albeit by a was suggested that sudden rainfall unsettles one-month lag. Immediate rainfall dilution 3- bottom in shallower part of the ponds effect on PO4 -P was displayed only in 1999. and causes an increase in desorption rate of P As in the case of temperature (vide supra) and from P-trapped biogenic sediment to the water to be expected, the results in Table 2 for these column. It takes a long time for this process to years suggest that the rainfall dilution effect 3- return to pre-rainfall conditions; to reach a new was dependent on background PO4 -P concen- equilibrium. tration (at 0 mm rainfall); i.e. on the C values 10 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

Table 2 Results from linear regression analysis (m = slope; C = intercept; R2 (coefficient of de- 3- termination) to express correlation strength) for inlet PO4 -P (dependent parameter) versus rainfall (independent parameter). N = number of months used per year or period in the analysis; monthly data for better fits are in parenthesis Period Response m C m/C R2 Comments 1995 Immediate 0.075 1.300 0.058 0.88 N = 5 (May-Oct., but not June) 1-month lag -0.072 8.788 -0.008 0.78 N =6 (May-Oct.) 1996 Immediate 0.040 1.296 0.031 0.76 N = 4 (June-Oct., but not Aug.) 1-month lag 0.033 1.633 0.020 0.41 N = 5 (June-Oct.) 1997 Immediate 0.026 1.300 0.020 0.87 N =6 (May-Nov., but not Aug.) 1-month lag 0.004 1.440 0.003 0.85 N = 5 (May-Nov., but not June & July) 1998 Immediate 0.011 1.230 0.009 0.68 N = 5 (May-Nov., but not Aug. & Oct.) 1-month lag -0.017 2.309 -0.007 0.38 N = 5 (May-Nov., but not July & Aug.) 1999 Immediate -0.007 2.050 -0.003 0.46 N = 5 (May-Oct., but not Aug.) 1-month lag -0.011 2.018 -0.006 0.65 N = 5 (May-Oct., but not June) 2000 Immediate 0.007 1.584 0.004 0.31 N = 4 (June-Oct.; Aug. excluded) 1-month lag -0.029 4.014 -0.007 0.38 N = 4 (June-Sep.; Oct. excluded) 2001 Immediate 0.016 0.650 0.025 0.93 N = 6 (May-Nov., but not July) 1-month lag -0.017 2.036 -0.008 0.70 N = 4 (May-Aug.; not Sep., Oct. & Nov.) 2002 Immediate 0.015 1.337 0.011 0.81 N = 6 (May-Nov., but not Aug.) 1-month lag -0.019 2.114 -0.009 0.28 N = 5 (May-Nov.; July & Sep. excluded) 2003 Immediate 0.004 1.309 0.003 0.04 N = 6 (May-Oct.) 1-month lag 0.005 1.280 0.004 0.06 N = 6 (May-Oct.) 2004 Immediate 0.009 0.834 0.010 0.27 N = 4 (May-Aug.) 1-month lag -0.006 1.837 -0.003 0.32 N = 4 (May-Aug.) 2005 Immediate 0.009 0.637 0.014 0.59 N = 5 (June-Nov., but not Sep.) 1-month lag 0.001 1.205 0.001 0.01 N = 6 (June-Nov.) 2006 Immediate 0.012 0.752 0.016 0.65 N = 6 (May-Oct.) 1-month lag -0.009 1.418 -0.006 0.26 N = 5 (June-Oct., but not May) 2007 Immediate 0.012 0.617 0.019 0.22 N = 6 (May-Oct.) 1-month lag 0.016 0.500 0.032 0.57 N = 6 (May-Oct.) 2008 Immediate 0.024 0.315 0.077 0.94 N =6 (May-Nov., but not Aug.) 1-month lag 0.010 0.928 0.011 0.27 N = 7 (May-Nov.) 2009 Immediate 0.036 1.211 0.030 0.77 N = 5 (May-Nov., but not Aug. & Oct.) 1-month lag -0.035 3.089 -0.011 0.74 N = 5 (May-Nov., but not July & Oct.) 2010 Immediate 0.019 0.526 0.035 0.79 N = 4 (May-Oct., but not Aug. & Sep.) 1-month lag -0.014 2.527 -0.006 0.76 N = 4 (May-Oct.; June & Aug. excluded) 2014 Immediate 0.006 2.263 0.003 0.37 N = 5 (June-Nov., but not Sep.) 1-month lag 0.026 1.638 0.016 0.88 N = 4 (June-Nov., but not July & Sep.)

3- On the average, the immediate rainfall effect per at no rain PO4 -P output. The 1-month lag 3- in increasing the CW influent PO4 -P was effect averaged about half this value (i.e. 0.012 3- 3- 0.023 ± 0.020 PO4 -P per mm depth of rainfall ± 0.011 PO4 -P per mm depth of rainfall per at E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 11

3- no rain PO4 -P output). In other words, ~2.3% hence can present challenges for modeling of 3- increase of the WWTP output PO4 -P (at zero simultaneous N and P-removal systems (Zuthi rainfall) is expected instantly per mm of et al., 2013). In fact, the influencing factors to 3- rainfall. Such adverse influence of rainfall after P (as TP and/or PO4 ) uptake are numerous. a month lag will only result in ~1.2% increase Kadlec (2016) noted three as more likely 3- of the original WWTP output PO4 -P (at zero candidates over the course of a year: a) water rainfall) per mm of rainfall. temperature; b) vegetation growth patterns; and c) patterns of water additions. Additional 3.2 Factors responsible for the seasonality factors include alkalinity, pH, carbon type, 3- in PO4 -P removal efficiency by the sludge quality, sludge settlement, dissolved CW oxygen (DO), COD content, load and COD/P ratio (Mulkerrins et al., 2004; Sani Unlike TAN, for which the CW removal et al., 2013; Zuthi et al., 2013). Apart from efficiency was more consistently dependent on 3- biological means, PO4 removal in CWs may influent concentrations in quadratic fashions be achieved also by physical and/or chemical with maximum vertex (Quagraine and Duncan, processes such as precipitation, adsorption and 2017), the relation between the removal chemisorption, which likewise may be 3- efficiency and the inlet PO4 -P was less impacted by some influencing factors in predictable (Fig. 2). As shown in the figure, biological processes such as pH and tempera- although quadratic relations were as well ture either synergistically or in an opposing displayed commonly; they showed at different manner (Silveira and O’Connor, 2013); thus stages as either of maximum vertex (e.g. 1999, 3- making mathematical modeling of PO4 even 2000, 2006, 2007 and 2008) or of minimum the more complicated. vertex (e.g. 1995, 1996 and 2004). In the The challenge therefore is with the selection previous article (Quagraine and Duncan, of the best subset of variables in ensuring 2017), we suggested that the displayed irrelevant predictor variables are excluded and polynomial relations may be artifact and that important ones maintained to yield the most the true relation between the removal statistically reliable outcome. Therefore, efficiency and the inlet substrate is more likely despite the numerous potential factors of linear. With such differences in the nature of 3- influence on PO4 removal or release in CW polynomial relations between years, the view treatment systems, the present paper maintains that the substrate removal efficiency is linearly focus on the effect of the three main seasonal related to the inlet concentration is re- 3- related factors: temperature, rainfall and investigated here also for PO4 -P. In fact, using concentration, which were previously noted to 1999 as an example, which shows a good 2 influence nutrient reduction (Quagraine and polynomial fit with R = 0.71 (Fig. 2), no 3- Duncan, 2017) on the PO4 removal efficiency. significant difference is observed in correlation All other factors of potential influence are strength for the linear fit (R2 = 0.70). treated as a composite. The inherent complexity of biological P removal processes makes mathematical 3.2.1 Multi-linear regression estimations of modeling of associated treatment systems some 3 seasonal contributing effects to tedious (Zuthi et al., 2013). For instance, 3- PO4 -P removal nitrification and denitrification processes may inhibit P-removal efficiency of a treatment As previously discussed (Quagraine and system under various operating conditions and Duncan, 2017) and also noted above, factors 12 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

3- Figure 2 Selected plots of inlet mean monthly phosphate (PO4 )-P to the SaskPower CW versus its percentage within the 2-decades investigatory period (1994-2014). SAs stand for the secondary axes (right and top) axes such as inlet concentration, ambient tempera- independent variables and the intercept are ture and rainfall demonstrate seasonality and shown in parenthesis to provide indication of 3- do influence substrate removals in various their statistical significances to influence PO4 ways: synergistically and/or antagonistically. -P removal. As discussed in Section 2.3.2, only * Table 3 shows estimated k1 k2, k3 and C for model fits with p value <0.2 for significance of particular years or within a specified period of F in the analysis of variance (ANOVA) for the years, which provide indications of the overall regression and for at least two respective influence of inlet concentration, independent variables, and of total monthly temperature, rainfall and all other contributing data per year/period (N) ≥5 were deemed 3- factors to PO4 -P removal in the CW. acceptable; outside this criterion, the data fit The coefficient of determination (R2) which was rejected and are excluded in Table 3. 3- indicates the fraction of the variation in PO4 - Fig. 3 depicts the general seasonal pattern of 3- P removal efficiency (dependent variable) PO4 -P removal during the annual CW operat- attributable to the three independent variables ing period of May to November, as well as the 3- (concentration, temperature and rainfall) is closeness of the predicted (calculated) PO4 -P shown for each period in column 6 of Table 3 removal efficiencies to the corresponding along with the p values (in parenthesis) of the observed data. The results here contradict the F statistics. The corresponding p values of the findings by Merlin et al. (2002), who reported E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 13

Table 3 MLR model fit estimates of inlet concentration (k1), temperature (k2), rainfall (k3) fac- 3- tors and auxiliary contributions (C*) to PO4 -P removal by the CW. Numbers in pa- renthesis are p values

2 k1 k2 k3 C* R Comments Year Group 1 1995 5.92 -2.00 1.50 -31.47 0.99 N = 5 (May-Oct.; Sep. excluded) {0.25} {0.18} {0.11} {0.24} {0.12} 1997 33.67 -2.82 -0.69 0.88 0.94 N = 7 (May-Nov.) {0.03} {0.04} {0.07} {0.96} {0.03} 1998 46.43 -4.91 0.02 -9.55 0.85 N = 7 (May-Nov.) {0.08} {0.03} {0.95} {0.71} {0.10} 2001 166.60 -4.15 2.65 -49.99 0.84 N = 7 (May-Nov.) {0.01} {0.16} {0.02} {0.23} {0.02} 2002 56.49 -3.62 -0.10 -61.18 0.88 N = 7 (May-Nov.) {0.03} {0.10} {0.66} {0.07} {0.07} 2008 44.46 -4.09 0.39 -57.81 0.87 N = 7 (May-Nov.) {0.02} {0.07} {0.31} {0.01} {0.07} 2009 26.67 -3.55 0.15 -13.54 0.99 N = 6 (May-Oct.) {0.004} {0.006} {0.13} {0.09} {0.01} Year Group 2 2003 88.10 -11.13 -0.22 -5.48 1.00 N = 5 (May-Oct.; Aug. excluded) {0.06} {0.07} {0.35} {0.88} {0.06} 2010 69.68 -10.74 0.16 -37.35 0.98 N = 6 (May-Oct.) {0.02} {0.03} {0.65} {0.32} {0.02} Period Group 1 1995-1996 12.43 -2.60 0.54 -3.89 0.99 N = 5 (May-Oct.; Sep. excluded) {0.13} {0.13} {0.23} {0.77) {0.11} 1995-2014 41.61 -3.13 0.13 -35.43 0.84 N = 7 (May-Nov.) {0.10} {0.06} {0.80} {0.18} {0.10} 1997-2014 43.79 -2.68 -0.08 -31.91 0.83 N = 7 (May-Nov.) {0.10} {0.11} {0.87} {0.21} {0.11}

higher removal efficiency to a maximum of occurred in August although also in July for 90% in summer than in winter with minima as some years (e.g. 2001 and 2010). More 3- low as 20-30%; and by Scholz (2016) who efficient PO4 -P removals were experienced in 3- reported no clear seasonal trend for PO4 -P spring (May) and in late fall (October/Novem- removal. However, the former and the latter ber). On the whole, with some minor excep- 3- reports were respectively with reference to tions, the PO4 -P removal tendencies exhibited horizontal-flow and vertical-flow subsurface in May seemed to have been fairly well CWs as against the FWS CW being investi- sustained through to July before the significant gated here. Generally, the most substantial release in August. This is consistent with initial 3- 3- PO4 -P release into the CW water column PO4 -P uptakes by plants, algae and bacteria 14 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 for growth during early stages of the annual increase or decrease). On the average, rainfall 3- CW operation period, followed by net higher effect in reducing PO4 -P concentration (likely microbial breakdown of organic P compounds by dilution) was 0.69 ± 0.92% reduction per 3- in various materials including: dead algae, mm of rainfall and in PO4 -P release as 0.27 ± 3- plant exudates, detritus materials, etc. to PO4 0.29% per mm of rainfall. Therefore, the CV -P into the water column. for the periods where k3 values were positive 3- (causing PO4 -P reduction) was 133.1% and 3.2.1.1 Temperature effect 104.6% for the negative cases (resulting in 3- release of PO4 -P reduction). Furthermore, as From Table 3, k2 (the temperature related an independent variable, the dependence of regression coefficient) was consistently nega- 3- PO4 -P removal efficiency on rainfall was tive suggesting that temperature favored a net 3- inconsistent (as shown by a significant release of PO4 -P likely via microbial action variability in the p-value) and in most cases of on detritus materials from sediment sources or no statistical significance even at 80% particulate (including dead algae) in the water confidence level (i.e. p >0.2). The most column (Kadlec and Wallace, 2009; Silveira 3- influence of rainfall on PO4 -P removal both in and O’Connor, 2013). Furthermore, the 3- magnitude (2.65% PO4 -P reduction per mm temperature dependence was generally statisti- of rainfall) and reliability (p = 0.02; i.e. at a cally relevant; at least, at 80% confidence level 98% confidence level) was shown in 2001. (p ≤0.20). This is in contrast to a report on a 3- vertical-flow CW where PO4 -P removal 3.2.1.3 Influent concentration effect appeared independent of temperature (Scholz, 2016). Amongst the years, the best fit was As shown in Table 3, the derived k1 values are obtained for 2009 and the temperature variable throughout the study period and the 3- influence on PO4 -P removal was statistically differences are notably large ranging from 3- very significant (p = 0.006). Based on the 6.0% to 166.6 per PO4 -P concentration in relative contribution of k2, the various mg/L. The variability in k1 suggests it is not a years/periods have been classified into two constant and that there are other related factors 3- groups. Group 1 shows 2.0 to 4.9% (3.4 ± to PO4 -P concentration responsible for the 3- 3- 0.9 %) more PO4 -P release (over inlet concentration dependent influence on PO4 -P concentration) per degree rise in temperature removal efficiency. Significant variability in k1 3- - and 10.9 ± 0.3% PO4 -P release for the Group was also noted for NO3 -N removal in an earlier 2 stages (Table 3). The coefficients of variation publication (Quagraine and Duncan, 2017) and - (CV) for these two groups are 26.1% and 2.5%, was attributed to the dependence on both NO3 respectively. It is uncertain the reason for the -N and TOC (or BOD); in fact, the dependence - higher temperature related regression coeffi- was on the ratio of TOC (or BOD) to NO3 -N. cient k2 in 2003 and 2010, but it is possible for Based on yearly mean and multi-year period the involvement of other temperature related mean data, a linear inverse correlation (r = factors to also be at play. -0.64; p = 0.02) is observed between k1 and 3- inlet PO4 -P for the total number of data sets 3.2.1.2 Rainfall effect shown in Table 3 (n = 12). In fact, excluding the earlier years of 1995 and 1996 to focus the In contrast to temperature, the rainfall effect 3- results solely on CS effluent as influent to the (k3) on PO4 -P removal was rather erratic both CW (n = 10), a more statistically significant (p in direction (positive or negative) and in the = 0.002) and stronger linear inverse relation magnitude of influence (i.e. extent of the E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 15

3- 3- Figure 3 Observed mean monthly PO4 -P removal efficiency versus calculated PO4 -P removal efficiency covering the periods 1995-1996, 1997-2014 and some selected years within the study period

(r = -0.84) is observed between k1 and inlet On the other hand, the negative relation 3- 3- PO4 -P (annual averages). Similarly, a modest between BOD and PO4 -P removal efficiency 3- (r = -0.67) but statistically significant linear may reflect release of PO4 from biodegrada- inverse correlation is observed between k1 and tion of organophosphorus content of organic inlet BOD, which improves significantly with matter in the water (measured as BOD). Fur- - exclusion of 2001 (r = -0.90; p = 0.0002). Thus, thermore, unlike the case for NO3 -N removal 3- k1 relates inversely to both PO4 -P (the where k 1 correlates linearly better with the ratio - substrate) and BOD. This is in contrast to the of BOD to NO3 -N than individually with BOD - - NO3 -N removal data (Quagraine and Duncan, and NO3 -N (Quagraine and Duncan, 2017), - 2017), where though k1 dependence on NO3 -N linear correlations individually between k1 and 3- (the substrate) is likewise negative, it was PO4 -P or BOD (vide supra) are better in terms positive with respect to BOD. Positive relation of reliability (p value) and in strength (r or R2) 3- between soluble and biodegradable organic than between k1 and BOD/PO4 -P ratio (r = - carbon and NO3 removal in wetland soils are 0.43; p = 0.17), which even worsens with ex- reported to reflect denitrification rates (D’An- clusion of the seeming anomalous data in 1995 - gelo and Reddy, 1999; Hume et al., 2002; and 2001 (vide supra). Thus, unlike NO3 Komor and Fox, 2002 and references therein). where the removal efficiency is influenced by 16 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

- the proportion of organic matter and NO3 -N relation over time is expected, and it is not 3- present, organic matter and PO4 -P are more strange considering the complexity of the CW 3- complexly related in influencing PO4 -P treatment system, which involves various removal efficiency. biological, physical and chemical processes. Compositional parameters which influence Nevertheless, Eq. 3 suggests that the variability 3- biological removal of PO4 are various, and may, in part, be attributed to co-dependence on apart from P-load and organic carbon content also pH and BOD. MLR analysis of the discussed above other factors such as volatile performance data of some small scale CWs in acid (VFA) content, cation concentration and TP removal has been reported and pH pH have also been reported (Mulkerrins et al., dependence was observed to be significant 2004). In fact, further analysis of the annual (Mohansingh et al., 2006), but its effect on the data from the present study reveals that the TP removal efficiency was inverse and 3- PO4 -P removal efficiency dependence on contrary to its demonstration in Eq. 3. Why this 3- 3- 3- PO4 is not only related to BOD and PO4 load apparent contradiction of pH effect on PO4 -P - but to pH as well (though not NO3 -N as others or TP removal (not only between these two have alleged (Adhishwar and Choudhary, cited reports, but generally in the literature)? - 2015). The purported negative effect of NO3 More detailed regression analysis would share on P removal could not be confirmed by a more light on this, as investigated for a future biological nutrient removal (BNR) method publication. (Vabolienė and Matuzevičius, 2005). Exclud- ing 2008, a positive linear relation is found 3.2.1.4 Auxiliary factors between k1 and pH (r = 0.80; p = 0.02). In fact, As shown in Table 3, C* though consistently but for 2001 and 2008, the MLR analysis shown as negative effect (except in 1997) was shows a significant dependence (p = 0.007) of 3- very variable suggesting net release of PO4 -P 3- the k1 values on PO4 -P, BOD and pH to from various potential non-point sources/ 2 account for 85% (R = 0.85) of its annual processes such as bio-mineralization of organic variability; albeit, BOD is a more reliable sediments, particulate matter, plant exudates predictor variable (p = 0.03) of k1 than either and dead algae and plants; physical processes 3- PO4 -P (p = 0.28) or pH (p = 0.27). The such as run-offs or perturbation of sediment equation of best fit is given below (Eq. 2): materials and physico-chemical processes such as desorption and/or dissolution of inorganic 3 k1 = 11.9 (PO4 -P) + 55.5 pH – 6.2 (BOD) – phosphate minerals. For these processes, 399.6 (2) various factors including DO, pH, sediment Or texture, flow rates etc. (as aggregate) would

3- 3 2 have significant influence (Mohansingh et al., ∆PO4 -P (%) = 11.9 (PO4 -P) + 55.5 pH* 3 3 2006). Therefore, the actual net influence of (PO4 -P) – 6.2 (BOD)* (PO4 -P) – 399.6 3 pH, for example, may be very different from as (PO4 -P) (3) shown in Eq. 3. 3- From Eq. 3, the PO4 dependence on its removal efficiency is indeed quadratic in nature 3.3 Seasonal performance, effluent and not just due to influences from other quality relating to influent quality, parameters such as temperature and rainfall as and vegetative conditions - seems to apply to NO3 -N removal (Quagraine and Duncan, 2017). As shown in Fig. 2, As a background for the discussion in this variability in the actual nature of the quadratic section, the reader should recall from above E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 17

3- that the PO4 removal exhibits some seasonal implications of the data at the various stages of patterns. Also, the removal efficiency depends the CW operation are also discussed. 3- on the influentPO4 concentration. The reader is also reminded that during the 2-decades of 3.3.1 Seasonal performance on treating the investigation two main wastewater qualities facultative lagoon discharge were fed into the CW for polishing: the early As the shown in Table 4, the CW removed years (from 1994-1995) of FL treated standard 3- PO4 -P very efficiently in 1994/95 when the and the remaining years of CS treated standard. influent to the CW was strictly a FL treated Further, in the literature, apart from variation in effluent. During these initial stages of treatment loading, the degree of coverage and 3- vegetation establishment, plant uptake of PO4 maturity of vegetation also influences the P 3- -P was expected to be high (Kadlec and (PO4 ) dynamics of a CW (Ciceka et al., 2006). Wallace, 2009). The order of decline in Plants, either submerged or emergent, are pre- 3- removing efficiency between the seasons: sumed to promote a major fraction of P (PO4 ) spring > fall > summer cannot be attributed removal in CWs; the former as more efficient 3- simply to variation in the PO4 -P load into the at low P concentrations (~0.1 mg/L) and the lat- CW as the removal in fall (with the least ter at higher concentrations (~1.0 mg/L) average inlet concentration-3.68 mg/L) was (Kadlec and Wallace, 2009). The denseness of higher than in summer (which was of higher emergent vegetation is also known to be im- - average influent concentration - 4.29 mg/L). portant in nutrient removal (e.g. NO3 -N) in 3- Temperature effect, as discussed above, was CWs (Bastviken et al., 2009); and PO4 -P is 3- adverse to PO4 -P and would explain the lower not expected to behave differently. However, removal efficiency in summer. Nevertheless, initial establishment of plants in a CW requires 3- care should be taken in interpreting such data more P (PO4 ) than needed to sustain the with limited number of samples (N); i.e. N = 1 mature annual cycle. This establishing require- (spring), N = 3 (summer), and N = 5 (fall). In ment occurs at rates which increase as the new fact, statistically, there is no difference between plant increases (Kadlec and Wallace, 3- summer and fall in their mean PO4 -P removal 2009). The flood that occurred in the Estevan efficiency (p = 0.28 (two-tail)). area in 2011 affected both the degree of vegetation coverage and the denseness of the Though based on only a single set of data, 3- emergent vegetation. the effluent PO4 -P of ~0.1 mg/L in spring met the expected make-up limit of ~0.33 mg/L The section discusses the average and 3- PO4 -P (vide supra) in avoiding (Ca3(PO4)2) median effluent water qualities and the CW scale deposition during condenser cooling treatment efficiencies at three different stages reuse application. However, the summer and of the CW operation based on the main MWW 3- fall average CW effluent PO4 -P of 2.75 and feed qualities and broad characterization of the 1.75 mg/L, respectively did not meet this vegetation coverage and/or CW maturation: i.e. requirement; suggesting that, even in the fresh a) 1994-1995: the infantile stages of the CW stages of operation, the CW alone was not and on treating FL effluent; b) 1996-2010: the 3- adequate in removing PO4 sufficiently from more established vegetation stages and on FL effluent during these seasons to prevent treating CS effluent; c) 2013-2014: the period Ca3(PO4)2 scale deposition and/or algae growth after the flood where the vegetation coverage in CTs during condenser cooling application. was being reestablished whilst still treating CS Further treatment or ample blending with other effluent. The summary of the results are 3- make-up sources less in PO4 is required to presented in Table 4. The operational 18 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 mitigate the scaling and bio-fouling potentials. showed the least removal efficiency (actually the most release) showed even the highest 3.3.2 Seasonal performance on treating average influent concentration amongst the conventional secondary wastewater three seasons (with p = 0.002 when the summer 3- plant discharge inlet mean PO4 -P concentration is compared to that in fall). As earlier discussed, 3.3.2.1 Pre-flood periods temperature consistently showed adverse 3- From 1996 to 2010, when the CW was more effect; resulting in the release of PO4 -P. Even 3- matured and the supplied SMWW for polishing though plant uptake of PO4 -P is high during was of CS treated standard, the CW generally summer, so are microbial activities on detritus 3- declined in treatment efficiency for PO4 -P. materials during this period; and in fact, even 3- However, similar order of decline in removing higher resulting in net discharge of PO4 -P efficiency between the seasons is apparent as in during this season (Fig. 3). This explains why the initial vegetation establishment stage: i.e. irrespective of the influent quality, the summer 3- spring > fall > summer. It is noteworthy performance in removing PO4 -P was always however, that unlike on treating facultative the poorest. lagoon effluent from 1994-1995, the mean of Although spring CW performance, in terms 3- 3- PO4 -P removal efficiency in summer (on of the mean removal efficiency of PO4 -P treating CS wastewater during the pre-flood appears to be better than in fall, this (as period) was statistically different from fall (p = indicated earlier) was statistically non- 0.02) or spring (p = 0.0004); albeit, there was significant and the effluent quality from the no statistical difference between that of spring CW was on the average rather better during the and fall (p = 0.18). Once again, this trend fall season (p = 0.015). The latter is attributable cannot be attributed simply to variation in the to the lower influent concentration into the CW 3- PO4 -P load into the CW as summer which

Table 4 Average ± standard deviation and (median) of effluent concentrations and removal 3- efficiencies of PO4 -P (in mg/L) during the spring (May), summer (June-August) and fall (September to November) seasons at three different stages of the 2-decade (1994- 2014) CW operation Season Average Influent Average Effluent Removal Efficiency (%) 1994-1995 (FL effluent) Spring 7.35 (7.35) 0.09 (0.09) 98.8 (98.8) Summer 4.29 ± 1.22 (4.10) 2.75 ± 0.99 (2.83) 34.9 ± 21.9 (45.6) Fall 3.68 ± 1.40 (3.60) 1.75 ± 1.50 (0.95) 56.3 ± 27.1 (59.3) 1996-2010 (CS effluent: pre-flood) Spring 1.66 ± 0.38 (1.63) 1.43 ± 0.24 (1.37) 11.0 ± 19.0 (11.1) Summer 1.84 ± 1.06 (1.69) 1.97 ± 0.65 (1.91) -28.6 ± 59.5 (-12.6) Fall 1.25 ± 0.54 (1.22) 1.16 ±0.49 (1.12) -1.51 ± 43.8 (8.0) 2013-2014 (CS effluent: post-flood) Spring - 0.38 (0.38) - Summer 2.15 ± 0.82 (2.20) 1.88 ± 0.98 (1.55) 11.0 ± 39.2 (3.7) Fall 2.42 ± 1.29 (2.30) 2.38 ± 0.91 (2.81) 16.6 ± 13.8 (23.9)

E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24 19

3- during the fall season. So, as far as PO4 pre-flood period, the CW effluent during the induced scaling or biofouling was concern, the post-flood period was least suited for 3- CW polished SMWW during the pre-flood condenser cooling application as far as PO4 period was more conducive for condenser induced scaling or biofouling were concern. In cooling application in fall than during any of fact, whether polishing FL or CS treated the other two seasons; summer being worst. MWW, during the initial plant establishing The poorer effluent quality in summer was (1994/1995) or re-establishing stages (post- statistically different from that in spring (p = flood period of 2013 and 2014), the CW 3.37 × 10-5) and in fall (1.11 × 10-8). Nonethe- effluent during spring season appeared better less, irrespective of the season, the CW effluent suited for the condenser cooling application quality was generally not very suitable for than in the summer or fall seasons; fall season 3- direct reuse in condenser cooling; further CW effluent PO4 -P quality being best only treatment or blending with other make-up after the plant vegetation were well established 3- sources less in PO4 was required to avoid (1997-2010). Ca3(PO4)2 scale formation during the conden- ser cooling process and/or algae growth in the CT especially during the warm summer CONCLUSIONS months. The manuscript centres on the effects of the

season-dependent variables n temperature, 3.3.2.2 Post-flood periods 3- rainfall, and inlet PO4 -P concentration on its With re-establishment of the vegetation cover- removal due to pond stabilization and through age after the flood, the plant demand for Pin a CW over a twenty year period. growth was high and is reflected in the general With respect to the effect of these factors on 3- 3- improved average removal efficiency of PO4 - pond stabilization removal of PO4 -P, P as compared to the pre-flood periods. Even correlation analysis was mainly used to the summer months experienced net average establish relationships existing between 3- 3- PO4 -P removal. The washing off of PO4 rich removal efficiencies and the various factors sources composed of detritus and/or top and how strong these relations are. The sediment materials by the flood could also following are some key findings: 3- 3- explain the reduced tendency for PO4 -P  PO4 -P discharge from the MWWTP release, typically experienced in summer. storage ponds to the CW generally Nevertheless, with the exception of the spring followed a seasonal or monthly cycle. data, the effluent quality was still poor for Highest concentrations occurred typically direct use as make-up for condenser cooling in June, followed by declining trend to application. Statistically, there was no signifi- minimum concentrations in August, which 3- cant difference between the meaneffluent PO4 was then followed by reversal in trend to -P concentration in summer and fall (p = 0.50), higher concentrations in September or neither was there any significant difference later in October. between their removal efficiencies (p = 0.78).  3- The magnitude of impact of both tempera- The post-flood poor CW effluent PO4 -P 3- ture and rainfall in reducing PO4 -P in the quality in summer and fall reflects the 3- MWWTP storage ponds were found to be relatively higher influent PO4 loads in concentration dependent, as also found comparison to the pre-flood periods. Unlike - previously for TAN and NO3 -N. polishing CS treated MWW during the 3-  Temperature effects on PO4 -P in these 20 E.K. Quagraine / Journal of Water Sustainability 1 (2018) 1-24

ponds were immediate and/or lagged for a erratic both in direction (positive or month depending on the actual conditions negative) and in magnitude of influence prevailing. (i.e. extent of the increase or decrease).  The effects of temperature was estimated Further, with only few exceptions, the 3- o as ~3.3% PO4 -P reduction of the (at 0 C) effect was generally found not to be background concentrations per degree rise. statistically significant (i.e. p >0.2). This was irrespective of whether the effect Nevertheless, on average, rainfall could be 3- was immediate or lagged. shown to cause inlet PO4 -P concentration - reduction by 0.69 ± 0.92% per mm of  Unlike TAN and NO3 -N, rainfall effect on 3- rainfall (attributable to dilution) or in some PO4 -P was not as obvious. Nonetheless, 3- such effect seemed to have been displayed cases, PO4 -P release by 0.27 ± 0.29% per for about half the time of investigation; mm of rainfall (likely due to sediment albeit by a one-month lag, which was perturbation). 3- 3- shown as ~0.7 PO4 -P reduction of (at zero  The removal efficiency of PO4 -P by the 3- rainfall) background concentrations per CW was dependent on the influent PO4 -P mm depth of rainfall. More commonly, the concentration, but the coefficient of this effect of rainfall was shown in the release variable (k1) was inconstant throughout the 3- of PO4 -P, which is attributed to sediment study period ranging from 6.0-166.6% per 3- perturbation (and/or P-rich runoff input) PO4 -P concentration in mg/L suggesting 3- into the water column; being displayed there are other co-factors to PO4 -P immediately with rainfall for all the years concentration responsible for the concen- 3- except one and sustaining such effect even tration dependent influence on PO4 -P a month later for some years. removal efficiency; evidence was provided Concerning the effects of temperature, to suggest pH and BOD as potential 3- co-factors. rainfall, and inlet of PO4 -P concentration and 3- other factors on PO4 -P removal by the CW, With respect to the reuse application of the correlation and MLR analyses were used to CW effluent for condenser cooling, but for the 3- establish the relationships and strength first spring season of operation where PO4 -P between the removal efficiency and the various concentration of only 0.09 mg/L was shown in factors, and to quantify the individual the effluent after ~99% removal, the CW on the 3- contributions of these factors to the PO4 -P whole was incapable of producing effluent 3- removal. The following are some key findings: PO4 -P concentration of the ≤0.33 mg/L  MLR analysis of the data for the various required in avoiding Ca3(PO4)2 scale individual years showed consistent statis- formation. tically significant adverse temperature ef- 3- fect on PO4 -P removal, at least at 80% confidence level (i.e. p ≤0.2); yet, the mag- ACKNOWLEDGEMENT nitude of the adverse temperature effect The author would like to thank Mr. Bruce was demonstrated generally in two princi- Duncan, under whom the Shand CW was pal ways: i.e. as ~3.4 ± 0.9% or 10.9 ± operated since the early stages until later in 3- 0.3% more PO4 -P release (over inlet con- 2013 when he relinquished that role to him. centration) per degree rise in temperature. The author would like to also thank the various  In contrast to temperature, the rainfall University of Regina co-op summer students 3- effect (k3) on PO4 -P removal was rather hired by SaskPower during summers, who E.K. 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