Benchmarking the sustainability of sludge handling systems in small wastewater treatment plants

Journal of Environmental Management 256 (2020) 109893

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Journal of Environmental Management journal homepage: http://www.elsevier.com/locate/jenvman

Research article

Benchmarking the sustainability of sludge handling systems in small wastewater treatment plants Greggory Archer *, Chao Jin, Wayne Parker University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada

A R T I C L E I N F O

A B S T R A C T

Keywords: Sludge processing Biosolids management Energy Greenhouse gas emissions Biosolids disposition Key performance indicators

A benchmarking strategy was developed to assess all aspects of sludge handling in small wastewater treatment plants and tested on a cross-section of Ontario facilities. Using operational data and on-site measurements, sustainability metrics that addressed energy consumption, chemical use, biosolids quality and disposition, and greenhouse gas (GHG) emissions were estimated. Electricity consumption for sludge handling ranged from 0.9 to 3.9 kW-hours per dry kilogram of raw sludge (kWh/dry kg) with thermo-alkali hydrolysis and auto-thermal thermophilic aerobic digestion (ATAD) processes consuming the least and most electricity for stabilization, respectively. Mechanical dewatering processes consumed between 2 and 5% of total sludge handling electricity, however, associated polymer dosages were higher than literature values in some cases. Disposition fuel re­ quirements for plants with dewatering were up to 85% lower than facilities without dewatering. Biosolids contaminant (pathogen/metals) contents were observed to be substantially below Non-Agricultural Source Material (NASM) requirements. The copper content of the hauled biosolids exhibited the highest concentration relative to the NASM limit among all plants studied, ranging from 14 to 37% among facilities practicing land application of biosolids. Four biosolid products met Class A requirements for E. coli content, including one product generated via long-term storage. Carbon emissions ranged from 119 to 299 kg CO2 equivalents per dry tonne of raw sludge (g CO2 eq./kg). Six facilities that practiced land application exhibited net-negative GHG emissions; the carbon credits gained from fertilizer production avoidance outweighed emissions associated with sludge processing and transportation. The results provide evidence that this practice is sustainable from a GHG emissions standpoint. The benchmarking approach developed and information gathered is beneficial to plant owners and operators seeking to better understand how their utility is performing relative to peers, identify areas of need and further investigation, and improve the sustainability of their operations.

1. Introduction Benchmarking is a strategy by which the sustainability of sludge handling in wastewater treatment plants (WWTPs) may be improved (Gernaey et al., 2014). Such a practice can provide owners and operators with a tool to evaluate their plant’s performance relative to others of similar capacity and scope of operation, and make informed decision-making based on the results. Historically, much of the bench­ marking of wastewater treatment operations has focused on a) broader, high-level metrics of overall WWTP process operations and performance (Vera et al., 2013; Yang et al., 2010), and b) large treatment facilities with advanced sludge processing (Bailey et al., 2014; Lindtner et al., 2008; Silva et al., 2016). Few studies (Belloir et al., 2015; Foladori et al., 2015; Hanna et al., 2017) have solely evaluated small WWTPs that have

limited capital, operating and human resources. Information gaps in the actual operation of such systems often exist and the quality and dispo­ sition of biosolids from these systems is typically not well documented. Yet, sludge processing and its associated management can account for upwards of 40% of the operational costs for a WWTP (Lindtner et al., 2008; Haslinger et al., 2016), and several studies have found that small WWTPs generally exhibited higher specific energy consumption than larger plants (Mizuta and Shimada, 2010; Yang et al., 2010; Silva and Rosa, 2015; Haslinger et al., 2016; Singh et al., 2016) since the former do not benefit from the economies of scale that larger facilities exhibit. Accordingly, improvements to the efficiency of process operation and management practices in such facilities could have a beneficial impact on environmental and economic sustainability (Ceschin and Gaziulusoy, 2016).

* Corresponding author. E-mail address: [email protected] (G. Archer). https://doi.org/10.1016/j.jenvman.2019.109893 Received 5 July 2019; Received in revised form 2 November 2019; Accepted 18 November 2019 Available online 9 December 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.

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Journal of Environmental Management 256 (2020) 109893

Regardless of the specific objective(s) of the investigation, bench­ marking studies have generally involved the selection of performance metrics [key performance indicators (KPIs)] that were evaluated for €m, 2012; Benedetti et al., each facility studied (Balm� er and Hellstro 2008; Haslinger et al., 2016; Krampe, 2013; Mamais et al., 2015; Silva and Rosa, 2015). The metrics involved a common normalizing basis for each response (e.g. unit volume of wastewater treated), and numerators that were specific to input of interest [e.g. kilowatt hours (kWh) for electricity consumption]. Energy utilization has been commonly evaluated (Longo et al., 2016), with some studies incorporating advanced statistical techniques to develop data-driven benchmarking models (Carlson et al., 2007; Hanna et al., 2017; Mizuta and Shimada, 2010). However, to obtain a data set large enough to generate the model(s), data acquisition was limited to obtaining utility bills for the entire plant to determine elec­ tricity consumption, and therefore information on the performance of individual unit processes was unavailable. Conversely, Belloir et al. (2015), Foladori et al. (2015), NYSERDA (1998; 2006) reported the gathering of on-site power measurements (submetering) on individual pieces of equipment. It was found that submetering is resource intensive but can provide more insight into the performance of individual processes. Thus, it assists plant owners and operators seeking to improve specific areas. For example, NYSERDA (2006) identified $6.4 million in savings (15% of operation costs) through their study. The studies that incorporated submetering employed similar audit methodologies. For motors that had a constant power draw, single measurements were coupled with motor run-times to calculate daily energy consumption. For motors that were equipped with variable fre­ quency drives (VFDs) or were otherwise manually adjusted based on process conditions, metering equipment was installed to capture tem­ poral variations in demand. While benchmarking can provide insight into metrics of the sus­ tainability of wastewater treatment, prior studies that assessed sludge handling systems were typically performed with different goals and did not involve benchmarking (Alvarez-Gaitan et al., 2016; Beavis and Lundie, 2003; Hospido et al., 2005; Johansson et al., 2008; Lundin et al., 2004; Murray et al., 2008; Niu et al., 2013; Remy et al., 2012). A common goal of such studies was to evaluate the impact of different sludge processing technologies and end-use scenarios on sustainability. Studies were conducted on the basis of either a single WWTP (either existing or hypothetical) or the cumulative production of several plants in a region, based on typical sludges generated at the plant or within the region. Despite the differing objectives, prior studies (Beavis and Lundie, 2003; Hospido et al., 2005; Lundin et al., 2004; Remy et al., 2012; Stefaniak et al., 2014) have employed systematic assessment frame­ works consistent with LCA practices that included the following core elements (ISO 14040, 2006; ISO 14044, 2006):

(Alvarez-Gaitan et al., 2016; Beavis and Lundie, 2003; Hospido et al., 2005; Johansson et al., 2008; Lundin et al., 2004; Murray et al., 2008; Niu et al., 2013). The capture of all system inputs and outputs repre­ sented a key difference between sustainability and traditional bench­ marking studies, given the latter usually only evaluated a single input (e. g. energy). Among the LCA impact categories selected for evaluation, global warming potential (GWP) is common, although some studies have evaluated other categories, including acidification, eutrophication, human toxicity, and ecotoxicity potential (Chai et al., 2015; Lundin et al., 2004). A variety of tools, typically a combination of LCA software packages, databases, literature values, and available models have been employed to convert inventoried inputs/outputs of each system to the desired impact category quantity. Such conversion calculations require knowledge or assumption of a pathway from any given LCA input (e.g. electricity production) to the impact category quantity. Taken collectively, there is a need for an assessment of sludge handling operations in small WWTPs, and the previous studies provide a general framework on which to base such an assessment. Hence, the objective of this study was to critically develop a benchmarking strategy for assessing the sustainability of sludge handling systems in small WWTPs. To achieve the objective, a systematic plant audit methodology was developed and implemented in ten WWTPs to evaluate a variety of sustainability metrics: energy consumption, chemical use, biosolids quality, biosolids disposition, and greenhouse gas emissions. While all of the facilities evaluated were located in Ontario, the benchmarking methodology developed could be applied to biosolids management practices in other jurisdictions. In particular, given that the practices of aerobic digestion, gravity thickening, and land application and land­ filling of biosolids are practiced elsewhere (Bastian, 1997; Kelessidis and Stasinakis, 2012; LeBlanc et al., 2009; Wang et al., 2009), the approach developed could be employed by small communities in such areas to better understand how their utility is performing relative to peers of similar capacity and scope, identify areas of need and further investi­ gation, and improve the long-term sustainability of their operations. 2. Materials and methods A methodology that was consistent with benchmarking and sus­ tainability methods established in the literature was developed to sys­ tematically evaluate sludge handling systems. This included development of a plant audit methodology that collected actual oper­ ating data to estimate benchmarking KPIs. Sludge production (dry weight basis) was employed as the FU and the system boundaries con­ sisted of those directly related to the operation, transport, and disposi­ tion of the sludge/biosolids. The methodology was tuned by employing it to characterize sludge handling practices in 10 WWTPs in Ontario that spanned a range of design and operating parameters. The outcome of this exercise was a database of KPI values that could be employed by other WWTPs to benchmark operations and assess their sustainability.

1. Selection of system functional unit (FU), that provides a normalizing basis for sustainability metrics; 2. Definition of system boundaries to identify inputs and outputs; 3. Selection of LCA impact categories to be evaluated; 4.Inventory of relevant inputs and outputs and translation to LCA impact categories.

2.1. Definition of KPIs The KPIs (Table 1) quantified aspects of energy consumption, chemical use, biosolids disposition, biosolids quality, and GHG emis­ sions. Where appropriate, the KPIs were normalized on the basis of raw sludge (dry mass) produced, defined as the mass of sludge entering the sludge handling process (from primary/secondary clarifiers) less any mass flows in return streams. Energy inputs represent a substantial portion of a treatment facility’s total operational costs and the GHG emissions associated with their production represent an environmental impact (Griffin and Hammond, 2019; Mac Kinnon et al., 2018). As such, reductions in this area without compromising plant performance can potentially improve economic and environmental sustainability. For all but one of the plants studied, electricity was the only form of energy consumed. Hence, energy KPIs

The FU concept is analogous to the denominator of a KPI in tradi­ tional benchmarking studies, and among sludge handling sustainability studies, the mass of sludge processed (dry weight basis) was typically selected as the FU (Alanya et al., 2015; Murray et al., 2008). This quantity represents the quantity of material being processed, regardless of wastewater volume treated or COD load to the WWTP. There was also broad agreement among the literature regarding the FU boundaries that should be employed for such systems. Several studies have included inputs and outputs related to the operation (i.e. process­ ing/treatment), transport, and disposal of the sludge/biosolids 2

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Table 1 Selected key performance indicators. KPI

Category

Metric or Description

1–6

Energy

kWh for thickening per dry kg

7 8 9–10 11 12 13 14 15 16

Energy Energy Chemicals Disposition Disposition Quality Quality Quality Quality

17 18–22

Quality GHG

kWh for aerobic digestion per dry kg of VSS destruction m3 of natural gas consumption per dry kg g of polymer used per dry kg Weighted average round-trip distance to end-use destination (km) Liters of fuel consumed by haulage trucks per dry kg Mean TP content of hauled biosolids (g/kg) Mean TN content of hauled biosolids (g/kg) Mean K content of hauled biosolids (g/kg) Mean metal concentration; dry mass basis ratio of hauled biosolids NASM limit Mean log pathogen (E. coli) content of hauled biosolids [log (CFU/g)] g CO2 eq. for g CO2 eq. for natural gas g CO2 eq. for chemical electricity consumption per dry kg consumption per dry kg consumption per dry kg

kWh ​ forstabilization per dry kg

kWh ​ fordewatering per dry kg

describing normalized electricity consumption (kWh/dry kg) were developed. Individual electricity consumption KPIs for each stage of the sludge treatment process (thickening, stabilization, dewatering, hold­ ing), odour control, and pumping were developed to describe which parts of the sludge processing sequences were the significant energy consumers. An additional KPI that normalized electricity consumption by volatile solids destruction in the digester was created as a measure of energy efficiency in this process. In addition, an energy KPI for natural gas was created to recognize this input to some sludge handling technologies. The purchase of chemicals represents an operational cost for plant owners and the GHG emissions associated with their production repre­ sent an environmental burden. In sludge handling in small WWTPs polymers are typically employed in mechanical thickening and dew­ atering to enhance the liquid-solid separation process. In addition, potassium-hydroxide (KOH) is employed in some processes for pH control and to boost the potassium content of the biosolids product. The chemical consumption KPIs were created to reflect these inputs. The disposal of biosolids represents an operational cost for waste­ water treatment plant owners, and the carbon emissions associated with trucking fuel consumption represent an environmental burden. Separate indicators that employed the average distance that the biosolids trav­ elled to their destination and the amount of fuel consumed (normalized to dry mass of solids processed) were created. The latter indicator was chosen to account for differences in capacity and fuel economy of truck fleets. Liquid biosolids are typically transported in tanker trucks with capacities of approximately 40 m3 per truck, while dewatered cake is often hauled in trucks that have smaller capacities and higher fuel efficiencies. KPIs for mean nutrient and log-mean E. coli concentrations were selected to identify the range of beneficial value (nutrients) and prox­ imity to the regulatory limits of the produced biosolids. In addition, a KPI relating the highest ratio of mean metal concentrations to their respective regulatory limits was selected to determine whether any metals were at risk of exceeding regulatory thresholds. In sludge handling trains, the expenditure of resources in upstream processes (i.e. dewatering) will impact on downstream operations (i.e. disposition). Hence, a KPI based upon CO2 emission was created to integrate all of the inputs and outputs of each system. This KPI was based upon published CO2 equivalents of the various components.

kWh for pumping per dry kg

kWh for odour control per dry kg

kWh for aerated holding per dry kg

g of KOH used per dry kg

g CO2 eq. for fuel consumption per dry kg

g CO2 eq. credits for land application per dry kg

employ anaerobic digestion. Only mechanical treatment systems were considered for evaluation; lagoon systems were excluded as sludge generation at these facilities is sporadic. However, if a mechanical plant incorporated a lagoon as part of its non-stabilization sludge handling process (e.g. for storage), it was considered for selection. In total, 210 facilities met the initial screening criteria. Ten were selected (Table 2) to capture a range of on-site sludge processing tech­ nologies (thickening, stabilization, dewatering), disposition practices (land application, landfill), operator type [public, private, Ontario Clean Water Agency (OCWA)], and geographical locations (Southern, Eastern, Northern Ontario). Although most small facilities in the province are not currently using highly innovative technologies (e.g. thermo-alkali hy­ drolysis, GeoTube™, etc.) (Jin and Parker, 2017), it was considered important to have such plants represented in the study to assess the extent to which newer technologies may impact the sustainability of operations, and provide baseline knowledge for other communities considering upgrades to or replacements of their existing process. Technologies such as centrifuge and rotary press dewatering are reasonably well-established in large WWTPs but are not common in small treatment facilities (Metcalf & Eddy, 2013). Thus their use in small plants was deemed to be innovative and were included in the ten plant sample. 2.3. Data gathering and analysis One of the goals of the study was to estimate KPI values for the WWTPs using actual operating data such that they could inform future benchmarking on the basis of actual operations. Hence, a comprehensive data gathering and quality assurance exercise was conducted to develop accurate KPI values. Information on the dimensions and types of treat­ ment processes employed at each facility were gathered from reports. Data describing flows (influent, waste/return sludge), chemical and natural gas use and biosolids quality (solids, nutrients, heavy metals, E. coli) were obtained from each WWTP for three years (2014–2016). To determine the power draw of the various processing equipment (blowers, pumps, dewatering units, etc.), spot measurements were collected on-site using a Fluke™ 1735 power logger. Power draw was determined to be constant over time since no major pieces of equipment incorporated variable frequency drives. Electricity consumption (kWh) was estimated by multiplying daily equipment run-times (obtained from plant records and discussions with plant operators) with measured power readings (kW). Natural gas consumption was calculated as the difference between baseline usage for plant-wide heating and the con­ sumption reported during stabilization operation. Biosolids disposition information [quantities and farm/landfill

2.2. Plant selection For the purposes of the study, a “small” plant was defined as one with a design hydraulic capacity of less than 10,000 m3/day that does not 3

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Table 2 Characteristics of WWTPs. ID

Operator

A B C D E F G H

OCWA Private Public OCWA Public Public Public Public

I J

OCWA OCWA

Thickening

Gravity Thickener

Gravity Thickener Rotary Disc Thickener

Stabilization Technology CAD CAD CAD CAD CAD CAD CAD Thermo-alkali Hydrolysis CAD ATAD

Dewatering Technology

Holding/Storage On-site lagoon Off-site lagoon Off-site storage

Centrifuge Rotary Press GeoTube™ Centrifuge Off-Site Drying Bed Rotary Press

GeoTube™ Aerated holding Aerated holding Aerated holding (WAS), On-site storage On-site storage

Odour Control

Disposition

Location

Biofilter

Agricultural Agricultural Agricultural Landfill Agricultural Agricultural Agricultural Agricultural

South South South North East South South South

Biofilter

Landfill Agricultural

North East

Biofilter

ATAD ¼ auto-thermal thermophilic aerobic digestion. CAD ¼ conventional aerobic digestion. OCWA ¼ Ontario Clean Water Agency.

address (es)] was obtained from haulage reports and Google Maps™ was employed to determine the driving distance from the WWTP to each destination. To calculate the normalized fuel consumption of each operation, the distance value was used in conjunction with truck fuel economy information obtained from the truck owner. A BioWin™ model was generated for each plant to obtain a solids mass balance for the sludge handling process, estimate net sludge pro­ duction, and screen for problematic data (Hulsbeek et al., 2002). The modelling exercise involved initial configuration to reflect reported operating conditions. Unknown return streams (e.g. digester decant, dewatering centrate) were then adjusted such that the predicted aera­ tion basin mixed-liquor suspended solids (MLSS) matched the reported values and predicted biosolids quantities matched the reported amounts. The quantity of solids destroyed was estimated from the BioWin™ model of each plant. A meta-data summary of the MLSS observed data employed in model fitting is presented in Table S1 of the Supplemental Material. In most cases, the facilities employed dedicated blowers for aerobic digesters and holding tanks. Hence, power draw was directly allocated to the sludge handling process of interest from the measurements taken on-site. However, there were some instances where the same blower supplied air to digesters and the wastewater treatment aeration basins, or to both the digester and aerated holding tank. In some cases infor­ mation on the air flow to each vessel was obtained to determine the percentage of air (and in turn, the proportion of electricity) supplied to the digester. Air flow information was not available for one plant and hence the calibrated BioWin™ model was employed to allocate air flows and the corresponding power consumption. In one plant where data availability was limited, the allocation of air flow was estimated on the relative volumes of the aerobic digesters and the aeration basin. Emission factors specific to Ontario (electricity, natural gas produc­ tion) or Canada (transportation fuel) were employed to calculate GHG emissions associated with each input in support of the estimation of KPIs 18–22. In other cases, literature values for chemical production (poly­ mer, KOH) and chemical fertilizer production (N, P, K) were utilized (Environment and Climate Change Canada, 2018; IPCC, 2006; Biograce, 2011; Recycled Organics Unit, 2006; Kongshaug, 1998). The latter fac­ tors were used to determine the carbon off-sets gained by using biosolids as a fertilizer through the avoidance of chemical fertilizer production for each nutrient (Alvarez-Gaitan et al., 2016; Beavis and Lundie, 2003; Hospido et al., 2005; Johansson et al., 2008; Lundin et al., 2004; Murray et al., 2008; Niu et al., 2013; Remy et al., 2012).

uncertainties in the estimated values (expressed as the standard devia­ tion as a percentage of the mean for each KPI) were characterized. Among the KPIs that involved raw sludge production, the variability in production represented the largest source of uncertainty. The standard deviations associated with raw sludge production were found to range from 11 to 34% of the mean values. Where appropriate, the results were statistically compared using pair-wise t-tests at a level of significance of 0.05 (Walpole et al., 2012). There was also uncertainty associated with some of the estimates provided by WWTP operators (e.g. polymer use), and in the partitioning of electricity draw when one blower supplied air to both liquid stream aeration basins and digesters. Where necessary, the implications of such uncertainties (quantitative and qualitative) on the extent to which conclusions could be drawn are discussed subsequently. Additional descriptive statistics for the KPIs evaluated are presented in Table S2 – S10 of the Supplemental Material. All ten plants within the study consumed electricity as part of the sludge treatment process. The contributions of individual processes to total electricity consumption are shown in Fig. 1, which also displays the uncertainty (standard deviation) associated with total consumption. Total consumption ranged from 0.9 to 3.9 kWh/dry kg of raw sludge among all plants studied. The 25th, 50th and 75th percentile values corresponded to 1.8, 2.2, and 2.7 kWh/dry kg. Notably, the lowest consumer (plant I) exhibited consumption that was statistically different than all other consumers (p < 0.05). However, as discussed earlier, there was unquantified uncertainty in plant I estimates as it did not employ a dedicated digester blower. Hence, the low KPI for plant I may not be a feasible goal for plants seeking to reduce electricity consumption. Regardless, the range in values indicate that possibilities for improve­ ment may exist within some of the higher consumers, and the lower consumers may provide insight into best management practices for sludge treatment. When the type of sludge handling technologies employed was considered, the facilities that did not practice conventional aerobic digestion exhibited the highest (ATAD) and fourth-highest (thermo-al­ kali hydrolysis) total electricity consumption, respectively. However, the latter facility consumed the least amount of electricity for stabili­ zation among all plants studied, which is noteworthy when considering the technology’s application in other facilities. If the process were to be implemented at a plant that did not require aerated holding or less intensive odour control (the two largest power consumers for sludge handling operations at plant H), the potential for electricity savings could be substantial relative to the conventional aerobic digestion pro­ cess. Conversely, the ATAD facility consumed the most energy for sta­ bilization, indicating that this technology was substantially more energy-intensive than the conventional aerobic digestion processes within the sample. Among the eight plants practicing conventional aerobic digestion, total electricity consumption ranged from 0.9 to 2.7

3. Results and discussion The benchmarking results associated with each system input and output were evaluated for each of the plants studied. Where possible, 4

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Fig. 1. Total electricity consumption per dry mass of raw sludge produced.

kWh/dry kg. For such plants, the 25th, 50th and 75th percentile cor­ responded to 1.6, 2.0, and 2.4 kWh/dry kg, respectively. Given the ubiquity of such technology in small WWTPs in Ontario (Jin and Parker, 2017), these values provide benchmarks for other plant owner’s that seek to determine how their facility is performing relative to others of similar scope and operation. Stabilization consumed the highest fraction of sludge handling electricity for all but two of the plants (F and H). With the exception of these two plants, at least 82% of the electricity consumption was for stabilization. The high proportion of electricity utilized for stabilization suggests that this process should be examined when seeking to reduce electricity consumption. Among all the plants studied, electricity con­ sumption for stabilization ranged from 0.3 to 3.8 kWh/dry kg. The 25th, 50th and 75th percentile values corresponded to 1.0, 1.6, and 2.5 kWh/ dry kg, respectively. Given that all the plants employed the same tech­ nology, the over two-fold increase in consumption between the 25th and 75th percentile indicates that opportunities may exist for process opti­ mization in some of the higher consumers. The electricity consumption of facilities that practiced aerobic digestion was also evaluated on the basis of VSS destruction (Fig. 2) as

an indicator of efficiency. From this analysis it was found that the ATAD plant had the statistically highest (p < 0.05) energy consumption (63 kWh/kg VSS destroyed). Among the eight facilities employing conven­ tional aerobic digestion, consumption ranged from 4.9 to 56 kWh/dry kg VSS. The 25th, 50th, and 75th percentile corresponded to 7.1, 8.7, and 19 kWh/dry kg VSS, respectively. The minimum value (4.9 kWh/kg VSS) was statistically lower than any other value observed (p < 0.05), while the four next highest consumers (6.5–8.8 kWh/kg VSS) exhibited substantially less consumption than the four highest consumers (15.1–63.1 kWh/kg VSS). Thus, the five lowest consumers could collectively serve as benchmarks for other utilities seeking to determine their performance relative to peers of similar scope and operation. The low consumption in plant C may have been due to the mix of primary and secondary sludge digested, the former is more readily biodegradable than secondary sludge (Metcalf & Eddy, 2013). The resulting mixture thus generally requires less air to achieve a given quantity of VSS destruction than pure secondary sludges (Metcalf & Eddy, 2013). One other facility generated primary sludge (plant B) and statistically exhibited the fourth lowest specific energy consumption, despite being the second highest overall consumer (Fig. 1). The findings reveal that

Fig. 2. Digester electricity consumption per dry mass of VSS destruction. 5

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evaluating metrics with different normalizing bases can provide insight into optimization opportunities that may have not been observed otherwise. Plants G and E exhibited over three- and six-fold greater energy consumption on the basis of VSS destruction than the median value, respectively (Fig. 2). To gain insight into why these facilities exhibited notably higher consumption, the hydraulic residence time (HRT) of each digester was examined and found to be 48 and 58 days HRT, respec­ tively. Both values were substantially higher than the MOECC (2008) design guideline of 15 days HRT, which suggests that both digesters are oversized based on the current loading to the digester, and hence the aeration requirements for mixing exceed that consumed for VSS destruction. Essentially, both units are effectively operating as aerated holding tanks in addition to their function as aerobic digesters and therefore appear to be inefficient on the basis of VSS destruction. The consumption of energy in dewatering that is employed to convert liquid biosolids into a cake was examined. Four of the WWTPs employed a mechanical process that required electricity as part of its operation while a fifth plant employed a passive process (GeoTube™) that involved storing aerobically digested biosolids in large geomembrane bags. In this process, leachate seeps through the pores of the bags and the dewatered solids are retained within the bag. Among plants with mechanical dewatering, the normalized power draw for centrifuge dewatering ranged from 0.06 to 0.10 kWh/dry kg while the two plants with rotary presses consumed between 0.07 and 0.09 kWh/ dry kg. Notably, the percentage of total sludge handling electricity consumed by the dewatering processes ranged from 2% to 5% of the total draw. Hence, it was found that the energy required to dewater liquid sludges into cake via mechanical dewatering was minor relative to other processes. One of WWTPs consumed natural gas and added KOH as part of the sludge treatment process (plant H). The natural gas consumption was found to be 0.04 m3/dry kg while the KOH usage was 19 g KOH/kg. The thermal hydrolysis technology employed at Plant H represents a novel technology for small WWTPs and hence it was not possible to compare against other plants. However, its performance with respect to GHG emissions relative to the other facilities will be subsequently discussed. Thickening and dewatering processes typically consume polymer to improve solids capture and increase cake solids content (Gable et al., 2013; Sharrer et al., 2010). In the current study only one facility employed polymer for thickening and this was used in conjunction with a rotary disc thickener where 12 g/kg was consumed and a 4.5% TS sludge product was generated. When dewatering was examined the GeoTube™ facility consumed the least polymer (9 g/kg) and generated a 9.2% TS product. Polymer usage for rotary press operation ranged from 20 to 28 g/kg and generated cakes ranging from 16.9 to 18.4% TS. Polymer usage for centrifuge operation ranged from 8 to 24 g/kg and generated cakes ranging from 17.2 to 22.5% TS. Notably, the lower centrifuge chemical usage value corresponded to higher TS content. The lower dosage was employed at the plant that processed mixed pri­ mary/secondary sludge, which typically exhibits higher dewaterability than pure secondary sludges (Metcalf & Eddy, 2013). The results obtained in the current study were compared with those that have been reported elsewhere (Metcalf & Eddy, 2013; Sanin et al., 2011) to assess whether operations were in line with common practice. Among the two rotary presses evaluated, the polymer usage and solids content exceeded the reported values. Rotary presses employed for dewatering aerobically digested waste activated sludges have been re­ ported to consume a maximum of 17.5 g/kg and achieve 28–45% solids. For centrifuge use, the referenced values were 10–15 g/kg to achieve between 18 and 25% solids The discrepancies between observed and reference values may indicate that excess polymer was being dosed in some cases, or that the polymers employed were less effective than those reported in literature. Disposition KPIs involving the average distance that biosolids were hauled and the associated normalized fuel consumption were evaluated

(Fig. 4). For the former calculation, the uncertainty in the estimates was assumed to be constant (0.5 km) since Google Maps™ was employed to obtain the exact address of each farm/landfill and uncertainty was therefore only associated with variation in distance travelled within a given farm. Uncertainty in fuel consumption was associated with the uncertainty in estimated raw sludge production for each plant. From Fig. 3 it can be seen that the average round-trip trucking dis­ tance ranged from 8 to 83 km. There was a substantial difference in trucking distances between the lower (<16 km) and upper (>52 km) five facilities studied. The three highest distances corresponded to plants (C, B, I) that did not have on-site storage, which necessitated additional trucking. In each case, transport involved trucking the biosolids from the WWTP to an off-site location (storage building, lagoon, drying bed) and subsequent trucking from this location to the final destination (farm, landfill). As shown in Fig. 3, fuel consumption normalized by raw sludge production ranged from 1 to 99 L/dry tonne. The broad range observed was influenced by multiple factors: the distance required for trucking, the capacity and fuel economy of the trucks employed for trans­ portation, and the presence of on-site dewatering. Notable observations were obtained when comparing facilities with similar trucking dis­ tances, but different dewatering practices. Among the three plants with the highest trucking distances, only plant C employed dewatering (Plant I employed an off-site drying bed). As a result, plant C consumed 85% and 72% less fuel than plants I and B, respectively. Plants E and G had similar trucking distances (53 and 54 km, respectively), but the former facility consumed 75% less fuel as a result of on-site dewatering (Geo­ Tube™). Similar observations were made for plants J and F, which had similar trucking distances and a substantial difference in fuel con­ sumption for the facility that employed dewatering (plant J). A broad range of trucking distances and normalized fuel consump­ tion were observed among the facilities studied. The difference between trucking distance and fuel consumption was largest when comparing facilities that employed chemically enhanced dewatering against those that did not. The results indicate that the implementation of dewatering to reduce trucking requirements is a consideration worthy of investi­ gation. From a sustainability standpoint, the reduced fuel consumption represents a savings in the carbon footprint associated with trucking. However, as will be discussed subsequently, the manufacture of poly­ mers can represent a substantial source of GHG emissions. KPIs were estimated on the basis of total phosphorus (TP), total ni­ trogen (TN), and potassium (K) content of the biosolids products as measures of product quality. The TP and TN contents of the product biosolids did not differ between facilities and were found to range from 19 to 40 g TP/dry kg and 25–69 g TN/dry kg respectively. Among the nine facilities that did not add K to their sludge during the treatment process, the K content ranged from 0.9 to 6.0 g K/dry kg (median of 3.1 g K/dry kg) while Plant J which added K produced biosolids with an average of 53 g K/dry kg (standard deviation of 26 g K/dry kg). Across all facilities studied, the TP, TN, and K (non-supplemented) contents were consistent with those found in practice (Metcalf & Eddy, 2013). The generation of GHG credits from land application of these nutrients as a fertilizer supplement will be discussed subsequently. The E. coli content of the biosolids were evaluated as a measure of product quality. In all cases, the E. coli values were at least 10 fold lower than the Ontario regulatory limit of 6.3 log (CFU/g) for agricultural land application. The contents ranged from 2.1 to 5.2 log (CFU/g), while the 25th, 50th, and 75th percentile corresponded to 2.3, 3.8, and 4.4 log (CFU/g), respectively. Four facilities generated a product that met USEPA Class A requirements for E. coli content (plants D, E, G, J). This result was expected for plants G and J, since both facilities employed stabilization processes (thermo-alkali hydrolysis and ATAD, respec­ tively) that are designed to disinfect the sludge (Metcalf & Eddy, 2013). The results were more notable for plants D and E that employed con­ ventional aerobic digestion. The latter facility employed the GeoTube™ process following the digestion process. As a form of long term storage, 6

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Journal of Environmental Management 256 (2020) 109893

Fig. 3. Disposition KPI results.

Fig. 4. Sludge handling GHG emissions per dry mass of raw sludge produced.

the GeoTube™ appears to function similar to a system implemented by Eyre et al. (2018) that also generated a Class A product with respect to E. coli content. Taken together, the observations indicate that long-term storage may emerge as a solution for obtaining a Class A product without substantial energy and labour inputs. The ratio of heavy metal concentrations to the NASM limit was evaluated as a measure of contamination. All concentrations were found to be below regulatory limits for agricultural land application in Ontario. The highest ratio was observed for copper at all plants, and ranged from 14 to 69% of the regulatory limit. Zinc and selenium were

the most common metals to be observed as either the second or third highest ratio for the plants evaluated. The former metal was observed in ten occurrences, while the latter was observed in seven instances. The only other metals observed as either the second or third highest ratio were molybdenum (two occurrences) and arsenic (one occurrence). The arsenic observation corresponded to a facility that did not practice land application (plant I). Viewed collectively, the heavy metal content of the biosolids were unlikely to limit agricultural land application (Aljerf and AlMasri, 2018). KPI values reflecting GHG emissions were estimated for all aspects of 7

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operation of each of the plants examined (Fig. 4). In this analysis GHG credits were calculated for plants that employed the biosolids in agri­ cultural land application where the nutrients were considered to offset the use of manufactured fertilizers. From Fig. 4 it can be seen that the net GHG emissions ranged from 119 to 299 g CO2/kg. Of the eight plants that practiced land application disposition, six exhibited net negative emissions, which ranged between 119 and 4 g CO2 eq./kg. In each case, the carbon credits gained from chemical fertilizer offset exceeded the emissions associated with plant operations and biosolids trucking. Of the plants studied, two facilities practiced land application but did not exhibit negative emissions. A third facility (plant H) exhibited negative emissions ( 4 g CO2 eq./kg) to a substantially lesser degree than other such plants within the sample [-119 to 86 g CO2 eq./kg]. Plant B exhibited emissions near zero (4 g CO2 eq./kg), while the emissions associated with plant J (128 g CO2 eq./kg) were substantially higher than its peers that practiced land application. The net positive emissions observed for plant B were primarily a result of the trucking emissions (137 g CO2 eq./kg), which exceeded those associated with electricity consumption (109 g CO2 eq./kg). In the case of plant J, emissions associated with energy consumption and chemical use exceeded the credits gained from fertilizer off-sets. Among the plants that employed polymers for dewatering, the car­ bon intensity impact was pronounced for facilities that were identified as using chemical quantities that were greater than those reported in the literature (plants J, D, H). Notably, carbon emissions associated with polymer use were similar to electricity-associated emissions for plants D and J. Conversely, the plants that used polymer quantities consistent with literature values (plants E and C) had amongst the lowest carbon footprints of all facilities. The results for plant C were particularly notable in that the facility had the highest distance required for trucking among all plants. Plant I had the highest net carbon emissions (299 g CO2 eq./kg) as it did not receive GHG credits for nutrients (biosolids were landfilled). This plant had 186 g CO2 eq./kg more emissions than plant (D) that also landfilled its biosolids. The latter plant practiced on-site dewatering, thereby providing an indication of the environmental benefits that such a technology can provide. Plant H received a larger K fertilizer credit ( 37 g CO2 eq./kg) than its peers, which off-set the emissions associated with the production of the KOH chemical used in the stabilization pro­ cess (36 g CO2 eq./kg). Its electricity-related emissions totaled 104 g CO2 eq./kg, of which 90 g CO2 eq./kg were collectively associated with aerated holding and odour control. If thermal hydrolysis were to be implemented at facilities without aerated holding or less stringent odour control requirements, the technology could exhibit emissions similar to the other observed sludge handling systems that were carbon sinks. The positive outcomes with respect to GHG KPIs were due in part to the low carbon intensity of electricity production in Ontario (Environ­ ment and Climate Change Canada, 2018). From a sustainability perspective, the outcome was noteworthy in two respects: a) land application is the most common disposal method for small WWTPs in Ontario (Jin and Parker, 2017), and b) all observations corresponded to facilities practicing conventional aerobic digestion, which is the most common stabilization technology among small WWTPs in Ontario (Jin and Parker, 2017). The results therefore indicate that when land application is practiced in combination with conventional treatment processes, sludge handling practices in Ontario can be sustainable from a GHG perspective.

digestion plants was 2.7 kWh/dry kg. The minimum stabilization con­ sumption corresponded to a thermo-alkali hydrolysis process. Mechanical dewatering processes represented 2–5% of total sludge handling power draw, however, chemical usage was found to be elevated in some cases. The normalized transportation fuel consumption varied from 1 to 99 L/dry tonne and it was found that dewatering pro­ vided a marked reduction in consumption, while only requiring a nominal amount of additional electricity. Elevated polymer consump­ tion was found to negatively impact on GHG emissions. Four facilities generated product that met Class A requirements for E. coli content, one of which was achieved through a low-tech long-term storage technology (GeoTube™). This appears to be cost-effective method to reduce the pathogen content biosolids. Carbon emissions ranged from 119 to 299 g CO2 eq./kg among all plants studied. Six of the eight facilities that practiced land application exhibited net-negative emissions, ranging from 119 to 4 g CO2 eq./ kg. The finding provides evidence that land application as a biosolids utilization practice contributes to sustainability from a GHG standpoint. The substantially different levels of normalized inputs/outputs observed in this study demonstrate the value of the developed bench­ marking methodology. It provides a means of identifying opportunities for optimization within systems, and a basis for assessing alternative technology implementation. From an operations and sustainability standpoint, the benchmarking approach can be employed to better un­ derstand how a utility is performing relative to peers of similar capacity and scope of operations and to identify areas for further investigation in support of initiatives to enhance sustainability of operations. 5. Acknowledgements The authors would like to acknowledge and extend thanks to the following parties who provided financial support for the research: (Note: none of the funding sources played any role in the design or conducting of the work, nor the preparation of the journal manuscript.) � � � � �

Ontario Ministry of Environment and Climate Change Ontario Clean Water Agency Oxford County Walker Environmental Southern Ontario Water Consortium

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jenvman.2019.109893. References Alanya, S., Dewulf, J., Duran, M., 2015. Comparison of overall resource consumption of biosolids management system processes using exergetic life cycle assessment. Environ. Sci. Technol. 49 (16), 9996–10006. Aljerf, L., AlMasri, N., 2018. A gateway to metal resistance: bacterial response to heavy metal toxicity in the biological environment. Ann. Sci. 2, 32–44. https://doi.org/ 10.29328/journal.aac.1001012. Alvarez-Gaitan, J.P., Short, M.D., Lundie, S., Stuetz, R., 2016. Towards a comprehensive greenhouse gas emissions inventory for biosolids. Water Res. 96, 299–307. Bailey, E.L., Whitfield, T., Revels, J., D’Adamo, P., 2014. Benchmarking of biosolids and residuals handling operations: building the measuring stick. Proc. Water Environ. Fed. (6), 2773–2797, 2014. Balm� er, P., Hellstr€ om, D., 2012. Performance indicators for wastewater treatment plants. Water Sci. Technol. 65 (7), 1304–1310. Bastian, R.K., 1997. The biosolids (sludge) treatment, beneficial use, and disposal situation in the USA. Eur. Water Pollut. Control 2 (7), 62–79. Beavis, P., Lundie, S., 2003. Integrated environmental assessment of tertiary and residuals treatment-LCA in the wastewater industry. Water Sci. Technol. 47 (7–8), 109–116. Belloir, C., Stanford, C., Soares, A., 2015. Energy benchmarking in wastewater treatment plants: the importance of site operation and layout. Environ. Technol. 36 (2), 260–269.

4. Conclusions A benchmarking strategy was developed and employed to charac­ terize the sustainability of sludge handling in small WWTPS. Among all plants studied, overall electricity consumption for sludge handling ranged from 0.9 to 3.9 kWh/dry kg (median: 2.2 kWh/dry kg). The maximum consumption corresponded to the facility practicing ATAD stabilization, while the highest value among conventional aerobic 8

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