Assessment of Multiple Sustainability Demands for Wastewater

Article pubs.acs.org/est

Assessment of Multiple Sustainability Demands for Wastewater Treatment Alternatives: A Refined Evaluation Scheme and Case Study Xu Wang,*,† Junxin Liu,*,† Nan-Qi Ren,‡ Han-Qing Yu,§ Duu-Jong Lee,∥ and Xuesong Guo† †

Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, P. R. China State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, 150090, P. R. China § Department of Chemistry, University of Science & Technology of China, Hefei, 230026, P. R. China ∥ Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan ‡

S Supporting Information *

ABSTRACT: Current estimation schemes as decision support tools for the selection of wastewater treatment alternatives focus primarily on the treatment efficiency, effluent quality, and environmental consequences for receiving water bodies. However, these schemes generally do not quantify the potential to convert pollutants in wastewater to recoverable resources. This study proposes a refined evaluation scheme for choices of wastewater treatment processes that quantifies not only adverse environmental effects but also bioenergy and nutrient recovery indices. An original means of data processing was established and clear estimate indicators were consequently obtained to allow a smooth overall estimation. An array of wastewater treatment alternatives that meet three effluent limits were used as case studies to demonstrate how the present scheme works, simultaneously, to identify optimum choices. It is concluded in the overall estimation that the lower sustainability of wastewater treatment contributed by increasingly stringent discharge demands was offset and mitigated by the resource-recovery scenarios involved, and the scenario of recovering nutrients via excess-sludge composting was of more benefit. Thus, before tightening wastewater discharge requirements, one should bear in mind the situation of multiple sustainability by setting a goal to achieve not only the greatest reduction in environmental burden but also the maximum resource-recovery benefits.

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generation through combustion.9 Other evolving examples of bioenergy recovery from wastewater include hydrogen production and electricity harvesting using microbial fuel cells.10−13 The wastewater treatment industry is striving for sustainability by reducing environmental footprints and enhancing resource recovery.14 Meeting these multiple demands simultaneously is often practically difficult. A context-specific assessment technique to estimate the performances of wastewater treatment alternatives and a means to resolve trade-offs among these alternatives is desired. To date, there are two representative estimation methods: the traditional effluentquality-based method focusing only on the treatment efficiency and effluent quality of the studied systems, and life cycle assessment (LCA) primarily concentrating on the environmental consequences of discharging effluent into natural

INTRODUCTION Biological wastewater treatment technology is facing challenges in terms of electricity consumption, environmental emission, and resource degradation in the face of increasingly sophisticated discharge requirements. The United States Environmental Protection Agency claimed that the electricity needs for wastewater treatment plants (WWTPs) in the U.S. will increase by 20% in the next 15 years, which is similar to forecasts for other developed countries.1,2 Increased production of excess-sludge due to stringent wastewater treatment standards further increases energy consumption and intensifies the environmental consequences of operating a WWTP.3,4 Moreover, WWTPs are regarded as a significant source of greenhouse gas (GHG) emissions.5 The concept of wastewater treatment design is experiencing a paradigm shift in that it now considers wastewater not only as a waste stream in need of disposal but also as a source of valuable chemicals and energy.3,6,7 Struvite (NH4MgPO4·6H2O) can be harvested from wastewater streams or sludge fermentation supernatant as fertilizer.8 The CH4-containing biogas generated from sludge anaerobic digestion can be used for power © 2012 American Chemical Society

Received: Revised: Accepted: Published: 5542

February April 18, April 24, April 24,

24, 2012 2012 2012 2012

dx.doi.org/10.1021/es300761x | Environ. Sci. Technol. 2012, 46, 5542−5549

Environmental Science & Technology

Article

Figure 1. Framework of the evaluation scheme proposed in this work.

environments.15 LCA can help reveal a number of previously unknown environmental impacts of WWTPs.16 However, there are still limitations to the use of these two assessment methods in evaluating performances of WWTPs.17−20 Considering that LCA is a very time- and effort-intensive process, it is not surprising that previous studies on wastewater/sludge treatment processes have only addressed small components of a comprehensive LCA, such as the effects of wastewater treatment processes on the natural environment; other sustainability considerations, such as the potential for resource recovery from wastewater/sludge, are not included in the current scope of LCA. Additionally, both the traditional effluent-quality-based method and LCA require detailed and high-quality data to enable smooth processing of an accurate assessment, and they generally cannot use field data collected from a WWTP operation. The objective of the present study is to propose a refined estimation scheme that considers adverse environmental impacts and resource recovery benefits. Using this proposed scheme, appropriate wastewater treatment options, including effluent regulations and preferred WWTP alternatives, can be rapidly identified for decision-makers. The scheme takes a novel process modeling approach to resolve the problems of

data gaps and data quality mentioned for other estimation approaches, and an original means of data processing is constructed and clear estimation indicators are consequently obtained to allow smooth overall estimation. The evaluation scheme is illustrated in a case study that compares an array of wastewater treatment systems that meet various effluent limits. Finally, overall evaluation outcomes are derived from this case study to demonstrate how an options approach can enhance the overall sustainability of a WWTP, simultaneously identifying the optimum wastewater treatment choices.

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METHODOLOGY Assessment Framework. Figure 1 shows the estimation framework comprising four steps: definition of the estimate boundary, data acquisition, data processing, and analysis of the results. The boundary definition is considered as follows. The objective of the scheme is to compare the combined sustainability of various wastewater treatment options. Therefore, an array of wastewater treatment systems including intersystem flows were selected as the estimate objects. Schemes in which wastewater and/or sludge were collected and transported were not considered since wastewater and/or 5543



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assumptions in the GHG calculation are presented in Table S1 and all GHGs were converted to the CO2 equivalent. In the case of energy consumption, the energy requirement for aeration, pumping for activated-sludge return and mixed liquor recirculation, liquid mixing in the anoxic and anaerobic unit, and heating for the sludge anaerobic digestion unit were considered.30 The BioWin aeration model was used to estimate the oxygen transfer rate (kg O2/d) and the electricity consumption for the aeration unit could be consequently calculated by assuming an aeration efficiency of 2 kg O2/kWh.31 Electricity consumption for all pumps was estimated from the individual flow rate and the assumed pumping head. Hydraulic efficiencies of pumps were estimated from standard curves with motor efficiency of 95%.32 The stirring powers for liquid mixing in the anoxic and anaerobic zones were assumed to be 5 W/m3 liquor.33 Energy demands to operate the sludge anaerobic digester mainly included the needs for heating. The amount of heat (kWh) required per wet metric ton (1000 kg) of sludge is calculated from the difference between the initial and desired temperatures multiplied by the specific heat capacity of sludge with 6% solids (1117 kWh/kg °C),33 and the heat loss from the digester with available heat transfer coefficients.33 In the case of an industrial-scale digester, the energy need for stirring is remarkably lower than that for the heating of sludge or heat loss from the digester, and thus, the energy consumption by the stirrer was negligible and was not taken into account herein in the estimation.34 Other energy consumption by the WWTP was assumed to be 20% of the total according to previous statistics.35 With regard to the determination of resource recovery indices, the N and P contents in excess sludge (kg N/d and kg P/d) were obtained by the BioWin activated sludge/anaerobic digestion (AS/AD) model using mass balance theory, while struvite generation (kg ISS/d) was estimated employing a kinetic model taken from the literature;36 the bioenergy recovery (kWh/m3 of treated wastewater) from biogas combustion was determined from the simulated biogas production (m3 CH4/d) multiplied by the assumed heat value of biogas (11.67 kWh/m3, converted from 42 MJ/m3) and total energy conversion rate (75%).30 Note that all estimation data obtained in this step had different units and various orders of magnitude, and they could not be used as clear indicators for overall estimates if employed directly in the assessment. Data processing was thus carried out by normalizing the estimation data collected in the data acquisition step. First, the minimal and maximal values of each estimation category were identified, on the basis of which the data set of each estimation category was then normalized to [0,1] using a general formula, eq 1 or 2. This approach of data processing is called normalization.

sludge were assumed to be shared by all systems, and thus, only the operation process was taken into account in the analysis. The functional unit of the study was the amount of wastewater treated at the WWTP per day with the subsequent sludge treatment expressed in units of cubic meters per day. In the present scheme, even if four assessment scenarios, namely environmental-footprint scenario, bioenergy-recovery scenario, nutrient-recovery scenario, and simultaneous bioenergy- and nutrient-recovery scenario, were considered at the same time for the sake of overall discussion, the appropriate assessed scenario could be selected according to actual needs. In the environmental-footprint scenario, the estimation of the WWTP focuses only on the adverse environmental impact and the estimation is made via inventory analysis, similar to lifecycle inventory assessment. Methane (from anaerobic digestion of excess sludge), which is claimed to be the best bioenergy recovery option,16 is considered in the bioenergy-recovery scenario. In the nutrient-recovery scenario, excess sludge is thickened, dewatered by a belt filter press, and then assumed to be transported to an off-plant composting facility to be recycled into agricultural soil. The N and P sludge content allows the substitution of the equivalent amount of N and P mineral fertilizer. In the simultaneous bioenergy- and nutrient-recovery scenario, methane is recovered from the sludge anaerobic digester and combusted to generate energy, struvite is harvested from the digester supernatant, and the dewatered digested sludge is further recycled on agricultural soils via composting. Note that the three scenarios of resource recovery assessment include balanced considerations of the adverse environmental impact and resource recovery benefit. Data acquisition is conducted principally via computer modeling and simulation. In other estimation approaches, unknown or incomplete data for WWTP evaluation are commonly associated with high levels of uncertainty since they are mainly obtained from fewer sites and/or under very different production conditions via field survey, personal interviews, or internal reports.21 Thus, to improve data quality and allow accurate evaluation, the required data in the proposed scheme for evaluation were gained via process modeling and simulation using code such as BioWin Simulator (V.3.0., BW31952).22−26 Through simulations, the flow and composition data of wastewater/sludge/air streams and performance characteristics can be obtained directly, and the inventory data for each assessment scenario are calculated subsequently. In quantifying adverse environmental consequences, the main estimation categories are chemical use, GHG emissions, and energy consumption. In a WWTP operation, the above three elements are regarded as the obvious contributors associated with abiotic depletion, global warming, ozone depletion, acidification, and ecotoxicity, which are the main impact categories used in environmental impact assessment.4,27,28 Thus, the combination of chemical use, energy consumption, and GHG emission was employed herein to rapidly indicate the overall environmental consequence. In the present scheme, the degree of chemical use was acquired directly from the BioWin Simulator. The GHG calculation considered the release of CH4, N2O, and CO2 gases. CH4 was calculated using mass balance and mass transfer models; N2O was determined via a combination of Intergovernmental Panel on Climate Change (IPCC) emission factors and relevant literature data; and CO2 was calculated only from energy consumption, since the CO2 emission from the oxidation of sewage organics is biogenic according to IPCC rules.29 Detailed

NFi =

PFi =

pi − min(p1 , p2 , ..., pn ) max(p1 , p2 , ..., pn ) − min(p1 , p2 , ..., pn )

(1)

qi − min(q1 , q2 , ..., qn) max(q1, q2 , ..., qn) − min(q1 , q2 , ..., qn)

(2)

After normalization, each wastewater treatment alternative has its own normalized factors, which consist of adverse normalized factors (chemical use, NFchem; energy consumption, NFener; GHG emissions, NFgree) and gain normalized factors (bioenergy recovery, PFbioe; nutrient recovery from sludge composting, 5544



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Figure 2. Simplified diagrams of all wastewater treatment alternatives with notation of optimum alternatives at each discharge level.

PFnitr and PFphos; and nutrient recovery from struvite generation, PFstru). Note that a value zero for the normalized factor means the null effect of the corresponding estimation category, whereas a value of 1 suggests the significant impact of the relevant estimation category. Additionally, the detailed definitions of NF and PF values are given by eqs S1−S7. With quantified NF and PF values, the respective synthesized factors for each assessment scenario can be calculated for the final result analysis. In the environmental-footprint scenario, the synthesized factor can be quantified via a general formula, eq 3. This formula shows that all the estimation categories of the adverse environmental effect will be considered. For the three resource recovery assessment scenarios, synthesized factors can be quantified via a general formula, eq 4. This formula states that the balance of the adverse environmental impact and resource recovery indices is considered. SF =

∑ waNF

(3)

SF =

∑ wg PF − ∑ waNF

(4)

Scheme section. The values of the synthesized factors range between −1 and 1, with a value of −1 indicating a very negative overall impact, and a value of 1 indicating a remarkable beneficial overall effect for the studied scenario. Detailed quantification rules of SF values in four assessment scenarios are presented in the Supporting Information (eqs S8, S12, S21, and S30).

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CASE STUDY Studied WWTPs. Municipal WWTPs applying anaerobic, anoxic, and oxic processes to remove organics and nutrients from wastewater are widely used.37−40 In this work, six typical wastewater treatment alternatives (Figure 2) were selected to treat 2 × 105 m3/d of raw municipal wastewater having a chemical oxygen demand (COD) of 500 mg/L and containing 50 mg N/L and 12 mg P/L to meet the three levels permitted by Chinese discharge regulations.41 In brief, Level 1 limits the COD to less than 100 mg/L, the NH3−N concentration to less than 25 mg N/L, and the total phosphorus (TP) concentration to less than 3 mg P/L, but there is no limitation to the total nitrogen (TN) discharge; Level 2 limits the COD to less than 60 mg/L, total nitrogen (TN) to less than 20 mg N/L, NH3−N to less than 8 mg N/L, and TP to less than 1 mg P/L; and Level 3 limits the COD to less than 50 mg/L, TN to less than 15 mg N/L, NH3−N to less than 5 mg N/L, and TP to less than 0.5 mg P/L. Thus, Level 3 is more stringent than Levels 1

Note that wg and wa are the weighting factors for the estimation categories, since a different estimation category will make a different contribution to the overall effect. In this study, however, we assumed all weighting factors to be 1. This assumption will be discussed in the Limitations of the Proposed 5545



−0.73 −0.09 0.27 −0.16 a

Very negative overall effect = −1, remarkable beneficial overall effect = 1. Beneficial effects are shown in bold.

A6 A5 A4

−0.53 −0.39 0.06 −0.15 −0.55 −0.13 0.27 −0.03

A3 A2

−0.44 −0.34 0.14 −0.17 −0.45 −0.28 0.19 −0.04

A1 A6

−0.64 0.08 0.34 −0.03 −0.65 0.06 0.31 −0.07

A5 A4

−0.24 −0.28 0.13 −0.01 −0.45 0.07 0.37 0.19

A3 A2

−0.36 −0.24 0.18 0.40 −0.36 −0.18 0.23 0.06

A1 A6

−0.43 −0.03 −0.05 −0.10 −0.43 −0.02 −0.13 −0.22

A5 A4 A2

−0.20 −0.43 −0.20 −0.43

A1

−0.20 −0.39 −0.06 −0.22

factors

SFnorm‑envi SFnorm‑bioe SFnorm‑nutr SFnorm‑simu

A3

−0.17 −0.38 −0.01 −0.12

Level 3 5546

Level 2

RESULTS AND DISCUSSION Wastewater Treatment Alternatives. Table 1 lists the results of the wastewater treatment alternatives that meet different wastewater discharge requirements. Among the studied wastewater treatment alternatives in Figure 2, the most sustainable options were A3 under the Level 1 discharge requirement, A4 under the Level 2 discharge requirement, and A2 under the Level 3 discharge requirement, with which a minimal negative environmental footprint is required for a sitespecific WWTP with no resource recovery steps. When bioenergy recovery is incorporated, A5 under the Level 1 discharge requirement, A6 under the Level 2 discharge requirement, and A5 under the Level 3 discharge requirement are the sustainable options. If nutrient recovery is considered, A3 under the Level 1 or Level 2 discharge requirement and A3 or A5 under the Level 3 discharge requirement are proposed. When simultaneous nutrient and bioenergy recovery is considered, A3 under the Level 1 or Level 3 discharge requirement and A2 under the Level 2 discharge requirement are the best. The optimum configuration depends on the demands imposed on the design. Overall Comparative Assessment. Overall effects of the three resource recovery scenarios on the sustainability of increasingly stringent discharge requirements are estimated (Figure 3). It is obvious that increasingly stringent discharge needs, such as those up to Level 3, yield increasing environmental burdens, even if limits are proposed prior to prevent the eutrophication of receiving waters. Additionally, by comparing the cases of resource recovery scenarios with those of “no recovery” in Figure 3, it is seen that the efforts made to meet stringent wastewater discharge standards are largely compensated for by the benefits of resource recovery. In the bioenergy-recovery scenario, there is a 4.2%−82.2% positive increment in the scores of the overall effect compared with the situations without bioenergy recovery. Nevertheless, the net overall effects are still governed by the environmental burden since additional chemicals and energy are required to operate sludge anaerobic digesters. Additionally, the situation where a bioenergy-recovery scenario is introduced to WWTPs that meet stringent discharge requirements (e.g., Level 3 here), and the situation where no recovery scenario is incorporated at WWTPs that meet lenient discharge requirements (e.g., Level 1 here), both have scores of the overall effect of −0.24. This result is interesting since it indicates that a stringent discharge requirement may be a beneficial option for sustainable wastewater treatment from the perspective of coupled environment protection and resource recovery.

Level 1

Table 1. Synthesized Factors for All Alternatives Meeting Varying Discharge Levels for Assessment of Multiple Sustainability Demandsa

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0.00 −0.14 0.24 0.15

and 2. None of the studied WWTPs have primary sedimentation tanks to maintain a suitable carbon-to-nitrogen ratio for denitrification. Important information about six wastewater treatment alternatives is given in Figure 2. The additional design parameters are listed in Table S2. The capacities of the wastewater treatment alternatives and the sludge treatment for achieving multiple demands were estimated according to the framework in Figure 1. Environmental parameters and the main characteristics of influent in the process design and modeling are presented in the Supporting Information (Table S3). A BioWin comprehensive model was established for modeling. The convergent solution was set as the solution for which the normalized residual for all variables was less than 10−3. All simulations were conducted on a Dell mobile workstation (M4500).

−0.71 −0.21 0.19 −0.27

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burden. The results presented in Figure 3B also imply that introducing a nutrient-recovery step through sludge composting to WWTPs that meet lenient discharge requirements (e.g., Level 1 here) does not provide greater benefits, and the overall effect is still controlled by the environmental burden. Relatively speaking, Level 2 clearly is the optimum trade-off point wherein moderate discharge limits combined with resource recovery can yield sustainable wastewater treatment when compared with the other two combinations of treatment and resource recovery (Levels 1 and 3) considered in this study. Thus, before tightening wastewater discharge requirements to Level 3, one should bear in mind the situation of multiple sustainability by setting a goal to achieve not only the greatest reduction in environmental burden but also maximum resource recovery benefits. Data Quality Assessment. This study was highly data intensive and the results are therefore largely dependent on the data quality. A qualitative and quantitative quality assessment of each data input is briefly presented (Table 2). Most of the calculation methods for the estimation categories in this study are widely used and it was thus possible to find corroborating accounting parameters for quantifications and further evaluations. The main uncertainty in the analysis is in the use of assumed parameters for N2O accounting. Herein, N2O was assumed to be released from biological wastewater treatment process, effluent discharge to the watercourse, and direct volatilization from excess sludge, which are regarded as the main emission pathways of N2O in WWTPs. As no well-proven N2O kinetic model is available for N2O prediction, some emission factors were summarized from the scarce literature on N2O quantification. Therefore, less confidence can be had in those numbers. Thus, further efforts should be undertaken toward incorporating the most accurate method for N2O quantification once the relevant prediction model becomes available. Moreover, with respect to the quantification of other energy consumptions in WWTPs, there have been few detailed studies and previously obtained statistical data were used in the study, for which there is less confidence. Limitations of the Proposed Scheme. Because each impact category has relative significance, the weighting of impact categories is supposed to be considered in an elaborated

Figure 3. Comparative estimates of the overall effects of the following resource-recovery scenarios adopted for the WWTPs that meet increasingly strict discharge levels: (A) bioenergy-recovery; (B) nutrient-recovery; (C) simultaneous bioenergy- and nutrient-recovery.

With stringent discharge requirements such as Level 3, there is a net overall beneficial effect with nutrients being recovered through sludge composting (Figure 3B). Comparing the bioenergy-recovery scenario and nutrient-recovery scenario, the latter has the greater overall benefits since the former requires additional chemicals and energy demands in the anaerobic digestion step, which increase the environmental Table 2. Results of Data Quality Assessmenta

chemical use energy consumption aeration pumping mixing heating others GHG emissions CH4 N2O CO2 resource recovery energy compost struvite

methodb

independ.c

represent.d

agee

geog.f

tech.g

average

1

3

1

2

2

1

1.7

2 2 2 2 3

3 3 3 3 4

1 1 1 1 2

2 2 2 2 3

2 2 2 2 3

1 1 1 1 2

1.8 1.8 1.8 1.8 2.8

2 3 2

3 1 3

1 3 1

2 3 2

2 3 2

1 3 1

1.8 2.7 1.8

2 1 1

3 3 3

1 1 1

2 2 2

2 2 2

1 1 1

1.8 1.7 1.7

a

Best quality = 1, worst quality = 5; the values of each category are defined in Table S4. bAcquisition method. cIndependence. dData representativeness. eData age. fGeographical correlation. gTechnological correlation. 5547



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assessment. To this end, weighting factors of each estimation category were included in the proposed scheme, but all were equally assumed to have a value of 1 in this case. It seems that this assumption would introduce uncertainty in the estimation results, since a different impact category may have different significance, and the estimation results may be affected by setting different weighting factors. In fact, the weighting element has always been a controversial issue owing to required incorporation of social, political, and ethical values. Furthermore, there is no best weighting method available, and there is no recommended set of weighting factors. Thus, before carrying out an in-depth investigation of the importance of each estimation category from a multiple value perspective, all the estimation categories for the adverse environmental effect and resource recovery were equally considered in this study. Because of the lack of related in-depth studies, future efforts should be directed toward proposing a preferred weighting method, investigating its effect on final evaluation results and identifying the preferred weighting factors for all estimation categories. Even if chemical use, energy consumption, and GHG emission are successfully employed herein to rapidly estimate the overall adverse environmental impact with benefits of time and effort savings, this work is not a “one size fits all” endeavor. For example, for facilitating the identification of the optimum alternative, all alternative wastewater treatment systems were compared for the same effluent limits, and the nitrogen and phosphorus loads in effluent made little difference among the alternatives; thus, no eutrophication-relevant impact category was included in the present scheme and eutrophication was not discussed. If there were further concerns on the quantification of a certain category, such as eutrophication, acidification, or ecotoxicity, a combination of the proposed scheme and LCA may be desirable. Implications of This Work. Wastewater and excess sludge handled in WWTPs are potential renewable resources. However, the use of such resources calls for the appropriate design of achievable sustainability strategies for wastewater treatment, including consideration of effluent standards and alternative treatment systems. This study proposed a rapid and elaborated scheme that considers impacts in assessing the multiple sustainability of a WWTP and offers guidance for decision-makers in choosing appropriate wastewater treatment options, taking into account effluent regulations and optimal WWTP alternatives. Additional works on the weighting of estimation categories are required in the future.

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ACKNOWLEDGMENTS

This study was supported by the National Science Foundation of China under Project 51138009 (Key Project) and Project 50921064 (National Creative Research Group).

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NOMENCLATURE NF adverse normalized factor, dimensionless PF gain normalized factor, dimensionless SF synthesized factor, dimensionless SFnorm‑envi synthesized factor for the environmental-footprint scenario, dimensionless SFnorm‑bioe synthesized factor for the bioenergy-recovery scenario, dimensionless SFnorm‑nutr synthesized factor for the nutrient-recovery scenario, dimensionless SFnorm‑simu synthesized factor for the simultaneous bioenergyand nutrient-recovery scenario, dimensionless p raw data of the adverse estimation category q raw data of the gain estimation category wg weighting factor for the gain estimation category wa weighting factor for the adverse estimation category Subscripts

n i g a

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total number of assessment objects serial number of the assessment object, i = 1, 2, 3, ..., n gain factor adverse factor

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ASSOCIATED CONTENT

S Supporting Information *

Additional information on the assessment method, data input, and data sources for the case study. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone/fax: +86 0 10 62849133; e-mail: [email protected] (J.L.); wangxu_offi[email protected] (X.W.). Notes

The authors declare no competing financial interest. 5548



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Assessment of Multiple Sustainability Demands for Wastewater

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