3.1. CLD Development
This section explores the interactions and feedback between WWC and WWTP systems and presents CLDs for different sectors such as the consumer, physical, financial, and environmental.
The total inflow volume received by a WWTP depends on the volume of inflow and infiltration (I&I) entering into the WWC pipe network system, and the volume of sewage generated by system users. While the revenue of the WWTP sector is generated based on consumers’ metered water, its operational expenses are based on the fraction of metered water plus the extraneous inflow and infiltration (I&I). Rehan et al. [
26] showed that the I&I makes a significant contribution to wastewater volume; on average, the monthly volume of collected wastewater is 25% higher than the corresponding volume of metered water, and at peak-flow exceeds it by 74% [
26].
The infiltration rate to the WWC pipe network system increases as WWC pipes deteriorate and their internal condition grade increases. Sewage generation is increased by population growth, which also affects the I&I flow rate due to WWC pipe network expansion in urban area development. The consequence of an increasing inflow volume is an increasing cost of operating WWTPs, and the need for capital investment to expand capacity. In contrast, it is assumed that decommissioning a WWTP will have no significant capital cost. Construction and operation of new WWTP capacities, as well as, the installation and operation of extended WWC pipe network will increase the energy footprint of the whole system.
To increase the fund balance, utilities need to increase revenues by increasing user fees. Since the wastewater collection and treatment fees are directly related to the metered volume of water, the response of users will be water-demand reduction which leads to a decrease of the energy footprint by reducing the energy-use in upstream water treatment and water distribution systems. Population growth will also increase the user-fee based revenues of WWC and WWT utilities. Development charges are another revenue stream for utilities, collected to cover the required capital work expenses due to urban development.
Qualitative relationships among the variables below are identified in CLDs, then parametrized in the SD model.
Figure 3,
Figure 4,
Figure 5 and
Figure 6 present the CLDs for the SD model of integrated WWC and WWTP systems.
Figure 3 reinforcing loop (R1) shows an increasing pipe deterioration rates increases the I&I flow rate which in turn decreases the pipes’ condition grade. Rehan [
19] showed that the unit maintenance cost of the WWC pipes will increase with an increase in pipe condition grade. Reinforcing loop (R2) shows a decrease in the WWC pipes’ condition grade as the result of increasing operational expenditures, decreasing utilities’ fund balance, and, subsequently, decreasing available funds for rehabilitation of the WWC pipes. To increase the fund balance, WWC utilities need to increase revenues by increasing user fees. Since the WWC fees are directly related to the metered volume of water, the response of users will be water-demand reduction.
Reinforcing loop (R3) shows that users’ water-conservation efforts result in decreasing WWC revenues, which means less available funds for utilities to spend.
Figure 4 shows the CLD for WWTP system. Reinforcing loop (R4) shows that users’ water-conservation will result in decreasing WWT revenues and decreasing WWT fund balance. Parkinson et al. [
18] have reported that an increase in the concentration of suspended solids (SS) and biological oxygen demand (BOD) in wastewater are a result of water-conservation scenarios. Min and Yeats [
27] have shown an increase in the operational cost of WWC and WWT services as a result of BOD and SS level increases. DeZellar and Maier [
28] argued that the total cost of WWT might be lower with a decrease of the total wastewater volume, but the unit cost of the operation and maintenance of WWTP increases due to non-routine operational problems such as clogging, changing bacterial activities, or malfunctioning of the biological treatment processes, and the extra chlorination and recirculation needed to prevent odor problems, etc.
Reinforcing loop (R5) shows the cause-and-effect-chain mechanism that exists between water conservation, sewage pollutant levels, operation, and maintenance costs of the WWTP systems, fund balances, and fee hikes.
Balancing loop (B1) shows that the reduction of total wastewater volume from water conservation will lower the operational and capital expenses and increase the fund balance. The increase of the fund balance will reduce the service-fee-increase rate, leading to a decrease in water conservation.
Figure 5 presents the interconnections between the WWC and WWTP systems. The total inflow volume received by a WWTP depends on the volume of inflow and infiltration (I&I) entering into the WWC pipe network system, and the volume of sewage generated by system users. While the revenue of the WWTP sector is generated based on consumers’ metered water, its operational expenses are based on the fraction of metered water plus the extraneous inflow and infiltration (I&I). Rehan et al. [
26] showed that the I&I makes a significant contribution to wastewater volume as the monthly volume of collected wastewater is 25% higher than the corresponding volume of metered water, and at peak-flow exceeds it by 74%.
Reinforcing loop (R6) shows that increasing the WWT fee will increase the water conservation rate, which in turn cause reduces the WWC revenue and the funds available for reinvestment and rehabilitation of the WWC pipes. This in turn leads to further deterioration of a WWC pipes (increases the condition grade), which increases I&I flowing into the WWTPs.
Marleni et al. [
29] demonstrated that water-use reduction in various water-demand management scenarios increases the concentration of sulfide and sulfate levels by 30% and 40%, respectively. These two compounds, which are the main source of hydrogen sulfide formation, will cause odor problems and corrosion of WWC pipes.
Reinforcing loop (R7) shows that the increased pollutant concentration, from water conservation, will increase WWC pipes’ blockages rate, resulting in reduced service performance of the wastewater pipe network, and increase the willingness of service users to accept WWT fee hikes and pay for service improvements.
Figure 6 shows impacts of population growth and urban densification on WWC and WWTP systems.
Growing population with urban development will cause the WWC pipe-network length to extend and incur capital and operational costs for both the WWC and WWTP utilities. Sewage generation will increase with population growth and the I&I flow rate will increase due to WWC pipe network expansion. On the revenue side, population growth will increase the user-fee based revenues for both WWC and WWT due to the increased volume of metered water. Development charges are collected from land developers to cover the required capital work expenses.
Figure 7 shows all developed interconnections and feedbacks (
Figure 3,
Figure 4,
Figure 5 and
Figure 6) between the WWTP and WWC physical, finance, and consumer sectors. It also depicts the related components contributing to GHG emissions from WWC and WWTP systems.
As discussed earlier, water conservation, as well as I&I reduction, will increase pollutant concentrations in wastewater. The increased BOD level will increase the methane gas yield as a main source of GHG emission from WWT processes [
23]. Construction and operation of new WWTP capacities, as well as the installation and operation of extended WWC pipe network, will increase the energy footprint of the whole system. Replacement of highly deteriorated pipes (ICG5) by open-cut trenching technologies can lead to traffic delays and consequently more GHG emissions from cars’ engine-fuel combustion [
30]. Water-demand reduction will also lead to a decrease in the energy footprint by reducing the energy-use in upstream water treatment and water distribution systems, which in turn will reduce GHG emission.
3.2. SD Model Parameterization
The Rehan [
19] and Ganjidoost [
22] developed their SD model using Stella® software, Research Version 9.1.4 [
31]. The four basic elements, as in any SD model, are the stock, flow, converter, and connector. Stocks represent the accumulation of physical or non-physical elements in a system (e.g., the length of sewer pipes in worst condition grade (Sewers_Grade5) presented in
Figure 8). Flows are used to model the inputs or outputs to the stock, and represent the activities in a system (e.g., the length of new wastewater pipes added to the network each year (New_WW_pipe_installation) presented in
Figure 8). Converters are used to incorporate the effects of changing variables in an SD model (e.g., the value for the Population_Growth_rate presented in
Figure 8); and, connectors represent the links between the convertors, stocks, and flow components of an SD model. The following section describes the SD model comprised of four sectors: (1) physical, (2) consumer, (3) finance, and (4) environment. It also presents new elements and changes made to the validated and calibrated models developed by Rehan et al. [
20,
21,
26] and Ganjidoost [
22] for WWC network system.
3.2.1. Physical Infrastructure Sector
This section provides a brief overview of the WWC pipe network SD model developed by Rehan, R. [
19] followed by the new model development for the WWTP system.
WWC Physical Model
Pipe inventories are made up of different pipe materials such as vitrified clay, concrete, polyvinyl chloride (PVC), ductile iron, etc., and are grouped into five classes, which are represented in
Figure 8 as stocks, based on their internal condition grade (ICG) as defined by the Water Research Center (WRc) in the United Kingdom [
32]. The method used in Rehan et al. [
26] is adapted to define the deterioration and infiltration rates.
New pipes with the best ICG are in the first stock class, whereas pipes in the worst condition belong to the fifth stock class. Today, PVC pipes are used in new pipe installation projects. Therefore, the new pipes, either for upgrading the ICG5 stock or for urban development and network expansion, are entered into the first PVC pipe stock.
WWTP Physical Model
The physical assets of WWTPs consist of electromechanical equipment, such as pumps, motors, aerators, mixers, tanks, basins, pipes, and buildings.
Figure 9 shows the modeling of WWTP assets at a strategic level, which is based on the WWTP capacity requirement.
The Required_Change_in_WWT_Capacity [m3/d] is equal with the difference between the Available_WWTP_Capacity [m3/d] and the Desired_Total_Capacity [m3/d], which is the sum of annual total wastewater flow (Annual_Total_WW_Flow [m3/d]) and the required reserve capacity (Initial_%_Reserve_Capacity [m3/d]).
The reserved capacity for the maximum seasonal, daily, and hourly peak wastewater flow can be estimated based on two methods: (1) the current reserve capacity of the WWTPs, or (2) based on the recommended standard defined by the Great Lake-Upper Mississippi River Board [
33]. The desired reserve capacity in this model is calculated based on the initial percentage reserve capacity percentage, which is assumed to be maintained for the entire simulation period.
A positive difference indicates that capacity construction must be initiated (Required_Building_WWT_Capacity [m3/d]), whereas a negative difference suggests the decommissioning of extra capacities. The annual total wastewater flow is estimated based on the sewage generation from residential and non-residential users and the annual I&I flow. The sewage generation rate depends on the population growth rate and water demand rates, and the I&I flow rate depends on the WWC pipes’ conditions.
Wastewater Composition Model
The concentration of SS and BOD is assumed to increase proportionally with declining wastewater volume flowing into WWTPs. The unit mass of BOD and SS per capita is assumed to be fixed in time and is calculated based on the annual mass of BOD and SS reported by the WWTP divided by the current population. Thus, the concentration of SS and BOD changes as the generated wastewater—which is a function of the water demand (WD) and the consumptive use fraction (CUF) of metered water—and I&I change over the simulation period. The concentration of BOD and SS are formulated as in Equations (1) and (2), respectively.
where
t [year] is the current time;
SS (t) [g/l] is the concentration of suspended solid in wastewater inflow at WWTP in year t;
SS0 [kg/capita/year] is the initial mass of suspended solid generation per capita;
365/1000 [(day/year)/(m3/liter)] is the conversion factor to convert days to year and liter to cubic meter;
WD (t) [liter/capita/day] is the average daily water demand of a residential user in year t;
CUF [%] is the percentage of water received by customers that is not returned as sewage to the WWC pipe network;
I&I [m3/year] is the annual inflow and infiltration volume to the WWC pipe network;
Population (t) is the population number in year t.
t [year] is the current time;
BOD [kg/capita/year] is the mass of dissolved oxygen needed by aerobic biological organisms to break down organic material presented in wastewater sample in year t;
BOD0 [kg/capita/year] is the initial BOD;
365/1000 [(day/year)/(m3/liter)] is the conversion factor to convert days to year and liter to cubic meter;
WD (t) [liter/capita/day] is the average daily water demand of a residential user in year t;
CUF [%] is the percentage of water received by customers that is not returned as sewage to the WWC pipe network;
I&I [m3/year] is the annual inflow and infiltration volume to the WWC pipe network;
Population (t) is the population number in year t.
3.2.2. Consumer Sector
Consumers reactions to incremental change of wastewater service fees are modeled based on Ganjidoost [
22] SD model. The daily water-use per capita or water demand is estimated as a function of the Price_Elasticity [-] of demand, User_Fee [
$/m
3], and Minimum_Water_Demand [liter/capita/day].
The price elasticity of demand, which is the percentage change in water demand per corresponding percentage change in the fee, is selected as −0.35, similar to the Rehan et al. [
24] model. The minimum water demand is considered to be 150 liters per capita per day (LPCD) [
26]. The modeling of the consumer sector is improved by decoupling non-residential and residential users, and by adding a population growth model. In Ganjidoost, A. [
22] model, water demand is calculated as the sum of residential, commercial, institutional, and industrial water demand divided by population, under the assumption that all customers experience the same price elasticity of water demand. However, this assumption does not address that the industrial users can often apply technological means to reuse and conserve water and significantly cut their water demands. Water and wastewater utilities also set different price rates for non-residential users, in consideration of their social and economic importance to the societies who are depending on them. The water demands, wastewater collection, and treatment fees for non-residential users are assumed to be fixed in the present model.
The expanded SD model is deemed to better represents the projection of user-fee based revenues, user-fee hike rates, and the wastewater volume collected and treated in the WWC and WWT models. A policy favoring fixed wastewater service fees for non-residential users indicates a strategy, whereby residential users are subsidizing the system, and the result is a more stable economic sector. The wastewater collection and treatment services are subsidized for commercial, institutional, and industrial users if their fee increase rate is lower than the residential fee-hike rates and vice versa.
Population growth has been modeled by an urban densification index (UDI) to represent various urban development scenarios. In 100% urban densification (UDI = 100%) new population is served within the current WWC pipe network, which avoids the need to install and operate new pipes. It also does not impact the WWTP system’s operation and capacity planning due to future I&I to the new parts of the WWC pipe network. In contrast, a no urban densification scenario (UDI = 0%) requires a growing WWC pipe network, which would incur capital and operational costs for both the WWC and WWTP utilities.
3.2.3. Finance Sector
In this section, the models developed for the WWC and WWT finance sectors are described.
WWC Finance Model
Figure 10 presents the model developed for the WWC finance sector. The operation and maintenance cost of pipes in different ICG-categories and the user-fee calculation are modeled similar to the Rehan, R. [
19] and Ganjidoost [
22] SD models.
The revenues are generated from the WWC_Fee [
$/m
3] and WWC_Service_Charges [
$/month]. The connection-service charges are based on the water-meter sizes and are collected monthly. The number of meters in each size is assumed to be increased by the same rate as the population growth rate. The service charges inflation is considered to be equal to the non-residential buildings construction-price-index (NRB CPI), which is reported as 3.7% per annum by Statistics Canada [
34].
The new parts of the model consist of separate fund-balance stocks for operational and capital expenses, which are presented as WWC_Op_FundBalance [
$] and WWC_Cap_FundBalance [
$], respectively in
Figure 10. In the advanced finance module, the generated revenues are primarily allocated to pay for the WWC_Maintenance_Expenses [
$/year] and WWC_Debt_Services [
$/year], and the remaining is transferred to the capital fund balance (WWC_Operation_Over_Fund [
$/year]) for paying the capital costs of WWC pipe network rehabilitation and expansion.
The WWC utility has the option to issue debt to maintain a zero-capital fund balance if the revenue from operational fund balance is not sufficient to pay the capital expenses. In the opposite scenario, the surplus revenue can be reserved for future capital expenses. Separation of the two fund balance accounts in the present model restricts payment for operational expenses from issued debt or reserved cash.
WWTP Finance Model
A new module is developed for the WWTPs finance sector. Similar to the WWC finance structure, surplus revenues are available to lower the fund-balance stocks presented in
Figure 11.
The revenues are generated from collecting the user-fees (WWT_Fee [
$/m
3]) and OverStrength_Charges [
$/year] and are primarily paid towards the operational expenses (WWT_OpEx [
$/year]). Development_Charges [
$/unit and
$/m
2] are allocated to recover the cost of developing new urban area and are governed by the Province of Ontario under Development Charge Act [
35]. If the utility has debt, the revenues from development charges are first allocated to pay debt services (WWT_DebtService [
$/year]), and the surplus becomes available for paying the capital expenses (WWT_CapEx [
$/year]).
The total development charge (DC) is the sum of the residential and non-residential development charges, which are calculated using Equation (3) and (4), respectively
where
t [year] is the current time;
DCresidential (t) [$/year] represents the revenue of issuing permits for residential building constructions in year t;
Nresidential [household/year] is the number of households added to the current population in year t;
UDI [%] represents the urban densification index;
S(t) [$] is the development-charge for single and attached houses in year t;
A(t) [$] is the development-charge for apartments and lodging units in year t.
t [year] is the current time;
DCnon-residential (t) [$/year] represents the revenue of issuing permits for construction of non-residential buildings in year t;
ADnon-residential [m2/year] is the new area permitted for building commercial, institutional or industrial buildings in year t;
UDI [%] represents the urban densification index;
NR (t) [$/m2] is the development-charge for non-residential area development in year t.
When UDI = 0%, the model will simulate the no urban densification scenario, in other words, non-residential area is developed at the same rate as the population growth rate, and only single houses and townhouses will be built to accommodate new population. In contrast, when the UDI = 100%, the model assumes that only apartments and lodging units will be built. The most probable policy would be a 50% urban densification.
If the cash-reserve scenario is selected, the surplus cash will be reserved to up to 50% of the replacement value of WWTPs in reserve (WWT_CapCash_Reserve [$/year]) for paying future capital expenses. If the revenues into capital fund balance (WWT_Cap_FundBalance [$]) are insufficient to pay for the capital-work expenses, the utility has the option to issue debt to maintain a zero-fund balance.
3.2.4. Environment Sector
Greenhouse gas (GHG) emissions are calculated and used as a proxy indicator for the environmental sustainability assessment. The following sections describe the GHG calculation model implemented into the SD Model.
GHG Calculation Model
The GHG module is developed to capture the variations and dynamics in GHG emissions as the results of different asset management scenarios. The annual total GHG emission is comprised of the following variables, which are described in the next subsections.
- ○
Annual GHG emission from WWT processes;
- ○
Annual GHG emission from electric energy-use;
- ○
Annual GHG emission from ICG5 pipes’ replacement, new pipes’ installations, and ICG4 pipes’ rehabilitation.
GHG Emission from WWT Processes:
Three sources of GHG emissions are attributed to the treatment processes: CO
2, CH
4, and N
2O gas emissions. The CO
2 gas emission is classified as “biogenic” emission—since it would otherwise br emitted trough natural process of decay—and is not accounted in most referred to protocols such as the Intergovernmental Panel on Climate Change (IPCC) [
36].
The annual methane gas CH
4 and nitrous oxide N
2O emissions are estimated based on the IPCC protocol [
36]. Annual methane gas emission is calculated using Equation (5) as
where
t [year] is the current time;
CH4 (t) [kg/year] represents the mass of methane gas emissions in year t;
Ui [%] represent the fraction of population in income group i as rural, urban high income, and urban low income;
Tij [%] indicates the treatment pathway j as centralized well managed aerobic treatment, overloaded aerobic treatment, anaerobic digester, etc., served for each group of people in different income groups or (i);
EFj [kg/year] is the emission factor in each treatment pathway;
IBOD(t) [mg/l] is the BOD concentration of wastewater inflow at WWTP in year t;
SBOD(t) [mg/l] is the BOD in removed sludge from WWTP in year t;
R [kg CH4 /year] represents the recovered methane gas from WWTP in each studied year.
In IPCC manual [
36], 95% of Canadians are classified as high-income people, and the most common WWT method are centralized aerobic WWT and lagoons for both domestic and industrial wastewater. In Ontario, almost the entire population is connected to centralized treatment systems, where secondary-mechanical treatment are applied to remove most of organic matters [
37].
Based on the IPCC manual [
36], the methane gas emission factor for well managed aerobic treatment systems is considered to be zero. The methane gas emission can be negative if the biogas from anaerobic treatment of wastewater sludge is used for heat and energy recovery.
The annual N
2O emission is calculated using Equation (6) as
where
t [year] is the current time;
N2O (t) [kg/year] represents the mass of nitrous oxide emissions in year t;
P (t) [capita] is the population in year t;
0.004 [kg/capita/year] is the mass of nitrous oxide emission per person per year (industrial and commercial discharges are also attributed to the residential users).
Therefore, the total annual GHG emissions from the WWT processes is calculated by Equation (7) as
where
t [year] is the current time;
GHG (t) [kg/year] represents the equivalent mass of CO2 gas emitted from WWT processes in year t;
23 [kg CO2/kg CH4] represents the relative global warming potential of CH4 gas compared to an equivalent mass of CO2 gas;
CH4 (t) [kg/year] is the annual CH4 emission calculated in Equation (5);
296 [kg CO2/kg N2O] represents the relative global warming potential of N2O gas compared to an equivalent mass of CO2 gas;
N2O [kg/year] is the annual N2O emission calculated in Equation (6).
GHG Emission from Electric Energy Use:
From a life-cycle perspective, energy-use accounting should be done for all life-cycle stages of a studied product or service, including the manufacturing of materials, construction of structures, operation and maintenance of wastewater-collection, and rehabilitation and renewal of infrastructure parts, as well as the disposal of waste materials and end-of-life components.
Based on a study done by the United State Environmental Protection Agency [
38], more than 95% of energy-use is attributed to the operational and maintenance stages of water and wastewater systems. Therefore, the energy footprint modeling is centered on the operation, maintenance, and rehabilitation activities. The energy footprints of WWC asset management activities are the sum of all activities listed below:
Annual_Energy_use_for_sewage_capital_works [gigajoule/year], which includes the energy used for new pipes installation and ICG4 pipes rehabilitation activities;
Annual_Energy_used_for_sewage_collection [gigajoule/year];
Annual_Energy_used_for_WWT [gigajoule/year];
Annual_energy_used_for_WT [gigajoule/year];
Annual_energy_used_for_water_distribution [gigajoule/year];
Annual_Energy_use_for_sludge_transportaiton [gigajoule/year] to a central treatment facility such as incineration plant or landfill site;
Sludge_treatment_Energy [gigajoule/year] produced or used in sludge treatment processes.
The GHG emission factors are used to convert the energy-use rate to kg CO
2 eq. for various energy resources. The GHG emission factor for one kwh electrical energy is calculated based on the energy resources used to generated 1 kwh electricity, which is considered to be 125 g/kwh in Ontario [
39].
GHG Emissions from Pipes’ Installation, Replacement, and Rehabilitation:
Rehabilitation activities, particularly when done by open-cut trenching technologies, can lead to traffic delays and consequently more GHG emissions from cars’ engine-fuel combustion [
30]. Average GHG emission factors for traffic delays are calculated using the methodology described in [
30]. The annual GHG emissions form traffic disturbances can be calculated using Equation (8) as
where
t [year] is the current time;
GHG_raod_ststem (t) [kg/year] represents the equivalent mass of CO2 emitted from traffic disturbances in year t;
2 [kg CO2eq.)/m] represents the GHG emission factor for rehabilitation of ICG4 pipes in year t by using trenchless technologies;
ICG4 (t) [m/year] is the length of ICG4 pipes rehabilitated in year t;
64 [kg CO2/m] represents the GHG emission factor for replacement of ICG5 or installation of new WWC pipes using open-cut technology in year t (daily traffic is assumed to be 3500 vehicles/day);
ICG5 [m/year] is the length of ICG5 pipes being replaced in year t;
NSP [m/year] is the length of new WWC pipes being installed in year t.