Advancing the Economic and Environmental Sustainability of the NEWgenerator Nonsewered Sanitation System

Abstract

Achieving safely managed sanitation and resource recovery in areas that are rural, geographically challenged, or experiencing rapidly increasing population density may not be feasible with centralized facilities due to space requirements, site-specific concerns, and high costs of sewer installation. Nonsewered sanitation (NSS) systems have the potential to provide safely managed sanitation and achieve strict wastewater treatment standards. One such NSS treatment technology is the NEWgenerator, which includes an anaerobic membrane bioreactor (AnMBR), nutrient recovery via ion exchange, and electrochlorination. The system has been shown to achieve robust treatment of real waste for over 100 users, but the technology's relative life cycle sustainability remains unclear. This study characterizes the financial viability and life cycle environmental impacts of the NEWgenerator and prioritizes opportunities to advance system sustainability through targeted improvements and deployment. The costs and greenhouse gas (GHG) emissions of the NEWgenerator (general case) leveraging grid electricity were 0.139 [0.113-0.168] USD cap^-1 day^-1 and 79.7 [55.0-112.3] kg CO₂-equiv cap^-1 year^-1, respectively. A transition to photovoltaic-generated electricity would increase costs to 0.145 [0.118-0.181] USD cap^-1 day^-1 but decrease GHG emissions to 56.1 [33.8-86.2] kg CO₂-equiv cap^-1 year^-1. The deployment location analysis demonstrated reduced median costs for deployment in China (-38%), India (-53%), Senegal (-31%), South Africa (-31%), and Uganda (-35%), but at comparable or increased GHG emissions (-2 to +16%). Targeted improvements revealed the relative change in median cost and GHG emissions to be -21 and -3% if loading is doubled (i.e., doubled users per unit), -30 and -12% with additional sludge drying, and +9 and -25% with the addition of a membrane contactor, respectively, with limited benefits (0-5% reductions) from an alternative photovoltaic battery, low-cost housing, or improved frontend operation. This research demonstrates that the NEWgenerator is a low-cost, low-emission NSS treatment technology with the potential for resource recovery to increase access to safe sanitation.

Figure 1

Process flow diagram summarizing the prefabricated NEWgenerator units (within the gray box with a black dashed border) and additional units included in this analysis (outside of the gray box). The color of the boxes represents ancillary units (white) and treatment processes included both in the design of the NEWgenerator and the onsite pre-treatment (orange). The boxes with purple triangles on the upper left corner represent unit processes from which fugitive emissions (e.g., N₂O) are released. There are two different energy configurations for the NEWgenerator: photovoltaic and grid-tied. The inputs to and outputs from the system and between units are categorized as liquids and solids (black arrows), electricity (yellow arrows), and recovered resources (red arrows). Outputs across the evaluated scenarios include four recovered resources: biogas from the anaerobic membrane bioreactor (AnMBR), treated sludge from the sludge pasteurization unit, liquid NH₃–N from the ion exchange unit in the nutrient capture system (NCS), and water from the electrochlorination (EC) unit to be recycled for use as flush water in the frontend.

Figure 2

Estimates of economic and environmental outcomes associated with the NEWgenerator under (A) two different energy configurations (photovoltaic, green; grid-tied, blue) and (B) different deployment contexts (the general case and five countries of interest). Shading in the kernel density maps in (A) represents the density of results from 10,000 Monte Carlo simulations (darker regions have a higher density of results). The horizontal position corresponds to per capita cost and the vertical position corresponds to per capita GHG emissions. Box and whisker plots represent the same data as the kernel density maps and show median, 25th/75th, 10th/90th, and 5th/95th percentiles with the center line, bottom/top of the box, lower/upper whiskers, and points below/above the whiskers, respectively. In (B), the points represent the baseline values and the error bars represent 5th/95th percentiles from 10,000 Monte Carlo simulations, and the color scheme is consistent with (A).

Figure 3

(A) Daily per capita cost estimates and (B) annual per capita life cycle GHG emissions for the NEWgenerator technology assuming 100 users, a 25-year lifetime, and 100,000 units produced at scale for two different energy configurations (photovoltaic and grid-tied). The shaded background bars depict the photovoltaic configuration (gray), and the unshaded background depicts the grid-tied configuration. The top panel shows the total breakdown of costs and GHG emissions by relative contributions from capital, O&M, electricity, labor, and direct (for GHG emissions only) to the median per capita cost. The lower panels further break down per capita cost and GHG emissions by each unit process. Error bars extend to the 5th and 95th percentile values from the uncertainty analysis.

Figure 4

(A) Daily per capita cost and (B) annual per capita GHG emissions were simulated based on the impact of increasing users (increasing hydraulic throughput and COD/nutrient loading rates) for the NEWgenerator general case. Two different energy configurations were simulated: (green, top values in annotation) photovoltaic and (blue, bottom values in annotation) grid-tied. The user capacity was simulated from 100 users (baseline or current) to 600 users, with per capita cost and emissions for current, double, and triple users highlighted with annotation. The value of 600 users was selected as the maximum value for the x-axes to clearly show diminishing financial benefits at higher user loadings. The median, 25th/75th, and 5th/95th are depicted by the solid line, shaded region, and dashed line, respectively, to represent the range of results from uncertainty analysis. Simulation results from the underutilization analysis (50 actual users with a system sized for 100 users) are provided in Figure S1 of the Supporting Information.

Figure 5

(A) Median change in daily per capita cost and (B) annual per capita GHG emissions of the NEWgenerator photovoltaic configuration general case from the baseline case were simulated for each targeted improvement to advance sustainability for the NEWgenerator. Error bars extend to the 5th and 95th percentile values from the uncertainty analysis.

Figure 6

(A) Impact of grid unit electricity cost (from 0.00–0.60 USD kWh^–1) and (B) grid electricity unit emissions (from 0–1 kg CO₂equiv kWh^–1) on the costs and impacts of the NEWgenerator. The (green) photovoltaic configuration per capita cost and life cycle GHG emissions are constant across all values, whereas the (blue) grid-tied configuration costs and impacts vary with grid characteristics. The country-specific values of grid electricity unit cost and unit emissions for the five countries of interest (China, India, Senegal, South Africa, and Uganda) are shown on the upper x-axes. The median, 25th/75th, and 5th/95th are depicted by the solid line, shaded region, and dashed line, respectively, to represent the range of results from uncertainty analysis.