Life Cycle Assessment of a Domestic Rainwater Harvesting System: A Case Study of Poland

Abstract

Protection of water resources is an urgent priority in the context of increasing freshwater scarcity. Sustainable and circular water management focuses on reducing water consumption as well as measures to recover and reuse alternative water sources. This study assesses the life cycle assessment (LCA) of a domestic rainwater harvesting (DRWH) system located in Poland. Moreover, the most significant environmental contributors and the quantification of each component’s role in the system’s overall footprint are assessed. The study used the OpenLCA tool and assumes 1 m³ of treated water as the functional unit. Findings reveal a highly concentrated impact distribution for the components. The high-density polyethylene (HDPE) tank dominates, which represents 78.69% of total environmental impacts and leads in 18 of the 18 categories examined. Its influence is greatest in non-renewable fossil energy use, where it accounts for 92% of the impact, and in photochemical oxidant formation, with contributions exceeding 90%. The data quality assessment (DQA) of the system resulted in uncertain temporal and geographical correlation. Further Monte Carlo simulations confirmed the uncertainties regarding climate change and energy-related impact categories. The methodology aligns with ISO 14044 guidelines, providing a foundation for evidence-based environmental management decisions.

1. Introduction

Sustainable and circular management of water resources is one of the most important challenges of modern society. Currently, in every region of the world, several actions are being taken to ensure safe access to water, both for people and economic activities, including agriculture and industry. This is a consequence of the water crisis that affects over 2.3 billion people globally, with projections showing that 4.3 billion could face chronic water scarcities by 2050 due to climate change, pollution, and population growth [1,2]. This crisis extends beyond developing regions to major urban centers, highlighting the need for sustainable water management and protection of its quality and quantity.

In recent years, various initiatives have been implemented, including tightening legal regulations in the field of water resource management [3], education, and social campaigns, as well as wide promotion of water saving in households and industry, and development of technologies aimed at optimizing water consumption and its recovery from municipal and industrial sewage, rainwater or gray water [4].

Considering household needs, the promising solution that can be widely used in various regions is DRWH systems, a decentralized solution to augment water supplies [5]. DRWH addresses water security by capturing rainfall from rooftops for non-potable uses such as toilet flushing, watering the garden, lawn, potted plants, car washing, laundry, thereby reducing demand from centralized municipal water and mitigating stormwater runoff, thereby reducing the risk of flooding and overloading of sewer systems [6,7,8,9]. For every technological solution for water recovery, including DRWH, it is important to use safe and effective rainwater treatment methods that include the removal of contaminants, such as sand, leaves, oils, heavy metals, and allow for its safe storage and use. Rainwater purification methods involve several stages. Initially, mechanical filtration is used, such as using mesh baskets or settling tanks to trap leaves, sand, and other larger contaminants. The next step may be sand-gravel filtration or synthetic media, which removes finer suspended particles. For water from contaminated areas, such as roofing felt or parking lots, grease and oil separators and sorption filters are used to absorb heavy metals and chemical compounds. For more advanced purification, UV lamps can be used for disinfection, or carbon filters, which improve the water’s odor and taste. The choice of method depends on the intended use of the rainwater—different systems are used for garden watering, while others are used for domestic use [10].

It should be emphasized that the use of these methods requires energy, material, and financial expenditure that can impact the environment and the economy. Therefore, they should be assessed using various methods, such as LCA, to quantify their environmental benefits and guide urban water management toward sustainability. Given the growing challenges of water security, there are some reported studies that concern DRWH systems as sustainable alternatives to centralized water utilities, which were evaluated with the LCA tool. Teston et al. [11] conducted a modular study of LCA that revealed that rainwater harvesting (RWH) reduced total system impacts by 11%, cut treatment plant loads by 23%, and addressed the high-impact consumer component, proving critical for sustainable urban water systems. Notably, this study also found that the overall chemical and energy demand in the water treatment and distribution system was reduced by installing the RWH system.

Several studies reported the significance of hybrid water conservation systems. Integrating various components enhances efficiency and reduces the overall environmental impact. Ghimire et al. [12] assessed the LCA of RWH and air-conditioning condensate harvesting (ACH) systems in commercial buildings, revealing that integrated RWH + ACH systems exhibit performance to the most effective standalone option (≤8% impact difference) while optimizing water conservation. Efficacy is influenced by building height, regional precipitation, and pumping energy requirements. It has been demonstrated that integrated systems provide an efficient and sustainable water solution, particularly in high-rise buildings where ACH is prevalent, offering a flexible framework for climate-resilient water management across various geographies and building types. Another study conducted in Colombia evaluated a hybrid rainwater–greywater system against a conventional centralized water supply, revealing that the hybrid system decreased potable water consumption by 42.5% (131 m³/year) and surpassed conventional systems in 12 of 13 environmental impact categories. However, operational-phase impacts remain dominant [13]. The results illustrate those environmental benefits of hybrid systems, emphasizing the necessity for geographic-specific data to enhance implementation across various contexts. The LCA and Life Cycle Cost (LCC) study in tropical Malaysia revealed that commercial hybrid rainwater–greywater systems (55.3% water savings, economically viable at $5.20/m³) and domestic rainwater harvesting (95.3% savings, economically viable at $2.00/m³) surpass centralized systems in both environmental and economic performance, although government subsidies are essential for their adoption [4]. Marinoski and Ghisi [14] conducted an LCA study to analyze hybrid rainwater–greywater systems in Brazilian residences, finding a 41.9% reduction in potable water usage, a 40% reduction in sewage generation, and a 36.1% reduction in energy consumption relative to conventional systems, with the operational phase contributing to high environmental impact. The method illustrated the hybrid system’s environmental efficacy across several impact categories, emphasizing the significance of gravity-fed designs and policy endorsement for broader use in residential water management. These studies examine the hybrid system and its significance from various parts of the world. Similar conclusions are made regarding the benefits of a hybrid system, high environmental impact during the operational phase, lower energy consumption compared to a conventional system, and reliability based on geographical conditions. Moreover, the selection of components and materials used for RWH installations plays a significant role in minimizing environmental impacts and optimizing the overall process and output. The study conducted by Rashid et al. [15] found that HDPE tanks have lower impacts than low-density polyethylene (LDPE), steel, or ferrocement tanks. Furthermore, small tanks with an area of 2000–3000 m³ without pumps were the best-case scenario, and further recycling of the tanks reduced environmental impacts. Thus, the selection of the most reliable component for designing the RWH system significantly elongates the life cycle of the installation. Furthermore, geographic variations significantly influence the performance of the DRWH system. In arid regions like Tehran, Iran, DRWH reliability drops to <50%, yet it still reduces ecosystem damage by 20–30% compared to tap water due to avoided treatment and distribution [1]. Additionally, temporal variation affects water-saving efficiency and RWH system performance [16,17,18]. Baran-Gurgul and Wałęga [19] analyzed the hydrological drought variability in Poland from 1973 to 2022 and found that lowland regions experience longer droughts than mountainous areas. Thus, data quality assessment becomes important to assess external factors.

The former discussion clarifies various LCA studies of RWH systems. It discusses hybrid systems and other components related to RWH systems, mainly in regions of the world that have struggled for many years with limited access to water. In turn, data on conducting LCAs for DRWH in the Baltic Sea region are very limited. This study identifies significant gaps in the LCA of DRWH systems by analyzing an integrated setup that includes storage tanks, pipes, pumps, membrane filters, and a UV lamp for an installation located in Poland. This study fills the regional gap for Poland’s water policy and technology assessment. The goal of this study is to quantify the environmental impacts, emphasizing tap water savings and energy autonomy through solar photovoltaics. Key significance of the study includes the utilization of 100% solar energy for the operational process, and the reuse of 95% treated rainwater in greenhouses and gardening.

2. Materials and Methods

In this work, an LCA analysis of the DRWH system was performed. The methods used and the case study installation are described below.

2.1. Description of Case Study Installation

In the present case study, LCA was performed on the installation at a residential place in Kłobuck, Poland, as presented in Figure 1. The installation was developed and constructed under the project titled “Water Management in Practice—Development of Comprehensive Solutions for Water Recovery and Enhancing Awareness of Water’s Crucial Role in the Transition to a Circular Economy,” aimed to enhance the efficiency of initiatives related to the circular management of water resources. The objective of this project was to enhance public knowledge regarding the specified problem and to devise novel solutions for the extraction of water from secondary sources [20]. One of the main goals was to develop a compact rainwater recovery installation, studied in this paper. The cumulative area of the RW catchment for the designated structures is 160 m² for the residential house and 45 m² for the utility building. There are two tanks, A and B, collecting RW with a volume of 3 m³ each. Unfiltered RW is collected in tanks A and B from the residential building and utilities building, respectively. Then, the RW is passed through a series of filtration membrane units. The unit comprises a triple-membrane adjustable linear filtration system. Followed by a UV lamp. Further, the filtered RW was stored in irrigation tanks. Filtered water was used for irrigation purposes in the greenhouse, gardening, and washing. Thereby, utilizing secondary sources of water and eliminating the use of tap water for irrigation and gardening. A detailed description of the installation and a presentation of its effectiveness are described in the previous study [5].

2.2. LCA Method

LCA is an environmental analysis method that examines the total life cycle of a process, system, or product, spotting its potential impacts on the environment. It is standardized and addresses the environmental aspects from the acquisition of raw material, through production, use, end-of-life treatment, recycling, and final disposal of a product. According to ISO 14040:2006 [21], four phases should be followed for a study: (1) the goal and scope definition phase, (2) the life cycle inventory (LCI) analysis, (3) the life cycle impact assessment (LCIA), and (4) the life cycle interpretation. The evaluation spans 18 environmental impact categories and applies the OpenLCA tool to identify the most significant environmental contributors and quantify each component’s role in the system’s overall footprint.

2.2.1. Goals and Scope

The goal and scope of the study were to evaluate the environmental impact of the DRWH system from operation and component analysis. Major components like HPDE tanks for storage, pumps, polyvinyl chloride (PVC) pipes for distribution, membrane filters, and UV lamp setup are assessed. Here, 100% solar energy is used as the source of electricity. The main purpose of the study is to evaluate the components used for the DRWH system. The assessment provides a systematic view of each component and its effect under the environmental impact category. The study highlights the importance of assessing environmental assessments, such as LCA of the technology and components installed. LCA of the system is advantageous to optimize the process and select the components with less environmental footprint. The boundaries of the system are presented in Figure 2. In this study, components of the system and energy utilization for the overall process are included. The arrow presented in the system boundary diagram represents the flow of water in the system. However, the construction, transportation phase, and end-of-life stages are not integrated in the study. The manufacturing phase of the components and the operational stage requirements of the system are presented.

2.2.2. Functional Unit

The functional unit (FU) represents the input and output data that are normalized in a mathematically consistent manner; the functional units and/or reference flows must be quantifiable and explicitly defined [21]. Multiple FU may be employed for the LCA of wastewater treatment based on the goal and scope of the study. Like the influent generated by one person equivalent [22], or the inflow quantity per day [23]. For this study, 1 m³ of treated RW is appropriate and practicable to serve as the functional unit. This metric streamlines data collection and inventory formation.

2.2.3. Impact Categories

The selection of appropriate life cycle impact assessment (LCIA) methodology significantly influences the scope and interpretation of the results, with categories like ReCiPe 2016, TRACI 2.1, and Environmental Footprint (EF) 3.1, offering different strengths and regional applicability. For this study, OpenLCA version 2.4 and ReCiPe 2016 v1.03, the midpoint (H) method is selected for holistic impact studies covering energy sources, air, water, land, and toxicity assessment. This approach includes 18 categories: Acidification: terrestrial, Climate change, Ecotoxicity: freshwater, Ecotoxicity: marine, Ecotoxicity: terrestrial, Energy resources: non-renewable, fossil, Eutrophication: freshwater, Eutrophication: marine, Human toxicity: carcinogenic, Human toxicity: non-carcinogenic, Ionizing radiation, Land use, Material resources: metals/minerals, Ozone depletion, Particulate matter formation, Photochemical oxidant formation: human health, Photochemical oxidant formation: terrestrial ecosystems, and Water use.

2.2.4. Life Cycle Inventory

LCI of various components used in RWH is analyzed. The inventory considers all the input and output data that were recorded in accordance with the daily operation of the units. Additionally, the tenure of the units was accounted for during the inclusion of all the material and energy data required per functional unit. Secondary data regarding the manufacturing of components, energy requirements, chemical use, and transportation were obtained from LCI databases like Ecoinvent and other data sources, as presented in

Table 1. LCI of the inputs/outputs for the RWH system.

Elements	Description	Input	Unit	Service Life (Years)	Database
Collected RW	RW collection from residential and utilities building	1.2	m3	Not applicable	Measured
Total electricity usage	Use of solar energy	47.27	kWh/m3	Not applicable	Measured
HDPE water tanks	3 storage tanks of 3 m3 each	312	kg	50	Ecoinvent 3.11
Pipes	PVC pipes of 30 m length and 32 mm diameter	18.14	kg	50	Modified from NIST [26]
Pump	3 pumps	3	Item	25	Ecoinvent 3.11
Membranes Filters	3 membranes	3.918	Kg	10	Modified from Lawler et al. [27]
UV lamp	1 lamp	1	Item	25	Ecoinvent 3.11
Elements	Description	Output	Unit	Service life (years)	References
Treated RW	RW storage after the filtration process	1	m3	Not applicable	Measured
Water loss	Water loss during the filtration process and distribution	0.2	m3	Not applicable	Measured

. The energy used for the overall process is supplied from installed onsite solar photovoltaic panels. Grid energy is not supplied to any component of the process, as it efficiently utilizes solar energy for daily operations. Furthermore, PVC pipes and HDPE tanks are used for the distribution and storage of RW. These building materials prolong the life of the installation and optimize the environmental footprint [24]. The set of membrane filters and UV lamp is used to treat the stored RW. The membrane filter removes the suspended and dissolved salts, which could potentially block the irrigation channel. Moreover, the UV chamber acts as a disinfection and eliminates microbial growth due to prolonged storage of RW. Lastly, the treated water is used for irrigation in greenhouses, gardening, and other washing purposes. Basic water quality parameters like pH, total dissolved solids (TDS), redox potential (ORP), conductivity, salinity, total phosphorus (TP), and turbidity are measured. It ensures the quality of treated RW to be used for non-potable purposes in line with the European Union (EU) Water Reuse Regulation 2020/741 [25].

2.2.5. Data Quality Assessment

Data quality assessment (DQA) in the LCA process evaluates the reliability, accuracy, and representativeness of the data used to model a product’s environmental impact. The present case study utilizes the Ecoinvent data quality system. It is based on the Pedigree Matrix approach integrated with uncertainty analysis [28]. The core purpose of the matrix is to identify weak spots in inventory data, compliance with data quality requirements, and to make informed decisions about data improvement. Five quality indicators are evaluated, viz. reliability (R), completeness (C), temporal correlation (T), geographical correlation (G), and future technological correlation (F). Each indicator is rated from 1 (Best) to 5 (Worst). As shown in

Table 2. Data quality assessment scores.

Impact Category	Reference Unit	R	C	T	G	F	Overall Score	Data Quality Rating
Acidification: terrestrial	kg SO2-Eq	3	3	5	4	3	3.6	Poor
Climate change	kg CO2-Eq	3	3	4	4	2	3.2	Fair
Ecotoxicity: freshwater	kg 1.4-DCB-Eq	1	1	3	1	1	1.4	Excellent
Ecotoxicity: marine	kg 1.4-DCB-Eq	1	1	3	1	1	1.4	Excellent
Ecotoxicity: terrestrial	kg 1.4-DCB-Eq	3	2	5	3	2	3.0	Fair
Energy resources: non-renewable, fossil	kg oil-Eq	1	1	2	1	1	1.2	Excellent
Eutrophication: freshwater	kg P-Eq	1	1	5	1	1	1.8	Good
Eutrophication: marine	kg N-Eq	1	1	4	2	1	1.8	Good
Human toxicity: carcinogenic	kg 1.4-DCB-Eq	1	1	2	1	1	1.2	Excellent
Human toxicity: non-carcinogenic	kg 1.4-DCB-Eq	1	1	4	1	1	1.6	Good
Ionizing radiation	kBq Co-60-Eq	1	1	5	1	1	1.8	Good
Land use	m2*a crop-Eq	2	3	4	3	2	2.8	Fair
Material resources: metals/minerals	kg Cu-Eq	2	2	4	1	1	2.0	Good
Ozone depletion	kg CFC-11-Eq	3	3	4	3	2	3.0	Fair
Particulate matter formation	kg PM2.5-Eq	3	3	5	4	3	3.6	Poor
Photochemical oxidant formation: human health	kg NOx-Eq	3	3	4	4	2	3.2	Fair
Photochemical oxidant formation: terrestrial ecosystems	kg NOx-Eq	3	3	4	4	2	3.2	Fair
Water use	m3	3	3	4	3	2	3.0	Fair

, the dataset presents a strong overall score ranging from 1 to 2 for R and C, resulting in ratings classified as “Excellent” to “Good” for the case of Poland. Nonetheless, significant systemic uncertainties compromise the site-specific validity of the research. Crucial mid-point impact categories, especially Climate Change, Photochemical Oxidant Formation, Acidification, and Particulate Matter, demonstrate inadequate geographical correlation (G = 4). Further suggesting that the foundational life cycle inventory data does not accurately reflect Poland’s unique energy landscape and industrial practices. Furthermore, numerous categories, including those designated as “Poor,” are afflicted by substantial temporal misalignment (T = 4–5 or 77.8%), incorporating obsolete foreground or background data. As a result, although the findings related to toxicity are dependable, the overall environmental assessment score of 2.38 for Poland is considerable with uncertainty, underscoring the need for incorporating regionally specific and temporally applicable inventory data to uphold analytical integrity and produce decision-relevant conclusions.

2.2.6. Monte Carlo Simulation

Monte Carlo simulation quantifies environmental impact uncertainty by executing 1000 iterations across 18 LCA impact categories. Therefore, 1000 iterations provide 95% confidence in point estimates and 90% confidence in percentile values (5th–95th percentile) for normally distributed categories. The uncertainty metric, Coefficient of Variation (CV = Standard Deviation/Mean × 100%), enables dimensionless comparison across environmental categories with vastly different units and magnitudes.

2.3. Limitations of the Study

The present study manages to work within several important methodological and data constraints that must be accepted. The most significant limitation is the study’s geographic applicability in five categories (rating G = 4). As the study employed the Ecoinvent database, the weighted electricity generation is much higher in Poland than average Central European countries. Also, broader industrial practices for producing HDPE (as per Ecoinvent’s database) for average European manufacturing conditions do not reflect Polish facilities specific to energy efficiency. The second limitation is temporal misalignment affecting 14 categories (rating T = 4–5). This underlines the inaccessibility of recent data related to manufacturing and electricity. This gap systematically overestimates the climate-related impacts. The functional unit of 1 m³ treated water is based on treated water for the year 2023. However, assumptions about rainfall variability, system yield, and rainwater collection efficiency introduce uncertainty. This uncertainty is not fully quantified in the analysis and could significantly impact various categories. The system boundary excludes the transportation and distribution of components, installation, and the construction phase. This study assesses a specific DRWH design in Poland, and it is limited by designated parameters and geographic context.

3. Results and Discussion

This study assesses the environmental impact of an RWH system with advanced treatment. The system processes 1.2 m³ of collected rainwater to yield 1 m³ of treated water, achieving 83.3% efficiency with 0.2 m³ losses. Storage of rainwater is made of HDPE tanks arranged as three units, providing substantial buffering but increasing material impacts compared to lighter, gravity-fed alternatives. The treatment comprises PVC piping, three pumps, and a UV disinfection lamp. This configuration illustrates the environmental implications of integrating advanced treatment into RWH systems. As presented in

Table 3. Impact categories result of LCI.

Impact Category	Reference Unit	Result
Acidification: terrestrial	kg SO2-Eq	3.549536
Climate change	kg CO2-Eq	1387.85
Ecotoxicity: freshwater	kg 1.4-DCB-Eq	44.82811
Ecotoxicity: marine	kg 1.4-DCB-Eq	60.066
Ecotoxicity: terrestrial	kg 1.4-DCB-Eq	2798.972
Energy resources: non-renewable, fossil	kg oil-Eq	712.7573
Eutrophication: freshwater	kg P-Eq	0.446889
Eutrophication: marine	kg N-Eq	0.046031
Human toxicity: carcinogenic	kg 1.4-DCB-Eq	135.8299
Human toxicity: non-carcinogenic	kg 1.4-DCB-Eq	1490.804
Ionizing radiation	kBq Co-60-Eq	25.99765
Land use	m2*a crop-Eq	11.31704
Material resources: metals/minerals	kg Cu-Eq	3.331223
Ozone depletion	kg CFC-11-Eq	0.000364
Particulate matter formation	kg PM2.5-Eq	2.020856
Photochemical oxidant formation: human health	kg NOx-Eq	3.019736
Photochemical oxidant formation: terrestrial ecosystems	kg NOx-Eq	3.381912
Water use	m3	5.997082

, the system generates 1387.85 kg CO₂-eq per m³ of treated water, exceeding typical RWH impacts of 400–800 kg CO₂-eq/m³ [8,12]. This increase arises from higher energy use due to the advanced filtration system. The calculation also considers the manufacturing process of all the components. However, prolonged use of the whole system reduces the overall impact on the environment. Also, the assumptions of the study are linked with data quality assessment for the reliability of results. The results have shown higher temporal and geographical mismatch. Considering the results, higher confidence impact categories like ecotoxicity (freshwater and marine), eutrophication (freshwater and marine), and ozone depletion correlate with the DQA scores, stating that the parameters align with the area of study. Lower confidence categories like Climate change and Ecotoxicity: terrestrial, show poor correlation. Further, the section explores toxicity analysis, component-specific analysis, and Monte Carlo analysis for the system.

3.1. Toxicity Impact Analysis

Human toxicity impacts from the system are higher, relative to typical water systems, with 1,4-DCB-equivalents of 135.8299 kg for carcinogenic and 1490.804 kg for non-carcinogenic effects per m³. These impacts primarily arise from the manufacturing of complex equipment, electronic UV components, and chemicals released during the membrane production [29]. The multi-barrier treatment configuration increases toxicity due to material complexity and intensive chemical processing. UV lamp manufacturing and electronic controls add further human toxicity burdens via rare earth element use and electronic waste pathways. Terrestrial ecotoxicity dominates at 2798.972 kg 1,4-DCB-equivalents, indicating significant environmental releases during material production. Freshwater and marine ecotoxicity reach 44.82811 and 60.066 kg 1,4-DCB-equivalents, respectively, reflecting aquatic risks from chemical emissions. HDPE tanks are a major contributor to ecotoxicity due to petrochemical and plastic manufacturing, though bio-based polyethylene could reduce impacts [30]. Eutrophication potentials are moderate: 0.4468 kg P-equivalents freshwater and 0.046 kg N-equivalents marine. These are mainly linked to material manufacture and energy generation, with membrane production and periodic replacement contributing. The system could offer net eutrophication benefits by removing nutrients from collected rainwater [11,31]. Water consumption is 5.997 m³ per m³ treated, reflecting high embedded water use in components manufacturing and energy production. These challenges are RWH’s intent to relieve pressure on conventional water resources. Despite global water stress, deployment in water-scarce regions would yield proportionally greater relative benefits, but increasing water demand remains a concern.

3.2. Component Impact Analysis

The HDPE tank system constitutes a substantial material and high material resource impact, with a 50-year service life is presented in Figure 3. It results in significant upfront overall environmental impacts, which is 78.69% as presented in Figure 4. HDPE production emits approximately 1.8–2.9 kg CO₂-eq per kilogram, yielding 560–900 kg CO₂-eq for tank manufacturing [30]. Comparative studies show HDPE tanks generally perform better than fiberglass in most impact categories, except ozone depletion, where fiberglass performs worse [32]. Moreover, metal depletion impacts from pump manufacturing are the second highest at 16.22%, among the components, which indicates opportunities for design and component recovery strategies. Electric motors demonstrate high metal recovery potential with appropriate end-of-life management, particularly for copper and rare earth elements. Membrane filtration components, with 10-year replacement cycles, contribute to ongoing impacts due to material turnover. Their production involves chemical processes that generate human toxicity and ecotoxicity through solvent use, polymer processing, and supporting material fabrication [32]. Membrane materials typically demonstrate limited recycling potential but may offer energy recovery value. Furthermore, the UV disinfection system adds both operational energy demands and manufacturing burdens from electronic, lamp, and control components. Emerging UV-LED systems offer potential reductions in environmental impact by improving energy efficiency and eliminating mercury, although current efficiencies remain lower than conventional UV lamps [33,34]. Agricultural integration with water harvesting represents an emerging application area with limited LCA precedent in academic literature. Thus, component analysis in irrigation areas supports overall environmental and economic impact minimization.

3.3. Monte Carlo Simulation

The present study models that there is a 3.8-million-fold absolute difference between climate change impacts (1389.62 kg CO₂-eq) and ozone depletion impacts (0.000363 kg CFC-11-eq), as presented in

Table 4. Monte Carlo simulation result of 1000 iterations.

Impact Category	Reference Unit	Mean	Standard Deviation	CV	Median	5% Percentile	95% Percentile
Acidification: terrestrial	kg SO2-Eq	3.56	0.36	10.11	3.53	3.00	4.17
Climate change	kg CO2-Eq	1389.62	128.98	9.28	1377.55	1191.21	1618.72
Ecotoxicity: freshwater	kg 1.4-DCB-Eq	45.11	11.32	25.09	43.42	30.12	65.01
Ecotoxicity: marine	kg 1.4-DCB-Eq	60.44	15.18	25.12	58.39	40.62	87.07
Ecotoxicity: terrestrial	kg 1.4-DCB-Eq	2799.69	533.71	19.06	2716.67	2066.50	3810.06
Energy resources: non-renewable. fossil	kg oil-Eq	713.65	66.90	9.37	708.79	610.63	830.28
Eutrophication: freshwater	kg P-Eq	0.45	0.22	48.89	0.40	0.21	0.83
Eutrophication: marine	kg N-Eq	0.05	0.00	0.00	0.05	0.04	0.05
Human toxicity: carcinogenic	kg 1.4-DCB-Eq	135.54	128.80	95.03	118.37	67.76	239.74
Human toxicity: non-carcinogenic	kg 1.4-DCB-Eq	1483.73	505.68	34.08	1378.31	873.75	2457.46
Ionizing radiation	kBq Co-60-Eq	26.32	32.78	124.54	15.26	3.71	87.97
Land use	m2*a crop-Eq	11.46	27.33	238.48	16.17	−18.54	28.64
Material resources: metals/minerals	kg Cu-Eq	3.32	0.55	16.57	3.26	2.52	4.29
Ozone depletion	kg CFC-11-Eq	0.00	0.00	0	0.00	0.00	0.00
Particulate matter formation	kg PM2.5-Eq	2.02	0.17	8.42	2.02	1.75	2.34
Photochemical oxidant formation: human health	kg NOx-Eq	3.03	0.31	10.23	3.00	2.56	3.57
Photochemical oxidant formation: terrestrial ecosystems	kg NOx-Eq	3.39	0.37	10.91	3.36	2.84	4.05
Water use	m3	6.00	0.58	9.67	5.97	5.05	6.98

. Despite this extreme range, normalized CV values (climate change 9.28%, ozone depletion 12.41%) indicate comparable relative uncertainty, essential for cross-category aggregation. Uncertainty sources differ systematically by category. Such as climate change (CV 9.28%), driven by electricity grid carbon intensity annual variation (±15–30%), methane management pathway selection (±20%), and biogenic carbon assumptions (±10–20%), which indicates the highest contributor in the system. Ecotoxicity: terrestrial and Human toxicity: non-carcinogenic shows the highest standard deviation, suggesting uncertainty. Moreover, Acidification: terrestrial, Ozone Depletion, and Eutrophication: marine show low variability in standard deviation and 5th-95th percentile range. Higher CV is observed in Land Use, Ionizing Radiation, and Human Toxicity: carcinogenic. For the majority of the impact categories, the data remains consistent, showing strong correlation with data quality and modeling.

It should be noted that a complex evaluation of water recovery systems is required for installations with different application purposes. This stems from the fact that in the process of striving for climate neutrality, which is a priority for the EU economy in the European Green Deal strategy (COM no. 640, 2019), all technologies should be as low-emission as possible. Therefore, conducting analyses, both environmental, social, and economic, is strongly recommended for water recovery systems. Furthermore, EU and national programs support the implementation of such solutions. In Poland, the most prominent program supporting water recovery is the ‘Moja Woda’ (My Water) program, introduced in 2020 [35]. It represents a policy instrument that supports sustainable water resource management at the household level. The primary objective is to mitigate the impacts of climate change, particularly droughts and pluvial flooding, by promoting small-scale retention of rainwater and meltwater. It offers residents of individual households financial subsidies for the installation of circular water solutions, such as retention tanks, infiltration systems, and domestic reuse infrastructure. This program seeks to reduce pressure on municipal stormwater systems, enhance local water retention capacity, and encourage pro-environmental behavior among citizens. Next to the positive results presented in this work, this program, ‘Moja Woda’, could contribute to further development and implementation of DRWH systems in Poland.

4. Conclusions

This study provides an environmental assessment of an advanced RWH system. It demonstrates the comprehensive environmental impact of all the components used for the study. Also, the DQA resulted in the worst temporal correlation among the five indicators. It is recommended to carry out modeling using the latest data set. Although the results of DQA are uncertain, regional data sets could significantly improve the rating of data quality. Furthermore, the operation and manufacturing of the components are assessed for calculating impact categories. HDPE tank manufacturing dominates environmental impacts, contributing the highest across 18 of 18 categories. Therefore, the prolonged life span of HDPE tanks could minimize the overall impact of the system. Secondly, pumps amounted to 16.22% of the total average impact among the components. Recovery of metals from pumps after end-of-life is recommended for reducing overall impact. Pipes, membranes, and UV lamps collectively accounted for around 5% of the total impact. This case study utilizes 100% renewable solar energy for the operation process, and surplus energy is transferred to the grid, maintaining a positive ratio. For this case study, the manufacturing of components is accountable for higher climate change and energy resource categories. Monte Carlo simulation proves high uncertainty about climate change and energy-related impact categories. It is primarily due to the manufacturing and temporal correlation of the data sets. Future RWH system development should prioritize energy optimization, material selection, geographical correlation, and an appropriate treatment scheme. This case study demonstrates how sustainable technologies can achieve favorable environmental outcomes and could be replicated based on material selectivity and process optimization for large-scale implementation.