Inclusive Evaluation of Industrial Wastewater Treatment Systems Based on Grey Water Footprint: Emphasizing Sustainable Development Goals

Abstract

This report presents a novel framework utilizing the grey water footprint (GWF) to comprehensively assess and compare industrial wastewater treatment plants (WWTPs). Unlike conventional methods that evaluate pollutant removal efficiency individually, the GWF approach integrates multiple pollutants, pollution loads, and regional water quality standards, offering a holistic evaluation critical for complex industrial wastewater treatment. Four innovative GWF-based criteria were developed: multi-pollutant GWF reduction, operational reliability, grey water footprint per carbon footprint (GWCF), and heavy metal pollution index (HPI) reduction. Case studies of activated sludge (AS) and membrane bioreactor (MBR) systems treating real industrial wastewater demonstrated the applicability of this framework. Results indicated that AS outperformed MBR across all criteria, highlighting GWF as a versatile index for sustainable wastewater management aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action).

Introduction

Industrial wastewater contains diverse pollutants such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), and heavy metals, posing significant risks to human health and ecosystems. Efficient wastewater treatment plants (WWTPs) are essential to mitigate these risks, contributing directly to SDG 6. Conventional evaluation methods focus on individual pollutant removal efficiencies, which are insufficient for complex industrial wastewater containing multiple pollutants and variable inflows.

The grey water footprint (GWF) offers an advanced assessment by quantifying the volume of freshwater required to assimilate pollutants based on regional water quality standards. This method aligns with SDG 6 by promoting sustainable water management and pollution control. Additionally, integrating GWF with carbon footprint (CF) and heavy metal pollution index (HPI) addresses energy consumption and toxicity concerns, supporting SDG 7 (Affordable and Clean Energy) and SDG 12 (Responsible Consumption and Production).

Materials and Methods

Case Study

The study was conducted at Jey Industrial Park WWTP in Isfahan province, Iran, featuring two parallel secondary treatment modules: activated sludge (AS) and membrane bioreactor (MBR). Both modules treat complex industrial wastewater from sectors including metal plating, textile dyeing, and food production. The facility’s operation and sampling protocols ensured robust data collection for GWF evaluation, supporting SDG 9 (Industry, Innovation, and Infrastructure).

Sampling and Analysis

• Weekly sampling over six months (October 2022–March 2023) from inlet and outlet of AS and MBR modules.
• Analysis of 36 pollutants, including heavy metals, using standardized methods (APHA, ASTM).
• Triplicate sampling ensured data reliability and accuracy.

Energy Consumption and Carbon Footprint Calculation

Energy consumption data were collected for both modules, with AS consuming 51.7 kWh/day and MBR 60.6 kWh/day. Carbon footprint (CF) was calculated based on Iran’s electricity generation mix, predominantly natural gas, aligning with SDG 13 by quantifying greenhouse gas emissions associated with wastewater treatment.

Grey Water Footprint (GWF) Calculation

1. GWF was calculated considering pollutant concentrations, flow rates, and regional maximum allowable concentrations (C_max), across four scenarios reflecting different water quality standards.
2. The maximum GWF among pollutants was used as the representative index for each module.

Combined Grey Water-Carbon Footprint (GWCF)

The GWCF index was introduced to evaluate the volume of grey water pollution reduced per unit of CO₂ emitted, integrating water quality improvement with climate impact mitigation, directly supporting SDG 13.

Heavy Metal Pollution Index (HPI) Reduction

HPI was calculated based on heavy metal concentrations and their regulatory standards to assess the reduction of hazardous pollutants, contributing to SDG 3 (Good Health and Well-being) by mitigating toxic risks.

Results

Pollution Removal Performance

• Both AS and MBR significantly reduced pollutant concentrations, with AS achieving higher average GWF removal efficiency (93.1%) compared to MBR (87.1%).
• Operational reliability, measured by reduction in GWF variation, was higher in AS (83.7%) than MBR (77.5%).
• GWCF indicated AS reduced more grey water pollution per unit CO₂ emitted (347.8 m³/kg-CO₂) than MBR (84.9 m³/kg-CO₂), highlighting better energy and environmental efficiency.
• HPI reduction was also superior in AS (56.7%) compared to MBR (50.4%), emphasizing enhanced heavy metal pollution control.

Flow Rates and Energy Intensity

Daily wastewater flow rates varied seasonally, influencing GWF calculations. AS operated at lower energy intensity (1.15 kWh/m³) than MBR (4.11 kWh/m³), supporting sustainable energy use (SDG 7).

Comparative Evaluation Using GWF-Based Criteria

1. Removal Efficiency: AS demonstrated higher multi-pollutant GWF removal efficiency.
2. Operational Reliability: AS provided more stable effluent quality with reduced GWF fluctuations.
3. Carbon Efficiency: AS achieved greater GWF reduction per CO₂ emitted.
4. Heavy Metals Reduction: AS showed enhanced reduction of heavy metal pollution risks.

Discussion

Advancement in Pollution Removal and Sustainable Water Management

The study confirms the critical role of WWTPs in reducing industrial water pollution, directly contributing to SDG 6. The GWF framework enables comprehensive assessment beyond conventional pollutant-by-pollutant evaluation, incorporating regional water quality standards and pollution loads.

Integration of Energy Consumption and Climate Impact

By integrating carbon footprint with GWF through the GWCF index, the framework addresses the water-energy-climate nexus, aligning with SDG 7 and SDG 13. The superior performance of AS in reducing both water pollution and greenhouse gas emissions underscores the importance of energy-efficient treatment technologies.

Addressing Toxicity and Health Risks

Inclusion of the heavy metal pollution index (HPI) within the GWF framework enhances evaluation of hazardous pollutant removal, supporting SDG 3 by safeguarding human health and ecosystems.

Conclusion

• The developed GWF-based framework provides an inclusive, multi-functional index for evaluating industrial WWTP performance, integrating pollutant removal, operational reliability, energy consumption, and toxicity reduction.
• Activated sludge (AS) outperformed membrane bioreactor (MBR) systems across all GWF-based criteria, demonstrating greater sustainability under strict water quality standards.
• The framework supports the achievement of multiple Sustainable Development Goals, including SDG 3 (Good Health and Well-being), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action).
• Further research is recommended to refine toxicity assessments and expand the application of GWF in wastewater treatment sustainability evaluations.

References

Data and detailed references supporting this report are available within the original study and supplementary materials.

1. Sustainable Development Goals (SDGs) Addressed or Connected

• SDG 6: Clean Water and Sanitation – The article focuses on industrial wastewater treatment, pollution removal, and water quality improvement, directly contributing to ensuring availability and sustainable management of water and sanitation for all.
• SDG 12: Responsible Consumption and Production – The study addresses sustainable industrial wastewater treatment practices, reducing pollution loads and improving operational efficiency, aligning with sustainable consumption and production patterns.
• SDG 13: Climate Action – The article integrates carbon footprint (CF) analysis and greenhouse gas (GHG) emissions related to wastewater treatment, addressing climate change mitigation through energy consumption optimization and GHG reduction.
• SDG 3: Good Health and Well-being – By reducing hazardous pollutants and heavy metals in wastewater, the study contributes to reducing health risks associated with water pollution.
• SDG 9: Industry, Innovation and Infrastructure – The development and evaluation of innovative wastewater treatment frameworks and technologies (activated sludge and membrane bioreactor) support sustainable industrial infrastructure and innovation.

2. Specific Targets Under Identified SDGs

• SDG 6: Clean Water and Sanitation
  • Target 6.3: Improve water quality by reducing pollution, minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater, and substantially increasing recycling and safe reuse globally.
  • Target 6.4: Increase water-use efficiency across all sectors and ensure sustainable withdrawals to address water scarcity.
• SDG 12: Responsible Consumption and Production
  • Target 12.4: Achieve environmentally sound management of chemicals and wastes throughout their life cycle to minimize adverse impacts on human health and the environment.
  • Target 12.5: Substantially reduce waste generation through prevention, reduction, recycling, and reuse.
• SDG 13: Climate Action
  • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
  • Target 13.3: Improve education, awareness-raising, and human and institutional capacity on climate change mitigation.
• SDG 3: Good Health and Well-being
  • Target 3.9: Substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination.
• SDG 9: Industry, Innovation and Infrastructure
  • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.

3. Indicators Mentioned or Implied for Measuring Progress

1. Grey Water Footprint (GWF) – Measures the volume of freshwater required to assimilate pollutants discharged by industrial wastewater treatment plants, considering multiple pollutants, pollution loads, and regional water quality standards. Used as a multi-functional index for overall pollution removal efficiency and operational reliability.
2. Multi-pollutant GWF Reduction (%) – Indicator of the percentage reduction in grey water footprint from inlet to outlet of treatment units, reflecting overall pollution abatement performance.
3. Operational Reliability (OR_e) – Quantifies the reduction in variation (standard error) of GWF from inlet to outlet, indicating stability and consistency of treatment performance.
4. Carbon Footprint (CF) – Quantifies greenhouse gas emissions (kg-CO₂) associated with energy consumption of wastewater treatment units, reflecting climate impact.
5. Grey Water Footprint per Carbon Footprint (GWCF) – Ratio of GWF reduction to CF, measuring water pollution abatement efficiency relative to carbon emissions, integrating water and air environmental impacts.
6. Heavy Metal Pollution Index (HPI) Reduction (%) – Measures the reduction of heavy metal pollution risk in treated wastewater, assessing the effectiveness of treatment units in mitigating hazardous pollutants.
7. Pollutant Concentration Removal (%) – Conventional indicators measuring percentage removal of specific pollutants such as COD, BOD, TSS, heavy metals, etc.
8. Energy Consumption (kWh/m³) – Energy intensity of treatment units, used to evaluate efficiency and environmental sustainability.

4. Table of SDGs, Targets, and Indicators

Source: nature.com