Grey Water Footprint Assessment Research Articles

Wastewater and Grey Water Footprint Assessment of the Olive Oil Production Process in Northwest Argentina

Argentina stands as the leading producer and exporter of olive products in the Americas, with the province of La Rioja as its main productive area. Since the 1990s, the olive grove cultivated area and related agro-industry in La Rioja have expanded. However, the resulting wastewater has generally been neglected. The water footprint (WF) provides information about the water volume consumed and polluted by a production process. Since the 1990s, agricultural and agro-industrial activities in La Rioja have experienced substantial growth. This study aims to analyze the generation, quality, and management of Oil Mill Wastewater (OMWW) using the grey WF of chloride and nitrate as an indicator and focusing on two olive mills (OM) in La Rioja. Additionally, it seeks to examine the relationship between the international trade of provincial olive oil and the estimated grey WF. For the diagnosis of OMWW generation, a description of the production process was made coupled with flow and physico-chemical characterization. The total grey WF was 8.69 and 45.5 L water/L olive oil for OM 1 and OM 2, respectively. Nitrate was identified as the critical pollutant. The grey virtual water export related to the export of olive oil was 5569 m3 for OM 1 and 28,000 m3 for OM 2. The provincial grey virtual water export related to olive oil was 161,955 m3 with major trade destinations including Spain, the United States, and Brazil. The article analyses for the first time the grey WF of olive oil industries and assess the related grey virtual water exports. This research represents a step forward in the knowledge of wastewater management in the olive oil sector and facilitates the search for solutions to minimize negative environmental impacts while promoting cleaner production.

Long-term global water pollution stress from crops production considering different driving forces

Increasingly intensive crop farming has been aggravating global water pollution. It is significant to retrospectively investigate world's crop production induced water pollution stress, facilitating sustainable agriculture in the future. In this study, nearly 60-yr grey water footprint (GWF) dynamics of global crop farming are examined at multi-scales covering 146 crops in 162 countries, based on a framework coupling GWF assessment with an advanced dynamic decomposition analysis (DDA). Results show that global crop GWF has increased by 1224.1 % in 1961–2018, presenting three stages of GWF growth: an accelerated period of 1961–1980 (P1), a linear period of 1980–1999 (P2), and a decelerated period of 1999–2018 (P3). The spatial and crop-wise patterns of GWF vary for the stages. Crop GWF generally shifts from developed (North America and Europe) to developing (Asia and South America) regions during P1-P2, with crop-wise GWF showing increased proportions in cash crops (Fruits, Vegetables, Oil crops, etc.) for P2-P3 and decreased in food crops (mainly Cereals). China, America, and India exhibit top three national crop GWF amounts and increases (in total 60.3 % of world's GWF growth). Overall, production efficiency and population scale are determinants of growing global crop GWF, with different drivers recognized for the stages. Fertilization level principally drives up world's crop GWF for P1 (50.7 %) while offsets GWF growth in P3 (−8.61 %). Proportion of rural population becomes the main contributor offsetting crop GWF growth especially in P3. Driving pattern of crop GWF evolution varies among continents, countries, and crop categories, as well. Based on these, we recommend optimizing regional/national crop activities expecting to enhance sustainability of global agriculture.

A novel uncertainty analysis method to improve the accuracy of agricultural grey water footprint evaluation considering the influence of production conditions

Agricultural grey water footprint (GWF) is an important index for quantitatively describing the impact of agricultural production on water environment, which can help evaluate regional water pollution risk (WPR). However, changes in agricultural production conditions lead to strong uncertainties in agricultural GWF. Therefore, advanced methods to deal with these uncertainties for improving the accuracy of regional WPR assessment are required. By combining min–max/max-max programming models with a GWF assessment framework, an integrated uncertainty analysis method for agricultural GWF affected by production conditions was proposed in this study. Based on the method, extreme scenarios of GWF evaluation were established to obtain lower and upper bounds of agricultural GWF. The proposed method was applied in the Guangdong Province in South China, and the following results were obtained. Agricultural GWFs in the Guangdong Province were [124.67, 237.37], [110.63, 204.90], and [104.04, 178.01] Gm3 in 2005, 2010, and 2015, respectively. Different contributing factors of agricultural GWF were shown in the minimum and maximum GWF scenarios. For crop planting, the contribution of dryland and paddy fields to GWF was larger than that of garden land in the minimum GWF scenario, whereas the contribution of garden land was greatest in the maximum GWF scenario. For livestock and poultry breeding, the contribution of livestock raised under dry cleaning condition to the GWF was larger than that under flushing condition in the minimum GWF scenario, whereas the opposite situation was observed in the maximum GWF scenario. Based on the interval values of agricultural GWF, the WPR of the cities of Zhanjiang, Maoming, Yunfu, Chaozhou, Foshan, and Dongguan were identified as unacceptable. By identifying the uncertainties within agricultural GWF evaluation induced by the variability of production conditions, the proposed method can provide helpful information for agricultural planning in the context of sustainable agricultural development.

Variation and internal-external driving forces of grey water footprint efficiency in China's Yellow River Basin.

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China's Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.

The Impact of Cattle Grazing on Recreational Ecosystem Services in a Park Area: a Gray Water Footprint Assessment

The Impact of Cattle Grazing on Recreational Ecosystem Services in a Park Area: a Gray Water Footprint Assessment

Grey water footprint assessment of domestic wastewater in Guangdong section of the Dongjiang River Basin

Water pollution in river basins has become one of the main factors in restricting regional social-economic development. Water footprint assessment at river basin level is a hot issue in the research on water for sustainable development. In this study, the grey water footprint method was introduced for domestic wastewater assessment. The approach can effectively reflect the impact of water pollution on the amount of available water resources by evaluating the degree of water pollution from the perspective of water quantity. In order to verify the approach, the region of Guangdong section of the Dongjiang River Basin (GD-DRB) was selected as a case. Based on multiple pollutants (i.e., CODCr, NH3-N, BOD5 and TP), grey water footprint of the river basin was calculated. The results showed that the grey water footprint of domestic water in the urban area of the region was higher than that in the rural area. The total amount of grey water footprint in the region was 28.41 billion m3. The main pollutants in the region were phosphorus and organic compounds.

An approach to quantifying the grey water footprint of agricultural productions in basins with impaired environment

Grey water footprint (GWF) is an indicator that converts the pollution loads to the equivalent freshwater volume in water footprint (WF) assessment. The conventional application of GWF is to account the embedded water required for the assimilation of pollution loads discharged by production processes. However, this research highlights a deficiency in GWF accounting and proposes an integrated methodology to reinforce GWF to consider the regional environmental concerns in the WF of agricultural productions. This methodology has three main consecutive steps. First, the soil and water assessment tool (SWAT) is used for basin simulation. Second, the WFs of agricultural productions are assessed by the modeling outcomes and according to the conventional approach of GWF accounting. In the third step, an amendment is introduced in WF assessment for accounting the embedded freshwater required for environmental enhancement. This amendment develops multiple-pollutant GWF assessment and increases the WFs of agricultural productions in environmentally impaired basins. This approach is verified in Zrebar Lake basin where farmlands are the main non-point sources and the lake experiences eutrophication. Here, the average WFs of rain-fed and irrigated crops are increased 8.9% and 5.8%, respectively by the developed methodology. In addition, the average ratio of GWF to WF of crops grows from 1.3% to 7.6% and the water pollution level increases more than 5.3 times in basin for WF sustainability assessment. This enhancement in the quantification of GWF is mainly due to the fact that the majority of the GWF of crops in the study area is related to nitrogen pollution loads, while phosphorous reduction is more critical for eutrophication control. Thus, this approach enables the multiple-pollutant GWF to consider the impacts of both pollution loads and ecological impairment in the WF of products. This perspective can promote GWF as an environmental indicator and extend its application for decision-making.

Grey water footprint assessment of geothermal water resources in the southeastern Anatolia region

Grey water footprint assessment of geothermal water resources in the southeastern Anatolia region

Blue Water Footprint and Grey Water Footprint Assessment of Block-Printed Batik-Making Process Coloured by Indigo (Indigofera sp.), Tingi (Ceriops sp.) and Mahogany (Swietenia sp.) Dyes

The Indonesian batik, which is usually produced by small and medium enterprises (SMEs) often consumes a lot of water and causing environmental pollution. Therefore, a study to map the water use for batik production is required. This study examines the Blue Water Footprint (BWF) and Grey Water Footprint (GWF) of block-printed batik making-process coloured by Indigo (Indigofera sp.), Mahogany (Swietenia sp.) and Tingi (Ceriops sp.) dyes produced by a batik SME in Jarum village of Klaten Regency, Indonesia. The average BWF of block-printed batik making-process coloured by natural dyes was lower than the GWF and Indigo consumes more water in dyeing, followed by Tingi and Mahogany. The dyeing process consumes less water for BWF (2.96 L/pc), while the biggest portion of BWF was contributed from the wax removal process (152.81 L/pc). Implementation of cleaner production by managing the grey water, such as the implementation of communal Wastewater Treatment Plant, adopting water conservation strategies and educating the craftsmen are necessary to achieve batik and water resource sustainability.

Grey water footprint assessment of groundwater resources in southeastern Turkey: effect of recharge

AbstractThis paper aimed to determine the grey water footprint (GWF) of groundwater resources. The effect of groundwater recharge on grey water footprint was investigated in Southeastern Turkey. In this paper, GWF has been monitored in October and March in terms of nitrate and arsenic concentrations for ten observation wells in the Harran Plain. In this study, a new approach was developed based on the GWF to determine the nitrate and arsenic pollution of groundwater. GWF of coagulation-flocculation and biochar adsorption processes were calculated and benchmarked with each other. In the second stage of the study, the effect of groundwater recharge using reclaimed municipal wastewater on GWF was investigated applying Monte Carlo simulation. The results revealed that nitrate led to higher GWF. According to the results, biochar application could reduce the GWF. Total reduction of GWF would be approximately 64.3% if groundwater recharge using reclaimed wastewater is applied.

Assessment of grey water footprint in paddy rice cultivation: Effects of field water management policies

Excessive use of fertilizers and manure has detrimentally affected surface and ground water resources due to high rates of nitrogen (N) deposition. Reducing the nitrogen-related grey water footprint (GWF) is crucial, otherwise it may lead to severe food production-related water pollution and a reduction in freshwater absorption capacity. This study explored the feasibility of farmland water management policies (WMPs) for GWF regulation in paddy rice fields. Four WMPs—frequent shallow irrigation (FSI), wet and shallow irrigation (WSI), controlled irrigation (CI), and rain-catching and controlled irrigation (RCCI)—were analyzed based on daily field water balance and nitrogen transportation in Nanjing, China. The results showed that GWF varied among the four WMPs. During 2015–2017, the GWF values under FSI, WSI, CI, and RCCI policies were 303.6, 366.4, 369.5, and 218.3 mm, respectively. The GWF under RCCI was the smallest for all the years, that under CI was the highest in the wet year (2016), and that under FSI was the highest in the dry year (2017). GWF increased with an increase in total field water inflow and precipitation. Additionally, changes in the irrigation and drainage methods could regulate and decrease GWF. Pollutant leaching risk increased with rising precipitation, which could be mitigated by reducing drainage in the early stages of growth. In East China, RCCI was observed to be the best policy for managing field water in paddy production with regard to GWF reduction. The findings of this study can aid in agricultural water resource management.

New insights from grey water footprint assessment: An industrial park level

Greywater is found to bring higher water pressure than freshwater consumption, thus grey water footprint (GWF) has received great attention as a comprehensive indicator to assess wastewater pollution. Industrial park is an important source for wastewater discharge, but few GWF studies have been conducted at the industrial park level due to lack of data availability. To fill such a gap, this study develops one GWF assessment model at the industrial park level by considering wastewater sources from enterprises, wastewater treatment plant (WWTP), residents and agriculture. Such a model is then tested in the Changzhou Economic and Technology Development Zone (CETDZ). Results show that non-connected enterprises were the dominant sources for wastewater discharge, particularly in terms of total phosphorus (TP) and NH3–N pollution. The critical pollutants for GWF in CETDZ include total nitrogen and TP, which are mainly from rural towns. The top ten companies for GWF are identified, which can help the local government to prepare more appropriate wastewater control policies. Several mitigation measures, including increasing connection rate of the non-connected enterprises, controlling key enterprises, recycling wastewater from WWTP, and reducing the background concentration of natural water bodies, are investigated, in which increasing connection rate is identified as the most effective one. Finally, several policy recommendations on increasing connection rate, such as implementing river dredging projects and artificial wetland technology, are proposed.

Gray water footprint assessment for pesticide mixtures applied to a sugarcane crop in Brazil: A comparison between two models

The concept of gray water footprint (GWF) was introduced as an indicator of the volume of freshwater required to dilute the total pollutant load, generated from a particular production process, to maintain water quality standards. In crop production, for example, the theoretical calculation of the GWF can be employed as a sound tool for assessing the environmental impacts due to the use of pesticides. In this context, we compared two models for estimating the GWF of pesticide mixtures applied to the soil in a sugarcane crop, in Pernambuco, Brazil. One model was proposed by Hoekstra et al. (2011) and calculates the GWF based on the maximum concentration limit acceptable in water. The other one was designed by Paraiba et al. (2014) and employs the concept of Concentration Addition. This latter model also takes into account the effect of toxicity of pesticide mixtures on aquatic organisms and water quality, considering the characteristics of the soil and cultivation data obtained through the field and physical-chemical tests pesticides used in agricultural activities. The model of Paraiba et al. (2014) has shown to be more conservative in determining the GWF of the pesticide mixtures (2.00 × 107 m³ ha−1) in the aquatic ecosystem, than the model of Hoekstra et al. (2011) (6.25 × 105 m³ ha−1 for Sulfentrazone). The results presented and discussed in this study reinforce the use of the GWF as an indicator of water quality for agricultural sustainability. Our work also contributes to a better understanding of the advantages and limitations of each model approach in the assessment of the volume of freshwater needed to dilute the contaminant load in agriculture activities as, for instance, in sugarcane crops.

Formation mechanism and step effect analysis of the crop gray water footprint in rice production

Gray water footprint (GWF) is a simple and efficient indicator to quantify water pollution that has received extensive attention. Previously, an agricultural GWF was estimated by assuming the nitrogen loss rate throughout the crop cycle, while the field chemicals accompanying water movement during crop growth were not considered. In this study, using field observations of water and nitrogen transport in Nanjing, China, the GWF was calculated at the time steps of a day (GWFd), sub-growth period (GWFs), and whole crop season (GWFw), and its formation mechanism and step effect in rice production were explored. Field drainage and leakage caused nitrogen loss and generated a GWF, with the latter occurring intermittently. The values of GWFd, GWFs, and GWFw from 2015 to 2018 were 0.42, 0.38, and 0.31 m3/kg, respectively, and the step effect exists temporally for the rice GWF. The GWF of paddy rice growth decreased as the measurement period increased and the time step was calculated using GWFd as a reference. Herein, it was determined that the GWFw used in previous studies may have greatly underestimated the adverse impact of the GWF on water quality, particularly in humid regions or years. Therefore, it is essential to identify the formation mechanism and appropriately select the time steps for GWF evaluation during rice production. This research provides methodological references for GWF assessment and reduction in crop production systems.

Grey water footprint of agricultural production: An assessment based on nitrogen surplus and high-resolution leaching runoff fractions in Turkey

Agricultural activities are responsible for three quarter of global nitrate (NO3) pollution. Many surface and ground water resources have been detrimentally affected from high rate of nitrogen (N) deposition due to excessive fertilizer and manure application. Grey water footprint (GWF) is one of the internationally accepted indicators quantifying the environmental effects of contaminants on water bodies. The main scope of this study is to assess GWF of agricultural nitrogen utilization in Turkey using Tier-1 approach, which is proposed by Water Footprint Network considering detailed spatially continuous soil, climate and agricultural data in order to provide quantifications at both provincial and basin level. Leaching runoff fractions of diffuse N loads are very important for GWF accounts. However, the previous studies are mainly relied on assigning constant leaching runoff fractions and superficial N applications rates. Nevertheless, many studies reported remarkable variations in leaching fractions due to heterogeneities in soil and water resources. To the author's knowledge, this is the first GWF assessment of the study area employing high resolution leaching runoff fractions which is estimated using soil texture, natural drainage and climate data maps. Nitrogen emissions and GWF accounts of 81 administrative provinces and 25 hydrological basins were quantified using provincial N application and surplus amounts. Accordingly, GWF of anthropogenic N accumulation is estimated to be 24.7 Gm3/y corresponding to an average water height of 114 mm per agricultural land area and 340 m3 per capita for the time period of five years (2007–2011). The water pollution level (WPL) is found to be critical (>0.75) at several river basins while the national average WPL is around 0.13. This study is expected to contribute to the national and international water and agricultural management and planning studies in order to decrease the water pollution levels by providing suitable information to policy-makers.

Improving potential of nitrogen linked gray water footprint in China’s intensive cropping systems

While many studies about gray water footprint (GWF) were carried out for different economic sectors, regions, production systems as well as methodologies, little research evaluates improving potential of the current situation. As the largest nitrogen (N) fertilizer consumption country in the world, leaching and runoff contributed half of the N release to waters, while associated GWF was not calculated based on local parameters, management and water quality standards. Different from previous studies, we firstly conducted a meta-analysis of N leaching and runoff parameters based on traditional and optimal management practices from the original field experiments rather than empirical parameters; then, distinguished the different influences of leaching and runoff on groundwater and surface water and applied corresponding standards for the GWF calculations; finally, estimated the improving potential of the GWF. The results show that N leaching and runoff could be reduced by 53%–84% and 34%–68%, respectively, by optimal fertilizer application and irrigation, and integrated management, while the corresponding GWF associated with leaching and runoff could be reduced by 53%–87% and 29%–59%, respectively, without sacrificing crop yields. The total GWF was 999.8 billion cubic meters in 2016, of which 45% was from leaching and 55% from runoff. GWF hotspots were concentrated in the middle and downstream of the Yangtze River basin, while the highest water pollution level (WPL) was found in northern China (e.g., in the Hai River basin, where N released to water exceeded the water resource dilution capacity). It was found that optimal fertilizer and irrigation management could reduce the GWF from existing cropping practices by 44%. We suggest that original experimental results and standards of different water pollution processes should be considered for the GWF calculations, and a GWF assessment under optimal crop management should be used as indicators to guide the technology and policy of water resource management and sustainable intensive agriculture development.

Grey water footprint assessment for a dye industry wastewater treatment plant using Monte Carlo simulation: influence of reuse on minimisation of the GWF

Grey water footprint was figured out for five scenarios and existing treatment process without reuse and with reuse process for a dye industry wastewater treatment plant. In this study, five treatment scenarios that include an electrocoagulation process (scenario-1), a Fenton process (scenario-2), a membrane distillation process (scenario-3), a reverse osmosis (scenario-4) and an ozonation process (scenario-5) were studied. Grey water footprint assessment was undertaken using Water Footprint Network method without reuse process. Then, Monte Carlo simulation was applied for the minimum grey water footprint. Finally, the grey water footprint was assessed with the reuse process. According to the results, the grey water footprint was found to decrease upon applying the reuse process. A significant reduction was approximately 182.5% with the reuse process for existing process. The results revealed that scenario-4 has the smallest grey water footprint value and scenario-1 has the highest grey water footprint value, after the reuse process.

Grey water footprint assessment for a dye industry wastewater treatment plant using Monte Carlo simulation: influence of reuse on minimisation of the GWF

Grey water footprint assessment for a dye industry wastewater treatment plant using Monte Carlo simulation: influence of reuse on minimisation of the GWF

China's industrial gray water footprint assessment and implications for investment in industrial wastewater treatment.

Industrial wastewater is the largest contributor of toxic pollutants and third-largest contributor of nutrients to bodies of water in China, and understanding the characteristics of such pollution is important for water pollution control. In this study, the industrial gray water footprint (GWF) of each industry sector in China's 31 provinces in 2015 was calculated to identify the pollution characteristics of industrial wastewater discharge and determine how to efficiently allocate investment to pollution reduction. We show that the total industrial GWF of China was 300 billion m3 in 2015 and that the major pollutants were petroleum pollutant (PP), ammonia nitrogen (NH3-N), volatile phenol (VP), and chemical oxygen demand (COD). The water pollution level (WPL) was higher than 1 in Ningxia, Shanxi, Hebei, Tianjin, Shanghai, Henan, and Shandong, indicating that industrial pollution exceeded the carrying capacity of local water bodies in these seven regions. Given equivalent total investment, a scenario that takes the total reduction of the industrial GWF weighted by the WPL in each region as the investment target can better allocate funds to control industrial wastewater pollution in regions with high WPLs relative to a scenario in which investment targets the reduction of the unweighted total industrial GWF. For further industrial GWF reduction in regions with high WPLs, it is crucial to adjust the industrial structure and to upgrade relevant technologies.

A hybrid water-quality-index and grey water footprint assessment approach for comprehensively evaluating water resources utilization considering multiple pollutants

The grey water footprint (GWF) is effective in helping to evaluate the sustainable and efficient use of water resources. Traditionally, GWFs have been used to assess individual pollutants wherein only the highest GWF value is typically used. However, multiple pollutants coexist in water, and the characteristics of water resources are defined collectively by all contaminants present. To date, few studies have attempted to specifically consider impacts of multiple pollutants on GWFs; however, GWFs based on multiple pollutants could provide a global picture of spatiotemporal changes in water quality as a whole. Accordingly, this study proposes an improved method to assess GWFs under multiple pollutants. The proposed method is to initially determine a wide range of GWFs, after which different GWF values are refined using a binary search algorithm until meeting a given water quality target. One commonly used method (the Water Quality Index (WQI) from the Canadian Council of Ministers of the Environment (CCME)) was used in this study to assess water quality based on multiple pollutants. We also calculated the water pollution level (WPL) based on associative GWFs and compared results to test the effectiveness of the improved method. China was used as a case study to illustrate this improved method. Results showed that compared to the traditional method, the improved method could reduce 25% of values of GWFs while maintaining a “Good” water quality ranking. Even under a 50% reduction, the improved method was still able to maintain a “Fair” ranking. Based on the traditional method, regions where WPLs were >1 totaled approximately 63.59% in 31 regions throughout China; however, corresponding results under different water quality targets based on the improved method were approximately 46.54% and 36.87%. The improved method provides an efficient tradeoff between water quality and quantity management. It has also shown that existing studies which only considered individual pollutants may have overestimated GWFs and WPLs values.