Why Don’t Scientists Follow the Water Footprint Assessment Manual? Example of One Study

Abstract

A recently published study by Parra-Orobio et al. looked at a water footprint assessment in low-income urban neighborhoods from developing countries; specifically, this is a case study of the Fátima site in the municipality of Gamarra, Colombia. However, that study deviates significantly from the water footprint methodology described in the Water Footprint Assessment Manual. Therefore, the results of the study cannot be compared with similar studies. In this Letter to the Editors, different parts of the application of the water footprint methodology used in the mentioned study are analyzed and several main deviations from the standard water footprint methodology are pointed out. It is, therefore, suggested that the authors of the article “Assessment of the Water Footprint in Low-Income Urban Neighborhoods from Developing Countries: Case Study Fátima (Gamarra, Colombia)” should expand or modify this article to clarify any deviations from the standard water footprint methodology.

1. Introduction

I read with great interest the study Water Footprint in Low-Income Urban Neighborhoods by Parra-Orobio et al. [1] (referred to as “the study” in further text). There are very few similar water footprint studies. Data collection for water footprint assessment is very complicated in these low-income communities and, therefore, the study has the potential to provide innovative results on water footprint values. Unfortunately, this study deviates significantly from the clearly defined Water Footprint methodology [2]. I will make this claim by analyzing the different parts of the water footprint calculation application used in the study [1].

2. Application of Blue Water Footprint Methodology

The study [1] states that the blue water footprint (WF_blue) “represents the fraction of freshwater that evaporates from different sources (roads, rivers, lakes, etc.), including water consumed by communities resulting from their typical activities or lost in different processes (refrigeration, transport, heating, storage, etc.)”. This is in line with the water footprint methodology [2], although the blue water footprint is defined more broadly as the water consumed that does not return to the water body. However, the calculation itself then does not follow this procedure, because the amount of water used to calculate WF_blue is not the amount of water ‘consumed’ by the inhabitants of the area of interest but the amount of water ‘used’ by them; this can be inferred both from the text of the study (“WF_blue was obtained through the volumetric data of water used for drinking and cooking”) and from the data presented in the Supplementary Material of study by Parra-Orobio et al. [1], where the average water footprint values per inhabitant activity are given (Table S1). This is the first major departure from the water footprint methodology [2], as WF_blue represents the amount of water that was consumed. Thus, WF_blue is the amount of water that is not available to other users in a given catchment and time. This is specifically stated in the glossary of terms on page 188 of the Water Footprint Assessment Manual [2]:

Blue water footprint—volume of surface and groundwater consumed as a result of the production of a good or service. Consumption refers to the volume of freshwater used and then evaporated or incorporated into a product. It also includes water abstracted from surface or groundwater in a catchment and returned to another catchment or the sea. It is the amount of water abstracted from groundwater or surface water that does not return to the catchment from which it was withdrawn.

According to the study [1], WF_blue of the Fátima neighborhood is 48.0 m³/month. Unfortunately, it is not clear from the article how this value was calculated, as the data provided in the study and in the attached Supplementary Material of study by Parra-Orobio et al. [1] do not indicate this. Thus, the study is neither auditable nor replicable. With 368 inhabitants in the Fátima neighborhood (this number is given in Section 2.2 of the study by Parra-Orobio et al. [1]) and WF_blue = 48.0 m³/month, the average per capita consumption is 0.130 m³/month or 4.35 L per capita per day. Yet, the study states, “On average, the water consumed for drinking and cooking by household members in the neighborhood, excluding pets, was 8.9 m³/month, equivalent to 74.2 Lpcd (Litres per capita per day)”. It is not clear how monthly water consumption of 8.9 m³/month for 368 inhabitants in 134 households leads to 74.2 Lpcd. For example, 74.2 Lpcd for 368 inhabitants represents 27.3 m³/day.

According to Figure 2 in the study [1], the blue water footprint included water consumed for drinking by children, adults, dogs and cats, and water used for cooking. It is unclear whether the amount of water used for cooking was also consumed (i.e., evaporated during cooking) or whether it was truly water used, some of which was returned to the water source. Similarly, some of the water drunk by children and adults is returned to water sources in the catchment area, even though “the area lacked a wastewater collection system” [1].

Conversely, it can be assumed that some of the other water use also evaporates and does not return to water sources due to the lack of wastewater collection systems. Therefore, it would be appropriate to include a part of the water used for laundry (“clothes washing”) in the blue water footprint value, in line with the methodology [2], as some water evaporates when drying wet laundry. Even if it is only 10% of the water used, this represents 0.435 m³/month and 0.166 m³/month in Stratum 1 and Stratum 2, respectively, according to the data presented in Table S1 in the Supplementary Material of the study by Parra-Orobio et al. [1]. Furthermore, it is also appropriate to include the fraction used for car washing in the water footprint, as in low-income communities, hand washing of vehicles, where most of the water soaks into the soil and is subsequently evapotranspired by surrounding vegetation, is more likely to be expected than washing vehicles in car washes connected to sewers.

3. Application of Grey Water Footprint Methodology

The second significant deviation from the Water Footprint Assessment Manual [2] is the equation used to calculate the grey water footprint. In the study [1], the equation used to calculate the grey water footprint (WF_grey) was:

$$W{F}_{grey}=\frac{L_{COD}}{C_{maxCOD}-C_{rbCOD}}+\frac{L_{TSS}}{C_{maxTSS}-C_{rbTSS}},$$

(1)

“where L_COD is the COD load at the discharge point (kg/s), L_TSS is the TSS load at the discharge point (kg/s), C_maxCOD is the maximum allowable concentration of COD in the receiving body (kg/m³), C_maxTSS is the maximum allowable concentration of TSS in the receiving body (kg/m³), C_rbCOD is the COD concentration in the receiving body (kg/m³), and C_rbTSS is the TSS concentration in the receiving body” [1]. COD represents chemical oxygen demand, and TSS represents total suspended solids.

However, the Water Footprint Assessment Manual [2] calculates WF_grey differently: “The grey water footprint is calculated by dividing the pollutant load (L, in mass/time) by the difference between the ambient water quality standard for that pollutant (the maximum acceptable concentration c_max, in mass/volume) and its natural concentration in the receiving water body (c_nat, in mass/volume)” [2] (page 30). “When a waste flow concerns more than one form of pollution, as is generally the case, the grey water footprint is determined by the pollutant that is most critical, that is the one that is associated with the largest pollutant-specific grey water footprint” [2] (page 39).

Thus, there are two differences between the Water Footprint Assessment Manual [2] and the study [1]:

• Instead of the actual concentration of pollutant XXX in the receiving water body (C_rbXXX) in Equation (1), the Water Footprint Assessment Manual uses the natural concentration in the receiving water body (C_nat).

The Water Footprint Assessment Manual [2] explains on page 32 “why the natural concentration is used as a reference and not the actual concentration in the receiving water body. The reason is that the grey water footprint is an indicator of appropriated assimilation capacity. The assimilation capacity of a receiving water body depends on the difference between the maximum allowable and the natural concentration of a substance”. As reported by Jamshidi et al. [3], “C_nat equals the concentration of pollutants in the receiving water on the condition that the interferences of human activities are eliminated”.

The Water Footprint Network has published supplementary guidance on the implementation of the three-tier approach for determining maximum and natural concentration values [4]. Liu [5] suggests that the grey water footprint calculation is highly sensitive to the water standards applied and highlights the need for standardizing the setting of water quality standards for a consistent grey water footprint assessment. However, many studies use national standards to determine maximum or natural concentrations. In the Czech Republic, for example, Standard ČSN 75 7221 [6] Water quality-Classification of surface water quality [7,8,9] is used. In China, in turn, China’s Environmental Quality Standards for Surface Water GB 3838-2002 [10] are used [11,12,13,14].

2.

Instead of using the sum of the grey water footprint values for COD and TSS, the Water Footprint Assessment Manual uses the higher of the two values.

From the structure of Equation (1) used in the study [1], it can be assumed that the addition of another pollutant would add another term to Equation (1) and, therefore, lead to an increase in the grey water footprint value even under unchanged conditions. However, I do not consider this a meaningful solution.

I was also struck by the choice of indicators for assessing the grey water footprint. Chemical oxygen demand (COD) and total suspended solids (TSSs) were chosen for the study [1]. This was based on the recommendations of the study by Cerón-Hernández et al. [15]. However, this referenced source focuses on drinking water production and not on wastewater management. Additionally, it does not use COD but biological oxygen demand (BOD₅) and dissolved oxygen. As there is no sewerage system in the area, the estimation of grey water footprint determinants is more challenging. According to long-term measured data available in the Czech Republic [16], the most common determining pollutant in untreated wastewater is N-NH₄⁺. Stejkalová et al. [17] analyzed the determinants of input in untreated wastewater according to the size of wastewater production, and in no size category; however, COD or suspended solids were the dominant determinants of the grey water footprint value (

Table 1. Chemicals determining the grey water footprint in untreated wastewater in % of cases.

Wastewater Production	BOD5	COD	SS	DIS	Ninorg	Ptot	N-NH4+
Cat. II (2001–8000 m3/year)	43	2	0	2	0	1	54
Cat. III (8001–20,000 m3/year)	17	1	0	0	0	2	81
Cat. IV (20,001–80,000 m3/year)	18	1	1	0	0	2	79
Cat. V (80,001–400,000 m3/year)	18	0	1	0	0	1	80
Cat. VI (400,001–4,000,000 m3/year)	19	0	3	3	0	1	74
Cat. VII (>4,000,000 m3/year)	30	0	13	1	0	1	55

BOD₅—biochemical oxygen demand; COD—chemical oxygen demand; SS—suspended solids; DIS—dissolved inorganic solids; N_inorg—inorganic nitrogen; P_tot—total phosphorus; N-NH₄⁺—ammonium nitrogen. Adapted from [17].

).

4. Grey Water Footprint Sustainability Assessment

A third significant deviation from the Water Footprint Assessment Manual [2] is the equation used to calculate the Water Pollution Level (WPL) in the grey water footprint sustainability assessment. The Water Footprint Assessment Manual [2] states on page 86 that WPL is an impact indicator “which measures the degree of pollution. It is defined as the fraction of the waste assimilation capacity consumed”. In the study [1], the equation used to calculate WPL is:

$$WPL=\frac{{\displaystyle \sum }W{F}_{grey}}{A{v}_{blue}}.$$

(2)

At the same time, Av_blue is defined as 20% of the runoff from the watershed [1]. However, the Water Footprint Assessment Manual [2] uses the entire runoff from the catchment to calculate the WPL—see Equation (55) on page 87 of the Water Footprint Assessment Manual [2]. This is logical because all of the runoff from the watershed serves to dilute the pollution discharged or, as stated on paged 86–87 of the Water Footprint Assessment Manual [2]: “A water pollution level of 100 per cent means that the waste assimilation capacity has been full consumed. When the water pollution level exceeds 100 per cent, ambient water quality standards are violated”. Therefore, it is not clear why the study [1] uses only 20% of the runoff to calculate the WPL. At the same time, the study, in defining Av_blue, refers to a publication by Hossain and Patra [18], which does not use the WPL but the Water Pollution Index, a different indicator that is calculated in a completely different way from the study [1]. At the same time, the study [1] lists the limits for assessing the sustainability of both the blue and grey water footprint in Table 1, which refers to the paper by Yuan and Lo [19], which, however, does not deal with either the water footprint or the WPL indicator at all but deals with indicators within the Water–Energy–Food Nexus concept.

5. Discussion and Conclusions

The study [1] in Low-Income Urban Neighborhoods provides interesting information about water use in these communities. The authors applied the approaches used in the water footprint methodology [2]. However, in their application, they deviated so much from the defined water footprint principles that their results cannot be compared with similar studies that applied the water footprint methodology according to the defined procedures in the Water Footprint Assessment Manual [2]. It is a great pity that these deviations are not documented and justified, as they may have a rational basis and could, if accepted by the scientific community, advance the development of the water footprint methodology. After reading the study [1], the reader is confused for a while, as it is not clear why the Water Footprint Assessment Manual [2] is not followed and why the referenced references have no relation to the methods described in the study.

It would be very helpful if the authors would expand or modify their article “Assessment of the Water Footprint in Low-Income Urban Neighborhoods from Developing Countries: Case Study Fátima (Gamarra, Colombia)” to clarify all of the above deviations from the standard water footprint methodology.