Understanding residential water demand: insights from a survey in a Mediterranean city

ABSTRACT

In order to formulate targeted water demand management policies, policymakers need to know how water is consumed in cities. However, domestic water consumption can be highly affected by diverse factors, with local specificities playing a major role. In this study, the determinant factors associated with residential water consumption in the city of Aveiro, Portugal, are examined based on data collected through a survey. The analysis was conducted to understand how sociodemographic and physical house factors influence the domestic water consumption. Income, family size, the presence of children and water saving fixtures were noticed to have a significant relationship with water consumption. However, no significant relationship was found with the year of construction of the building.

KEYWORDS:

• water demand
• water consumption drivers
• household characteristics
• housing characteristics
• water conservation practices

1. Introduction

Water scarcity is one of the most pressing problems across the globe and is worsening. The instability of water resources is increasing, not only in areas that have typically already experienced water stress, but also in new areas, motivated by hydrological pattern alteration driven by climate changes (Otaki, Ueda, and Sakura 2017). Additionally, population growth and the consequent urbanization progression and increased industrial activity continue to escalate the demand for freshwater (Chen, Ngo, and Guo 2013; Kumar et al. 2019; Murwirapachena 2021).

To enhance water resilience in urban areas, a deep understanding of the determinants of domestic water consumption is in need. This is essential in order to design and implement effective water demand management policies and to obtain accurate water consumption forecasts. Long-term water demand tends to be driven by house and household characteristics (Donkor et al. 2014; Polebitski, Palmer, and Waddell 2011). Therefore, several studies have been conducted to increase our understanding of how these determinants can influence domestic water consumption (e.g. Cominola et al. 2023; Mazzoni et al. 2023). Housing characteristics are the physical features of properties that influence water use efficiency and water needs of households. Typically, the variables used to evaluate the relationship between the house and the water consumption are, for example, the house age (e.g. H. Chang, Parandvash, and Shandas 2010; Reynaud 2013; Ouyang et al. 2014), number of bathrooms and/or bedrooms, indoor and outdoor water facilities (e.g. H. Chang et al. 2017; Domene and Saurí 2006; Ojeda de la Cruz et al. 2017), lot and house size (e.g. Domene and Saurí 2006; Mini, Hogue, and Pincetl 2014), presence of garden or garden size (e.g. Breyer, Chang, and Parandvash 2012; Domene and Saurí 2006; Larson et al. 2009; Mini, Hogue, and Pincetl 2014) and the presence of water-efficient devices (e.g. Gam and Regeb 2021; Turner et al. 2005). Household characteristics include socioeconomic and demographic attributes of households that can influence habits and tendencies to use water. Usually, the variables used to relate household and water consumption patterns are income (e.g. Alharsha et al. 2022; Bich-Ngoc et al. 2022; Fielding et al. 2012; Harlan et al. 2009; March and Saurí 2010; Veiga, Kalbusch, and Henning 2022), number of adults and children (e.g. Bich-Ngoc et al. 2022; Domene and Saurí 2006), demographics and education levels of household members (e.g. André and Carvalho 2014; Fielding et al. 2012; House-Peters, Pratty, and Chang 2010; Schleich and Hillenbrand 2009).

Some authors (e.g. Breyer, Chang, and Parandvash 2012; Mini, Hogue, and Pincetl 2014; Wong, Zhang, and Chen 2010; Xenochristou, Kapelan, and Hutton 2020) identified the influence of seasonal variables, such as climate (e.g. temperature and rainfall), on short to medium-term water demand. Others also refer to the influence of spatial effects, observing that water consumption of households tends to be similar to their neighbours, independently of their demographic and economic characteristics (e.g. Wentz and Gober 2007; H. Chang, Parandvash, and Shandas 2010) or that higher population densities are associated with lower average water demand (March and Saurí 2010). The water price is also identified as a driver by some authors (e.g. Baerenklau, Schwabe, and Dinar 2014; Domene and Saurí 2006; Grafton et al. 2011; Mini, Hogue, and Pincetl 2014; Reynaud 2013; Yoo et al. 2014).

For Portugal, the number of studies focused on the determinants of domestic water consumption is scarce. Martins and Fortunato (2007) have estimated a Portuguese residential water demand equation to explain the effects of the pricing policy on water demand, while Cabral et al. (2016) evaluated the influence of weather conditions on urban water consumption using available continuous flow measurements from existing district metered areas in Lisbon and Setubal districts. Matos et al. (2014) evaluated possible relations between the indoor water end-use patterns and socio-demographic characteristics of the household, namely the number of residents, the presence/absence of children/elders and the income and education levels. However, there are some studies in the Mediterranean area on user-level water consumption, where some of them are related with non-residential water uses (e.g. Deyà-Tortella et al. 2019; Mazzoni et al. 2022).

This study aims to identify the house and household variables that can influence residential water consumption in the city of Aveiro. Determinant factors regarding house and household characteristics associated with water consumption were examined. This was made based on data collected from an online survey distributed to inhabitants of Aveiro city between July and September 2022. To the best of our knowledge, no study has ever been conducted where user-level data was used, since most of the studies performed for Portugal were based on aggregated data (e.g. Cabral et al. 2016; Martins and Fortunato 2007). The exception is the study by Matos et al. (2014), but it extends over three cities with a sample of only 36 households, and not all of them complete (the maximum number of replies in any of the questions was only 23).

2. Determinants of domestic water consumption

Various studies have identified the influence of sociodemographic and demographic attributes of households and housing characteristics on water consumption, such as income, household size, age distribution, and education level of household members (e.g. Kenney et al. 2008; Willis et al. 2011, 2013).

Households with higher income usually present higher water consumption than households with lower incomes (e.g. Guhathakurta and Gober 2007; Kenney et al. 2008; Loh and Coghlan 2003; Willis et al. 2013). In low-income households, the financial reasons motivate the need for saving water. In high-income households, however, two opposing effects can be observed. On the one hand, since high-income households usually have higher-education levels, they generally have stronger water conservation intentions and tend to have more water-efficient appliances (e.g. Gilg and Barr 2006; Lam 2006). On the other hand, high-income households tend to have higher living standards (e.g. more frequent and longer showers/baths, large garden irrigation needs, pools), which imply the use of larger amounts of water to support them (e.g. Fielding et al. 2012; Harlan et al. 2009). Some studies (e.g. De Oliver 1999; Gregory and Di Leo 2003) studies find that higher education can be associated with higher water use. This indicates that income and education levels should not be considered independently, since they tend to be correlated (higher income level is usually associated with higher education level), and should also consider other context variables, namely the housing outdoor uses.

The higher the number of members of a household, the larger the total water demand (e.g. Wentz and Gober 2007). However, water demand per capita does not always increase with household size. The reason is related to an ‘economy-of-scale’ effect, since various uses of water, such as house cleaning, gardening and cooking, are shared activities. Consequently, the increase in water consumption with the number of household members is not linear (Veiga, Kalbusch, and Henning 2022). Nonetheless, economies of scale regarding cannot be generally achieved in small households, and there is an optimum household size (Arbués, Garcia-Valinas, and Martinez-Espiñeira 2003).

The age of the household members is another relevant driver of domestic water consumption. However, studies on occupants’ age and water demand present contradictory results. Some authors (e.g. Fox, McIntosh, and Jeffrey 2009; Schleich and Hillenbrand 2009) reported that older people generally use less water per capita than younger people. This is justified by the fact that households with children tend to experience more frequent and more prolonged micro-component events, such as showers (e.g. Abu-Bakar, Williams, and Hallett 2023; Balling, Gober, and Jones 2008; Willis et al. 2013). Other authors (e.g. Kenney et al. 2008; Martínez-Espiñeira 2003; Martins and Fortunato 2007; Musolesi and Nosvelli 2007; Willis et al. 2009), however, obtained a negative relationship between per capita water use and the share of elderly people living in households. The justification presented is that this population group are retired individuals that spend a greater proportion of their time at home and, thus, their full water needs are satisfied there and not elsewhere (e.g. work and school).

House characteristics associated with its size, such as number of bedrooms, number of bathrooms, lot size and house size, are positively related to residential water use (e.g. Gam and Regeb 2021; Renwick and Green 2000; Stoker and Rothfeder 2014). Gam and Rejeb (2021) found that, in Tunisia, household water demand increases 0.53% per additional bedroom. The year of construction of the building is often reported as an important factor of water demand, mainly attributed to the fact that recent buildings tend to have more efficient water fixtures (e.g. Guhathakurta and Gober 2007; Harlan et al. 2009; Rockaway et al. 2011). Regarding water-saving devices, research has shown that replacing existing household appliances by water-efficient alternatives, such as dual-flush toilets, tap aerators and low-flow showerheads, can reduce water use by 9% to 50% (e.g. Inman and Jeffrey 2006; Mayer et al. 2004). However, a rebound effect was observed by some authors (e.g. Stewart et al. 2013). Despite the installation of water-efficient fixtures, water demand increased in several households. This might be explained by the ‘offsetting behaviour’, where residents increase the number and duration of the uses since they know that they are using water-conserving devices and end up using more water than previously (e.g. Campbell, Johnson, and Larson 2004).

3. Methodology

3.1 Study area

This study was performed in the city of Aveiro, which is located in the central part of Portugal, by the coast. It is a medium-size city in the context of Portugal and the capital of the district of Aveiro. With around 80 954 inhabitants in an area of 200 km², it presents an average population density of 404 inhabitants per km² (INE 2021). It is composed of 10 civil parishes: São Jacinto, Cacia, Eixo e Eirol, Esgueira, Santa Joana, Glória e Vera Cruz, São Bernardo, Oliveirinha, Requeixo, Nossa Senhora de Fátima e Nariz, and Aradas (Figure 1).

Figure 1. Aveiro district location and its municipalities, with municipalities water supplied by ADRA highlighted.

AdRA – Águas da Região de Aveiro is the water utility responsible for the management of the water system of the Aveiro region. It was founded in 2009, in the scope of the public partnership contract for the integrated management of the public water supply and wastewater drainage of 10 municipalities in the Aveiro region. It covers an area of 1,500 km², serving a population of about 350 000 inhabitants.

3.2 Survey design

A survey was applied using FormsUA (https://forms.ua.pt/) that allows to create online surveys using the LimeSurvey software (http://www.limesurvey.org). This method was preferred as it did not involve direct costs, allowed anonymous participation, and the implementation and dissemination were much faster than the more traditional approaches, such as in-person, over-the-phone or mail surveys. Although some authors (e.g. Almulhim and Aina 2022; Grafton et al. 2011; Maykot and Ghisi 2020) show concern about the quality of online responses compared to traditional survey methods, Deutskens et al. (2006) obtained identical results when comparing data from mail and online surveys in service research.

Survey questions were organized into six sections: i) general information; ii) water consumption data; iii) household socioeconomic characteristics; iv) housing characteristics; v) behavioural characteristics and vi) water conservation practices (Table 1). The survey contains a combination of open-ended and closed-ended questions, the latter measured in either a binary (Yes/No), categorical or Likert-type scale. When applicable, the categorical close-ended questions were measured using the categories adopted by the Portuguese Bureau of Statistics census, namely household income, household members’ age, education level and year of construction of the building. Water conservation practices were measured on 5-point Likert scale: ‘Very frequently’, ‘Often’, ‘Don’t know’, ‘Occasionally’ and ‘Rarely’. The ‘Don’t know’ answer was interpreted as a non-conscientious behaviour, implying that the user may behave in a way in some occasions and in a different way in others. Water consumption data consist of average daily water consumption values in litres/day, measured by the water utility at the entrance of each respondent’s dwelling. It should be noticed that, at the beginning of the survey, the following notification was highlighted: ‘When starting to reply to this survey, you should have in hand a water billing sheet no more than 3 months old’. At the section where the respondents filled their water consumption, were provided instructions on where to search for the average daily water consumption, in the billing sheet, as the water utility provides historical information to their clients. For this reason, the water consumption data is expected to be exempted from seasonal factors.

Table 1. Description of the survey variables.

Section 1: General information
House location	Civil parish
Section 2: Water consumption
Water consumption	Billed average daily water consumption (litres/day)
Section 3: Household socioeconomic characteristics
Ownership	Owned, rented or other
Age	Age of the household members
Sex	Male, Female, Not specified
Education level	5 categories (less than high school, high school, bachelor, master, doctorate)
Income	Average monthly income of household
Household size	Number of family members living in the house
Section 4: Housing characteristics
Property type	Apartment, detached house, house
Year of construction	Year of construction of the building
House size	House area (m^2)
Outdoor space	House has outdoor space (e.g. garden, balcony, porch)
Number of rooms	Number of rooms
Number of bathrooms	Number of bathrooms
Presence of water efficient fixtures	Presence of tap aerators, dual flush toilet and efficient shower
Section 5: Behavioural characteristics
Number of washing machine uses per week	12 categories (from 0 to more than 10)
Number of dishwasher uses per week	12 categories (from 0 to more than 10)
Number of shower/bath uses per week	16 categories (from 0 to more than 14)
Number of daily meals at home	4 categories (from 0 to 3)
Section 6: Water conservation practices
Turn off water while brushing teeth
Turn off water while lathering hands
Turn off water while shampooing hair and soaping body
Taking short showers
Taking shower instead of bath
Washer and dishwasher full whenever possible
Avoid using running water to wash fruits and vegetables
Avoid using running water to wash dishes
Collect rainwater/Recycle wastewater

3.3 Data collection

Preliminarily, the survey was tested among colleagues for ease of understanding and content validity. It was asked to criticize the survey for ambiguity, clarity and appropriateness of the parameters evaluated. The feedback was used to revise and improve the questions. In total, the survey was designed by 4 people (the authors and a person from the area of Design) and checked by 10 people (from academia, research and industry).

The survey was distributed by e-mail to several mailing lists. Water distribution companies and other institutions involved in the water sector were also asked to distribute the survey, along with universities and research centres. It was also advertised through different social networks (such as Facebook and LinkedIn). The survey was made available to the respondents through a link, between July and September 2022. Participation was voluntary, and the confidentiality of the participants was guaranteed. All the requirements of the Portuguese General Data Protection Regulation (RGPD) were satisfied. The responses were considered valid only if the survey was fully completed.

4. Results

4.1 Sample characteristics

A total of 76 answered surveys were received, for the city of Aveiro, corresponding to 53 fully answered and 23 incomplete surveys. The incomplete surveys were discarded, being only analysed the 53 valid replies, respecting to 163 individuals (107 adults and 56 children). Most of the surveyed dwellings were located in the parish of União da Glória and Vera Cruz (67%) that corresponds to the Aveiro city centre. Since the University of Aveiro plays a significant role in the city, it was not surprising that five of the replies were from apartments with only students aged between 20 and 29.

To the best of our knowledge, this is the largest sample size in any study on water consumption determinants in Portugal. Furthermore, being concentrated in a single city and almost in a single parish, the spatial effects can be neglected. Figure 2 shows the distribution of the number of responses by the different parishes in the municipality of Aveiro.

Figure 2. Distribution of the number of complete answers by parishes in the city of Aveiro.

Figure 3 presents the household demographic characteristics of the surveyed households. In general, the studied households were gender balanced, and most of their members are between 35 and 49 (42%), and between 5 and 19 years old (27%). Considering only the adults in each household surveyed, the educational level was generally high, with most households having members with higher education (94%). In fact, considering only the highest education level, all households had at least one adult with higher education. Most of the households were composed by two adults (79%) and no children (43%).

Figure 3. Household demographic characteristics: a) Age; b) education level of adults; c) number of children; d) number of adults.

Size-wise, 57% of the respondents belonged to small families (3–4 members), most of the members were employed or studying (90%), and a large portion of the dwellings were owned (66%). Regarding household income, the distribution was quite uniform for the four major groups considered (Figure 4). Within the lower group, the households with income below 360€ were all student apartments. Within the highest income group, 84% had monthly incomes below 3570€.

Figure 4. Household socioeconomic characteristics: a) Size; b) Income; c) Employment; d) ownership.

Most of the dwellings were apartments (85%) and were built between 1991 and 2022 (69%) (Figure 5). The average living area (without considering garden and outdoor spaces) of the surveyed dwellings was 142 m², and 48% presented an outdoor space. The presence of garden or vegetable garden aimed at capturing the potential for high outdoor water demand and represented the outdoor space in 39% of the dwellings with outdoor spaces, but the majority (61%) had other outdoor spaces, mainly balconies, which imply lower outdoor water consumption. Considering all dwellings (with and without outdoor spaces), only 18% had garden or vegetables garden.

Figure 5. House characteristics: a) Type of dwelling; b) Year of construction; c) Area; d) Type of outdoor space.

4.2 Water consumption

On average, the sampled households consumed a total of 299 l/day, with large majority having a daily water consumption between 90 and 350 l/day (Figure 6).

Figure 6. Water consumption frequency.

The average daily per-capita water consumption value, 99 l/capita/day, is within the range of values obtained in previous studies for southern European countries, namely Italy, Portugal and Spain, although it is in the lower end (Figure 7). Detailed information on data of water consumption for the studies used to build Figure 7 is presented in Table A1, of the Appendix, which presents per capita daily domestic water consumption in different countries. An effort was made to present studies that reported per capita domestic water consumption and not gross per capita water consumption (that corresponds to the total amount of water used in a certain network divided by the population connected to that same network, thus including leaks and non-domestic uses).

Figure 7. Box-plot of the per-capita water consumption in different countries. References: Mayer et al. (1999), Miguel et al. (1999), Kloss-Trebaczkiewicz and Osuch-Pajdzinska (2000), DeOreo et al. (2001), Loh and Coghlan (2003), Roberts (2005), Cohim et al. (2009), Dantas et al. (2006), Keshavarzi et al. (2006), Oliveira et al. (2006), Ywashima et al. (2006), Ghisi and Ferreira (2007), Heinrich (2007), Lu and Smout (2008), Heinrich (2009), Willis et al. (2009), Sivakumaran and Aramaki (2010), Al-Amin et al. (2011), Beal et al. (2011), Lee et al. (2012), Fan et al. (2013), Matos et al. (2013), Marianski et al. (2014), Hussien et al. (2016), Sant’Ana and Mazzega (2018), Bonoli et al. (2019), Saurí (2020), Muloiwa et al. (2021), McCarton et al. (2022).

A large dispersion in the water consumption values is observed, even for studies pertaining to the same country. For the city of Córdoba, Spain, Miguel et al. (1999) observed a substantial decrease in domestic water consumption from 199 l/capita/day in 1980 to 142 l/capita/day in 1997. More recently, Saurí (2020) observed a similar trend when analysing domestic water consumption in the seven largest Spanish cities, from an average of 135 l/capita/day in 2003 to 110 l/capita/day in 2016 (Figure 7 reports values for the year of 2013, the most recent year that had data for the seven cities). Saurí (2020) attributed this declining trend to multiple factors, namely, to structural causes (linked to conscious mid- to long-term actions performed by individuals and organizations) and to contingent causes (for Spain, drought and economic crisis). As in Portugal, the environmental and economic scenarios are similar, a declining trend in water consumption might be one of the explanations for the difference between the 146 l/capita/day reported by Matos et al. (2013) and the 99 l/capita/day obtained in the present study. Another aspect that may explain the decreasing temporal trend in some locations is the technological advances that took place in the time frame in which the studies were carried out (the oldest ones are from 1999). However, this is more applicable to the most developed regions, where water service coverage and quality is high for decades. In less developed countries, the savings from technological advances are, in many cases, lost to the increasing water supply service coverage and quality and comfort demands from the individuals, resulting in a growing per capita water consumption.

4.3 Household characteristics

The positive effect of income is widely accepted and demonstrated in the literature (e.g. Guhathakurta and Gober 2007; Kenney et al. 2008). In this study, the same effect is found, but the results indicate the existence of a non-linear effect. Considering the full sample, there is a significant difference between the lower-income group and the remaining. This may indicate that only the lower-income households need to restrain their water consumption due to financial reasons. Furthermore, since the effect of income may be affected by the number of elements in the household, focusing only on families of 3–4 elements (57% of the sampled households – see Figure 4), the same pattern is observed (Table 2). This effect might be related to higher living standards of wealthier households, combined with the lower effort to pay for water in relation to the lower-income households. However, since water is relatively inexpensive in Portugal, the influence seems to affect only the lowest income group.

Table 2. Average water consumption at different household income levels.

Income	Average water consumption (l/capita/day)
All households
1355€<	80
1356–2325€	116
2326–3570€	97
>3570€	101
Households with 3–4 elements
1355€<	69
1356–2325€	105
2326–3570€	108
>3570€	106

It is worth referring that 4 out of the 10 households on the lower income level correspond to four out of the five students’ apartments, which are amongst the households with the lowest water consumption per capita. In these households, the income reported does not reflect the corresponding wealth since there is the financial support from the family. Additionally, other aspects, such as returning to the parents’ home on the weekends and during periods without classes, may contribute to the lower water consumption values. Still, excluding the students’ apartments, for the lowest income households, the average per capita water consumption is 106 l/capita/day for the households with up to 2 elements, and 60 l/capita/day for the households with 3–4 elements. This seems to indicate that income affects only the water consumption of the households of the lowest income level and depends on the household size.

In principle, the higher the number of members of a household, the larger the water demand is supposed to be (Wentz and Gober 2007). The same was verified in this study, the higher the number of members of a household, the larger the water demand is supposed to be (Figure 8).

Figure 8. Water consumption for different household sizes.

However, the economy of scale effect documented in literature (e.g. Beal et al. 2011) is also verified, as water consumption per capita decreases from 1 to 5–6 family members (Table 3), regardless of the income level.

Table 3. Average water consumption under different size family groups.

Household size	Average water consumption (l/capita/day)
1 (One person)	122
2 (Two persons)	98
3–4 (Small family)	99
5–6 (Large family)	88

Research has found that households with more education often have stronger intentions to conserve water and have environmental concerns in general (e.g. Gilg and Barr 2006; Lam 1999). Analysing the education level of the surveyed households shows that, in the majority, at least one of the family members presents a high or very high education level, holding a bachelor's, master's or doctoral degree. Considering all income levels, the average water consumption reveals small differences between different education levels. However, when analysing the education-level effect controlling the income, the results reveal a trend towards lower water consumption as the education level increases, independently of the household size (Table 4).

Table 4. Average water consumption per capita by household education level.

Income	Education level	Average water consumption (l/capita/day)
All	Graduate	104
	Master	95
	Doctoral	99
1355€<	Graduate	75
	Master	92
	Doctoral	-
1356–2325€	Graduate	146
	Master	106
	Doctoral	102
2326–3570€	Graduate	109
	Master	95
	Doctoral	83
>3570€	Graduate	116
	Master	62
	Doctoral	102

Household members age can also influence water consumption. According to some studies (e.g. Fox, McIntosh, and Jeffrey 2009; Schleich and Hillenbrand 2009), water consumption tends to decrease when the age of the occupants increases. This was not confirmed directly in the present study since most households include elements of different age groups. However, indirectly, the results are consistent with this observation since, for the same household size, the presence of children in surveyed dwellings led to higher average water consumption (Figure 9). The water consumption increase with the presence of children is consistent with the results of other authors (e.g. Abu-Bakar, Williams, and Hallett 2023; Balling, Gober, and Jones 2008; Willis et al. 2013).

Figure 9. Water consumption and the presence of children in household.

4.4 Housing characteristics

In general, the factors that measure a house's physical size, such as the number of bedrooms/bathrooms and lot or house size, are positively related to residential water use (e.g. H. Chang et al. 2017; Domene and Saurí 2006; Turner et al. 2005). Results from this study show that there is only a slight increment in water consumption with the increase in the physical size of the house (Figure 10).

Figure 10. Water consumption for different number of bedrooms or bathrooms.

The effect of the year of construction of the building in water consumption is not clear, having observed opposite conclusions in the literature. House-Peters et al. (2010) have successfully linked higher water consumption with newer properties. The explanation for this effect is that new dwellings are often bigger and more expensive, which may be a proxy for income and the existence of outdoor spaces. Also, older houses had fewer water fixtures and appliances. On the other hand, other authors (e.g. Guhathakurta and Gober 2007; Harlan et al. 2009; Rockaway et al. 2011) defend that recent buildings are expected to contribute to lower water consumption since they possess more efficient water fixtures. Finally, Bich-Ngoc et al. (2022) observed that, for the region of Wallonia, Belgium, the year of construction had no significant effect on water consumption. In this study, the non-significant effect of the year of construction was verified. Despite the variability, in the present study, there is no identifiable pattern between the water consumption per capita and the year of construction of the building (Figure 11). Also, the recent increase in rehabilitation of existing buildings, which was not captured in the present study, may introduce a bias on the relation between the year of construction and water consumption (the construction year may not correctly represent the characteristics of the water installation and fixtures if they were refurbished).

Figure 11. Per-capita water consumption in buildings with different years of construction.

Moreover, water fixtures with advanced technology generally improve water efficiency (Inman and Jeffrey 2006; Mayer et al. 2004). As a result, questions were asked regarding water-efficient fixtures installed at surveyed dwellings. Although the majority of the respondents knew if their dwellings possessed dual flush toilets, almost 40% did not know if their taps and showerheads were efficient or not. Disregarding the ‘don’t know’ answers, results showed that a large portion of the dwellings have dual flush toilet (74%), followed by tap aerators (50%) and efficient shower heads (36%). Moreover, it was verified that 15% have tap aerators and dual flush toilet, 6% dual flush toilet and efficient showerhead, 2% tap aerators and efficient showerhead, and 13% tap aerators, dual flush toilet and efficient showerhead. Table 5 presents the average water consumption associated with all combinations of water-efficient fixtures present in the households surveyed. The results do not allow us to observe a consistent trend linking the use of efficient fixtures with lower water consumption, revealing that water consumption depends on multiple factors.

Table 5. Water consumption for the different water fixtures installed at dwellings.

Water-efficient fixtures	Water consumption (l/capita/day)
None	94
Tap aerators	105
Efficient shower	69
Tap aerators + Dual flush toilet	68
Tap aerators + Efficient shower	77
Dual flush toilet + Efficient shower	121
Tap aerators + Dual flush toilet + Efficient shower	96

4.5 Behavioural variables

Behavioural variables regarding the kitchen and bathroom use were evaluated by quantifying the ‘Washing machine and dishwasher use’, ‘Shower use’ and ‘Number of meals’ using open-ended questions regarding the use of the washing machine and dishwasher in the household, the use of the shower and the number of meals consumed at home, per week. Results show that dwellings with more intense washing machine and dishwasher use present higher water consumption values (Table 6). Similarly, dwellings of higher water consumption groups present higher shower use and number of meals consumed at home (Table 7).

Table 6. Water appliances utilization under different consumption groups.

Water appliances use	Water consumption (l/capita/day)
	<60	60–90	90–120	>120
Weekly
Dishwasher (uses)	3.6	5.1	4.8	4.0
Washing machine (uses)	2.7	4.3	4.7	4.0
Weekly per capita
Dishwasher (uses)	1.3	1.6	1.3	1.7
Washing machine (uses)	0.9	1.3	1.3	1.6

Table 7. Shower and kitchen use (meals) under different consumption groups.

Water consumption activities	Water consumption (l/capita/day)
	<60	60–90	90–120	>120
Weekly
Shower use (number of baths)	12.3	19.3	22.3	17.3
Kitchen use (number of meals)	38.5	50.8	57.3	44.7
Weekly per capita
Shower use (number of baths)	4.4	5.9	6.1	6.8
Kitchen use (number of meals)	2.0	2.2	2.4	2.6

The literature review conducted did not reveal similar assessments, but the results obtained are logical. Assuming that most individuals shower every day (common practice in Portugal) and considering three main meals, the results indicate that households that have more intensive use of the shower and consume more meals at home, instead of outside (e.g. showering at the gym or eating out), have higher in-home water consumption.

4.6 Water conservation practices and environmental concerns

In the survey, the respondents could inform on their water conservation practices. From the nine available practices, three were adopted by the majority of the residents. The most frequently followed water conservation practice was to take a shower instead of a bath to save water (96% - very frequently), followed by turning off the tap, while brushing teeth (68% - very frequently) and using the washing machine and dishwasher with full loads (64% - very frequently) (Figure 12). These results are consistent with the findings of Jorgensen et al. (2013) and Liu et al. (2016), regarding shortening of the shower times and turning off the water while teeth brushing.

Figure 12. Frequency of water saving behaviours of surveyed households.

Considering the scale for conservation practices from 1 – ‘Rarely’ to 5 – ‘Very frequently’, the level of water conservation behaviour was analysed for different water consumption groups (Table 8). Results show that, generally, the lower water consumption groups have higher levels of water conservation behaviour than the groups with higher water consumption. This trend is particularly noticeable for the actions: ‘turn off water while lathering hands’ and ‘avoid using running water to wash dishes’.

Table 8. Water conservation behaviours under different consumption groups.

Behaviours	Water consumption (l/capita/day)
	<60	60–90	90–120	>120
Turn off water while brushing teeth	4.3	4.6	4.6	4.2
Turn off water while lathering hands	4.3	4.2	3.7	2.8
Turn off water while shampooing hair and soaping body	3.0	3.2	3.1	3.3
Taking short showers	3.2	3.6	4.3	3.2
Taking shower instead of bath	5.0	5.0	5.0	4.6
Washing machine and dishwasher full whenever possible	4.8	4.8	4.7	4.2
Avoid using running water to wash fruits and vegetables	3.0	2.9	2.7	2.4
Avoid using running water to wash dishes	4.7	3.6	3.4	3.1
Collect rainwater/Recycle wastewater	1.8	1.8	1.8	1.7

5. Limitations

The major limitation of the study is clearly the data from the survey. In addition to the sample size limitation, the adoption of a method without interviewer lacks positive reinforcement, enthusiasm, and accountability, and restricts the participation of individuals with literacy and/or technology limitations. On the other hand, it provides a greater sense of privacy, self-pacing, and flexibility to complete surveys at any moment (L. Chang and Krosnick 2009). This restricts the statistical analysis that can be done because the sample is neither large enough to provide high confidence level and low margin of error nor it is adequately distributed to capture all subgroups of the population.

As in any survey study, the potential for sample bias exists. In fact, the sample is not representative of the population in Aveiro, but rather represents a subset of the residents. This can be easily concluded from the demographics of the sample, which does not capture the elderly (the oldest participant was less than 69 years old and only three participants were between 60 and 69 years old). But the strongest bias is probably the education level, which tends to be correlated with the income. According to the 2021 Census (INE 2021), the proportion of Aveiro residents over 24 years old with higher education (bachelor, master or doctorate) was 30%. In the parish of ‘Glória e Vera Cruz’, where most of the participants were from, the proportion was almost 50%, which is still significantly lower than the proportion of participants with higher education in the sample. This, in turn, has an effect on the income, which is higher than the national average (around 1 294€ in 2021, according to Pordata (2022)) considering that more than half of the households had incomes above 2 325€.

6. Conclusions

This work studied the domestic water consumption in the city of Aveiro, Portugal. The influence of housing, demographic and socio-economic factors on per capita water consumption was investigated using a comprehensive on-line questionnaire survey. The collected data was analysed, and the average water consumption was calculated considering the total average water consumption present in the water bill divided by the number of household members. The results revealed that the average per capita water consumption is 99 l/capita/day, close to the values obtained for other southern European countries.

Results indicated that there is a relationship between water consumption in a household and the size of the household, presence of children, and income. It is also observed that, for the same income group, households with higher education levels tend to consume less water than the households with lower education levels. However, is not observed a correlation between the per-capita domestic water consumption and the year of construction of the buildings. Moreover, behavioural variables show that dwellings with higher washing machine and shower use and with higher number of meals consumed at home, per capita, present higher water consumption. Additionally, results also show that the households with lower water consumption per capita are the ones with stronger water conservation attitudes. This finding suggests the importance that water conservation habits can play in contributing to reducing the amount of water used in a household.

Summarizing, the key-findings of this study were:

• The size of the household, presence of children and income positively influence water consumption. On the other hand, water consumption decreases for higher levels of education.

• Did not find a correlation between water consumption and the year of construction of the building.

• Households with stronger water conservation attitudes present lower water consumption.

Despite the limitations of this study, the results have provided insights into which and how some household and housing characteristics can influence water consumption in the city of Aveiro. This can contribute to support water utilities decisions in establishing priorities for measures to promote water-efficient use and estimate potential savings with their application.

Consent to participate

Since the paper article is a prospective study involving human participants, consent to participate was required for this research. Participation was voluntary, and the confidentiality of the participants was guaranteed. All the requirements of the Portuguese General Data Protection Regulation (RGPD) were satisfied.

Ethical approval

The study involves human data, so ethics approval was required for this research. The ethics approval was made by the institutional research committee (Conselho de Ética e Deontologia da Universidade de Aveiro).

Acknowledgements

This research was developed in the scope of R&D project ‘WDS – Water Data Solutions,” co-funded by the European Regional Development Fund (ERDF) of the European Union, through the COMPETE 2020 of Portugal 2020 – POCI-01-0247-FEDER-047224. The financial support of FCT (‘Fundação para a Ciência e a Tecnologia’ – Portugal) is gratefully acknowledged through the project UIDB/04450/2020 (RISCO) by Sandra Costa and Inês Meireles. Vitor Sousa is grateful for the Foundation for Science and Technology’s support through funding UIDB/04625/2020 from the research unit CERIS.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly. Therefore, due to the sensitive nature of the research, supporting data is not available.

Additional information

Funding

This work was supported by the European Regional Development Fund [POCI-01-0247-FEDER-047224]; Fundação para a Ciência e a Tecnologia [UIDB/04625/2020]; Fundação para a Ciência e a Tecnologia [UIDB/04450/2020].

Appendix

Table A1. Per-capita water consumption in different countries.

Continent	Country	City/Region	Water consumption (lpcd)	Reference
NORTH AMERICA	Canada	Waterloo and Cambridge	267	Mayer et al. (1999)
	USA	Boulder	245	Mayer et al. (1999)
	USA	California	175	DeOreo et al. (2001)
	USA	Denver	262	Mayer et al. (1999)
	USA	Eugene	316	Mayer et al. (1999)
	USA	Las Virgenes	263	Mayer et al. (1999)
	USA	Lompoc	249	Mayer et al. (1999)
	USA	Phoenix	294	Mayer et al. (1999)
	USA	San Diego	221	Mayer et al. (1999)
	USA	Seattle	217	Mayer et al. (1999)
	USA	Seattle	240.7	DeOreo et al. (2001)
	USA	Seattle	151	DeOreo et al. (2001)
	USA	Tampa	249	Mayer et al. (1999)
	USA	Tampa	146	Mayer et al., 2004)
	USA	Tempe and Scottsdale	308	Mayer et al. (1999)
	USA	Walnut Valley	257	Mayer et al. (1999)
SOUTH AMERICA	Brazil	Salvador	80	Cohim et al. (2008)
	Brazil	Itajubá	117	Dantas et al. (2006)
	Brazil	Paulínia	113	Ywashima et al. (2006)
	Brazil	Goiânia	95	Oliveira et al. (2006)
	Brazil	Florianópolis	172.3	Ghisi and Ferreira (2007)
	Brazil	Florianópolis	168	Marionski et al. (2014)
	Brazil	Federal District	181.3	Sant’Ana and Mazzega (2018)
OCEANIA	Australia	Birsbane	143.7	Beal et al. (2011)
	Australia	Gold Coast	157.2	Willis et al. (2009)
	Australia	Gold Coast	140.8	Beal et al. (2011)
	Australia	Ipswich	111.2	Beal et al. (2011)
	Australia	Melbourne	169	Roberts (2005)
	Australia	Perth	155	Loh and Coghlan (2003)
	Australia	Sunshine Coast	170.8	Beal et al. (2011)
	New Zealand	Auckland	177	Heinrich (2009)
	New Zealand	Kapiti Coast	184.2	Heinrich (2007)
ASIA	Bangladesh	Manikganj	83.2	Al-Amin et al. (2011)
	China	Harbin	136	Lu and Smout (2008)
	China	Weinan, Yanglin – Wugong & Baoji	70.2	Fan et al. (2013)
	South Korea	55 cities	165.8	Lee et al. (2012)
	Sri Lanka	Trincomalee	139	Sivakumaran and Aramaki (2010)
MIDDLE EAST	Iran	Ramjerd area	121.7	Keshavarzi et al. (2006)
	Iraqi Kurdistan	Duhok	271	Hussien et al. (2016)
AFRICA	South Africa	Tohoyandou	190	Muloiwa et al. (2021)
EUROPE	Ireland	Ballinabrannagh	100.8	McCarton et al. (2022)
	Italy	Bologna	123	Bonoli et al. (2019)
	Poland	Warsaw	206	Kloss-Trebaczkiewicz and Osuch-Pajdzinska (2000)
	Poland	Bialystok	123	Kloss-Trebaczkiewicz and Osuch-Pajdzinska (2000)
	Portugal	Valpaços, Vila Real & Porto	146.1	Matos et al. (2013)
	Spain	Barcelona	106	Saurí (2020)
	Spain	Córdoba	142	Miguel et al. (1999)
	Spain	Madrid	125	Saurí (2020)
	Spain	Murcia	109	Saurí (2020)
	Spain	Palma Mallorca	116	Saurí (2020)
	Spain	Seville	111	Saurí (2020)
	Spain	Valencia	102.5	Saurí (2020)
	Spain	Zaragoza	99	Saurí (2020)
	Portugal	Aveiro	99	Present study