Assessing the relevance of intervening parameters on the per capita water consumption rates in Brazilian urban communities

Abstract

The determination of the per capita water consumption is an essential step in the design of water supply systems. The present paper examines the influence of several variables on the per capita water consumption, based on 96 different towns in the Brazilian state of Minas Gerais. The set of samples was categorised firstly into five different classes and three distinct population intervals and, finally, was presented in overall terms. The paper also attempts to explain the relative influence of the intervening factors for each population range, and presents a multivariate regression model for the per capita water consumption rates for towns with populations ranging from 50,000 to 100,000 inhabitants. For this population range, the significant variables were: (a) percentage of water metering, (b) water tariff, and (c) energy consumption by commercial and industrial activities.

Seasonal Variation of Rainy and Dry Season Per Capita Water Consumption in Freetown City Sierra Leone

“…The monthly average family income is broadly consistent with the Government of Sierra Leone Civil Service Code: Regulations and Rules governing income and salary scales and the UN Salary scales for staff in the General Service and related categories [27]. The correlation coefficient R can be used to evaluate the strength of the relationship between variables [28,29]. The analysis of the data suggests a strong relationship between household occupancy (i.e., the number of people in the household) and total water consumption (R = 0.64), whilst there is a negative relationship between household total per capita consumption and household size (R = −0.728).…”

Influência De Indicadores Socioeconômicos No Consumo Per Capita De Água Para Os Municípios Do Estado Da Bahia

“…Correlação positiva entre IDHM e consumo per capita de água também foi observada em municípios dos estados de Minas Gerais (FERNANDES NETO et al, 2005) e de Goiás (AQUINO et al, 2017), sugerindo que quanto maior a qualidade de vida da população, maior é seu consumo médio diário de água.…”

Classifying households for water demand forecasting using physical property characteristics

“…Factors used to explain variation in water demand Agthe and Billings (2002) Water price, value per bedroom, number of bedrooms, apartment age, indoor water saving devices, swimming pools, vacancy rates Bradley (2004) Economy, employment, property type, household size Billings and Jones, 1996 Garden, personal income Clarke et al (1997) Income, property type, property size, tenure Dandy et al (1997) Water price Day and Howe (2003) Changes in technology, socio-economic, garden, water use behaviour, demography, land use de Lourdes Fernandes Neto et al (2005) Water price, temperature, rainfall Durga Rao (2005) Distance from city, land use cover, population density, soils Foster and Beattie (1979) Water price, income, number of persons per meter, rainfall Hall et al (1988) Appliance ownership, appliance volumetric water use, frequency of use Herrington (1996) Water using appliance ownership, water use behaviour changes Huei (1990) Number of rooms per house, number of persons, employment type and percentage employed. Koo et al (2005) Employment, population Kowalski and Marshalsay (2005) Socio-economic group, occupancy, house type Letpalangsunti et al (1999) Day of the week Liu et al (2003) Water price, income, household size Martinez-Espineira (2002) Household size, occupancy, climate Metzner (1989) Number of persons per household and household composition, climate Power et al (1981) White goods installation, appliance ownership Russac et al (1991) Property type, household size, appliance ownership Syme et al (2004) Income, garden interest, lifestyle, conservation attitudes Tamada et al (1993) Temperature, weather Troy and Holloway (2004) Dwelling type Weber (1989) Rainfall, temperature, water prices Whitford (1972) Water price, housing patterns, appliance water use Zhou et al (2002) Temperature and rainfall to property size, but only imprecisely through a banding system based on value.…”