(C) PLOS One This story was originally published by PLOS One and is unaltered. . . . . . . . . . . Assessing inequalities in urban water security through geospatial analysis [1] ['Juliana Marçal', 'Water Innovation', 'Research Centre', 'Wirc', 'Department Of Chemical Engineering', 'University Of Bath', 'Bath', 'United Kingdom', 'Water Informatics In Science', 'Engineering'] Date: 2024-06 Urban water security evaluation The task of populating the list of indicators revealed different levels of data availability and granularity for the city of Campinas. Several indicators only had values for the entire city, especially for water quantity, climate change and governance. This process allowed us to audit the accessibility of free data for this case study and to note the impacts on the following assessment. Data at a small scale may be further available within stakeholders’ organisations, however, for transparency reasons only freely accessible data were used in this study. Most the of granular data available issued from a decennial national survey carried out by the Brazilian Institute of Geography and Statistics [64]. Incorporating small scale monitoring to the local agenda and making that information available is important to better investigate certain aspects, especially in terms of governance and risks and climate change. Information such as temperature differences in the urban space can provide insights on urban heat island fluctuations for instance. These have been found to be related to urbanisation pattern and having influence on public health [79], therefore, detailed information on spatial distribution of temperature in urban areas can prompt public action and help improve different dimensions of water security. Nevertheless, small-scale free information from the state or municipality was difficult to find. Data for some indicators, such as diversity of sources (A1.3), metering level (A4.3) and water loss (A4.4) (Dimension A), were only available for the city scale, therefore, all the sectors received the same score and a study of inequality in the city was not possible. This was also the case for several aspects of dimensions C and D, for which data at a small scale was less available. This hinders the assessment on the urban water security heterogeneity since it is difficult to conclude if this is related to homogeneity of the urban area or if there was not enough data to translate the existing variability. The results of the assessment at city and sector scales are presented to each of the four dimensions in Figs 2–5. These show the scores attributed for the city as bars and the scores calculated for sectors as circular markers. The size of the circular marker indicates the population living in each sector. The scores range from 1 to 0, with desirable characteristics given ‘1’ and undesirable values, ‘0’. This visualisation shows the interest of our framework since it highlights the dispersion existent within the studied area for high scoring indicators, such is the case of affordability (A3.3) and access to wastewater collection (A3.2). When aggregating the categories for the four dimensions for the sectors in the city, the spatial distribution of the results can be visualised, as seen in Fig 6. Different scores are visibly distributed in the urban area, given an indication of existing spatial inequalities of water security. These results show less differentiation for dimensions C and D, for which granular data was less available. Nonetheless, even with the challenge of data availability, adding the spatial dimension to water security assessment allowed us to show, for all four dimensions considered, some variability in the aggregated scores. The results support the need to investigate inequality within the city boundary rather than considering the average value for the entire urban area. PPT PowerPoint slide PNG larger image TIFF original image Download: Fig 6. Spatial distribution of water security. Aggregated results for (A): Drinking water and human well-being (B): Ecosystems (C): Water related hazards and climate change and (D): Economic and social development. Labels on the maps show the highest and lowest scores found for each dimension. Territorial units basemap source: Campinas geospatial database from the Campinas Municipal Council https://informacao-didc.campinas.sp.gov.br/metadados.php [69], freely available to use. https://doi.org/10.1371/journal.pwat.0000213.g006 The results for the Drinking water and human well-being dimension (A) show that, in general, few districts have a below average score (Fig 6A), while diversity can be observed within the municipality when investigating separate categories and indicators (Fig 2). Water quantity (A1) was found to be the most concerning category for the case study, with the lowest scores in the dimension, and water stress (A1.5) being the main challenge for the city (see Fig 2). The high concentration of people and economic activities in the region, associated with decreasing water availability over the years results in constant pressure in the basin’s water resources and a low score for the city. The region has faced water crises in 2014 and 2016, while the available water quantity is a continuous concern of local organisations [80]. Regarding accessibility to services (A3), Campinas has been able to establish very good conditions in the urban area. Yet, it is possible to see markers with low score, representing sectors where challenges are still present as shown in Fig 2. Data on sewage coverage (A3.2) for instance, showed some deficiency in the infrastructure of certain sectors in the outskirts of the city. For the last decade a plan to achieve universal sanitation has been implemented by the water utility [66]: for the time scale of this study, 83% of the population had access to sewage collation, a percentage that increased to 94% in 2020 [72]. According to the Sustainable Cities Program, Campinas has achieved the goals for water supply and sewage collection and treatment from the SDG 6 but still faces challenges regarding water loss [81]. In terms of reliability of services (infrastructure reliability (A4)), measures of non-scheduled maintenance services (service reliability (A4.2)) were found for the different sewage collection systems, allowing visualisation of some variability between the sectors, especially highlighting low scores in the outskirts and south of the municipality. As for public health and well-being (A5), with little incidence of gastrointestinal infections (incidence of water-borne diseases (A5.1)) throughout the territory, the main component leading to diversity in this category was accessibility to green social areas (recreational opportunities (A5.2)). A very dispersed set of results showed an unequal distribution of scores, with districts in the centre having good access to parks and gardens and therefore high scores while sectors at the outskirts of the city received low scores. The heterogeneity of scores was more prominent for the dimension Ecosystems (B)(see Fig 6B) that also had the lower score, ranging between 0.34 and 0.74 for the urban sectors. Investigation of the categories of this dimension showed that indicators related to green coverage and environmental diseases, from the Environment (B1) category, presented relative low average scores and high dispersion within the city boundary (Fig 3). Campinas, as many other urban areas in tropical and subtropical regions, faces challenges with environmental safety (B1.2)—or water-vectored—diseases, such as dengue fever. These are related to high population density, irregular supply, waste management, etc [82, 83]. The results also demonstrate challenges regarding green coverage (green areas (B1.1)). These are common to the urban context, due to the urbanisation process and high urbanisation rate in the city (in Campinas, of about 98%) [64]. In terms of the pollution control (B2) category, intra-city granular data for groundwater and surface water quality (B2.1 and B2.2) were not available, and therefore, little differentiation was observed for these aspects. As for wastewater treatment rate (B2.3), data from wastewater collection systems allowed us to verify diversity within the city. For the time scale analysed, improvement was required in some sectors, especially in the south of the city. However, substantial investment has taken place in the last decade which improves the score for this indicator. The wastewater treatment rate in the city increased from 72% in 2010 to 89% in 2020, with the water utility goal expected to be reaching 100% by 2025 [72]. A reuse water station, using membrane bioreactor (MBR) technology, is installed and in operation since 2012 in the south of the city. For this area, high removal efficiency is accompanied by high energy consumption, leading in some sectors to relatively low scores for the energy usage efficiency (B3.1) indicator [72] (Fig 3). Other districts that have their wastewater treated by energy demanding activated sludge and aerated ponds technology, also had lower scores for this indicator. As to wastewater reuse (B3.2), the practice is still limited due to legislation restrictions, resulting in a very low score overall. However, with a second water reuse station inaugurated in 2021, there is great potential to improve usage efficiency in the city of Campinas for the next decade [72]. As for dimension C: Water related hazards and climate change, in terms of water hazards (C1), Campinas did not face any drought during the decade preceding the evaluation date [84], and, although it has faced several flood events, the proportion of flood prone areas varies considerably in the sectors (see Fig 4). As for preparedness (C2), a wide distribution of drainage infrastructure and people living in hazardous areas was found. Nonetheless, due to lack of available granular data for other indicators in the dimension, possible existing spatial variation was attenuated and rendered virtually invisible in the final visualisation map (see Fig 6C). Related to the SDG 13—urgent action to combat climate change and its impacts [85], the scores of dimension C are supported by the results found in the Sustainable Cities Program of which Campinas has taken part since 2012 [81]. This program monitors participant cities in Brazil and evaluates them in terms of the Sustainable Development Index, adopting SDG indicators. According to their results, Campinas scores highly in terms of climate change performance, which also included greenhouse gas emissions and strategies for risk management and prevention of natural disasters. For dimension D—Economic and social development, the spatial distribution of the aggregated score was similar to dimension C. It is less noticeable but still exists (see Figs 5 and 6D). This is expected in view of data collection challenges and low sample sizes obtained for some indicators in these dimensions: the lack of data granularity prevents the grasp of urban inequalities. Governance (D1) aspects in particular were only feasible at the city scale and therefore, no distinction is made for the sectors. Granularity was available for social aspects (D2) indicators and therefore, it was possible to observe a distribution of scores in the city for this category (see Fig 5). Gender equality (D2.6) results showed low scores throughout the municipality with only few sectors with a scores above 0.5. This was confirmed by the a similar low score received by the city of Campinas in the Sustainable Cities Program [81] for the SDG 5—Achieve gender equality and empower all women and girls, considering participation of women in decision making positions, wage inequality among others, major challenges were identified in order to achieve this specific goal. Interestingly, the score for income inequality (D2.3) was smaller for the city than for the sectors, an indication that the sectors are somewhat homogeneous, but differences can be found between them. This is supported by the results of average income (D2.4) that show a great dispersion of results (see Fig 5). As for economic development (D3) indicators, data were available only for the city scale, and translated the favourable economic position of the city—Campinas is a relatively wealthy city with one of the highest GDPs of the state [64]. The use of granular data and spatial visualisation clearly highlights the intra-urban variability for the different water security aspects. Similar to the results of Tholiya and Chaudhary [17] on the performance water supply services and Doeffinger and Hall [14] on sub-national water security assessment, the geospatial visualisation demonstrates the heterogeneity of the studied area. This helps to expose vulnerable regions, and therefore, could inform and support effective decision making. [END] --- [1] Url: https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000213 Published and (C) by PLOS One Content appears here under this condition or license: Creative Commons - Attribution BY 4.0. via Magical.Fish Gopher News Feeds: gopher://magical.fish/1/feeds/news/plosone/