From the global to the subnational scale: Landing the compositional monitoring of drinking water and sanitation services.

Monitoring of access to water and sanitation services is stipulated in Sustainable Development Goals (SDG) 6.1 and 6.2, respectively. The monitoring is carried out with a global, regional and country vision. However, in most developing countries, decentralization of services in water and sanitation management has tended to the sub-national level or has shared responsibilities between national and sub-national governments. Management at the subnational level becomes more important, since everything that is done there will impact the objectives and goals of the country. However, little or nothing progress has been made in harmonizing global indicators with those at the subnational level. Therefore, in this study we have proposed a way to disaggregate information and form WASH ladders at the subnational level. The results show using disaggregated data to interpolate models at the subnational level requires overcoming three main points: the validation of the data through statistical methods, interpolation techniques that go according to the compositional characteristics of the data and the incorporation of the uncertainty of the data into the model results. It also shows that subnational behavior is heterogeneous, which a general analysis does not capture correctly, i.e., there is a masking effect of subnational trends that the country's trend does not represent. However, these have been exceptional cases in some specific categories. Finally, the applicability of non-linear models is contrasted in a broader context, an issue that is still under discussion for its application to global monitoring. This study also provides a way to disaggregate information from the global to the sub-national level, allowing any sector analyst to replicate the methodology in a broader context.

A new measure of hygiene inequality applied to urban-rural comparison.

Access to hygiene services remains one of the most urgent challenges facing countries, especially low-income ones. This has become much more critical in the current context of the COVID-19 pandemic. The WHO/UNICEF Joint Monitoring Program globally monitors access to hygiene service levels. As data are in three parts with a constant sum and a positive value, they are compositional data. Inequality is monitored in disaggregated data; in the urban-rural case, this is done through a simple difference between the urban and rural service levels. However, this simple form of calculation does not take into account the characteristics of the data, which can lead to erroneous interpretations of the results. Therefore, we propose an alternative measure of inequality that uses a ternary diagram and does not infringe on the data properties. The results of the new urban-rural inequality measure show spatial heterogeneity. The highest inequality occurs in Colombia, with a value of 37.1 percentage points, and the lowest in Turkmenistan, with a value of zero. Our results also show that 73 of the 76 countries evaluated have higher basic hygiene services in urban areas than in rural areas. This means that urban households have more availability of a handwashing facility on-premises with soap and water than rural households. Likewise, by subdividing the ternary diagram into ternary parcels, we could group and rank the countries based on hygiene service conditions in a hierarchical order using tripartite information. Finally, our study finds that a multivariate measure of inequality can be important for the public policies of the sector with a general vision, which underscores the value of making evidence-based decisions.

Spatially-structured human mortality modelling using air pollutants with a compositional approach.

The human mortality models with a demographic approach are performed in function of time. The addition of information (social, economic, and environmental) in the structure of demographic models allows fitting observed values better. Air pollution influences human mortality and could be used as an environmental covariate in the demographic models. The levels of air pollutants describe quantitatively the parts of a whole (air), called composition, and their statistical treatment should consider this characteristic in the modelling process. This article evaluated the association between human mortality data with levels of air pollutants as a composition using a spatially-structured model. The spatially-structured modelling approach in the human mortality data captures the spatial heterogeneity of air pollutant concentrations (local environmental conditions). Human mortality data is defined as the number of deaths, and in this work, it was analyzed with both total and disaggregated presentation. The disaggregation was by (i) sex and (ii) sex and age-group. A likelihood ratio test suggested the model with air pollutants as covariates treated under a compositional approach (proposed model) is more appropriate than the model based only on time explanatory variable in yearly basis. The proposed model was evaluated in 48 counties in Spain, each with its mortality and air pollution dataset. The modelling approach in this work presented adequate quality model indexes and could be applied to make short-term predictions with different air pollution scenarios.

Influence of atmospheric parameters on human mortality data at different geographical levels.

Human mortality data are often modeled using a demographic approach as a function of time. This approach does not present an adequate fit model for the number of deaths with great variability. For this reason, additional information (social, economic and environmental) is required for complementing and improving demographic modelling. This article evaluated the association between human mortality data (segregated by age and sex) with meteorological and air pollutant covariates at three geographical levels: country, macro-climate regions and county. The modelling was based on a generalized linear modelling framework and takes into account the common characteristic of overdispersion in human mortality data through the application of negative binomial distribution. The proposed approach improved the dynamic behavior of the Farrington-like model (basic demographic model) and took into account the extreme meteorological and natural air pollution events. Notably, the proposed modelling worked well in cases where the amount of data was scarce.

Spatio-temporal air pollution modelling using a compositional approach.

Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m-3), sulfur dioxide (SO2, µg·m-3), ozone (O3, µg·m-3), nitrogen dioxide (NO2, µg·m-3), and particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5, µg·m-3). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.

Preprocessing alternatives for compositional data related to water, sanitation and hygiene.

The Sustainable Development Goals (SDGs) 6.1 and 6.2 measure the progress of urban and rural populations in their access to different levels of water, sanitation and hygiene (WASH) services, based on multiple sources of information. Service levels add up to 100%; therefore, they are compositional data (CoDa). Despite evidence of zero value, missing data and outliers in the sources of information, the treatment of these irregularities with different statistical techniques has not yet been analyzed for CoDa in the WASH sector. Thus, the results may present biased estimates, and the decisions based on these results will not necessarily be appropriate. In this article, we therefore: i) evaluate methodological imputation alternatives that address the problem of having either zero values or missing values, or both simultaneously; and ii) propose the need to complement the point-to-point identification of the WHO/UNICEF Joint Monitoring Program (JMP) with other robust alternatives, to deal with outliers depending on the number of data points. These suggestions have been considered here using statistics for CoDa with isometric log-ratio (ilr) transformation. A selection of illustrative cases is presented to compare performance of different alternatives.

Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda.

The Sustainable Development Goals (SDGs) are presented as integrated and indivisible. Therefore, for monitoring purposes, conventional indicator-based frameworks need to be combined with approaches that capture and describe the links and interdependencies between the Goals and their targets. In this study, we propose a data-driven Bayesian network (BN) approach to identify and interpret SDGs interlinkages. We focus our analysis on the interlinkages of SDG 6, related to water and sanitation, across the whole 2030 Agenda, using SDG global available data corresponding to 179 countries, 16 goals, 28 targets and 44 indicators. To analyze and validate the BN results, we first demonstrate the robustness of the BN approach in identifying indicator relationships (i.e. consistent results throughout different country sample sizes). Second, we show the coherency of the results by comparing them with an exhaustive study developed by UN-Water. As an added value, our data-driven approach provides further interlinkages, which are contrasted against the existing literature. We conclude that the approach adopted is useful to accommodate a thorough analysis and interpretation of the complexities and interdependencies of the SDGs.

Bayesian network modelling of hierarchical composite indicators.

The water, sanitation and hygiene (WaSH) sector has witnessed the development of multiple tools for multidimensional monitoring. Hierarchical and composite indicator (CI)-based conceptual frameworks provide one illustrative example. However, this approach does not address the existing interrelationship of the indicators they comprise. Bayesian networks (BNs) are increasingly being exploited to assess WaSH issues and to support planning and decision-making processes. Here, we aim to evaluate the validity, reliability and feasibility of BNs in replicating an existing CI-based conceptual framework. We adopt a data-driven approach and propose a semi-automatic methodology. As a pilot study, we used the regional monitoring initiative Rural Water Supply and Sanitation Information System (SIASAR). Data from two different countries are processed and analysed to calibrate and validate the model and the method. The major findings show: i) the model inference capacity improves when structure is provided to the networks (according to the CI-based framework); ii) key components that explain a pre-defined objective variable are reduced and quantified (implying important advantages in data updating); and iii) interlinkages among these components can be identified (which might enhance multi- and trans-disciplinary actions). We conclude that BNs accurately replicate the CI-based conceptual framework, with great potential for a wider application.

Monitoring sanitation and hygiene in the 2030 Agenda for Sustainable Development: A review through the lens of human rights.

International monitoring of drinking water and sanitation has been jointly carried out by WHO and UNICEF through their Joint Monitoring Programme (JMP). With the end of the Millennium Development Goals (MDGs) era in 2015, the JMP has proposed a post-2015 framework for integrated monitoring of water and sanitation targets included in the Sustainable Development Goal no. 6. This article discusses how each element of the proposed sanitation target and corresponding indicators can be understood from a human rights perspective. Building on the MDGs, and although some of the weaknesses and gaps persist, the discussion suggests that the post-2015 proposal is a step forward towards a monitoring framework where human rights elements related to sanitation are effectively promoted. In addition, to support the interpretation and implementation of the normative content of human rights obligations related to sanitation, the study proposes a reduced set of easy-to-assess indicators to measure the normative criteria of this right, which are then grouped in a multidimensional framework to describe increasing levels of sanitation service. To do this, the study combines literature review and specific local experience from three case studies. It is shown that the proposed monitoring tools, namely the indicators and the multidimensional indicator framework, provide guidance on monitoring the human right to sanitation. In doing so, they might ultimately help sector stakeholders in the realization of this right.

Impacts on effluent contaminants from mine sites: risk assessment, fate, and distribution of pollution at basin scale.

The environmental implications of mining activities are of worldwide concern. An environmental evaluation at the basin level was conducted because of widespread mining in Cajamarca in Northern Peru. A sediment monitoring program was developed at the Jequetepeque basin, located in Cajamarca. A total of 16 sites were monitored at three different times between June 2009 and July 2010, and a total of 42 samples were collected. All samples were analyzed by microwave digestion and by a sequential extraction scheme following the three-stage European Community Bureau of Reference (three-stage BCR) protocol. Trace element mobilization from the sediments to the water column was assessed by the risk assessment code (RAC). Spatial and temporal distribution of trace elements was evaluated by principal component analysis and hierarchical cluster analysis. Cd, Zn, As, and Pb showed the highest concentrations independent of season. Notably, Cu concentration and mobility increased during the wet season for all samples. Additionally, Hg concentration and mobility increased during the wet season near the mine sites. According to the enrichment factor, the highest enrichments of Cd, Zn, Pb, and As were related to mine runoff. The effect of trace elements near the mine sites at the Jequetepeque basin was considered a significant threat to the environment due to Cd, Zn, Pb, and As, and the concentrations of Cu and Hg were also considered a concern. This work establishes a baseline for the environmental quality status of the Jequetepeque basin that may support water quality management in Peru.

Water-sanitation-hygiene mapping: an improved approach for data collection at local level.

Strategic planning and appropriate development and management of water and sanitation services are strongly supported by accurate and accessible data. If adequately exploited, these data might assist water managers with performance monitoring, benchmarking comparisons, policy progress evaluation, resources allocation, and decision making. A variety of tools and techniques are in place to collect such information. However, some methodological weaknesses arise when developing an instrument for routine data collection, particularly at local level: i) comparability problems due to heterogeneity of indicators, ii) poor reliability of collected data, iii) inadequate combination of different information sources, and iv) statistical validity of produced estimates when disaggregated into small geographic subareas. This study proposes an improved approach for water, sanitation and hygiene (WASH) data collection at decentralised level in low income settings, as an attempt to overcome previous shortcomings. The ultimate aim is to provide local policymakers with strong evidences to inform their planning decisions. The survey design takes the Water Point Mapping (WPM) as a starting point to record all available water sources at a particular location. This information is then linked to data produced by a household survey. Different survey instruments are implemented to collect reliable data by employing a variety of techniques, such as structured questionnaires, direct observation and water quality testing. The collected data is finally validated through simple statistical analysis, which in turn produces valuable outputs that might feed into the decision-making process. In order to demonstrate the applicability of the method, outcomes produced from three different case studies (Homa Bay District-Kenya-; Kibondo District-Tanzania-; and Municipality of Manhiça-Mozambique-) are presented.

Trace metal content of sediments close to mine sites in the Andean region.

This study is a preliminary examination of heavy metal pollution in sediments close to two mine sites in the upper part of the Jequetepeque River Basin, Peru. Sediment concentrations of Al, As, Cd, Cu, Cr, Fe, Hg, Ni, Pb, Sb, Sn, and Zn were analyzed. A comparative study of the trace metal content of sediments shows that the highest concentrations are found at the closest points to the mine sites in both cases. The sediment quality analysis was performed using the threshold effect level of the Canadian guidelines (TEL). The sediment samples analyzed show that potential ecological risk is caused frequently at both sites by As, Cd, Cu, Hg, Pb, and Zn. The long-term influence of sediment metals in the environment is also assessed by sequential extraction scheme analysis (SES). The availability of metals in sediments is assessed, and it is considered a significant threat to the environment for As, Cd, and Sb close to one mine site and Cr and Hg close to the other mine site. Statistical analysis of sediment samples provides a characterization of both subbasins, showing low concentrations of a specific set of metals and identifies the main characteristics of the different pollution sources. A tentative relationship between pollution sources and possible ecological risk is established.