Dr Andreas Nocker

Carla Costa

Zelma Chemane

Dinis Namburete

Abdul Gafur

João Maweia

Adriana Caifaz

Rafael Da Câmara

Vasco Helio Cossa

Susie Parente

Rita Kinghan

Sergio Couto

Osorio Mutumane

Shinjini Macamo

Mehta

Keywords: Monitoring and Evaluation, peri-urban area, risk of contamination scoring, standard faecal indicator bacteria, sanitation, hygiene-related infrastructures, survey, observations, interactive mapping

Many organisations aim to improve sanitation for the urban poor in developing countries. However, evaluating the impact of their activities on people's daily lives is often difficult. Thus both cost effective and systematic procedures to monitor the effectiveness and sustainability of such activities are urgently needed. Three existing procedures for estimating the degree of faecal contamination and for assessing the state of the hygiene-related infrastructure in communal areas were applied with minor modifications to six low income districts in Maputo City, Mozambique: (1) a survey conducted among local experts, (2) microbiological testing for faecal indicator bacteria from shoe surfaces and (3) an observational approach. Both the microbiological and observational data were collected while completing transect walks within the districts. Observations systematically categorized four actual and potential sources of contamination: faecal contamination from human sources, faecal contamination from animal sources, infrastructure deficiencies and solid waste and mud. While too many factors were found to affect outcomes for both the microbiological and survey approaches, the observational methodology appeared promising and was further developed into an interactive map. Using freely available software, first steps for developing an interactive monitoring mapping tool were made. The tool comprised photographic evidence assigned to GPS coordinates allowing for revisiting the corresponding sites for reassessment. Interactive monitoring mapping has the potential to be a cost effective and time saving tool for systematic monitoring.

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LIST OF TABLES

 2000). One of the greatest challenges faced during the implementation of projects in developing countries over the past decades is the difficulties associated with monitoring and evaluating the impact of organisations' activities on people's daily lives [START_REF] Blum | Measuring the impact of water supply and sanitation investments on diarrhoeal diseases: problems of methodology[END_REF]. Thus cost effective and systematic procedures to monitor the effectiveness and sustainability of such interventions are urgently needed [START_REF] Blum | Measuring the impact of water supply and sanitation investments on diarrhoeal diseases: problems of methodology[END_REF]. Although providing in part valuable data, very often the monitoring procedures are limited to assessing the hygiene within households [START_REF] Pickering | Bacterial hand contamination among Tanzanian mothers varies temporally and following household activities[END_REF][START_REF] Pickering | Fecal contamination and diarrheal pathogens on surfaces and in soils among Tanzanian households with and without improved sanitation[END_REF]. However people can readily come into contact with many sources of contamination outside this area in the urban environment [START_REF] Pickering | Fecal contamination and diarrheal pathogens on surfaces and in soils among Tanzanian households with and without improved sanitation[END_REF]) and potentially acquire diseases. One of the greatest risks is the exposure to faecal material by contaminated water or objects leading to faecal oral transmission [START_REF] Carr | Water quality: guidelines, standards and health. Assessment of risk and risk management for water-related infectious disease[END_REF][START_REF] Gundry | A systematic review of the health outcomes related to household water quality in developing countries[END_REF]).

Nevertheless there is little quantitative data on the degree of exposure to faecal contamination existing in the local environment comprising communal areas such as streets, alleyways, public areas or any other area outside the house.

Many household surveys have been conducted in different contexts to assess various behaviours related to water and sanitation [START_REF] Daniels | A case control study of the impact of improved sanitation on diarrhoea morbidity in Lesotho[END_REF][START_REF] Vanderslice | Drinking-water quality, sanitation, and breast-feeding: their interactive effects on infant health[END_REF][START_REF] Tumwine | Diarrhoea and effects of different water sources, sanitation and hygiene behaviour in East Africa[END_REF]. These studies tend to be either time consuming or to require complex logistics as well as experienced and highly qualified field supervisors or both (UNICEF 2009). Manun'Ebo et al.

(1997) also highlighted that interviewees excessively report "desirable" behaviours. More recently, participatory monitoring surveys have been developed to evaluate hygiene practices. These procedures are very powerful and rewarding but also tend to be time-consuming and to require significant training [START_REF] Shordt | Action Monitoring for Effectiveness: Improving water, hygiene and environmental sanitation programmes[END_REF]. No published paper was found where local experts were interviewed to evaluate the level of faecal contamination in the local environment.

Microbiological testing are commonly used procedures to quantify contamination within the environment. Microbial pathogens are widespread in the environment (including contaminated soil and water) with disease typically spread via the faecal-oral route (WHO 2008). Pathogens can comprise a wide variety of viruses, bacteria and protozoa (WHO 2008). As testing for all these pathogens is economically and technically not practicable [START_REF] Edberg | Escherichia coli: the best biological drinking water indicator for public health protection[END_REF], assessment of microbial contamination is typically based on the quantification of faecal indicator bacteria [START_REF] Edberg | Escherichia coli: the best biological drinking water indicator for public health protection[END_REF] which, like pathogens, are shed into environment through faecal contamination (WHO 2006;WHO 2008).

Positive testing of indicator bacteria thus indirectly indicates the potential presence of pathogens [START_REF] Feachem | Sanitation and disease: health aspects of excreta and wastewater management, report number 11616[END_REF].

The most common indicator bacteria are Escherichia coli (E.coli) which belong to the group of faecal coliforms [START_REF] Edberg | Escherichia coli: the best biological drinking water indicator for public health protection[END_REF]. Faecal coliforms are naturally found in high numbers (10 9 cells per gram of faeces) in the guts of human and other warm blooded animals and are typically non-pathogenic [START_REF] Edberg | Escherichia coli: the best biological drinking water indicator for public health protection[END_REF]. Once in the environment, faecal indicator bacteria such as E.coli are exposed to factors which affect its survival such as temperature, moisture, ultraviolet light, pH, soil type, nutrient availability and competition with other microorganisms [START_REF] Martin | An investigation of faecal pathogen survival in a synthetic stool[END_REF]. Depending on the conditions, survival time is estimated to range between 4-12 weeks for E.coli and between 3-10 weeks for faecal coliforms [START_REF] Martin | An investigation of faecal pathogen survival in a synthetic stool[END_REF].

A wide array of testing methods has been developed for faecal coliforms (including E.coli), in particular for testing water and food [START_REF] Wohlsen | Evaluation of the methods for enumerating coliform bacteria from water samples using precise reference standards[END_REF][START_REF] Dufour | Real-time monitoring technologies for indicator bacteria and pathogens in shellfish and shellfish harvesting. In: Safe management of shellfish and harvest waters[END_REF]. For instance, the drinking water standards from the WHO (1997) are based on enumeration of faecal coliforms in drinking water supply networks. In the United Kingdom like in many other countries, no E.coli should be found in a 100 mL sample (DWI 2010).

Another group of indicator bacteria are the so-called total coliforms which comprise the faecal coliforms (WHO 2008). In contrast to faecal coliforms, which originate from human or animal faeces, the group of total coliforms also comprises members that can be of environmental origin (WHO 2008), meaning they can replicate in the environment.

One of the procedures to enumerate both E.coli and total coliforms is the 3M [START_REF] Matner | Efficacy of Petrifilm™ E. coli count plates for E. coli and coliform enumeration[END_REF]3M 2001). A comparison between 8 methods to count both E.coli and other coliforms in water samples revealed that the Petrifilm TM was the most reliable method [START_REF] Wohlsen | Evaluation of the methods for enumerating coliform bacteria from water samples using precise reference standards[END_REF]). In addition to food [START_REF] Matner | Efficacy of Petrifilm™ E. coli count plates for E. coli and coliform enumeration[END_REF][START_REF] Priego | Evaluation of Petrifilm series 2000 as a possible rapid method to count coliforms in foods[END_REF] and environmental water samples [START_REF] Vail | Enumeration of waterborne Escherichia coli with Petrifilm plates: comparison to standard methods[END_REF], the Petrifilm TM technology was also validated for soil samples with a detection limit of 10 CFU g -1 [START_REF] Samarajeewa | Evaluation of Petrifilm TM EC method for enumeration of E. coli from soil[END_REF]. While the 3M TM Petrifilm TM technology is not appropriate to detect the presence of low numbers of faecal indicator bacteria in 100 mL water samples as required by the drinking water standards [START_REF] Vail | Enumeration of waterborne Escherichia coli with Petrifilm plates: comparison to standard methods[END_REF][START_REF] Bain | A summary catalogue of microbial drinking water tests for low and medium resource settings[END_REF], it is particularly suitable for enumeration of samples containing high numbers of coliforms as expected to be found in shoe sampling (Chambers et al. 2009).

In this study, microbial testing was not conducted in water but samples were taken on the sole of shoes after walking in districts. A recent study demonstrated that shoes can transport faecal bacteria from outside to inside the household plot in a faecally contaminated Alaskan area (Chambers et al. 2009).

Thus both faecal indicator bacteria E.coli and coliforms and the 3M TM Petrifilm TM

E.coli/Coliform

Count Plat microbiological kit were considered to be the most appropriate indicators and technology for shoe sampling.

Several observational methods exist to assess or identify actual and potential sources of faecal contamination. Sanitary inspections are field evaluations designed to record all the existing and potential sources of faecal contamination which could be hazardous to a community (WHO 1997). Such inspections were initially developed for groundwater, surface drinking water sources, piped networks and recreational water systems [START_REF] Figueras | Monitoring bathing waters, a practical guide to the design and implementation of assessments and monitoring programmes[END_REF][START_REF] Kinzelman | Indicators, sanitary surveys and source attribution techniques[END_REF]. For the latter, they are mainly used to assist in the analysis of the microbiological results [START_REF] Figueras | Monitoring bathing waters, a practical guide to the design and implementation of assessments and monitoring programmes[END_REF]. These inspections were also applied for sanitation purposes where they allow both comparative assessments and ranking priority opportunities for improvement in a given area (Smith & Husary 2000). They are rapid and easy to use recording tools (Smith & Husary 2000). However, their application in the sanitation sector is limited to assess the facilities quality such as latrines (Smith & Husary 2000). Another type of observational method established by the WHO (1997) is the inspection using a risk of contamination (ROC) scoring system. The respective sanitary inspection forms comprise a set of questions listing the potential sanitary hazards. At the end of the inspections, the hazards are summed up so as to obtain a risk score where a high score indicates a high probability for a contamination to occur (WHO 1997). A limited number of studies [START_REF] Godfrey | Microbiological risk assessment and management of shallow groundwater sources in Lichinga, Mozambique[END_REF][START_REF] Luby | Tubewell water quality and predictors of contamination in three flood-prone areas in Bangladesh[END_REF][START_REF] Parker | An assessment of microbiological water quality of six water source categories in north-east Uganda[END_REF][START_REF] Mushi | Sanitary inspection of wells using risk-of-contamination scoring indicates a high predictive ability for bacterial faecal pollution in the periurban tropical lowlands of Dar es Salaam, Tanzania[END_REF]) applied this method and compared the results with microbial testing to know if the external characteristics assessed by such surveys could accurately predict microbial contamination levels. [START_REF] Luby | Tubewell water quality and predictors of contamination in three flood-prone areas in Bangladesh[END_REF] and [START_REF] Parker | An assessment of microbiological water quality of six water source categories in north-east Uganda[END_REF] showed that such sanitary surveys cannot foretell water quality accurately. [START_REF] Mushi | Sanitary inspection of wells using risk-of-contamination scoring indicates a high predictive ability for bacterial faecal pollution in the periurban tropical lowlands of Dar es Salaam, Tanzania[END_REF] obtained satisfactory results, however the authors acknowledged that the specificities of the study area caused an overestimation of the predictive ability.

In order to identify risk factors for infection among children aged between 7 to 14 years, [START_REF] Milroy | Evaluating sanitary quality and classifying urban sectors according to environmental conditions[END_REF] developed an approach to both quantitatively score and rank sanitary conditions in thirty districts in Brazil. Districts were categorized according to environmental quality criteria. Data were collected in the streets to record both the absence and the quality of infrastructure which contributed to impact on health, such as quality of housing, paving, water supply, sewage disposal, drainage and household waste disposal. The scoring system allowed districts to be ranked with regards to their respective sanitary situations, in order to identify specific sanitation needs as well as to prioritize poorest districts in terms of sanitation infrastructure. Despite the promising results obtained, this methodology requires to provide an extensive training to a field team of architects and civil engineers. Many organizations do not have the capacity (time and budget) to implement such a monitoring approach.

Quantitative microbial risk assessment (QMRA) is a systematic procedure for estimating human health hazards due to the exposure to a particular pathogen [START_REF] Howard | Quantitative microbial risk assessment to estimate health risks attributable to water supply: Can the technique be applied in developing countries with limited data?[END_REF]. The WHO (2008) promoted the use of such a risk assessment tool in combination with risk management for managing and controlling risks associated with drinking water networks. Initially established for water networks in developed countries, [START_REF] Howard | Quantitative microbial risk assessment to estimate health risks attributable to water supply: Can the technique be applied in developing countries with limited data?[END_REF] successfully applied a simplified procedure in Kampala, Uganda. Such a procedure, even simplified, might not be appropriate in locations with reduced data and resources [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]).

This study aimed at assessing the degree of faecal contamination in communal areas. The procedures comprising microbiological testing of faecal indicator bacteria, an observational procedure and a survey, were designed in previous studies [START_REF] Hirst | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF][START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]) and applied to Antananarivo, Madagascar and Lusaka, Zambia. The objective of this project was to further evaluate the usefulness of these approaches by applying them to low income districts in Maputo, Mozambique. A systematic comparison was hoped to identify the most promising procedure and potential for further development. To be promising, the procedure should: (i) be easy to apply in developing countries, (ii) provide reliable data, (iii) be repeatable and (iv) be transposable in different contexts.

MATERIALS AND METHODS

Study area

With respect to the MDGs Goal 7, the capital city Maputo City shows promising results with a proportion of improved sanitation of 84.6% in 2008 (UNDP 2010). (Table 1 andFigure 1) were chosen to be the intervention districts, i.e. the districts where WSUP would implement significant interventions to improve the sanitary situation, whereas the districts 4, 5 and 6 were selected to be the control districts, i.e. where no major improvement is planned in the four coming years neither by WSUP nor by any other NGOs. Self reported diarrhoea is the main outcome of this evaluation. The environmental exposure to faecal indicator bacteria measured via finger prints is a second outcome.

According to [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF], the main application and usage of the three procedures to assess the faecal contamination are to evaluate such a contamination related to flooding during low rain periods and to help to identify districts which should receive priority sanitation and hygiene interventions.

The districts 1 to 6 are then suitable study locations to assess the degree of faecal contamination. A possible link is hoped to be made between the data from the Health Impact Survey and the data from this paper (WSUP 2013).

Table 1. Official names of the six administrative districts and their allocated number for this study The transect walks were therefore drawn to lie within district boundaries, but took into consideration the differences in shape between each respective district. ). The spread sheet document was given to the interviewees as a practical interface for answering. The spread sheet was asked to be returned once completed. The community developers interviewed were asked to rate from 1 to 5 the overall degree of faecal contamination for the concerned districts where 1 indicated a not significant or very low occurrence and 5 indicated an extreme level of faecal contamination or an extremely widespread occurrence. They were also asked to rate from 1 to 5 how widespread they perceived some situations to be (Table 2). These situations belonged to 2 categories: human sources of faecal contamination and infrastructures deficiencies. iii -open defecation 1-5 vi -defecation in plastic bags 1-5 iv -direct drainage of latrines to the ground or informal drains 1-5 v -direct drainage of latrines to formal drains 1-5 i -faecal contamination as a result of flooding 1-5 ii -flooding 1-5 vii -inappropriate disposal of sludge from latrines 1-5 viii -discharge or overflow from sewers 1-5

District name District number

Infrastructure deficiencies

Rating scale 20 20

Questions Category

Maximun score per category

Human sources of faecal contamination

The first two questions were quantitatively analysed. The answers related to the two categories, human sources of faecal contamination and infrastructures deficiencies, were treated to be comparable with the results of the observational procedure. Scores were summed up within both categories. The maximum score per category was 20.

Microbiological testing

Transect walks were completed while wearing ordinary shoes. Shoes were washed with antibacterial soap, disinfected with medical ethanol 90 % and dried in the evening preceding the walk. They were disinfected and dried a second time at least two hours before starting the transect walks. Once disinfected and dried, shoes were then stored into two separate zip plastic bags to avoid any contamination. A new set of plastic bags was utilized for each district. Shoes were sampled immediately after completion of the walk. Shoe sampling involved two major components: sterile transport swabs and a taped cardboard template. Before going to the field, 3 mL of sterile water were added into the sterile transport swabs and a common cardboard was cut out in its middle to delimit a squared sampling surface of 2.5 cm times to 2.5 cm, i.e. a total sampling surface of 6.25 cm 2 . The whole cardboard template was then taped to facilitate cleaning and to avoid any cross contamination between the three shoe sampling locations which were left toe, left heel and right heel. After completion of the walk, the cardboard was strongly held and successively placed over these shoe sampling locations, a swab was rolled systematically three times vertically and three times horizontally within the sampling shoe surface of 6.25 cm 2 . Back at the WSUP office, 7 mL of sterile water were added to the transport swabs using 15 mL sterile disposable centrifuge tubes and 1 mL sterile disposable pipettes. Dilutions of a factor ten were also systematically realized for the three samples as the 3M TM Petrifilms TM does not allow enumerating more than 150 colonies. The difference between the time of collection of the last shoe sampled and the time of plating the first Petrifilms TM went from 30 minutes to 180 minutes. Samples were re-suspended 10 minutes before plating. Thus Petrifilms TM were used to plate a total of six samples: three no diluted samples and three samples diluted by a factor of ten. Both a blank of the sterile water and a blank of a randomly chosen disinfected shoe location were taken for each district to ensure that colonies plated were solely coming from the district environment. A couple of minutes after plating, the eight samples were placed in a taped cardboard pocket maintained by four elastic bands. The cardboard pocket containing the Petrifilms TM was then placed into a money belt which was worn by the researcher to provide 24 hours of incubation. The cardboard pocket was taped to prevent sweat from being absorbed by both the cardboard itself and the Petrifilms TM .

Referring to the 3M TM Interpretation guide (3M 2001) and using back and front lighting both coliforms (red colonies with bubbles) and E.coli (blue colonies with bubbles), colonies were enumerated after the incubation period to obtain values in CFU mL -1 . Pictures of the Petrifilms TM were also taken to provide reliable records.

No literature was found to state whether the picking of coliforms is proportional to the distance walked or not. In this study, the researcher took the assumption that the distance is a factor which influence the number of coliforms picked up under the sole of the shoes and thus the number of coliforms counted on the Petrifilms TM . The transect walk lengths for the six districts were not homogeneous. The longest transect walk obtained for the district 3 (3,106 m) constituted 135 % of the shortest transect walk (district 5; 2,319 m). Both E.coli and Total coliforms enumerated colonies were normalised to 1,000 m walked to reduce such an influence and to allow comparisons. The normalised distance of 1, 000 m corresponds to a range of transect walk length percentages which range from 32 % (district 3) to 43 % (district 5).

Observational procedure

The observational procedure involved walking within a square of 300 m per 300 m in a district following a pre-defined transect route called transect walk. The methodology was based on the procedure developed by [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] Observations were recorded either as number of incidences (A) or as cumulative distance in meters (B) like described in Table 4. Number of incidences and cumulative distances in meters were summed up for the nine segments, so as to get for each type of observation either the total number of incidences or the total distance walked in meters for the entire transect walk.

According to the weighting system developed by [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF], weighting factors were applied to convert total number of incidences and total distance in meters for each type of observation into an observational score. Such as in Raven's study (2012) weighting factor values were chosen to depict if the type of observation was a direct source of faecal contamination and what the degree of exposure was. The weighting system proposed by [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] was amended to allowing total scores to range from 0 to 5 for each type of observation. The Table 4 gives the detailed weighting system for each type of observation. Scores for the type of observations which belonged to the same category were summed so as to get a total score for each of the four categories.

The Erreur ! Source du renvoi introuvable. gives the score range or weighting system for each category. Whereas the total score ranged from 20 to 100 in Raven's study (2012), in this study the total score ranged from 0 to 100. 4. Observational checklist and scorecard. Whilst walking, both number of incidences (A) and cumulative distance in meters (B) per segments were counted. Both A and B were summed up for each types of observations for the nine segments which constitute a whole transect walk. The scoring system allowed converting the total counting for each type of observations into a score ranging from 0 to 5.

Development of an interactive mapping tool

As suggested by [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF], a GPS device (Garmin TM 60) was used on the day the transect walks were completed in order to improve the reproducibility of the method. When defining the transect route using Google TM Earth prior to completion of the transect walk, the resolution of the map did not allow to localize impassable streets such as blind alleys in very dense areas.

Coordinates of intermediary points along the transect walk were systematically taken to adjust the route initially drawn. Coordinates as well as pictures were systematically taken for each type of observations of the checklist (Table 4) encountered while walking. Coordinates were taken in degree minutes seconds.

After the transect walk was completed, both coordinates and pictures were processed immediately after the transect walk was completed and on the following day. Before processing the data, characteristics for each type of observations such as size and colour were defined. Similar characteristics for observations ranked under a same category were chosen for consistency, e.g. same colour. Measurements at specific points were represented with point symbols and linear observations were represented with lines. Pictures were processed to be integrated into info-bubbles. The parameters for the creation and the selection of the different characteristics of the info-bubbles were defined in the online Google TM spreadsheet. To be displayed in the info-bubbles in Google TM Earth, the pictures were selected and then exported via Picasa TM 3 to Google TM + so as so to obtain for each of them an URL address in the public domain. The URL address was copied into the Google TM spreadsheet. Then a simple click was needed on Google TM Earth to update the link between the information contained in the spreadsheet and the information available in Google TM Earth.

RESULTS AND DISCUSSION

Three existing procedures were applied in 6 low income districts of Maputo City, Mozambique; this comprised microbiological testing of faecal indicator bacteria, an observational procedure and a survey..

Survey results

A survey was conducted to assess the degree of contamination for a whole area in each district. For this purpose local experts from WSUP were asked to weight on a scoring scale of 1 to 5 how widespread/frequent they perceived situations described in the questionnaire to be (Appendix A.1). In total four local experts participated to the survey, one per district. None of the local expert had a sufficient knowledge to answer with respect to districts 3 and 6. The Table 5 gives the relative percentages and the number of answers of each category distributed according to the score collected for the four districts. For these four districts, scores ranged from 2 to 5 in both categories. While local experts perceived situations related to human sources of faecal contamination to be "occasional, a sporadic issue" at 75 % (12 out 16 answers), they perceived situations related to infrastructure deficiencies to be "extremely widespread" at 62.5 % (10 out of 16 answers). Normalised average E.coli and total coliforms counts per 6.25 cm 2 of shoe surface per 1,000 m and the associated range as well as standard deviation calculated for the six districts are presented in Table 6. The ranges revealed substantial differences in both E. coli and total coliforms counts obtained between the six districts (Figure 2 and Figure 3). The standard deviation expressed as a percentage of the average revealed an important variation of the measurements from a same district (Table 6). Overall, lowest numbers were obtained in district 1 whereas highest numbers were obtained in district 4 (Figure 2 and Figure 3). Thus, district 1 is relatively the cleanest and the district 4 the dirtiest in terms of faecal contamination of the local environment according to microbial results. In particular, district 4 shows the highest average counts for both E.coli and total coliforms (1,083 counts) as well as both E.coli range (174 counts) and standard deviation (240). For this district only, a rainy event occurred during the night preceding the completion of the transect walk whereas the other transect walks were completed under similar climatic conditions in terms of air temperature (~20-25 ºC) and exposure to sunlight. In a beach sand environment, moisture was found to significantly influence the survival of E.coli [START_REF] Mika | Pilot-and bench-scale testing of fecal indicator bacteria survival in marine beach sand near point sources[END_REF]). An assessment of faecal contamination on surfaces and soils revealed that faecal indicator bacteria such as E.coli were found in higher levels in soils with greater moisture content [START_REF] Pickering | Fecal contamination and diarrheal pathogens on surfaces and in soils among Tanzanian households with and without improved sanitation[END_REF]. In addition, [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]

E.coli

Total coliforms (2012) found that other parameters such as exposure to sunlight and initial microbial community might affect the level of faecal indicator bacteria obtained [START_REF] Martin | An investigation of faecal pathogen survival in a synthetic stool[END_REF] as described in the introduction. Thus, important variations of both E.coli and total coliforms obtained from the five districts, apart from district 1 and in particular for district 4, might be due to the influence of these numerous parameters. Consequently, the microbiological numbers obtained in Maputo do not reveal the degree of contamination solely caused by human, animal or infrastructure sources.

Additional limitations was also observed by previous research in the case of Maputo. The transect walks were completed only once per district in Lusaka [START_REF] Hirst | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF], Antananarivo [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] and Maputo, which suggests two implications. Firstly, despite the attempt to increase the number of values by taking triplicate [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] or even sextuplicate datasets [START_REF] Hirst | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] in each district, the data remain very poor due to the given surface of approximately 90,000 m 2 which was meant to be assessed by the transect walks. Multiple repetitions of transect walks were suggested by the two previous researchers [START_REF] Hirst | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF][START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]) as a major opportunity to increase the size of the data sets. It could consist of repeating the same transect walk a statistically sufficient number of times following the exact same route during the same day (e.g. by several persons), in order to check that under similar conditions the count of faecal indicator bacteria is even between the different shoes sampled. The assumption made that pick up of coliforms is proportional to the distance walked would also need to be evaluated to validate the reproducibility of the method. To do so, the transect walks would need to be repeated several times in each district. For each district, the random procedure used to draw the transect route will necessarily create different routes between the different repetitions. It would be expected that the distances of each type of segments (i.e. Segment 1-2, Segment 2-3, etc.) would differ from one trial to another and consequently the total distances would vary. When designing this possible study, the other factors discussed in this paper would have to be taken into consideration in order to collect meaningful data. The equipment used for incubation, a money belt, does not allow the researcher to wear more than 8 to 10 Petrifilms TM at the same time [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF], without jeopardizing incubating requirements (37ºC for 24 hours). Many repetitions would then be required in order to get a sufficient data sets to allow statistical analysis. Secondly, the completion of one transect walk per district during the dry season is questionable with regard to the goal of the approach. This procedure of assessing the degree of faecal contamination during the dry season was developed because it was considered to be difficult to assess it during the rainy season due to frequent rainy events [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]. As moisture and rainy events seemed to greatly affect the counts of faecal indicator bacteria, relying on quantitative data obtained during the dry season which would not be representative of rains or flooding. The transect walk could be completed during both seasons.

Another limitation found in the three locations (Antananarivo, Lusaka and Maputo) is the sensitivity to "hot spots", i.e. very localized and highly concentrated sources of faecal contamination. Such "hot spots" are supposed to be found in both human and animal excreta as well as in any soil surfaces faecally contaminated likes muddy areas, street covers with solid waste, etc.

These "hot spots" were not necessarily obviously detectable while walking in the district. The author might have walked into "hot spots" without being aware of it.

Rainy events might flush away these localized "hot spots", spread them locally and make them more faded within the environment. When these "hot spots" are localized within the environment, it can be assumed that their contact with shoes is also localized. For instance, a researchers might have excreta stuck on a localized area on the sole of one of their shoes. Thus, these "hot spots" might be an additional factor which participates to the important variations of faecal indicator bacteria counts obtained.

Observational procedure results

The application of the observational approach consisted in quantifying a number of parameters falling under four different categories: (1) human sources of faecal contamination (2) animal sources of faecal contamination (3) infrastructure deficiencies and (4) solid waste and mud. This list was included in Table 4. As the total distances of transect walks varied between districts (ranging between 2,319 m and 3,106 m), the number of incidences were normalised to 1,000 m. In addition to normalisation, incidences were weighted according to a scorecard and summed up to get normalised weighted incidences for each category, as presented in Figure 4. In agreement with the microbiological approach, the lowest degree of faecal contamination from both animal and human sources, dirt/waste and infrastructure deficiencies was found in district 1 (a total of five normalised weighted incidences, Figure 4). District 2 showed the highest degree of faecal contamination from both animal and human sources, dirt/waste and infrastructure deficiencies (13 normalised weighted incidences, Figure 4), whereas highest levels of faecal indicator bacteria were found in district 4 (Figure 2 and Figure 3). The number of normalised weighted incidences related to infrastructure deficiencies (8) contributed greatly (62.5%) to the highest degree of faecal contamination, dirt/waste and infrastructure deficiencies found for the district 2. For the six districts, the most recurring normalised weighted incidences of observations for 1,000 m were: muddy area open-plan coverage During transect walks, small plastic pipes coming from inside household plots were observed to leak into the streets or drains. Both the local guide and the local experts confirmed these pipes were not originating from latrines. They are commonly utilized to evacuate grey water (used for washing food and clothes) into the street. Thus, these pipes were not directly recorded as a distinct type of observation. Nevertheless, they were indirectly recorded through muddy areas, which are partly created by the grey water leaking from these pipes. As faecal contamination might be found in the water used to wash food or cloths, a future study could potentially record these pipes as a type of observation.

Although some improvements were brought to the existing methodology [START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]) such as (1) the simplification of the weighting system through amendments of the types of observations and the creation of four categories, Similarly to the microbiological procedure, some limitations related to the transect walk method are also applicable to the observational approach. For instance, the number of incidences recorded from the two categories infrastructure deficiencies (e.g. sewers and formal open drains overflows, leakages) and solid waste and mud (e.g. muddy areas both open coverage and spotty occurrence), might be much higher during the rainy season whereas transect walks were completed only once per district during the dry season.

Efforts were made to improve both the reproducibility of the method and the comparison of the results between the different districts. The reproducibility was improved using a GPS device which allowed to localise points, segments and observational incidences more accurately. The normalisation of the length of the transect walks to a distance of 1,000 m enabled comparison of the results between the different districts. However the normalisation did not consider the frequency for which a same segment or a part of a segment was taken. For the six districts, the number of incidences were not equally distributed between the nine segments of the transect walk. The more the segments which comprise a high number of incidences are taken, the higher the final score of the district will be. Consequently, one could either repeat multiple times the number of transect walks for each district or consider the frequency to overcome this issue. But repeating the number of transect walks for each district multiple times might jeopardise the cost effectiveness of the procedure, and considering the frequency might greatly complicate it.

Three out of the six districts are supposed to receive significant water sanitation and hygiene interventions over the four following years, whereas three others are not supposed to receive any significant intervention over this period (Schmidt & Norman n.d). Applying the three monitoring methodologies in these specific districts would allow two levels of comparative assessments with regard to the pre intervention and post intervention evaluations. On the one hand the three methodologies could be compared over time. On the other hand the three methodologies could be compared with the results of the HIS pre-intervention and post intervention surveys. In particular with respect to diarrhoea occurrence, fingerprints might be interpreted to build up a relationship between the degree of faecal contamination of the local environment and the degree of faecal contamination obtained by the fingerprints. One would have to be very careful with regard to the important quantity and variety of parameters involved in the contamination of inhabitant's fingers. Contamination on hands were shown to come from food [START_REF] Pickering | Bacterial hand contamination among Tanzanian mothers varies temporally and following household activities[END_REF], water [START_REF] Mattioli | Hands and water as vectors of diarrheal pathogens in Bagamoyo, Tanzania[END_REF], household surfaces [START_REF] Pickering | Fecal contamination and diarrheal pathogens on surfaces and in soils among Tanzanian households with and without improved sanitation[END_REF], after defecation [START_REF] Curtis | Review: Domestic hygiene and diarrhoea -pinpointing the problem[END_REF] and even currency notes [START_REF] Bhat | An assessment of oral health risk associated with handling of currency notes[END_REF][START_REF] Feglo | Bacterial load on Ghanaian currency notes[END_REF]. Little information about the faecal contamination pathways is available even if a current study [START_REF] Korschun | Center for Global Safe Water launches study to assess risks of fecal contamination in low-income urban environments[END_REF]) is aiming to fill in this knowledge gap.

Survey and observational procedure results comparison

The comparison of the results between the survey and the observational methodologies were not based on correlation analysis due to the limited size of the data sets obtained for both methodologies. The microbiological methodology was neither compared to the survey more fully nor to the observational methodologies, because microbiological results were shown to be affected by too many factors. The Figure 5 shows the weighted score from the survey (weighted incidences of contamination) and the normalised weighted incidences of contamination from the observational procedure for the districts 1, 2, 3 and 5.

While the observational procedure did not record any incidence of human sources of faecal contamination (zero value for the four districts), the survey procedure ranged from 8 to 11 weighted incidences of such contamination.

Situations related to infrastructure deficiencies were weighted in both procedures with the survey ranging weighted incidences from 12 to 20 and the observations ranging normalised weighted incidences from 2 to 8. In the survey, the results of the ranking of perceived incidences of infrastructure deficiencies in four districts was district 1, district 5, district 3 and district 2, where district 1 has the highest number of incidences and district 2 has the lowest number of incidences. However, the observation results showed the opposite result, with a ranking order of district 2 (highest number of incidences), district 3, district 1, and district 5 (lowest number of incidences).

Despite the relevant ideas to use local knowledge and to try to get an overview for the whole area of each district, which can be compared with the observational data sets, the survey procedure presented some limitations.

Firstly, the number of situations assessed was limited to two categories instead of four for the observational procedure. Secondly, as a limited number of local experts were interviewed per district, this did not allow any statistical analysis, which would have mitigated the influence of the subjectivity of interviewee's answers. In particular, each interviewee might not be an expert in each of the situations described in the questionnaire. Thirdly, although it was simplified compared to Raven (2012), the weighting system is not developed enough yet for the results to be fully comparable with the observational results. Further developments could focus on increasing the number of interviewees per district, amending the situations assessed as well as the weighting system to be fully comparable with the observational procedure so as to get a more systematic and reproducible procedure.

Opportunities offered by the interactive map

While too many factors were found to influence outcomes for the microbiological and survey approaches, the observational approach appeared promising and was further developed into an interactive mapping tool. The interactive map was created by using the free version of the programme Google TM Earth. Two main components were developed into the interactive map: symbols (Figure 6) and info-bubbles (Figure 7). Symbols served to represent each type of observations of the observational checklist (Appendix B.1). Similar colours were assigned to symbols belonging to the same category. For instance, symbols from the category 'human sources of faecal contamination' were all coloured in green as shown the in Figure 6. In the Google TM Earth places panel (Appendix C), symbols associated to the observations were ranked following the same structure as the observational checklist. The user can easily display a specific observation or even every observation from the same category, by ticking the corresponding box in the place panel.

The developed interactive mapping tool has several advantages. The first advantage is the simplicity of the Google TM Earth programme which is very intuitive. The user can display the different symbols and info-bubbles created by simple clicks in the place panel. The second advantage of this tool is its interactive aspect: important zoom and different view configurations are possible. However, this study did not allow the tool to be fully developed and some limitations need to be addressed. For example, using a free version of Google TM Earth did not allow any importation of data from spreadsheets such as coordinates. Each data point was manually entered directly into the Google TM Earth interface, which was a time consuming process. In addition, the free version of Google TM Earth restricts the number of info-bubbles created up to 400 per user account (Google Earth 2013). Nevertheless, such an interactive mapping tool presents the potential to be a cost effective (free software), easy to use (simple interface) and systematic (observational checklist) monitoring tool for comparative assessments. For instance, if a similar research is conducted after WSUP interventions, the maps from the pre-intervention and post-intervention could be compared to easily assess changes.

Future research should focus on (1) facilitating the importation of data while still using free versions of programmes, (2) adding modules to the free software used in this study or possibly software could be created. A tablet could be readily utilised in the field with the developed software. Such equipment could allow recording of incidences directly, measuring the distance walked and drawing the route (as it could utilise integrated GPS), take pictures and associate them with the location. The map could immediately be produced once the walk had finished. Completed with local authority representatives, the walk and the associated interactive map could then be used as an effective communication tool.

C.2.2 The Control and Intervention districts

Three elements are assigned to the Control and Intervention districts folders (Figure 15): boundaries, chef office locations and district numbers assigned for the study (Table 1).

The Figure 15 gives the symbols for the Control and Intervention districts for both the Places Panel and the Map viewer. The Figure 16 gives a snapshot of the boundaries, chef offices and district numbers as they appear for the user on the Google TM Earth Interface. represented by a specific placemark on the Google TM Earth Interface. The Figure 34 gives the legend for each type of observation. Placemarks (or type of observations) falling under a category share a similar color. The same color code was applied in the Figure 4.

Figure 1 .Figure 4 .Figure 6 .Figure 7 .Figure 9 -Figure 11 -Figure 13 -Figure 14 -Figure 15 -Figure 18 -Figure 19 -Figure 20 -Figure 21 -Figure 22 -Figure 23 -Figure 24 -

 146791113141518192021222324 Figure 1. Peri-urban studied districts and associated transect walk segments. 16 Figure 3. Normalised total coliform counts per 6.25 cm 2 of shoe surface per 1,000 m walked in sampling districts 1 to 6. .............................................. 27 Figure 4. Normalised weighted incidences of contamination per 1,000 m walked by districts. ................................................................................................. 32 Figure 6. Infrastructure deficiencies in the district 2, Maputo City .................... 38 Figure 7. Example of an info-bubble in district 2, Maputo City.......................... 38 Figure 9 -Function of some elements of the Google TM Earth Toolbar used in the study. ................................................................................................... 58 Figure 10 -Description on how to use the Navigation Controls ....................... 58 Figure 11 -Location Status bar ....................................................................... 59 Figure 12 -Places Panel ................................................................................. 59 Figure 13 -Link between the Places Panel and the Map viewer -Step i. ....... 61 Figure 14 -Link between the Places Panel and the Map viewer -Step ii. ....... 61 Figure 15 -Symbols for the Control and Intervention districts on both the Places Panel and the Map viewer. ............................................................ 62 Figure 16 -Boundaries, chef office locations and district numbers for both the Control and Intervention districts as they appear on the Google TM Earth Interface. .................................................................................................... 62 Figure 17 -How to use the interactive monitoring mapping tool-Step 1 and 2. .................................................................................................................. 63 Figure 18 -How to use the interactive monitoring mapping tool -Step 3. ....... 64 Figure 19 -How to use the interactive monitoring mapping tool -Step 4. ....... 65 Figure 20 -How to use the interactive monitoring mapping tool -Step 5. ....... 65 Figure 21 -How to use the interactive monitoring mapping tool -Steps 6 and 7. .................................................................................................................. 66 Figure 22 -How to use the interactive monitoring mapping tool -Step 8. ....... 67 Figure 23 -How to use the interactive monitoring mapping tool -Step 9. ....... 68 Figure 24-How to use the interactive monitoring mapping tool -Step 10. ...... 68 Figure 25 -How to use the interactive monitoring mapping tool -Step 11 ...... 69 Figure 26 -How to use the interactive monitoring mapping tool -Step 12. ..... 70

  Mozambique has a tropical to subtropical climate where the rainy season lasts from November to April (McSweeney et al. 2010). Coastal locations such as Maputo City are exposed to destructive hurricanes and cyclones which substantially contribute to the seasonal rainfall (McSweeney et al. 2010; Campos et al. 2012). Located downstream of two important river systems Maputo is subject to both flooding and droughts (Campos et al. 2012). High water tables associated with heavy rainfall cause latrines, sewers and open drains to overflow in the low income districts of Maputo City (Schaub-Jones 2005; UN-HABITAT 2010; Campos et al. 2012). A Health Impact Survey (HIS) was conducted by the organization Water & Sanitation for the Urban Poor from October 2012 to March 2013, in six low income districts of Maputo City (WSUP 2013; Schmidt & Norman n.d). This HIS is a four-year study which aims at evaluating the impact of an inclusive urban Water, Sanitation and Hygiene (WASH) campaign in these districts. According to a set of specific criteria (Schmidt & Norman n.d), the districts 1, 2 and 3

Figure 1 .

 1 Figure 1. Peri-urban studied districts and associated transect walk segments. The districts for which boundaries are coloured in green are the intervention districts of the WSUP Health Impact Survey and the red districts are the control districts. The procedure was to draw transect walks within a 300m by 300m square, for each district.

  conducted among the local community developers of the organization WSUP. As fieldworkers they were considered to be in the best position to answer to district-specific questions. The questionnaire was exactly the same as the one used by[START_REF] Hirst | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] and Raven (2012). Each WSUP local community developer works in a specific district. For this reason, only one local fieldworker was available per district for interview. The questionnaire was sent by e-mail to provide interviewees with sufficient time to fully understand the overall goal of the questionnaire and the meaning of each single question. Both a WORD-processed and a spread sheet document were sent. The word processed document defined the term of faecal contamination of the local environment, gave examples of sources and types of faecal contamination and included the four questions (A.1

  but was slightly adapted. The free geographical program Google TM Earth was used to obtain a map for each district. The orientation of the Google TM Earth map view can easily be changed by the user. The first step to define the transect route is to ensure that the orientation of the map view coincides with North. Whenever the district chef office was located at 300 m or more from the district boundaries it was assigned to be the Point 1. If not, a grid reference was randomly generated[START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] to locate a new point. A new grid reference was generated until a point was located at 300 m or more from the district boundaries. This point was assigned to the Point 1. Then, a square of 300 m from all sides which was including Point 1 was drawn and was gridded so as to get nine sub-squares of equal size within the main square. An online random number generator[START_REF] Urbaniak | [END_REF] was used to randomly allocate numbers from 2 to 10 into the nine sub-squares. The pre-defined route was then obtained by drawing the segments or paths between two consecutive points: the first segment 1-2 was drawn by connecting Point 1 to Point 2 and so on until reaching Point 10. A transect walk was then including nine segments.While walking following the pre-defined route, observations of actual and potential sources of faecal contamination were systematically recorded according to an existing observational checklist[START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF] further developed (B.1). Two main improvements were brought: (i) observations were ranked into four categories: human sources of faecal contamination, animal sources of faecal contamination, infrastructure deficiencies and solid waste and mud, (ii) new types of observations were added and the existing types of observations were amended so as to obtain a total of twenty types of observations.

Figure 3 .

 3 Figure 3. Normalised total coliform counts per 6.25 cm 2 of shoe surface per 1,000 m walked in sampling districts 1 to 6. For each district, the minimum, the maximum and the average values are obtained from a data sets of three values. Error bars represent the full range of samples collected from two shoe soles.

  Figure 4 reveals that no incidence of human sources of faecal contamination was recorded in the six districts. The observations for this category were related to open defecation, toilets leakages to drains or to the ground and contamination related to pit emptying (Appendix B.1).

( 9 )

 9 , drinking water pipes visibly exposed to dirt (6), observations of livestock/cattle (5), incidences of solid waste on the street (4) and observations of open dumps. The absence of incidences of faecal contamination from human sources might be explained by three reasons. Firstly, a practice of open defecation is not common in low income areas of Maputo, according to local experts. Secondly, most of the inhabitants of the observed districts have latrines inside their plots (Schaub-Jones 2005). Since the procedure is restricted to observations located outside the plot, it was not possible to record any incidences of faecal contamination related to latrine leakages or emptying located inside the plot. Thirdly, the transect walk procedure which was completed only once per district might not allow to record "occasional" or "sporadic" incidences such as open defecation as suggested by the survey results.

Figure 4 .

 4 Figure 4. Normalised weighted incidences of contamination per 1,000 m walked by districts. No incidences of faecal contamination from human sources were recorded.

  (2) the normalisation of the weighted observations per 1,000 m walked and (3) an increased range of a non-normalised weighted score per district from 20-100[START_REF] Raven | Integrative assessment of the degree of faecal contamination in low-income urban environments[END_REF]) to 0-100, some limitations remain. The weighting factors applied in this study remain arbitrary because of the lack of validated studies. There is no evidence of how animal sources of faecal contamination impact on human health. The weighting system could be adapted as soon as the knowledge gap of the relation between the sources of faecal contamination and the impact on human health is bridged. Meanwhile the Figure4provides insights for relative comparisons between the six districts indicating what the most recurring actual and potential sources of faecal contamination are.

Figure 5 .

 5 Figure 5. Weighted incidences of faecal contamination from the survey and normalised weighted incidences of contamination from observations for the districts 1, 2, 3 and 5. None local expert had sufficient knowledge to answer both districts 2 and 6 therefore no answer was collected for these districts. Blue crosses represent a zero value for the category Faecal Contamination from Human Sources (Observations).

Figure 6 .

 6 Figure 6. Infrastructure deficiencies in the district 2, Maputo City, Mozambique.

Figure 7 .

 7 Figure 7. Example of an info-bubble in district 2, Maputo City, Mozambique.

Figure 9 -

 9 Figure 9 -Function of some elements of the Google TM Earth Toolbar used in the study.

Figure 10 -Figure 13 -

 1013 Figure 10 -Description on how to use the Navigation Controls

Figure 14 -

 14 Figure 14 -Link between the Places Panel and the Map viewer -Step ii.

Figure

  Figure 15 -Symbols for the Control and Intervention districts on both the Places Panel and the Map viewer.

Figure 19 -

 19 Figure 19 -How to use the interactive monitoring mapping tool -Step 4.

Figure 20 -

 20 Figure 20 -How to use the interactive monitoring mapping tool -Step 5.

Figure 22 -

 22 Figure 22 -How to use the interactive monitoring mapping tool -Step 8.

Figure 25 -

 25 Figure 25 -How to use the interactive monitoring mapping tool -Step 11

Figure 29 -

 29 Figure 29 -How to use the interactive monitoring mapping tool -Steps 16 & 17.

Figure 30 -

 30 Figure 30 -How to use the interactive monitoring mapping tool -Step 18.

Table 1 .

 1 Official names of the six administrative districts and their allocated number for this study ................................................................................. 16

Table 2 .

 2 Survey questions and associated rating scale and maximum score per category. .................................................................................................... 17

Table 3 .

 3 Score range of the four observational categories. .............................. 21

Table 4 .

 4 Observational checklist and scorecard. Whilst walking, both number of incidences (A) and cumulative distance in meters (B) per segments were counted.. .................................................................................................... 22

Table 5 .

 5 Relative percentages and number of answers on how widespread/frequent the situations for each category (Human Sources of Faecal Contamination and Infrastructure Deficiencies) were perceived by the 4 local experts. ..

	In the pursuit of the Millennium Development Goal (MDG) Target 10, which
	aims at halving the proportion of people without access to safe drinking water
	and basic sanitation by 2015 (UN 2003), there are many organizations which
	support programmes to improve sanitation, hygiene and infrastructures for the
	urban poor population in developing countries. Despite these efforts to reduce
	the occurrence of diarrhoeal diseases via such improvements, more than 2.2
	million deaths each year are estimated to be caused by gastrointestinal
	infections such as diarrhoea (WHO & UNICEF

.................................................................................. 25 Table 6. Standard deviation (STDEVA) and standard deviation expressed as a percentage of the average (%) for the normalised average E.coli and total coliform counts, per 6.25 cm 2 of shoe surface per 1,000 m distance, collected during transect walks in districts 1 to 6. ...................................... 28 INTRODUCTION

Table 2 . Survey questions and associated rating scale and maximum score per category.

 2

	A. Rate the level of faecal contamination:	1-5	NA
	B. Indicate how widespread the following situations are:

Table 3 . Score range of the four observational categories.

 3

	Category	Score range
	Human Sources of Faecal Contamination	0-30
	Animal Sources of Faecal Contamination	0-10
	Infrastructures Deficiencies	0-35
	Solid Waste and Mud	0-25
	TOTAL	0-100

Table

Table 5 . Relative percentages and number of answers on how widespread/frequent the situations for each category (Human Sources of Faecal Contamination and Infrastructure Deficiencies) were perceived by the 4 local experts (n=4) on a scale of 1 to 5 for the districts 1, 2, 3 and 5. Rating scale is: (1) does not occur, (2) occasional, sporadic issue, (3) not very common, but still a significant problem, (4) common, major problem and (5) extremely widespread.

 5

				Rating scale
		1	2	3	4	5
	Human Sources of
	Faecal Contamination	0% (0)	75% (12)	12.5% (2)	0% (0)	12.5% (2)
	(n=4)
	Infrastructure
	Deficiencies	0% (0)	18.75% (3)	12.5% (2)	6.25% (1)	62.5% (10)
	(n=4)

greatest difference measured in samples collected in district 4. Intermediate microbial contamination was found in all other districts.

Table 6 . Standard deviation (STDEVA) and standard deviation expressed as a percentage of the average (%) for the normalised average E.coli and total coliform counts, per 6.25 cm 2 of shoe surface per 1,000 m distance, collected during transect walks in districts 1 to 6.

 6

Figure 26 -How to use the interactive monitoring mapping tool -Step 12.Figure 27 -How to use the interactive monitoring mapping tool -Step 13.

ACKNOWLEDGEMENTS

I would like to express my appreciation to Guy Norman from the organization Water & Sanitation for the Urban Poor (WSUP) who gave me the opportunity to

gratitude to Dr Andreas Nocker, my research supervisor, for his patient guidance, enthusiastic encouragement and useful critiques of this research work. Assistance provided by the staff from Cranfield University was greatly appreciated. , my sister, and my grandparents for their important support and encouragement throughout all my studies.

Finally, I wish to thank my friends from Arradon, Rennes, and Strasbourg

Microbiological testing results

E. coli and total coliform numbers recovered from surfaces of shoes used during transect walks in each district are shown in Figure 2 and Figure 3. Whilst the number of E. coli per 6.25 cm 2 of shoe surface ranged from 0 to 185, the numbers of total coliforms were in the range between 0 and 1,083. Lowest numbers of both indicator groups were obtained in district 1, highest numbers were measured in district 4. For both E.coli and total coliforms, substantial differences were observed between minimal and maximal numbers with the CONCLUSION Three procedures to assess the degree of faecal contamination in low income environments were applied in six low income districts of Maputo City, Mozambique. The procedures were microbiological testing of faecal indicator bacteria, an observational procedure and a survey to rank districts in terms of faecal contamination. Results from both the microbial testing and survey were obtained from a limited data set and appeared to be affected by many factors.

The results should therefore be considered with caution. The limitations for both the microbial testing and the survey found in the existing literature and for this particular research project, led to the conclusion that these approaches were not promising. The observational procedure was found to have most potential and was thus further developed. Using freely available software, an interactive map prototype was established. The tool utilises photographic evidence with images assigned to GPS coordinates, allowing for the corresponding sites to be re-visited for reassessment. Interactive monitoring mapping has the potential to:

(i) be easy to apply in developing countries, (ii) provide reliable data, (iii) be repeatable and (iv) be transposable in different contexts.

This project suggests that future efforts should focus on the full development of an interactive map and the importation of data whilst using free versions of programmes. Additional modules could potentially be added to the free software used in this study. A tablet device is suggested to be the most efficient platform allowing direct route recording, tracking of incidences, and measuring the distances and drawing the route followed via an integrated GPS. Incidences could be supported by pictures with assigned GPS coordinates. Maps could be produced immediately after completion of a transect walk and data exchanged with local authorities and funding organizations. 1. Little or no significant faecal contamination (as expected in London or Paris, for example) 2. Minor/occasional faecal contamination (as expected in a smart residential area of an African city, for example) 3. Moderate faecal contamination: some children may come into contact with faecal contamination in particular locations or at particular times, but this would not be a common occurrence 4. Major faecal contamination, possibly extreme during periods of flooding: children playing on the streets are at significant risk of coming into contact with faecal contamination, at least during rainy periods 5. Extreme faecal contamination throughout much of the year: children playing on the streets will very probably come into contact with faecal contamination The Places Panel allows the user to list and organise its data (Figure 12). For instance, in order to locate, save, organize and revisit placemarks. The key point to understand how the interactive monitoring mapping tool works is to understand the link between the data saved and organised in the Places

APPENDICES Appendix A Survey approach

Panel and the symbols displayed on the Map viewer. To make visible or invisible a symbol on the Map viewer, the user only needs to tick or untick a box in the Place Panels. As an example, follow the two steps below:

i.

In the Places Panel, untick all the boxes. The Map viewer is "empty", i.e.

does not contain anything except the existing background (Figure 13).

ii. Tick the boxes for the folders 'Control districts' and 'Intervention districts'. Lines, numbers and symbols are now displayed in the Map viewer (Figure 14).

C.2.3 The transect walks and the recorded observations

The interactive monitoring mapping tool was designed to give a visual and practical means to analyse the results from the observational procedure (p. 20).

Both the points and segments defined by the procedure to draw the route followed by the researcher on the field and the observations recorded while completing the transect walk are incorporated in this interactive map. The steps below introduce the user to this tool.

NB:

To facilitate the navigation within the Places Panel, always aim at collapsing as much as arrows as possible.

1. Untick all the boxes and collapse the arrows to the maximum (Figure 17). 19).

6. Zoom towards these points either with the Navigation Controls or by double clicking on the folder 'Points'. These points are the points selected to draw the transect walk route, as described by the observational procedure (p.31).

7. Tick the box related to the folder 'Segments' (to make visible the route followed by the researcher (from Point 1 to Point 2, Point 2 to Point 3, etc) as shown in Figure 21. NB: To display specifically some points and/or segments, expand either the folder 'Points' and/or 'Segments' and tick/untick the relevant boxes. The Figure 33 gives the legend for the points and segments.

8. The folder 'Symbols' is structure accordingly to the observational checklist (Table 4). This folder has the same four main categories than the observational checklist as well as the same type of observations. Expand the folder 'Symbols'. Ensure to collapse arrows to the maximum so as to obtain only the four folders (or categories): Human sources, Animal sources, Infrastructures deficiencies and Solid Waste and Mud (Figure 22).

C.2.4 The info-bubbles

The same general principle applies to display the info-bubbles.

14. In the folder 'District 2 -Mafalala', untick the folder 'Symbols' (Figure 28).

15. In 'My Places', tick the folder 'Link to -Spreadhseet'. The view will automatically unzoom to get an overview of the six districts (Figure 28). The Figure 31 and Figure 32 give the nature of the information found in the infobubbles.