Since the late 20th century most people in rural Bangladesh use hand pumped tube wells to extract groundwater as their primary source of water. In 1993 it was officially recognized that many of the Holocene groundwater aquifers contain elevated levels of arsenic (As) and that millions of people in Bangladesh are exposed and at risk for mass poisoning. The need to focus efforts to areas with scarce supply of safe drinking water has raised the need to quickly locate and identify these areas. Mitigation options evaluated in this study focus on 1) how to target As safe aquifers and 2) how to identify As safe tube wells An attempt to target As safe shallow aquifers based on sediment color was evaluated. The majority of 30 new tube wells screened in off-white sand sediments have As safe water with respect to both the WHO and the national guideline, but including the failed attempts to target off-white sediments at shallow depths, the success rate of the method was considered to be too low. This study also attempt to validate platform color as a rapid low-cost screening tool for As by relating platform color to water chemistry in 103 tube wells. Different classification methods were tested to develop recommendations for the future use of this method. The results showed that a simplified color classification was better than a detailed classification at predicting water chemistry of iron (Fe), manganese (Mn) and As. Recently, Mn has also emerged as a possible widespread problem in the Bengal Basin. Although not thoroughly investigated, present evidence indicates that a high concentration of Mn in drinking water affects the intellectual function in children. The occurrence of both low As and low Mn concentrations in shallow aquifers was found to be very unlikely since only one out of 133 tested wells had this water chemistry composition. Instead it was showed that the highest Mn concentrations occur in As-safe aquifers. Therefore WHO should consider reintroducing their previous health based guideline value for Mn to highlight the potential risk of excessive exposure, since more people risk being exposed to Mn when As-safe shallow aquifers are targeted.

Most of the drinking water supply in rural Bangladesh comes from groundwater collected using shallow tubewells. The tubewells, usually shallow because of the increased cost involved in deeper tubewells, have been installed by local drillers. A Sediment Color Tool was developed, with input from local drillers, that associated the arsenic concentration with specific sediment colors, in order to help the drillers install safe tubewells. This tool was digitized into the phone application, ASMITAS, to reduce subjectivity in sediment color determination due to human error or surrounding conditions, when used with a color sensor. The purpose of this study was to carry out a preliminary assessment of the application performance and usability, and the results provided by the application for color identification. 35 sediments were used and assigned into 4 different data sets to allow for comparison. Two data sets were assigned a Munsell color manually, while two were assigned the Munsell Soil Color (or Red-Green-Blue color) through use of the digital app. The sensor, the Nix Color Sensor Pro 2, was validated through a literature review and is considered accurate in identifying the color of the soil sediments. The data sets were compared based on the Delta E 2000 formula to determine the color difference between the data sets. The most relevant result of this method was between the Red-Green-Blue that the Nix Sensor originally provided to the application versus the closest matching Munsell code that the application could provide. It showed that the library from which the Munsell color was drawn was not yet expansive enough to accurately identify all sediments that may be scanned. Cyan-Magenta-Yellow-Black color comparisons were made to ascertain which aspects of the color are the most difficult to identify. It was found that both the sensor and the human eye had difficulties in identifying differences in the yellow percentage of several of the samples. The results showed that there may be greater need for distinction of which yellow percentages of Cyan-Magenta-Yellow-Black belong to which color sediment. Overall, the application appears to have a small number of less prominent features and functions to improve on prior to the publication of the application. At this stage of development, the main goal lies in the improvement and building of the Munsell color code reference library and the library of arsenic concentrations associated with each sediment color within the application, in order to improve the accuracy of the results. / Största delen av dricksvattenförsörjningen i lantlig Bangladesh kommer från grundvatten som samlas in med rörbrunnar. Rörbrunnarna, som vanligtvis är grunda som en följd av kostnaderna, har installerats av lokala borrare. Under 2007 antogs det att färgen på sedimentet i vilket rörbrunnarna placeras ger en indikation på arsenikens koncentration. Därför utvecklades Sediment Color Tool med input från de lokala borrarna. Verktyget vidareutvecklades till en digital app, ASMITAS, för att minska subjektiviteten i markfärgbestämning på grund av mänskliga fel eller omgivande förhållanden. Syftet med denna studie var att utvärdera applikationens prestanda i detta stadie av dess utveckling och färgidentifiering som genomförts av applikationen. 35 sediment användes i första bedömningen och klassificerades fyra gånger i fyra olika datamängder för att möjliggöra jämförelse. Två datamängder tilldelades en Munsell Soil Color manuellt, medan två tilldelades sin färg genom användning av den digitala appen. Sensorn som användes, Nix Color Sensor Pro 2, validerades genom en litteraturöversikt och anses vara korrekt när det gäller att identifiera färgen på sediment. De fyra datamängderna jämfördes visuellt med användning av färgbrickor. De jämfördes baserat på DE2000-formeln för att bestämma färgskillnaden mellan datamängden. Det mest avslöjande resultatet med denna metod var mellan dem två digitalt förvärvade datamängderna. Resultatet föreslår att referensbiblioteket i ASMITAS, från vilket matchen togs, ännu inte var tillräckligt stort för att identifiera alla sedimentprover noggrant för att ej vara märkbar för det mänskliga ögat. Cyan-Magenta-Gul-Svart jämförelser gjordes för att se vilka aspekter av färgen som är svårast att identifiera. Resultaten visade att både sensorn och det mänskliga ögat hade svårigheter att identifiera skillnader i den gula procentandelen av flera av proverna och sedimentfärgerna. Resultaten visade att det kan finnas ett större behov av åtskillnad av vilka gula procentsatser som tillhör vilken färg av sediment (och motsvarande arsenikkoncentration). Det finns ytterligare aspekter och funktioner av appen som är mindre centrala för dess prestanda som bör förbättras innan applikationen publiceras. I detta utvecklingsstadium ligger emellertid huvudmålet i förbättring och uppbyggnad av Munsell- färgkodreferensbiblioteket och biblioteket med arsenik-koncentrationer som är kopplad till varje sedimentfärg i applikationen. Detta för att öka resultatets noggrannhet.