Protocols for microbially induced carbonate precipitation (MICP) have been extensively studied in the literature to optimise the process with regard to the amount of injected chemicals, the ratio of urea to calcium chloride, the method of injection and injection intervals, and the population of the bacteria, usually using fine- to medium-grained poorly graded sands. This study assesses the effect of varying urease activities, which have not been studied systematically, and population densities of the bacteria on the uniformity of cementation in very coarse sands (considered poor candidates for treatment). A procedure for producing bacteria with the desired urease activities was developed and qPCR tests were conducted to measure the counts of the RNA of the Ure-C genes. Sand biocementaton experiments followed, showing that slower rates of MICP reactions promote more effective and uniform cementation. Lowering urease activity, in particular, results in progressively more uniformly cemented samples and it is proven to be effective enough when its value is less than 10 mmol/L/h. The work presented highlights the importance of urease activity in controlling the quality and quantity of calcium carbonate cements.

Microbially induced calcium carbonate precipitation (MICP) is an innovative biocementation technique that facilitates the formation of calcium carbonate within a pore network. Initially gaining prominence in the field of geotechnical engineering, MICP has attracted significant attention since its inception (the last three decades) and expanded its reach across various engineering disciplines. Examples include rock mechanics, geology and the oil and gas industry fields through the generation of rock-like specimens, and plugging of fractures, in civil and architectural engineering and material science for concrete repair, protection, and for self-healing of building materials, and in environmental engineering for the study of biomimetic materials. In response to this burgeoning interest, the current paper aims to present a comprehensive review of the main biochemical mechanisms underlying MICP (bacterial ureolytic activity, reactions duration and settling times, and chemical solution properties), their direct relevance to altering hydraulic and mechanical properties, both at the microscale and macroscale responses, and the precipitation mechanisms, particularly in relation to water resources and hydrology applications. Four main categories of relevant applications are identified, namely, the groundwater and soil remediation, the applications related to the generation of a low hydraulic conductivity barrier, those related to gaining cohesion, and the applications related to fluid flow studies in artificially generated porous media. Moreover, this comprehensive review not only aims to identify the existing applications of MICP within hydrological fields but also strives to propose novel and promising applications that can further expand its utility in this domain. Along with the investigation of the potential of MICP to revolutionize water resources and hydrology, it is imperative to delve deeper into its environmental implications to ensure sustainable and ecologically responsible implementation.

Water supply pipes age, deteriorate and break, which puts at risk the continuous provision of safe potable water endangering the public health in cities. Risk management methods are increasingly applied to optimise the capital investment for pipe replacement and rehabilitation, taking into account the probability and hydraulic impact of pipe breaks. As part of this process, however, historic pipe break data and statistical methods should be utilised to gather causal insights for past breaks to inform operational changes and/or capital investment decisions in order to reduce future breaks. This paper presents a comparative study of statistical and machine learning methods to carry out an exploratory causal analysis for historic pipe breaks in an operational water supply network. Regression models for count data and probabilistic models have been developed. The performance of these models was assessed and enhanced with the introduction of interactions and the inclusion of different network representations.