Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial technologies. In the present review, we show across 20 water monitoring analytes and 90 references that carbon nanotube-based electrochemical sensors, chemistors and field-effect transistors (chemFET) can meet these needs. A set of 126 additional references provide context and supporting information. After introducing water quality monitoring challenges, the general operation and fabrication principles of CNT water quality sensors are summarized. They are sorted by target analytes (pH, micronutrients and metal ions, nitrogen, hardness, dissolved oxygen, disinfectants, sulfur and miscellaneous) and compared in terms of performances (limit of detection, sensitivity and detection range) and functionalization strategies. For each analyte, the references with best performances are discussed. Overall, the most frequently investigated analytes are H⁺ (pH) and lead (with 18% of references each), then cadmium (14%) and nitrite (11%). Micronutrients and toxic metals cover 40% of all references. Electrochemical sensors (73%) have been more investigated than chemistors (14%) or FETs (12%). Limits of detection in the ppt range have been reached, for instance Cu(II) detection with a liquid-gated chemFET using SWCNT functionalized with peptide-enhanced polyaniline or Pb(II) detection with stripping voltammetry using MWCNT functionalized with ionic liquid-dithizone based bucky-gel. The large majority of reports address functionalized CNTs (82%) instead of pristine or carboxyl-functionalized CNTs. For analytes where comparison is possible, FET-based and electrochemical transduction yield better performances than chemistors (Cu(II), Hg(II), Ca(II), H₂O₂); non-functionalized CNTs may yield better performances than functionalized ones (Zn(II), pH and chlorine).

The recent developments in both nanotechnologies and wireless technologies have enabled the rise of small, low-cost and energy-efficient environmental sensing devices. Many projects involving dense sensor networks deployments have followed, in particular, within the Smart City trend. If such deployments are now within economical and technical reach, their maintenance and reliability remain, however, a challenge. In particular, reaching, then maintaining, the targeted quality of measurement throughout deployment duration is an important issue. Indeed, factory calibration is too expensive for systematic application to low-cost sensors, as these sensors are usually prone to drifting because of premature aging. In addition, there are concerns about the applicability of factory calibration to field conditions. These challenges have fostered many researches on in situ calibration. In situ means that the sensors are calibrated without removing them from their deployment location, preferably without physical intervention, often leveraging their communication capabilities. It is a critical challenge for the economical sustainability of networks with large-scale deployments. In this paper, we focus on in situ calibration methods for environmental sensor networks. We propose a taxonomy of the methodologies in the literature. Our classification relies on both the architecture of the network of sensors and the algorithmic principles of the calibration methods. This review allows us to identify and discuss two main challenges: how to improve the performance evaluation of such methods and how to enable a quantified comparison of these strategies?

The assessment of pavement conditions and their evolution with time is a crucial component for the establishment of pavement quality management (QM) plans and the implementation of QM practices. An effective pavement management system (PMS) is based on pavement conditions data continuously collected along the lifetime of a road. These data are used to model the pavement response, evaluate its performances, and trigger the necessary maintenance actions when they do not meet previously defined performance indicators. In the last decades, pavement monitoring via embedded sensing technologies has attracted more and more attention. Indeed, the integration of sensors in the road pavement allows the assessment of the complete history of pavement conditions, starting from sensor installation. Once the technologies are stabilized, collecting this information is expected to help road managers to define more effective asset management plans. This paper first proposes an overview of the most used devices for pavement instrumentation, categorized according to the measured parameters. Then a review of some prominent instrumented sections is presented by focusing on the methodology used for data interpretation.