This work describes the technical features and the performance of two different types of metal-oxide semiconductor sensors, based on ZnO:Ga thin films and SnO₂-G nanofibrous layers, for tropospheric ozone monitoring in ambient air. These nanostructures were tested and compared with commercial metal-oxide semiconductor sensors under controlled laboratory conditions and in a field campaign during summer 2021 in Monfragüe National Park (western Spain). The paper also details the design of the electronic device developed for this purpose. A machine learning algorithm based on Support Vector Regression (SVR) allowed the conversion of the resistive values into ozone concentration, which was evaluated afterward. The results showed that the manufactured sensors performed similarly to the commercial sensors in terms of R² (0.94 and 0.95) and RMSE (5.21 and 4.83 μg∙m⁻³). Moreover, a novel uncertainty calculation based on European guides for air quality sensor testing was conducted, in which the manufactured sensors outperformed the commercial ones.

Calculating analytical uncertainties as a part of method validation is a relevant aspect of field and laboratory practices in instrumental analytical chemistry subjects, which usually require complex algorithms. This work describes the development and didactic use of an automatic and straightforward informatics tool, implemented in an Excel macro, for calculating and interpreting the uncertainty of an analytical method against a reference method on field measurements. The software was initially developed for field testing of low-cost air quality monitoring analytical methods against reference methods, and the present work shows its adaptation to a didactic environment. The uncertainty calculation software was implemented through an Excel macro based on Visual Basic as a graphical user interface. It finds a best-fit line that describes the relation between concentrations determined by the candidate and reference methods. The software generates the analytical validation results (slope and intercept with their respective confidence limits, and expanded uncertainty of a concentration determined by the candidate method), hiding the intermediate functions and calculations. The Excel interface eases uncertainty calculations for undergraduate students, although the background mathematics can be quickly unveiled to students for didactic purposes. This tool has been applied to a laboratory exercise focused on validating experimental results obtained in the measurement of ozone levels in ambient air by passive sampling and spectrophotometric detection. The uncertainty calculation software has proved valuable by providing the student a resource to check the analytical quality of the data generated in the laboratory, while assimilating the fundamentals behind the calculations.

Screening and quantification of emerging contaminants in water is of enormous relevance due to its scarcity and harmful effects on aquatic life and human health. We present a simple and cost-effective electrochemical cell for determination of the antidepressant venlafaxine, an emerging contaminant included in the EU Watch list 2022. The cell consists of pencil leads used as electrodes and a microcentrifuge tube. Modification of the working electrode with carbon nanomaterials improved the signal. Cell-related (e.g., type of pencil leads or electroactive area) as well as experimental (e.g., pH, accumulation potential and time, and scan rate) parameters were thoroughly optimized. The adsorptive nature of venlafaxine process allowed the use of an adsorptive stripping square wave voltammetry methodology to increase the sensitivity. Under optimized variables, a linear range from 0.8 to 10 μmol L^-1 with a correlation coefficient of 0.996, a sensitivity of 1.48 μmol L^-1, a LOD of 0.4 μmol L^-1 and a RSD of 2.4 % were achieved. Selectivity was also studied, especially with respect to the main metabolite, o-desmethylvenlafaxine. The methodology distinguishes its signal from that of the main compound, allowing its determination. A similar linear range was obtained for the metabolite, with a LOD of 0.6 μmol L^-1. The platform developed was applied for venlafaxine quantification in spiked wastewaters from the Febros plant in Portugal, obtaining satisfactory recoveries. Furthermore, the versatility of pencil leads made it possible to combine them with modified paper for sampling and buffering in order to decentralize the determination, showing promising results.