Status and advances in technologies for phosphorus species detection and characterization in natural environment- A comprehensive review.

Poor recovery of phosphorus (P) across natural environment (water, soil, sediment, and biological sources) is causing rapid depletion of phosphate rocks and continuous accumulation of P in natural waters, resulting in deteriorated water quality and aquatic lives. Accurate detection and characterization of various P species using suitable analytical methods provide a comprehensive understanding of the biogeochemical cycle of P and thus help its proper management in the environment. This paper aims to provide a comprehensive review of the analytical methods used for P speciation in natural environment by dividing them into five broad categories (i.e., chemical, biological, molecular, staining microscopy, and sensors) and highlighting the suitability (i.e., targeted species, sample matrix), detection limit, advantages-limitations, and reference studies of all methods under each category. This can be useful in designing studies involving P detection and characterization across environmental matrices by providing insights about a wide range of analytical methods based on the end user application needs of individual studies.

Quantitative Method for Comparative Assessment of Particle Removal Efficiency of Fabric Masks as Alternatives to Standard Surgical Masks for PPE.

In response to the COVID-19 pandemic, cloth masks are being used to control the spread of virus, but the efficacy of these loose-fitting masks is not well known. Here, tools and methods typically used to assess tight-fitting respirators were modified to quantify the efficacy of community-produced and commercially produced fabric masks as personal protective equipment. Two particle counters concurrently sample ambient air and air inside the masks; mask performance is evaluated by mean particle removal efficiency and statistical variability when worn as designed and with a nylon overlayer, to independently assess fit and material. Worn as designed, both commercial surgical masks and cloth masks had widely varying effectiveness (53%-75% and 28%-91% particle removal efficiency, respectively). Most surgical-style masks improved with the nylon overlayer, indicating poor fit. This rapid testing method uses widely available hardware, requires only a few calculations from collected data, and provides both a holistic and aspect-wise evaluation of mask performance.

Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of statistical representativeness to natural water samples. The accuracy of ANN results improves monotonically with the statistical representativeness of the training set (error decreases by ∼5×), while the expanded neural network architecture contributes a further factor of 2-3.5 decrease in error when trained with the most representative sample set. Results using the most statistically accurate set of training samples (which retain environmentally relevant ion concentrations but avoid the potential interference of humic acids) demonstrated accurate, unbiased quantification of nitrate and ammonium at natural environmental levels (±20% down to <10 µM), as well as the major ions Na(+), K(+), Ca(2+), Mg(2+), Cl(-), and SO4(2-), in unprocessed samples. These results show promise for the development of new in situ instrumentation for the support of scientific field work.

Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels.

A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non-linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures. Conductivity measurements and the concept of charge balance are incorporated into the ANN structure, resulting in (1) removal of estimation bias typically seen with use of ISEs in mixtures of unknown composition and (2) improvement of signal estimation by an order of magnitude or more for both major and minor constituents relative to use of ISEs as stand-alone sensors and error reduction by 30-50% relative to use of standard ANN models. This method is suggested as an alternative to parameterization of traditional models (e.g., Nikolsky-Eisenman), for which parameters are strongly dependent on both analyte concentration and temperature, and to standard ANN models which have no mechanism for incorporation of system knowledge. Network architecture and weighting are presented for the base case where the dot product can be used to relate ion concentrations to both conductivity and charge balance as well as for an extension to log-normalized data where the model can no longer be represented in this manner. While parameterization in this case study is analyte-dependent, the architecture is generalizable, allowing application of this method to other environmental problems for which mathematical constraints can be explicitly stated.

Towards an automated, standardized protocol for determination of equilibrium potential of ion-selective electrodes.

An automated real-time method for determination of ISE steady state value and response time is developed, following most recent IUPAC recommendations. Specifically, detection of the 'steady state' is related to (1) the time derivative of the emf as it reaches a limiting value (ΔE/Δt(limit), e.g., 0.1-1.0 mV min(-1)) and (2) the duration of time for which the absolute value of the time derivative remains less than this limiting value (stability window, denoted win(st)). A suite of representative ISEs, including glass, solid state, and polymer-based electrodes, is examined to determine sensitivity of results to parameterization choice. Measurements taken over a wide range of concentration values and in un-processed samples (i.e., without use of ionic strength adjustment) provide insight into behavior of ISEs in applications where analyte concentrations span a wide range and/or sample pre-processing may not be an option, e.g., use of sensors for in situ environmental sampling. Results show that declared steady state emf is strongly sensitive to variations in ΔE/Δt(limit) but relatively unaffected by changes in the stability window when win(st) ≥30 s. Linearity of calibration curves produced, quantified by root mean squared error (RMSE) against a linear fit, improves as ΔE/Δt(limit) decreases, however the percentage of measurements which reach a declared steady state within the prescribed sample window (∼6.5 min) falls with corresponding decreases in the ΔE/Δt(limit) parameter. Response time, defined as the time required to reach declared steady emf, is also a strong function of parameterization. Dependence of response times on sample composition and/or ISE membrane composition and type are also discussed; results for ISEs in samples comprised exclusively of interfering ions are included. In general, limiting emf derivatives of {0.25-0.4 mV min(-1)} and stability windows of {30-40s} achieve both good analytical accuracy and compliance with potentially short sampling window requirements. Methodology based on use of these parameters can improve sampling speed and accuracy as well as promote inter-comparison of data and ISE characterizations among research teams.

Field testing of lake water chemistry with a portable and an AUV-based mass spectrometer.

Two mass spectrometers (MS) are tested for the measurement of volatile substances, such as hydrocarbons and metabolic gases, in natural waters. KOALA is a backpackable MS operated from above the water surface, in which samples are pumped through a flow cell using a syringe. NEREUS is an underwater instrument hosted by an autonomous underwater vehicle (AUV) that is linked to a communications network to provide chemical data in real time. The mass analyzers of the two MS are nearly identical cycloids, and both use flat-plate membrane inlets. Testing took place in an eutrophic, thermally stratified lake exhibiting steep chemical gradients and significant levels of methane. KOALA provided rapid multispecies analysis of dissolved gases, with a detection limit for methane of 0.1 ppm (readily extendable to 0.01 ppm) and savings of time of at least a factor of 10 compared to that of conventional analysis. The AUV-mounted NEREUS additionally provided rapid spatial coverage and the capability of performing chemical surveys autonomously. Tests demonstrated the need for temperature control of a membrane inlet when steep thermal gradients are present in a water body, as well as the benefits of co-locating all sensors on the AUV to avoid interference from chemically different waters entering and draining from the free-flooding outer hull. The ability to measure dissolved volatiles provided by MS offers potential for complementarity with ionic sensors in the study of natural waters, such as in the case of the carbonate system.