Ozone (O₃) is a potent oxidant associated with adverse health effects. Low-cost O₃ sensors, such as metal oxide (MO) sensors, can complement regulatory O₃ measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM₂.₅ concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O₃ although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O₃ federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O₃ concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O₃ concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU’s MO oxidizing species and temperature measurements and DAQ’s solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R² = 0.491), and the ANN exhibited good performance (R² = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O₃ for five months after the calibration period and the results showed moderate correlations (R²s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O₃ levels.

Passive air samplers (PAS) were evaluated for measuring indoor concentrations of phthalates, novel brominated flame retardants (N-BFRs), polybrominated diphenyl ethers (PBDEs), and organophosphate esters (OPEs). Sampling rates were obtained from a 50-day calibration study for two newly introduced PAS, polydimethylsiloxane (PDMS) or silicone rubber PAS (one with and one without a coating of styrene divinyl benzene co-polymer, XAD) and the commonly used polyurethane foam (PUF) PAS. Average sampling rates normalized to PAS surface area were 1.5 ± 1.1 m³ day⁻¹ dm⁻² for both unsheltered PDMS and XAD-PDMS, and 0.90 m³ ± 0.6 day⁻¹dm⁻² for partially sheltered PUF. These values were derived based on the compound-specific sampling rates measured here and in the literature for the PAS tested, to reasonably account for site-specific variability of sampling rates.PDMS and PUF were co-deployed for three weeks in 51 homes located in Ottawa and Toronto, Canada. Duplicate PUF and PDMS samplers gave concentrations within 10% of each other. PDMS and PUF-derived air concentrations were not statistically different for gas-phase compounds. PUF had a higher detection of particle-phase compounds such as some OPEs. Phthalate and OPE air concentrations were ∼100 times higher than those of N-BFRs and PBDEs. Concentrations were not systematically related to PM₁₀, temperature or relative humidity.We conclude that both PAS provide replicable estimates of indoor concentrations of these targeted semi-volatile organic compounds (SVOCs) over a three-week deployment period. However, PUF is advantageous for collecting a wider range of compounds including those in the particle phase.

An integrative passive sampler consisting of a C18 Empore® disk receiving phase saturated with n-octanol and fitted with low-density polyethylene diffusion membrane was calibrated for the measurement of time-weighted average concentrations of hydrophobic micropollutants, including polyaromatic hydrocarbons and organochlorine pesticides, in water. The effect of temperature and water turbulence on kinetic and thermodynamic parameters characterising the exchange of analytes between the sampler and water was studied in a flow-through system under controlled conditions. It was found that the absorption of test analytes from water to the sampler is related to their desorption to water. This allows for the in situ calibration of the uptake of pollutants using offload kinetics of performance reference compounds. The sampling kinetics are dependent on temperature, and for most of the tested analytes also on the flow velocity. Sampler–water partition coefficients did not significantly change with temperature.

Carbon sequestration in agricultural soils is controlled by the balance of added organic residues and microbial oxidation of both residues and native organic matter (OM) as moderated by management and tillage. The PC-based model CQESTR predicts decomposition of residues, organic amendments and soil OM, based on cropping practices. CQESTR uses RUSLE (Revised Universal Soil Loss Equation) crop rotation and management practice, crop production, and operation databases. These data are supplemented with residue nitrogen and soil OM, bulk density, and layer thickness. CQESTR was calibrated with soil carbon data from 70-year-long experiments at the Research Center at Pendleton, OR. The calibrated model provides estimates with a 95% confidence interval of 0.33% OM. Validation at 11 independent sites resulted in a matching of observed with calculated OM with a 95% confidence interval of 0.55% OM. A 12th site, with a history of severe erosion, provided a poor match.

Hexa-mix-chlorinated/brominated benzenes (HXBs), a group of newly found analogues of hexachlorobenzene (HCB) and hexabromobenzene (HBB), may exhibit similar environmental risks and toxicities as HCB and HBB, and therefore possess high interests in environmental and toxicological research. Yet information regarding HXBs in the environment remains scarce. In this study, we developed an isotope dilution method for quantitative and semiquantitative determination of five HXBs in fly ash, soil and air using gas chromatography high resolution mass spectrometry (GC-HRMS) in multiple ion detection mode. The samples were Soxhlet-extracted and purified with multilayer composite silica gel-alumina columns, followed by GC-HRMS detection. Identification of HXBs was conducted by the comparison between theoretical and detected mass spectra using paired-samples T test and cosine similarity analysis. Two HXBs (C₆BrCl₅ and C₆Br₄Cl₂) with reference standards were quantitatively determined while the rest three (C₆Br₂Cl₄, C₆Br₃Cl₃ and C₆Br₅Cl) without reference standards were semiquantitatively analyzed by sharing the calibration curves of C₆BrCl₅ and C₆Br₄Cl₂ in cooperation with isotopologue distribution computation. The accuracies for C₆BrCl₅ and C₆Br₄Cl₂ were 87.3–107.8% with relative standard deviations (RSD) of 2.8–5.0%. The method limits of quantification of the HXBs were 0.10 ng/g in fly ash and soil samples and 0.09 pg/m³ in ambient air samples. The recoveries ranged from 42.7% to 102.1% with RSD of 3.7–13.9%. This method has been successfully applied to the analysis of the HXBs in the environmental samples. The total concentrations of HXBs in the fly ash, soil and ambient air samples were 19.48 ng/g, 10.44 ng/g and 5.13 pg/m³, respectively, which accounted for 10.6%, 0.4% and 10.8% of the corresponding total concentrations of HCB and HBB. This study provides a reference method for quantitative and/or semiquantitative analyses of novel mix-halogenated organic compounds, and sheds light on the full picture of HXBs pollution in the environment.

A recently developed modified polar organic chemical integrative sampler (POCIS) provides a means for monitoring perfluorinated chemicals (PFCs) in water. However, changes in external flow rates may alter POCIS sampling behaviour and consequently affect estimated water concentrations of analytes. In this work, uptake kinetics of selected PFCs, over 15 days, were investigated. A flow-through channel system was employed with spiked river water at flow rates between 0.02 and 0.34 m s−1. PFC sampling rates (Rs) (0.09–0.29 L d−1 depending on analyte and flow rate) increased from the lowest to highest flow rate employed for some PFCs (MW ≤ 464) but not for others (MW ≥ 500). Rs's for some of these smaller PFCs were increasingly less sensitive to flow rate as this increased within the range investigated. This device shows promise as a sampling tool to support monitoring efforts for PFCs in a range of flow rate conditions.

The Polar Organic Chemical Integrative Sampler (POCIS) is a new tool for the sampling of organic pollutants in water. We tested this device for the monitoring of pharmaceuticals in hospital wastewater. After calibration, a field application was carried out in a French hospital for six pharmaceutical compounds (Atenolol, Prednisolone, Methylprednisolone, Sulfamethoxazole, Ofloxacin, Ketoprofen).POCIS were calibrated in tap water and wastewater in laboratory conditions close to relevant environmental conditions (temperature, flow velocity). Sampling rates (Rs) were determined and we observed a significant increase with flow velocity and temperature. Whatever the compound, the Rs value was lower in wastewater and the linear phase of uptake was shorter.POCIS were deployed in a hospital sewage pipe during four days and the estimated water concentrations were close to those obtained with twenty-four hour composite samples.

Triolein embedded cellulose acetate membrane (TECAM) passive samplers provide potentially powerful tool for monitoring time weighted average concentrations (CTWA) of trace hydrophobic organic contaminants in water. To study the field performance of TECAM, exchange kinetics of chemicals between water and TECAM were studied at different temperature and water flow rates. Results showed that the uptake rate constant (kᵤ) in TECAM was less sensitive to temperature variation than the SPMD and Chemcatcher. The kᵤ in TECAM was sensitive to even a slight change of the flow rate, which required the field calibration using performance reference compounds (PRCs). To estimate CTWA by TECAM, both empirical model and WBL model were compared in laboratory conditions, and only small differences were observed between the predicted and measured kᵤ. Field validation was conducted to test the sampler performance alongside spot sampling. A good agreement of water concentration was obtained by the two sampling techniques.

Air–soil exchange is an important process governing the fate of polycyclic aromatic hydrocarbons (PAHs). A novel passive air sampler was designed and tested for measuring the vertical concentration profile of 4 low molecular weight PAHs in gaseous phase (PAHLMW₄) in near soil surface air. Air at various heights from 5 to 520 mm above the ground was sampled by polyurethane foam disks held in down-faced cartridges. The samplers were tested at three sites: A: an extremely contaminated site, B: a site near A, and C: a background site on a university campus. Vertical concentration gradients were revealed for PAHLMW₄ within a thin layer close to soil surface at the three sites. PAH concentrations either decreased (Site A) or increased (Sites B and C) with height, suggesting either deposition to or evaporation from soils. The sampler is a useful tool for investigating air–soil exchange of gaseous phase semi-volatile organic chemicals.

Widespread soil contamination threatens living standards and weakens global efforts towards the Sustainable Development Goals (SDGs). Detailed soil mapping is needed to guide effective countermeasures and sustainable remediation operations. Here, we review visible and infrared reflectance spectroscopy (VIRS) based detection methods in combination with machine learning. To date, proximal, airborne and spaceborne carrier devices have been employed for soil contamination detection, allowing large areas to be covered at low cost and with minimal secondary environmental impact. In this way, soil contaminants can be monitored remotely, either directly or through correlation with soil components (e.g. Fe-oxides, soil organic matter, clay minerals). Observed vegetation reflectance spectra has also been proven an effective indicator for mapping soil pollution. Calibration models based on machine learning are used to interpret spectral data and predict soil contamination levels. The algorithms used for this include partial least squares regression, neural networks, and random forest. The processes underlying each of these approaches are outlined in this review. Finally, current challenges and future research directions are explored and discussed.