One of the best approaches for improving the assessment of metal toxicity in aquatic organisms is to study their organotropism (i.e., the distribution of metals among organs) through a dynamical approach (i.e., via kinetic experiments of metal bioaccumulation), to identify the tissues/organs that play a key role in metal regulation (e.g., storage or excretion). This study aims at comparing the organ-specific metal accumulation of a non-essential (Cd) and an essential metal (Zn), at their environmentally relevant exposure concentrations, in the gammarid Gammarus fossarum. Gammarids were exposed for 7 days to ¹⁰⁹Cd- or ⁶⁵Zn-radiolabeled water at a concentration of 52.1 and 416 ng.L⁻¹ (stable equivalent), respectively, and then placed in clean water for 21 days. At different time intervals, the target organs (i.e., caeca, cephalons, intestines, gills, and remaining tissues) were collected and ¹⁰⁹Cd or ⁶⁵Zn contents were quantified by gamma-spectrometry. A one-compartment toxicokinetic (TK) model was fitted by Bayesian inference to each organ/metal dataset in order to establish TK parameters. Our results indicate: i) a contrasting distribution pattern of concentrations at the end of the accumulation phase (7ᵗʰ day): gills > caeca ≈ intestines > cephalons > remaining tissues for Cd and intestines > caeca > gills > cephalons > remaining tissues for Zn; ii) a slower elimination of Cd than of Zn by all organs, especially in the gills in which the Cd concentration remained constant during the 21-day depuration phase, whereas Zn concentrations decreased sharply in all organs after 24 h in the depuration phase; iii) a major role of intestines in the uptake of waterborne Cd and Zn at environmentally relevant concentrations.

Plastics are produced and used in large quantities worldwide (e.g. as food packaging). In line with this, plastic particles are found throughout the ecosphere and in various foods. As a result, plastics are also present in energy-rich waste biomass derived from the food industry, supermarkets, restaurants, etc. These waste streams are a valuable source for biogas production but can also be used to feed insects that in turn upcycle it into new high-value biomass. In both applications, the remaining residue can be used as fertilizer. Due to the present plastic particles, these applications could pose a continued threat to the environment, and both human and animal health. Therefore, the need of determining the (micro)plastic content to assess the potential danger is rising. In this research, a closed-vessel microwave-assisted acid digestion method was developed to accurately determine meso- and microplastic contents in food (waste) matrices by solubilising this food matrix. Polyvinyl chloride (PVC) food packaging foil was used to develop the method, using a full factorial design with three parameters (nitric acid concentration (c(HNO₃)), temperature (T), and time (t)). According to this model, the best practical conditions were c(HNO₃) = 0.50 mol/L, T = 170 °C, and t = 5.00 min. Subsequently, the method was tested on five other plastics, namely high- and low-density polyethylene (HDPE and LDPE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET), mixed with a food matrix, resulting in a mean plastic recovery of 102.2 ± 4.1%. Additionally, the polymers were not oxidised during the microwave digestion. For PVC and PS hardly any degradation was found, while HDPE, LDPE, and PP showed slight chain degradation, although without recovery loss. In conclusion, the method is an accurate approach to quantify the total meso- and microplastic content in food (waste) matrices with minimal change in their intrinsic characteristics.

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM₂.₅) in high-density cities, few studies have revealed the vertical differences in PM₂.₅ exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM₂.₅ exposure that combines a high-resolution LUR model with a vertical PM₂.₅ variation model to investigate the results of horizontal and vertical mobile PM₂.₅ monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM₂.₅ variation, respectively. Vertical measurements of PM₂.₅ from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM₂.₅ changes at a certain height relative to the ground. The vertical variation model for PM₂.₅ was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM₂.₅ exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM₂.₅, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Multiwalled carbon nanotubes (MWCNTs) were oxidized using a mixture of H₂SO₄ and HNO₃, and the oxidized MWCNTS were decorated with magnetite (Fe₃O₄). Finally, poly-N-isopropyl acrylamide-co-butyl acrylate (P-NIPAM) was added to obtain P-NIPAM/Fe/MWCNT nanocomposites. The nanosorbents were characterized by various techniques, including X-ray diffraction, transmission electron microscopy, scanning electron microscopy, thermogravimetric analysis, and Brunauer–Emmett–Teller analysis. The P-NIPAM/Fe/MWCNT nanocomposites exhibited increased surface hydrophobicity. Owing to their higher adsorption capacity, their kerosene removal efficiency was 95%; by contrast, the as-prepared, oxidized, and magnetite-decorated MWCNTs had removal efficiencies of 45%, 55%, and 68%, respectively. The P-NIPAM/Fe/MWCNT nanocomposites exhibited a sorbent capacity of 8.1 g/g for kerosene removal from water. The highest kerosene removal efficiency from water was obtained at a process time of 45 min, sorbent dose of 0.005 g, solution temperature of 40 °C, and pH 3.5. The P-NIPAM/Fe/MWCNTs showed excellent stability after four cycles of kerosene removal from water followed by regeneration. The reason may be the increase in the positive charge of the polymer at pH 3.5 and the increased adsorption affinity of the adsorbent toward the kerosene contaminant. The pseudo second-order model was found to be the most suitable model for studying the kinetics of the adsorption reaction.

To better evaluate the potential risks of aquatic polycyclic aromatic compounds (PACs), multiple approaches have been implemented in this study to assess the human health and ecological risks of parent, nitrated and oxygenated polycyclic aromatic hydrocarbons (PAHs, NPAHs and OPAHs) in the surface water of Jialing and Yangtze Rivers in downtown Chongqing in southwestern China. The concentrations of ∑PAHs (334 ± 125 ng L⁻¹) were much higher than those of ∑OPAHs (20.2 ± 7.49 ng L⁻¹) in the two rivers, while NPAHs were barely detected. Concentrations of detected PACs were higher in wet season than dry season, probably resulted from the elevated particle input due to heavy rainfall in wet season. Concentrations of PAHs were higher in the particulate phase than dissolved phase, while OPAHs levels showed a reverse pattern. The partition coefficients (Kₚ) of PACs in the water-SPM (suspended particulate matter) system were mainly affected by SPM concentrations and octanol/water partition coefficients of specific PACs. Human health risks calculated from non-probabilistic risk assessment model and probabilistic risk assessment model based on Monte Carlo simulation showed similar data pattern with slight difference in absolute values. Both models revealed potential or even severe human health risks contributed mainly by dermal exposure to aquatic PACs in this study. Furthermore, these models also manifested that infant stage was highly sensitive for PAC exposure. Sensitivity analysis indicated that health risk results was most sensitive to Benzo[a]pyrene equivalent toxic concentration (BaPₑq), followed by showering time and daily water intake volume. Levels of ecological risks and contributions of individual PACs differed from models based on different quality values. The adequacy of toxicity data was crucial for the reliability of ecological risk assessment.

We investigated the competitive effects of different fractions of wastewater treatment plant effluent organic matter (EfOM) on adsorption of an organic micro pollutant (OMP), propranolol (PRO), in a fixed bed column packed with magnetic tyre char (MTC). The results showed that the presence of EfOM inhibited PRO adsorption in wastewater leading to decreased PRO adsorption capacity from 5.86 to 2.03 mg/g due to competitive effects and pore blockage by smaller EfOM fractions. Characterization of EfOM using size exclusion chromatography (LC-OCD) showed that the principal factor controlling EfOM adsorption was pore size distribution. Low molecular weight neutrals had the highest adsorption onto MTC while humic substances were the least interfering fraction. Effect of important parameters such as contact time, linear velocity and bed height/diameter ratio on MTC performance was studied in large-lab scale columns. Linear velocity and contact time were found to be effective in increasing adsorption capacity of PRO on MTC and delaying breakthrough time. Increase in linear velocity from 0.64 cm/min to 1.29 cm/min increased mass transfer and dispersion, resulting in considerable rise of adsorbed amount (5.86 mg/g to 22.58 mg/g) and increase in breakthrough time (15.8–62.7 h). Efficiency of non-equilibrium Hydrus model considering dispersion and mass transfer mechanism was demonstrated for real wastewater and scale up purposes. Ball milling for degradation of adsorbed PRO and regeneration of MTC resulted in 79% degradation of PRO was achieved after 5 h milling (550 rpm), while the addition of quartz sand increased the efficiency to 92%.

The objective of this study was to demonstrate the feasibility and the relevance of combining biochar with the Fenton process for the simultaneous improvement of polycyclic aromatic hydrocarbons (PAHs) degradation and immobilization of heavy metals (HMs) in real soil remediation processes at circumneutral pH. The evaluation of PAHs degradation results was performed through multivariate statistical tools, including principal component analysis (PCA) and partial least squares (PLS). PCA showed that the level of biochar amendment decisively affected the degree of degradation of total PAHs, highlighting the role of biochar in catalyzing the Fenton reaction. Moreover, the PLS model was used to interpret the important features of each PAH's physico-chemical properties and its correlation to degradation efficiency. The electron affinity of PAHs correlated positively with the degradation efficiency only if the level of biochar amendment sat at 5%, explained by the ability of biochar to transfer the electrons to PAHs, improving the Fenton-like degradation. Moreover, the addition of biochar reduced the mobilization of HMs by their fixation on their surface, reducing the Fenton-induced metal leaching from the destruction of metal-organic complexes. In overall, these results on the high immobilization rate of HMs accompanied with additional moderate PAHs degradation highlighted the advantages of using a biochar-assisted Fenton-like reaction for sustainable remediation of technogenic soil.

The volatilities of ambient organic aerosol (OA) components are important to forecasting OA formation with models. However, providing the OA volatility distribution inputs for models is challenging, and models often rely on measurements from chamber experiments. We measured the volatility of submicron ambient OA in Seoul during May/June of 2019 by connecting a thermodenuder to an Aerodyne Time-of-Flight Aerosol Mass Spectrometer (AMS). We calculated a volatility basis set (VBS) of the organic aerosol with a thermodenuder mass transfer model and data from the thermodenuder set to various temperatures (30–200 °C). We found a large discrepancy between the measured ambient VBS and a reference VBS used in air quality models, with the ambient organics being less volatile. The results suggest that a modeling study that tries to account for this discrepancy may be needed to identify the impact it has on modeling outcomes. Chamber experiments aiming to determine VBSs for specific chemical systems should address limitations caused by wall losses and incomplete modeling parameters.

Particulate Matter (PM₂.₅) pollution in China has been a primary concern for public health in recent years, which requires banks to appropriately control their credit supply to industries with high pollution, high energy consumption, and surplus capacity. For this reason, this paper examines economic determinants of PM₂.₅ concentrations and incorporates the spatial spillover effect of bank credit by employing the spatial Durbin model (SDM) under the stochastic impacts by regression on population, affluence and technology framework. Using China's provincial dataset from 1998 to 2016, the main findings are as follows: First, there is evidence in support of spatial dependence of PM₂.₅ concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM₂.₅ concentrations in a province tend to increase as the level of its own urbanization increases, but they decrease as its own human capital and bank credit increase. Meanwhile, the level of neighboring urbanization positively influences a province's PM₂.₅ concentrations, whereas neighboring population size, industrialization, trade openness, and bank credit present negative impacts. Third, indirect effects of the SDM indicate significant and negative spatial spillover effect of bank credit on PM₂.₅ concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM₂.₅ pollution in China from a cross-provincial collaboration perspective.

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.