Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors – random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Single-chemical thresholds cannot comprehensively evaluate the risk of chemical mixture exposure in indoor air. Moreover, a large number of researches have focused on short-term and high-concentration co-exposure scenarios related to different species, based on diverse endpoints, which hampers the application and improvement of existing risk evaluation models of chemical mixture exposures. More importantly, current risk evaluation models are not user-friendly for construction practitioners who do not have sufficient toxicological knowledge. Therefore, in this study, an inhalation experiment system and a hazard index (HI) were developed to investigate the risks associated with low-concentration and long-term inhalation exposure scenarios of formaldehyde and benzene, individually and combined, based on Drosophila melanogaster mortality. The results showed that the system exhibited good reproducibility in providing stable exposure concentrations during D. melanogaster life cycle. Furthermore, in a range of experimental concentrations, the interaction between formaldehyde and benzene was additive or synergistic, which was concentration- and ratio-dependent. This study is of great significance in harmonising and providing toxicity data under long-term and low-concentration exposure scenarios, which is beneficial for establishing a new user-friendly risk evaluation model for indoor chemical mixture exposures. It should be noted that the proposed HI value could indicate the hazard degrees of long-term inhalation exposures of formaldehyde and benzene, individually and combined, to D. melanogaster. However, the applicability of this index requires further experiments to evaluate the exposure risks of other volatile organic compounds (VOCs) to D. melanogaster.

Synthetic microfibers have been identified as the most prevalent type of microplastic in samples from aquatic, atmospheric, and terrestrial environments across the globe. Apparel washing has shown to be a major source of microfiber pollution. We used California as a case study to estimate the magnitude and fate of microfiber emissions, and to evaluate potential mitigation approaches. First, we quantified synthetic microfiber emissions and fate from apparel washing in California by developing a material flow model which connects California-specific data on synthetic fiber consumption, apparel washing, microfiber generation, and wastewater and biosolid management practices. Next, we used the model to assess the effectiveness of different interventions to reduce microfiber emissions to natural environments. We estimate that in 2019 as much as 2.2 kilotons (kt) of synthetic microfibers were generated by apparel washing in California, a 26% increase since 2008. The majority entered terrestrial environments (1.6 kt), followed by landfills (0.4 kt), waterbodies (0.1 kt), and incineration (0.1 kt). California's wastewater treatment network was estimated to divert 95% of microfibers from waterbodies, mainly to terrestrial environments and primarily via land application of biosolids. Our analysis also reveals that application of biosolids on agricultural lands facilitates a directional flow of microfibers from higher-income urban counties to lower-income rural communities. Without interventions, annual synthetic microfiber emissions to California's natural environments are expected to increase by 17% to 2.1 kt by 2026. Further increasing the microfiber retention efficiency at the wastewater treatment plant would increase emissions to terrestrial environments, which suggests that microfibers should be removed before entering the wastewater system. In our model, full adoption of in-line filters in washing machines decreased annual synthetic microfiber emissions to natural environments by 79% to 0.5 kt and offered the largest reduction of all modeled scenarios.

With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.

Mine waste classification preceding mining constitutes a proactive solution to classify and segregate mine waste into geo-environmental domains based upon the magnitude of their environmental risks. However, upstream classification requires multi-disciplinary and integrated approaches. This study integrates geological modeling and kinetic modeling to inform upstream mine waste classification based on the pH generated from the main acid-generating and acid-neutralizing reactions once the mine solid waste is stored in oxidizing conditions. Geological models were used to depict the ante-mining spatial distribution of the main reactive minerals: pyrite, albite and calcite. Subsequently, the corresponding block models were created. The dimension of the elementary voxels for each block model was set at 40х40х40 m for this study. The kinetic modeling approach was performed using PHREEQC and VS2DRTI to consider unsaturated conditions. The kinetic modeling simulated a 1D column for each voxel. The column simulates the excavated state of the hosting rock involving kinetic reactions and unsaturated flow under highly oxidizing conditions. Subsequently, the resulting pH for different intervals of time was assigned to its respective voxel. The outcome consists of a spatio-temporal visualization of the pH defining ante-mining geo-environmental domains, thereby providing the opportunity for formulating proactive management measures regarding the hazardous geo-environmental domains.

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.