Plant-based products such as essential oils and other extracts have been used for centuries due to their beneficial properties. Currently, their use is widely disseminated through a variety of industries and new applications are continuously emerging. For these reasons, they are produced industrially in large quantities and consequently they have the potential to reach the environment. However, the potential effects that these products have on the ecosystems’ health are mostly unknown. In recent years, the scientific community started to focus on the possible toxic effects of essential oils and plant extracts towards non-target organisms. As a result, an increasing body of knowledge has emerged. This review describes the current state of the art on the toxic effects that essential oils and plant extracts have towards organisms from different trophic levels, including producers, primary consumers, and secondary consumers. The majority of the studies (76.5%) focuses on the aquatic environment, particularly in aquatic invertebrates (45.1%) with only 23.5% of the studies focusing on the potential toxicity of plant-derived products on terrestrial ecosystems.While some essential oils and extracts have been described to have no toxic effects to the selected organisms or the toxic effects were only observable at high concentrations, others were reported to be toxic at concentrations below the limit set by international regulations, some of them at very low concentrations. In fact, L(E)C₅₀ values as low as 0.0336 mg.L⁻¹, 0.0005 mg.L⁻¹ and 0.0053 mg.L⁻¹ were described for microalgae, crustaceans and fish, respectively. Generally, essential oils exhibit higher toxicity than extracts. However, when the extracts are obtained from plants that are known to produce toxic metabolites, the extracts can be more toxic than essential oils.Overall, and despite being generally considered “eco-friendly” products and safer than they synthetic counterparts, some essential oils and plant extracts are toxic towards non-target organisms. Given the increasing interest from industry on these plant-based products further research using international standardized protocols is mandatory.

Hydroxyapatite (HAP) can effectively immobilize soil heavy metals, but excess phosphate would be released to aquatic ecosystem, resulting in eutrophication. This study investigated the effects of ferrihydrite (FH) on the HAP immobilization of copper (Cu) and cadmium (Cd) and their reduction of phosphorus release under flooding-drainage alternation conditions. Results showed that the incorporation of HAP and FH significantly increased soil solution pH and decreased Cu²⁺ and Cd²⁺ concentrations. Applications of FH, HAP, and FH-HAP (FH and HAP combination) can all enhance soil pH and reduce CaCl₂-extractable and exchangeable Cu and Cd, but HAP addition increased soluble phosphate by 6.60–7.77 times compared to control. However, FH-HAP application can significantly reduce phosphate release by 92.7–99.7% compared to HAP application. FH-HAP was the most effective to reduce exchangeable Cu and Cd by 49.8–93.4% and 50.9–88.8% and decreased labile and moderately labile phosphorus by 34.0–74.4% and 13.5–18.6%, respectively, while increased stable phosphorus by 22–45.1% than single HAP. All FH treatments significantly increased amorphous iron oxides by the factors of 4.66–20.8, but only 3% and 5% of FH applications slightly enhanced crystal iron oxides by the factors of 0.81–1.27. The major implication is that the combination of FH and HAP can not only immobilize of Cu and Cd, but also reduce the risk of phosphate release by HAP addition.

Freshwater ecosystems are recognized as non-negligible sources of plastic contamination for the marine environment that is the final acceptor of 53 thousand tons of plastic per year. In this context, microplastic particles are well known to directly pose a great threat to freshwater organisms, they also indirectly affect the aquatic ecosystem by adsorbing and acting as a vector for the transport of other pollutants (“Trojan horse effect”). Polystyrene is one of the most widely produced plastics on a global scale, and it is among the most abundant microplastic particles found in freshwaters. Nevertheless, to date few studies have focused on the eco-genotoxic effects on freshwater organisms caused by polystyrene microplastic particles (PS-MPs) in combination with other pollutants such as pharmaceuticals and pesticides. The aim of this study is to investigate chronic and sub-chronic effects of the microplastic polystyrene beads (PS-MP, 1.0 μm) both as individual xenobiotic and in combination (binary/ternary mixtures) with the acicloguanosine antiviral drug acyclovir (AC), and the neonicotinoid broad-spectrum insecticide imidacloprid (IMD) in one of the most sensitive non-target organisms of the freshwater food chain: the cladoceran crustacean Ceriodaphnia dubia. Considering that the individually selected xenobiotics have different modes of action and/or different biological sites, the Bliss independence was used as reference model for this research. Basically, when C. dubia neonates were exposed for 24 h to the mixtures during Comet assay, mostly an antagonistic genotoxic effect was observed. When neonates were exposed to the mixtures for 7 days, mostly an additive chronic toxic effect occurred at concentrations very close or even overlapping to the environmental ones ranging from units to tens of ng/L for PS-MPs, from tenths/hundredths to units of μg/L for AC and from units to hundreds of μg/L for IMD, revealing great environmental concern.

Air pollution remains the major environmental problem globally. There is extensive evidence showing that the variety of air pollutants from environmental and occupational exposures cause adverse effects to our health. The clinical symptoms of those effects may present at a late stage, so surveillance is difficult to manage. Several biomarkers have been used for the early detection of health issues following exposure to air pollution, including the use of telomere length which indicates cellular senescence in response to oxidative stress. Oxidative stress is one of the most plausible mechanisms associated with exposure to air pollutants. Some specific contexts including age groups, gender, ethnicity, occupations, and health conditions, showed significant alterations in telomere length after exposure to air pollutants. Several reports demonstrated both negative and positive associations between telomere length and air pollution, the studies using different concentrations and exposure times to air pollution on the study of telomere lengths. Surprisingly, some studies reported that low levels of exposure to air pollutants (lower than regulated levels) caused the alterations in telomere length. Those findings suggest that telomere length could be one of most practical biomarkers in air pollution surveillance. Therefore, this review aimed to summarize and discuss the relationship between telomere length and exposure to air pollution. The knowledge from this review will be beneficial for the planning of public health to reduce health problems in the general population, particularly in vulnerable people, who still live in areas with high air pollution.

Vehicle emissions are an important source of nitrated aromatic compounds (NACs) in particulate size smaller 2.5 μm (PM₂.₅), which adversely affect human health and biodiversity, especially in urban areas. In this study, filter-based PM₂.₅ samples were collected during October 14–19, 2019, in a busy urban tunnel (approximately 35,000 vehicles per day) in south China to identify PM₂.₅-bound NACs. Among them, 2,8-dinitrodibenzothiophene, 3-nitrodibenzofuran and 2-nitrodibenzothiophene were the most abundant nitrated polycyclic aromatic hydrocarbons (NPAHs), while 2-methyl-4-nitrophenol, 2,4-dinitrophenol, 3-methyl-4-nitrophenol and 4-nitrophenol were the most abundant nitrophenols (NPs). The observed mean fleet emission factors (EFs) of NPAHs and NPs were 2.2 ± 2.1 and 7.7 ± 4.1 μg km⁻¹, and were 2.9 ± 2.7 and 10.2 ± 5.4 μg km⁻¹ if excluding electric and liquefied petroleum gas vehicles, respectively. Regression analysis revealed that diesel vehicles (DVs) had NPAH-EFs (55.3 ± 5.3 μg km⁻¹) approximately 180 times higher than gasoline vehicles (GVs) (0.3 ± 0.2 μg km⁻¹), and NP-EFs (120.6 ± 25.8 μg km⁻¹) approximately 30 times higher than GVs (4.1 ± 0.2 μg km⁻¹), and thus 89% NPAH emissions and 56% NP emissions from the onroad fleets were contributed by DVs although DVs only accounted for 3.3% in the fleets. Methanol solution-based light absorption measurements demonstrated that the mean incremental light absorption for methanol-soluble brown carbon at 365 nm was 6.8 ± 2.2 Mm⁻¹, of which the 44 detected NACs only contributed about 1%. The mean EF of the 7 toxic NACs was approximately 3% that of the 16 priority PAHs; However, their benzo(a)pyrene toxic equivalence quotients (TEQBₐP) could reach over 25% that of the PAHs. Moreover, 6-nitrochrysene mainly from DVs contributed 93% of the total TEQBₐP of the NACs. This study demonstrated that enhancing DV emission control in urban areas could benefit the reduction of exposure to air toxins such as 6-nitrochrysene.

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Solanum nigrum L. is a Cd hyperaccumulator, but the potential for continuous remediation, or different planting methods have not been fully characterized. The potential for continuous phytoremediation of Cd-contaminated farmland soil (2.08 mg kg⁻¹ Cd) by 2 planting methods (flowering harvest twice a year and maturity harvest once a year) was studied in a 3-year pot experiment. The total Cd accumulation (ug plant⁻¹) of the 3-year flowering stage treatments was 26.3% higher than that of the maturity stage treatments, which was mainly due to that flowering harvest twice a year caused 65.5% increase of shoot biomass. Similarly, the Cd decreased concentration in soil and Cd removal rate in the flowering stage treatments were 29.2% and 27.9% higher than that in the maturity stage treatments, respectively. After 3 years of phytoremediation, the extractable Cd concentration in soil was reduced by 36.4% in the flowering stage treatments and by 27.6% in the maturity stage treatments, which also led to the same decreasing trend of Cd accumulation of S. nigrum. In conclusion, the study results have demonstrated that the planting mode of two harvests a year at the flowering stage seems to be a viable option to apply for continuous phytoremediation of Cd-contaminated farmland soil.

More than 20 years after the Water Framework Directive was adopted, there are still major gaps in the sanitary status of small rivers and waterbodies at the head of basins. These small streams supply water to a large number of wetlands that support a rich biodiversity. Many of these waterbodies are fishponds whose production is destined for human consumption or for the restocking of other aquatic environments. However, these ecosystems are exposed to contaminants, including pesticides and their transformation products. This work aims to provide information on the distribution, diversity, and concentrations of agricultural contaminants in abiotic and biotic compartments from a fishpond located at the head of watersheds. A total of 20 pesticides and 20 transformation products were analyzed by HPLC-ESI-MS/MS in water and sediment sampled monthly throughout a fish production cycle, and in three fish species at the beginning and end of the cycle.The highest mean concentrations were found for metazachlor-OXA (519.48 ± 56.52 ng.L⁻¹) in water and benzamide (4.23 ± 0.17 ng g⁻¹ dry wt.) in sediment. Up to 20 contaminants were detected per water sample and 26 per sediment sample. The transformation products of atrazine (banned in Europe since 2003 but still widely used in other parts of the world), flufenacet, imidacloprid (banned in France since 2018), metazachlor, and metolachlor were more concentrated than their parent compounds. Fewer contaminants were detected in fish and principally prosulfocarb accumulated in organisms during the cycle.Our work brings innovative data on the contamination of small waterbodies located at the head of a basin. The transformation products with the highest frequency of occurrence and concentrations should be prioritized for further environmental monitoring studies, and specific toxicity thresholds should be defined. Few contaminants were found in fish, but the results challenge the widely use of prosulfocarb.

Urbanization has destroyed river ecosystems, leading to eutrophication. Heavy metals are frequently observed in urban rivers, and the joint effects of eutrophication and heavy metals on microbial communities, especially on fungal communities, have not been adequately explored. In this study, we explored the effect of sediment physicochemical factors and heavy metals on the microbial diversity, community structure, and functions of bacterial and fungal communities from a black-odorous river in Wuhu, China. Twenty-four samples were collected, and the diversity and structure of fungal and bacterial communities were determined by high-throughput sequencing. Proteobacteria and Rozellomycota were the main phyla in the bacterial and fungal communities, respectively. The results showed different distribution patterns of bacterial and fungal communities along the river. Physicochemical factors and heavy metals exhibited different effects on microbial variation. Specifically, pH and Cr negatively affected bacterial α-diversity, whereas total phosphorus and Cr significantly affected fungal α-diversity. Variance partitioning analysis revealed that physicochemical factors explained more of the bacterial community structure than heavy metals (49.5% vs. 36.6%), with pH and total phosphorus being the dominant factors. Opposite patterns were observed for fungal community structure, with heavy metals contributing the most (48.0%). A similar influence pattern was observed for the predicted functions of the two communities. This study suggests that heavy metals in eutrophication rivers are essential factors that shift the microbial variation and should be considered in urban river evaluation and remediation.

Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition, fuzzy C-means clustering and bidirectional gated recurrent unit. Firstly, the original water quality data is decomposed into several subseries by empirical wavelet transform, and then, the decomposed subseries are recombined by fuzzy C-means clustering. Next, for each clustered series, bidirectional gated recurrent unit is applied to develop prediction model. Finally, the forecast result is obtained by the summation of the predictions for the subseries. The proposed forecast model is evaluated by the water quality data of Poyang Lake, China. Results show that the proposed forecast model provides highly accurate forecast result for all of the six water quality data: the average of MAPE of the forecast results for the six water quality datasets is 4.59% for 7 day ahead prediction. Furthermore, our model shows better forecast performance than the other models. Particularly, compared with the single BiGRU model, MAPE decreased by 32.86% in average. Results demonstrate that the proposed forecast model can be used effectively for water quality forecasting.