Monitoring beach plastic contamination across space and time is necessary for understanding its sources and ecological effects, and for guiding mitigation. This is logistically and financially challenging, especially for microplastics. Citizen science represents an option for sampling accessible sites to support long term monitoring, but challenges persist around data validation. Here we test a simple citizen science methodology to monitor visible microplastic contamination on sandy beaches using a standard quadrat unit (50 cm × 50 cm x 5 cm depth) sieved to 1 mm, to support the analysis of microplastic on two islands within the marine protected area of the Galápagos Archipelago, Ecuador (San Cristóbal and Santa Cruz islands). High school and university students undertook supervised sampling of two beaches in 2019–2020 collecting over 7000 particles. A sub-sample of the suspected microplastics collected (n = 2,213, ∼30% total) were analysed using FTIR spectrometry, confirming 93% of particles >1 mm visually identified by students were microplastics or rubber, validating this method as a crowd-sourced indicator for microplastic contamination. These data provide important insights into the plastic contamination of Galápagos, revealing plastic abundances of 0–2524 particles m⁻² over the two beaches (the highest reported in Galápagos). Strong accumulation gradients were measured parallel to the waterline at Punta Pitt (San Cristobal island) and perpendicular to the waterline at Tortuga Bay (Santa Cruz island), where four-fold higher concentrations were recorded at the sea turtle nesting habitat on the back-beach dune. No significant seasonal trends were measured during one year. These results demonstrate the value of citizen science in filling spatiotemporal knowledge gaps of beach contamination to support intervention design and conservation.

Polycyclic aromatic hydrocarbons (PAHs) constitute a significant environmental pollution group that reaches toxic levels with anthropogenic activities. The adverse effects of nanoplastics accumulating in ecosystems with the degradation of plastic wastes are also a growing concern. Previous studies have generally focused on the impact of single PAH or plastic fragments exposure on plants. However, it is well recognized that these contaminants co-exist at varying rates in agricultural soil and water resources. Therefore, it is critical to elucidate the phytotoxicity and interaction mechanisms of mixed pollutants. The current study was designed to comparatively investigate the single and combined effects of anthracene (ANT, 100 mg L⁻¹), fluorene (FLU, 100 mg L⁻¹) and polystyrene nanoplastics (PS, 100 mg L⁻¹) contaminations in wheat. Plants exposed to single ANT, FLU and PS treatments demonstrated decline in growth, water content, high stomatal limitations and oxidative damage. The effect of ANT + FLU on these parameters was more detrimental. In addition, ANT and/or FLU treatments significantly suppressed photosynthetic capacity as determined by carbon assimilation rate (A) and chlorophyll a fluorescence transient. The antioxidant system was not fully activated (decreased superoxide dismutase, peroxidase and glutathione reductase) under ANT + FLU, then hydrogen peroxide (H₂O₂) content (by 2.7-fold) and thiobarbituric acid reactive substances (TBARS) (by 2.8-fold) increased. Interestingly, ANT + PS and FLU + PS improved the growth, water relations and gas exchange parameters. The presence of nanoplastics recovered the adverse effects of ANT and FLU on growth by protecting the photosynthetic photochemistry and reducing oxidative stress. PAH plus PS reduced the ANT and FLU accumulation in wheat leaves. In parallel, the increased antioxidant system, regeneration of ascorbate, glutathione and glutathione redox status observed under ANT + PS and FLU + PS. These findings will provide an information about the phytotoxicity mechanisms of mixed pollutants in the environment.

For the first time, we used targeted metabolome to investigate the effects of pH-aluminum (Al) interactions on energy-rich compounds and their metabolites (ECMs) and phytohormones in sweet orange (Citrus sinensis) roots. The concentration of total ECMs (TECMs) was reduced by Al-toxicity in 4.0-treated roots, but unaffected significantly in pH 3.0-treated roots. However, the concentrations of most ECMs and TECMs were not lower in pH 4.0 + 1.0 mM Al-treated roots (P4AR) than in pH 3.0 + 1.0 mM Al-treated roots (P3AR). Increased pH improved the adaptability of ECMs to Al-toxicity in roots. For example, increased pH improved the utilization efficiency of ECMs and the conversion of organic phosphorus (P) from P-containing ECMs into available phosphate in Al-treated roots. We identified upregulated cytokinins (CKs), downregulated jasmonic acid (JA), methyl jasmonate (MEJA) and jasmonates (JAs), and unaltered indole-3-acetic acid (IAA) and salicylic acid (SA) in P3AR vs pH 3.0 + 0 mM Al-treated roots (P3R); upregulated JA, JAs and IAA, downregulated total CKs, and unaltered MEJA and SA in P4AR vs pH 4.0 + 0 mM Al-treated roots (P4R); and upregulated CKs, downregulated JA, MEJA, JAs and SA, and unaltered IAA in P3AR vs P4AR. Generally viewed, raised pH-mediated increments of JA, MEJA, total JAs, SA and IAA concentrations and reduction of CKs concentration in Al-treated roots might help to maintain nutrient homeostasis, increase Al-toxicity-induced exudation of organic acid anions and the compartmentation of Al in vacuole, and reduce oxidative stress and Al uptake, thereby conferring root Al-tolerance. In short, elevated pH-mediated mitigation of root Al-stress involved the regulation of ECMs and phytohormones.

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.

The extensive application of nanomaterials has increased their levels in soil environments. Therefore, clarifying the process of environmental migration is important for environmental safety and human health. In this study, alfalfa was used to determine the effects of different doses of ZnO nanoparticles (NPs) on the growth of alfalfa and the soil environment. Results showed that the alfalfa biomass was inversely proportional to the exposure concentration of ZnO NPs. The Zn concentration in the alfalfa tissue and the exposure dose presented a significant positive correlation. A high concentration of ZnO NPs decreased the nitrogen-fixing area of root nodules while the number of bacteroids and root nodules, which in turn affected the nitrogen-fixing ability of alfalfa. At the same time, it caused different degrees of damage to the root nodules and root tip cells of alfalfa. A high dose of ZnO NPs decreased the relative abundance and diversity of the soil microorganisms. Therefore, short-term and high-dose exposure of ZnO NPs causes multiple toxicities in plants and soil environments.

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.

Heavy metals (HMs) in soil and water bodies greatly threaten human health. The wide separation of HMs urges the necessity to develop an expert system for HMs prediction and detection. In the current perspective, several propositions are discussed to design an innovative intelligence system for HMs prediction and detection in soil and water bodies. The intelligence system incorporates the Edge Cloud Server (ECS) data center, an innovative deep learning predictive model and the Federated Learning (FL) technology. The ECS data center is based on satellite sensing sources under human expertise ruling and HMs in-situ measurement. The FL system comprises a machine learning (ML) technique that trains an algorithm across multiple decentralized edge servers holding local data samples without exchanging them or breaching data privacy. The expected outcomes of the intelligence system are to quantify the soil and water bodies' HMs, develop new modified HMs pollution contamination indices and provide decision-makers and environmental experts with an appropriate vision of soil, surface water, and crop health.

The occurrence of organic pollutants in soil is a major environmental concern. These compounds can reach the soil in different ways. Point sources, related to pesticides that are used intentionally, can be applied directly to the soil, or reach the soil indirectly due to application to the aerial parts of crops. On the other hand, non-point sources, which reach soils collaterally during irrigation and/or fertilization, or due to the proximity of plots to industrialized urban centers. Long-range transport of global organic pollutants must also be taken into account. In this study, 218 pesticides, 49 persistent organic pollutants, 37 pharmaceutical active compounds and 6 anticoagulant rodenticides were analyzed in 139 agricultural soil samples collected between 2018 and 2020 in the Macaronesia. This region comprised four inhabited archipelagos (Azores, Canary Islands, Cape Verde, and Madeira) for which agriculture is an important and traditional economic activity. To our knowledge, this is the first study on the levels of organic compound contamination of agricultural soils of the Macaronesia. As expected, the most frequently detected compounds were pesticides, mainly fungicides and insecticides. The Canary Islands presented the highest number of residues, with particularly high concentrations of DDT metabolites (p,p’ DDE: 149.5 ± 473.4 ng g⁻¹; p,p’ DDD: 16.6 ± 35.6 ng g⁻¹) and of the recently used pesticide fenbutatin oxide (302.1 ± 589.7 ng g⁻¹). Cape Verde was the archipelago with the least contaminated soils. Very few pharmaceutical active compounds have been detected in all archipelagos (eprinomectin, fenbendazole, oxfendazole and sulfadiazine). These results highlight the need to promote soil monitoring programs and to establish maximum residue limits in soils, which currently do not exist at either continental or local level.

Biodegradable cellulosic pulp foams with robustness and water resistance are urgently needed in nowadays to replace petroleum-based plastic foams for environmental sustainability. In this work, a facile protocol to fabricate robust poly-lactic acid (PLA) coated cellulose foams (PCCF) was developed through a combined water-based foaming and PLA melt-coating process using pulp as the raw material. In the synthesis, the so-called PLA coating was realized through melting PLA powders dispersed between fibers by an in-situ heating and post cooling process. Performance tests revealed that the incorporation of PLA coating significantly enhances mechanical strength, water stability, and biodegradability of the synthesized PCCF samples compared with conventional cellulosic foams. Specifically, the low-density PCCF were observed with mechanical strength up to 81.24 kPa, high water stability, and more than 95% degradation in 56 days. As the fabrication process is simple and pulp is highly cost competitive, our proposed synthesis strategy makes the PCCF a promising substitute for petroleum-based plastic foams at large-scale production.

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.