Current mitigation strategies to offset marine plastic pollution, a global concern, typically rely on preventing floating debris from reaching coastal ecosystems. Specifically, clean-up technologies are designed to collect plastics by removing debris from the aquatic environment such as rivers and estuaries. However, to date, there is little published data on their potential impact on riverine and estuarine organisms and ecosystems. Multiple parameters might play a role in the chances of biota and organic debris being unintentionally caught within a mechanical clean-up system, but their exact contribution to a potential impact is unknown. Here, we identified four clusters of parameters that can potentially determine the bycatch: (i) the environmental conditions in which the clean-up system is deployed, (ii) the traits of the biota the system interacts with, (iii) the traits of plastic items present in the system, and, (iv) the design and operation of the clean-up mechanism itself. To efficiently quantify and assess the influence of each of the clusters on bycatch, we suggest the use of transparent and objective tools. In particular, we discuss the use of Bayesian Belief Networks (BBNs) as a promising probabilistic modelling method for an evidence-based trade-off between removal efficiency and bycatch. We argue that BBN probabilistic models are a valuable tool to assist stakeholders, prior to the deployment of any clean-up technology, in selecting the best-suited mechanism to collect floating plastic debris while managing potential adverse effects on the ecosystem.

Removal of multi-ionic contaminants from water resources has been a major challenge faced during the treatment of water for drinking and industrial applications. In the present study, varying composition of magnesium doped hydroxyapatite (Mg-HAp) and zeolite nanocomposite embedded porous polymeric beads were synthesized using solvent displacement method and its sorption efficiency towards multi-ion contaminant (such as Ag, Al, As, Ba, Be, Cd, Co, Cr, Cu, Mn, Ni, Pb, Se, Tl, Th, U, V and Zn) was investigated in aqueous solution and spiked groundwater. The prepared beads were characterized using suitable techniques like high-resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) equation. The surface area and pore radius of the beads varied from 6.996 to 66.469 m²/g and 1.698–3.960 nm respectively according to the composition of the bead. The control bead without nanocomposite showed maximum surface area. Multi-ion adsorptions onto beads were confirmed using an inductively coupled plasma-optical emission spectrophotometer (ICP-OES) and X-ray photoelectron spectrophotometer (XPS). The sorption efficiency was high at pH 5 owing to its anionic surface charge leading to an increase in affinity towards the cations. For validating field application, selected high performance beads were tested in multi-ion spiked groundwater. The results indicated that the Mg-HAp nanocomposite bead dominate all the other bead compositions with more than 90% removal efficiency for most of the multi-ion contaminants. The feasible adsorption mechanism has been discussed. This adsorption study revealed that the Mg-HAp nanocomposite bead is a promising material that is cost-effective, non-toxic, biodegradable, eco-friendly and highly efficient towards the removal of multi-ionic contaminants from groundwater.

Two areas in central-southern Italy Land of Fires in Campania and Valley of Sacco river in Lazio are known to be contaminated sites, the first due to illegal fly-tipping and toxic fires, and the second due to an intensive industrial exploitation done by no-scruple companies and crooked public administration offices with dramatic consequences for environment and resident people. The work is intended to contribute to Human BioMonitoring (HBM) studies conducted in these areas on healthy young male population by a semiconductor gas sensor array trained by SPME-GC/MS. Human semen, blood and urine were investigated. The fingerprinting of the Volatile Organic Compounds (VOCs) by a gas sensors system allowed to discriminate the different contamination of the two areas and was able to predict the chemical concentration of several VOCs identified by GC/MS.

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM₂.₅ concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM₂.₅ concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from −56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM₂.₅ concentrations over inland areas ranging from 0.27 ppbV to −7.17 ppbV and 0.89 μg/m³ to −2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to −2.86 ppbV and 3.41 μg/m³ to −7.47 μg/m³. The decreases in inland ozone and PM₂.₅ concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM₂.₅ formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM₂.₅ concentrations due to reduced reactions of PM₂.₅ precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM₂.₅ concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM₂.₅ inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

The exposure of Caenorhabditis elegans to polystyrene (PS) beads of a wide range of sizes impedes feeding, by reducing food consumption, and has been linked to inhibitory effects on the reproductive capacity of this nematode, as determined in standardized toxicity tests. Lipid storage provides energy for longevity, growth, and reproduction and may influence the organismal response to stress, including the food deprivation resulting from microplastics exposure. However, the effects of microplastics on energy storage have not been investigated in detail. In this study, C. elegans was exposed to ingestible sizes of PS beads in a standardized toxicity test (96 h) and in a multigeneration test (∼21 days), after which lipid storage was quantitatively analyzed in individual adults using coherent anti-Stokes Raman scattering (CARS) microscopy. The results showed that lipid storage distribution in C. elegans was altered when worms were exposed to microplastics in form of PS beads. For example, when exposed to 0.1-μm PS beads, the lipid droplet count was 93% higher, the droplets were up to 56% larger, and the area of the nematode body covered by lipids was up to 79% higher than in unexposed nematodes. The measured values tended to increase as PS bead sizes decreased. Cultivating the nematodes for 96 h under restricted food conditions in the absence of beads reproduced the altered lipid storage and suggested that it was triggered by food deprivation, including that induced by the dilutional effects of PS bead exposure. Our study demonstrates the utility of CARS microscopy to comprehensively image the smaller microplastics (<10 μm) ingested by nematodes and possibly other biota in investigations of the effects at the level of the individual organism.

In this study, we backward traced and controlled the pollution of organophosphorus flame retardants (OPFRs) in aquaculture areas from the standpoints of terminal treatment, migration and transformation resistance, and source molecular substitution technology. A regulatory plan to considerably reduce the combined biotoxicity of fish exposed to OPFRs in aquaculture areas and significantly improves the biodegradation of sewage treatment and the efficiency of soil plant-microorganism combined remediation was formulated. Environmentally friendly alternatives of OPFRs were designed. The supplementation scheme of aquatic feed significantly alleviates the toxicity risk of fish exposure to OPFRs in aquafarm (reduced by 121.02%). The regulatory scheme of external stimulus to enhance the biodegradation of OPFRs in wastewater treatment process included an H₂O₂ concentration of 400 mg/L, voltage gradient of 1.5 V/m, and pH of 6.5 can improve the degradation capacity of OPFRs molecules by 88.86%. The degradation of OPFRs can be enhanced by plant-microorganism combined remediation (up to 98.64%) by growing plants whose primary function is phytoextraction in soils dominated by Sphingopyxis sp. and Rhodococcus sp. A 3D-QSAR pharmacophore model based on apoptosis toxicity, mitochondrial dysfunction, oxidative stress response, reproductive, neurotoxicity, gill-inhalation combined toxicity of fish exposed to OPFRs in aquafarm was fabricated. The recommended aquatic feed scheme and the control scheme of enhanced degradation of OPFRs by sewage treatment and soil environment had better applicability for the new-designed OPFRs substitution molecules (the maximum combined toxicity/degradation is reduced/increased by 75.46% and 63.24%, respectively). In this paper, a technical scheme of OPFRs terminal treatment, process regulation, and source control was applied as a cradle-to-grave approach to reduce the ecological toxicity risk of fish exposed to OPFRs in aquaculture areas providing theoretical support for the realization of OPFRs environmental pollution control.

Irregular or unnatural artificial light causes severe environmental stress on the survival and health of organisms, which is rapidly becoming a widespread new type of environmental pollution. A series of disruptive behaviors to body homeostasis brought about by light pollution, including metabolic abnormalities, are likely to be the result of circadian rhythm disturbances. Recently, the proposed role of light pollution in metabolic dysregulation has accelerated it into an emerging field. Hence, the regulatory role of light pollution in mammalian metabolic homeostasis is reviewed in this contribution. Light at night is the most widely affected type of light pollution, which disrupts metabolic homeostasis largely due to its disruption of daily food intake patterns, alterations of hormone levels such as melatonin and glucocorticoids, and changes in the rhythm of inflammatory factor production. Besides, light pollution impairs mammalian metabolic processes in an intensity-, photoperiod-, and wavelength-dependent manner, and is also affected by species, gender, and diets. Nevertheless, metabolic disorders triggered by light pollution are not irreversible to some extent. Potential interventions such as melatonin supplementation, recovery to the LD cycle, time-restricted feeding, voluntary exercise, wearing blue light-shied goggles, and bright morning light therapy open a bright avenue to prevent light pollution. This work will help strengthen the relationship between light information and metabolic homeostasis and provide new insights for the better prevention of metabolic disorders and light pollution.

Human-induced nitrogen (N) and phosphorus (P) enrichment have profound effects on grassland net primary production (NPP) and species richness. However, a comprehensive understanding of the relative contribution of N vs. P addition and their interaction on grassland NPP increase and species loss remains elusive. We compiled data from 80 field manipulative studies and conducted a meta-analysis (2107 observations world-wide) to evaluate the individual and combined effects of N and P addition on grassland NPP and species richness. We found that both N addition and P addition significantly enhanced grassland above-ground NPP (ANPP; 33.2% and 14.2%, respectively), but did not affect total NPP, below-ground NPP (BNPP), and species evenness. Species richness significantly decreased with N addition (11.7%; by decreasing forbs) probably due to strong decreased soil pH, but not with P addition. The combined effects of N and P addition were generally stronger than the individual effects of N or P addition, and we found the synergistic effects on ANPP, and additive effects on total NPP, BNPP, species richness, and evenness within the combinations of N and P addition. In addition, N and P addition effects were strongly affected by moderator variables (e.g. climate and fertilization type, duration and amount of fertilizer addition). These results demonstrate a higher relative contribution of N than P addition to grassland NPP increase and species loss, although the effects varied across climate and fertilization types. The existing data also reveals that more long-term (≥5 years) experimental studies that combine N and P and test multifactor effects in different climate zones (particularly in boreal grasslands) are needed to provide a more solid basis for forecasting grassland community response and C sequestration response to nutrient enrichment at the global scale.

Urban and regional ozone (O₃) pollution is a public health concern and causes damage to ecosystems. Due to the diverse emission sources of O₃ precursors and the complex interactions of air dispersion and chemistry, identifying the contributing sources of O₃ pollution requires integrated analysis to guide emission reduction plans. In this study, the meteorological characteristics leading to O₃ polluted days (in which the maximum daily 8–h average O₃ concentration is higher than the China Class II National O₃ Standard (160 μg/m³)) in Guangzhou (GZ, China) were analyzed based on data from 2019. The O₃ formation regimes and source apportionments under various prevailing wind directions were evaluated using a Response Surface Modeling (RSM) approach. The results showed that O₃ polluted days in 2019 could be classified into four types of synoptic patterns (i.e., cyclone, anticyclone, trough, and high pressure approaching to sea) and were strongly correlated with high ambient temperature, low relative humidity, low wind speed, variable prevailing wind directions. Additionally, the cyclone pattern strongly promoted O₃ formation due to its peripheral subsidence. The O₃ formation was nitrogen oxides (NOₓ)-limited under the northerly wind, while volatile organic compounds (VOC)-limited under other prevailing wind directions. Anthropogenic emissions contributed largely to the O₃ formation (54–78%) under the westerly, southwesterly, easterly, southeasterly, or southerly wind, but only moderately (35–47%) under the northerly or northeasterly wind. Furthermore, as for anthropogenic contributions, local emission contributions were the largest (39–60%) regardless of prevailing wind directions, especially the local NOₓ contributions (19–43%); the dominant upwind regional emissions contributed 12–46% (e.g., contributions from Dongguan were 12–20% under the southeasterly wind). The emission control strategies for O₃ polluted days should focus on local emission sources in conjunction with the emission reduction of upwind regional sources.

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.