Microplastics (<5 mm) have been found in many fish species, from most marine environments. However, the mechanisms underlying microplastic ingestion by fish are still unclear, although they are important to determine the pathway of microplastics along marine food webs. Here we conducted experiments in the laboratory to examine microplastic ingestion (capture and swallowing) and egestion by juveniles of the planktivorous palm ruff, Seriolella violacea (Centrolophidae). As expected, fish captured preferentially black microplastics, similar to food pellets, whereas microplastics of other colours (blue, translucent, and yellow) were mostly co-captured when floating close to food pellets. Microplastics captured without food were almost always spit out, and were only swallowed when they were mixed with food in the fish's mouth. Food probably produced a ‘gustatory trap’ that impeded the fish to discriminate and reject the microplastics. Most fish (93% of total) egested all the microplastics after 7 days, on average, and 49 days at most, substantially longer than food pellets (<2 days). No acute detrimental effects of microplastics on fish were observable, but potential sublethal effects of microplastics on the fish physiological and behavioural responses still need to be tested. This study highlights that visually-oriented planktivorous fish, many species of which are of commercial value and ecological importance within marine food webs, are susceptible to ingest microplastics resembling or floating close to their planktonic prey.

The gut microbiome is sensitive to diet and environmental exposures and is involved in the regulation of host metabolism. Additionally, gut inflammation is an independent risk factor for the development of metabolic diseases, specifically atherosclerosis and diabetes. Exposures to dioxin-like pollutants occur primarily via ingestion of contaminated foods and are linked to increased risk of developing cardiometabolic diseases. We aimed to elucidate the detrimental impacts of dioxin-like pollutant exposure on gut microbiota and host gut health and metabolism in a mouse model of cardiometabolic disease. We utilized 16S rRNA sequencing, metabolomics, and regression modeling to examine the impact of PCB 126 on the microbiome and host metabolism and gut health. 16S rRNA sequencing showed that gut microbiota populations shifted at the phylum and genus levels in ways that mimic observations seen in chronic inflammatory diseases. PCB 126 reduced cecum alpha diversity (0.60 fold change; p = 0.001) and significantly increased the Firmicutes to Bacteroidetes ratio (1.63 fold change; p = 0.044). Toxicant exposed mice exhibited quantifiable concentrations of PCB 126 in the colon, upregulation of Cyp1a1 gene expression, and increased markers of intestinal inflammation. Also, a significant correlation between circulating Glucagon-like peptide-1 (GLP-1) and Bifidobacterium was evident and dependent on toxicant exposure. PCB 126 exposure disrupted the gut microbiota and host metabolism and increased intestinal and systemic inflammation. These data imply that the deleterious effects of dioxin-like pollutants may be initiated in the gut, and the modulation of gut microbiota may be a sensitive marker of pollutant exposures.

Phytophthora capsici, an economically devastating oomycete pathogen, causes devastating disease epidemics on a wide range of vegetable plants and pose a grave threat to global vegetables production. Heavy metals and acid pH are newly co-occurring stresses to soil micro-organisms, but what can be expected for mycelia growth and virulence and how they injure the oomycetes (especially P. capsici) remains unknown. Here, the effects of different heavy metals (Cu²⁺, Cr²⁺, and Hg²⁺) on mycelia growth and virulence were investigated at different pHs (4.0 vs. 7.0) and the plausible molecular and physiological mechanisms were analyzed. In the present study, we compared the effective inhibition of different heavy metals (Cu²⁺, Cr²⁺, and Hg²⁺) and acid pH on a previously genome sequenced P. capsici virulent strain LT1534. Both stress factors independently affected its mycelia growth and sporulation. Next, we investigated whether ROS participated in the pH-inhibited mycelial growth, finding that the ROS scavenger, catalase (CAT), significantly inhibited the acid pH-induced ROS in mycelia. Additionally, because MAPK specially transmits different stress responsive signals in environment into cells, we employed CAT and a p38-MAPK pathway inhibitor to investigate ROS and p38-MAPK roles in heavy metal-inhibited mycelia growth at different pHs (4.0 vs. 7.0), finding that they significantly inhibited growth. Furthermore, ROS and p38-MAPK influenced the heavy metal-induced TBARS content, total antioxidant capacity (TAC), and CAT activity at different pHs, and also reduced the expression of infection-related laccases (PcLAC2) and an effector-related protein (PcNLP14). We propose that acid pH stress accelerates how heavy metals inhibit mycelium growth, sporulation, and virulence change in P. capsici, and posit that ROS and p38-MAPK function to regulate the molecular and physiological mechanisms underlying this toxicity. Although these stresses induce molecular and physiological challenges to oomycetes, much remains to be known the mechanisms dedicated to resolve these environmental stresses.

Effects of antibiotics on the transformation of nitrate and the associated N₂O release in paddy fields are obscure. Using soil slurry experiments combined with ¹⁵N tracer techniques, the influence of tetracycline and sulfamethazine (applied alone and in combination) on the denitrification, anaerobic ammonium oxidation (anammox), dissimilatory nitrate reduction to ammonium (DNRA) and N₂O release rates in the paddy soil were investigated, while genes related to nitrate reduction and antibiotic resistance were quantified to explore the microbial mechanisms behind the antibiotics’ effects. The potential rates of denitrification, anammox, and DNRA were significantly (p < 0.05) reduced, which were mainly attributed to the inhibitory effects of the antibiotics on nitrate-reducing microbes. However, the N₂O release rates were significantly (p < 0.05) stimulated by the antibiotic treatments (0.6–6000 μg kg⁻¹ soil dry weight), which were caused by the different inhibition effects of antibiotics on N₂O production and N₂O reduction as suggest by the changes in abundance of nirS (nitrite reduction step) and nosZ (N₂O reduction to N₂ step) genes. Antibiotic resistance gene (tetA, tetG, sulI, and sulIII) abundances were significantly (p < 0.05) increased under high antibiotic exposure concentrations (>600 μg kg⁻¹ soil dry weight). Our results suggest that the widespread occurrence of antibiotics in paddy soils may pose significant eco-environmental risks (nitrate accumulation and greenhouse effects) by altering nitrate transformation processes.

Attention has been paid to the environmental distribution and fate of nanomedicines. However, their effects on the toxicity of environmental pollutants are lack of knowledge. In this study, the negatively charged poly (ethylene glycol)-b-poly (L-lactide-co-glycolide) (mPEG-PLA) and positively charged polyethyleneimine-palmitate (PEI-PA) nanomicelles were synthesized and served as model drug carriers to study the interaction and combined toxicity with dichlorodiphenyltrichloroethane (DDT). DDT exerted limited effect on the biointerfacial behavior of mPEG-PLA nanomicelles, whereas it significantly mitigated the attachment of PEI-PA nanomicelles on the model cell membrane as monitored by quartz crystal microbalance with dissipation (QCM-D). The cytotoxicity of DDT towards NIH 3T3 cells was greatly decreased by either co-treatment or pre-treatment with the nanomicelles according to the results of real-time cell analysis (RTCA). The cell viability of NIH 3T3 exposed to DDT was increased up to 90% by the co-treatment with mPEG-PLA nanomicelles. Three possible reasons were proposed: (1) decreased amount of free DDT in the cell culture medium due to the partitioning of DDT into nanomicelles; (2) mitigated cellular uptake of nanomicelle-DDT complexes due to the complex agglomeration or electrostatic repulsion between complexes and cell membrane; (3) detoxification effect in the lysosome upon endocytosis of nanomicelle-DDT complexes.

Pollution risk from the discharge of industrial waste or accidental spills during transportation poses a considerable threat to the security of rivers. The ability to quickly identify the pollution source is extremely important to enable emergency disposal of pollutants. This study proposes a new approach for point source identification of sudden water pollution in rivers, which aims to determine where (source location), when (release time) and how much pollutant (released mass) was introduced into the river. Based on the backward probability method (BPM) and the linear regression model (LR), the proposed LR–BPM converts the ill-posed problem of source identification into an optimization model, which is solved using a Differential Evolution Algorithm (DEA). The decoupled parameters of released mass are not dependent on prior information, which improves the identification efficiency. A hypothetical case study with a different number of pollution sources was conducted to test the proposed approach, and the largest relative errors for identified location, release time, and released mass in all tests were not greater than 10%. Uncertainty in the LR–BPM is mainly due to a problem with model equifinality, but averaging the results of repeated tests greatly reduces errors. Furthermore, increasing the gauging sections further improves identification results. A real-world case study examines the applicability of the LR–BPM in practice, where it is demonstrated to be more accurate and time-saving than two existing approaches, Bayesian–MCMC and basic DEA.

CO2-driven acidification and emerging contaminants, such as pharmaceuticals, pose new threats for the maintenance of natural populations of marine organisms by interfering with their normal biochemical pathways and defences. The combined effects of seawater acidification, as predicted in climate change scenarios, and an emerging contaminant (the non-steroidal anti-inflammatory drug, NSAID, diclofenac) on oxidative stress-related parameters were investigated in the Mediterranean mussel Mytilus galloprovincialis and the Manila clam Ruditapes philippinarum. A flow-through system was used to carry out a three-week exposure experiment with the bivalves. First, the animals were exposed to only three pH values for 7 days. The pH was manipulated by dissolving CO2 in the seawater to obtain two reduced pH treatments (pH −0.4 units and pH −0.7 units), which were compared with seawater at the natural pH level (8.1). Thereafter, the bivalves were concomitantly exposed to the three experimental pH values and environmentally relevant concentrations of diclofenac (0.00, 0.05 and 0.50 μg/L) for an additional 14 days. The activities of superoxide dismutase, catalase and cyclooxygenase, and lipid peroxidation and DNA strand-break formation were measured in both the gills and digestive gland after 7, 14 and 21 days of exposure to each experimental condition. The results show that the biochemical parameters measured in both the mussels and clams were more influenced by the reduced pH than by the contaminant or the pH*contaminant interaction, although the biomarker variation patterns differed depending on the species and tissues analysed. Generally, due to increases in its antioxidant defence, M. galloprovincialis was more resistant than R. philippinarum to both diclofenac exposure and reduced pH. Conversely, reduced pH induced a significant decrease in COX activity in both the gills and digestive gland of clams, possibly resulting in the increased DNA damage observed in the digestive gland tissue.

The urbanization and industrialization of cities around the coastal region of the Bohai Sea have produced large amounts of sewage sludge from sewage treatment plants. Research on the biodegradation of nonylphenol (NP) and the influencing factors of such biodegradation during sewage sludge composting is important to control pollution caused by land application of sewage sludge. The present study investigated the effect of aeration on NP biodegradation and the microbe community during aerobic composting under two intermittent aeration treatments in a full-scale plant of sewage sludge, sawdust, and returned compost at a ratio of 6:3:1. The results showed that 65% of NP was biodegraded and that Bacillus was the dominant bacterial species in the mesophilic phase. The amount of NP biodegraded in the mesophilic phase was 68.3%, which accounted for 64.6% of the total amount of biodegraded NP. The amount of NP biodegraded under high-volume aeration was 19.6% higher than that under low-volume aeration. Bacillus was dominant for 60.9% of the composting period under high-volume aeration, compared to 22.7% dominance under low-volume aeration. In the thermophilic phase, high-volume aeration promoted the biodegradation of NP and Bacillus remained the dominant bacterial species. In the cooling and stable phases, the contents of NP underwent insignificant change while different dominant bacteria were observed in the two treatments. NP was mostly biodegraded by Bacillus, and the rate of biodegradation was significantly correlated with the abundance of Bacillus (r = 0.63, p < 0.05). Under aeration, Bacillus remained the dominant bacteria, especially in the thermal phase; this phenomenon possibly increased the biodegradation efficiency of NP. High-volume aeration accelerated the activity and prolonged the survival of Bacillus. The risk of organic pollution could be decreased prior to sewage sludge reuse in soil by adjusting the ventilation strategies of aerobic compost measurements.

Emissions from the transportation sector are rapidly changing worldwide; however, the interplay of such emission changes in the face of climate change are not as well understood. This two-part study examines the impact of projected emissions from the U.S. transportation sector (Part I) on ambient air quality in the face of climate change (Part II). In Part I of this study, we describe the methodology and results of a novel Technology Driver Model (see graphical abstract) that includes 1) transportation emission projections (including on-road vehicles, non-road engines, aircraft, rail, and ship) derived from a dynamic technology model that accounts for various technology and policy options under an IPCC emission scenario, and 2) the configuration/evaluation of a dynamically downscaled Weather Research and Forecasting/Community Multiscale Air Quality modeling system.By 2046–2050, the annual domain-average transportation emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH3), and sulfur dioxide (SO2) are projected to decrease over the continental U.S. The decreases in gaseous emissions are mainly due to reduced emissions from on-road vehicles and non-road engines, which exhibit spatial and seasonal variations across the U.S. Although particulate matter (PM) emissions widely decrease, some areas in the U.S. experience relatively large increases due to increases in ship emissions. The on-road vehicle emissions dominate the emission changes for CO, NOx, VOC, and NH3, while emissions from both the on-road and non-road modes have strong contributions to PM and SO2 emission changes. The evaluation of the baseline 2005 WRF simulation indicates that annual biases are close to or within the acceptable criteria for meteorological performance in the literature, and there is an overall good agreement in the 2005 CMAQ simulations of chemical variables against both surface and satellite observations.

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990–2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China.