River floodplain ecosystems host one of the highest freshwater molluscan biodiversity on Earth. However, multiple human disturbances, such as loss of hydrological connectivity and deterioration of water quality, are seriously threatening most floodplain lakes throughout the world. Given the high imperilment rate of freshwater molluscs but the scarcity of studies examining the anthropogenic effects on this fauna, we test the response of mollusc assemblages to river-lake disconnection and eutrophication in 30 lakes in the Yangtze River floodplain, China. The species richness of entire Mollusca, Gastropoda and Bivalvia and 6 dominant families were all much lower at disconnected lakes than that in connected lakes, and decreased with increasing water eutrophication. The assemblage structure differed significantly among four lake groups for datasets based on entire Mollusca, Gastropoda and Bivalvia, indicating the serious impacts of hydrological disconnection and eutrophication. Moreover, the connected lakes showed significantly lower values of average taxonomic distinctness (Δ⁺) but higher values of variation in taxonomic distinctness (Λ⁺) than disconnected lakes. Such variations were triggered by the extirpation of congeneric and endemic species (mainly from families Unionidae and Viviparidae), which giving a waring of the loss of mollusc endemism in this region. In general, the present study showed that river-lake disconnection and deterioration of water quality resulted in serious biodiversity declines of both gastropods and bivalves in the Yangtze River floodplain lakes. A systematic approach including restoration of river-lake connectivity and habitats and improvement of water quality should be implemented in the conservation planning in this large river floodplain.

The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1–24, 2020) period to the Lock (March 25–April 19, 2020) and Tri (April 20–May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM₂.₅ concentrations and an enhancement in the O₃ concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O₃ formation, comprising 89.4%, 83.1%, and 88.9% of the O₃ sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O₃ concentrations at the surface layer. Compared with the Pre1 period, the O₃ enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O₃ removal rate by horizontal transport. During the Tri period, a slower consumption of O₃ by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O₃. Emission and aerosol processes constituted the major positive contributions to the net surface PM₂.₅, accounting for a total of 48.7%, 38.4%, and 42.5% of PM₂.₅ sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM₂.₅ concentrations during the Lock and Tri periods were primarily explained by the weaker PM₂.₅ production from emission and aerosol processes. The increased vertical transport rate of PM₂.₅ from the surface layer to the upper layers was also a reason for the decrease in the PM₂.₅ during the Lock periods.

The association of co-exposure to polycyclic aromatic hydrocarbons (PAHs) and phthalates (PAEs) with blood cell-based inflammatory biomarkers is largely unknown. We conducted a panel study of 144 children aged 4–12 years, with up to 3 repeated visits across 3 seasons. For each visit, we collected the first-morning urine for 4 consecutive days and fasting blood on the day of physical examination. We developed a gas chromatography/tandem mass spectrometry method to detect the metabolites of 10 PAHs (OH-PAHs) and 10 PAEs (mPAEs) in urine samples. We employed linear mixed-effects models to evaluate the individual associations of each OH-PAH and mPAE with blood cell-based inflammatory biomarkers over different lag times. Bayesian kernel machine regression (BKMR) and quantile g-computation were used to evaluate the overall associations of OH-PAHs and mPAEs mixtures with blood cell-based inflammatory biomarkers. After multiple adjustments, we found positive associations of summed hydroxylphenanthrene (∑OHPHE), summed OH-PAHs, and mono-n-butyl phthalate with inflammatory biomarkers such as neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and the systemic immune-inflammation index (SII) at lag 0 (the day of physical examination). Each 1% increase in ∑OHPHE was related to a 0.18% (95% confidence interval: 0.10%, 0.25%) increase in SII, which was the strongest among the above associations. The results of BKMR and quantile g-computation suggested that co-exposure to PAHs and PAEs mixture was associated with an elevated white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and SII, to which ∑OHPHE and 1-hydroxypyrene (1-OHPYR) might be the major contributors. In addition, gender and age modified the associations of ∑OHPHE and 1-OHPYR with inflammatory biomarkers, where girls and younger children were more susceptible. In conclusion, co-exposure to PAHs and PAEs was associated with elevated inflammation in children, in which ∑OHPHE and 1-OHPYR might play important roles.

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.

Crude oil contaminant is one of the major problem to environment and its removal process considered as most challenging tool currently across the world. In this degradation study, crude oil hydrocarbons are degraded on various pH optimization conditions (pH 2, 4,6,7,8 and 10) by using two biosurfactant producing bacterial strains Pseudomonas aeruginosa PP3 and Pseudomonas aeruginosa PP4. During crude oil biodegradation, degradative enzymes alkane hydroxylase and alcohol dehydrogenase were examined and found to be higher in PP4 than PP3. Biodegradation efficiency (BE) of crude oil by both PP3 and PP4 were analysed by gas chromatography mass spectroscopy (GCMS). Based on strain PP3, the highest BE was observed in pH 2 and pH 4 were found to be 62% and 69% than pH 6, 7, 8 and 10 (47%, 47%, 49% and 45%). It reveals that PP3 was survived effectively in acidic condition and utilized the crude oil hydrocarbons. In contrast, the highest BE of PP4 was observed in pH 7 (78%) than pH4 (68%) and pH's 2, 6, 8 and 10 (52%, 52%, 43% and 53%) respectively. FTIR spectra results revealed that the presence of different functional group of hydrocarbons (OH, –CH₃, CO, C–H) in crude oil. GCMS results confirmed that both strains PP3 and PP4 were survived in acidic condition and utilized the crude oil hydrocarbons as sole carbon sources. This is the first observation on biodegradation of crude oil by the novel strains of Pseudomonas aeruginosa in acidic condition with higher BE. Overall, the extracellular enzymes and surface active compounds (biosurfactant) produced by bacterial strains were played a key role in crude oil biodegradation process.

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

Currently little is known about the adsorption behaviors of metalloids on microplastics (MPs) and their complex toxic effects on aquatic plants. Herein, we investigated the adsorption behaviors of arsenic (As(III) and As(V)) on three types of MPs (polystyrene, polyvinyl chloride, and polyethylene) with four different particle sizes (100, 10, 1, and 0.1 μm). Compared with the short-term exposure experiment, co-toxicity of polystyrene nanoplastics (PS-NPs) and As on two submerged macrophytes (Vallisneria denseserrulata and Potamogeton crispus) were explored through two relatively longer 14-day-cultivation experiments in summer and spring, respectively. The adsorption results showed that As entered the internal surface adsorption site of MPs at 24 h and fully combined to reach equilibrium. The adsorption capacity also enhanced with the increase of MPs concentrations, which generated more adsorption sites for binding with MPs. The presence of PS-NPs increased the absorption of As on macrophytes by 36.2–47.2%. More serious damage of leaf structure by combined PS-NPs and As was observed by transmission electron microscope. The larger harms by the co-toxicity of MPs and As were also reflected by the changes in physiochemical characteristics (e.g. photosynthesis) and the enhancement of oxidative damage of macrophytes. This work provides a clear theoretical basis for the behavior of PS-NPs as carrier with other contaminants on submerged macrophytes, and clearly evaluates the co-toxicity of NPs and metalloids in complex aquatic environments.

Denitrification, as both origins and sinks of N₂O, occurs extensively, and is of critical importance for regulating N₂O emissions in acidified soils. However, whether soil acidification stimulates N₂O emissions, and if so for what reason contributes to stimulate the emissions is uncertain and how the N₂O fractions from fungal (ffD) and bacterial (fbD) denitrification change with soil pH is unclear. Thus, a pH gradient (6.2, 7.1, 8.7) was set via manipulating cropland soils (initial pH 8.7) in North China to illustrate the effect of soil acidification on fungal and bacterial denitrification after the addition of KNO₃ and glucose. For source partitioning, we used and compared SP/δ¹⁸O mapping approach (SP/δ¹⁸O MAP) and acetylene inhibition technique combined isotope two endmember mixing model (AIT-IEM). The results showed significantly higher N₂O emissions in the acidified soils (pH 6.2 and pH 7.1) compared with the initial soil (pH 8.7). The cumulative N₂O emissions during the whole incubation period (15 days) ranged from 7.1 mg N kg⁻¹ for pH 8.7–18.9 mg N kg⁻¹ for pH 6.2. With the addition of glucose, relative to treatments without glucose, this emission also increased with the decrement of pH values, and were significantly stimulated. Similarly, the highest N₂O emissions and N₂O/(N₂O + N₂) ratios (rN₂O) were observed in the pH 6.2 treatment. But the difference was the highest cumulative N₂O + N₂ emissions, which were recorded in the pH 7.1 treatment based on SP/δ¹⁸O MAP. Based on both approaches, ffD values slightly increased with the acidification of soil, and bacterial denitrification was the dominant pathway in all treatments. The SP/δ¹⁸O MAP data indicated that both the rN₂O and ffD were lower compared to AIT-IEM. It has been known for long that low pH may lead to high rN₂O of denitrification and ffD, but our documentation of a pervasive pH-control of rN₂O and ffD by utilizing combined SP/δ¹⁸O MAP and AIT-IEM is new. The results of the evaluated N₂O emissions by acidified soils are finely explained by high rN₂O and enhanced ffD. We argue that soil pH management should be high on the agenda for mitigating N₂O emissions in the future, particularly for regions where long-term excessive nitrogen fertilizer is likely to acidify the soils.

The importance of per-, poly-fluoroalkyl substances (PFASs) effects on riverine microbiomes is receiving increased recognition in the environmental sciences. However, few studies have explored how PFASs affect microbiomes across trophic levels, specifically through predator-prey interactions. This study examined the community profiles of planktonic archaea, bacteria, fungi, algae, protozoa, and metazoa in a semi-industrial and agricultural river alongside their interactions with 15 detected PFASs. As abiotic factors, PFASs affected community coalescence more than biogenic substances (p < 0.05). For biotic regulations, sub-communities in rare biospheres (including always rare taxa-ART and critically rare taxa-CRT) contributed to spatial community coalescence more than sub-communities in abundant biospheres (always abundant taxa-AAT and critically abundant taxa-CAT) (p < 0.05). Metazoa-bacteria (Modularity = 1.971) and protozoa-fungi (1.723) were determined to be the most stable predator-prey networks. Based on pathway models, short-chain PFBA (C4) was shown to weaken the trophic transfer efficiencies from heterotrophic bacteria (HB) to heterotrophic flagellates (HF) (p < 0.05). Long-chain PFTeDA (C14) promoted HB to amoeba (p < 0.05), which we postulate is the pathway for PFTeDA to enter the microbial food chain. Our preliminary results elucidated the influence of PFASs on planktonic microbial food webs and highlighted the need to consider protecting and remediating riverine ecosystems containing PFASs.

With the continued increase of global ammonia emission, the damage to human or animal caused by ammonia pollution has attracted wide attention. The noncoding RNAs have been reported to regulate a variety of biological processes under different environmental stimulation via ceRNA (competing endogenous RNA) networks. Autophagy is a hallmark of tissue damage from air pollution. However, the specific role of circular RNAs (circRNAs) in the injury of intestinal tissue caused by autophagy remains unclear. Here, we established 42-days old ammonia-exposed broiler models and observed that autophagy flux in broiler jejunum was activated under ammonia exposure. Meanwhile, a total of eight significantly dysregulated expressed circRNAs were obtained and a circRNAs-miRNAs-genes interaction networks were constructed by bioinformatics analysis. Furthermore, an axis named circRNA-IGLL1/miR-15a/RNF43 was predicted to participate in the excessive autophagy by targeting RNF43. The target relationship was proved by dual-luciferase reporter assay in vitro. Mechanistically, downregulated circRNA-IGLL1 could suppress the expression of RNF43 in ammonia-exposed jejunum and the Wnt/β-catenin pathway was activated. Inhibition of miR-15a reversed autophagy caused by downregulated circRNA-IGLL1. CircRNA-IGLL1 could competitively bind miR-15a to regulate RNF43 expression, thus modulating the occurrence of autophagy. Taken together, our results showed that circRNA-IGLL1/miR-15a/RNF43 axis is involved in ammonia-induced intestinal autophagy in broilers.