Remediation of neonicotinoid-contaminated soils using peanut shell biochar and composted chicken manure: Transformation mechanisms of geochemical fractions.

Soil remediation techniques are promising approaches to relieve the adverse environmental impacts in soils caused by neonicotinoids application. This study systematically investigated the remediation mechanisms for peanut shell biochar (PSB) and composted chicken manure (CCM) on neonicotinoid-contaminated soils from the perspective of transformation of geochemical fractions by combining a 3-step sequential extraction procedure and non-steady state model. The neonicotinoid geochemical fractions were divided into labile, moderate-adsorbed, stable-adsorbed, bound, and degradable fractions. The PSB and CCM addition stimulated the neonicotinoid transformation in soils from labile fraction to moderate-adsorbed and stable-adsorbed fractions. Compared with unamended soils, the labile fractions decreased from 47.6% ± 11.8% of the initial concentrations to 12.1 ± 9.3% in PSB-amended soils, and 7.1 ± 4.9% in PSB and CCM-amended soils, while the proportions of moderate-adsorbed and stable-adsorbed fractions correspondingly increased by 1.8-2.4 times and 2.3-4.8 times, respectively. A small proportion (<4.8%) in bound fractions suggested there were rather limited bound-residues after 48 days incubation. The PSB stimulated the -NO2-containing neonicotinoid-degraders, which promoted the degradable fractions of corresponding neonicotinoids by 8.2 ± 6.3%. Degradable fraction of neonicotinoids was the dominant fate in soils, which accounted for 58.3 ± 16.7%. The findings made beneficial theoretical supplements and provided valuable empirical evidence for the remediation of neonicotinoid-contaminated soils.

Spatiotemporal dynamics and modeling of thiacloprid in paddy multimedia systems with the effect of wetting-drying cycles.

The widespread presence of thiacloprid (THI), a neonicotinoid, raises concerns for human health and the aquatic environment due to its persistence, toxicity, and bioaccumulation. The fate of THI in paddy multimedia systems is mainly governed by irrigation practices, but the potential impacts remain poorly documented. This study investigated the effects of water management practices on THI spatiotemporal dynamics in paddy multimedia systems by combining soil column experiments and a non-steady-state multimedia model. The results indicated the wetting-drying cycle (WDC) irrigation reduced THI occurrences in environmental phases (i.e., soil, interstitial water, and overlying water) and accelerated the THI loss through the THI aerobic degradation process. THI occurrences in the soil and water phases decreased from 18.8% for conventional flooding (CF) treatment to 9.2% for severe wetting-drying cycle (SW) treatment after 29 days, while the half-lives shortened from 11.1 days to 7.3 days, respectively. Meanwhile, the WDC decreased THI outflow from leakage water, which reduced the THI risk of leaching. There was no significant difference in THI plant uptake and volatilization between CF and WDC treatments. The mean proportions of THI fate in paddy multimedia systems followed the order: THI degradation (57.7%), outflow from leakage water (25.5%), occurrence in soil (12.4%), plant uptake (3.4%), occurrence in interstitial water (0.7%), occurrence in overlying water (0.3%), volatilization (<0.1%) after 29 days. The sensitivity analysis identified the soil organic carbon partition coefficient (KOC) as the most sensitive parameter affecting THI's fate. In addition, the topsoil layers of 0-4 cm were the main sink of THI, holding 67% of THI occurrence in the soil phase. The THI occurrence in interstitial water was distributed evenly throughout the soil profile. These findings made beneficial theoretical supplements and provided valuable empirical evidence for water management practices to reduce the THI ecological risk.

Optimal allocation of agricultural water and land resources integrated with virtual water trade: A perspective on spatial virtual water coordination.

Agricultural production consumes the majority of global freshwater resources. The worsening water scarcity has imposed significant stress on agricultural production when regions seek food self-sufficiency. To seek optimal allocation of spatial agricultural water and land resources in each water function zone of the objective region, a multi-objective optimization model was developed to tackle the trade-offs between the water-saving objective and the economic benefit objective considering virtual water trade (VWT). The cultivated area of each crop in each water function zone was taken into account as the decision variable, while a set of strong constraints were used to restrict land resources and water availability. Then, a decomposition-simplex method aggregation algorithm (DSMA) was proposed to solve this nonlinear, bounding-constrained, and multi-objective optimization model. Based on the quantitative analysis of the spatial blue and green virtual water in each agricultural product, the proposed methodology was applied to a real-world, provincial-scale region in China (i.e., Jiangsu Province). The optimized results provided 18 Pareto solutions to reallocate the land resources in the 21 IV-level water function zones of Jiangsu Province, considering four major rainy-season crops and two dry-season crops. Compared to the actual scenario, the superior scheme increased by 7.95% (5.6 × 109 RMB) for economic trade and decreased by 1.77% (2.0 × 109 m3) for agricultural water consumption. It was mainly because the potential of spatial blue and green virtual water in Jiangsu was fully exploited by improving spatial land resource allocation. The food security of Jiangsu could be guaranteed by achieving self-sufficiency in the superior scheme, and the total VWT in the optimal scheme was 2.2 times more than the actual scenario. The results provided a systematic decision-support methodology from the perspective of spatial virtual water coordination, yet, the methodology is widely applicable.

Surface modification significantly changed the effects of nano-polystyrene on sediment microbial communities and nitrogen metabolism.

Nanoplastics are ubiquitous in the natural environment, and their ecological risks have received considerable attention. Surface modification is common for nanoplastics and an essential factor affecting their toxicity. However, studies on the potential effects of nanoplastics and their surface-modified forms on functional communities in aquatic systems are still scarce. This study investigated the effects of nano-polystyrene (nPS), amino-modified nPS (nPS-NH2), and carboxylated nPS (nPS-COOH) particles on sediment bacterial and fungal communities and their functions over a 60-day incubation period. The results showed that the fungal community was significantly inhibited by nPS-NH2 exposure, while the bacterial community diversity remained relatively stable in all nPS treatments. Proteobacteria and Ascomycota were the dominant phyla for the bacterial and fungal communities, respectively. Nitrification was inhibited in all nPS treatments, while denitrification was enhanced for nPS-NH2 and nPS-COOH treatments. The activity of four key denitrification enzymes (NAR, NIR, NOR, and NOS) was also significantly improved by nPS, resulting in increased nitrogen and nitrous oxide gas production, and decreased nitrate concentrations in the overlying water. This showed the total increased effect of nPS on the activity of denitrifiers. Our results suggest that surface modification significantly affects the effects of nPS on microbial communities and functions. The potential impacts of nPS on ecological functions should be elucidated with more attention.

Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models.

Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R2adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.

Changes in Microeukaryotic Communities in the Grand Canal of China in Response to Floods.

Floods are frequent natural disasters and could have serious impacts on aquatic environments. Eukaryotic communities in artificial canals influenced by floods remain largely unexplored. This study investigated the spatiotemporal variabilities among eukaryotes in response to floods in the Grand Canal, China. Generally, 781,078 sequence reads were obtained from 18S rRNA gene sequencing, with 304,721 and 476,357 sequence reads detected before and after flooding, respectively. Sediment samples collected after the floods exhibited a higher degree of richness and biodiversity but lower evenness than those before the floods. The eukaryotic communities changed from Fungi-dominated before floods to Stramenopile-dominated after floods. The spatial turnover of various species was the main contributor to the longitudinal construction of eukaryotes both before the floods (ßSIM = 0.7054) and after the floods (ßSIM = 0.6858). Some eukaryotic groups responded strongly to floods and might pose unpredictable risks to human health and environmental health. For example, Pezizomycetes, Catenulida, Glomeromycetes, Ellipura, etc. disappeared after the floods. Conversely, Lepocinclis, Synurale, Hibberdiales, Acineta, Diptera, and Rhinosporidium were all frequently detected after the floods, but not prior to the floods. Functional analyses revealed amino acid metabolism, carbohydrate metabolism, translation, and energy metabolism as the main metabolic pathways, predicting great potential for these processes in the Grand Canal.

Ecotoxicological effects of the antidepressant fluoxetine and its removal by the typical freshwater microalgae Chlorella pyrenoidosa.

The antidepressant fluoxetine (FLX) has gained increasing attention due to its frequent detection in aquatic environments and negative effects on non-target organisms. However, knowledge on the ecotoxicological effects of FLX and its removal by microalgae is still limited. In this study, the ecotoxicological effects of FLX (10 -1000 µg/L) were assessed using batch cultures of the freshwater microalgae Chlorella pyrenoidosa for 10 days based on changes in growth, antioxidant response, and photosynthetic process. The removal efficiency, removal mechanism, and degradation pathway of FLX by C. pyrenoidosa were also investigated. The results showed that the growth of C. pyrenoidosa was inhibited by FLX with a 4 d EC50 of 0.464 mg/L. Additionally, FLX significantly inhibited photosynthesis and caused oxidative stress on day 4. However, C. pyrenoidosa can produce resistance and acclimatize to FLX, as reflected by the declining growth inhibition rate, recovered photosynthetic efficiency, and disappearance of oxidative stress on day 10. Despite the toxicity of FLX, C. pyrenoidosa showed 41.2%- 100% removal of FLX after 10 days of exposure. Biodegradation was the primary removal mechanism, accounting for 88.2%- 92.8% of the total removal of FLX. A total of five metabolites were found in the degradation processes of FLX, which showed less toxicity than FLX. The main degradation pathways were proposed as demethylation, O-dealkylation, hydroxylation, and N-acylation. Our results not only highlight the potential application of microalgae in FLX purification, but also provide insight into the fate and ecological risk of FLX in aquatic environments.

Combined remediation effects of biochar and organic fertilizer on immobilization and dissipation of neonicotinoids in soils.

Neonicotinoid (NEO) pesticides have become a potential risk to ecological safety and human health after application. The combined use of biochar and organic fertilizer (OF) is a promising approach to reduce pesticide adverse effects and improve soil fertility in agricultural soils. However, the combined remediation effects of biochar and OF on immobilization and dissipation of NEOs in soils have not previously been systematically investigated. In this study, biochars derived from peanut shell prepared at low/high pyrolysis temperatures (PS400 and PS900) were combined with composted chicken manure (CCM) as an example for OF to remediate contaminated soils toward six typical NEOs, nitenpyram (NIT), thiamethoxam (THIA), clothianidin (CLO), imidacloprid (IMI), acetamiprid (ACE), thiacloprid (THI). Results shown that both biochars and CCM were effective in improving soil sorption capacity and immobilization efficiency. The Freundlich affinity parameters (Kf) of NEOs in soils increased 7.2-12.0 times after the combined remediation of biochar and CCM, and the Kf of six NEOs had negative correlation with their lipophilicity (p < 0.05), which followed by THI > ACE ≈ IMI > CLO > THIA > NIT. Meanwhile, NEOs-abiotic degradation was accelerated by biochar, CCM and their combined addition by adjusting soil pH and stimulating hydrolysis action. Biotic degradation was dominant in NEOs dissipation processes in amended soils, and the contribution ratios of biotic degradation (CRbio) were in the range of 25.4-99.0%. The combined use of biochar and CCM selectively stimulated the relative abundance of NEOs-degraders, which simplified abiotic degradation of -NO2-containing NEOs (viz., NIT, THIA, CLO, and IMI), but inhibited -C≡N-containing NEOs (viz., ACE and THI). The combined remediation provided a strategy for immobilizing NEOs and facilitating dissipation of -NO2-containing NEOs in soils. The results in this study provide valuable information for policymakers and decision-makers to choose appropriate soil remediation approaches with respect to the NEO types.

Sulfur heterogeneity: A non-negligible factor in manipulating growth and lipid accumulation of Scenedesmus obliquus at a relatively high ratio of carbon to nitrogen.

Algal biodiesel has been becoming a focus in the field of bioenergy worldwide. In this study, effects of heterogeneous sulfur (SO42-, SO32- and S2-) on growth and lipid accumulation of Scenedesmus obliquus cultured in wastewater with a C/N ratio of 30 were investigated, respectively. The results shown that SO42-, the optimal sulfur source, could trigger cell growth in a concentration-dependent manner. However, SO32- was superior to the others in boosting carbon uptake of cells, which was subject to NH4+-N concentration. Only SO42- could simultaneously increase lipid content and productivity of cells with a dominant component of oleic acid (C18:1n9c) occupying approximately 40% in fatty acid profile. Additionally, the genes encoding enzymes such as CDIPT, ADPRM, DPP1, pmtA and BTA1 involved in the uppermost lipid-related pathway (glycerophospholipid metabolism) were identified facing different sulfur source regardless of the concentration changes. These findings may facilitate nutrition management efforts to enhance microalgae-based biofuel production.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning.

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj2: 0.907-0.999), robustness (QBOOT2: 0.900-0.937) and predictability (Rext2: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds.

Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (KPE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the KPE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (Kmic-w) and the LDPE-micelle partition coefficient (KPE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the KPE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental KPE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.

Full-Length Transcriptome of Thalassiosira weissflogii as a Reference Resource and Mining of Chitin-Related Genes.

ß-Chitin produced by diatoms is expected to have significant economic and ecological value due to its structure, which consists of parallel chains of chitin, its properties and the high abundance of diatoms. Nevertheless, few studies have functionally characterised chitin-related genes in diatoms owing to the lack of omics-based information. In this study, we first compared the chitin content of three representative Thalassiosira species. Cell wall glycosidic linkage analysis and chitin/chitosan staining assays showed that Thalassiosira weissflogii was an appropriate candidate chitin producer. A full-length (FL) transcriptome of T. weissflogii was obtained via PacBio sequencing. In total, the FL transcriptome comprised 23,362 annotated unigenes, 710 long non-coding RNAs (lncRNAs), 363 transcription factors (TFs), 3113 alternative splicing (AS) events and 3295 simple sequence repeats (SSRs). More specifically, 234 genes related to chitin metabolism were identified and the complete biosynthetic pathways of chitin and chitosan were explored. The information presented here will facilitate T. weissflogii molecular research and the exploitation of ß-chitin-derived high-value enzymes and products.

Effects of sulfate ions on growth and lipid synthesis of Scenedesmus obliquus in synthetic wastewater with various carbon-to-nitrogen ratios altered by different ammonium and nitrate additions.

Producing biodiesel from microalgae is a promising strategy to upgrade energy structure. In this study, effects of sulfate (SO42-) on lipid synthesis of Scenedesmus obliquus (S. obliquus) cultivated in synthetic wastewater with different carbon to nitrogen (C/N) ratios regulated by ammonium (NH4+-N) and nitrate (NO3--N), separately, were investigated. The results shown that SO42- could dramatically increase cell growth preferring to NH4+-N supply. And SO42- addition could improve its carbon and nitrogen utilization potential for boosting lipid productivity leading α-linolenic acid (C18:3n3) to occupy a dominant component (38.96%) in NH4+-N group at a C/N ratio of 7.5. Additionally, SO42- could enhance the enrichment and expression of up-regulated genes annotated in key enzymes such as GK, GNPAT, CRLS, plc and DEGS involved in glycerolipid, glycerophospholipid and sphingolipid metabolic pathways, resulting in carbon metabolism enhancement and sulfatide accumulation. This study brings a comprehensive view towards nutritional regulation of lipid synthesis in microalgae.

Influence of urban morphological parameters on the distribution and diffusion of air pollutants: A case study in China.

Air pollution has a serious fallout on human health, and the influences of the different urban morphological characteristics on air pollutants cannot be ignored. In this study, the relationship between urban morphology and air quality (wind speed, CO, and PM2.5) in residential neighborhoods at the meso-microscale was investigated. The changes in the microclimate and pollutant diffusion distribution in the neighborhood under diverse weather conditions were simulated by Computational Fluid Dynamics (CFD). This study identified five key urban morphological parameters (Building Density, Average Building Height, Standard Deviation of Building Height, Mean Building Volume, and Degree of Enclosure) which significantly impacted the diffusion and distribution of pollutants in the neighborhood. The findings of this study suggested that three specific strategies (e.g. volume of a single building should be reduced, DE should be increased) and one comprehensive strategy (the width and height of the single building should be reduced while the number of single buildings should be increased) could be illustrated as an optimized approach of urban planning to relief the air pollution. The result of the combined effects could provide a reference for mitigating air pollution in sustainable urban environments.

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches.

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (KPE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (KPE-W), LDPE-air (KPE-A) and LDPE-seawater (KPE-SW) partition coefficients. These developed models have satisfying predictability (R2adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSEtra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q2ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSEext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown KPE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

Genome-wide identification of chitinase genes in Thalassiosira pseudonana and analysis of their expression under abiotic stresses.

BACKGROUND: The nitrogen-containing polysaccharide chitin is the second most abundant biopolymer on earth and is found in the cell walls of diatoms, where it serves as a scaffold for biosilica deposition. Diatom chitin is an important source of carbon and nitrogen in the marine environment, but surprisingly little is known about basic chitinase metabolism in diatoms. RESULTS: Here, we identify and fully characterize 24 chitinase genes from the model centric diatom Thalassiosira pseudonana. We demonstrate that their expression is broadly upregulated under abiotic stresses, despite the fact that chitinase activity itself remains unchanged, and we discuss several explanations for this result. We also examine the potential transcriptional complexity of the intron-rich T. pseudonana chitinase genes and provide evidence for two separate tandem duplication events during their evolution. CONCLUSIONS: Given the many applications of chitin and chitin derivatives in suture production, wound healing, drug delivery, and other processes, new insight into diatom chitin metabolism has both theoretical and practical value.

Effects of residue removal and tillage on greenhouse gas emissions in continuous corn systems as simulated with RZWQM2.

Agricultural production is a major source of carbon dioxide (CO2) and nitrous oxide (N2O) globally. The effects of conservation practices on soil CO2 and N2O emissions remain a high degree of uncertainty. In this study, soil CO2 and N2O emissions under different residue and tillage practices in an irrigated, continuous corn system, were investigated using the Root Zone Water Quality Model (RZWQM2). Combinations of no/high stover removal (NR and HR, respectively) and no-till/conventional tillage (NT and CT, respectively) field experiments were tested over the four crop-years (Apr. 2011-Apr. 2015). The model was calibrated using the NRCT, and validated with other treatments. The simulation results showed that soil volumetric water content (VWC) in the NR treatments (i.e., NRCT and NRNT) was 1.3%-1.9% higher than that in the HR treatments (i.e., HRCT and HRNT) averaged across the four years. A higher amount of CO2 and N2O emissions were simulated in the NRCT across the four years (annual average: 7034 kg C/ha/yr for CO2 and 3.8 kg N/ha/yr for N2O), and lower emissions were in the HRNT (annual average: 6329 kg C/ha/yr and 3.7 kg N/ha/yr for N2O). A long-term simulation (2001-2015) suggested that the CO2 and N2O emissions were closely correlated with the stover removal degree (SRD), tillage, VWC, soil temperature (ST), years in management (Y), and fertilizer application. Stover and tillage practices had cumulative effects on CO2 emissions. The simulated annual CO2 emissions in 1st year from NRCT, NRNT, and HRCT were 7.8%, 0.0%, and 7.7% higher than that from HRNT, respectively; then the emissions in 15th year were 63.6%, 47.7%, and 29.1% higher, respectively. Meanwhile, there were no cumulative effects on N2O emissions. The results also demonstrated that the RZWQM2 is a promising tool for evaluating the long-term effects of CO2 and N2O emissions on different conservation practices.

Immobilization altering the growth behavior, ammonium uptake and amino acid synthesis of Chlorella vulgaris at different concentrations of carbon and nitrogen.

Nitrogen recycling by microalgae has aroused considerable attention. In this study, immobilized Chlorellavulgaris with 5-day mixotrophic cultivation to recover ammonium (NH4+-N) were systematically investigated under various sodium acetate (CH3COONa) and ammonium chloride (NH4Cl) concentrations, and evaluated by comparison with suspended cells. The results revealed that, unlike suspended cells, NH4+-N uptake by immobilized cells was not in direct proportion to chemical oxygen demand (COD) concentrations. The immobilized cells to NH4+-N uptake was all inferior to that of suspended cells, presenting the maximum rate of 68.92% in group of 30 mg/L NH4+-N and 200 mg/L COD. Free amino acids in immobilized cells such as glutamate (Glu), arginine (Arg), proline (Pro) and leucine (Leu) were more sensitive to NH4+-N assimilation, as higher values observed by suspended cells. Low carbon-nitrogen (C/N) ratio showed remarkable benefits to amino acid synthesis. These results could provide a reference for manipulating the algal system and biomass accumulation.

Combined effects of environmentally relevant concentrations of diclofenac and cadmium on Chironomus riparius larvae.

The nonsteroidal anti-inflammatory drug diclofenac (DCF) is considered a contaminant of emerging concern. DCF can co-exist with heavy metals in aquatic environments, causing unexpected risks to aquatic organisms. This study aimed to assess the combined effects of DCF and cadmium (Cd) at environmentally relevant concentrations on the bioconcentration and status of oxidative stress and detoxification in Chironomus riparius larvae. The larvae were exposed to DCF (2 and 20 µg L-1) and Cd (5 and 50 µg L-1) alone or in mixtures for 48 h. The combined exposure to DCF and Cd was found to reciprocally facilitate the accumulation of each compound in larvae compared with single exposures. As indicated by the antioxidant enzyme activities, reduced glutathione levels, and malondialdehyde contents, the low concentration of the mixture (2 µg L-1 DCF + 5 µg L-1 Cd) did not alter the oxidative stress status in larvae, while the high concentration of the mixture (20 µg L-1 DCF + 50 µg L-1 Cd) induced stronger oxidative damage to larvae compared with single exposures. The expression levels of eight genes (CuZnSOD, MnSOD, CAT, GSTd3, GSTe1, GSTs4, CYP4G, and CYP9AT2) significantly decreased due to the high concentration of the mixture compared with single exposures in most cases. Overall, the results suggest that the mixture of DCF and Cd might exert greater ecological risks to aquatic insects compared with their individual compounds.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches.

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (KPOM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log KPOM-w with R2adj of 0.825. The values of statistical parameters including R2adj, Q2ext, RMSEtra and RMSEext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.