Using ethanol in gasoline is considered one of the most significant goals in the 2030 agenda, which has been set a 15-year plan in order to achieve it since 2015. Appropriately, this project was planned for predicting the value of the most important engine parameters such as the equivalence air-fuel ratio (φ), fuel consumption (ṁf), and brake thermal efficiency nb. th, and brake-specific fuel consumption (BSFC) by regression models. According to the protocol of this project, first, the determined percentages of ethanol were added (0, 20, 40, 60, and 80%) to gasoline at different engine speeds (850, 1000, 2000, 3000, and 4000 rpm and the New European Driving Cycle test). After testing, calculating, mathematical programming, and fitting the regression models for the two SI-engine (TU5 and EF7) with different properties of engine design,12 regression equations have been determined for each of the ‘ (positive linear model), (ṁf) (negative linear model), nb.th (negative second-order polynomial model), and BSFC (positive second-order polynomial model), respectively. Clearly, these 48 regression equations with different line slopes will be able to predict the exact value of the ‘, (ṁf), nb.th, and BSFC for each concentration of ethanol at different engine speeds in order to help automotive industries for trend predicting them in other similar engines.

Intermediate-volatility and semi-volatile organic compounds (I/SVOCs) are key precursors of secondary organic aerosol (SOA). However, the comprehensive characterization of I/SVOCs has long been an analytical challenge. Here, we develop a novel method of speciating and quantifying I/SVOCs using two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-ToF-MS) by constructing class-screening programs based on their characteristic fragments and mass spectrum patterns. Using this new approach, we then present a comprehensive analysis of gaseous I/SVOC emissions from heavy-duty diesel vehicles (HDDVs). Over three-thousand compounds are identified and classified into twenty-one categories. The dominant compound groups of I/SVCOs emitted by HDDVs are alkanes (including normal and branched alkanes, 37–66%), benzylic alcohols (7–20%), alkenes (3–11%), cycloalkanes (3–9%), and benzylic ketones (1–4%). Oxygenated I/SVOCs (O–I/SVOCs, e.g., benzylic alcohols and ketones) are first quantified and account for >20% of the total I/SVOC mass. Advanced aftertreatment devices largely reduce the total I/SVOC emissions but increase the proportion of O–I/SVOCs. With the speciation data, we successfully map the I/SVOCs into the two-dimensional volatility basis set space, which facilitates a better estimation of SOA. As aging time goes by, approximate 45% difference between the two scenarios after seven-day aging is observed, which confirms the significant impact of speciated I/SVOC emission data on SOA prediction.

Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8-OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value (HSV), Brovey, principal component analysis (PCA), Gram-Schmidt (GS), wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least squares regression (PLSR) model developed on genetic algorithm (GA)-selected laboratory visible-near infrared-shortwave infrared (VNIR–SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements’ prediction models.

In recent decades, nanotechnology has rapidly developed. Therefore, there is growing concern about the potential environmental risks of nanoparticles (NPs). Caenorhabditis elegans (C. elegans) has been used as a powerful tool for studying the potential ecotoxicological impacts of nanomaterials from the whole animal level to single cell level, especially in the area of reproduction. In this review, we discuss the reproductive toxicity of common nanomaterials in C. elegans, such as metal-based nanomaterial (silver nanoparticles (NPs), gold NPs, zinc oxide NPs, copper oxide NPs), carbon-based nanomaterial (graphene oxide, multi-walled carbon nanotubes, fullerene nanoparticles), polymeric NPs, silica NPs, quantum dots, and the potential mechanisms involved. This insights into the toxic effects of existing nanomaterials on the human reproductive system. In addition, we summarize how the physicochemical properties (e.g., size, charge, surface modification, shape) of nanomaterials influence their reproductive toxicity. Overall, using C. elegans as a platform to develop rapid detection techniques and prediction methods for nanomaterial reproductive toxicity is expected to reduce the gap between biosafety evaluation of nanomaterials and their application.

Accurate prediction of the colloid-driven transport of radionuclides in porous media is critical for the long-term safety assessment of radioactive waste disposal repository. However, the co-transport and corelease process of radionuclides with colloids have not been well documented, the intrinsic mechanisms for colloids-driven retention/transport of radionuclides are still pending for further discussion. Thus the controlling factors and governing mechanisms of co-transport and co-release behavior of Eu(III) with bentonite colloids (BC) were discussed and quantified by combining laboratory-scale column experiments, colloid filtration theory and advection dispersion equation model. The results showed that the role of colloids in facilitating or retarding the Eu(III) transport in porous media varied with cations concentration, pH, and humic acid (HA). The transport of Eu(III) was facilitated by the dispersed colloids under the low ionic strength and high pH conditions, while was impeded by the aggregated colloids cluster. The enhancement of Eu(III) transport was not monotonically risen with the increase of colloids concentration, the most optimized colloids concentration in facilitating Eu(III) transport was approximately 150 mg L⁻¹. HA showed significant promotion on both Eu(III) and colloid transport because of not only its strong Eu(III) complexion ability but also the increased dispersion of HA-coated colloid particles. The HA and BC displayed a synergistic effect on Eu(III) transport, the co-transport occurred by forming the ternary BC-HA-Eu(III) hybrid. The transport patterns could be simulated well with a two-site model that used the advection dispersion equation by reflecting the blocking effect. The retarded Eu(III) on the stationary phase was released and remobilized by the introduction of colloids, or by a transient reduction in cation concentration. The findings are essential for predicting the geological fate and the migration risk of radionuclides in the repository environment.

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.

This study represents the first quantitative global prediction of the mass distribution of six widespread polymers, plus plastic fibers and rubber across four environmental compartments and 11 sub-compartments. The approach used probabilistic material flow analysis for 2015, with model input values and transfer coefficients between compartments taken from literature. We estimated that 3.2 ± 1.8 Mt/year of polyethylene, 1.3 ± 0.8 Mt/year of polypropylene, 0.5 ± 0.3 Mt/year of polystyrene, 0.3 ± 0.15 Mt/year of polyvinyl chloride, 1.6 ± 0.9 Mt/year of polyethylene terephthalate and 2.4 ± 1.2 Mt/year of plastic fibers enter the environment. Combining all plastic, including rubber, 4.9 ± 1.3, 4.8 ± 1.9 and 1.8 ± 1.2 Mt/year accumulated in the soil, ocean, and freshwater, respectively. Urban soils and ocean shorelines were predicted as hotspots for plastic accumulation, accounting for 33% and 25% of total plastic, respectively. The floor of freshwater systems and the ocean were predicted as hotspots for high density plastic such as polyethylene terephthalate, polyvinyl chloride and plastic fibers. Furthermore, 59% of environmental rubber was predicted to accumulate in soil. The findings of this study provide baseline data for quantifying plastic transport and accumulation, which can inform future ecotoxicity studies and risk assessments, as well as targeting efforts to mitigate plastic pollution.

Environmental exposure to 4-nonylphenol (4-NP) is extensive, and studies related to human risk assessment must continue. Especially, prediction of toxicodynamics (TDs) related to reproductive toxicity in males is very important in risk-level assessment and management of 4-NP. This study aimed to develop a physiologically-based-toxicokinetic-toxicodynamic (PBTK-TD) model that added a TD prostate model to the previously reported 4-n-nonylphenol (4-n-NP) physiologically-based-pharmacokinetic (PBPK) model. Modeling was performed under the assumption of similar TKs between 4-n-NP and 4-NP because TK experiments on 4-NP, a random-mixture, are practically difficult. This study was very important to quantitatively predict the TKs and TDs of 4-NP by age at exposure using an advanced PBTK-TD model that reflected physiological-changes according to age. TD-modeling was performed based on the reported toxic effects of 4-NP on RWPE-1 cells, a human-prostate-epithelial-cell-line. Through a meta-analysis of reported human physiological data, body weight, tissue volume, and blood flow rate patterns according to age were mathematically modeled. These relationships were reflected in the PBTK-TD model for 4-NP so that the 4-NP TK and TD changes according to age and their differences could be confirmed. Differences in TK and TD parameters of 4-NP at various ages were not large, within 3.61-fold. Point-of-departure (POD) and reference-doses for each age estimated using the model varied as 426.37–795.24 and 42.64–79.52 μg/kg/day, but the differences (in POD or reference doses between ages) were not large, at less than 1.87-times. The PBTK-TD model simulation predicted that even a 100-fold 4-NP PODₘₐₙ dose would not have large toxicity to the prostate. With a focus on TDs, the predicted maximum possible exposure of 4-NP was as high as 6.06–23.60 mg/kg/day. Several toxicity-related values estimated by the dose-response curve were higher than those calculated, depending upon the PK or TK, which would be useful as a new exposure limit for prostate toxicity of 4-NP.

Co-contamination of organic pollutants and heavy metals is universal in the natural environment. Dibutyl phthalate (DBP), a typical plasticizer, frequently coexists with cadmium (Cd) in nature. However, little attention has been given to the impacts of co-contamination by DBP and Cd on microbial communities or the responses of microbes. To address this, a microcosm experiment was conducted by supplying the exogenous DBP-degrading bacterium Glutamicibacter nicotianae ZM05 to investigate the interplay among DBP-Cd co-contamination, the exogenous DBP-degrading bacterium G. nicotianae ZM05, and indigenous microorganisms. To adapt to co-contamination stress, microbial communities adjust their diversity, interactions, and functions. The stability of the microbial community decreased under co-contamination, as evidenced by lower diversity, simpler network, and fewer ecological niches. Microbial interactions were strengthened, as evidenced by enriched pathways related to microbial communications. Meanwhile, interactions between microorganisms enhanced the environmental fitness of the exogenous DBP-degrading bacterium ZM05. Based on co-occurrence network prediction and coculture experiments, metabolic interactions between the non-DBP-degrading bacterium Cupriavidus metallidurans ZM16 and ZM05 were proven. Strain ZM16 utilized protocatechuic acid, a DBP downstream metabolite, to relieve acid inhibition and adsorbed Cd to relieve toxic stress. These findings help to explain the responses of bacterial and fungal communities to DBP-Cd co-contamination and provide new insights for the construction of degrading consortia for bioremediation.

Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.