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.

The inherent oxidation potential (OP) of atmospheric particulate matter has been shown to be an important metric in assessing the biological activity of inhaled particulate matter and is associated with the composition of PM₂.₅. The current study examined the chemical composition of 388 personal PM₂.₅ samples collected from students and guards living in urban and suburban areas of Beijing, and assessed the ability to predict OP from the calculated metrics of carcinogenic risk, represented by ELCR (excess lifetime cancer risk), non-carcinogenic risk represented by HI (hazard index), and the composition and sources of the particulate matter using multiple linear regression methods. The correlations between calculated ELCR and HI and the measured OP were 0.37 and 0.7, respectively. HI was a better predictor of OP than ELCR. The prediction models based on pollutants (Model_1) and pollution sources (Model_2) were constructed by multiple linear regression method, and Pearson correlation coefficients between the predicted results of Model_1 and Model_2 with the measured volume normalized OP are 0.81 and 0.80, showing good prediction ability. Previous investigations in Europe and North America have developed location-specific relationships between the chemical composition of particulate matter and OP using regression methods. We also examined the ability of relationships between OP and composition, sources, developed in Europe and North America, to predict the OP of particulate matter in Beijing from the composition and sources determined in Beijing. The relationships developed in Europe and North America provided good predictive ability in Beijing and it suggests that these relationships can be used to predict OP from the chemical composition measured in other regions of the world.

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Current studies in China at the national scale were less focused on NO₂ exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO₂ predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO₂, TROPOspheric Monitoring Instrument NO₂, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ concentrations from 2013 to 2019 across China at 1×1 km² resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R² = 0.72) and the spatial (R² = 0.85) variations of the NO₂ predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R² > 0.68) or regions far away from monitors (CV R² > 0.63). We identified a clear decreasing trend of NO₂ exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO₂ variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

The epidemiological evidence supporting putative associations between air pollution and health-related outcomes continues to grow at an accelerated pace with a considerable heterogeneity and with varying consistency based on the outcomes assessed, the examined surveillance system, and the geographic region. We aimed to evaluate the strength of this evidence base, to identify robust associations as well as to evaluate effect variation. An overview of reviews (umbrella review) methodology was implemented. PubMed and Scopus were systematically screened (inception-3/2020) for systematic reviews and meta-analyses examining the association between air pollutants, including CO, NOX, NO₂, O₃, PM₁₀, PM₂.₅, and SO₂ and human health outcomes. The quality of systematic reviews was evaluated using AMSTAR. The strength of evidence was categorized as: strong, highly suggestive, suggestive, or weak. The criteria included statistical significance of the random-effects meta-analytical estimate and of the effect estimate of the largest study in a meta-analysis, heterogeneity between studies, 95% prediction intervals, and bias related to small study effects. Seventy-five systematic reviews of low to moderate methodological quality reported 548 meta-analyses on the associations between outdoor air quality and human health. Of these, 57% (N = 313) were not statistically significant. Strong evidence supported 13 associations (2%) between elevated PM₂.₅, PM₁₀, NO₂, and SO₂ concentrations and increased risk of cardiorespiratory or pregnancy/birth-related outcomes. Twenty-three (4%) highly suggestive associations were identified on elevated PM₂.₅, PM₁₀, O₃, NO₂, and SO₂ concentrations and increased risk of cardiorespiratory, kidney, autoimmune, neurodegenerative, cancer or pregnancy/birth-related outcomes. Sixty-seven (12%), and 132 (24%) meta-analyses were graded as suggestive, and weak, respectively. Despite the abundance of research on the association between outdoor air quality and human health, the meta-analyses of epidemiological studies in the field provide evidence to support robust associations only for cardiorespiratory or pregnancy/birth-related outcomes.

Biochemical oxidation and reduction are key processes in treating biological wastewater and they require the presence of electron acceptors. The functional impact of electron acceptors on microbiomes provides strategies for improving the treatment efficiency. This research focused on two of the most important electron acceptors, nitrate and oxygen. Molecule ecological network, null model, and functional prediction based on high-throughput sequencing were used to analyze the microbiomes features and assembly mechanism. The results revealed nitrate via the homogeneous selection (74.0%) decreased species diversity, while oxygen via the homogeneous selection (51.1%) and dispersal limitation (29.6%) increased the complexity of community structure. Microbes that were more strongly homogeneously selected for assembly included polyphosphate accumulating organisms (PAOs), such as Pseudomonas and variovorax in the nitrate impacted community; Pseudomonas, Candidatus_Accumulibacter, Thermomonas and Dechloromonas, in the oxygen impacted community. Nitrate simplified species interaction and increased the abundance of functional genes involving in tricarboxylic acid cycle (TCA cycle), electron transfer, nitrogen metabolism, and membrane transport. These findings contribute to our knowledge of assembly process and interactions among microorganisms and lay a theoretical basis for future microbial regulation strategies in wastewater treatment.

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.

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors – random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

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.

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O₃, NO₂, NO, PM₂.₅, and PM₁₀, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.