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.

Small-scale experiments performed at SINTEF, Norway in 2011–12 led to the development of a modified Weber scaling algorithm. The algorithm predicts initial oil droplet sizes (d50) from a subsea oil and gas blowout. It was quickly implemented in a high number of operational oil spill models used to predict fate and effect of subsea oil releases both in academia and in the oil industry. This paper presents experimental data from large-scale experiments generating oil droplet data in a more realistic multi-millimeter size range for a subsea blow-out. This new data shows a very high correlation with predictions from the modified Weber scaling algorithm both for untreated oil and oil treated by dispersant injection. This finding is opposed to earlier studies predicting significantly smaller droplets, using a similar approach for estimating droplet sizes, but with calibration coefficients that we mean are not representative of the turbulence present in such releases. | publishedVersion

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.

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.

In recent years, the naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil Cd content and bioavailability among different parent materials, the previous land classification scheme based on total soil Cd content as the classification standard, has certain shortcomings. This study aims to explore the factors influencing soil Cd bioavailability in typical karst areas of Guilin and to suggest a scientific and effective farmland use management plan based on the prediction model. A total of 9393 and 8883 topsoil samples were collected from karst and non-karst areas, respectively. Meanwhile, 149 and 145 rice samples were collected together with rhizosphere soil in karst and non-karst areas, respectively. The results showed that the higher CaO level in the karst area was a key factor leading to elevated soil pH value. Although Cd was highly enriched in karst soils, the higher pH value and adsorption of Mn oxidation inhibited Cd mobility in soils. Conversely, the Cd content in non-karst soils was lower, whereas the Cd level in rice grains was higher. To select the optimal prediction model based on the correlation between Cd bioaccumulation factors and geochemical parameters of soil, artificial neural network (ANN) and linear regression prediction models were established in this study. The ANN prediction model was more accurate than the traditional linear regression model according to the evaluation parameters of the test set. Furthermore, a new land classification scheme based on an ANN prediction model and soil Cd concentration is proposed in this study, making full use of the spatial resources of farmland to ensure safe rice consumption.

The theory-guided air quality model solves the mathematical equations of chemical and physical processes in pollution transportation numerically. While the data-driven model, as another scientific research paradigm with powerful extraction of complex high-level abstractions, has shown unique advantages in the PM₂.₅ prediction applications. In this paper, to combine the two advantages of strong interpretability and feature extraction capability, we integrated the partial differential equation of PM₂.₅ dispersion with deep learning methods based on the newly proposed DPGN model. We extended its ability to perform long-term multi-step prediction and used advection and diffusion effects as additional constraints for graph neural network training. We used hourly PM₂.₅ monitoring data to verify the validity of the proposed model, and the experimental results showed that our model achieved higher prediction accuracy than the baseline models. Besides, our model significantly improved the correct prediction rate of pollution exceedance days. Finally, we used the GNNExplainer model to explore the subgraph structure that is most relevant to the prediction to interpret the results. We found that the hybrid model is more biased in selecting stations with Granger causality when predicting.

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.

Airborne bacteria may absorb the substance from the atmospheric particles and play a role in biogeochemical cycling. However, these studies focused on a few culturable bacteria and the samples were usually collected from one site. The metabolic potential of a majority of airborne bacteria on a regional scale and their driving factors remain unknown. In this study, we collected particulates with aerodynamic diameter ≤2.5 μm (PM₂.₅) from 8 cities that represent different regions across China and analyzed the samples via high-throughput sequencing of 16S rRNA genes, quantitative polymerase chain reaction (qPCR) analysis, and functional database prediction. Based on the FAPROTAX database, 326 (80.69%), 191 (47.28%) and 45 (11.14%) bacterial genera are possible to conduct the pathways of carbon, nitrogen, and sulfur cycles, respectively. The pathway analysis indicated that airborne bacteria may lead to the decrease in organic carbon while the increase in ammonium and sulfate in PM₂.₅ samples, all of which are the important components of PM₂.₅. Among the 19 environmental factors studied including air pollutants, meteorological factors, and geographical conditions, PM₂.₅ concentration manifested the strongest correlations with the functional genes for the transformation of ammonium and sulfate. Moreover, the PM₂.₅ concentration rather than the sampling site will drive the distribution of functional genera. Thus, a bi-directional relationship between PM₂.₅ and bacterial metabolism is suggested. Our findings shed light on the potential bacterial pathway for the biogeochemical cycling in the atmosphere and the important role of PM₂.₅, offering a new perspective for atmospheric ecology and pollution control.

Despite streams and rivers play a critical role as conduits of terrestrially produced organic carbon to the atmosphere, fluvial CO₂ and CH₄ are seldom integrated into regional carbon budgets. High spatial variability hinders our ability to understand how local and longitudinal controls affect underlying processes of riverine CO₂ and CH₄ and challenge the prediction and upscaling across large areas. Here, we conducted a survey of fluvial CO₂ and CH₄ concentrations spanning multiple stream orders within an agriculturally impacted region, the North China Plain. We explored the spatial patterns of fluvial CO₂ and CH₄ concentrations, and then examined whether catchment and network properties and water chemical parameters can explain the variations in both carbon gases. Streams and rivers were systematically supersaturated with CO₂ and CH₄ with the mean concentrations being 111 and 0.63 μmol L⁻¹, respectively. Spatial variability of both gases was regulated by network properties and catchment features. Fluvial CO₂ and CH₄ declined longitudinally and could be modeled as functions of stream order, dissolved oxygen, and water temperature. Both models explained about half of the variability and reflected longitudinal and local drivers simultaneously, albeit CO₂ was more local-influenced and CH₄ more longitudinal-influenced. Our empirical models in this work contribute to the upscaling and prediction of CO₂ and CH₄ emissions from streams and rivers and the understanding of proximal and remote controls on spatial patterns of both gases in agriculturally impacted regions.