Particulate Matter Index Research Articles

Disparities of compound exposure of particulate matter (PM2.5) and heat index using citywide monitoring networks

As epicenters of environmental change, cities face multiple threats from environmental hazards, including poor air quality and extreme heat. The complex features of urban environments, such as varying landscapes and emission sources, significantly affect these phenomena. This study investigated the combined exposure to PM2.5 and extreme heat within Chicago, considering socioeconomic aspects to gage environmental justice issues. This study utilized high-resolution environmental datasets and Random Forest Spatial Interpolation (RFSI) to generate hourly Heat Index and PM2.5 maps at a spatial resolution of 250 m. The RFSI showed robust performance, with cross-validation R2 ranging from 0.78 to 0.98 (air temperature (Ta): 0.98, relative humidity (RH): 0.93, and PM2.5: 0.84) and RMSE from 0.84 to 5.4 (Ta: 1.09, RH: 5.40, and PM2.5: 0.84). With this result, this research visualized the spatial variations in extreme heat, as measured by the Heat Index and PM2.5 levels, and identified areas with critical exposure that are potentially harmful to the health of vulnerable populations. Furthermore, this study found the spatial disparities in exposure linked to socioeconomic factors by conducting a Welch ANOVA test. These findings can inform the development of targeted interventions considering the temporal-spatial disparities of heat and air pollution levels.

The Role of Environmental Exposures on Survival After Non-Small Cell Lung Cancer Resection

BackgroundSocioeconomic status and pollution exposure have been described as risk factors for poor survival in patients with non-small cell lung cancer (NSCLC). However, the relationship between these factors is complex and inadequately studied. This study aimed to evaluate the relationship between environmental and social factors and their impact on survival after NSCLC resection. MethodsA prospective database for all patients with NSCLC who underwent primary resection from 2006 to 2021 was analyzed. Ambient fine particulate matter (air pollution smaller than 2.5 μm [PM2.5]), greenness, and deprivation index (a measure of neighborhood-level material deprivation composed of 6 factors) were linked to individual patients by geocoding their residential address. ResultsA total of 661 patients who underwent pulmonary resection for NSCLC were evaluated. Black patients had increased levels of community deprivation compared with White patients; however, there was no difference in PM2.5 exposure or overall survival between races. Increased PM2.5 exposure was an independent predictor of worse survival on univariable and multivariable analysis (hazard ratio, 1.06; P = .003). ConclusionsIncreased PM2.5 exposure is associated with worse overall survival in resected NSCLC and was a more significant factor than race and material deprivation in this population. Interventions to reduce environmental air pollution could improve lung cancer survival.

Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020)

Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM1, PM2.5–1, and PM10–2.5), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 %CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.

Hedging against air pollution using an option pricing model based on a fine particulate matter index

Hedging against air pollution using an option pricing model based on a fine particulate matter index

Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning

This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM1, PM2.5, PM10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (σ). RMSE ranged between 0.65 and 1 μg/m3, while σ has values between 2.75 and 4.1 μg/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square - Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and σ vary between 8 and 9.1 μg/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 μg/m3, while the σ ranged between 4.44 and 6.26 μg/m3, further validating the precision of our forecasting model.

Soot formation of renewable gasoline: From fuel chemistry to particulate emissions from engines

Renewable fuels are essential to meet climate protection goals, especially for decarbonizing the existing vehicle fleet. In addition, optimized and specially designed fuels allow emissions to be avoided before they occur. This paper presents fuel effects on emission characteristics of six chemically complex gasoline blends compliant with the DIN EN 228 standard and one ethanol/gasoline blend, focusing on particle emissions. First, the sooting propensity of the fuels was examined using different soot indices, such as the Particulate Matter Index (PMI), the Oxygen Extended Sooting Index (OESI), the Yield Sooting Index (YSI), and the soot threshold (φST), which is drawn on measurements of the particle number (PN) density in the exhaust gas of a laminar flat flame. Second, PN emissions (particle number concentration) from a production-calibrated turbo gasoline direct injection engine with four cylinders were investigated. Third, soot precursor chemistry was investigated using a laminar flow reactor at atmospheric pressure. Finally, correlations to fuels’ volatility and chemical characteristics were made for the complete data set. Based on the soot precursor chemistry in the homogeneous gas phase reaction, the analysis shows that engine PN emissions are primarily influenced by fuel chemistry for the investigated engine conditions. In particular, the composition of the hydrocarbon fraction is decisive, with the heavy C9+ aromatics and the upper distillate significantly influencing soot formation. In order to reduce PN engine emissions, future fuels should contain fewer soot-increasing hydrocarbon fractions.

Particulate Matter Emissions in Gasoline Direct-Injection Spark-Ignition Engines: Sources, Fuel Dependency, and Quantities

This study investigates sources of particulate matter (PM) from a direct-injected spark-ignition (DISI) engine, all of which are likely the result of poor mixture formation, although through varying mechanisms. Nine unique gasoline formulations, including both oxygenated and non-oxygenated fuels, with varying distillation curves and concentrations of different hydrocarbon classes were employed to investigate the effect of fuel on soot formation. The engine experiments were further supported by reacting and non-reacting experiments in a spray-chamber.The metal-engine experimental results indicated that engine-out PM emissions of the nine fuels generally conforms to their sooting tendency, here quantified by Particulate Matter Index (PMI). Discrepancies with the engine-out PM emissions and PMI were observed under transient operating condition, which is not reflective of the test conditions used to formulate the PMI. A diisobutylene surrogate blend and a high-olefin full-boiling range fuel were investigated further in the optically-accessible engine, showing soot generation from different sources. These differences came from the variation in their spray characteristics, as confirmed in the non-reactive spray-chamber experiments.While the earlier tests were conducted with a fouled injector, the injector was later cleaned and fuels were tested in the optical engine. High olefin fuel and diisobutylene blend showed significantly lower soot emissions with clean injector. E30 fuel exhibited spray impingement and subsequent diffusive combustion, known as pool fire, on the piston top characterized by a yellow flame due to soot luminosity. A final round of experiments in the spray-chamber mimic this pool fire mechanism. A stoichiometric flame was initiated after a spray has impinged on a wall and formed a film. Results showed a large time delay between the flame passing over the fuel film and the formation of soot mass. The slow soot formation via pyrolysis in spray-chamber suggest that pyrolysis may not be a dominating soot-production pathway in SI engines, even for conditions with significant wall wetting. The findings of this study highlight the many considerations which influence the effective design of a combustion system when the minimization of PM emissions is a goal, and the ways in which fuel formulation can change soot-production pathways in DISI engines.

Air pollution and refraining from visiting health facilities: a cross-sectional study of domestic migrants in China

BackgroundLocal environmental factors are associated with health and healthcare-seeking behaviors. However, there is a paucity in the literature documenting the link between air pollution and healthcare-seeking behaviors. This study aimed to address the gap in the literature through a cross-sectional study of domestic migrants in China.MethodsData were extracted from the 2017 China Migrants Dynamic Survey (n = 10,051) and linked to the official air pollution indicators measured by particulate matter (PM2.5 and PM10) and air quality index (AQI) in the residential municipalities (n = 310) of the study participants over the survey period. Probit regression models were established to determine the association between air pollution and refraining from visiting health facilities after adjustment for variations in the predisposing, enabling and needs factors. Thermal inversion intensity was adopted as an instrumental variable to overcome potential endogeneity.ResultsOne unit (µg/m3) increase in monthly average PM2.5 was associated with 1.8% increase in the probability of refraining from visiting health facilities. The direction and significance of the link remained unchanged when PM2.5 was replaced by AQI or PM10. Higher probability of refraining from visiting health facilities was also associated with overwork (β = 0.066, p = 0.041) and good self-related health (β = 0.171, p = 0.006); whereas, lower probability of refraining from visiting health facilities was associated with short-distance (inter-county) migration (β=-0.085, p = 0.048), exposure to health education (β=-0.142, p < 0.001), a high sense of local belonging (β=-0.082, p = 0.018), and having hypertension/diabetes (β=-0.169, p = 0.005).ConclusionAir pollution is a significant predictor of refraining from visiting health facilities in domestic migrants in China.

Influence of land use and occupation on the chemical and physical fractions of organic matter in cultivated and native areas in the Atlantic Forest biome

This study quantified the C content of the chemical and physical fractions of SOM in different management systems in an Argisoil of sandy texture. The study was carried out in a reference area of Native Forest (NF), and in three managed areas: Permanent Pasture (PP), No-Tillage System (NTS) and an area of Private Natural Heritage Reserve (PNHR) in the process of natural regeneration. Soil samples were collected in the layers 0.0-0.05, 0.05-0.10 and 0.10-0.20 m. We assessed the soil density (Sd), total organic carbon (TOC) content, chemical fractionation of SOM with determination of the C contents of the fulvic acids (FA), humic acids (HA) and humin (HUM), with subsequent calculations of the HA/FA ratios, AE/HUM, stock (StockC), physical granulometric fractionation and determination of C contents of particulate organic matter (C-POM) and carbon management index (CMI). Higher TOC contents were observed for the NF area. The C-HA and C-HUM contents were higher in the NF and NTS. NF showed higher C-POM levels in all layers evaluated. For the C-MOM, the NTS area was superior to the other managed areas. The managed areas had lower StockPOM values than the NF. The managed areas had lower CMI values in relation to NF. The NTS area showed that, even in crop succession, it contributes to the improvement of the soil organic fraction over the adoption time. On the other hand, the areas of PP and PNHR showed that inadequate management favors the reduction of edaphic quality.&#x0D; Keywords: carbon in soil, carbon management index, chemical fractionation, physical fractionation.

Impact of Short-Term Air Pollution on Respiratory Infections: A Time-Series Analysis of COVID-19 Cases in California during the 2020 Wildfire Season.

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

Fuel Effects on Advanced Compression Ignition Load Limits

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In order to maximize the efficiency of light-duty gasoline engines, the Co-Optimization of Fuels and Engines (Co-Optima) initiative from the U.S. Department of Energy is investigating multi-mode combustion strategies. Multi-mode combustion can be describe as using conventional spark-ignited combustion at high loads, and at the part-load operating conditions, various advanced compression ignition (ACI) strategies are being investigated to increase efficiency. Of particular interest to the Co-Optima initiative is the extent to which optimal fuel properties and compositions can enable higher efficiency ACI combustion over larger portions of the operating map. Extending the speed-load range of these ACI modes can enable greater part-load efficiency improvements for multi-mode combustion strategies. In this manuscript, we investigate fuel effects for six different fuels, including four with a research octane number (RON) of 98 and differing fuel chemistries, iso-octane, and a market representative E10 fuel, on the load limits for two different ACI strategies: spark-assisted compression ignition (SACI) and partial fuel stratification-gasoline compression ignition (PFS-GCI) operation. Experimental results show that limits to intake boosting limit high load operation for most fuels, but high smoke emissions for high particulate matter index (PMI) fuels under SACI conditions could also be a limitation. Contrastingly, low load is limited by combustion efficiency, but these effects have more pronounced variation with fuel chemistry for PFS-GCI than with SACI. Additional, distinct effects affecting autoignition timing and peak heat release at higher speeds were identified for fuels having different low temperature heat release (LTHR) propensities for both ACI modes.&lt;/div&gt;&lt;/div&gt;

An Assessment on Ethanol-Blended Gasoline/Diesel Fuels on Cancer Risk and Mortality.

Although cancer is traditionally considered a genetic disease, the epigenetic abnormalities, including DNA hypermethylation, histone deacetylation, and/or microRNA dysregulation, have been demonstrated as a hallmark of cancer. Compared with gene mutations, aberrant epigenetic changes occur more frequently, and cellular epigenome is more susceptible to change by environmental factors. Excess cancer risks are positively associated with exposure to occupational and environmental chemical carcinogens, including those from gasoline combustion exhausted in vehicles. Of note, previous studies proposed particulate matter index (PMI) as a measure for gasoline sooting tendency, and showed that, compared with the other molecules in gasoline, 1,2,4–Trimethylbenzene, 2–methylnaphthalene and toluene significantly contribute to PMI of the gasoline blends. Mechanistically, both epigenome and genome are important in carcinogenicity, and the genotoxicity of chemical agents has been thoroughly studied. However, less effort has been put into studying the epigenotoxicity. Moreover, as the blending of ethanol into gasoline substitutes for carcinogens, like benzene, toluene, xylene, butadiene, and polycyclic aromatic hydrocarbons, etc., a reduction of secondary aromatics has been achieved in the atmosphere. This may lead to diminished cancer initiation and progression through altered cellular epigenetic landscape. The present review summarizes the most important findings in the literature on the association between exposures to carcinogens from gasoline combustion, cancer epigenetics and the potential epigenetic impacts of biofuels.

On Optical Semi-Quantitative Spectral Study of Low-Speed Pre-Ignition Sources in Spark Ignition Engines

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Low-Speed pre-ignition (LSPI) in modern-day, heavily downsized, boosted, and direct-injection spark ignition (SI) engines is a well-known problem. Several mechanisms contribute towards stochastic pre-ignition (SPI), the most prominent being crevice material droplet induced and deposit induced pre-ignition mechanisms. The droplet mechanism is typically dominated by the detergent additives present in the lubricant formulation; more specifically calcium and sodium-based detergent additives correlate strongly with the increased LSPI rates. Deposits flaking off the combustion chamber surfaces can also induce LSPI under certain conditions.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;This study aimed to develop an optical method designed to investigate the nature of pre-ignition precursors. Southwest Research Institute (SwRI) utilized an optically accessible GM 2.0 L LHU engine to study the pre-ignition phenomenon and studied the nature of pre-ignition precursors using spectral information from one of the cylinders in this engine. A custom, simplified borescope was employed to gather visible light from the combustion chamber to detect the glowing pre-ignition precursors. Further, this light signal was sent to an array of four photomultiplier tube (PMT) detectors which were tuned to visible light emission wavelength bands of interest. Three of the four wavelengths (555, 623, 645 nm) were selected to be representative of calcium and CaOH while the fourth wavelength of 715 nm (red emission) was selected to be representative of glowing soot-like aggregates. A high calcium first-generation Dexos™ 1 compliant oil with moderate LSPI activity was used in this study. The analysis revealed that the precursors contained both the signals for calcium as well as the deposit wavelengths in different proportions. More specifically, it was observed that in a series of LSPI events, the first event was usually more biased towards calcium wavelengths suggesting a pre-ignition initiated by the droplet ejecting from the crevice. A relative semi-quantitative metric was developed to compare the light signals for different frequencies to further delve into the pre-ignition mechanism. Two additional fuels were tested with different particulate matter index (PMI) values to assess the impact of PMI on pre-ignition precursors. A bulk comparison of around 30,000 combustion cycles of the two additional test fuels revealed that the high PMI fuel showed more bias towards a deposit induced mechanism while the lower PMI fuel exhibited signs of both droplet and deposit induced pre-ignition precursors.&lt;/div&gt;&lt;/div&gt;

Market Fuel Effects on Low Speed Pre-Ignition

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Low-Speed Pre-ignition (LSPI) is an undesirable abnormal combustion phenomenon observed in turbocharged, direct-injection spark-ignition engines and is characterized by early heat release, high cylinder pressures and severe, potentially damaging knock. LSPI has been studied for more than a decade and engine design, operating conditions and fuel and engine oil formulations have all been identified as contributing factors. A significant focus on engine oil has led to the establishment of the Sequence IX engine test and the second-generation of GM dexos® oil requirements, as well as a convergence of engine oil detergent causality. Conclusions about the effects of fuel on LSPI have been more varied, but as part of a recently completed research consortium, the LSPI tendency of market fuels with a range of properties, including composition, boiling point distribution, ethanol content and particulate matter index (PMI) were evaluated. Tests were performed in a 2-liter GM LHU engine and each test comprised of at least 24 repeats of a high-load, low-speed, steady-state test segment, with engine boundary conditions and calibration adjusted to amplify LSPI. All market fuel tests were bracketed by baseline tests and severity adjustments were made to account for changes in engine condition. It was found that the PMI and certain boiling points correlated the best with the frequency of LSPI events. In addition, decreased LSPI severity, quantified by the peak-to-peak knock values, was found to correspond with increased octane numbers and higher ethanol content of the market fuels.&lt;/div&gt;&lt;/div&gt;

Fuel Effects on Engine-out Emissions Part 2 - Fuel Properties Correlations

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Particulate matter emissions from internal combustion engines have become an increasingly important area of focus for development teams in recent years. This is due to greater regulatory scrutiny on vehicles globally, and especially on particulate emissions. The chemical composition and bulk physical properties of the fuel have been shown to influence the particulate number emissions characteristics. Although some predictive models have been proposed, the causality of specific properties or constituents has not been demonstrated due to the co-linearity of the variables considered in previous studies.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In this work, fuels were formulated to capture the expected variation in three key properties of United States (US) market gasoline fuels. Specifically, total aromatics, volatility, and particulate matter index (PMI) were varied across market extremes within regulatory limits--while holding other properties constant. Engine dyno tests were performed with the formulated fuels on a 4-cylinder 2.3 L turbocharged direct injection spark-ignited engine, operated between 2 and 16 bar BMEP.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;ANOVA analysis was used to demonstrate that both low volatility and high PMI independently increased particle number (PN) emissions at high load at a statistically significant level, whereas total aromatics was only significant at low-load. However, neither low-volatility nor high PMI was universally bad for PN emissions, and other properties also played an important role. For example, at &amp;lt;12 bar BMEP the lowest PN formulated fuel was a low-volatility, high PMI, low aromatics fuel.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The nine formulated fuels from this work along with ten fuels tested in Part 1 of this work were used to correlate PN and THC emissions with various fuel properties, within the repeatability of the test. For PN emissions, most of the fuel-to-fuel differences could be explained across the entire load range using a combination of 5 parameters: FBP, T90, A9&lt;sup&gt;+&lt;/sup&gt;, IP9&lt;sup&gt;+&lt;/sup&gt;, and C11. A9&lt;sup&gt;+&lt;/sup&gt; and FBP were always positively correlated with emissions, whereas IP9&lt;sup&gt;+&lt;/sup&gt; was negatively correlated, and T90 and C11 switched from negative to positive depending on load. For THC, only three parameters were required, and T70 was by far the best predictor of emissions—accounting for 50-70% of the difference between fuels.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The work highlights the critical properties that determine engine out emissions characteristics of different fuels, and shows a path to formulating fuels that can simultaneously reduce PN and THC from vehicles.&lt;/div&gt;&lt;/div&gt;

Analysis of Weather Patterns Related to Wintertime Particulate Matter Concentration in Seoul and a CMIP6-Based Air Quality Projection

This study analyzes the relationship between various atmospheric fields and the observed PM10 concentrations in the Seoul metropolitan area, South Korea, during the winters of the 2001–2014 period to find suitable atmospheric indices for predicting high PM10 episodes in the region. The analysis shows that PM10 concentration in the metropolitan area is mainly affected by the intensity of horizontal ventilation and the 500 hPa high-pressure system over the Korean peninsula. The modified Korea particulate matter index (MKPI) is proposed based on a 10 m wind speed for surface ventilation and 500 hPa zonal wind for the intensity of a 500 hPa high-pressure system over the Korean peninsula. It is found that a positive MKPI value is closely correlated with the occurrence of high PM10 concentration episodes, and hence, can be used as a predictor for high PM10 episodes in the area. A future projection of the MKPI using two three-member general circulation model (GCM) ensembles with four shared socioeconomic pathway (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) shows that positive MKPI events and high PM10 episodes are expected to increase by 5.4−16.4% depending on the SSP scenarios in the 2081−2100 period from the present-day period of 1995−2014.

Ability of Particulate Matter Index to describe sooting tendency of various gasoline formulations in a stratified-charge spark-ignition engine

This study investigates the ability of Particulate Matter Index (PMI) to describe the sooting behavior of various gasoline formulations in a stratified-charge (SC) spark-ignition engine. The engine was operated at 2000 rpm with an intake pressure of 130 kPa where soot formation is known to primarily occur in the bulk gases. Exhaust soot emissions were measured for nine test fuels at various exhaust gas recirculation levels. A comparison between measured soot levels and PMI shows that PMI is a relatively poor predictor of the sooting tendency of the tested fuels under lean SC combustion. Among the fuels, three fuels, namely the di-isobutylene blend, High Olefin, and E30 fuels exhibit measured soot behavior opposite of that predicted by PMI. Optical diagnostics were utilized to further investigate the in-cylinder phenomena for these three fuels. Analysis of natural luminosity and diffused back-illumination extinction imaging suggests that fuel-induced differences in the amount of soot formed are responsible for a majority of the discrepancy in measured versus predicted sooting tendency. Fuel-induced differences in soot oxidation and spray development seem to play minor roles. Because the combustion and air-fuel mixing processes for lean SC combustion are different from conventional stoichiometric operation it is hypothesized that the PMI correlation needs to be modified to account for differences in stoichiometric air-fuel ratio and level of oxygenation between fuels. Furthermore, the role of fuel volatility in PMI possibly needs to be de-emphasized for SC operation with fuel injection into compression-heated gases.

Impact of ethanol blending into gasoline on aromatic compound evaporation and particle emissions from a gasoline direct injection engine

This study examined the interactions of ethanol with aromatic compounds on aromatic species evolution during gasoline evaporation and the consequent impacts on particle emissions from a single-cylinder, gasoline direct injection engine. From a chemical kinetic standpoint, the dilution of the aromatic species in gasoline by ethanol blending should reduce particle emissions because aromatics are the primary species that form particles during gasoline combustion. However, increased evaporative cooling from the ethanol and ethanol’s effect on when aromatics in the fuel droplet (or surface film or pool) evaporate can drive an increase in particle emissions. The objective of this study was to quantify these competing effects. The results show that a combination of ethanol’s increased evaporative cooling and impact on aromatic compound vapor-liquid equilibrium can extend droplet lifetime and cause aromatics to evaporate later in the droplet evaporation process than would be the case without ethanol. When fuels were burned in a direct-injection single-cylinder engine at high speed (2500 rpm) and relatively high load, ethanol blending caused an increase in particle emissions for fuels containing a low vapor pressure aromatic. At a lower speed (1500 rpm), no statistically significant increase in particle emissions was observed, likely because more time was available for evaporation and mixing to occur. In contrast, particle emissions from a fuel blended with a high vapor pressure aromatic were either insensitive to or reduced by ethanol blending, likewise to engine operating conditions. Current models for predicting particle emissions from gasoline engines, such as particulate matter index (PMI), generally lack variables for non-linear interactions between ethanol and gasoline hydrocarbons. To identify nonlinear interactions between predictor variables that might better explain the observed particle emissions, the least absolute shrinkage and selection operator (LASSO) regularized regression approach was employed. Among the predictor variables evaluated was the Yield Sooting Index (YSI) which is as an improved measure to quantify the aromatic’s chemical tendency to generate particle emissions. Following this analysis, two variables were selected based on their frequent appearance. The first was ethanol mole percent multiplied by aromatic mole percent divided by aromatic vapor pressure [(EtOH% * Aro%)/Aro VP@443 K]. The second was aromatic mole percent multiplied by aromatic YSI divided by aromatic molecular weight [(Aro% * Aro YSI)/Aro MW]. The results of linear regression including only these two combined explanatory variables achieved an r2 = 0.959, substantially improved over the PMI model (r2 = 0.688) and provides insight into how the PMI formalism might be modified to account for non-linear oxygenate-hydrocarbon coupling.

A reduced PM index for evaluating the effect of fuel properties on the particulate matter emissions from gasoline vehicles

Particulate matter (PM) emissions have become an increasingly noticeable concern for gasoline vehicles, especially for gasoline direct injection (GDI) one. The PM index (PMI) model developed by Aikiwa et al. provides an effective model linking the fuel properties to the PM emissions from gasoline vehicles. In this study, a more practical reduced PM index (RPMI) was developed based on the detailed analysis of 22 fuels with various physical properties and chemical compositions. Two approaches of statistical mathematics of correlation analysis and multivariate linear regression (MLR) were adopted in the modeling process. The RPMI involving two global fuel properties of T90 (distillation temperature of 90% by volume) and T70 (distillation temperature of 70% by volume) was tested to be statistically valid. In addition, the model was verified through the engine bench tests and the vehicle emissions tests. The results reveal that the RPMI showed good correlations to the engine-out particle number (PN) emissions under all of the typical test conditions, including PFI (port fuel injection) mode, GDI (gasoline direct injection) mode and compound injection (PFI + GDI) mode. In addition, the RPMI demonstrated a significantly high correlation to vehicle-out PN emissions over the New European Driving Cycle (NEDC) with the determination coefficient R2 = 0.947. Moreover, a moderate correlation (R2 = 0.801) of the index to the filter PM mass emissions was observed. In the light of no additional components analysis is needed except for the legitimate tests of fuels, the RPMI has the potential to be a useful tool for original equipment manufacturers (OEMs) and environmental certification departments to expediently evaluate the fuel effect on PM emissions.

Effects of the Particulate Matter Index and Particulate Evaluation Index of the Primary Reference Fuel on Particulate Emissions from Gasoline Direct Injection Vehicles

The purpose of this experimental study was to evaluate the range of particulate mass (PM) and particulate number (PN) results from gasoline direct injection (GDI) vehicles by using four test fuels with a range of particulate matter index (PMI) from 1.38 to 2.39 and particulate evaluation index (PEI) from 0.89 to 1.92. The properties of four test fuels were analyzed with detailed hydrocarbon analysis (DHA). Two passenger cars with a GDI engine were tested with four test fuels by conducting the China 6 test procedure, which is equivalent to the worldwide harmonized light-duty vehicle test procedure (WLTP). When the fuels could meet the China 6 primary reference fuel standard with PMI from 1.38 to 2.04 and PEI from 0.89 to 1.59, the PM variation of Vehicle B was from 1.94 mg/km to 3.32 mg/km and of Vehicle A was from 2.55 mg/km to 4.15 mg/km, respectively. In addition, the PN variation of Vehicle B was from 1.57 × 1012 #/km to 3.38 × 1012 #/km and of Vehicle A was from 3.02 × 1012 #/km to 4.80 × 1012 #/km. It was noted that the two different cars had a unique response and sensitivity by using the different fuels, but PMI and PEI did trend with both the PM and the PN response. All PM and PN results from the two cars had an excellent correlation R2 &gt; 0.94 with PMI and R2 &gt; 0.90 with PEI. Therefore, PMI/PEI would be the appropriate specification for sooting tendency in reference fuel standards of emission regulations.