BACKGROUND:Exposure to particulate matter, hazardous gases, and noise among workers causes millions of deaths, injuries, and disabilities annually. These exposures are not measured frequently, and most workers are never monitored because resources for monitoring are limited globally. Even in high-income countries, just two or three samples per workplace inspection are collected on average. Considerably larger sample sizes are required to estimate measures of central tendency and upper percentiles of common exposure distributions. Therefore, interventions and epidemiological studies often rely on poor estimates of those parameters. OBJECTIVE:The objective of this study was to demonstrate that a small team can cost-effectively measure all workers' exposures to multiple hazards within a facility (~100 workers) in a single day while minimizing participant burden and loss of productivity. METHODS:We deployed novel, compact personal monitors-called AirPens-to measure exposures to total particulates, formaldehyde, and A-weighted noise among workers at a furniture manufacturing facility during one full work shift. AirPens were preloaded with sampling media and preprogrammed to minimize field deployment time. On-board sensor data were used to identify sampling anomalies, confirm that AirPens were worn, and validate sampling results. Valid samples were then used to estimate exposure distribution parameters. RESULTS:A team of five people deployed 83 AirPens at the facility. After quality assurance screening, 67 PM, 67 noise, and 22 formaldehyde samples remained valid (or 72, 67, and 31, respectively if samples below the limit of detection are counted as valid). PM and noise exposure distribution parameters (e.g., arithmetic and geometric mean, 95th percentile) were estimated with high certainty. Uncertainty (i.e., confidence intervals) grew several fold when smaller sample sizes were analyzed. SIGNIFICANCE:Use of streamlined multi-hazard monitoring technology enables dramatically higher sample throughput for workplace exposure assessment. This approach reduces the uncertainty of workplace risk assessment and facilitates more in-depth analyses of personal exposure. IMPACT STATEMENT:Comprehensive monitoring of personal exposure to air pollution is lacking due to technological and logistical limitations inherent to established monitoring methods. This lack of monitoring limits occupational health practice and epidemiologic research, which rely on sufficient sample sizes to support expert judgments or statistical inferences. This work demonstrates a new wearable sampler for particle, gas, and noise hazards, designed to overcome typical barriers to personal exposure assessment (e.g., cost, time, participant burden). Results show that this new sampling approach can produce measurements of similar quality, but on a much larger scale, compared to established technology.

Residents of agricultural communities may experience higher exposures to pesticides due to their proximity to agricultural operations. We applied a novel measurement approach, using Ultrasonic Personal Air Samplers (UPAS), to quantify particulate matter and organophosphate pesticides in air in California's Central Valley. We collected 124 personal, 126 in-home, and 32 outdoor air samples with 66 adults from 37 rural households in 2023 and 2024. We detected chlorpyrifos, acephate, malathion, diazinon, and naled in air samples. We detected gas-phase chlorpyrifos in 63% of personal samples and 86% of homeseven though use of chlorpyrifos has been banned in California (with few exceptions) since January 2021at 24 h average concentrations ranging up to 13 ng m-3 (personal) and 5.8 ng m-3 (in-home). We did not detect chlorpyrifos in outdoor air samples. Using linear mixed models, we found that higher indoor air temperatures and having more carpets/rugs were associated with higher indoor chlorpyrifos concentrations. The concentrations we measured were well below the California Department of Pesticide Regulation's health screening level of 510 ng m-3 for chronic exposure to chlorpyrifos in air; nevertheless, our results suggest that persistent chlorpyrifos in home environments continues to contribute to nondietary exposure among California residents.

Indoor nitrogen dioxide (NO _2 ) and fine particulate matter (PM _2.5 ) are concerns in U.S. households, especially those that cook using gas or propane stoves. Exposures to these and other indoor pollutants are linked to a variety of adverse health outcomes, including asthma morbidity, that disproportionately affect low-income households. We conducted a cross-sectional study of 138 homes in four low-income rural communities in California’s San Joaquin Valley, comparing air pollutant concentrations between households that participated in a state electrification program and households using propane or natural gas for cooking. In each home, pollutants were monitored for approximately one month using personal air monitors and for 48 h using reference-grade instruments. Median 48-h average indoor NO _2 concentrations were 63% lower in electric stove homes (electric: 6.0 ppb, gas: 16.0 ppb, p < 0.001). No electric stove homes had 48-h indoor NO _2 concentrations exceeding the California annual guideline of 30 ppb, while 17% of gas homes did. Additionally, no electric stove homes had 1-h rolling-average NO _2 concentrations exceeding the 100-ppb level deemed unhealthy for sensitive groups by the U.S. Environmental Protection Agency, whereas 41% of gas homes exceeded this threshold. PM _2.5 concentrations were similar across groups, indicating that cooking-related emissions from food were the dominant contributor to PM _2.5 mass concentrations rather than particles generated from gas combustion. Our evaluation of monitoring durations showed that two to four days of NO _2 data and one week of PM _2.5 data provided reliable estimates of longer-term averages, suggesting that shorter campaigns may yield robust estimates of indoor air quality. These results support the provision of electric cooking technologies as a strategy to address air quality-related health risks in rural, low-income communities and provide new evidence from an understudied population that can inform future indoor air quality research and energy transition policies.

California’s San Joaquin Valley experiences some of the worst particulate matter (PM) air pollution in the U.S., but PM2.5 and PM10 exposures in agricultural communities are understudied. We collaborated with rural residents living adjacent to large-scale agricultural production and processing activities to assess 24-h-average personal and indoor PM2.5 and PM10 concentrations during different seasons. We visited 35 participants from 18 households during December 2023, May 2024, and the September 2024 harvest season to collect PM samples and survey data. Mixed effects linear regression models (with random effects for participant or household) assessed associations between natural log-transformed PM concentrations and regional ambient PM, harvest season, as well as participant/household characteristics. Participants were mostly female (69%) and Hispanic/Latino(a) (100%). Median household distance to processing facility silos was 633 m. Median personal exposures to PM2.5 and PM10 were 11.1 and 45.5 µg m−3. Median indoor PM2.5 and PM10 levels were 12.9 and 24.3 µg m−3. Overall, 29% of personal and indoor PM2.5 samples and 33% of personal and indoor PM10 samples exceeded WHO 24-h air quality guidelines (15 µg m−3 PM2.5, 45 µg m−3 PM10). The factors most strongly associated with personal and indoor PM were household members working in agriculture and regional ambient PM measures.

Reliable assessment of personal exposure to air pollution remains a challenge due to the limitations of monitoring technology. Recent technology developments, such as reductions in the size and cost of samplers as well as the incorporation of continuous sensors for location, activity, and exposure (i.e., global positioning systems [GPS], accelerometers, and low-cost pollutant sensors), have advanced our ability to assess personal exposure to air pollution. This study evaluated the upgraded Ultrasonic Personal Air Sampler (UPAS v2.1 PLUS) as a tool for quantifying time-integrated indoor and personal exposure to particulate matter (PM) and black carbon (BC) among a panel of participants in California's Central Valley and exploring personal exposures in different microenvironments using time/location-resolved PM2.5 data. Three field campaigns demonstrated that filter-derived PM10, PM2.5, PM10 BC, and PM2.5 BC concentrations measured using the UPAS were linear, unbiased, and precise compared to those measured using conventional personal sampling equipment. Time-resolved PM2.5, GPS, and light intensity data from the UPAS allowed for personal PM2.5 exposure assessment across microenvironments. The majority of daily PM2.5 exposure occurred inside the home. Participants with higher out-of-home PM2.5 exposures received those exposures primarily in agricultural and in-transit environments, in accordance with their self-reported occupational exposures. This study demonstrated the UPAS v2.1 PLUS is a reliable and valid tool for characterizing indoor air pollution and personal exposures in both temporal and spatial dimensions. Its enhanced capabilities should reduce the burden of personal activity logging in the field and enable accurate and precise estimation of exposures for epidemiological and community-based research.Copyright (c) 2024 American Association for Aerosol Research

As cities and states across the United States increasingly commit to building decarbonization, gas stoves are garnering public health attention because, in addition to contributing to greenhouse gas emissions, they may pose a respiratory health risk. Disadvantaged groups, as defined by demographic, socioeconomic, and residential factors, are often late adopters of new technology. To ensure that disadvantaged groups are not left behind from this transition, WE ACT for Environmental Justice, a New York City community-based environmental justice organization, implemented the first pilot of gas-to-electric stove transition in low-income housing. The goal of this mixed-methods study was to evaluate the effect of this intervention on indoor air quality and to characterize the distinct experiences of low-income residents. Twenty low-income households were recruited and randomized to an intervention (replacement of gas stove with induction stove) and a control arm. Between October 2021 and July 2022, three 168-hr long monitoring campaigns were conducted to assess indoor air quality (NO2, CO, and PM2.5) and stove use pre- and postintervention. The impact of cooking events on indoor air quality was further evaluated during controlled cooking tests carried out in both gas and induction homes. To identify key characteristics of the end-user experience throughout this intervention, participants were invited to join focus group discussions. Between baseline and endline, 168-hr average NO2 and CO concentrations decreased in both study arms, likely due to seasonality factors. Still, the induction arm showed a 56 % reduction (95 % CI: -61.9 %, -15.2 %) in mean daily NO2 concentration compared to the gas arm. During controlled cooking tests, the median background NO2 concentration (18 ppb) in gas homes rose to 197 ppb and negligibly changed in induction homes. During focus group discussions, participants unanimously reported being pleased with the transition and highlighted quality of life improvements resulting from the unexpected intervention's ability to address energy insecurity concerns. Taken together, our quantitative and qualitative results suggest that decarbonization energy transitions can improve health by reducing indoor NO2 but need to extend beyond single appliance swap-out to address health issues resulting from energy insecurity.

We would like to take this opportunity to thank all of Environmental Science: Atmospheres's reviewers for helping to preserve quality and integrity in chemical science literature. We would also like to highlight the Outstanding Reviewers for Environmental Science: Atmospheres in 2023.

Most evaluations of low-cost aerosol sensors have focused on their measurement bias compared to regulatory monitors. Few evaluations have applied fundamental principles of aerosol science to increase our understanding of how such sensors work and could be improved. We examined the Plantower PMS5003 sensor's internal geometry, laser properties, photodiode responses, microprocessor output, flow rates, and response to mono- and poly-disperse aerosols. We developed a physics-based model of particle light scattering within the sensor, which we used to predict counting and sizing efficiency for 0.30 to 10 mu m particles. We found that the PMS5003 counts single particle scattering events, acting like an imperfect optical particle counter, rather than a nephelometer. As particle flow is not focused into the core of the laser beam, >99% of particles that flow through the PMS5003 miss the laser, and those that intercept the laser usually miss the focal point and are subsequently undersized, resulting in erroneous size distribution data. Our model predictions of PMS5003 response to varying particle diameters, aerosol compositions, and relative humidity were consistent with laboratory data. Computational fluid dynamics simulations of the PurpleAir monitor housing showed that for wind-speeds less than 3 m s(-1), fine and coarse particles were representatively aspired to the PMS5003 inlet. Our measurements and models explain why the PurpleAir overstates regulatory PM2.5 in some locations but not others; why the PurpleAir PM10 is unresponsive to windblown dust; and why it reports a similar particle size distribution for coarse particles as it does for smoke and ambient background aerosol.

Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.

Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O-3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O-3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O-3 (21 to 7 ppb); additionally, correlation between HHB and reference O-3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 mu g m(-3) (6.1% relative standard deviation [RSD]) and 40.1 mu g m(-3) (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).

Knock in spark-ignited (SI) engines is initiated by autoignition of the unburned gasses upstream of spark-ignited, propagating, turbulent premixed flames. Knock propensity of fuel/air mixtures is typically quan-tified using research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are measured using single-cylinder, variable compression ratio engines. In this study, knock propensity of SI fuels was quantified via observations of end-gas autoignition (EGAI) in un-burned gasses upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 < PRF < 100) were ignited using an Nd:YAG laser over a range of temperatures and pressures, all in excess of 545 K and 16.1 bar. Laser ignition produced outwardly-propagating premixed flames. High-speed pressure measurements and schlieren images indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (i.e., EGAI fraction) varied with fuel reactivity (i.e., octane number) and the time-integrated temperature of the end-gas prior to ignition. Flame propaga-tion rates, which were measured using schlieren images, were only weakly correlated with octane num -ber but were affected by turbulence caused by variation in piston timing. Under conditions of low tur-bulence, measured flame propagation rates approached one-dimensional premixed laminar flame speed computations performed at the same conditions. Experiments were simulated with a three-dimensional CONVERGETM model using reduced chemical kinetics (121 species, 538 reactions). The simulations accu-rately captured the measured flame propagation rates, as well as the variation in EGAI fraction with fuel reactivity and time-integrated end-gas temperature. The simulations also revealed low-temperature heat release as well as formaldehyde and hydrogen peroxide formation in the end-gas upstream of the propa-gating flame, which increased the temperature and degree of chain branching in the end-gas, ultimately leading to EGAI. (C) 2021 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Studies that characterize the performance of low-cost particulate matter (PM) sensors are needed to help practitioners understand the accuracy and precision of the mass and number concentrations reported by different models. We evaluated Plantower PMS5003, Sensirion SPS30, and Amphenol SM-UART-04L PM sensors in the laboratory by exposing them to: (1) four different polydisperse aerosols (ammonium sulfate, Arizona road dust, NIST Urban PM, and wood smoke) at concentrations ranging from 10 to 1000 mu g m(-3), (2) hygroscopic and hydrophobic aerosols (ammonium sulfate and oil) in an environment with varying relative humidity (15%-90%), (3) polystyrene latex spheres (PSL) ranging from 0.1 to 2.0 mu m in diameter, and (4) extremely high concentrations of Arizona road dust (18-h mean total PM = 33,000 mu g m(-3); 18-h mean PM2.5 = 7300 mu g m(-3)). Linear models relating PMS5003- and SPS30-reported PM2.5 concentrations to TEOM-reported ammonium sulfate concentrations up to 1025 mu g m(-3), nebulized Arizona road dust concentrations up to 540 mu g m(-3), and NIST Urban PM concentrations up to 330 mu g m(-3) had R-2 >= 0.97; however, an F-test identified a significant lack of fit between the model and the data for each sensor/aerosol combination. Ratios of filter-derived to PMS5003-reported PM2.5 concentrations were 1.4, 1.7, 1.0, 0.4, and 4.3 for ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, wood smoke, and oil mist, respectively. For SPS30 sensors, these ratios were 1.6, 2.1, 2.1, 0.6, and 2.2, respectively. Collocated PMS5003 sensors were less precise than collocated SPS30 sensors when measuring ammonium sulfate, nebulized Arizona road dust, NIST Urban PM, oil mist, or PSL. Our results indicated that particle count data reported by the PMS5003 were not reliable. The number size distribution reported by the PMS5003 (a) did not agree with APS data and (b) remained roughly constant whether the sensors were exposed to 0.1 mu m PSL, 0.27 mu m PSL, 0.72 mu m PSL, 2.0 mu m PSL, or any of the other laboratory-generated aerosols. The size distribution reported by the SPS30 did not always agree with APS data, but did shift towards larger particle sizes when the sensors were exposed to 0.72 PSL, 2.0 mu m PSL, oil mist, or Arizona road dust from a fluidized bed generator. The proportions of PM mass assigned as PM1, PM2.5, and PM10 by all three sensor models shifted as the PSL size increased. After the sensors were exposed to high concentrations of Arizona road dust for 18 h, PM2.5 concentrations reported by SPS30 sensors remained consistent, whereas 3/8 PMS5003 sensors and 2/7 SM-UART-04L sensors began reporting erroneously high values.

In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application-inferring time-resolved aerosol concentration from a low-cost differential pressure sensor. The objective is to estimate a monotone function and make inference on the scaled first derivative of the function. We proposed Bayesian nonparametric monotone regression, which uses a Bernstein polynomial basis to construct the regression function and puts a Dirichlet process prior on the regression coefficients. The base measure of the Dirichlet process is a finite mixture of a mass point at zero and a truncated normal. This construction imposes monotonicity while clustering the basis functions. Clustering the basis functions reduces the parameter space and allows the estimated regression function to be linear. With the proposed approach we can make closed-formed inference on the derivative of the estimated function including full quantification of uncertainty. In a simulation study the proposed method performs similar to other monotone regression approaches when the true function is wavy but performs better when the true function is linear. We apply the method to estimate time-resolved aerosol concentration with a newly developed portable aerosol monitor. TheRpackagebnmris made available to implement the method.

Low-cost aerosol monitors can provide more spatially- and temporally-resolved data on ambient fine particulate matter (PM2.5) concentrations than are available from regulatory monitoring networks; however, concentrations reported by low-cost monitors are sometimes inaccurate. We investigated laboratory- and field-based approaches for calibrating low-cost PurpleAir monitors. First, we investigated the linearity of the PurpleAir response to NIST Urban PM and derived a laboratory-based gravimetric correction factor. Then, we co-located PurpleAirs with portable filter samplers at 15 outdoor sites spanning 3 x 3-km in Fort Collins, CO, USA. We evaluated whether PM2.5 correction factors calculated using ambient relative humidity data improved the accuracy of PurpleAir monitors (relative to reference filter samplers operated at 16.7 L min(-1)). We also (1) evaluated gravimetric correction factors derived from periodic co-locations with portable filter samplers and (2) compared PM2.5 concentrations measured using portable and reference filter samplers. Both before and after field deployment, a linear model relating NIST Urban PM concentrations reported by a tapered element oscillating microbalance and PurpleAir monitors ("PM2.5 ATM") had R-2 = 99%; however, an F-test identified a significant lack of fit between the model and the data. The laboratory-based correction did not translate to the field. Over a 35-day period, time-averaged ambient PM2.5 concentrations and RHs measured during 72- or 48-h filter samples ranged from 1.5 to 8.3 mu m(-3) and 47%-77%, respectively. Corrections calculated using ambient RH data increased the fraction of time-averaged PurpleAir PM2.5 concentrations that were within 20% of the reference concentration from 24% (for uncorrected measurements) to 66%. Corrections derived from monthly, weekly, and concurrent in-field co-locations with portable filter samplers increased the fraction of time-averaged PurpleAir PM2.5 concentrations that were within 20% of the reference to 46%, 54%, and 72%. PM2.5 concentrations measured using portable filter samplers were within 20% of the reference for 69% of samples.

Fine particulate air pollution (PM2.5) is a health hazard with numerous indoor and outdoor sources. Versatile monitors are needed to characterize PM2.5 sources, concentrations, and exposures in a range of locations and applications. Whereas low-cost light-scattering PM sensors provide real-time measurements with limited accuracy, gravimetric samples provide more accurate, albeit time-integrated, measurements. When used together, low-cost sensor data can be corrected to gravimetric samples. Here we describe the development of a portable PM2.5 monitor that features a low-cost sensor in line with an active filter sampler. Laboratory tests were conducted to determine (1) the accuracy and precision of PM2.5 concentrations derived from the filter sample and (2) correction factors for the low-cost sensor response to ammonium sulfate, Arizona road dust, urban particulate matter, and match smoke. Filter samples collected at 0.25 and 1.0 L min-1 had mean biases of -10% and -4%, relative to a tapered element oscillating microbalance, and a relative standard deviation (RSD) that ranged from 1% to 17%. The low-cost sensor correction factor varied with the test aerosol, sample flow rate, and between individual monitors. Gravimetric correction reduced the bias and RSD of ∼1 hour average concentrations measured by low-cost sensors in three collocated monitors. A week-long field experiment was also conducted to investigate how the monitor could be used to learn about sources of residential air pollution. Field data were used to identify: (1) pollution events resulting from cooking and use of a wood furnace and (2) variations in the number of air changes per hour inside the residence.

Essential oils are mostly used in aromatherapy and their popularity has grown rapidly for the last decade. However, the industry has a substantial low-value waste stream, because downstream industries require therapeutic-grade oil. These waste stream oils can be used in the transport and agricultural sectors. This study investigated the influence of various essential oil blends on the emission characteristics of a multi-cylinder diesel engine. Orange, eucalyptus and tea tree oil were blended with diesel at 5% and 10% by volume, neat diesel and a 10% waste cooking biodiesel-diesel blend were also tested for comparison. The major constituents of orange oil and eucalyptus oil are limonene and 1,8-cineole respectively, and the main constituents of tea tree oil are terpinen-4-ol, γ-terpinene and α-terpinene. Orange oil contains negligible amounts of oxygen, whereas eucalyptus oil and tea tree oil contain 8.4% and 5.4% respectively. Compared to neat diesel, all the essential oil blends exhibited similar or slightly higher density, similar heating value, lower viscosity, flash point, and cetane number, and higher surface tension. However, only orange oil and eucalyptus oil blends exhibited oxidation stability above the minimum standards. Interestingly, blending eucalyptus oil increased the oxidation stability of diesel. Tea tree oil blends emitted the most carbon monoxide (CO) while orange oil and eucalyptus oil blends emitted the least CO and nitrogen oxide (NOX). Although eucalyptus oil and tea tree oil blends contain similar levels of oxygen, they exhibited opposite NOX emission trends, which might be attributed to the dissimilar and complex bonding of oxygen molecules into the structures. Particle number emission of essential oils were load dependent, however, all essential oil belnds emitted higher particulate mass at all loads.

Waste management cost for Australia is increasing every year, and thus, it is important to find alternative ways to use the waste. For example, essential oil has a significant waste stream that can be utilized in vehicles of their producers. However, some of the essential oils contain oxygen which considerably affects engine performance, emission, and combustion characteristics of diesel engines. Thus, this research paper will try to evaluate the essential oils as a replacement of diesel fuel to operate a multicylider diesel engine. For this study, two essential oils are selected which contain different oxygenated functional groups, tea tree oil (5.4% oxygen) and eucalyptus oil (8.4% oxygen), with an aim to evaluate the effect of these functional groups on engine performance and emission parameters. These oils were blended with neat diesel (0% oxygen) to obtain a blend cotaining 2.2% oxygen by weight. The blends produced similar brake power; however, brake-specific fuel consumption (BSFC) increased for eucalyptus oil blends (2.4-3.7%) and tea tree oil blends (3.9-5.3%). Essential oil-diesel blends resulted in less CO and increased NOx emission, produced similar peak pressure, and indicated mean effective pressure. The results then lead to the conclusion that oxygenated essential oils can have a role to reduce dependency of agricultural sector on diesel in the near future.