Particle Measurements Research Articles

Evaluation of Current and Future Aviation Fuels at High-Pressure Rich-Quench-Lean-Type Combustor Conditions

Abstract Decarbonizing the aviation sector is one of the biggest challenges to minimize climate change effects associated with carbon dioxide emission. Sustainable aviation fuels will play a major role in this context especially for long-distance flights where hydrogen or all-electric propulsion systems do not present a feasible alternative. In addition, aromatic-free sustainable aviation fuels offer a promising solution to lower soot emissions of aeroengines. Jet A-1 as reference fuel, two blends, and two neat synthesized paraffinic kerosenes (SPKs) from hydroprocessed esters and fatty acids (HEFA) were tested to characterize the combustion behavior of drop-in/near drop-in fuels. Experiments in an optically accessible high-pressure rich-quench-lean (RQL)-type combustor at typical aeroengine conditions were performed using optical diagnostics, exhaust gas, and particle sampling measurements to delineate the fuel effects on spray, flame, and emission characteristics. Measurements were performed at pressures up to 10 bar, air preheating temperatures up to 773 K and primary zone equivalence ratios up to 1.66. Liquid fuel distribution and fuel placement were found to be sensitive to fuel properties. By contrast, no pronounced influence on flame position and shape were observed. As a consequence, NOx and CO emissions of the tested fuels differed only moderately. However, particulate matter measurements at the fuel richest operating condition showed a clear fuel ranking with the lowest particle emissions evident for the two neat HEFA-SPK samples. Moreover, the particle volume concentration at the fuel richest condition followed the expected trend with fuel H-content.

Impact of Design Dimension Optimization on Capacitive Sensor Performance for Particulate Matter Detection and Measurement

Particulate matter (PM) emissions from exhaust gases are major pollutants and cause serious health problems. Diesel particulate filters (DPF) are used for monitoring and trapping particulates from exhaust gases. For sensing these particulates sensors are used downstream of a DPF. The present study proposes an interdigitated electrode (IDE) capacitive sensor for detecting and measuring deposited particulates on the sensor surface. The sensor dimensions are optimized to detect and measure the least deposition of particulates. The paper presents improvement in sensor performance, a high selectivity of 65.70% with an accuracy of 87.26%. Dimension optimization extracts capacitance of 581 pF manifolds 10 times more than the reference sensor from the literature. The study presents a sensor with a thin sensing layer manufactured by optical lithography and a lift-off method for IDE. Measurements and testing showed that the sensor measures the lowest particulate mass of 0.0045 mg in 3.4 ms

The level, source, and health outcome of PM2.5 exposure in Southwest Iran

IntroductionDusty storms considerably increase airborne particles in dry and semi-dry locations, such as deserts with no plants and strong winds. Therefore, the environment and people are affected severely. Ahvaz, an important metropolis, is often polluted by neighboring nations. The present research studies the concentration, source, and calculation of these particles' effects. Material and methodFor health consequences evaluation, the WHO suggests the Air Quality Health Impact Evaluation Programmed (Air Q 2.2.3). Khuzestan Meteorology Office recorded particulate matter measurements on both hazy and clear days. The data was gathered voluminously in 2023. ResultAccording to data collected from Khuzestan province's meteorology documents, 49 days in 2023 had very unsafe air quality. The most polluted months in terms of the number of dust days are as follows: January (14 days) > December (12 days) > November (11 days) > August (5 days) > May (3 days) > September (2 days) > March, February, June, July, October (1 day) > April (0 days). HYSPLIT maps indicate that Iran causes dust in March, the Great Arabian Desert in December and August, Iraq in April, September, and October, Kuwait in January, Turkey in February and July, Egypt in June and May, and Oman in November. ConclusionThe meteorology database reveals that Ahvaz is highly polluted and that 49 days had unacceptable dust levels. Based on assessments obtained employing the Air Q+ programs, the people of Ahvaz encountered heart disease, respiratory disease, and stroke caused by their exposure to PM2.5 particulates.

In-Situ Turbulence and Particulate Measurements in Support of the BOLT II Flight Experiment

The results of a measurement campaign to characterize the freestream atmospheric turbulence intensity and particulate concentrations experienced by the Boundary Layer Transition (BOLT) II flight experiment are described. These measurements were obtained using custom high-altitude balloon-borne payloads using a modified commercial optical particle counter and a high-bandwidth coldwire thermometer and hotwire anemometer, designed for low cost and light weight, with telemetered data so that physical payload recovery is not necessary. Sixteen flights were carried out over a 10-day campaign, with viable data returned from 14 flights, including four centered on the BOLT II launch with 1-h cadence. Turbulence and particulate measurements are shown vs altitude for all 14 data sets, along with statistics derived from the measurements, quantifying the BOLT II atmospheric environment for use in assessing the impact of these freestream disturbances on the hypersonic-vehicle boundary layer.

Airborne particulate matter measurement and prediction with machine learning techniques

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Particulate matter emissions from open pit mines; measurement methodologies, instruments, and research undertaken

Particulate matter is one of the primary pollutants in open pit mining operations. Measurements must be taken to control particulate matter created during open pit mining activities and to compare them to the regulatory limits. Numerous studies have been undertaken to estimate particulate matter emissions produced by open pit mining. It was discovered that the research were largely conducted on coal mines (69.4%), with little study done in other mining types. Research studies on particulate matter estimation took into consideration mostly machine characteristics (loader bucket volume, truck capacity, number of truck wheels etc.) and atmospheric conditions (air temperature, wind speed, relative humidity etc.). This study emphasizes on particulate matter measurement methods along with other measuring parameters and equipment for particulate matter estimation (TSP, PM10, PM4, PM2.5, and PM1).

Development of low-cost air particulate IoT measurement using HM-3301 sensor

Abstract Air quality measurement is essentially important to create a safe and healthier environment. In this study, the accuracy and precision of the Grove HM-3301 sensor were evaluated and compared with the data taken from a standard air quality monitoring meter. Using the low-cost air particulate measuring sensor, the measuring results of the PM1, PM2.5, and PM10 concentration was fairly accurate, with a relative bias smaller than 30 %. The accuracy of the HM-3301 sensor can be improved using single-point calibration, with relative bias reduced to the limit of 13 % as tested in the particle mass concentration limit within 500 μg/m3. With improved accuracy, the air particulate IoT system using the HM-3301 sensor can be employed in practical applications to assess air quality. With a development cost of 90 USD, the PM measurement IoT system is suitable to be applied in monitoring outdoor and indoor air quality.

Pollutants In the Selected 3 – Bus Station Microenvironments in Enugu Metropolis

Idling buses in bus station micro-environments potentially pollute ambient air with health implications for commuters with long waiting times. This study aimed at finding the severity of air pollutants in selected three bus station microenvironments in Enugu metropolis, South Eastern Nigeria with the objective to determine the air quality index. Real time measurements of ambient particulates (PM10 and PM2.5) and gaseous pollutants such as SO2, NO2 and CO were monitored, in conjunction with meteorological parameters (wind speed, temperature and relative humidity) on idling buses in selected three bus stations and a control station in Enugu Metropolis. The study spanned through September 2016 to August 2018, covering dry and wet seasons. Hand-held Aeroqual Series - 200 gas monitor/sensors, particulates laser meter and ambient weather anemometer were used to collect data. Results indicated that the daily highest mean concentrations of pollutants were as follows: PM10=228µg/m3 – Young Shall Grow; PM2.5=99.5µg/m3 – Young-Shall-Grow; SO2=0.81ppm – Ifesinachi; NO2= 0.068ppm – Young-Shall-Grow; CO= 7.376ppm – ABC, consistently maintained higher concentrations than those at the control station. AQI was calculated with AQI calculator. The air quality index (AQI) results showed a severe health issue in the selected 3-bus station micro environments. This implies that commuters are highly at risk if they cluster idling buses at bus stations. This study can assist the relevant authorities to set strict ambient air quality objectives for pollutants in transport micro environments.

Wax screen-based fabrication of paper devices for the determination of iron in particulates of selected welding fumes in Addis Ababa, Ethiopia

In this study a paper-based analytical device was developed using screen printing technique for the determination of particulate iron from welding fumes. The operational parameters such as volume and concentration of 1,10-phenanthroline, volume and concentration of hydroxylamine were optimized by the Box Behnken Design (BBD) using Minitab. Additionally, the µ-PAD's sample solution holding capacity and reaction time were also optimized. Under the optimized condition, particulate metal concentrations were colorimetrically quantified usinga handheld mobile phone and image processing software, ImageJ. Very good analytical performance such as good linearity of the calibration curve and better selectivity was observed by the developed method. The limit of detection for Fe3+ assay was 4.6 mg/L; which is adequate to determine the threshold concentration limit of particulate iron set by the regulatory bodies (6 mg/L). The µ-PAD revealed 95-99% recovery compared with the UV-Vis spectrophotometry (98-100%). Furthermore, welding fume samples were collected in Addis Ababa over five days, using mixed cellulose ester filters and the findings show a high concentration that exceeds the standard levels. Analyzing particulate iron with µ-PADs yielded results consistent with UV-Vis spectrophotometry, suggesting µ-PADs' potential application for occupational particulate iron exposure measurement, eliminating the need for expensive analytical devices.
 KEY WORDS: µ-PADs, Particulate iron, Response surface methodology, Wax screen-printing, Welding fume, Colorimetric detection
 Bull. Chem. Soc. Ethiop. 2024, 38(3), 563-576. 
 DOI: https://dx.doi.org/10.4314/bcse.v38i3.2 

An Ocean of Particles Characterization of Particulate Trace Elements by the GEOTRACES Program

The GEOTRACES program has greatly expanded measurements of trace elements, which serve as key nutrients, harmful contaminants, and tracers of ocean processes and past conditions. Many elements tend to associate with particulate matter, and GEOTRACES has been particularly valuable for growing our understanding of this fraction. Focusing on the micronutrient iron as an example, GEOTRACES data demonstrate that the majority of iron in the ocean is particulate. Chemically labile particulate iron, likely available for biological use, is also often more abundant than dissolved forms, particularly near continents and in the deep sea. This highlights the need to consider the particulate fraction in conceptual and numeric ocean models. Direct comparisons of particle-sampling methods highlight both the abundance of small particles (<0.45–0.8 μm), whose biogeochemical roles are still poorly known, and the difficulty in consistently capturing large, faster-sinking particles. In situ pumps with 0.8 μm filters often capture less small particulate iron than bottle-collected samples filtered onto 0.45 μm filters, but they can also capture more material near some sources. GEOTRACES datasets contain nearly sevenfold more dissolved than particulate iron measurements, and ongoing efforts to pair these measurements are needed in order to fully understand the cycles of iron and other important elements.

Assessing CMAQ model discrepancies in a heavily polluted air basin using UAV vertical profiles and sensitivity analyses

An unmanned aerial vehicle was deployed daily in Riverside, CA from August to November 2020, capturing vertical ozone and particulate matter measurements.

The impact of air pollution on cash holdings of firms in Vietnam

Cash holding is an important decision that affects many other choices in a firm. Previous studies have revealed a range of factors affecting cash holdings, but little is known about a macro variable: air pollution, which is quite a pressing issue recently and globally. This study examines the association between air pollution and cash holdings using a sample of listed firms in Vietnam, a developing country that has a worrisome level of air pollution. The research sample covers non-financial firms listed on the Vietnam Stock Exchange from 2016 to 2021, retrieved from Thomson Refinitiv. Furthermore, the authors also gather information on air pollution using the particulate matter measurement index (PM2.5) over the years 2016 to 2021 from the contribution of General Consulate of U.S. in Vietnam. The current research employs conventional estimation strategies including OLS, Fixed effects model, and Random effects model. In contrast to previous studies suggesting a positive linkage, we find that air pollution is not significantly related to cash holdings of firms in Vietnam. This could be due to the own situations in Vietnam and that air pollution could reduce (risky) investments. We further find that leverage tends to make air pollution have a more negative effect on cash holdings for firms in Vietnam. The findings bring important implications, and are robust to several techniques to ensure the robustness of findings

Particulate emissions from biomass power plants: a practical review and measurement uncertainty issues

This paper follows a practical approach to evaluate particulate matter emissions from biomass-fuelled power plants and analysis measurement uncertainties. Sixty-seven measurement reports from 11 biomass power plants, all equipped with fabric filters as dust control device, were analysed. Particulate matter emission measurements by gravimetrical standard reference method were chosen to provide the most reliable values to investigate the influence of bag filter parameters, such as media age or bag filter length. 92.1% of total dust emission measurements were below lower BAT-associated emission levels (BAT-AELs) for waste incineration. Data also showed that bag filter media older than 6 years can still secure very low dust emission levels. This work also gives a summary of findings and problems with uncertainty of automated measuring systems for dust emissions in biomass combustion applications in practice and quality assurance level one (QAL1) process related to very low emission levels such as lower best available techniques reference document (BREF) values. Practical examples on issues of continuous dust emission monitoring devices are also given.

Automatic weighing system vs. manual weighing precision comparison in PM-loaded filter measurements under different humidity conditions

Currently, there is a lack of research directly comparing the precision of automatic weighing systems and manual weighing in the context of particulate matter (PM) filter equilibration and measurements under different humidity conditions. During experimental measurements, three different types of PM-loaded filters were weighed using manual and automatic balances. During manual weighing, every filter was weighed twice in three different relative humidity conditions. The same procedure was done using an automated weighing system. In most cases, it was found that under relative humidities in the range of 30–55% RH, the manual and automated methods can be treated as referential. Regarding device stability, very slight but overall better precision was found for 30% RH, suggesting that 40 CFR Part 50, Appendix L requirements regarding conditioning humidity (30–40% RH) seem more suitable than those presented in the PN-EN 12341:2014 standard (45–50% RH). Understanding the effects of the influence of the RH% on PM mass measurements is a matter of great importance, because water vapor condensed on a filter can affect the particulate matter concentrations. This is especially important in areas where regulatory limits are exceeded. Calculation of uncertainty in the PM mass measurements is therefore crucial for determining the actual sample mass and improving air monitoring practices. In a nutshell, the experimental results obtained clearly describe how changing RH% conditions affect the PM weighing precision during manual and automated measurements.

Aerosol size distribution retrieved from optical depth measurements in Tamanrasset and Blida

Since we are often faced with limited, insufficient or contaminated observations in remote sensing. We will employ a theoretical technique for the retrieval of the particle size distribution. In this study, the columnar aerosol size distributions have been inferred by numerically inverting particulate optical depth measurements obtained in Blida and Tamanrasset from AERONET database at wavelengths region between 0.340 - 1.640 . Our results are in good agreement with those found by other authors and the correlation between the aerosol optical thickness measurements and the reproduced AOTs by the inverted columnar aerosol size distribution is very good with a correlation coefficient more than 0.97.

Anti-contamination calibration method for ultra-fine dry chemical agent: Achieving wide measurement range and high measurement accuracy

Achieving real-time measurements of ultra-fine dry chemical agents (UDCA) is extremely challenging, with their complex composition, high injection concentrations, and susceptibility to contamination. In this study, a high-concentration particulate matter measurement method was proposed based on the extinction principle to reduce the powder contamination effect during calibrations. Through theoretical analysis and experimental verification, a functional relationship was established. Calibration experiments were conducted using sodium bicarbonate (SBA) and potassium bicarbonate (PBA) UDCAs, which are the most representative. The results showed that the measurement ranges of SBA and PBA reached 227.15 g/m3 and 122.55 g/m3, respectively. Compared with the uncorrected calibration method, the maximum indicated errors of SBA and PBA with the powder contamination effect corrected were reduced from 11.25% and 22.68% to 5.98% and 9.21%. This method provides a strategy for measuring high concentration powder, especially for achieving the measurement of UDCA in aircraft engine compartments.

Arrays of glass wedges for multi-dimensional optical diagnostics.

There is a common need in the advancement of optical diagnostic techniques to increase the dimensionality of measurements. For example, point measurements could be improved to multi-point, line, planar, volumetric, or time-resolved volumetric measurements. In this work, a unique optical element is presented to enable multi-dimensional measurements, namely, an array of glass wedges. A light source is passed through the wedges, and different portions of the illumination are refracted by different amounts depending on the glass wedge angle. Subsequent optics can be used to focus the light to multiple points, lines, or planes. Basic characterization of a glass wedge array is presented. Additional wedge-array configurations are discussed, including the use of a periodic intensity mask for multi-planar measurements via structured illumination. The utility of this optical element is briefly demonstrated in (a)multi-planar flame particulate measurements, (b)multi-point femtosecond-laser electronic excitation tagging for flow velocimetry, and (c)multi-line nitric oxide molecular tagging velocimetry in a hypersonic shock-tunnel. One significant advantage of this optical component is its compatibility with high-energy laser sources, which may be a limiting factor with other beam-splitting or beam-forming elements such as some diffractive optics. Additionally, an array of glass wedges is simple and easily customizable compared to other methods for forming multiple closely spaced illumination patterns. Suggestions for further development and applications are discussed.

Optimal static light scattering detection angle for particulate matter size and concentration measurement

The serious pollution of particulate matter (PM) causes many adverse effects on human health and industrial production. High-precision, real-time monitor of atmospheric particulate concentration is becoming increasingly important. In this study, a method to find the optimal detection angle to measure the mass concentration of PM is proposed. To find an optimal angle, a motorized rotary system is used to measure the light scattering signal of particles from 0° to 180°. The new method obtains parameters such as mass concentration factor, deviation, mass concentration coefficient and deviation coefficient, which can be measured more accurately. Through theoretical calculation and experimental analysis, the optimal detection angle is 45°. At the optimal angle, the correlation between the scattered light intensity and the mass concentration of spherical SiO2 particles reaches 0.9836. Then, the mass concentration of PM is inverted and calculated at the optimal angle. The results showed that the correlation coefficient R2 of different particle sizes is larger than 0.952, and the measurement results were highly correlated.

KAJIAN EKSPERIMENTAL PENGARUH PENGGUNAAN ELECTRONIC CONTROL UNIT AFTERMARKET PADA DAYA, TORSI, EMISI DAN KONSUMSI BAHAN BAKAR SEPEDA MOTOR 150CC

To improve motorbike performance, simple modifications are often applied using an aftermarket Electronic Control Unit (ECU). However, scientific studies on this particular topic are still limited. Therefore, the purpose of this research is to examine the effect of the aftermarket Juken 5 ECU on the power, torque, emissions, and fuel consumption of the Yamaha R15-V3 bike. The results of the dynamometer testing showed that the motorcycle with the Juken 5 ECU experienced an average increase of 8.09 ± 1.30% in power and 7.79 ± 1.31% in torque, respectively, at 5500 – 11000 rpm. However, the concentrations of Carbon Monoxide and Hydrocarbon emissions also increased by 170% and 360%, respectively. Particulate emission measurements also revealed that the Juken 5 ECU usage resulted in a significant increase in particulate counts. Compared to the standard ECU, motorcycles equipped with the Juken 5 ECU produced 89% and 145% more particulates in the size range of 0.3 – 0.374 μm and 0.374 – 2.685 μm, respectively. The performance and emission data analysis results indicated that the increase in power and torque achieved by the Juken 5 ECU is simply due to more fuel being injected into the combustion chamber. This finding was supported by the results of fuel consumption testing, where using the aftermarket ECU increased motorcycle fuel consumption by 63.6%.

Combustion and emission characteristics of dimethyl ether/gasoline DFSI engine under different excess air coefficients

Dimethyl ether (DME) was a carbon neutral alternative fuel for engines.The combustion and emission characteristics of a DME/gasoline Dual Fuel Spark Ignition (DFSI) engine with various excess air coefficients (λs) at different DME ratios (ΦDME) were investigated experimentally in this study.The in-cylinder pressures along with their relevant crank angle were used for the combustion characteristics calculation. The light five component exhaust gas analyzer and particle analyzer were employed for the gaseous and particulate matter emission measurement, respectively. Results showed that the DME/gasoline dual fuel combustion strategy can enhance the Brake Mean Effective Pressure (BMEP) by up to 12.6% because the high diffusivity, superior LTR and fast LFS of DME. Although adding DME was beneficial for the reduction of gaseous emissions (HC, CO and NOx) from DME/gasoline DFSI engines, the gaseous emissions were mainly determined by the variation of λ s. Particularly, up to 93.4% of the CO emissions at stoichiometric condition can be reduced after increasing λ to 1.3. The fast evaporation rate and superior LTR of DME helped to suppress PN emissions, but larger DME ratio (ΦDME > 5%) was needed for λ >1.2.