Federal Reference Method Research Articles

Precision and Accuracy Analysis of PM2.5 Light-Scattering Sensor: Field and Laboratory Experiments

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with the established standards set by the US Environmental Protection Agency (US EPA). This study evaluates the accuracy and reliability of the DustBoy, a commonly used PM2.5 monitoring device in Thailand. A comparative analysis was conducted between the DustBoy and the US EPA’s Federal Reference Method (FRM) and Federal Equivalent Method (FEM). The research involved both laboratory and field testing, where the DustBoy’s performance was analyzed at various particulate matter concentration levels and environmental conditions. The study demonstrated that the DustBoy readings diverged from those of standard monitors at higher PM2.5 concentrations; however, a positive correlation between the devices remained evident. Below 100 µg/m3, the DustBoy overestimated PM concentrations compared to the FRM devices but underestimated them compared to the FEM devices. At higher concentrations, the DustBoy showed a significant overestimation, although the data trends aligned with those of standard devices. The sensor performance was also affected by factors such as the sensor age and device model. Corrections were developed to adjust the DustBoy readings to match the reference devices more closely, enhancing the accuracy post-adjustment. These corrections will refine the DustBoy’s public data reporting and serve as guidelines for other low-cost sensors in Thailand.

Combined inertial and pleated filter for efficient PM1.0 separation and sampling

A reliable measuring method of particulate matter less than 1 µm (PM1.0) is essential to clarify the correlation between the PM1.0 mass concentration and the impact of human health, and to establish effective PM1.0 control strategies. In this study, a new PM1.0 sampler, the inertial filter/pleated filter sampler (I/P sampler), was designed to collect particles larger than 1 μm in the inertial filter and PM1.0 in the pleated filter during sampling. The inertial filter with small filtration area successfully separated particles larger than 1 μm by reducing diffusion and increasing inertia, while the pleated filter with large filtration area efficiently collected PM1.0 by lowering the filtration velocity to promote diffusional collection.The I/P sampler was used to measure PM1.0 mass concentration through field tests, with results compared with those of conventional sampling devices for PM1.0 (cyclone sampler, cascade impactor, beta attenuation monitor [BAM], and EPA-approved federal reference method [E-FRM] sampler). Field measurements indicated a good correlation between the I/P sampler and conventional samplers, with regression slopes ranging from 0.90 to 1.07 in summer and 1.10 to 1.37 in winter. In addition, the measured mass concentrations decreased in the order of I/P sampler, cyclone sampler, E-FRM sampler, BAM, and cascade impactor.

Evaluation of Fine Particulate Matter (PM2.5) Concentrations Measured by Collocated Federal Reference Method and Federal Equivalent Method Monitors in the U.S.

Evaluation of Fine Particulate Matter (PM2.5) Concentrations Measured by Collocated Federal Reference Method and Federal Equivalent Method Monitors in the U.S.

ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES

This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer promising solutions to overcome these challenges. Through an analysis of case studies and applications in various environmental settings, including urban areas, industrial zones, and indoor environments, the review highlights the effectiveness of monitoring networks in enhancing spatial and temporal resolution, as well as the need for community engagement and real-time monitoring solutions. Furthermore, technological innovations such as sensor fusion, data analytics, and artificial intelligence hold great promise for improving the accuracy, reliability, and accessibility of PM2.5 monitoring data. Regulatory agencies and policymakers play a crucial role in advancing PM2.5 monitoring by harmonizing monitoring standards, strengthening quality assurance measures, and developing evidence-based regulations to mitigate air pollution and protect public health. In conclusion, international cooperation and collaboration are essential for addressing transboundary air pollution and global environmental challenges. Regional monitoring networks and international agreements provide frameworks for data sharing, standardization of monitoring practices, and collaborative research efforts. To this end, stakeholders can leverage PM2.5 monitoring by adopting new technologies, improving data quality, and supporting evidence-based actions to safeguard public health, the environment, and sustainability

Particulate matter annual emission factors for dairy facilities and cattle feedlots of Texas, USA

The direct emission of inhalable particulate matters (PM2.5 and PM10) from agricultural sources imposes major concern to human health. It is imperative to keep regular update of the pollutant emissions to scrutinize the sources and take necessary preventive measures. Two representative animal feeding operations, one dairy and one feedlot of Texas were selected during the year 2021 for summer and winter sampling using programmable Federal Reference Method (FRM) samplers and Texas A&M University (TAMU) designed samplers. Additionally, for quick and simple emission concentration determination, handheld particle counters were utilized parallel to the FRM and TAMU designed samplers. The air quality index showed that the PM2.5 pollutant fell into ‘Moderate’ category for about 20.93% times and 13.89% times in the dairy and the feedlot respectively. The maximum mean of daily PM10 concentration observed in the Feedlot during the summer was 1676.3 μg/m3 which can be aggravating. The correlation developed using the emission data from the FRM and handheld samplers demonstrated linear relationship and 0.66 of goodness of fit. During the summer, maximum mean PM2.5 emission factors (EF) of 0.84 kg 1000 hd−1 d−1 and 8.09 kg 1000 hd−1 d−1 were determined for the dairy and for the feedlot respectively. However, the winter and the summer PM10 EF for the dairy did not differ significantly (P > 0.05). Although there has not been enough record of PM2.5 annual EF, the current study PM10 annual EF was benchmarked with some of the previously published EFs. This investigation on the air pollutant EFs from the animal feeding operations can greatly benefit in preventing the pollution and updating the most recent EFs. However, continuous monitoring and further research are suggested to improve data quality and develop strategic management practices.

Aerosol Thermodynamics: Nitrate Loss from Regulatory PM2.5 Filters in California.

Fine particulate matter (PM2.5) mass concentrations reported by regulatory networks are declining across the United States. It is well established that ammonium nitrate contributes substantially to the PM2.5 mass in the western United States, and that Teflon filters commonly used by regulatory monitors are subject to negative mass artifacts due to ammonium nitrate volatilization. This study focuses on the San Joaquin Valley (SJV), an environmental justice (EJ) and agricultural region with persistently poor air quality. The SJV is a serious nonattainment area of PM2.5 National Ambient Air Quality Standards (NAAQS) with substantial nitrate mass concentrations. We explicitly model the chemical thermodynamic equilibrium of the ammonium nitrate-nitric acid systems and quantify volatilization across California as a function of the deliquescence point relative humidity (%DRH). Nitrate loss is estimated at all federal reference method (FRM) and federal equivalent method (FEM) monitors from 2001 to 2021. Nearly 20% of PM2.5 mass is lost from filters in the SJV area, especially during winter and fall when particulate nitrate mass is most abundant. All decadal PM2.5 trends calculated from reported measurements in Kern, Tulare, and Fresno counties in the SJV show greater decline in PM2.5 mass when nitrate loss is accounted for, up to a factor of 20 in Kern county. This suggests PM2.5 mass concentrations reported in regulatory networks are biased low relative to the actual atmospheric burden, notably in an EJ area that lags behind most of the country's air quality improvements.

Field intercomparison of continuous ambient FRM and FEM NO2 instruments in the Athabasca Oil Sands Region, Alberta, Canada and the potential impact on ambient regulatory compliance

ABSTRACT The Canadian Federal Government promulgated new and lower NO2 Ambient Air Quality Standards (CAAQS) that went into effect in 2020 with additional decreases scheduled for 2025. The new hourly and annual NO2 CAAQS are 60 and 17 ppb, respectively, and the 2025 hourly and annual CAAQS are 42 and 12 ppb, respectively. The province of Alberta has also promulgated Ambient Air Quality Objectives (AAAQO) for NO2 currently set to 159 and 24 ppb on an hourly and annual basis, respectively. The Wood Buffalo Environmental Association (WBEA) in northeastern Alberta, Canada monitors NO2 at 21 community and industrial sites throughout the Athabasca Oil Sands Region (AOSR), for regulatory compliance using Thermo-Environmental (TEI) Model 42i Federal Reference Method (FRM) designated NO-NO2-NOx analyzers. The 42i measures NO directly via NO-O3 chemiluminescence, and NOx following the reduction of oxidized nitrogen to NO by a heated internal molybdenum converter. The difference between the NOx and NO channels is reported as NO2. This study presents the results of a three-year (2018–2021) WBEA comparison of four continuous NO2 analyzers: TEI 42i FRM; the API Model T500U cavity attenuated phase shift (CAPS) Federal Equivalent Method (FEM); a total reactive odd nitrogen analyzer (TEI Model 42i-Y); and a TEI 42i equipped with an external photolytic converter. The study showed that NO2 data from all analyzers were highly correlated and in general agreement, with r2 values (vs. the CAPS) ranging from 0.990–0.997 and slopes ranging from 0.933–0.992. Mean NO2 concentrations over the study period ranged from 7.2–7.5 ppb. Differences between the TEI 42i, TEI 42i-Y, and PhoNO, relative to the CAPS were all positive and highly significant (p < 0.0001), based upon nonparametric tests. The potential impact from the selection of different FRM/FEM measurement methods on current and future Canadian 2025 regulatory compliance in the region is evaluated. Implications: The study objective was to compare/evaluate different regulatory NO2 measurement techniques from a regional monitoring authority in a routine network operational context. Relatively small NO2 differences resulted in significant differences with respect to regulatory compliance triggers, particularly hourly standards based on daily extreme value statistics (e.g., 99th percentiles). For example, mean hourly NO2 △ differences ranged from 0.02–0.26 ppb over the study period but resulted in 2–3 ppb differences in the 3-year hourly CAAQS metrics. These differences could affect regulatory CAAQS and LARP compliance (management level) at monitoring sites observed during 2019 annual and 2020 hourly LARP trigger exceedances.

Evaluation of Almond Harvest Dust Abatement Strategies Using an Aerial Drone Particle Monitoring System

This study demonstrates the feasibility of a mobile aerial drone particle monitoring system (DPMS) to measure and detect changes in harvest dust levels based on moderate adjustments to harvester settings. When compared to an earlier harvester, a new harvester operated at standard settings produced 35% fewer PM2.5s, 32% fewer PM10s, and 42% fewer TSPs. Increasing the ground speed had an adverse effect on dust mitigation, while reducing it by half only offered a slightly more favorable margin. The mutual effects of some meteorological factors were found to be slightly correlated with PM10 and TSP readings and caused significant variability in PM2.5 readings. The current findings show similar trends to PM reduction estimates of previous studies, with only a nominal difference of 10 to 15% points. Overall, the DPMS was found to perform well within an acceptable statistical confidence level. The use of DPMSs could reduce the logistical needs, complexity issues, and feedback times often experienced using the Federal Reference Method (FRM). Further investigation is needed to verify its robustness and to develop potential correlations with the FRM under different orchard location and management practices. At this stage, the current aerial DPMS should be considered a rapid screening tool not to replace the FRM, but rather to complement it in evaluating the feasibility of dust abatement strategies for the almond industry.

Source identification of PM2.5 during the COVID-19 lockdown in Bangkok and the metropolitan region by ion beam analysis (IBA) and positive matrix factorization (PMF) techniques

PM2.5 pollution has significant impacts on human health and has been a persistent problem in Bangkok and its metropolitan area for many decades. To effectively address the issue, source identification is crucial. This study was aimed at determining the sources of PM2.5 in three regions; Pathumwan district in Bangkok, Mueang district in Samut Sakhon province, and Mueang district in Samut Prakan province. PM2.5 sampling was performed according to the Federal Reference Method (FRM). A combined total of 135 samples were collected across all three locations, over a 24-h period from December 2021 to February 2022 with 46.2 mm polytetrafluoroethylene (PTFE) membranes. The filters were analyzed using particle accelerator-based ion beam analysis techniques; Proton-induced X-ray emission, proton-induced gamma-ray emission, and proton elastic scattering analysis. Positive matrix factorization was used for source apportionment for the three locations. The results indicated that the main contributors to PM2.5 in Bangkok, Samut Prakan, and Samut Sakhon were biomass/solid waste burning (45.6%), traffic (43.7%), and construction (36.0%), respectively. These preliminary findings further supported the need for expanding these types of studies to implement specific strategies for a reduction of PM2.5 level in high activity cities and which could then be applied to other urban areas around the world.

The Unignorable Near-ground PM2.5, UFP, PAHs, and BC Levels around a Traffic Prohibited Night Market

In some special densely populated areas, the background atmospheric fine particulate matter (PM2.5) concentration is very high, which makes near-ground (NG) exposure a major problem endangering human health. In our study, the night market in Chiayi City was selected as the research object and collected the 24-hour PM2.5 samples through the federal reference method (FRM), characterizing the mass concentration, water-soluble ionic components, carbon specious, metal compositions and source contributions of PM2.5. To better analyze the contribution of traffic sources under different sampling conditions, the mobile real-time monitoring system was used to analyze the quality of NG-PM2.5, the number of ultra-fine particles (UFP), the concentration of black carbon (BC) and total polycyclic aromatic hydrocarbons (PAH) before and after the traffic restriction. Results indicated the concentration of PM2.5 was 7.26–58.6 mg m−3. In chemical analysis, secondary contents e.g., carbonaceous and ionic components accounted for ~60% of the PM2.5, supporting the importance of long-range transport. However, the traffic contribution accounted for ~30% and hardly changed between different samples, which was not conducive to source apportionment. Through traffic restriction, it was found that all kinds of pollutants increased significantly before restriction, and even after restriction, the concentrations of PM2.5 and BC increased 131% and 151% in low concentration season. In the high concentration season, the traffic restriction significantly reduced the NG-UFP and NG-PAH concentration by 27% and 55%, respectively, but NG-BC and NG-PM2.5 was almost unaffected. Therefore, besides the contribution of traffic source, emissions from cooking activities are very important for the increase of NG-PM2.5 levels in the night market area.

Summary of PM2.5 Measurement Artifacts Associated with the Teledyne T640 PM Mass Analyzer Under Controlled Chamber Experimental Conditions Using Polydisperse Ammonium Sulfate Aerosols and Biomass Smoke

Particulate matter (PM) is a major primary pollutant emitted during wildland fires that has the potential to pose significant health risks to individuals/communities who live and work in areas impacted by smoke events. Limiting exposure is the principle measure available to mitigate health impacts of smoke and therefore the accurate determination of ambient PM concentrations during wildland fire events is critical to protecting public health. However, monitoring air pollutants in smoke impacted environments has proven challenging in that measurement interferences or sampling conditions can result in both positive and negative artifacts. The EPA has performed research on methods for the measurement of PM2.5 in a series of laboratory based studies including evaluation in smoke. This manuscript will summarize the results of the laboratory based evaluation of federal equivalent method (FEM) analyzers for PM2.5 with particular attention being given to the Teledyne-API Model T640 PM Mass analyzer, as compared to the filter-based federal reference method (FRM). The T640 is an optical-based PM analyzer and has been gaining wide use by state and local agencies in monitoring for PM2.5 U.S. National Ambient Air Quality Standards (NAAQS) attainment. At present, the T640 (includes both T640 and T640X) comprises ~40% of the PM2.5 FEM monitors in U.S. regulatory monitoring networks. In addition, the T640 has increasingly been employed for the higher time resolution comparison/evaluation of low-cost PM sensors including during smoke impacted events. Results from controlled non-smoke laboratory studies using generated ammonium sulfate aerosols, demonstrated a generally negative T640 measurement artifact that was significantly related to the PM2.5 concentration and particle size distribution. Results from biomass burning chamber studies demonstrated positive and negative artifacts significantly associated with PM2.5 concentration and optical wavelength dependent absorption properties of the smoke aerosol. Implications The results detailed in this product will provide state and local air monitoring agencies with the tools and knowledge to address PM2.5 measurement challenges in areas frequently impact by wildland fire smoke. The observed large positive and negative artifacts in the T640 PM mass determination has the potential to result in false exceedances of the PM2.5 NAAQS or in the disqualification of monitoring data through an exceptional event designation. In addition, the observed artifacts in smoke impacted air will have a detrimental effect on providing reliable public information when wildfires occur and also in identifying reference measurements for small sensor evaluation studies. Other PM2.5 FEMs such as the BAM-1022 perform better in smoke and are comparable to the filter based FRM. Care must be taken in choosing high time resolution FEM monitors that will be operated at smoke impacted sites. Accurate methods, such as the FRM and BAM-1022 will reduce the burden of developing and reviewing exceptional event request packages, data loss/disqualification, and provide states with tools to adequately evaluate public exposure risks and provide accurate public health messaging during wildfire/smoke events.

Measurement of visible dust emissions during almond nut-picking operations at various harvester settings

ABSTRACT Peak ambient dusts levels in the San Joaquin Valley (SJV) are often coincident with peak agricultural harvest seasons, particularly for nut orchards. Federal reference methods (FRMs), consist of mass-based sampling techniques, are designated to measure and analyze ambient dust concentrations. However, FRMs generally require meticulous attention to sampling details, tedious labor and logistics, and time-consuming dispersion calculations. This study used opacity-based techniques - the U.S. Environmental Protection Agency (EPA) Method 9 and the Digital Compliance Opacity Technique (DCOT) - to quickly measure relative dust intensity at different harvester settings, and to evaluate visible dust emission reductions using a new/low-dust nut harvester. Results indicate that old/conventional harvester at standard settings (3 mph ground speed and 900 rpm blower speed) produced visible dusts with opacities ranging from 23.3% - 29.0%. The dust plume generated from new/low-dust harvesters at similar settings registered lower opacity readings, with an average of 13.94%. The lowest average dust opacity (7.8%) was achieved when the separation fan was turned off. The present results validated the previous FRM emission findings that an old/conventional harvester has the highest PM emissions, but with a potential to achieve 50% reduction by adopting new/low-dust harvesters. Compared to an old harvester, the relative intensity of dusts was reduced by 40% - 51% for low-dust harvesters operated at lower ground speeds. Further dust reductions could be achieved by lowering the fan speed (58%) and operating at no fan/blower setting (66.4%). Both visible emission evaluation (VEE) techniques were able to provide rapid quantitative feedback on the effects of operational changes on emitted harvest dusts. At this stage, opacity-based techniques can potentially be adapted as a screening tool for identifying best management practices (BMP) in reducing harvest dusts, rather than to solely rely on FRM mass-based techniques. Implication: U.S. EPA VEE techniques are established, robust methods of dust measurement systems that can be adopted to evaluate visible dust mitigation strategies in the SJV, alongside the existing FRM protocols. The use of Method 9 and DCOT, as both non-disruptive and rapid dust measurement tools, can bring immediate impact concerning the goal of the almond industry to significantly reduce its PM emissions in the next coming years.

Particulate Matter Emission Factors for Dairy Facilities and Cattle Feedlots during Summertime in Texas.

Particulate matter (PM) emissions from dairies and feedlot sources require regular emission factor update. Likewise, development of simple measurement technique to accurately measure pollution concentration is warranted to limit the impact of air pollution and take necessary actions. During June of 2020, a dairy facility from central Texas and a feedlot from the Texas Panhandle region, titled as Dairy B and Feedlot C, respectively, were chosen for measurement of PM emissions in the state of Texas to represent dairy facilities and cattle feedlots PM emission rates. Four stations, each assigned with an EPA-approved Federal Reference Method (FRM) sampler, Texas A&M University (TAMU) designed sampler and handheld non-FRM AEROCET (MET One Instruments) sampler for collocation, were selected within each sampling locations. Drones were also utilized mounted with a handheld AEROCET sampler for simultaneously sampling at a certain height. PM2.5 emissions of Dairy B were all below 24-h PM2.5 standard of 35 μg m-3 as specified by National Ambient Air Quality Standards (NAAQS) even at the 98th percentile. The PM ratio between regulated PM10 to PM2.5 was determined to make an estimate of relative percentage of coarser particles to fine particles in both feedlot and the dairy representative animal facilities. The maximum mean emission factor determined using AERMOD for PM2.5 and PM10 was found to be 0.53 and 7.09 kg 1000-hd-1 d-1, respectively, for the dairy facility while 8.93 and 33.42 kg 1000-hd-1 d-1, respectively, for the feedlot. A conversion factor and correlation matrix were developed in this study to relate non-FRM sampler data from the handheld AERCET samplers with FRM samplers. Cheaper handheld samplers (AEROCETs) may play a potential role in quick and relatively instant measurement of PM emissions to initiate necessary preventive actions to control PM emission from dairy facility and feedlot sources.

A Technical Overview on Beta-Attenuation Method for the Monitoring of Particulate Matter in Ambient Air

Beta-attenuation technique is one of the widely used real-time technique for ambient particulate matter (PM) measurements since it allows continuous measurement while requiring minimal operator attention. Like any other technique, this method has several limitations that have been recorded in many studies. Beta-attenuation technique is dependent on the meteorological conditions as well as operational factors, which lead to over- or underestimation in the mass measurements in comparison to the reference method. However, the factors that affect its measurement and the variations in its performance under different conditions are not listed or reviewed in a comprehensive manner in a single document. Also, the systematic advancement of its development and implementation in ambient air measurements have not been documented in literature used by the air quality community. It is important for the user of this technique to have a detailed understanding of its principle and operation. Consequently, this article discusses the research, development, technology, and measurement of beta-attenuation method in depth. Although this review emphasizes primarily PM10 measurement results but some PM2.5 studies are also included. Our review reveals that federal equivalent method (FEM) designated beta gauge monitors in various studies performed better (with slope < 1.5 and intercept < 2 µg m−3) during high RH ambient conditions against reference or federal reference method (FRM). Studies related to PM10 showed that cut-off size, high mass loading and high ambient RH (> 80%) have impact on beta gauge measurements. Therefore, it is recommended to clean inlet once a week and use smart heater to control RH at or below 35%. PM2.5 studies also confirm the effect of relative humidity on beta gauge measurements.

Development of an integrated duty cycle test method to assess cordwood stove performance

ABSTRACT The US Environmental Protection Agency’s (EPA’s) New Source Performance Standards (NSPS) for Residential Wood Heaters (RWH) require certification emission testing of prototype appliances. In 2015, EPA revised those standards to further reduce particulate matter emissions from this critical source. However, to achieve that goal, lower emissions measured in certification tests must reflect lower emissions when the appliance is operated in homes. Woodstove certification tests have used either the Federal Reference Method (FRM), a crib wood method, or a cordwood testing method developed by ASTM International that was designated as a broadly applicable Alternative Test Method (ATM) by the EPA until December 2021, when that status was revoked. There is broad agreement that the FRM and ASTM procedures do not simulate typical fueling and operating of wood stoves in the field, raising questions about the efficacy of the current program. Effective emission reduction efforts require robust, accurate, and reproducible test methods. With input from a range of stakeholders, the Northeast States for Coordinated Air Use Management (NESCAUM) developed the Integrated Duty Cycle Test Method for Certification of Wood-Fired Stoves Using Cordwood (IDC), a cordwood testing protocol designed to improve the efficacy of residential wood heater certification testing. That method was approved by EPA as a broadly applicable ATM in 2021. IDC test runs assess appliance performance under a range of operating and fueling conditions representative of typical consumer use patterns. Unlike previous test methods, the IDC protocol requires three replicate runs to assess appliance performance variability. Including variable fueling and operating conditions, along with the requirement for replicates runs, will increase the effectiveness of certification testing and promote the development of improved wood stove technology. This paper reports on experiments conducted to develop and test the IDC method. Implications: Residential wood heating is one of the largest sources of primary particulate matter pollution nationwide. EPA’s New Source Performance Standards (NSPS) establish emission limits for this source category and require certification testing of prototype wood appliances to demonstrate compliance with those limits. However, the operating and fueling requirements in NSPS compliance testing protocols do not represent typical conditions in the field. We developed a new testing approach, the Integrated-Duty Cycle (IDC) Test Method, to address the shortcomings of current certification test approaches. The IDC procedure for cordwood stoves, which was approved by EPA as a broadly applicable alternative test method in 2021, assesses appliance operations over various operating and fueling conditions representing typical consumer use patterns in an integrated run and requires three replicate runs to enable the assessment of variability in stove performance. Stoves certified with this method will be equipped to meet the NSPS limits consistently in field operation.

Online measurement of PM from residential wood heaters in a dilution tunnel

ABSTRACT The U.S. Environmental Protection Agency (US EPA) requires residential wood heaters (RWHs) to meet particulate matter (PM) emission limits in order to lower ambient concentrations and reduce public exposure. The current US EPA dilution tunnel PM measurement methods for RWHs were developed several decades ago and use manual filter samples to generate a single PM value for tests that can last more than 12 hours for stoves and 30 hours for central heating appliances. This approach results in averaging periods of high and low emissions together and provides limited data on emissions over the entire burn profile. Over the last decade, the U.S. ambient fine particulate monitoring network has transitioned to the routine use of online automated methods. However, stationary source measurement methods have not made this transition. There are no substantial technical issues in implementing real-time automated methods to measure PM for RWH emission certification purposes. The Thermo Scientific Tapered Element Oscillating Microbalance (TEOM™) has been widely used for ambient PM measurements. It is a true inertial mass measurement with high time resolution and sensitivity. This work compares measurements obtained using a Thermo 1400 or 1405 TEOM with ASTM E2515 manual filter samples, the current US EPA Federal Reference Method, for 172 test runs across a wide range of stoves and PM loading conditions. The TEOM measurements used the same filter media, similar filter face velocities, and filter temperatures as manual methods. PM measurements were well correlated (R2 > 0.9), with TEOM values typically lower by 5% to 10%. TEOM data capture was high, with filter changes resulting in ~5 minutes of lost data, usually once or twice during a multi-hour test. We discuss differences between the two methods, such as post-sampling equilibration and measurement of PM on sample train surfaces (probe “catch”). We also provide examples of substantial non-water semi-volatile mass loss during sampling. Implications: Measurement methods for continuous PM and our understanding of their performance has dramatically improved over the last thirty years. Highly time-resolved measurements of PM from residential wood heating appliances in an appliance certification testing context provide additional insight into both appliance performance and the suitability of the test method to assess that performance. This continuous measurement approach offers new opportunities to replace traditional US regulatory PM sampling integrated manual source methods like ASTM E2515 or EPA Method 5G testing. For measurement of combustion products that can have a wide range of physical and chemical characteristics, the TEOM’s actual mass measurement principle has advantages over the sensitivity of surrogate methods to different aerosols for use in a regulatory program. Although the TEOM is commonly used to measure ambient PM, it can readily be configured to meet the needs of continuous emission testing.

Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring

ABSTRACT Many low-cost particle sensors are available for routine air quality monitoring of PM2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent AirTM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent AirTM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R2 = 0.75, mean bias error of −1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R2 = 0.57, mean bias error of −1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent AirTM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent AirTM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements. Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021.

Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

Atmospheric PM2.5 near an Urban-Industrial Complex during Air-pollution Episodes with Various Meteorological Conditions

This study investigates the atmospheric fine particulate matter (PM2.5) issue caused by the multi-effect of complicated sources, terrain, and meteorology at Southern Taiwan. Three sampling stations represent an urban, a rural, and a coastal sites near an urban-industrial complex. The atmospheric PM2.5 were measured during a pollution episode from November to February, when the reference samples were collected in April. The sample was collected with a constant-flow sampler with the Federal Reference Method performance. After determining the PM2.5 mass, their chemical compositions of ions, metals, and carbons were analyzed for the different properties caused by the multi-factors. The chemical mass balance (CMB) model was employed to evaluate the emission contributions. Additionally, an inverse trajectory model is used to analyze the pollutant transport and support the CMB results. The air-pollution episodes occurred within the winter to spring. The PM2.5 were composed of 51-69% ions, 18-31% carbonaceous species, and 1.5-3.0% metals. The SO42-, NH4+ and NO3- contributed 92-96% of the ions. Most of the organic/elemental carbon ratios were low, suggesting more primary carbon emissions. The metal contents were minor and dominated by Fe and Zn. The CMB model indicated the PM2.5 were dominated by 24% secondary SO42-, 14.7% traffics, 8.3% petrochemical emissions, 6.8% soil dust, and 4.5% sintering plant emission. For non-episode days, the PM2.5 were contributed by 34.9% traffics, 30% the secondary SO42-, 10.3% secondary NO3- , and 6.8% soil dust. Nevertheless, the frequent sea-land breeze might lead to more powerful wind eddies and bring the primary PM2.5 and the aerosol precursors from the emission areas. Consequently, the uncontrollable meteorological changings would lead to the pollution issues at the lowly convective area. Therefore, the averaging emissions of the PM2.5 and precursors should be lowered; meanwhile, the rapid controls of the primary emissions are suggested when the high-level PM2.5 are forecasted.

Evaluation of small form factor, filter-based PM2.5 samplers for temporary non-regulatory monitoring during wildland fire smoke events

Wildland fire activity and associated emission of particulate matter air pollution is increasing in the United States over the last two decades due primarily to a combination of increased temperature, drought, and historically high forest fuel loading. The regulatory monitoring networks in the Unites States are mostly concentrated in larger population centers where anthropogenic air pollution sources are concentrated. Smaller population centers in areas more likely to be impacted by wildland fire smoke in many instances lack adequate observational air quality data. Several commercially available small form factor filter-based PM2.5 samplers (SFFFS) were evaluated under typical ambient and simulated near-to mid-field wildland fire smoke conditions to evaluate their accuracy for use in temporary deployments during prescribed and wildfire events. The performance of all the SFFFS tested versus the designated federal reference methods (FRM) was acceptable in determining PM2.5 concentration in both ambient (2.7–14.0 μg m−3) and chamber smoke environments (24.6–3044.6 μg m−3) with accuracies ranging from ∼92 to 98%. However, only the ARA Instruments model N-FRM Sampler was found to provide PM2.5 mass measurement accuracies that meet FRM guideline performance specifications under both typical ambient (97.3 ± 1.9%) and simulated wildland fire conditions (98.2 ± 1.4%).