The U.S. EPA wildland fire sensor challenge: Performance and evaluation of solver submitted multi-pollutant sensor systems

Summary of PM2.5 measurement artifacts associated with the Teledyne T640 PM Mass Monitor 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) monitors for PM2.5 with particular attention being given to the Teledyne-API Model T640 PM Mass monitor, as compared to the filter-based federal reference method (FRM). The T640 is an optical-based PM monitor 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 T640×) comprises ~44% 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 paper will provide state and local air monitoring agencies with the tools and knowledge to address PM2.5 measurement challenges in areas frequently impacted by wildland fire smoke. The observed large positive and negative artifacts in the T640 PM mass determination have 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.

Ginger ring compounds as an inhibitor of spike binding protein of alpha, beta, gamma and delta variants of SARS-CoV-2: An in-silico study

The available drugs against coronavirus disease 2019 (COVOD-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are limited. This study aimed to identify ginger-derived compounds that might neutralize SARS-CoV-2 and prevent its entry into host cells. Ring compounds of ginger were screened against spike (S) protein of alpha, beta, gamma, and delta variants of SARS-CoV-2. The S protein FASTA sequence was retrieved from Global Initiative on Sharing Avian Influenza Data (GISAID) and converted into ".pdb" format using Open Babel tool. A total of 306 compounds were identified from ginger through food and phyto-databases. Out of those, 38 ring compounds were subjected to docking analysis using CB Dock online program which implies AutoDock Vina for docking. The Vina score was recorded, which reflects the affinity between ligands and receptors. Further, the Protein Ligand Interaction Profiler (PLIP) program for detecting the type of interaction between ligand-receptor was used. SwissADME was used to compute druglikeness parameters and pharmacokinetics characteristics. Furthermore, energy minimization was performed by using Swiss PDB Viewer (SPDBV) and energy after minimization was recorded. Molecular dynamic simulation was performed to find the stability of protein-ligand complex and root-mean- square deviation (RMSD) as well as root-mean-square fluctuation (RMSF) were calculated and recorded by using myPresto v5.0. Our study suggested that 17 out of 38 ring compounds of ginger were very likely to bind the S protein of SARS-CoV-2. Seventeen out of 38 ring compounds showed high affinity of binding with S protein of alpha, beta, gamma, and delta variants of SARS-CoV-2. The RMSD showed the stability of the complex was parallel to the S protein monomer. These computer-aided predictions give an insight into the possibility of ginger ring compounds as potential anti-SARS-CoV-2 worthy of in vitro investigations.

The impact of fire-specific PM2.5 calibration on health effect analyses

The quantification of PM_2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as 'fire') is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM_2.5 estimates. First, we calibrated CMAQ-based non-fire PM_2.5 to ground PM_2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM_2.5 concentrations in the second stage by multiplying the calibrated non-fire PM_2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM_2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM_2.5 better agreed with ground PM_2.5 observations with a 10-fold cross-validated (CV) R² of 0.79 compared to CMAQ-based PM_2.5 estimates with R² of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM_2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95% confidence interval (CI) = 1.021-1.130 in October; RR = 1.191, 95% CI = 1.099-1.291 in November; RR for respiratory disease = 1.078, 95% CI = 1.005-1.157 in October; RR = 1.153, 95% CI = 1.045-1.272 in November. However, the results were inconsistent when non-calibrated PM_2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.

Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke

PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries for real-time air quality information, particularly during wildfire smoke episodes. Uncorrected PurpleAir data can be biased and may exhibit a nonlinear response at extreme smoke concentrations (>300 µg/m3). This bias and nonlinearity result in a disagreement with the traditional ambient monitoring network, leading to the public’s confusion during smoke episodes. These sensors must be evaluated during smoke-impacted times and then corrected for bias, to ensure that accurate data are reported. The nearby public PurpleAir sensor and monitor pairs were identified during the summer of 2020 and were used to supplement the data from collocated pairs to develop an extended U.S.-wide correction for high concentrations. We evaluated several correction schemes to identify an optimal correction, using the previously developed U.S.-wide correction, up to 300 µg/m3, transitioning to a quadradic fit above 400 µg/m3. The correction reduces the bias at each air quality index (AQI) breakpoint; most ambient collocations that were studied met the Environmental Protection Agency’s (EPA) performance targets (twelve of the thirteen ambient sensors met the EPA’s targets) and some smoke-impacted sites (5 out of 15 met the EPA’s performance targets in terms of the 1-h averages). This correction can also be used to improve the comparability of PurpleAir sensor data with regulatory-grade monitors when they are collectively analyzed or shown together on public information websites; the methods developed in this paper can also be used to correct future air-sensor types. The PurpleAir network is already filling in spatial and temporal gaps in the regulatory monitoring network and providing valuable air-quality information during smoke episodes.

Fuel layer specific pollutant emission factors for fire prone forest ecosystems of the western U.S. and Canada

Wildland fires are a major source of gases and aerosols, and the production, dispersion, and transformation of fire emissions have significant ambient air quality impacts and climate interactions. The increase in wildfire area burned and severity across the United States and Canada in recent decades has led to increased interest in expanding the use of prescribed fires as a forest management tool. While the primary goal of prescribed fire use is to limit the loss of life and property and ecosystem damage by constraining the growth and severity of future wildfires, a potential additional benefit of prescribed fire - reduction in the adverse impacts of smoke production and greenhouse gas (GHG) emissions - has recently gained the interest of land management agencies and policy makers in the United States and other nations. The evaluation of prescribed fire/wildfire scenarios and the potential mitigation of adverse impacts on air quality and GHGs requires fuel layer specific pollutant emission factors (EFs) for fire prone forest ecosystems. Our study addresses this need with laboratory experiments measuring EFs for carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), ethyne (C₂H₂), formaldehyde (H₂CO), formic acid (CH₂O₂), hydrogen cyanide (HCN), fine particulate matter (PM_2.5), nitric oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and total reduced sulfur (TRS) for the burning of individual fuel components from three forest ecosystems which account for a large share of wildfire burned area and emissions in the western United States and Canada - Douglas fir, ponderosa pine, and black spruce/jack pine.

Evaluation of Cairpol and Aeroqual Air Sensors in Biomass Burning Plumes

Cairpol and Aeroqual air quality sensors measuring CO, CO₂, NO₂, and other species were tested in fresh biomass burning plumes in field and laboratory environments. We evaluated sensors by comparing 1-minute sensor measurements to collocated reference instrument measurements. Sensors were evaluated based on the coefficient of determination (r ²) between the sensor and reference measurements, by the accuracy, collocated precision, root mean square error (RMSE), and other metrics. In general, CO and CO₂ sensors performed well (in terms of accuracy and r ² values) compared to NO₂ sensors. Cairpol CO and NO₂ sensors had better sensor-versus-sensor agreement (e.g., collocated precision) than Aeroqual CO and NO₂ sensors of the same species. Tests of other sensors (e.g., NH₃, H₂S, VOC, NMHC) provided more inconsistent results and need further study. Aeroqual NO₂ sensors had an apparent O₃ interference that was not observed in the Cairpol NO₂ sensors. Although the sensor accuracy lags that of reference-level monitors, with location-specific calibrations they have the potential to provide useful data about community air quality and personal exposure to smoke impacts.

Evaluation of two collocated federal equivalent method PM2.5 instruments over a wide range of concentrations in Sarajevo, Bosnia and Herzegovina

Two widely used PM_2.5 monitors in the United States (U.S.) designated as federal equivalent methods (FEMs) by the U.S. Environmental Protection Agency were collocated for 15 months in Sarajevo, Bosnia and Herzegovina (BiH) to evaluate their comparability. With differing measurement principles, the FEMs (Met One BAM-1020 and Teledyne API T640) exhibited unique responses to the significant range in PM_2.5 over the study period. During the winter months when concentrations greatly increased (e.g., daily PM_2.5 > 100 μg m^-3), the BAM-1020 had intermittent malfunctioning nozzle contact to the collection tape, resulting in periods of data invalidation. Increased operator observation and doubling the cleaning frequency were required to maintain proper operation. The hourly data from the BAM-1020, which detects PM_2.5 via beta-attenuation of particles loaded to the collection tape, indicated higher noise at concentrations below 40 μg m^-3 relative to the T640, which detects PM_2.5 via an optical method. Above this concentration threshold, the two instruments appear to have comparable hourly fluctuations in the data. Relative to the BAM-1020, the T640 reported higher concentrations when PM_2.5 is above 80 μg m^-3. A linear regression equation was developed and applied to adjust T640 PM_2.5 high concentration values, resulting in 24-hr average T640adj PM_2.5 values closely matching that from the BAM-1020 for the full concentration range. Based on the T640adj values, the annual average for Sarajevo was calculated at the site to be 42 μg m^-3, with significant seasonality resulting in over 7-fold higher concentrations in the months of December-January compared to June-July.

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 PM_2.5 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 PM_2.5 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 PM_2.5 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%).

Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA

Prescribed burning (PB) is a prominent source of PM_2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM_2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM_2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM_2.5 concentrations. Simulated PM_2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM_2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM_2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R² of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM_2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM_2.5 fields to provide better estimates of hourly exposures to smoke from PB.Implications: Prescribed burning emissions are a major cause of elevated PM_2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM_2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Comparison of ozone measurement methods in biomass burning smoke: an evaluation under field and laboratory conditions

In recent years wildland fires in the United States have had significant impacts on local and regional air quality and negative human health outcomes. Although the primary health concerns from wildland fires come from fine particulate matter (PM2.5), large increases in ozone (O3) have been observed downwind of wildland fire plumes (DeBell et al., 2004; Bytnerowicz et al., 2010; Preisler et al., 2010; Jaffe et al., 2012; Bytnerowicz et al., 2013; Jaffe et al., 2013; Lu et al., 2016; Lindaas et al., 2017; McClure and Jaffe, 2018; Liu et al., 2018; Baylon et al., 2018; Buysse et al., 2019). Conditions generated in and around wildland fire plumes, including the presence of interfering chemical species, can make the accurate measurement of O3 concentrations using the ultraviolet (UV) photometric method challenging if not impossible. UV photometric method instruments are prone to interferences by volatile organic compounds (VOCs) that are present at high concentrations in wildland fire smoke. Four different O3 measurement methodologies were deployed in a mobile sampling platform downwind of active prescribed grassland fire lines in Kansas and Oregon and during controlled chamber burns at the United States Forest Service, Rocky Mountain Research Station Fire Sciences Laboratory in Missoula, Montana. We demonstrate that the Federal Reference Method (FRM) nitric oxide (NO) chemiluminescence monitors and Federal Equivalent Method (FEM) gas-phase (NO) chemical scrubber UV photometric O3 monitors are relatively interference-free, even in near-field combustion plumes. In contrast, FEM UV photometric O3 monitors using solid-phase catalytic scrubbers show positive artifacts that are positively correlated with carbon monoxide (CO) and total gas-phase hydrocarbon (THC), two indicator species of biomass burning. Of the two catalytic scrubber UV photometric methods evaluated, the instruments that included a Nafion® tube dryer in the sample introduction system had artifacts an order of magnitude smaller than the instrument with no humidity correction. We hypothesize that Nafion®-permeating VOCs (such as aromatic hydrocarbons) could be a significant source of interference for catalytic scrubber UV photometric O3 monitors and that the inclusion of a Nafion® tube dryer assists with the mitigation of these interferences. The chemiluminescence FRM method is highly recommended for accurate measurements of O3 in wildland fire plume studies and at regulatory ambient monitoring sites frequently impacted by wildland fire smoke.