Gaseous, PM2.5 Mass, and Speciated Emission Factors from Laboratory Chamber Peat Combustion

Peat fuels representing four biomes of boreal (western Russia and Siberia), temperate (northern Alaska, USA), subtropical (northern and southern Florida, USA), and tropical (Borneo, Malaysia) regions were burned in a laboratory chamber to determine gas and particle emission factors (EFs). Tests with 25 % fuel moisture were conducted with predominant smoldering combustion conditions (average modified combustion efficiency (MCE) =0.82+/-0.08). Average fuel-based EFCO2 (carbon dioxide) are highest (1400 +/- 38 g kg(-1)) and lowest (1073 +/- 63 g kg(-1)) for the Alaskan and Russian peats, respectively. EFCO (carbon monoxide) and EFCH4 (methane) are similar to 12 %15 % and similar to 0.3 %0.9 % of EFCO2, in the range of 157171 and 310 g kg(-1), respectively. EFs for nitrogen species are at the same magnitude as EFCH4, with an average of 5.6 +/- 4.8 and 4.7 +/- 3.1 g kg(-1) for EFNH3 (ammonia) and EFHCN (hydrogen cyanide); 1.9+/-1.1 g kg(-1) for EFNOx (nitrogen oxides); and 2.4+/-1.4 and 2.0 +/- 0.7 g kg(-1) for EFNOy (total reactive nitrogen) and EFN2O (nitrous oxide). An oxidation flow reactor (OFR) was used to simulate atmospheric aging times of similar to 2 and similar to 7 d to compare fresh (upstream) and aged (downstream) emissions. Filter-based EFPM2.5 varied by \u3e 4-fold (1461 g kg(-1)) without appreciable changes between fresh and aged emissions. The majority of EFPM2.5 consists of EFOC (organic carbon), with EFOC / EFPM2.5 ratios in the range of 52 %98 % for fresh emissions and similar to 14 %23 % degradation after aging. Reductions of EFOC (similar to 79 g kg(-1)) after aging are most apparent for boreal peats, with the largest degradation in low-temperature OC1 that evolves at \u3c 140 degrees C, indicating the loss of high-vapor-pressure semivolatile organic compounds upon aging. The highest EFLevoglucosan is found for Russian peat (similar to 16 g kg(-1)), with similar to 35 %50 % degradation after aging. EFs for water-soluble OC (EFWSOC) account for similar to 20 %62 % of fresh EFOC. The majority (\u3e 95 %) of the total emitted carbon is in the gas phase, with 54 %75 % CO2, followed by 8 %30 % CO. Nitrogen in the measured species explains 24 %52 % of the consumed fuel nitrogen, with an average of 35 +/- 11 %, consistent with past studies that report similar to 1/3 to 2/3 of the fuel nitrogen measured in biomass smoke. The majority (\u3e 99 %) of the total emitted nitrogen is in the gas phase, with an average of 16.7 % as NH3 and 9.5 % as HCN center dot N2O and NOy constituted 5.7 % and 2.9 % of consumed fuel nitrogen. EFs from this study can be used to refine current emission inventories

Changes in PM2.5 Peat Combustion Source Profiles with Atmospheric Aging in an Oxidation Flow Reactor

Smoke from laboratory chamber burning of peat fuels from Russia, Siberia, the USA (Alaska and Florida), and Malaysia representing boreal, temperate, subtropical, and tropical regions was sampled before and after passing through a potential-aerosol-mass oxidation flow reactor (PAM-OFR) to simulate intermediately aged (∼2 d) and well-aged (∼7 d) source profiles. Species abundances in PM2.5 between aged and fresh profiles varied by several orders of magnitude with two distinguishable clusters, centered around 0.1 % for reactive and ionic species and centered around 10 % for carbon. Organic carbon (OC) accounted for 58 %–85 % of PM2.5 mass in fresh profiles with low elemental carbon (EC) abundances (0.67 %–4.4 %). OC abundances decreased by 20 %–33 % for well-aged profiles, with reductions of 3 %–14 % for the volatile OC fractions (e.g., OC1 and OC2, thermally evolved at 140 and 280 ∘C). Ratios of organic matter (OM) to OC abundances increased by 12 %–19 % from intermediately aged to well-aged smoke. Ratios of ammonia (NH3) to PM2.5 decreased after intermediate aging. Well-aged NH+4 and NO−3 abundances increased to 7 %–8 % of PM2.5 mass, associated with decreases in NH3, low-temperature OC, and levoglucosan abundances for Siberia, Alaska, and Everglades (Florida) peats. Elevated levoglucosan was found for Russian peats, accounting for 35 %–39 % and 20 %–25 % of PM2.5 mass for fresh and aged profiles, respectively. The water-soluble organic carbon (WSOC) fractions of PM2.5 were over 2-fold higher in fresh Russian peat (37.0±2.7 %) than in Malaysian (14.6±0.9 %) peat. While Russian peat OC emissions were largely water-soluble, Malaysian peat emissions were mostly water-insoluble, with WSOC ∕ OC ratios of 0.59–0.71 and 0.18–0.40, respectively. This study shows significant differences between fresh and aged peat combustion profiles among the four biomes that can be used to establish speciated emission inventories for atmospheric modeling and receptor model source apportionment. A sufficient aging time (∼7 d) is needed to allow gas-to-particle partitioning of semi-volatilized species, gas-phase oxidation, and particle volatilization to achieve representative source profiles for regional-scale source apportionment

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector, edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses

Feasibility of coupling a thermal/optical carbon analyzer to a quadrupole mass spectrometer for enhanced PM2.5 speciation

A thermal/optical carbon analyzer (TOA), normally used for quantification of organic carbon (OC) and elemental carbon (EC) in PM2.5 (fine particulate matter) speciation networks, was adapted to direct thermally evolved gases to an electron impact quadrupole mass spectrometer (QMS), creating a TOA-QMS. This approach produces spectra similar to those obtained by the Aerodyne aerosol mass spectrometer (AMS), but the ratios of the mass to charge (m/z) signals differ and must be remeasured using laboratory-generated standards. Linear relationships are found between TOA-QMS signals and ammonium (NH4+), nitrate (NO3-), and sulfate (SO42-) standards. For ambient samples, however, positive deviations are found for SO42-, compensated by negative deviations for NO3-, at higher concentrations. This indicates the utility of mixed-compound standards for calibration or separate calibration curves for low and high ion concentrations. The sum of the QMS signals across all m/z after removal of the NH4+, NO3-, and SO42- signals was highly correlated with the carbon content of oxalic acid (C2H2O4) standards. For ambient samples, the OC derived from the TOA-QMS method was the same as the OC derived from the standard IMPROVE_A TOA method. This method has the potential to reduce complexity and costs for speciation networks, especially for highly polluted urban areas such as those in Asia and Africa.Implications: Ammonium, nitrate, and sulfate can be quantified by the same thermal evolution analysis applied to organic and elemental carbon. This holds the potential to replace multiple parallel filter samples and separate laboratory analyses with a single filter and a single analysis to account for a large portion of the PM2.5 mass concentration

Determinants of personal exposure to fine particulate matter (PM2.5) adult subjects in Hong Kong

Personal monitoring for fine particulate matter (PM2.5) was conducted for adults (48 subjects, 18-63 years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p = 0.02) and occupation (p < 0.001) were significant factors affecting the strength of the personal-ambient PM2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM2.5 was the dominant predictor of (R-2 = 0.12-0.59, p < 0.01) and the largest contributor (>37.3%) to personal exposures for PM2.5 mass and most components. For all subjects, a one-unit (2.72 mu g/m(3)) increase in ambient PM2.5 was associated with a 0.75 mu g/m(3) (95% CI: 0.59-0.94 mu g/m(3)) increase in personal PM2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM2.5 components in personal exposure (R-beta(2) = 0.06-0.63, p < 0.05), contributing to 3.0-70.4% of the variability. For onehour extra time spent at home, in transit; and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures. (C) 2018 Elsevier B.V. All rights reserved

A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Air quality time series consists of complex linear and non-linear patterns and are difficult to forecast. Box-Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their inability to predict extreme events. Artificial neural networks (ANN) can recognize non-linear patterns that include extremes. A novel hybrid model combining ARIMA and ANN to improve forecast accuracy for an area with limited air quality and meteorological data was applied to Temuco, Chile, where residential wood burning is a major pollution source during cold winters, using surface meteorological and PM10 measurements. Experimental results indicated that the hybrid model can be an effective tool to improve the PM10 forecasting accuracy obtained by either of the models used separately, and compared with a deterministic MLR. The hybrid model was able to capture 100% and 80% of alert and pre-emergency episodes, respectively. This approach demonstrates the potential to be applied to air quality forecasting in other cities and countries

Constraining Aerosol Optical Models Using Ground-Based, Collocated Particle Size and Mass Measurements in Variable Air Mass Regimes During the 7-SEAS/Dongsha Experiment

During the spring of 2010, NASA Goddard's COMMIT ground-based mobile laboratory was stationed on Dongsha Island off the southwest coast of Taiwan, in preparation for the upcoming 2012 7-SEAS field campaign. The measurement period offered a unique opportunity for conducting detailed investigations of the optical properties of aerosols associated with different air mass regimes including background maritime and those contaminated by anthropogenic air pollution and mineral dust. What appears to be the first time for this region, a shortwave optical closure experiment for both scattering and absorption was attempted over a 12-day period during which aerosols exhibited the most change. Constraints to the optical model included combined SMPS and APS number concentration data for a continuum of fine and coarse-mode particle sizes up to PM2.5. We also take advantage of an IMPROVE chemical sampler to help constrain aerosol composition and mass partitioning of key elemental species including sea-salt, particulate organic matter, soil, non sea-salt sulphate, nitrate, and elemental carbon. Our results demonstrate that the observed aerosol scattering and absorption for these diverse air masses are reasonably captured by the model, where peak aerosol events and transitions between key aerosols types are evident. Signatures of heavy polluted aerosol composed mostly of ammonium and non sea-salt sulphate mixed with some dust with transitions to background sea-salt conditions are apparent in the absorption data, which is particularly reassuring owing to the large variability in the imaginary component of the refractive indices. Extinctive features at significantly smaller time scales than the one-day sample period of IMPROVE are more difficult to reproduce, as this requires further knowledge concerning the source apportionment of major chemical components in the model. Consistency between the measured and modeled optical parameters serves as an important link for advancing remote sensing and climate research studies in dynamic aerosol-rich environments like Dongsha

Optimization and evaluation of multi-bed adsorbent tube method in collection of volatile organic compounds

The feasibility of using adsorbent tubes to collect volatile organic compounds (VOCs) has been demonstrated since the 1990's and standardized as Compendium Method TO-17 by the U.S. Environmental Protection Agency (U.S EPA). This paper investigates sampling and analytical variables on concentrations of 57 ozone (O-3) precursors (C-2-C-12 aliphatic and aromatic VOCs) specified for the Photochemical Assessment Monitoring Station (PAMS). Laboratory and field tests examined multi-bed adsorbent tubes containing a sorbate combination of Tenax TA, Carbograph 1 TD, and Carboxen 1003. Analyte stabilities were influenced by both collection tube temperature and ambient O-3 concentrations. Analytes degraded during storage, while blank levels were elevated by passive adsorption. Adsorbent tube storage under cold temperatures (- 10 degrees C) in a preservation container filled with solid silica gel and anhydrous calcium sulfate (CaSO4) ensured sample integrity. A high efficiency (> 99%) O-3 scrubber (i.e., copper coil tube filled with saturated potassium iodide [KM removed O-3 (i.e., < 200 ppbv) from the air stream with a sampling capacity of 30 h. Water vapor scrubbers interfered with VOC measurements. The optimal thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) desorption time of 8 min was found at 330 degrees C. Good linearity (R-2 > 0.995) was achieved for individual analyte calibrations (with the exception of acetylene) for mixing ratios of 0.08-1.96 ppbv. The method detection limits (MDLs) were below 0.055 ppbv for a 3 L sample volume. Replicate analyses showed relative standard deviations (RSDs) of < 10%, with the majority of the analytes within < 5%