Ann M. Dillner

Size-resolved particulate matter composition in Beijing during pollution and dust events

Each spring, Beijing, China, experiences dust storms which cause high particulate matter concentrations. Beijing also has many anthropogenic sources of particulate matter including the large Capitol Steel Company. On the basis of measured size segregated, speciated particulate matter concentrations, and calculated back trajectories, three types of pollution events occurred in Beijing from 22 March to 1 April 2001: dust storms, urban pollution events, and an industrial pollution event. For each event type, the source of each measured element is determined to be soil or anthropogenic and profiles are created that characterize the particulate matter composition. Dust storms are associated with winds traveling from desert regions and high total suspended particle (TSP) and PM2.5 concentrations. Sixty-two percent of TSP is due to elements with oxides and 98% of that is from soil. Urban pollution events have smaller particulate concentrations but 49% of the TSP is from soil, indicating that dust is a major component of the particulate matter even when there is not an active dust storm. The industrial pollution event is characterized by winds from the southwest, the location of the Capitol Steel Company, and high particulate concentrations. PM2.5 mass and acidic ion concentrations are highest during the industrial pollution event as are Mn, Zn, As, Rb, Cd, Cs and Pb concentrations. These elements can be used as tracers for industrial pollution from the steel mill complex. The industrial pollution is potentially more detrimental to human health than dust storms due to higher PM2.5 concentrations and higher acidic ion and toxic particulate matter concentrations.

Measuring the Mass Extinction Efficiency of Elemental Carbon in Rural Aerosol

Previous measurements of the mass absorption efficiency of ambient elemental carbon (EC) indicate that EC optical properties vary with location and imply that the variations may be due to different particle size distributions and composition at different locations (Liousse et al. 1993). For this reason, optical properties appropriate to regional characteristics of EC, determined over the wavelengths of light significant for aerosol extinction, are needed to adequately model the radiative impact of this species. Here we present a method for measuring one of these properties, the mass extinction efficiency (m 2 g -1 ) of EC, as a function of particle size and wavelength of light. In this method, size segregated atmospheric aerosol particles are collected on Nucleopore filters. The filter samples are extracted in a mixture of 30% isopropanol and 70% deionized distilled water to form a suspension of insoluble EC particles. Transmission of light through the extraction liquid is measured over wavelengths from 300 to 800 nm using a spectrophotometer. The transmission measurements taken through the liquid extract are mathematically converted to EC extinction coefficients in air. Although the conversion is a function of a parameter determined from Mie theory, which assumes monodisperse, spherical particles with a known density and refractive index relative to the medium, the method is shown to be reasonably insensitive to these assumptions. Using EC mass concentration obtained from a parallel sample, the EC mass extinction efficiency (in air) is calculated from the extinction coefficient (in air). This method is applied to a rural Midwestern, midcontinental aerosol. In general, the EC mass extinction efficiency in air is highest at lower wavelengths and for smaller particles. For particles with diameters between 0.09 and 2.7 w m and an assumed density of 1.9 g cm -3 , the measured EC mass extinction efficiency at 550 nm ranges from 7.3 to 1.7 m 2 g -1 .

A Novel Method Using Polyurethane Foam (PUF) Substrates to Determine Trace Element Concentrations in Size-Segregated Atmospheric Particulate Matter on Short Time Scales

A PUF-ICP-MS method has been developed that can quantify a large number of elements in size-resolved ambient aerosol samples collected over short-time intervals (four hours in this study). The pre-cleaning and digestion protocols developed enabled the quantification of 29 elements (including Hg) in ambient PM sampled onto a PUF substrate. The microwave-assisted acid digestion method affects a total dissolution of both aerosol and substrate, producing a colorless solution with no trace of aerosol, PUF, or polypropylene particulates, and is compatible with many analytical techniques (e.g., ICP-MS, CVAF, GFAA). For the large majority of elements the PUF-ICP-MS method provides superior elemental detection capabilities than traditional XRF approaches and importantly, method detection limits (MDLs) for most studied elements are lower than achieved with a 4-hour MOUDI approach using optimized ICP-MS analysis. For example, MDLs for V, Sr, Cd, Sb, and Hg are respectively 0.15, 0.18, 0.034, 0.051, and 0.0055 (solvent cleaning only for Hg) ng m−3. Only one-third, or less, of the PUF substrate is required for the ICP-MS analysis leaving the majority of the substrate available for additional characterization, e.g., organic speciation, of the same air mass. A demonstration study conducted in Phoenix, Arizona, shows that this new method can quantitatively resolve differences in element concentrations on short time scales and has the potential to be a powerful new tool for identifying sources of trace and ultra-trace elements. The highly size and time resolved data can also provide useful information for the study of health effects of individual or groups of elements.

A Temperature Calibration Procedure for the Sunset Laboratory Carbon Aerosol Analysis Lab Instrument

The Sunset Laboratory Carbon Aerosol Analysis Lab Instrument is widely used for thermal-optical analysis (TOA) of ambient particulate matter samples to measure total carbon (TC), organic carbon (OC), and elemental carbon (EC), and often thermal sub-fractions of OC and EC. TOA operating protocols include a series of plateau temperatures at which the thermal sub-fractions evolve. The temperatures have conventionally been measured by a sensor located in the sample oven but away from the filter sample. However, the TOA protocol used by the Interagency Monitoring of Protected Visual Environments (IMPROVE) network and recently adopted by the U.S. Environmental Protection Agency (EPA) Speciation Trends Network (STN) and Chemical Speciation Network (CSN) specify temperatures to be achieved at the filter. Our goal was to develop a simple calibration method to obtain the target filter temperatures in a Sunset Instrument. This method showed good agreement with the IMPROVE/STN/CSN method and has the advantages of not damaging oven components and of providing a direct comparison of sample oven sensor and filter temperatures at the TOA protocol-specified temperatures. Calibrations performed on four Sunset Instruments yielded different sensor/filter temperature relationships. Ambient PM 2.5 samples analyzed using IMPROVE_A temperatures at the oven sensor compared to IMPROVE_A temperatures at the filter yielded statistically insignificant differences for TC, OC, and EC but statistically significant differences for the carbon sub-fraction concentrations. Temperature calibrations should be performed on each Sunset Instrument to ensure comparability in the carbon sub-fractions being reported, and a simple method has been provided for performing these calibrations.

Particulate Matter Sample Deposit Geometry and Effective Filter Face Velocities

Abstract Aerosol filter face velocities can be underestimated when the sample deposit area does not cover the entire face of the filter. In many aerosol samplers, Teflon filters are backed with a metal support screen. In these samplers, air flows through the filter only in the small area upstream of each hole in the screen, resulting in a sample deposit that is an array of tiny dots that mimics the array of holes. Thus, the filter deposit area is smaller than the total filter area and the effective face velocity is greater than that calculated from the sample deposit envelope. The Inter-agency Monitoring of Protected Visual Environments (IMPROVE) network has used filter holders with two different screen hole arrays. The U.S. Environmental Protection Agency’s Chemical Speciation Network (CSN) and the Federal Reference Method samplers also use a metal support screen, but with much smaller screen holes than IMPROVE. These networks also use larger filters and lower flow rates than those used in IMPROVE. Filter face velocities for all of these networks that are calculated using the actual deposit array area range from 1.6 to 3.5 times larger than those calculated incorrectly using the entire sample deposit envelope.

Predicting ambient aerosol thermal–optical reflectance measurements from infrared spectra: elemental carbon

Elemental carbon (EC) is an important constituent of atmospheric particulate matter because it absorbs solar radiation influencing climate and visibility and it adversely affects human health. The EC measured by thermal methods such as thermal-optical reflectance (TOR) is operationally defined as the carbon that volatilizes from quartz filter samples at elevated temperatures in the presence of oxygen. Here, methods are presented to accurately predict TOR EC using Fourier transform infrared (FT-IR) absorbance spectra from atmospheric particulate matter collected on polytetrafluoroethylene (PTFE or Teflon) filters. This method is similar to the procedure developed for OC in prior work (Dillner and Takahama, 2015). Transmittance FT-IR analysis is rapid, inexpensive and nondestructive to the PTFE filter samples which are routinely collected for mass and elemental analysis in monitoring networks. FT-IR absorbance spectra are obtained from 794 filter samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least squares regression is used to calibrate sample FT-IR absorbance spectra to collocated TOR EC measurements. The FT-IR spectra are divided into calibration and test sets. Two calibrations are developed: one developed from uniform distribution of samples across the EC mass range (Uniform EC) and one developed from a uniform distribution of Low EC mass samples (EC < 2.4 mu g, Low Uniform EC). A hybrid approach which applies the Low EC calibration to Low EC samples and the Uniform EC calibration to all other samples is used to produce predictions for Low EC samples that have mean error on par with parallel TOR EC samples in the same mass range and an estimate of the minimum detection limit (MDL) that is on par with TOR EC MDL. For all samples, this hybrid approach leads to precise and accurate TOR EC predictions by FT-IR as indicated by high coefficient of determination (R-2; 0.96), no bias (0.00 mu gm(-3), a concentration value based on the nominal IMPROVE sample volume of 32.8m(3)), low error (0.03 mu g m(-3)) and reasonable normalized error (21 %). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision and accuracy to collocated TOR measurements. Only the normalized error is higher for the FT-IR EC measurements than for collocated TOR. FT-IR spectra are also divided into calibration and test sets by the ratios OC/EC and ammonium/EC to determine the impact of OC and ammonium on EC prediction. We conclude that FT-IR analysis with partial least squares regression is a robust method for accurately predicting TOR EC in IMPROVE network samples, providing complementary information to TOR OC predictions (Dillner and Takahama, 2015) and the organic functional group composition and organic matter estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade‐offs, but the cost of model parsimony and the potential for underfitting on achievable prediction errors are difficult to anticipate. We propose a metric that penalizes growing model variance against decreasing bias as additional latent variables are added. The magnitude of the penalty is scaled by a user‐defined parameter that is formulated to provide a constraint on the fractional increase in root mean square error of cross‐validation (RMSECV) when selecting a parsimonious model over the conventional minimum RMSECV solution. We evaluate this approach for quantification of four organic functional groups using 238 laboratory standards and 750 complex atmospheric organic aerosol mixtures with mid‐infrared spectroscopy. Parametric variation of this penalty demonstrates that increase in prediction errors due to underfitting is bounded by the magnitude of the penalty for samples similar to laboratory standards used for model training and validation. Imposing an ensemble of penalties corresponding to a 0–30% allowable increase in RMSECV through sum of ranking differences leads to the selection of a model that increases the actual RMSECV up to 20% for laboratory standards but achieves an 85% reduction in the mean error in predicted concentrations for environmental mixtures. Partial least squares models developed with laboratory mixtures can provide useful predictions in complex environmental samples, but may benefit from protection against overfitting.

Preparation of lead (Pb) X-ray fluorescence reference materials for the EPA Pb monitoring program and the IMPROVE network using an aerosol deposition method

ABSTRACT X-ray fluorescence (XRF) is a commonly used analytical method to quantify lead (Pb), a toxic element, in atmospheric aerosol. The commercially available reference materials used for calibrating XRF do not mimic the concentrations and filter materials of particulate matter (PM) monitoring networks. In this study, we described an aerosol deposition method to generate Pb reference materials (RMs) over a range of concentrations to serve several purposes for the US Environmental Protection Agency (EPA) and Interagency Monitoring of PROtected Visual Environments (IMPROVE) monitoring networks including laboratory auditing, federal equivalency method evaluation, and calibration and quality control of XRF instruments. The RMs were generated using a laboratory-built aerosol chamber equipped with a federal reference sampler at concentration levels ranging from 0.0125 to 0.70 μg/m3. XRF analysis at UC Davis was demonstrated to be equivalent to a US and EU reference method, inductively coupled plasma—mass spectrometry (ICP-MS), for measuring Pb on RMs following a methodology described in the United States and international standards. The Pb concentrations on subsets of the RMs were verified by three other XRF laboratories with different analyzers and/or quantification methods and were shown to be equivalent to the UC Davis XRF analysis. The generated RMs were demonstrated to have short and long-term stability, satisfying an additional requirement of reference materials. Copyright

The impact of the 2016 Fort McMurray Horse River Wildfire on ambient air pollution levels in the Athabasca Oil Sands Region, Alberta, Canada

An unprecedented wildfire impacted the northern Alberta city of Fort McMurray in May 2016 causing a mandatory city wide evacuation and the loss of 2,400 homes and commercial structures. A two-hectare wildfire was discovered on May 1, grew to ~157,000 ha by May 5, and continued to burn an estimated ~590,000 ha by June 13. A comprehensive air monitoring network operated by the Wood Buffalo Environmental Association (WBEA) in and around Fort McMurray provided essential health-related real-time air quality data to firefighters during the emergency, and provided a rare opportunity to elucidate the impact of gaseous and particulate matter emissions on near-field communities and regional air pollution concentrations. The WBEA network recorded 188 fire-related exceedances of 1-hr and 24-hr Alberta Ambient Air Quality Objectives. Two air monitoring sites within Fort McMurray recorded mean/maximum 1-hr PM2.5 concentrations of 291/5229 μg m−3 (AMS-6) and 293/3259 μg m−3 (AMS-7) during fire impact periods. High correlations (r2 = 0.83–0.97) between biomass combustion related gases (carbon monoxide (CO), non-methane hydrocarbons (NMHC), total hydrocarbons (THC), total reduced sulfur (TRS), ammonia) and PM2.5 were observed at the sites. Filter-based 24-hr integrated PM2.5 samples collected every 6 days showed maximum concentrations of 267 μg m−3 (AMS-6) and 394 μg m−3 (AMS-7). Normalized excess emission ratios relative to CO were 149.87 ± 3.37 μg m−3 ppm−1 (PM2.5), 0.274 ± 0.002 ppm ppm−1 (THC), 0.169 ± 0.001 ppm ppm−1 (NMHC), 0.104 ± 0.001 ppm ppm−1 (CH4), 0.694 ± 0.007 ppb ppm−1 (TRS), 0.519 ± 0.040 ppb ppm−1 (SO2), 0.412 ± 0.045 ppb ppm−1 (NO), 1.968 ± 0.053 ppb ppm−1 (NO2), and 2.337 ± 0.077 ppb ppm−1 (NOX). A subset of PM2.5 filter samples was analyzed for trace elements, major ions, organic carbon, elemental carbon, and carbohydrates. Sample mass reconstruction and fire specific emission profiles are presented and discussed. Potential fire-related photometric ozone instrument positive interferences were observed and were positively correlated with NO and NMHC.

Ambient aerosol composition by infrared spectroscopy and partial least squares in the chemical speciation network: Multilevel modeling for elemental carbon

ABSTRACT Fourier transform infrared spectroscopy (FT-IR) has been used to predict elemental carbon (EC) on polytetrafluoroethylene (PTFE) filter samples from the United States Environmental Protection Agencys Chemical Speciation Network (CSN). This study provides a proof-of-principle demonstration of using multilevel modeling to determine thermal/optical reflectance (TOR) equivalent EC (a.k.a., FT-IR EC) on PTFE samples collected in the CSN. Initially, spectra from nine geographically disperse sites were pooled and calibrated directly to collocated TOR EC measurements. The FT-IR EC quantified in test samples was deemed substandard when judged against an earlier study, e.g., R2 = 0.760 and median absolute deviation (MAD) = 26.7%. Upon scrutinizing each samples absolute prediction error and squared Mahalanobis distance, Elizabeth, NJ predictions were found to exhibit atypical systematic errors, motivating the development of a multilevel classification and calibration procedure. Atypical Elizabeth spectra were distinguished from the (typical) CSN spectra by training a partial least-square discriminant analysis. Predicting EC using calibrations dedicated to either atypical or typical samples produced a satisfactory improvement in overall performance (R2 = 0.886, MAD = 19.8%). Analysis of the atypical FT-IR spectra and select TOR thermal fractions suggested that Elizabeth samples contained elevated levels of diesel particulate matter as evidenced by the use of organic nitrogen functional groups for prediction, very low average OC/EC, and minimal charring during TOR speciation. FT-IR EC from the other eight sites was predominately determined by aliphatic C-H, C = C aromatic, and functional groups associated with oxidation. This study provides preliminary confirmation that FT-IR EC may be accurately determined from source-oriented calibrations under a combined classification and calibration methodology. Copyright