Thermal Optical Reflectance Research Articles

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

ABSTRACTFourier transform infrared spectroscopy (FT-IR) has been used to predict elemental carbon (EC) on polytetrafluoroethylene (PTFE) filter samples from the United States Environmental Protection Agency's 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 sample's 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 w...

Aerosol emissions factors from traditional biomass cookstoves in India: insights from field measurements

Abstract. Residential solid biomass cookstoves are important sources of aerosol emissions in India. Cookstove emissions rates are largely based on laboratory experiments conducted using the standard water-boiling test, but real-world emissions are often higher owing to different stove designs, fuels, and cooking methods. Constraining mass emissions factors (EFs) for prevalent cookstoves is important because they serve as inputs to bottom-up emissions inventories used to evaluate health and climate impacts. Real-world EFs were measured during winter 2015 for a traditional cookstove (chulha) burning fuel wood, agricultural residue, and dung from different regions of India. Average (±95 % confidence interval) EFs for fuel wood, agricultural residue, and dung were (1) PM2.5 mass: 10.5 (7.7–13.4) g kg−1, 11.1 (7.7–15.5) g kg−1, and 22.6 (14.9–32.9) g kg−1, respectively; (2) elemental carbon (EC): 0.9 (0.6–1.4) g kg−1, 1.6 (0.6–3.0) g kg−1, and 1.0 (0.4–2.0) g kg−1, respectively; and (3) organic carbon (OC): 4.9 (3.2–7.1) g kg−1, 7.0 (3.5–12.5) g kg−1, and 12.9 (4.2–15.01) g kg−1, respectively. The mean (±95 % confidence interval) OC ∕ EC mass ratios were 6.5 (4.5–9.1), 7.6 (4.4–12.2), and 12.7 (6.5–23.3), respectively, with OC and EC quantified by the IMPROVE_A thermal-optical reflectance protocol. These real-world EFs are higher than those from previous laboratory-based measurements. Combustion conditions have larger effects on EFs than the fuel types. We also report the carbon mass fractions of our aerosol samples determined using the thermal-optical reflectance method. The mass fraction profiles are consistent between the three fuel categories but markedly different from those reported in past literature – including the source profiles for wood stove PM2.5 emissions developed as inputs to receptor modeling studies conducted by the Central Pollution Control Board of India. Thermally stable OC (OC3 in the IMPROVE_A protocol) contributed nearly 50 % of the total carbon mass for emissions from all fuels.

Characterization of carbonaceous fractions in PM2.5 and PM10 over a typical industrial city in central China.

Aerosol samples of PM2.5 and PM10 were collected every 6days from March 2012 to February 2013 in Huangshi, a typical industrial city in central China, to investigate the characteristics, relationships, and sources of carbonaceous species. The PM2.5 and PM10 samples were analyzed for organic carbon (OC), elemental carbon (EC), char, and soot using the thermal/optical reflectance (TOR) method following the IMPROVE_A protocol. PM2.5 and PM10 concentrations ranged from 29.37 to 501.43μgm-3 and from 50.42 to 330.07μgm-3, with average levels of 104.90 and 151.23μgm-3, respectively. The 24-h average level of PM2.5 was about three times the US EPA standard of 35μgm-3, and significantly exceeds the Class II National Air Quality Standard of China of 75μgm-3. The seasonal cycles of PM mass and OC concentrations were higher during winter than in summer. EC and char concentrations were generally highest during winter but lowest in spring, while higher soot concentrations occurred in summer. This seasonal variation could be attributed to different seasonal meteorological conditions and changes in source contributions. Strong correlations between OC and EC were found for both PM2.5 and PM10 in winter and fall, while char and soot showed a moderate correlation in summer and winter. The average OC/EC ratios were 5.11 and 4.46 for PM2.5 and PM10, respectively, with individual OC/EC ratios nearly always exceeding 2.0. Higher char/soot ratios during the four seasons indicated that coal combustion and biomass burning were the major sources for carbonaceous aerosol in Huangshi. Contrary to expectations, secondary organic carbon (SOC) which is estimated using the EC tracer method exhibited spring maximum and summer minimum, suggesting that photochemical activity is not a leading factor in the formation of secondary organic aerosols in the study area. The contribution of SOC to OC concentration for PM2.5 and PM10 were 47.33 and 45.38%, respectively, implying that SOC was an important component of OC mass. The serious air pollution in haze-fog episode was strongly correlated with the emissions of pollutants from biomass burning and the meteorological conditions.

Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: implications for inter-protocol data conversion

Abstract. Organic carbon (OC) and elemental carbon (EC) are operationally defined by analytical methods. As a result, OC and EC measurements are protocol dependent, leading to uncertainties in their quantification. In this study, more than 1300 Hong Kong samples were analyzed using both National Institute for Occupational Safety and Health (NIOSH) thermal optical transmittance (TOT) and Interagency Monitoring of Protected Visual Environment (IMPROVE) thermal optical reflectance (TOR) protocols to explore the cause of EC disagreement between the two protocols. EC discrepancy mainly (83 %) arises from a difference in peak inert mode temperature, which determines the allocation of OC4NSH, while the rest (17 %) is attributed to a difference in the optical method (transmittance vs. reflectance) applied for the charring correction. Evidence shows that the magnitude of the EC discrepancy is positively correlated with the intensity of the biomass burning signal, whereby biomass burning increases the fraction of OC4NSH and widens the disagreement in the inter-protocol EC determination. It is also found that the EC discrepancy is positively correlated with the abundance of metal oxide in the samples. Two approaches (M1 and M2) that translate NIOSH TOT OC and EC data into IMPROVE TOR OC and EC data are proposed. M1 uses direct relationship between ECNSH_TOT and ECIMP_TOR for reconstruction: M1 : ECIMP_TOR = a × ECNSH_TOT + b; while M2 deconstructs ECIMP_TOR into several terms based on analysis principles and applies regression only on the unknown terms: M2 : ECIMP_TOR = AECNSH + OC4NSH − (a × PCNSH_TOR + b), where AECNSH, apparent EC by the NIOSH protocol, is the carbon that evolves in the He–O2 analysis stage, OC4NSH is the carbon that evolves at the fourth temperature step of the pure helium analysis stage of NIOSH, and PCNSH_TOR is the pyrolyzed carbon as determined by the NIOSH protocol. The implementation of M1 to all urban site data (without considering seasonal specificity) yields the following equation: M1(urban data) : ECIMP_TOR = 2.20 × ECNSH_TOT − 0.05. While both M1 and M2 are acceptable, M2 with site-specific parameters provides the best reconstruction performance. Secondary OC (SOC) estimation using OC and EC by the two protocols is compared. An analysis of the usability of reconstructed ECIMP_TOR and OCIMP_TOR suggests that the reconstructed values are not suitable for SOC estimation due to the poor reconstruction of the OC / EC ratio.

Ambient aerosol composition by infrared spectroscopy and partial least-squares in the chemical speciation network: Organic carbon with functional group identification

ABSTRACTThe Fourier-transform infrared (FT-IR) spectra of ambient fine aerosols were used with partial least-squares (PLS) regression to accurately, inexpensively, and nondestructively predict organic carbon (OC) on polytetrafluoroethylene (PTFE) filters in the U.S. Environmental Protection Agencies' Chemical Speciation Network (CSN). Recently, a similar FT-IR method was used for OC determination in the rural United States Interagency Monitoring of PROtected Visual Environments network, with the present work extending the method to urban aerosols with low mass loadings. In the present study, FT-IR spectra were calibrated to collocated thermal/optical reflectance (TOR) OC measurements following numerical processing with a second derivative filter, backward Monte Carlo unimportant variable elimination, and a quadratic discriminant analysis-PLS vapor correction routine. After processing and vapor correcting spectra, the number of model components (latent variables) were reduced from thirty-five to three with...

Characteristics of chemical components in PM2.5 at a plateau city, South-west China

A sampling campaign including summer, autumn and winter of 2014 and spring of 2015 was accomplished to obtain the characteristic of chemical components in PM2.5 at three sites of Kunming, a plateau city in South-west China. Nine kinds of water-soluble inorganic ions (WSI), organic and element carbon (OC and EC) in PM2.5 were analyzed by ion chromatography and thermal optical reflectance method, respectively. Results showed that the average concentrations of total WSI, OC and EC were 22.85±10.95 µg·m-3, 17.83±9.57 µg·m-3 and 5.11±4.29 µg·m-3, respectively. They totally accounted for 53.0% of PM2.5. Secondary organic and inorganic aerosols (SOA and SIA) were also assessed by the minimum ratio of OC/EC, nitrogen and sulfur oxidation ratios. The annual average concentrations of SOA and SIA totally accounted for 28.3% of the PM2.5 concentration. The low proportion suggested the primary emission was the main source of PM2.5 in Kunming. However, secondary pollution in the plateau city should also not be ignorable, due to the appropriate temperature and strong solar radiation, which can promote the atmospheric photochemical reactions.

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: sparse methods for statistical selection of relevant absorption bands

Abstract. Various vibrational modes present in molecular mixtures of laboratory and atmospheric aerosols give rise to complex Fourier transform infrared (FT-IR) absorption spectra. Such spectra can be chemically informative, but they often require sophisticated algorithms for quantitative characterization of aerosol composition. Naïve statistical calibration models developed for quantification employ the full suite of wavenumbers available from a set of spectra, leading to loss of mechanistic interpretation between chemical composition and the resulting changes in absorption patterns that underpin their predictive capability. Using sparse representations of the same set of spectra, alternative calibration models can be built in which only a select group of absorption bands are used to make quantitative prediction of various aerosol properties. Such models are desirable as they allow us to relate predicted properties to their underlying molecular structure. In this work, we present an evaluation of four algorithms for achieving sparsity in FT-IR spectroscopy calibration models. Sparse calibration models exclude unnecessary wavenumbers from infrared spectra during the model building process, permitting identification and evaluation of the most relevant vibrational modes of molecules in complex aerosol mixtures required to make quantitative predictions of various measures of aerosol composition. We study two types of models: one which predicts alcohol COH, carboxylic COH, alkane CH, and carbonyl CO functional group (FG) abundances in ambient samples based on laboratory calibration standards and another which predicts thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) mass in new ambient samples by direct calibration of infrared spectra to a set of ambient samples reserved for calibration. We describe the development and selection of each calibration model and evaluate the effect of sparsity on prediction performance. Finally, we ascribe interpretation to absorption bands used in quantitative prediction of FGs and TOR OC and EC concentrations.

An automated baseline correction protocol for infrared spectra of atmospheric aerosols collected on polytetrafluoroethylene (Teflon) filters

Abstract. A growing body of research on statistical applications for characterization of atmospheric aerosol Fourier transform infrared (FT-IR) samples collected on polytetrafluoroethylene (PTFE) filters (e.g., Russell et al., 2011; Ruthenburg et al., 2014) and a rising interest in analyzing FT-IR samples collected by air quality monitoring networks call for an automated PTFE baseline correction solution. The existing polynomial technique (Takahama et al., 2013) is not scalable to a project with a large number of aerosol samples because it contains many parameters and requires expert intervention. Therefore, the question of how to develop an automated method for baseline correcting hundreds to thousands of ambient aerosol spectra given the variability in both environmental mixture composition and PTFE baselines remains. This study approaches the question by detailing the statistical protocol, which allows for the precise definition of analyte and background subregions, applies nonparametric smoothing splines to reproduce sample-specific PTFE variations, and integrates performance metrics from atmospheric aerosol and blank samples alike in the smoothing parameter selection. Referencing 794 atmospheric aerosol samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011, we start by identifying key FT-IR signal characteristics, such as non-negative absorbance or analyte segment transformation, to capture sample-specific transitions between background and analyte. While referring to qualitative properties of PTFE background, the goal of smoothing splines interpolation is to learn the baseline structure in the background region to predict the baseline structure in the analyte region. We then validate the model by comparing smoothing splines baseline-corrected spectra with uncorrected and polynomial baseline (PB)-corrected equivalents via three statistical applications: (1) clustering analysis, (2) functional group quantification, and (3) thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) predictions. The discrepancy rate for a four-cluster solution is 10 %. For all functional groups but carboxylic COH the discrepancy is ≤ 10 %. Performance metrics obtained from TOR OC and EC predictions (R2 ≥ 0.94 %, bias ≤ 0.01 µg m−3, and error ≤ 0.04 µg m−3) are on a par with those obtained from uncorrected and PB-corrected spectra. The proposed protocol leads to visually and analytically similar estimates as those generated by the polynomial method. More importantly, the automated solution allows us and future users to evaluate its analytical reproducibility while minimizing reducible user bias. We anticipate the protocol will enable FT-IR researchers and data analysts to quickly and reliably analyze a large amount of data and connect them to a variety of available statistical learning methods to be applied to analyte absorbances isolated in atmospheric aerosol samples.

Predicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

Abstract. Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change, and reduced visibility. Typically OC and EC concentrations are measured using thermal–optical methods such as thermal–optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the functional group composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2 = 0.97 and R2 = 0.95 for OC and EC respectively), and for samples from 9 of the 11 sites not included in the calibration set (R2 = 0.96 and R2 = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error; we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the magnitude of mean error when applying a calibration model to a new set of samples.

Characteristics of Respirable Elemental Carbon (EC) Exposures of Household Waste Collectors

The objectives of this study to characterize exposure to respirable elemental carbon (EC), organic carbon (OC) and total carbon (TC) in relation to waste-handling activities and vehicle characteristics among workers who collect household wastes, and to examine the relationships among EC, OC and TC. A total of 72 household waste collectors were selected for exposure assessment over a full workday and most (70 of 72) exposures were collected from diesel emissions that underwent catalytic after-treatment by diesel particulate filters (DPFs). The exposure assessments were conducted from June through September 2014. Airborne EC and OC from the breathing zone were collected on pre-fired quartz filters and quantified using the thermal optical reflectance method. The average EC exposure level of the household waste collectors was 7.2 μg m^(–3) with a range of 2.0-30.4 μg m^(–3). A significant relationship between EC and TC exposure levels was observed (logTC = 0.38 × logEC + 3.22, p ＜ 0.0001, adjusted R^2 = 0.23). EC level (μg m^(–3)), truck age (＜ 10 year-old vs. ≥ 10 year-old), type of waste collection job (collector vs. driver), current smoking status (yes vs. no) and month were found to significantly influence the level of TC exposure (n = 70, adjusted R^2 = 0.56, p ＜ 0.0001). The average exposure to EC of household waste collectors can be categorized into the relatively low exposure group when compared to other DE exposure jobs. TC was not a best surrogate for DE exposure in household waste collection environments because it was affected by other OC interferences that were not generated from diesel engines.

Characteristics and Sources of Black Carbon in Atmospheric Dustfall Particles from Huangshi, China

Concentrations of carbonaceous particles in atmospheric dustfall particles in Huangshi, an industrial city in central China, were determined using a thermal-optical reflectance method. The black carbon (BC) contents in ninety-five dustfall samples ranged from 4.3 to 64.9 g kg^(-1) with an average of 17.0 g kg^(-1). These values were higher than those in world background soils and demonstrated serious contamination of the environment in this city. Overall, BC accounted for 17.6-71.3% (mean: 42.0%) of the organic carbon (OC), and BC and OC were positively correlated (r^2 = 0.90). Average char and soot contents were 8.01 g kg^(-1) and 8.65 g kg^(-1), respectively, and char/soot ratios ranged from 0.28 to 1.97 with an average of 1.01. All the measured carbonaceous species positively correlated with each another, suggesting their common sources. BC, char, and soot showed large spatial distribution variability, with high levels of BC adjacent to the presumed emission sources, such as a power plant and railway line. Analyses of BC/OC and char/soot ratios indicate major impacts from fossil fuel combustion, especially motor vehicle emissions and coal combustion. Industrial dusts related to coal use appear to be the major contributor to BC in dustfall, and this is likely related to the extensive industrial activities in the city, including metal smelting.

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

Abstract. 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 μg, 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 (R2; 0.96), no bias (0.00 μg m−3, a concentration value based on the nominal IMPROVE sample volume of 32.8 m3), low error (0.03 μg 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).

Spatial and seasonal variations of atmospheric particulate carbon fractions and identification of secondary sources at urban sites in North India.

An intensive measurement campaign was undertaken to characterize eight fractions of organic carbon (OC) and elemental carbon (EC) in particulate matter (PM) at four urban sites with different pollution characteristics during summer, post-monsoon, and winter at Kanpur, India. Speciation samplers were used to collect particulate samples on quartz filters followed by analysis of OC and EC using Interagency Monitoring of Protected Visual Environments (IMPROVE)-based thermal/optical reflectance (TOR) method. Based on 24-h average results at each site, the highest levels of OC and EC were observed during winter as 96.7 ± 26.9 and 31.8 ± 9.8μg/m(3) at residential site and traffic site, respectively. The levels of OC at residential sites during winter appeared to be more than twice of that during summer. The site close to the road traffic had the least value of OC/EC, as 1.77 ± 0.28 during post-monsoon, and the site influenced by emissions of domestic cooking and heating had the highest value of OC/EC, as 4.05 ± 0.79 during winter. The average abundances of OC1, OC2, OC3, OC4, OP, EC1, EC2, and EC3 in total carbon (TC) at all sites for three seasons were 10.03, 19.04, 20.03, 12.32, 10.53, 33.39, 3.21, and 1.99%, respectively. A sharp increase in levels of OC1 and EC1-OP during winter at two residential sites revealed that biomass burning could be a significant contributor to carbonaceous aerosols. From the application of EC-tracer method, it was observed that contribution of secondary organic carbon (SOC) to PM mass increased from 5% during post-monsoon to 16% during winter at residential sites and from 2% during post-monsoon to 7% during winter at traffic sites. Therefore, it could be inferred that increase in primary emissions coupled with unfavorable meteorological conditions could cause particle agglomeration and hygroscopic growth, leading to unpleasant pollution episode during winter.

Measurements of emission factors of PM2.5, OC, EC, and BC for household stoves of coal combustion in China

As follow-up efforts for measurements on emission factors (EFs) of fine particulate matter (PM2.5) and its carbonaceous fractions for China's household coal stoves, a large-sized dilution sampling system was designed to test a total of 20 coal/stove combinations, which involve five coals with wide-ranged geological maturities and three stoves. Coal smoke was simultaneously collected onto quartz filter for organic carbon (OC) and elemental carbon (EC) analyses by thermal-optical reflectance (TOR) protocol and monitored online for optical black carbon (BC) by Aethalometer. The mean EFs based on burned fuel weight of PM2.5, OC, EC, and BC are 4.25 ± 2.45, 1.11 ± 0.72, 1.43 ± 1.17, and 0.60 ± 0.42 g/kg for bituminous coal, and 1.44 ± 0.67, 0.05 ± 0.02, 0.04 ± 0.02, and 0.01 ± 0.01 g/kg for anthracite, respectively. Significant differences are observed among the EFs for various coal/stove combinations, which are attributable to the differences of coal maturity, burning style and stove efficiency. Although the EFs of BC and EC are closely correlated (r = 0.97), the average BC/EC ratio is only 0.39, indicating a significant gap between the two methods; and the optical attenuation cross-section (σ) for fresh coal smoke can be deduced as 6.47 m2/g, much lower than the manufacturer's preset value of 16.6 m2/g for Aethalometer.

Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: organic carbon

Abstract. Organic carbon (OC) can constitute 50% or more of the mass of atmospheric particulate matter. Typically, organic carbon is measured from a quartz fiber filter that has been exposed to a volume of ambient air and analyzed using thermal methods such as thermal-optical reflectance (TOR). Here, methods are presented that show the feasibility of using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters to accurately predict TOR OC. This work marks an initial step in proposing a method that can reduce the operating costs of large air quality monitoring networks with an inexpensive, non-destructive analysis technique using routinely collected PTFE filter samples which, in addition to OC concentrations, can concurrently provide information regarding the composition of organic aerosol. This feasibility study suggests that the minimum detection limit and errors (or uncertainty) of FT-IR predictions are on par with TOR OC such that evaluation of long-term trends and epidemiological studies would not be significantly impacted. To develop and test the method, FT-IR absorbance spectra are obtained from 794 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 TOR OC. The FTIR spectra are divided into calibration and test sets by sampling site and date. The calibration produces precise and accurate TOR OC predictions of the test set samples by FT-IR as indicated by high coefficient of variation (R2; 0.96), low bias (0.02 μg m−3, the nominal IMPROVE sample volume is 32.8 m3), low error (0.08 μg m−3) and low normalized error (11%). 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 to collocated TOR measurements. FT-IR spectra are also divided into calibration and test sets by OC mass and by OM / OC ratio, which reflects the organic composition of the particulate matter and is obtained from organic functional group composition; these divisions also leads to precise and accurate OC predictions. Low OC concentrations have higher bias and normalized error due to TOR analytical errors and artifact-correction errors, not due to the range of OC mass of the samples in the calibration set. However, samples with low OC mass can be used to predict samples with high OC mass, indicating that the calibration is linear. Using samples in the calibration set that have different OM / OC or ammonium / OC distributions than the test set leads to only a modest increase in bias and normalized error in the predicted samples. We conclude that FT-IR analysis with partial least-squares regression is a robust method for accurately predicting TOR OC in IMPROVE network samples – providing complementary information to the organic functional group composition and organic aerosol mass estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).

Size distribution characteristics of carbonaceous aerosol in a rural location in northwestern China

Size-segregated aerosol samples were collected in Dunhuang, China during 2012. The carbonaceous aerosols were quantified with the thermal/optical reflectance (TOR) method. In this study, two modes were observed in the size distribution of respective carbonaceous components. The first mode was the fine particle mode, and the second mode was in the coarse size range. OC, OP, and char showed a bimodal size distribution, with the fine particles as the major fraction in summer, autumn, and winter. Each of these components exhibited a similar seasonal pattern with the highest concentrations in winter and the lowest concentrations in summer. The highest soot (EC2 + 3, sum of EC2 and EC3) concentrations were in spring and the lowest concentrations were in fall, both in the fine and coarse modes. The correlations between different carbon fractions in size-segregated aerosol samples are discussed. OC and EC in Dunhuang are derived from coal combustion, biomass burning, and motor exhaust. The highest values of char/soot occurred in autumn, and lower values were found in the winter due to increased coal combustion.

Seasonal variation of urban carbonaceous aerosols in a typical city Nanjing in Yangtze River Delta, China

The Yangtze River Delta (YRD) is one of the regions with the most dynamic economy and severe atmospheric pollution in China. In order to characterize the particle features, especially the carbonaceous component in the YRD, particulate matter smaller than 2.5 μm (PM2.5) and 10 μm (PM10) samples in each season were collected in urban Nanjing, a typical city that locates in the west part of the YRD. The organic carbon (OC) and elemental carbon (EC) was differentiated using the thermal optical reflectance method. The average concentrations of PM2.5, OC and EC during the study periods were observed to be 117.6, 13.8, and 5.3 μg/m3 respectively, with all the highest levels in winter. The mass fraction of the Total carbonaceous aerosol (TCA) in PM2.5 was estimated at 23% on average, lower than those reported for other cities in the YRD. The OC and EC correlated well in all the seasons, especially in spring and winter, implying that OC and EC were attributed to common emission sources. Good correlation was observed between OC and estimated K+ from biomass burning in the harvest season in autumn and summer, indicating biomass burning a significant source of carbonaceous aerosols. This could also be confirmed by the lower fraction of OC3 + OC4 in OC during autumn and summer. The secondary organic carbon (SOC) estimated by EC-tracer method was the highest in winter (7.3 μg/m3) followed by autumn (6.7 μg/m3), summer (3.7 μg/m3) and spring (2.0 μg/m3). However, the SOC/OC in winter was not as high as that in summer and autumn, implying the high concentration of OC in winter was probably due to the stable weather but not mainly caused by SOC formation. The high SOC/OC ratio in summer was attributed to stronger oxidation, which could be suggested by higher sulfur oxidation ratio (SOR).

Characterization of fine particulate black carbon in Guangzhou, a megacity of South China

Continuous measurement of fine particulate black carbon (BC) was conducted at an urban site of Guangzhou in South China from December 2007 to December 2008. The daily average BC concentrations ranged from 0.6 to 20.5μg m−3, with an average value of 4.7μg m−3, which was substantially higher than those observed in the urban areas of other developed countries. Diurnal fluctuations of BC were marked with two peaks, one in the morning rush hour (08:00LT) and the other in the late evening hour (21:00–22:00LT), while the lowest BC concentrations were observed in the afternoon. Ambient BC concentrations displayed significant seasonal and diurnal variations with higher values in winter and spring, followed by lower concentrations during autumn and summer. Wind speed, wind direction and temperature were important meteorological factors that affected BC concentrations. A clearly negative correlation (r=−0.50, p<0.01) between BC concentrations and wind speed was found during the study period. A specific investigation was conducted to determine the relationship between optical BC and thermal–optical–reflectance elemental carbon (TOR EC) in distinct seasons. Although significant correlations between BC and EC were obtained (r>0.92, p<0.01), the regression slopes (ΔBC/ΔEC) slightly deviated from each other with values of 0.79, 1.18, and 0.81 in winter, spring and summer, respectively, possibly due to the distinct mixing states and source variations in different seasons. The calculated experimental attenuation coefficient showed a higher value (19.3m2g−1) in Guangzhou than the one recommended for typical Aethalometer measurements.

Carbonaceous particulate matter characterization in an urban and a rural site in the Philippines

Concentrations of organic carbon (OC) and elemental carbon (EC) in PM2.5 were measured at an urban (Valenzuela City, Metro Manila) and a rural (Angat, Bulacan) site in the Philippines from September 2011 to August 2012 by thermal–optical reflectance analysis following IMPROVE–A protocol. Results show that OC (8.00μg m−3) and EC (6.63μg m−3) levels in Valenzuela were 2–3 times higher than those in Angat (OC: 4.08μg m−3, EC: 2.29μg m−3). The total carbon contributions (OC+EC) to PM2.5 mass for the urban and rural site were 38.9% and 19.7% respectively. Compared to neighboring countries in Asia, the Philippine sites have intermediate OC concentrations and greatly elevated EC levels. These suggest the presence of highly inefficient combustion sources and highlight the need for the regulation of such emissions. Valenzuela was dominated by OC2, OC3, and EC1 (carbon fractions evolving at 280°C and 480°C in pure He phase and 580°C in He/O2 phase of the analysis, respectively) which points to vehicular, industrial, and cooking sectors as the possible main sources. While generally having lower concentrations and being less EC–dominated, Angat had remarkably higher levels of the EC2 fraction which suggests a unique EC source in the area. Conditional Probability Function (CPF) for Valenzuela OC and EC show similar results pointing towards the 30°, 150°, and 210° direction, indicating common sources for these species. Detailed survey of the surrounding area is needed to ascertain the identities of the sources present in these directions.

Characteristics of carbonaceous aerosols emitted from peatland fire in Riau, Sumatra, Indonesia

Biomass burning is a significant source of fine particulate matter (PM2.5). Forest, bush, and peat fires in Kalimantan and Sumatra, Indonesia are major sources of transboundary haze pollution in Southeast Asia. However, limited data exist regarding the chemical characteristics of aerosols at sources. We conducted intensive field studies in Riau Province, Sumatra, Indonesia, during the peatland fire and non-burning seasons in 2012. We characterized PM2.5 carbonaceous aerosols emitted from peatland fire based on ground-based source-dominated sampling. PM2.5 aerosols were collected with two mini-volume samplers using Teflon and quartz fiber filters. Background aerosols were also sampled during the transition period between the non-burning and fire seasons. We analyzed the carbonaceous content (organic carbon (OC) and elemental carbon (EC)) by a thermal optical reflectance utilizing the IMPROVE_A protocol and the major organic components of the aerosols by a gas chromatography/mass spectrometry. PM2.5 aerosols emitted from peatland fire were observed in high concentrations of 7120 ± 3620 μg m−3 and were primarily composed of OC (71.0 ± 5.11% of PM2.5 mass). Levoglucosan exhibited the highest total ion current and was present at concentrations of 464 ± 183 μg m−3. The OC/EC ratios (36.4 ± 9.08), abundances of eight thermally-derived carbon fractions, OC/Levoglucosan ratios (10.6 ± 1.96), and Levoglucosan/Mannosan ratios (10.6 ± 2.03) represent a signature profile that is inherent in peatland fire. These data will be useful in identifying contributions from single or multiple species in atmospheric aerosol samples collected from peatland fires.