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Joint Measurements of PM2.5 and Light-Absorptive PM in Woodsmoke-Dominated Ambient and Plume Environments

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Joint Measurements of PM2.5 and Light-Absorptive PM in Woodsmoke-Dominated Ambient and Plume Environments Atmos. Chem. Phys., 17, 11441–11452, 2017 https://doi.org/10.5194/acp-17-11441-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Joint measurements of PM2:5 and light-absorptive PM in woodsmoke-dominated ambient and plume environments K. Max Zhang1, George Allen2, Bo Yang1, Geng Chen1,3, Jiajun Gu1, James Schwab4, Dirk Felton5, and Oliver Rattigan5 1Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA 2Northeast States for Coordinated Air Use Management, Boston, MA, USA 3Faculty of Maritime Transportation, Ningbo University, Ningbo, Zhejiang Province, China 4Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA 5Division of Air Resources, New York State Department of Environmental Conservation, Albany, NY, USA Correspondence to: K. Max Zhang ([email protected]) Received: 8 March 2017 – Discussion started: 2 May 2017 Revised: 18 August 2017 – Accepted: 21 August 2017 – Published: 26 September 2017 Abstract. DC, also referred to as Delta-C, measures en- demand. While we observed reproducible PM2:5–DC rela- hanced light absorption of particulate matter (PM) samples tionships in similar woodsmoke-dominated ambient environ- at the near-ultraviolet (UV) range relative to the near-infrared ments, those relationships differ significantly with different range, which has been proposed previously as a woodsmoke environments, and among individual woodsmoke sources. marker due to the presence of enhanced UV light-absorbing Our analysis also indicates the potential for PM2:5–DC re- materials from wood combustion. In this paper, we further lationships to be utilized to distinguish different combustion evaluated the applications and limitations of using DC as and operating conditions of woodsmoke sources, and that both a qualitative and semi-quantitative woodsmoke marker DC–heating-demand relationships could be adopted to es- via joint continuous measurements of PM2:5 (by nephelome- timate woodsmoke emissions. However, future studies are ter pDR-1500) and light-absorptive PM (by 2-wavelength needed to elucidate those relationships. and 7-wavelength Aethalometer®) in three northeastern US cities/towns including Rutland, VT; Saranac Lake, NY and Ithaca, NY. Residential wood combustion has shown to be the predominant source of wintertime primary PM2:5 emis- 1 Introduction sions in both Rutland and Saranac Lake, where we conducted ambient measurements. In Ithaca, we performed woodsmoke Woodsmoke resulting from anthropogenic activities is a plume measurements. We compared the pDR-1500 against widespread air pollution problem in many parts of the a FEM PM2:5 sampler (BAM 1020), and identified a close world. For example, residential woodsmoke is estimated agreement between the two instruments in a woodsmoke- to account for 20 % of total stationary and mobile poly- dominated ambient environment. The analysis of seasonal cyclic organic matter emissions, and 50 % of all area- and diurnal trends of DC, black carbon (BC, 880 nm) and source air-toxic cancer risks according to the 2011 National PM2:5 concentrations supports the use of DC as an adequate Air Toxics Assessment in the US (https://www.epa.gov/ qualitative marker. The strong linear relationships between national-air-toxics-assessment). It is reported that around PM2:5 and DC in both woodsmoke-dominated ambient and 35 % of total fine particulate matter (PM2:5) emissions in the plume environments suggest that DC can reasonably serve as United Kingdom came from domestic wood burning in 2015, a semi-quantitative woodsmoke marker. We propose a DC- while road transport only contributed around 13 % of the to- based indicator for woodsmoke emission, which has shown tal PM2:5 emissions (DEFRA, 2016). In addition to its con- to exhibit a relatively strong linear relationship with heating tribution to regional air quality, residential woodsmoke may cause significant near-source air-quality impacts due to rela- Published by Copernicus Publications on behalf of the European Geosciences Union. 11442 K. M. Zhang et al.: Joint measurements of PM2:5 and light-absorptive PM tively low stack heights and low exhaust temperatures. While The concept of DC (also referred to as Delta-C) originated in some sense wood burning products may be considered nat- from using the level of UV enhancement as a marker for ural substances, the health effects of wood smoke are found woodsmoke PM (Allen et al., 2004). Traditionally, DC was to be comparable to those of fossil-fuel combustion sources calculated by the difference between the 370 and 880 nm sig- (Naeher et al., 2007). nals, i.e., DC D BC (370 nm) – BC (880 nm), due to the avail- Chemicals that are enriched in woodsmoke relative to ability of two-channel Aethalometer models. But the con- other sources have been used to quantify woodsmoke impacts cept is not limited to those two particular wavelengths. Fig- on ambient PM. Among them, levoglucosan, a sugar anhy- ure1a indicates that woodsmoke UV enhancement starts ap- dride derived from the pyrolysis of the major wood polymer pearing at 660 nm, and more enhancement can be expected cellulose, has been used extensively as a molecular marker at even shorter wavelength (than 370 nm) not available in for woodsmoke because it is emitted at high concentrations current Aethalometer models. Studies show that woodsmoke and is relatively stable in the atmosphere (Fine et al., 2001; enhancement peaks at ∼ 300 nm (Kirchstetter et al., 2004; Simoneit et al., 1999). However, detecting levoglucosan in Kirchstetter and Thatcher, 2012). It is possible that includ- PM samples at present requires detailed chemical analysis, ing shorter wavelengths in future instrumentation would im- and the related information is not widely available. prove the sensitivity to woodsmoke PM (Olson et al., 2015). The widely deployed Aethalometer® has made possi- Another approach taking advantage of UV enhancement (or ble continuous aerosol light-absorption measurements, com- wavelength dependence of the aerosol absorption coefficient monly referred to as black carbon (BC), at either two wave- in general), as reported by Sandradewi et al.(2008a), de- lengths (880 and 370 nm) or seven wavelengths (370, 470, rives light absorption Ångström exponents (AAE, or α) from 520, 590, 660, 880 and 950 nm). Allen et al.(2004) first multi-wavelength Aethalometer readings. α is close to 1 proposed using enhanced light absorption of ambient par- for traffic sources, and varies for woodsmoke, but is gener- ticulate matter (PM) at 370 nm relative to 880 nm, due to ally much larger than 1. Assuming a certain value of α for the presence of light-absorbing materials from wood com- woodsmoke, Sandradewi et al.(2008b) conducted a quantita- bustion near the ultraviolet (UV) range, as a marker for tive analysis of source contributions to PM. This approach of- woodsmoke PM. Figure1 depicts the distinct responses of a ten requires light-absorption measurements at multiple wave- seven-wavelength Aethalometer (Magee Scientific AE-33) to lengths to have a reliable estimate for α (Chen et al., 2015). woodsmoke (Fig.1a) and diesel (Fig.1b) plumes, providing Sandradewi et al.(2008b) showed that using different pairs a context for our discussions in this paper. The source of the of wavelengths led to different values of α for woodsmoke. diesel plume was a backup diesel generator, and the measure- Since the ambient data to be presented in this paper were ment was conducted in 2015. The woodsmoke plume data collected by a two-wavelength Aethalometer and given the was collected near a residential woodstove source in early uncertainties associated with values of α for woodsmoke, we 2016. Note that the purpose of Fig.1 is to reveal the qualita- did not perform a direct source apportionment analysis sim- tive differences between the two sources rather than making ilar to that presented by Sandradewi et al.(2008a), but pre- a quantitative comparison. sented a qualitative analysis to be presented later (Sect. 3.2). The wavelength-dependent responses to woodsmoke were Wang et al.(2011) reported a strong correlation between clearly shown in Fig.1a. At longer wavelengths, there DC and woodsmoke markers such as levoglucosan during the were virtually no differences in the signals from the 880 heating season, and no statistically significant correlation be- and 950 nm channels. At shorter wavelengths, the 370 nm tween DC and vehicle exhaust markers based on field data channel recorded the highest reading. We refer to the aug- collected in Rochester, NY. A follow-up study from the same mented responses at shorter wavelengths compared to the research group used DC as an input variable in source appor- 880 and 950 nm as “UV enhancement”. In contrast, virtu- tionment models, where it was found to play an important ally no wavelength-dependence (i.e., no UV enhancement) role in separating traffic emissions (especially diesel) from was observed for diesel exhaust (Fig.1b). There are some wood combustion emissions (Wang et al., 2012). Allen et al. slight discrepancies among the different wavelength chan- (2011) adopted DC as a woodsmoke marker for their fixed- nels, likely due to the limitations of the real-time dynamic site measurements in northern New York State, and revealed spot loading correction used by the AE-33 Aethalometer. temporally and spatially resolved patterns of woodsmoke PM The patterns of the wavelength-dependent responses shown (Fuller et al., 2014). However, Harrison et al.(2013) ana- in Fig.1 were consistent with the findings from several previ- lyzed data for DC from an Aethalometer network in the UK ous studies, which suggested that UV absorbing compounds and suggested the presence of other UV absorbing contrib- are enriched in biomass-combustion PM but scarce in diesel utors (such as coal burning) to the DC signal. Laboratory PM (Chen et al., 2015; Olson et al., 2015) or traffic-related experiments conducted by Olson et al.(2015) showed that PM (Kirchstetter et al., 2004).
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