Advertisement

Air Quality, Atmosphere & Health

, Volume 8, Issue 3, pp 243–263 | Cite as

Mass reconstruction methods for PM2.5: a review

  • Judith C. Chow
  • Douglas H. Lowenthal
  • L.-W. Antony Chen
  • Xiaoliang Wang
  • John G. Watson
Open Access
Article

Abstract

Major components of suspended particulate matter (PM) are inorganic ions, organic matter (OM), elemental carbon (EC), geological minerals, salt, non-mineral elements, and water. Since oxygen (O) and hydrogen (H) are not directly measured in chemical speciation networks, more than ten weighting equations have been applied to account for their presence, thereby approximating gravimetric mass. Assumptions for these weights are not the same under all circumstances. OM is estimated from an organic carbon (OC) multiplier (f) that ranges from 1.4 to 1.8 in most studies, but f can be larger for highly polar compounds from biomass burning and secondary organic aerosols. The mineral content of fugitive dust is estimated from elemental markers, while the water-soluble content is accounted for as inorganic ions or salt. Part of the discrepancy between measured and reconstructed PM mass is due to the measurement process, including: (1) organic vapors adsorbed on quartz-fiber filters; (2) evaporation of volatile ammonium nitrate and OM between the weighed Teflon-membrane filter and the nylon-membrane and/or quartz-fiber filters on which ions and carbon are measured; and (3) liquid water retained on soluble constituents during filter weighing. The widely used IMPROVE equations were developed to characterize particle light extinction in U.S. national parks, and variants of this approach have been tested in a large variety of environments. Important factors for improving agreement between measured and reconstructed PM mass are the f multiplier for converting OC to OM and accounting for OC sampling artifacts.

Keywords

PM2.5 Mass closure Chemical speciation Organic matter Sampling artifact 

Introduction

Particles with aerodynamic diameters <2.5 μm (PM2.5) and 10 μm (PM10) mass concentrations are regulated by the National Ambient Air Quality Standards (NAAQS; Bachmann 2007; Chow et al. 2007a) in the USA, with variations being adopted in other countries (Cao et al. 2013). For compliance monitoring, ambient particles are collected over 24-h durations onto filters that are weighed before and after sampling (Chow 1995; Watson and Chow 2011). Chemically speciated PM is needed to better understand pollution sources, atmospheric processing (e.g., transport and transformation), temporal and spatial variations and long-term trends, as well as adverse health and environmental consequences. PM2.5 mass and chemical components (i.e., ions, elements, and carbon) have been acquired in the National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) non-urban network, and the US Environmental Protection Agency (EPA) urban Chemical Speciation Network (CSN; Solomon et al. 2014; USEPA 2015) on an every-third- or sixth-day schedule since 1987/1988 and 1999/2000, respectively. Measurement protocols for the US PM2.5 networks are documented by Chow et al. (2010) and Solomon et al. (2014). Sampling and chemical analysis methods vary in these and other long-term networks and in special studies from the USA and elsewhere (e.g., Dabek-Zlotorzynska et al. 2011; Zhang et al. 2012).

Chow and Watson (2013) summarize different PM chemical analysis methods. The major PM components measured to explain gravimetric mass include: (1) anions (e.g., chloride (Cl), nitrate (NO3 ), and sulfate (SO4 =)) and cations (e.g., water-soluble sodium (Na+), potassium (K+), and ammonium (NH4 +)); (2) elements, including metals (up to 51 elements from sodium (Na) to uranium (U)); and (3) organic carbon (OC) and elemental carbon (EC) and their carbon fractions. To accommodate chemical speciation, at least two types of sampling substrates (i.e., Teflon-membrane and quartz-fiber filters) are needed (Chow 1995). IMPROVE and CSN use three parallel channels, in which mass by gravimetry and elements by X-ray fluorescence (XRF; Watson et al. 1999) are measured on Teflon-membrane filters; ions by ion chromatography (IC; Chow and Watson 1999) are measured on nylon-membrane filters preceded by a sodium carbonate (Na2CO3) denuder (Ashbaugh and Eldred 2004) to remove nitric acid (HNO3); and OC and EC by thermal/optical carbon analysis (Chow et al. 1993, 2007a, 2011) are measured on quartz-fiber filters. PM components include carbon (C), hydrogen (H), nitrogen (N), sulfur (S), oxygen (O), and a wide variety of other elements. Owing to practical analytical limitations (Chow and Watson 2013), most networks do not measure H and O associated with OC, geological minerals, and liquid water—with the exception of the IMPROVE network, where H was quantified from 1988 to 2010 (Nejedly et al. 1997). As a result, the sum of the measured species is often lower than the gravimetric mass. Watson (2004) specifies a percent mass explained of 100 ± 20 % for source apportionment models, and this is a reasonably good criteria for mass reconstruction.

PM mass reconstruction (also called mass closure or material balance) applies multipliers to several of the measured species to estimate unmeasured components. Mass reconstruction is used to: (1) identify and correct potential measurement errors as part of data validation efforts (Chow et al. 1994a; Malm et al. 2011; Watson et al. 2001); (2) understand temporal and spatial variations of chemical composition (Hand et al. 2014; Malm et al. 2011); and (3) estimate source contributions to PM and light extinction (Chow and Watson 2013; Watson 2002). Mass reconstruction attempts to achieve closure between gravimetric mass and the sum of major components with assumptions to account for unmeasured species, but without double counting. For example, when SO4 = is included, elemental S is omitted; inclusion of elemental chlorine (Cl) excludes water-soluble Cl; and the same applies for elemental potassium (K) and water-soluble potassium (K+) (Chow et al. 1994a). Although this review focuses on PM2.5, a similar approach is applicable for PM10. As PM2.5 is part of PM10, mass reconstruction should be conducted for both PM2.5 and PMcoarse (i.e., PM10–2.5) when PM10 speciation is available (e.g., Chow et al. 2002a).

Various approaches have been taken for PM mass reconstruction (e.g., Frank 2006; Hand et al. 2011; Malm et al. 2011)—the widely used 11 equations are documented in “Commonly applied reconstructed mass equations.” Applications of these equations to past studies (summarized in the supplemental material) are enumerated in “Applications of mass reconstruction equations to special studies.” To provide a perspective on the fraction of mass explained, examples of mass reconstruction applications for the long-term US IMPROVE network are given in “Evaluation of mass reconstruction through analysis of large data sets.” Various regression techniques have been used to derive multipliers for major PM components and to examine the adequacy of using the IMPROVE equations for mass reconstruction. Major factors that bias mass reconstruction (e.g., the use of an OC multiplier to estimate organic matter (OM), carbon sampling and analysis artifact, ammonium and nitrate volatilization, and particle-bound water on Teflon-membrane filters) are discussed in “Major factors influencing mass reconstruction.” This review examined hundreds of prior studies and intends to: (1) track the evolution and approaches for mass reconstruction; (2) discuss the adequacy of each approach; and (3) address major PM sampling and analysis issues that influence mass reconstruction.

Commonly applied reconstructed mass equations

Table 1 summarizes 11 PM mass reconstruction methods (i.e., Eqs. 1 to 11, sequence in chronological order of publication) that have been applied to data acquired since the late 1970s. Some variations from other studies are referenced. Reconstructed mass (RM) is expressed as the sum of its seven representative chemical components, including: (1) inorganic ions; (2) OM or OC; (3) EC, also referred to as “black carbon” (BC), “soot,” or light absorbing carbon (LAC); (4) geological minerals (or materials), often referred to as “dust,” “soil,” or “crustal material;” (5) salt (sea salt near oceans and inland seas, but also deriving from wintertime de-icing material and desert playas); (6) trace elements (other elements that are not accounted for as minerals, as from fly ash); and (7) “others,” or “remaining mass,” representing other unaccounted or unidentified components. As such, RM equations take the following form:
Table 1

Summary of the 11 mass reconstruction equations and their major chemical components

Equation No. (reference)/study area

Inorganic ions

Organic mass/organic carbon (OM/OC) ratio

Elemental carbon (EC)

Geological mineralsa

Saltb

Trace elementsc

Others

Equation 1 (Macias et al. 1981)/Page, AZ

(NH4)2SO4 + NH4NO3

1.5d

Yes

1.89Al + 2.14Si + 1.4Ca + 1.2K + 1.43Fe (assuming Al2O3, SiO2, CaO, K2O, and Fe2O3)

None

1.25Cu + 1.24Zn + 1.08Pb (assuming CuO, ZnO, and PbO)

None

Equation 2 (Solomon et al. 1989)/Los Angeles, CA

SO4 = + NO3  + NH4 +

1.4

Yes

1.89Al + 2.14Si + 1.4Ca + 1.43Fe (no oxides were specified)

None

Sum of all species measured by XRF (excluding S, Al, Si, Ca, and Fe) plus Na+ and Mg++ measured by AAS

None

Equation 3 (Chow et al. 1994b)/Los Angeles, CA

SO4 = + NO3  + NH4 +

1.4

Yes

As in Eq. 2 (assuming Al2O3, SiO2, CaO, and Fe2O3)

None

Sum of 40 elements (Na to U) by XRF excluding S, Al, Si, Ca, and Fe

None

Equation 4 (Malm et al. 1994)/IMPROVE network

4.125S as (NH4)2SO4

1.4

Yes

2.2Al + 2.49Si + 1.63Ca + 1.94Ti + 2.42Fe (assuming Al2O3, SiO2, CaO, Fe2O3, and FeO (in equal amounts), TiO2, and K2O (assuming that soil K is 0.6Fe), with all oxide multipliers by 1.16 to account for other missing compounds)

None

None

None

NO3 was excluded due to the concern that NO3 can volatilize from the Teflon-membrane filters but not from the Nylon filter

Equation 5 (Chow et al. 1996)/San Joaquin Valley, CA

SO4 = + NO3  + NH4 +

1.4

Yes

As in Eq. 2

Na+ + Cl

As in Eq. 2: also excluding Na+, K+, and Cl

None

Equation 6 (Andrews et al. 2000)/Great Smoky Mountains National Park, TN

SO4 = + NO3  + NH4 +

1.4

Yes

As in Eq. 2 plus 1.67Ti (assuming Al2O3, SiO2, CaO, K2O, TiO2, and Fe2O3)

None

Sum of remaining species (excluding S, Al, Si, Fe, Ti, Ca, and K; see Table S-1 of Andrews et al. 2000)

None

(MOUDI sampler NH4 + was estimated by HEADS SO4 =/NH4 + ratio)

Equation 7 (Malm et al. 2000); original IMPROVE Eq.)/ IMPROVE network

4.125S (as (NH4)2SO4) + 1.29NO3 (as NH4NO3)

1.4

Yes

As in Eq. 4

None

None

None

Equation 8 (Maenhaut et al. 2002)/Melpitz, Germany

SO4 = + NO3  + NH4 +

1.4

Yes

As in Eq. 4

Cl + 1.4486Na

Sum of all non-sea salt and non-crustal elements, excluding S and K.

Non-crustal K (K − 0.6Fe)

Equation 9 (DeBell et al. 2006)/IMPROVE network

4.125S (as (NH4)2SO4) + 1.29NO3 (as NH4NO3)

1.8

Yes

As in Eq. 4

None

None

None

Equation 10 (Hand et al. 2011; revised IMPROVE Eq.)/IMPROVE network

1.375 SO4 = (as (NH4)2SO4)e + 1.29NO3 (as NH4NO3)

1.8

Yes

As in Eq. 4

1.8Cl

None

None

Equation 11 (Simon et al. 2011)/IMPROVE network

(NH4)2SO4 + NH4NO3

1.8

Yes

3.48Si + 1.63Ca + 2.42Fe + 1.94Ti

1.8Cl

None

Non-crustal K = 1.2 × (K − 0.6Fe)

(NH 4 ) 2 SO 4 ammonium sulfate, NH 4 NO 3 ammonium nitrate, S sulfur, SO 4 = sulfate, NH 4 + ammonium, NO 3 nitrate, MOUDI, Multi-Orifice Uniform Deposit Impactor, HEADS Harvard-EPA Annular Denuder System

aGeological minerals include: aluminum (Al), aluminum oxide (Al2O3), silicon (Si); silicon oxide (SiO2), potassium (K); potassium oxide (K2O); calcium (Ca); calcium oxide (CaO), titanium (Ti), titanium oxide (TiO2), iron (Fe), ferric oxide (FeO), and ferrous oxide (Fe2O3)

bSalt includes: sea salt, chloride (Cl), potassium ion (K+), and sodium ion (Na+)

cTrace elements include: barium (Ba), chromium (Cr), copper (Cu), lead (Pb), vanadium (V), zinc (Zn), copper oxide (CuO), lead oxide (PbO), and zinc oxide (ZnO); measurement methods are X-ray fluorescence (XRF) and atomic absorption spectroscopy (AAS)

dBased on assumed organic compound composition proportional to CH2O0.25

eHand et al. (2011) estimated (NH4)2SO4 from the SO4 = concentration as 1.375 × SO4 = to account for unmeasured NH4 +

$$ \mathrm{R}\mathrm{M} = \mathrm{Inorganic}\ \mathrm{ions} + \mathrm{O}\mathrm{M} + \mathrm{E}\mathrm{C} + \mathrm{Geological}\ \mathrm{minerals} + \mathrm{Salts} + \mathrm{Trace}\ \mathrm{elements} + \mathrm{O}\mathrm{thers} $$
(A)

Each of these components can derive from a variety of sources, though they are often dominated by a few sources. Minerals, for example, do not include OM that might be associated with engine exhaust or bioaerosols deposited onto roadways or agricultural soils. These would be included in the OM fraction. Similarly, some fugitive dust sources include salts, but these would be accounted for in the salt fraction; sulfates and nitrates that react with salt (Hoffman et al. 2004) would be accounted for in the inorganic ion fraction. The background and assumptions related to these RM components are described in the following subsections.

Inorganic ions

In addition to commonly measured anions and cations by IC, automated colorimetric (AC), atomic absorption spectroscopy (AAS), and inductively coupled plasma-atomic emissions spectroscopy (ICP-AES) have also been applied for ionic speciation (Chow and Watson 2013). Depending on the measurements available, the following methods are used to determine their mass contributions:
  • In the absence of NH4 + measurement, SO4 = and NO3 are assumed to be neutralized to ammonium sulfate ((NH4)2SO4) and ammonium nitrate (NH4NO3), with the NH4 + fraction accounted for by stoichiometric multipliers: 1.375SO4 = and 1.29NO3 , respectively (i.e., Eqs. 1, 10, and 11 in Table 1). An ion balance based on molar equivalence between the measured anions and cations should be applied to verify the extent of neutralization (Chow et al. 1994b).

  • SO4 =, NO3 , and NH4 + are summed without weighting factors (i.e., Eqs. 2, 3, 5, 6, and 8). This does not account for H when SO4 = is incompletely neutralized by NH4 + as in sulfuric acid (H2SO4), ammonium bisulfate (NH4HSO4), or letovicite ((NH4)3H(SO4)2).

  • When only S is measured, it is assumed to be neutralized (NH4)2SO4 (i.e., 4.125S in Eqs. 7 and 9) and summed with either NO3 (Landis et al. 2001) or NH4NO3 (1.29NO3 in Eqs. 7 and 9). If NO3 is not measured, NH4NO3 is assumed to be negligible (Malm et al. 1994, Eq. 4). This assumption is valid only when the NO3 concentration is low, as it is for some non-urban, eastern US IMPROVE sites but not for others (Pitchford et al. 2009). Abundant NO3 has been found in several urban areas, especially during fall and winter (Green et al. 2015).

Assuming 1.29NO3 for NH4NO3 may not be valid when HNO3 reacts with suspended dust to form calcium nitrate (Ca(NO3)2) or when it reacts with sodium chloride (NaCl) from a marine intrusion or suspension from an alkaline playa to form sodium nitrate (NaNO3) (Hoffman et al. 2004). Lee et al. (2008) noted the presence of PM2.5 Ca(NO3)2 at several IMPROVE sites owing to a coarse particle NO3 tail that extended below 2.5 μm. Harrison et al. (2003) applied Eq. 7 for PM2.5 ions and added NaNO3 for PM10–2.5. Several studies used front filter NO3 (i.e., non-volatilized NO3 from Teflon-membrane or quartz-fiber filters), as volatilized NO3 is not part of the gravimetric mass (Chow et al. 2002a). Ma et al. (2001) estimated NH4NO3 as 2.857 N, with N measured by an elemental analyzer, which is commonly applied to fuel assays. The presence of ammonium chloride (NH4Cl) in PM2.5 was noted by Kelly et al. (2013) for Utah’s Salt Lake valley; by Pant et al. (2015) in New Delhi, India, where there is abundant trash burning; and by Levin et al. (2010) for biomass burning samples.

Elemental S has been commonly measured by XRF or proton-induced X-ray emission (PIXE) analyses (Watson et al. 1999). Based on molecular weight, 3S can be used to estimate SO4 =, assuming that all S is water-soluble SO4 =. This is not the case when: (1) S is associated with insoluble organic compounds such as mercaptans; (2) S is not completely water-soluble, as is the case for minerals such as gypsum (CaSO4·2H2O) and pyrite (FeS2); or (3) S consists of sulfur dioxide (SO2) adsorbed onto soot or other particles (Watson 2002).

For coastal environments, non-sea-salt sulfate (i.e., nssSO4 = = SO4 = − 0.252Na+, based on SO4 =/Na+ molar ratio in sea water) can be estimated (Sciare et al. 2003). Summed nssSO4 = + NO3  + NH4 + has been applied to estimate contributions from inorganic ions (Cheung et al. 2011; Maenhaut et al. 2008; Mkoma et al. 2009; Querol et al. 2001; Terzi et al. 2010). Zhang et al. (2013) also included K+ (a marker for biomass burning) as an additional inorganic ion.

Since NH4 + is not quantified in the IMPROVE network, (NH4)2SO4 is estimated by 4.125S (Eq. 7). Due to variations between SO4 = (by IC) and S (by XRF) ratios, Hand et al. (2011) used 1.375SO4 = (Eq. 10). Both the original (Eq. 7) and the revised (Eq. 10) IMPROVE equations have been the foundation for reconstructing light extinction in the USA under the Regional Haze Rule (now termed the Clean Air Visibility Rule; Pitchford et al. 2007; USEPA 2001; Watson 2002).

Organic mass/organic carbon (OM/OC)

To account for the unmeasured H, O, N, and S in organic compounds, a conversion factor (or multiplier) is used to transform OC to OM, i.e.,
$$ \mathrm{O}\mathrm{M} = f \times \mathrm{O}\mathrm{C} $$
(B)

The f multipliers of 1.4 and 1.8 in Table 1 are not site or time specific. Depending on the extent of OM oxidation and secondary organic aerosol (SOA) formation, values for f vary from 1.2 for fresh aerosol in urban areas (Chow et al. 2002a, b) to 2.6 for aged aerosol (Countess et al. 1980; Robinson et al. 2007, 2010; Roy et al. 2011; Turpin and Lim 2001). For example, benzo(a)pyrene (C20H12), an indicator of incomplete fuel combustion found in engine exhaust (Lowenthal et al. 1994) has an f = 1.05; whereas cellulose (C6H10O5) n , a major component of unburned biological material, has an f = 2.25 (Cerqueira et al. 2010; Puxbaum and Tenze-Kunit 2003; Sanchez-Ochoa et al. 2007).

The origins for f = 1.2–1.5 result from circular reasoning with limited measurements. Macias et al. (1981, Eq. 1) used 1.5 based on an assumed organic composition proportional to CH2O0.25. Solomon et al. (1989, Eq. 2) used 1.4, citing Gray et al. (1986), who used both 1.2 and 1.4 for studies in California’s South Coast Air Basin (SoCAB). The f = 1.2 originated from Countess et al. (1980), based on the analysis of ambient carboxylic acid (C16: (C + H + O)/C = 1.3), polynuclear aromatic ((C + H)/C = 1.08), and aliphatic compounds ((C + H)/C = 1.17) (van Vaeck and van Cauwenberghe 1978) in Denver, CO. Ma et al. (2001) used 1.4 but cited Countess et al. (1980). As noted by Andrews et al. (2000) and Watson (2002), the 1.4 derives from Grosjean and Friedlander (1975), based on two Los Angeles total suspended particle (TSP) samples. The ratios of C to the sum of C, H, N, and O was 0.66 for oxygenated organics and 0.86 for aliphatics; the inverses are 1.5 and 1.2, respectively. Gray et al. (1986) referred to White and Roberts (1977), who used f = 1.4 to construct a chemical light extinction budget based on Grosjean and Friedlander (1975). Harrison et al. (2003) used 1.4 for urban background sites in Birmingham, UK, and 1.3 for roadside sites in London, UK, citing Russell (2003).

Chow et al. (1994b; 1996, Eqs. 3 and 5, respectively) used 1.4, citing Solomon et al. (1989). Andrews et al. (2000, Eq. 6) also used 1.4, citing both White and Roberts (1977) and Grosjean and Friedlander (1975). Maenhaut et al. (2002, Eq. 8) used 1.4 for samples from Melpitz, Germany, citing Turpin et al. (2000). DeBell et al. (2006, Eq. 9) and Hand et al. (2011, Eq. 10) increased the f from 1.4 to 1.8 for the revised IMPROVE equation (Eq. 10) based on non-urban aerosols (e.g., El-Zanan et al. 2005) and regression analysis by Malm and Hand (2007). The average regression coefficient was 1.7 for OC across all IMPROVE sites for years 1988–2003. This is similar to the f = 1.8 used by Maenhaut et al. (2008) for samples from K-puszta, an EUSAAR station in Hungary, and by Mkoma et al. (2009) for a rural site in East Africa.

Several studies (e.g., Mkoma et al. 2009; Ni et al. 2013; Remoundaki et al. 2013; Terzi et al. 2010; Vecchi et al. 2008; Viana et al. 2007) used an f multiplier of 1.6, whereas f = 1.7 was reported by others (e.g., Guinot et al. 2007; Putaud et al. 2000; Rees et al. 2004). The value of the f multiplier under different situations remains the subject of current research. Biomass burning (especially during the smoldering phase) may require a higher f multiplier as it contains many oxygenated organic compounds (Chen et al. 2010; Chow et al. 2007b), such as levoglucosan (C6H10O5), a wood smoke marker (Simoneit et al. 1999) with the same chemical formula but a structure that differs from cellulose. For laboratory-generated vegetative burning, Levin et al. (2010) reported f = 1.55, consistent with a finding of f = ∼1.5 by Reid et al. (2005). Aiken et al. (2008) reported f = 1.55–1.7 for primary biomass combustion emissions in Mexico City, lower than 1.9–2.1 found by Polidori et al. (2008) in Pittsburgh, PA, during winter and 2.2–2.6 suggested by Turpin and Lim (2001).

Elemental carbon

The RM equation in Table 1 contain EC without any multiplier. Since OC and EC are operationally defined, absolute OC and EC concentrations and the ratio of OC to EC vary by carbon analysis method (Watson et al. 2005).

Geological minerals

Geological “minerals” might better represent geological “material,” as only assumed oxides of mineral elements (e.g., aluminum (Al), silicon (Si), calcium (Ca), K, titanium (Ti), and iron (Fe)) are included to calculate geological mass. These elements have been measured by XRF, PIXE (e.g., Maenhaut et al. 2008), and, in some cases, instrumental neutron activation analysis (INAA; Maenhaut et al. 2001; Siddique and Waheed 2014) or ICP-mass spectrometry (ICP-MS). Most researchers use one of the five soil formulae listed in Table 1. Macias et al. (1981, Eq. 1) expressed minerals as the sum of the oxides of Al, Si, Ca, K, and Fe assuming the common oxide forms of Al2O3, SiO2, CaO, K2O, and Fe2O3, respectively (Pettijohn 1975). Several studies eliminated the 1.2 K (Eq. 2), except for Andrews et al. (2000, Eq. 6), Kleindienst et al. (2010), and Ni et al. (2013), which also included 1.67Ti. A higher value (1.95Ca) was used by Terzi et al. (2010) and Remoundaki et al. (2013) to account for both CaO and CaCO3.

The IMPROVE “soil” formula (Malm et al. 1994, Eq. 4), applied in Eqs. 7–10, follows Macias et al. (1981, Eq. 1) with the following modifications: (1) iron oxides are equally divided between Fe2O3 and FeO; (2) K in soil is estimated as 0.6Fe, based on the composition of coarse particles (Cahill et al. 1986), because some PM2.5 K is emitted by biomass burning; and (3) titanium dioxide (TiO2) is included. All of the initial element coefficients are then multiplied by 1.16 to account for unmeasured O, therefore:
$$ \mathrm{Geological}\ \mathrm{minerals} = 2.2\mathrm{A}\mathrm{l} + 2.49\mathrm{S}\mathrm{i} + 1.63\mathrm{C}\mathrm{a} + 1.94\mathrm{T}\mathrm{i} + 2.42\mathrm{F}\mathrm{e} $$
(C)

The IMPROVE “soil” formula (Eq. C) has been applied in several other studies (e.g., Chan et al. 1997; Pant et al. 2015). Rogula-Kozlowska et al. (2012) applied Eq. C but supplemented with 2.4K based on the stoichiometric concentration of K2O. Due to the uncertainties associated with Al by XRF (McDade 2008), Simon et al. (2011, Eq. 11) eliminated Al and used 3.48Si, based on the Al to Si ratio (0.46) in IMPROVE samples. Landis et al. (2001) also eliminated Al but used 3.79Si, citing uncertainties in quantifying Al by energy-dispersive XRF. Hueglin et al. (2005) estimated Si in Eq. 1 as 3.41Al (Mason 1966) and also included 1.66Mg.

Single crustal elements have also been used to estimate the geological mineral contribution to PM mass. Si is the most abundant element (10–20 %) in the earth’s crust besides O (Chow et al. 2003; Houck et al. 1989). Countess et al. (1980) used 3.5Si, and Ma et al. (2001) used 4.807Si (assuming 20.8 % Si in soil; Scheff and Valiozis 1990). Using Al as a soil marker (Duce et al. 1980), Ho et al. (2006) used 13.77Al, Hsu et al. (2008) used 12.5Al, and Zhang et al. (2013) used 14.29Al. Besides 4.3Ca (from gypsum), Harrison et al. (2003) used the sum of 9Fe for background and 3.5Fe to 5.5Fe for roadside sites, assuming 11–29 % of Fe in fugitive dust. Putaud et al. (2000) summed non-sea-salt (nss)K+, nssCa++, and gravimetric analyses of water insoluble species as residues (600 °C for 8 h) to estimate minerals. Since geological minerals are not a major component of PM2.5, variations in the assumptions regarding metal oxides or multipliers do not contribute to large variations in RM.

Salt

Chow et al. (1996, Eq. 5) and Rogula-Kozlowska et al. (2012) used the sum of Na+ and Cl to track summertime transport of marine aerosol in California. Others (e.g., Maenhaut et al. 2002, Eq. 8, 2008; Mkoma et al. 2009; Viana et al. 2007) used Cl + 1.4486Na, based on the ratio of the sum of all elements (except Cl) to Na in sea water (Riley and Chester 1971). Ohta and Okita (1994) used 3.27Na+, and others (e.g., Chan et al. 1997; Chow et al. 2007a; Ho et al. 2006; Siddique and Waheed 2014; Yan et al. 2012) used 2.54Na+, whereas Harrison et al. (2003) and Joseph et al. (2012) used 1.65Cl to represent salt content.

PM2.5 Na is a conservative marker for salt (Lowenthal and Kumar 2006; White 2008), but it suffers self-absorption interferences by XRF (Dzubay and Nelson 1975; Formenti et al. 2010; Watson et al. 1999). Therefore, 1.8Cl, based on the abundance of Cl in sea water (White 2008), is used in the revised IMPROVE equation (Eq. 10). This approach is reasonable when: (1) there is no depletion of Cl in salt aerosols from reaction with H2SO4 or HNO3; (2) hydrochloride acid (HCl) is retained on the nylon-membrane filter, i.e., the preceding Na2CO3 denuder to remove HNO3 (Channel 2 of the IMPROVE sampler) does not remove any HCl; and (3) HCl only originated from reactions of acids with salt particles. In any case, 1.8Cl is a lower limit to estimate salt, assuming that Cl is measured accurately by IC (Chow and Watson 1999). With advances in chromatographic techniques, the Cl signal in the chromatogram no longer overlaps the deionized distilled water dip and can be determined quantitatively. As Cl may be depleted under vacuum by XRF analysis, Cl is a logical choice to estimate salt concentration. More water-soluble species in salt sources (e.g., sea water; Pytkowicz and Kester 1971) could be measured to reduce the uncertainty.

Depletion of Cl occurs as H2SO4 or HNO3 reacts with sea salt, which exchanges Cl for SO4 = or NO3 , respectively. This will increase the sea salt mass as SO4 = (MW = 96) and NO3 (MW = 62) are heavier than Cl (MW = 35) (Bardouki et al. 2003). For coastal samples from Canada, Yao and Zhang (2012) hypothesized Cl replacement with di-nitrogen pentoxide (N2O5), instead of HNO3, and that SO4 = may be associated with Cl depletion under acidic conditions. Sciare et al. (2003) defined sea salt (ss) as the sum of Na+, Cl, ssCa++, ssK+, water-soluble magnesium (Mg++), and ssSO4 =; Zhang et al. (2013) substituted ssMg++ for Mg++, whereas Hsu et al. (2010) used the sum of Na+, Cl, and Mg++.

Trace elements

Minor or trace elements, excluding geological species, can be added to the RM. Macias et al. (1981, Eq. 1) summed the trace elements in the form of CuO, ZnO, and PbO. Other studies (i.e., Eqs. 3, 5, 6, and 8) summed remaining elements by XRF, excluding S and the geological elements, with the exception of Solomon et al. (1989, Eq. 2), who also included Na+ and Mg++. Trace elements are more pronounced in coarse particles or at sampling sites near industrial facilities contaminated with toxic metals (Chow et al. 2002b) when some elements are not accounted for by the mineral formulae in Table 1. More complicated trace element oxides (TEOs; sum of oxides for vanadium (V), manganese (Mn), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), selenium (Se), strontium (Sr), phosphorus (P), chromium (Cr), and K) were used by Landis et al. (2001) and Zhang et al. (2013), but this component accounted for a small fraction (0.5–1.6 %) of PM2.5 mass. Therefore, summing the remaining elements may be sufficient.

Others

The remaining mass may be attributed to measurement errors, improper multiplier(s), missing source(s), and/or particle-bound water (e.g., Frank 2006; Malm et al. 2011). This component could represent negative mass if RM overestimates gravimetric mass.

Non-crustal K was estimated as “Others” by Maenhaut et al. (2002), Simon et al. (2011), and Yan et al. (2012) based on either K − 0.6Fe (Eq. 8) or 1.2 × (K − 0.6Fe) (Eq. 11), respectively. Organic acids (sum of acetate, fomite, methane sulfonate, pyruvate, and oxalate) were added to RM by Putaud et al. (2000).

Applications of mass reconstruction equations to special studies

Supplemental Table S-1 summarizes previous studies which give rise to the 11 RM equations in Table 1. Only a subset of equations (i.e., Eqs. 1, 2, 3, 5, and 8) are applied in these short-term special studies. Concerns over visibility degradation in the southwestern USA prompted the establishment of the Western Fine Particle Network that measured size segregated mass and elements during 1977–1981 (Flocchini et al. 1981). As part of the Denver Winter Haze Study and Project VISTTA, Countess et al. (1980) and Macias et al. (1981) started using RM to determine sources of haze-causing aerosol in uban Denver and non-urban Grand Canyon areas, respectively. Equation 1 was developed by Macias et al. (1981) for PM samples at two remote desert sites near Page, AZ. SO4 = was not completely neutralized based on the molar ratio of NH4 + to SO4 = (1.65 instead of 2.0). RM accounted for 75–93 % of PM2.5 and 50–69 % of PM15–2.5. Low PM15–2.5 RMs were attributed to the absence of carbon measurements.

For nine sites in the SoCAB (Solomon et al. 1989, Eq. 2), RM accounted for 86–94 % (averaging 92 %) of annual PM10. Average measured NH4 + concentrations were 17 % lower than those estimated from (NH4)2SO4 and NH4NO3, consistent with sulfates being slightly acidic or some of the nitrates being present as NaNO3. In another SoCAB study (Chow et al. 1994b, Eq. 3), RM accounted for 70–80 % of PM2.5 and 80–85 % of PM10 at nine sites during summer; unexplained mass was 5 % lower at six sites during fall. Chow et al. (1994b) measured OC on tandem quartz-fiber filter packs (i.e., OC on quartz-fiber front filter as OCQF, followed by a quartz-fiber backup filter as OCQBQ) to estimate adsorption of volatile organic compounds (VOCs; Chow et al. 2006a; Subramanian et al. 2004; Turpin et al. 1994), but large variations were found in OCQBQ. Average OC field blanks (OCFB) are commonly subtracted from OCQF (Chow et al. 2010; Watson et al. 2009). In such cases, RM uses blank subtracted values.

In central California (Chow et al. 1996, Eq. 5), RM accounted for >90 % of PM2.5 and PM10 at ten sites. At PM concentrations <30 μg/m3, the RM often exceeded the measured PM mass. This was in part attributed to OCQF that was not blank-corrected as OCQBQ > OCQF in 168 out of 584 (29 %) samples during ozone episodes. Uncorrected OCQF may be affected by a combination of positive (adsorption) and negative (volatilization) biases (Chow et al. 2010; Watson et al. 2009).

In Melpitz, Germany, RM accounted for 86 % of PM2 and 116 % of PM10–2 (Maenhaut et al. 2002, Eq. 8). OC was overestimated owing to adsorption of VOCs on quartz-fiber filters, as PM mass was 21 % higher from the quartz-fiber than the collocated Nuclepore-membrane filters. Water associated with hygroscopic species was not accounted for by gravimetry. Considering that the sum of inorganic ions accounted for 34 % of the PM10–2, the associated water at 50 % filter equilibration RH could have accounted for the overestimation of PM10–2 mass.

Evaluation of mass reconstruction through analysis of large data sets

Several studies have evaluated RM in the IMPROVE network (see Eqs. 4, 6, 7, and 9–11 in Table 1), the largest and most consistently acquired chemical speciation data set in the world. Malm et al. (1994, Eq. 4) first applied the IMPROVE “soil” formula (Eq. C) to 36 sites, and RM accounted for 75–80 % of PM2.5, consistent with an OM underestimation using 1.4OC. Andrews et al. (2000, Eq. 6) reported low RM (58–67 % of PM2.1) among four different types of samplers at Great Smoky Mountains National Park. Replacing SO4 = with (NH4)2SO4 increased RM by 6 %. The corresponding IMPROVE samples yielded RM as 83 % of measured mass. Andrews et al. (2000) attributed the mass deficit to: (1) underestimation of geological minerals; (2) water retention on the Teflon-membrane filter deposit; and (3) underestimation of OM. However, the mineral contribution was too small to account for the deficit. The RM deficiency was reduced to 15–23 % after estimating water content; hygroscopic organics may result in additional particle-bound water (Saxena and Hildemann 1996). In addition to the low OM (1.4OC) estimate, subtracting OCQBQ over-corrected for organic vapor adsorption (Andrews et al. 2000).

Lowenthal and Kumar (2003) applied Eq. 7 to 59 IMPROVE sites from 1988 to 1999. RM averaged 88 %, ranging 61–98 % of PM2.5. Incorporating Na, Cl, and trace elements increased RM by 30 % at the coastal Point Reyes site but had a small effect (∼3 %) at other sites. RM accounted for a larger fraction during winter than summer at 51 of 59 sites.

At ∼40 % RH (i.e., IMPROVE filter equilibration conditions for gravimetric analysis), (NH4)2SO4 and NH4NO3 (Eq. 7) absorb about 0.3 and 0.2 g of water/g of dry compound, respectively, assuming supersaturated (NH4)2SO4 (Chan et al. 1992; Tang and Munkelwitz 1994). The addition of water would increase RM by 11 % in summer and 12 % in winter. A more hygroscopic form of SO4 = or H2SO4 is needed during summer to account for the observed seasonal differences. However, this assumption cannot be tested without measured NH4 + or H+ and would not explain the discrepancies when SO4 = levels are low.

Using 2.1OC (Turpin and Lim 2001) increased RM by 14 % in summer and 16 % in winter (which overestimated measured PM2.5). A lower f may be applicable in winter due to lower photochemical activity (i.e., less unmeasured O in OM). For IMPROVE sites, monthly median OCQBQ (acquired at ∼5 % of IMPROVE sites) was used for blank subtraction, assuming VOCs adsorbed on both QF and QBQ became saturated (Watson et al. 2009). During 1990–1999, monthly median OCQBQ in summer were 0.155 μg/m3 (∼3 % of PM2.5) higher than winter. Gaseous organic adsorption and seasonal effects in the OC multiplier, evaluated by Lowenthal and Kumar (2003), narrowed the seasonal RM deficit.

PM2.5 sampling methods in both the IMPROVE network and CSN result in artifacts for RM (DeBell et al. 2006, Eq. 9; Hand et al. 2011, Eq. 10). Malm et al. (2011) addressed the uncertainties in PM2.5 gravimetric and speciation measurements. PM2.5 ions (e.g., Cl, NO3 , and SO4 =) are measured on a nylon-membrane filter after a denuder to remove HNO3, which captures both non-volatilized and volatilized NO3 . Particulate NH4NO3 exists in equilibrium with gaseous HNO3 and ammonia (NH3) (Hering and Cass 1999) depending on temperature, pressure, and RH. During sampling, NO3 can evaporate as HNO3 due to the pressure drop across the filter and be re-absorbed as volatilized NO3 . However, volatilized NO3 is not part of the gravimetric mass, resulting in a negative artifact, which is most prominent during summer. The uptake of water by sulfates, nitrates, and organics during weighing (at ∼40 % RH) counterbalances NO3 volatilization from the Teflon-membrane filter (Chow et al. 2005).

Blank subtraction is applied to OCQF for IMPROVE samples but not for CSN samples (Chow et al. 2010; Watson et al. 2009). For the period prior to 2007/2008, carbon analysis followed the STN_TOT protocol in CSN (thermal/optical transmittance; Peterson and Richards 2002) and the IMPROVE_TOR protocol in IMPROVE (thermal/optical reflectance; Chow et al. 1993). Although total carbon (TC = OC + EC) is comparable, STN_TOT reports higher OC and lower EC than the IMPROVE_A_TOR protocol (Chow et al. 2007c). Malm et al. (2011) used collocated measurements in order to relate CSN to IMPROVE carbon concentrations using ordinary least squares (OLS; unweighted) regression:
$$ {\mathrm{PM}}_{2.5} = \mathrm{a}1 \times 1.375\ {\mathrm{SO}}_4^{=} + \mathrm{a}2 \times 1.29\ {\mathrm{NO}}_3^{-} + \mathrm{a}3 \times \mathrm{O}\mathrm{C} + \mathrm{a}4 \times \mathrm{O}\mathrm{ther} $$
(D)
where “Other” is the sum of EC, geological minerals, and salt (DeBell et al. 2006, Eq. 9). The two regression coefficients, a1 and a2, should equal unity if SO4 = and NO3 are present as (NH4)2SO4 and NH4NO3, respectively. Equation D assumes no water uptake at weighing equilibration conditions and no NH4NO3 evaporation during sampling. a3 is the OC multiplier (f) and a4 = 1 if the weighting factors for geological minerals and salt are correct. For 168 IMPROVE sites during 1988–2008, average a1, a2, a3, and a4 values were 1.12, 0.75, 1.60, and 1.06, respectively. This implies a 12 % contribution from water mass associated with (NH4)2SO4 during weighing, a net loss of 25 % NH4NO3 during sampling, and an OC multiplier of 1.6 with 6 % more EC, geological minerals, and salt. A higher a3 for OC was found during summer (f = 1.7) than winter (f = 1.42), with a lower a2 during summer showing more NH4NO3 evaporation, as expected.
Different regression analyses were conducted for 708 IMPROVE samples at the urban Fresno Supersite (Watson et al. 2000) from 2004 to 2010, as shown in Table 2. Ordinary weighted least squares (OWLS) regression takes into account the measurement uncertainty of the independent variable (i.e., PM2.5), while effective variance (EV) regression takes into account the uncertainties of both the independent and dependent variables and should provide the most realistic results. Table 2 shows that average PM2.5 NO3 (3.9 ± 4.9 μg/m3) and OC (3.2 ± 2.5 μg/m3) were the major components, with 1.33 ± 1.26 μg/m3 for SO4 =. The average EC, geological minerals, and salt concentrations were 0.93, 1.42, and 0.27 μg/m3, respectively. Without accounting for measurement uncertainties, a large OLS a1 of 1.61 for SO4 = yields an increment (1.61–1.00 = 0.61) five times higher than the 0.12 increment (i.e., regression coefficient of 1.12) from Malm et al. (2011)—this is inconsistent with 30–40 % RH weighing conditions. The 8 % NO3 volatilization (i.e., a2 = 0.92) and an OC multiplier (a3) of 1.67 in Table 2 seem reasonable for typical ion concentrations. The geological mineral mass is overestimated (a4 = 0.59) by the IMPROVE “soil” formula (Eq. C).
Table 2

Regression coefficients for mass reconstruction (Eq. D) using various regression methods for Interagency Monitoring of Protected Visual Environments (IMPROVE) network samples collected at urban Fresno supersite in CA from 3 September 2004 to 31 December 2010

 

OLSb

OWLSc

EVd

Average ± standard deviatione

Minimum–maximum

Categorya

 Coefficient a1 (SO4 =)

1.61

0.90

0.93

  

 Coefficient a2 (NO3 )

0.92

0.85

0.88

  

 Coefficient a3 (OC)

1.67

1.74

1.71

  

 Coefficient a4 (Other)

0.59

0.78

0.78

  

Species

 Avg. SO4 = (μg/m3)

   

1.33 ± 1.26

0.079–25

 Avg. NO3 (μg/m3)

   

3.9 ± 4.9

0.138–38

 Avg. OC (μg/m3)

   

3.2 ± 2.5

0.54–24

 Avg. Other (μ/m3)

   

2.6 ± 1.8

0.53–26

a http://views.cira.colostate.edu/web/. To ensure data quality, only samples with species concentrations exceeding their uncertainties were included for regression analyses

bOrdinary least squares − no weighting

cOrdinary weighted least squares − weighting depends on uncertainty of independent variable

dEffective variance least squares − weighting depends on uncertainties of both the independent (i.e., SO4 =, NO3 , OC, and Other) and dependent variables (Watson et al. 1984)

eAverage and calculated ranges are as follows (number of samples in all averages = 708)

Table 2 shows a1 < 1 (0.90–0.93) by OWLS and EV regression methods, implying SO4 = is somewhat acidic in Fresno, which is probably not the case. NH3 is abundant in this agricultural region (e.g., Chow et al. 1998, 1999, 2006b). The a2 of 0.85–0.88 is slightly lower than 0.92 in OLS, but it is consistent with NO3 volatilization. The a3 is 2–4 % higher in OWLS (1.74) and EV (1.71) than OLS (1.67), but a4 (0.78) is ∼30 % higher than OLS (0.59). The high a1 and low a4 in the OLS regression are not realistic. However, the regressions in all cases are statistically significant and the squared multiple correlations (r 2) are 0.98 or 0.99. Hand et al. (2011) and Malm et al. (2011) provide insights into sampling and analytical artifacts in long-term PM2.5 networks. However, the example illustrated for Fresno indicates limitations on generalizing from a single dataset and one statistical approach.

Simon et al. (2011, Eq. 11) employed data screening procedures to eliminate suspect or physically unreasonable concentrations. Data sets with correlation coefficients (r) among explanatory variables greater than the absolute value of 0.85 were eliminated; whereas EC was removed due to correlations with OC. However, the effects of collinearity fell along a continuum, and selecting the level of correlation that can be tolerated is subjective. OLS regression was found to produce more bias in regression coefficients than OWLS or EV. Overall, the estimated median OC multipliers (a3) at the 50th percentile were lower for winter (f = 1.39) and fall (f = 1.59) and comparable between spring (f = 1.83) and summer (f = 1.81). The lowest median a3 (f = 1.29) was estimated at western sites during winter. Simon et al. (2011) concluded that more realistic and unbiased estimates of the OC multiplier were obtained using an “error in variables” regression and eliminating EC.

Major factors influencing mass reconstruction

The key factors affecting RM are examined for: (1) the OC multiplier (f); (2) sampling artifacts; (3) carbon analysis methods; (4) ammonium and nitrate volatilization; and (5) water uptake on Teflon-membrane filter deposits at different equilibration RHs.

Measurement of the OC multiplier (f) to estimate OM

Several aerosol extraction (a combination of water, organic solvents, and/or solid-phase extraction) and analytical methods (e.g., elemental analysis, Fourier-transform infrared (FTIR) spectroscopy, quadrupole-aerosol mass spectrometer (Q-AMS), etc.) have been applied to estimate the f multiplier (i.e., the OM/OC ratio). As shown in Table 3, the results from these direct measurements are variable with f = 1.27–2.2. Aircraft sampling with FTIR often yielded f = ∼1.3–1.4 (Gilardoni et al. 2007; Maria et al. 2002; Russell 2003) with a higher f multiplier (1.6–1.8) found by Takahama et al. (2011). Lower f values (∼1.4) were also found for personal and indoor sampling (Reff et al. 2007), for ship emissions (∼1.6 by Gilardoni et al. 2007), and for urban areas (∼1.6 by Day et al. 2010; Hawkins and Russell 2010; Ruthenburg et al. 2014). Higher f values (∼2.0 to 2.2) were typically found for aged aerosols sampled in remote areas (e.g., Gilardoni et al. 2007; Takahama et al. 2011).
Table 3

Examples of OM/OC ratio determined in various studies at urban and remote locations

Study

Particle size

Method/descriptiona

OM/OC (ratio)

Location

Season (sampling period)

Urban/sub-urban

Remote

Krivacsy et al. (2001)

PM2.5

Used total organic carbon (TOC) analyzer to determine TC and WSOC

 

1.9

High alpine research station, Jungfraujoch, Switzerland (in the Swiss alps; elevation 3580 m above sea level (asl))

July to August 1998

Used solid-phase extraction on a copolymer sorbent

Analyzed C, H, N, and S of OM by elemental analyzer with estimated O

Determined OM mass by gravimetry

Kisset al. (2002)

PM1.5

Used total organic carbon (TOC) analyzer to determine TC and WSOC

 

1.93 ± 0.038 (ranged from 1.9 to 2.0)

Rural K-puszta site with mixed forest, Hungary

January to September 2000

Used solid-phase extraction on a copolymer sorbent

Analyzed C, H, N, and S of OM by elemental analyzer with estimated O

Determined OM mass by gravimetry

Maria et al. (2002, 2003)

PM1

Calculated OC and OM from FTIR and compare with thermal/optical OC

 

1.27 ± 0.02 to 1.49 ± 0.28

Aircraft sampling over northeast Asia during the ACE-Asia Campaign

April and May 2001

A 4-solvent rinsing procedure was used to separate functional groups into fractions of increasing hygroscopicity

Used carbon monoxide (CO) vs. FTIR OC ratios to classify back trajectory clusters into 10 groups

Russell (2003)

Submicron PM

FTIR, estimated OC from the number of carbon bonds and OM from the molecular mass of each functional group

 

1.36 ± 0.13 (1.2–1.6)

Aircraft and ship-based sampling in the Caribbean and northeastern Asiab

March to April and July 2001

El-Zanan et al. (2005)

PM2.5

After sequential solvent extraction with dichloromethane, acetone, and water, the dried residue was weighed for OM and analyzed for OC by TOR OC. The water extracts were also analyzed for ions (Cl-, NO3 -, SO4 =, Na+, K+, and NH4 +) to subtract inorganic ion mass.

 

1.92 ± 0.40 (1.58–2.58) 2.07 by mass balance

U.S. National Parks (5 sites)c

Annual (1988–2003)

Zhang et al. (2005)

PM1

Inorganic ions (e.g., sulfates, nitrates, ammonium) and organics by AMS, followed by deconvolution of AMS mass spectrum to identify HOAs and OOAs.

Averaged 1.8 with 1.2 for HOA and 2.2 for OOA

 

Pittsburgh, PA

September 2002

Yu et al. (2005a, b)

PM1.5

Used water and solvent extraction followed by GC/MS analysis for WSOC and solvent-soluble OC

 

Daytime 2.0 ± 0.3 (1.4–2.5). Nighttime 1.8 ± 0.2 (1.3–2.0)

Great Smoky Mountains National Park, TN

July to August 2005

Chen and Yu (2007)

PM2.5

Determined OM by combining heating, gravimetric, and chemical constituents

2.1 ± 0.3

 

Sub-urban site at Clearwater, Hong Kong

October 2003 to June 2005

Gilardoni et al. (2007)

PM1

FTIR and comparison with IC-PILS for speciated carboxylic acids

 

1.4 ± 0.12

Aircraft sampling of Ohio power plant emissions and regional background (12 flights)

Summer 2004

1.6 ± 0.4

Ship sampling in the Gulf of Maine

1.5 ± 0.16

Appldore Island, ME

1.6 ± 0.14

Chebogue Point, Nova Scotia, Canada

Reff et al. (2007)

PM2.5

FTIR for aliphatic (CH) and carbonyl (C=O and [(C=O)−OH] by partial least squares (PLS) equation

Outdoor 1.7–2.6

 

219 non-smoking homes in LA county, CA, Elizabeth, NJ, and Houston, TX

Summer 1999 to Spring 2001

Indoor 1.3–1.7 (average 1.45 ± 0.17)

Personal 1.3–1.6 (average 1.4 ± 0.11)

Aiken et al. (2008)

PM1

Elemental analysis by AMS

Average 1.71 with 1.2–1.3 for HOA, 1.85–2.45 for OOA; and 1.6–1.7 for BBOA

 

Mexico City, Mexicod

March 2006

Cozic et al. (2008)

PM1

OM by Q-AMS, normalized to OC by OC/EC TOT carbon analyzer

 

1.84

Jungfraujoch, Switzerland

February and March 2005

Polidori et al. (2008)

PM2.5

Used a combination of polarity-based extraction/fractionation method, determine OM by gravimetry and OC by thermal/optical analysis (polarity generally increases as organic oxygen content increases)

OM/OC ratios increase with increasing polarity: 1.37 for hexane, 1.66 for dichloromethane, 1.89 for ethyl acetate, 2.11 for acetone, and 2.25 for methanol extractions. Annual average ratios with (OM/OCtotal) and without (OM/OCextract) non-extractable material were 2.05 ± 0.18 and 1.91 ± 0.24, respectively

 

Pittsburgh, PA

Annual (July 2001–July 2002)

Gilardoni et al. (2009)

PM1

FTIR

1.8

 

Mexico City, Mexico

March 2006

2.0

Altzomoni (60 km SE of Mexico City, Mexico)

Day et al. (2010)

PM1

FTIR and comparison of OM with Q-AMS

1.66e

 

La Jolla, CA

February and March 2009

Hawkins and Russell (2010)

PM1

FTIR and comparison with Q-AMS

1.55 ± 0.17

 

La Jolla, CA

June to September 2008

Takahama et al. (2011)

Submicron PM

FTIR and comparison with ACSM

 

2.0–2.2

Whistler Mountain, BC, Canada

March and April 2009

1.6–1.8

Aircraft sampling over Mexico and the Gulf of Mexico coast (12 flights)

May to September 2009

Ruthenburg et al. (2014)

PM2.5

FTIR

 

1.83

Mesa Verde, CO

Annual (2011) at seven IMPROVE sites

 

1.79

Olympia, WA

 

1.78

Proctor Maple R.F., VT

 

1.71

St. Marks, FL

 

1.73

Trapper Creek, AK

1.56

 

Phoenix, AZ

PM particulate matter, PM x PM with diameter smaller than x micrometers at 50 % cut-point, HOA hydrocarbon-like organic aerosol (represent gasoline and diesel engine exhaust emissions), OOA oxygenated organic aerosol (contains more oxygen atoms than HOAs, resemble humic-like substance, and have been associated with secondary organic aerosol), BBOA biomass burning organic aerosol

aMeasurement methods include aerosol chemical speciation monitor (ACSM), aerodyne aerosol mass spectrometer (AMS), quadrupole-aerosol mass spectrometer (Q-AMS), ion chromatography-particle into liquid sampler (IC-PILS), Fourier transform infrared analysis (FTIR), total carbon (TC), thermal/optical reflectance (TOR), thermal/optical transmittance (TOT), water-soluble organic carbon (WSOC), water-soluble organic matter (WSOM)

bDuring the aerosol characterization experiment (ACE)-Asia study in the western Pacific and the Passing Efficiency of the Low Turbulence Inlet Experiment (PELTI) study in the Caribbean

cSites are Acadia, ME; Great Smoky Mountains, TN; Big Bend, TN; Indian Gardens, Grand Canyon, AZ; and Mount Rainier, WA

dDuring the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign, ground-based sampling was done at the T0 Supersite at the Instituto Mexicano del Petróleo (IMP) and aircraft data were collected aboard the NCAR C-130 aircraft over the city

eEstimated based on the sum of carbon mass in the functional groups (Russel 2003)

Weighing samples before and after solvent extraction (Japar et al. 1984) resulted in f = 1.4 for diesel exhaust samples. In Pittsburgh, PA, Polidori et al. (2008) found that f increased with increasing polarity with f higher in summer (June and July) and winter (December and January) than in spring (March) and fall (October and November). High summer and winter values (f = 2.08–2.11) were attributed to biomass burning and residential wood combustion (RWC), respectively. Accounting for both solvent extractable and non-extractable material, the annual average f was estimated to be 2.05 ± 0.18.

Based on AMS measurements and multivariate analyses (e.g., principle component analysis (PCA), regression analysis, and positive matrix factorization (PMF)), Zhang et al. (2005) and Aiken et al (2008) reported average f = 1.7–1.8 with f = 1.2–1.3 for hydrocarbon-like organic aerosols (HOAs) and f = 1.9–2.5 for oxygenated OA (OOA). Aiken et al. (2008) also reported f = 1.6–1.7 for biomass burning OA (BBOA). Based on a series of field studies, Philip et al. (2014) parameterize OM/OC from AMS measurements using f = 1.3 for primary organic aerosol and f = 2.1 for OOA. The OM/OC ratio is determined as 1.3(f POA) + 2.1(1–f POA), where f POA is the primary organic aerosol (POA) fraction of the AMS data, a proxy for combustion emissions (derived from ambient NO2 measurements). The OM/OC ratios ranged from 1.7 to 2.1.

The f multiplier is expected to be higher in rural than in urban areas due to oxidation and/or addition of SOA during transport. However, the results in Table 3 do not show systematic variations. Organic compounds vary by location, season, and time of day. Site-specific f values need to be measured.

Sampling and analysis artifacts

Different approaches to sampling and analysis introduce uncertainties and systematic biases, including carbon sampling artifacts, thermally evolved carbon analysis methods, ammonium and nitrate volatilization, and particle-bound water on Teflon-membrane filters. The following subsections address these measurement uncertainties.

Carbon sampling artifacts and carbon analysis by thermal evolution

As noted, PM2.5 sampling onto quartz-fiber filters is accompanied by positive (e.g., VOC adsorption) and negative (e.g., volatilization during and after sample collection) OC artifacts (Chow et al. 2010; Putaud et al. 2000; Turpin et al. 1994; Watson et al. 2009). Positive artifacts (e.g., estimated by field blank (OCFB), backup filter (OCQBQ), preceding organic denuders, and regression analyses) often exceed negative artifacts (ten Brink 2004; Watson et al. 2009). OC artifacts may bias EC values by as much as ∼50 %, especially by TOT, as light attenuation due to charring of the adsorbed organics within the filter has greater influence than charring of the surface particle deposit in TOR (Chen et al. 2004; Chow et al. 2004).

In a review of carbon comparison studies, Watson et al. (2005) found EC differed by up to a factor of seven (Schmidt et al. 2001) among 19 thermal evolution methods. Table 4 summarizes the three most widely applied thermal/optical carbon analysis protocols (i.e., IMPROVE_A_TOR, STN_TOT, and EUSAAR_2_TOT). The US long-term networks (e.g., IMPROVE and CSN) apply the IMPROVE_A_TOR protocol (USEPA 2006). The European Union EUSAAR-2 protocol (Cavalli et al. 2010; Panteliadis et al. 2015) is similar to the IMPROVE_A temperature protocol with variations in selected temperature plateaus and shorter (70–180 s) residence times. Higher OC values in TOT can result in lower OM/OC ratios and might bias RM.
Table 4

Comparison of common thermal/optical protocols: IMPROVE_A, STN, and EUSAAR_2

Carbon fraction

Atmosphered

IMPROVE_A_TORa

STN_TOTb

EUSAAR_2_TOTc

Temp. (°C)

Residence time (s)e

Temp. (°C)

Residence time (s)

Temp. (°C)

Residence time (s)

OC1

Inert

140

80–580

310

60

200

120

OC2

Inert

280

80–580

480

60

300

150

OC3

Inert

480

80–580

615

60

450

180

OC4

Inert

580

80–580

900

90

650

180

Oven coolingf

NA

NA

NA

30

NA

30

EC1

Oxidizing

580

80–580

600

45

500

120

EC2

Oxidizing

740

80–580

675

45

550

120

EC3

Oxidizing

840

80–580

750

45

700

70

EC4

Oxidizing

NA

NA

825

45

850

80

EC5

Oxidizing

NA

NA

920

120

NA

NA

NA not applicable

aThe non-urban Interagency Monitoring of Protected Visual Environments (IMPROVE) network and urban Chemical Speciation Network (CSN), measures and reports both thermal/optical reflectance (TOR), and thermal/optical transmittance (TOT), following the IMPROVE_A_TOR protocol (Chow et al. 2007b, 2011)

bSpeciation Trends Network (STN), also called NIOSH-like protocol (Peterson and Richards 2002)

cEuropean Supersites for Atmospheric Aerosol Research, EUSAAR_2, protocol (Cavalli et al. 2010)

dInert atmosphere ultra-high purity (UHP) helium (He) for OC analysis. Oxidizing atmosphere 98 % He/2 % oxygen (O2) for all protocols

eRamping to the next temperature or atmosphere begins when the flame ionization detector (FID) response returns to either baseline or a constant value; these times represent minimum and maximum times to be spent in any segment, respectively

fAt the end of OC analysis, a cooling blower turns on for ∼30 s. EC analysis starts ∼10 s after the introduction of 98 % He/2 % O2

Ammonium and nitrate volatilization

Compared with total particulate NO3 , Chow et al. (2005) found volatilized NO3 losses ranging from <10 % during cold months to >80 % during warm months (from the front quartz-fiber filter) for urban and non-urban sites. The amount of NH4NO3 volatilization from the Teflon-membrane filter can be estimated by a thermodynamic model (Hering and Cass 1999; Mozurkewich 1993), but this is only possible when gaseous HNO3 and NH3, total particle NO3 , temperature, and RH are known (Chow et al. 2005; Stelson et al. 1979). Volatilized NO3 is not considered in the USEPA’s (1997) PM2.5 Federal Reference Method (FRM) for compliance monitoring. However, for evaluating light extinction or health effects, it is necessary to account for NO3 volatilization during sampling.

Yu et al. (2005c) noted that gaseous HNO3 interacts with nylon filters and retains HNO3 that volatilized from NH4NO3. However, losses of NH4 + (i.e., gaseous NH3) from nylon filters after a Na2CO3 denuder for the selected six IMPROVE sites ranged from 10 to 28 % (monthly average) during summer. Yu et al (2006) found that, for individual samples, the NH4 + losses spread between 1 and 65 %. NH4 + volatilization is enhanced by increasing temperature and RH, and with the fraction of total NH x (sum of NH3 and NH4 +) present as NH3 (Chen et al. 2014).

Losses of NH4 + after sampling need to be investigated. Non-volatilized NH4 + can be acquired on Teflon-membrane or quartz-fiber filters without preceding denuders. Ideally, both non-volatilized and volatilized NH4 + should be acquired on a parallel channel, using a preceding citric acid denuder to remove NH3, followed by a quartz-fiber filter with a citric acid impregnated cellulose-fiber backup filter (e.g., Chow 1995; Chow et al. 1998).

Particle-bound water on the Teflon-membrane filter

The influence of particle-bound and particle-adsorbed water on PM has been explored in several studies (e.g., Frank 2006; Malm et al. 2011; Malm and Day 2001; Perrino et al. 2013; Rees et al. 2004; Temesi et al. 2001). Water associated with PM was estimated by Harrison et al. (2003) by applying 1.29 to the sum of (NH4)2SO4 and NH4NO3 concentrations and in others (e.g., Murillo et al. 2012; Siddique and Waheed 2014) by multiplying 0.32 to the sum of NH4 + and SO4 =.

Hygroscopic salts (e.g., (NH4)2SO4, NH4NO3, and NaCl) absorb water as a function of RH (Chan et al. 1992; Tang and Munkelwitz 1994). At the deliquescence RH (DRH; ∼80 %), dry (NH4)2SO4 particles start to absorb water and the amount rises with increasing RH. The hydrated particle retains water below the DRH until it re-crystallizes at the efflorescence RH (ERH) of ∼30–40 %, the hysteresis effect (e.g., Han and Martin 1999). Acidic H2SO4 absorbs and desorbs water continuously with changes in RH, without exhibiting deliquescence or efflorescence. The DRH and ERH of pure NH4NO3 are 62 and 32 %, respectively. Tang et al. (1997) found that sea salt begins to deliquesce at low RH in the presence of Mg++ and Ca++, but that most of the material deliquesces between 70 and 74 %, the DRH of NaCl. Day et al. (2000) and Malm et al. (2003) found little evidence for deliquescence or efflorescence in ambient aerosols at the IMPROVE sites.

At RH >80 %, water may constitute more than 50 % of PM2.5 mass (Chen et al. 2003; McMurry 2000). If particles were hydrated during sample collection, the sample filters may retain water for weighing (equilibration of RH 30–40 %; USEPA 1997), unless they were dried below ERH between sample collection and weighing. Based on theoretical thermaldynamic modeling of salt mixtures, Pilinis et al. (1989) found that aerosol may contain up to 30 % water for RH = ∼20–50 %. McInnes et al. (1996) observed that water associated with sea salt particles contributed 26 % of the mass at 40 % RH. Speer et al. (2003) measured changes in PM2.5 mass as a function of RH in a humidity-controlled chamber (increased from 4 to 94 % in 5 % increments and then decreased similarly to 12 %) using a beta attenuation monitor (BAM) on Teflon-membrane filters. For samples collected at Research Triangle Park, NC, Speer et al (2003) observed hysteresis in most cases.

The water-soluble organic carbon (WSOC) portion of OM can enhance or inhibit water absorption by inorganic salts (Facchini et al. 1999; Mircea et al. 2002; Saxena et al. 1995; Saxena and Hildemann 1997). At Great Smoky Mountains National Park during the summer of 2006, Lowenthal et al. (2009) reported the water uptake as 5 % PM2.5 WSOC at 45 % RH and 33 % at 80 % RH. Based on thermodynamic modeling (Chen et al. 2003; Clegg et al. 1998; Tang and Munkelwitz 1994), ∼80 % of the measured water can be associated with SO4 = and NO3 . Speer et al. (2003) attributed the ∼20 % “residual water” to organics; the amount of water per unit mass of organics was ∼50 % of that associated with (NH4)2SO4 (per unit mass) at 60–80 % RH. Conversely, Engelhart et al. (2011) determined that water growth of aerosols in Crete, Greece, was consistent with thermodynamic modeling based on inorganic constituents alone. Water mass on the Teflon-membrane filter can be determined by weighing the filter under equilibrium conditions (30–40 % RH), drying the filter completely in a desiccator, and then rapidly re-weighing.

Recent advances in thermodynamic models have incorporated some organic compounds to estimate the associated water activity (Clegg et al. 2001, 2008; Clegg and Seinfeld 2006). However, most of the organic species have not been identified, and their thermodynamic properties are uncertain (Saxena and Hildemann 1996; Sempéré and Kawamura 1994). While thermodynamic modeling may provide insights on particle-bound water, the most straightforward means is through direct gravimetric analysis over a range of RHs.

Summary and conclusions

As PM2.5 mass concentration has been regulated in NAAQS to protect public health and welfare, it is important to understand the particle composition in order to: (1) examine the causes of elevated concentrations; (2) attribute ambient concentrations to air pollution sources; (3) relate toxic components to public health and ecosystems; and (4) associate particle scattering and absorption properties with visibility impairment, the Earth’s radiation balance, and climate change. With advances in sampling and analysis techniques, the demand for characterizing the chemical, physical, and optical properties of atmospheric aerosol is increasing worldwide. The validity of mass and chemical measurements needs to be examined prior to or in conjunction with air-quality modeling to develop pollution control strategies and reduce human exposure to hazardous pollutants.

Mass reconstruction is a simple and useful tool for validating the consistencies and addressing uncertainties among mass and chemical measurements. The reconstruction of measured mass was started by Countess et al. (1980) and Macias et al. (1981) as PM chemical speciation for ions, carbon, and elements became available. The 11 reconstructed mass (RM) equations examined here provide history and insight into the evolution of RM. Major PM components include: (1) major inorganic ions (e.g., SO4 =, NO3 , and NH4 +); (2) OC and its multiplier (f) to estimate OM, (3) EC, (4) geological minerals (based on estimated metal oxides), (5) salt, (6) trace elements (excluding double counting of ions and crustal components in geological minerals), and (7) others (as remaining mass including particle-bound water). The remaining mass can be negative when RM overestimates the gravimetric mass.

For inorganic ions, either the sum of (NH4)2SO4 and NH4NO3 (calculated by their respective stoichiometric multiplier as 1.375SO4 and 1.29NO3 ) or the sum of SO4 =, NO3 , and NH4 + is most commonly applied. For coastal environments, variations account for non-sea salt SO4 = (nssSO4), CaSO4, Na(NO3)2, and NH4Cl. The assumption that SO4 = is completely neutralized as (NH4)2SO4 overestimates SO4 = mass when non-neutralized (acidic) sulfates are present. Summing of SO4 =, NO3 , and NH4 + will not account for H associated with partially neutralized SO4 = (e.g., NH4HSO4). Ion balances should be applied to ensure the molar equivalence between the measured anions and cations and to justify the degree of neutralization. NH4 + measurements should be included in routine monitoring networks and special studies, preferably on a quartz-fiber filter or with preceding citric acid denuder and citric acid impregnated backup filter that can capture both non-volatilized and volatilized NH4 +, respectively.

PM2.5 NH4NO3 may evaporate from Teflon-membrane and quartz-fiber filters during warm, non-winter periods, but its contribution to RM is expected to be highest during winter when low temperatures and high RH favor the particle phase. Ammonium and nitrate volatilization during sampling does not affect mass reconstruction. However, positive bias in RM is expected for CSN and the IMPROVE network where total particulate NO3 measured on a nylon-membrane filter includes volatilized NO3 that is not part of the gravimetric mass on Teflon-membrane filters. To account for this bias, gaseous HNO3 can be removed with a preceding denuder and volatilized NO3 can be collected on a nylon filter or salt-impregnated filter behind one of the filters.

The OC multiplier (f) ranges from 1.2 to 2.6, depending on the extent of OM oxidation. The most commonly applied multipliers are 1.4 for urban and 1.8 for non-urban sites. The f multiplier is expected to be highest in non-urban areas due to oxidation and/or addition of secondary organic compounds during transport. Organic compounds vary by location, season, and time of day. Site-specific f values need to be measured. Future studies should focus on direct measurement of the OM/OC ratio at urban and remote locations with sampling periods covering warm and cold seasons.

Organic sampling artifacts need to be quantified using preceding carbon denuders, field blanks, and/or backup filters. Subtracting averaged field blanks from OC is the most convenient way to remove passive organic adsorption. Different thermal/optical carbon analysis protocols may result in additional uncertainties. The analysis protocol used in the CSN prior to 2007/2008 overestimated OC and consistently led to high-biased RM. Consistent carbon analysis protocol should be applied nationwide and internationally. Among the seven PM2.5 components, EC is the most straightforward as a single component without any multiplier. However, the abundance of EC is method dependent as OC and EC are operationally defined.

For geological minerals containing Al, Si, Ca, and Fe, compounds are assumed to be Al2O3, SiO2, CaO, and Fe2O3, respectively, with variations including or excluding FeO, K2O, and TiO2. The IMPROVE “soil” formula applies a factor of 1.16 to account for unmeasured compounds and tends to overestimate geological minerals. This can be examined empirically by measuring the chemical composition of local geological samples after subtracting OM and ionic concentrations. Since geological minerals are not a major component of PM2.5, variations in the assumptions regarding metal oxides or multipliers do not contribute to large variations in RM, but they are important for PM10–2.5 and PM10 RMs. Trace elements as a sum of remaining elements by XRF (excluding S and geological elements) or as complicated trace element oxides only account for a small fraction (0.5–1.6 %) of PM2.5 mass.

There is no standard method to estimate salt. It is mainly based on: (1) the sum of elements (excluding Cl and Cl) to Na or Na+ ratio in seawater; (2) straight sum of Na+ and Cl; or (3) estimated as 1.8Cl as in the revised IMPROVE equation. Depletion of Cl by reaction with sea salt particles with a strong acid (e.g., H2SO4 and HNO3) is difficult to estimate without additional measurement. However, the salt component should be accounted for at sampling sites near coastal areas, salt lakes, or desert playas, as it may comprise up to 20–30 % of PM2.5 mass.

Potential bias in measured mass due to the absorption of water by hygroscopic species on the Teflon-membrane filter from which PM2.5 mass is determined can be estimated theoretically from concentrations of water-soluble species measured on nylon-membrane or quartz-fiber filters using a thermodynamic model.

In conclusion, the principal sources of uncertainty are: (1) ammonium and nitrate volatilization and inconsistency between total particulate NO3 on nylon-membrane filters and non-volatilized NO3 on Teflon-membrane filters; (2) unknown OC multipliers (f) to estimate OM; (3) inaccurately accounting for OC sampling artifacts; (4) differences among OC and EC analytical protocols; (5) inaccurate conversion of crustal element concentrations to geological minerals; (6) various degrees of Cl depletion at coastal locations; and (7) particle-bound water on the Teflon-membrane filter deposits. Reasonably accurate PM2.5 mass reconstruction can be accomplished by minimizing sampling artifacts and conducting comprehensive chemical analyses to ensure mass closure.

Notes

Acknowledgments

This work was jointly sponsored by the National Park Service (NPS) IMPROVE Contract No. P11PC00036, the National Science Foundation (CHE-1214163), and the San Joaquin Valley Air Pollution Control District (Contract No. 11-10 PM). The authors wish to thank Miss Iris Saltus for her help in assembling and editing the manuscript.

Supplementary material

11869_2015_338_MOESM1_ESM.docx (92 kb)
ESM 1 (DOCX 89 kb)

References

  1. Aiken AC, DeCarlo PF, Kroll JH, Worsnop DR, Huffman JA, Docherty KS, Ulbrich IM, Mohr C, Kimmel JR, Sueper D, Sun Y, Zhang Q, Trimborn A, Northway M, Ziemann PJ, Canagaratna MR, Onasch TB, Alfarra MR, Prevot ASH, Dommen J, Duplissy J, Metzger A, Baltensperger U, Jimenez JL (2008) O/C and OM/OC ratios of primary, secondary, and ambient organic aerosols with high-resolution time-of-flight aerosol mass spectrometry. Environ Sci Technol 42:4478–4485Google Scholar
  2. Andrews E, Saxena P, Musarra S, Hildemann LM, Koutrakis P, McMurry PH, Olmez I, White WH (2000) Concentration and composition of atmospheric aerosols from the 1995 SEAVS Experiment and a review of the closure between chemical and gravimetric measurements. J Air Waste Manage Assoc 50:648–664Google Scholar
  3. Ashbaugh LL, Eldred RA (2004) Loss of particle nitrate from Teflon sampling filters: effects on measured gravimetric mass in California and in the IMPROVE Network. J Air Waste Manage Assoc 54:93–104Google Scholar
  4. Bachmann JD (2007) Will the circle be unbroken: a history of the US national ambient air quality standards—2007 Critical Review. J Air Waste Manage Assoc 57:652–697Google Scholar
  5. Bardouki H, Liakakou H, Economou C, Sciare J, Smolk J, Zdmal V, Eleftheriadis K, Lazaridis M, Dye C, Mihalopoulos N (2003) Chemical composition of size-resolved atmospheric aerosols in the eastern Mediterranean during summer and winter. Atmos Environ 37:195–208Google Scholar
  6. Cahill TA, Eldred RA, Feeney PJ (1986) Particulate monitoring and data analysis for the National Park Service 1982–1985. University of California, Davis, CaliforniaGoogle Scholar
  7. Cao JJ, Chow JC, Lee SC, Watson JG (2013) Evolution of PM2.5 measurements and standards in the U.S. and future perspectives for China. AAQR 13:1197–1121Google Scholar
  8. Cavalli F, Viana M, Yttri KE, Genberg J, Putaud JP (2010) Toward a standardized thermal-optical protocol for measuring atmospheric organic and elemental carbon: The EUSAAR protocol. Atmos Meas Tech 3:79–89Google Scholar
  9. Cerqueira M, Marques D, Caseiro A, Pio C (2010) Experimental evidence for a significant contribution of cellulose to indoor aerosol mass concentration. Atmos Environ 44:867–871Google Scholar
  10. Chan CK, Flagan RC, Seinfeld JH (1992) Water activities of NH4NO3/(NH4)2SO4 solutions. Atmos Environ 26A:1661–1673Google Scholar
  11. Chan YC, Simpson RW, McTainsh GH, Vowles PD, Cohen DD, Bailey GM (1997) Characterisation of chemical species in PM2.5 PM10 aerosols in Brisbane. Australia Atmos Environ 31:3773–3785Google Scholar
  12. Chen L-WA, Doddridge BG, Chow JC, Dickerson RR, Ryan WF, Mueller PK (2003) Analysis of summertime PM2.5 and haze episode in the mid-Atlantic region. J Air Waste Manag Assoc 53:946–956Google Scholar
  13. Chen L-WA, Chow JC, Watson JG, Moosmüller H, Arnott WP (2004) Modeling reflectance and transmittance of quartz-fiber filter samples containing elemental carbon particles: Implications for thermal/optical analysis. J Aerosol Sci 35:765–780Google Scholar
  14. Chen L-WA, Verburg P, Shackelford A, Zhu D, Susfalk R, Chow JC, Watson JG (2010) Moisture effects on carbon and nitrogen emission from burning of wildland biomass. Atmos Chem Phys 10:6617–6625Google Scholar
  15. Chen X, Yu JZ (2007) Measurement of organic mass to organic carbon ratio in ambient aerosol samples using a gravimetric technique in combination with chemical analysis. Atmos Environ 41:8857–8864Google Scholar
  16. Chen X, Day D, Schichtel B, Malm W, Matzoll AK, Mojica J, McDade CE, Hardison ED, Hardison DL, Walters S, De Water MV, Collett JL (2014) Seasonal ambient ammonia and ammonium concentrations in a pilot IMPROVE NHx monitoring network in the western United States. Atmos Environ 91:118–126Google Scholar
  17. Cheung K, Daher N, Kam W, Shafer MM, Ning Z, Schauer JJ, Sioutas C (2011) Spatial and temporal variation of chemical composition and mass closure of ambient coarse particulate matter (PM10-2.5) in the Los Angeles area. Atmos Environ 45:2651–2662Google Scholar
  18. Chow JC, Watson JG, Pritchett LC, Pierson WR, Frazier CA, Purcell RG (1993) The DRI thermal/optical reflectance carbon analysis system: description, evaluation and applications in U.S. air quality studies. Atmos Environ 27A:1185–1201Google Scholar
  19. Chow JC, Fujita EM, Watson JG, Lu Z, Lawson DR, Ashbaugh LL (1994a) Evaluation of filter-based aerosol measurements during the 1987 Southern California Air Quality Study. Environ Monit Assess 30:49–80Google Scholar
  20. Chow JC, Watson JG, Fujita EM, Lu Z, Lawson DR, Ashbaugh LL (1994b) Temporal and spatial variations of PM2.5 and PM10 aerosol in the Southern California Air Quality Study. Atmos Environ 28:2061–2080Google Scholar
  21. Chow JC (1995) Critical review: measurement methods to determine compliance with ambient air quality standards for suspended particles. J Air Waste Manage Assoc 45:320–382Google Scholar
  22. Chow JC, Watson JG, Lu Z, Lowenthal DH, Frazier CA, Solomon PA, Thuillier RH, Magliano KL (1996) Descriptive analysis of PM2.5 and PM10 at regionally representative locations during SJVAQS/AUSPEX. Atmos Environ 30:2079–2112Google Scholar
  23. Chow JC, Watson JG, Lowenthal DH, Egami RT, Solomon PA, Thuillier RH, Magliano KL, Ranzieri AJ (1998) Spatial and temporal variations of particulate precursor gases and photochemical reaction products during SJVAQS/AUSPEX ozone episodes. Atmos Environ 32:2835–2844Google Scholar
  24. Chow JC, Watson JG (1999) Ion chromatography in elemental analysis of airborne particles. In: Landsberger S, Creatchman M (eds) Elemental Analysis of Airborne Particles, vol 1. Gordon and Breach Science, Amsterdam, pp 97–137Google Scholar
  25. Chow JC, Watson JG, Lowenthal DH, Hackney R, Magliano KL, Lehrman DE, Smith TB (1999) Temporal variations of PM2.5, PM10, and gaseous precursors during the 1995 Integrated monitoring study in central California. J Air Waste Manage Assoc 49:PM16–PM24Google Scholar
  26. Chow JC, Watson JG, Edgerton SA, Vega E (2002a) Chemical composition of PM10 and PM2.5 in Mexico City during winter 1997. Sci Total Environ 287:177–201Google Scholar
  27. Chow JC, Watson JG, Edgerton SA, Vega E, Ortiz E (2002b) Spatial differences in outdoor PM10 mass and aerosol composition in Mexico City. J Air Waste Manage Assoc 52:423–434Google Scholar
  28. Chow JC, Watson JG, Ashbaugh LL, Magliano KL (2003) Similarities and differences in PM10 chemical source profiles for geological dust from the San Joaquin Valley, California. Atmos Environ 37:1317–1340Google Scholar
  29. Chow JC, Watson JG, Chen L-WA, Arnott WP, Moosmüller H, Fung KK (2004) Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ Sci Technol 38:4414–4422Google Scholar
  30. Chow JC, Watson JG, Lowenthal DH, Magliano KL (2005) Loss of PM2.5 nitrate from filter samples in central California. J Air Waste Manage Assoc 55:1158–1168Google Scholar
  31. Chow JC, Watson JG, Lowenthal DH, Chen L-WA, Magliano KL (2006a) Particulate carbon measurements in California’s San Joaquin Valley. Chemosphere 62:337–348Google Scholar
  32. Chow JC, Chen L-WA, Watson JG, Lowenthal DH, Magliano KL, Turkiewicz K, Lehrman DE (2006b) PM2.5 chemical composition and spatiotemporal variability during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). J Geophys Res - Atmos 111:1–17Google Scholar
  33. Chow JC, Watson JG, Feldman HJ, Nolan J, Wallerstein BR, Hidy GM, Lioy PJ, McKee HC, Mobley JD, Bauges K, Bachmann JD (2007a) 2007 critical review discussion - will the circle be unbroken: a history of the U.S. National ambient air quality standards. J Air Waste Manage Assoc 57:1151–1163Google Scholar
  34. Chow JC, Watson JG, Lowenthal DH, Chen L-WA, Zielinska B, Mazzoleni LR, Magliano KL (2007b) Evaluation of organic markers for chemical mass balance source apportionment at the Fresno supersite. Atmos Chem Phys 7:1741–1754Google Scholar
  35. Chow JC, Watson JG, Chen L-WA, Chang M-CO, Robinson NF, Trimble DL, Kohl SD (2007c) The IMPROVE_A temperature protocol for thermal/optical carbon analysis: maintaining consistency with a long-term database. J Air Waste Manage Assoc 57:1014–1023Google Scholar
  36. Chow JC, Watson JG, Chen L-WA, Rice J, Frank NH (2010) Quantification of PM2.5 organic carbon sampling artifacts in US networks. Atmos Chem Phys 10:5223–5239Google Scholar
  37. Chow JC, Watson JG, Robles J, Wang XL, Chen L-WA, Trimble DL, Kohl SD, Tropp RJ, Fung KK (2011) Quality assurance and quality control for thermal/optical analysis of aerosol samples for organic and elemental carbon. Anal Bioanal Chem 401:3141–3152Google Scholar
  38. Chow JC, Watson JG (2013) Chemical analyses of particle filter deposits. In: Ruzer L, Harley NH (eds) Aerosols handbook: measurement, dosimetry, and health effects, 2nd edn. CRC Press/Taylor & Francis, New York, NY, pp 179–204Google Scholar
  39. Clegg SL, Brimblecombe P, Wexler AS (1998) Thermodynamic model of the system H+–NH4 +-SO4 2 -–NO3–H2O at tropospheric temperatures. J Phys Chem 102:2137–2154Google Scholar
  40. Clegg SL, Seinfeld JH, Brimblecombe P (2001) Thermodynamic modelling of aqueous aerosols containing electrolytes and dissolved organic compounds. J Aerosol Sci 32:713–738Google Scholar
  41. Clegg SL, Seinfeld JH (2006) Thermodynamic models of aqueous solutions containing inorganic electrolytes and dicarboxylic acids at 298.15 K. 1. The acids as non-dissociating components. J Phys Chem A 110:5692–5717Google Scholar
  42. Clegg SL, Kleeman MJ, Griffin RJ, Seinfeld JH (2008) Effects of uncertainties in the thermodynamic properties of aerosol components in an air quality model—part 1: treatment of inorganic electrolytes and organic compounds in the condensed phase. Atmos Chem Phys 8:1057–1085Google Scholar
  43. Countess RJ, Wolff GT, Cadle SH (1980) The Denver winter aerosol: a comprehensive chemical characterization. J Air Pollut Control Assoc 30:1194–1200Google Scholar
  44. Cozic J, Verheggen B, Weingartner E, Crosier J, Bower KN, Flynn M, Coe H, Henning S, Steinbacher M, Henne S, Coen MC, Petzold A, Baltensperger U (2008) Chemical composition of free tropospheric aerosol for PM1 and coarse mode at the high alpine site Jungfraujoch. Atmos Chem Phys 8:407–423Google Scholar
  45. Dabek-Zlotorzynska E, Dann TF, Martinelango PK, Celo V, Brook JR, Mathieu D, Ding LY, Austin CC (2011) Canadian National Air Pollution Surveillance (NAPS) PM2.5 speciation program: methodology and PM2.5 chemical composition for the years 2003–2008. Atmos Environ 45:673–686Google Scholar
  46. Day DA, Liu S, Russell LM, Ziemann PJ (2010) Organonitrate group concentrations in submicron particles with high nitrate and organic fractions in coastal southern California. Atmos Environ 44:1970–1979Google Scholar
  47. Day DE, Malm WC, Kreidenweis SM (2000) Aerosol light scattering measurements as a function of relative humidity. J Air Waste Manage Assoc 50:710–716Google Scholar
  48. DeBell LJ, Gebhart KA, Hand JL, Malm WC, Pitchford ML, Schichtel BA, White WH (2006) Spatial and seasonal patterns and temporal variability of haze and its constituents in the United States: Report IV. National Parks Service, Fort Collins, COGoogle Scholar
  49. Duce RA, Unni CK, Ray BJ, Prospero JM, Merrill JT (1980) Long-range atmospheric transport of soil dust from Asia to the tropical North Pacific: temporal variability. Science 209:1522–1524Google Scholar
  50. Dzubay TG, Nelson RO (1975) Self absorption corrections for x-ray fluorescence analysis of aerosols. In: Pickles WL, Barrett CS, Newkirk JB, Rund CO (eds) Advances in X-ray analysis, vol 18. Plenum Publishing Corporation, New York, NY, pp 619–631Google Scholar
  51. El-Zanan HS, Lowenthal DH, Zielinska B, Chow JC, Kumar NK (2005) Determination of the organic aerosol mass to organic carbon ratio in IMPROVE samples. Chemosphere 60:485–496Google Scholar
  52. Engelhart GJ, Hildebrandt L, Kostenidou E, Mihalopoulos N, Donahue NM, Pandis SN (2011) Water content of aged aerosol. Atmos Chem Phys 11:911–920Google Scholar
  53. Facchini MC, Mircea M, Fuzzi S, Charlson RJ (1999) Cloud albedo enhancement by surface-active organic solutes in growing droplets. Nature 401:257–259Google Scholar
  54. Flocchini RG, Cahill TA, Pitchford ML, Eldred RA, Feeney PJ, Ashbaugh LL (1981) Characterization of particles in the arid west. Atmos Environ 15:2017–2030Google Scholar
  55. Formenti P, Nava S, Prati P, Chevaillier S, Klaver A, Lafon S, Mazzei F, Calzolai G, Chiari M (2010) Self-attenuation artifacts and correction factors of light element measurements by X-ray analysis: implication for mineral dust composition studies. J Geophys Res-Atmos 115. doi: 10.1029/2009JD012701
  56. Frank NH (2006) Retained nitrate, hydrated sulfates, and carbonaceous mass in federal reference method fine particulate matter for six eastern cities. JAWMA 56:500–511Google Scholar
  57. Gilardoni S, Russell LM, Sorooshian A, Flagan RC, Seinfeld JH, Bates TS, Quinn PK, Allan JD, Williams B, Goldstein AH, Onasch TB, Worsnop DR (2007) Regional variation of organic functional groups in aerosol particles on four US east coast platforms during the International consortium for atmospheric research on transport and transformation 2004 campaign. J Geophys Res-Atmos 112. doi: 10.1029/2006JD007737
  58. Gilardoni S, Liu S, Takahama S, Russell LM, Allan JD, Steinbrecher R, Jimenez JL, De Carlo PF, Dunlea EJ, Baumgardner D (2009) Characterization of organic ambient aerosol during MIRAGE 2006 on three platforms. Atmos Chem Phys 9:5417–5432Google Scholar
  59. Gray HA, Cass GR, Huntzicker JJ, Heyerdahl EK, Rau JA (1986) Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles. Environ Sci Technol 20:580–589Google Scholar
  60. Green MC, Chow JC, Watson JG, Dick K, Inouye D (2015) Effects of snow cover and atmospheric stability on winter PM2.5 concentrations in western US valleys. J Appl Meteorol Climatol. doi: 10.1175/JAMC-D-14-0191.1
  61. Grosjean D, Friedlander SK (1975) Gas-particle distribution factors for organic and other pollutants in the Los Angeles atmosphere. J Air Pollut Control Assoc 25:1038–1044Google Scholar
  62. Guinot B, Cachier H, Oikonomou K (2007) Geochemical perspectives from a new aerosol chemical mass closure. Atmos Chem Phys 7:1657–1670Google Scholar
  63. Han JH, Martin ST (1999) Heterogeneous nucleation of the efflorescence of (NH4)2SO4 particles internally mixed with Al2O3, TiO2, and ZrO2. J Geophys Res-Atmos 104:3543–3553Google Scholar
  64. Hand JL, Copeland SA, McDade CE, Day DE, Moore JCT, Dillner AM, Pitchford ML, Indresand H, Schichtel BA, Malm WC, Watson JG (2011) Spatial and seasonal patterns and temporal variability of haze and its constituents in the United States, IMPROVE Report V. Cooperative Institute for Research in the Atmosphere, Fort CollinsGoogle Scholar
  65. Hand JL, Schichtel BA, Malm WC, Pitchford M, Frank NH (2014) Spatial and seasonal patterns in urban influence on regional concentrations of speciated aerosols across the United States. J Geophys Res-Atmos 119:12832–12849Google Scholar
  66. Harrison RM, Jones AM, Lawrence RG (2003) A pragmatic mass closure model for airborne particulate matter at urban background and roadside sites. Atmos Environ 37:4927–4933Google Scholar
  67. Hawkins LN, Russell LM (2010) Oxidation of ketone groups in transported biomass burning aerosol from the 2008 Northern California Lightning Series fires. Atmos Environ 44:4142–4154Google Scholar
  68. Hering SV, Cass GR (1999) The magnitude of bias in the measurement of PM2.5 arising from volatilization of particulate nitrate from Teflon filters. J Air Waste Manage Assoc 49:725–733Google Scholar
  69. Ho KF, Lee SC, Cao JJ, Chow JC, Watson JG, Chan CK (2006) Seasonal variations and mass closure analysis of particulate matter in Hong Kong. Sci Total Environ 355:276–287Google Scholar
  70. Hoffman RC, Laskin A, Finlayson-Pitts BJ (2004) Sodium nitrate particles: physical and chemical properties during hydration and dehydration, and implications for aged sea salt aerosols. J Aerosol Sci 35:869–887Google Scholar
  71. Houck JE, Chow JC, Watson JG, Simons CA, Pritchett LC, Goulet JM, Frazier CA (1989) Determination of particle size distribution and chemical composition of particulate matter from selected sources in the San Joaquin Valley. Final Report. A6-175-32 OMNI Environmental Services Inc. and Desert Research Institute, Beaverton and RenoGoogle Scholar
  72. Hsu SC, Liu SC, Huang YT, Lung SCC, Tsai FJ, Tu JY, Kao SJ (2008) A criterion for identifying Asian dust events based on Al concentration data collected from northern Taiwan between 2002 and early 2007. J Geophys Res-Atmos 113. doi: 10.1029/2007JD009574
  73. Hsu SC, Liu SC, Arimoto R, Shiah FK, Gong GC, Huang YT, Kao SJ, Chen JP, Lin FJ, Lin CY, Huang JC, Tsai FJ, Lung SCC (2010) Effects of acidic processing, transport history, and dust and sea salt loadings on the dissolution of iron from Asian dust. J Geophys Res-Atmos 115. doi: 10.1029/2009JD013442
  74. Hueglin C, Gehrig R, Baltensperger U, Gysel M, Monn C, Vonmont H (2005) Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland. Atmos Environ 39:637–651Google Scholar
  75. Japar SM, Szkarlat AC, Gorse RA Jr, Heyerdahl EK, Johnson RL, Rau JA, Huntzicker JJ (1984) Comparison of solvent extraction and thermal-optical carbon analysis methods: application to diesel vehicle exhaust aerosol. Environ Sci Technol 18:231–234Google Scholar
  76. Joseph AE, Unnikrishnan S, Kumar R (2012) Chemical characterization and mass closure of fine aerosol for different land use patterns in Mumbai city. AAQR 12:61–72Google Scholar
  77. Kelly KE, Kotchenruther R, Kuprov R, Silcox GD (2013) Receptor model source attributions for Utah’s Salt Lake City airshed and the impacts of wintertime secondary ammonium nitrate and ammonium chloride aerosol. J Air Waste Manage Assoc 63:575–590Google Scholar
  78. Kiss G, Varga B, Galambos I, Ganszky I (2002) Characterization of water-soluble organic matter isolated from atmospheric fine aerosol. J. Geophys. Res 107:ICC 1-1-ICC 1-8Google Scholar
  79. Kleindienst TE, Lewandowski M, Offenberg JH, Edney EO, Jaoui M, Zheng M, Ding XA, Edgerton ES (2010) Contribution of primary and secondary sources to organic aerosol and PM2.5 at SEARCH network sites. J Air Waste Manage Assoc 60:1388–1399Google Scholar
  80. Krivacsy Z, Gelencser A, Kiss G, Meszaros E, Molnar A, Hoffer A, Meszaros T, Sarvari Z, Temesi D, Varga B, Baltensperger U, Nyeki S, Weingartner E (2001) Study on the chemical character of water soluble organic compounds in fine atmospheric aerosol at the Jungfraujoch. J Atmos Chem 39:235–259Google Scholar
  81. Landis MS, Norris GA, Williams RW, Weinstein JP (2001) Personal exposures to PM2.5 mass and trace elements in Baltimore, MD, USA. Atmos Environ 35:6511–6524Google Scholar
  82. Lee T, Yu XY, Ayres B, Kreidenweis SM, Malm WC, Collett JL Jr (2008) Observations of fine and coarse particle nitrate at several rural locations in the United States. Atmos Environ 42:2720–2732Google Scholar
  83. Levin EJT, McMeeking GR, Carrico CM, Mack LE, Kreidenweis SM, Wold CE, Moosmüller H, Arnott WP, Hao WM, Collett JL, Jr., Malm WC (2010) Biomass burning smoke aerosol properties measured during fire laboratory at Missoula experiments (FLAME). J Geophys Res-Atmos 115, D18210, doi: 10.1029/2009JD013601
  84. Lowenthal DH, Zielinska B, Chow JC, Watson JG, Gautam M, Ferguson DH, Neuroth GR, Stevens KD (1994) Characterization of heavy-duty diesel vehicle emissions. Atmos Environ 28:731–743Google Scholar
  85. Lowenthal DH, Kumar NK (2003) PM2.5 mass and light extinction reconstruction in IMPROVE. J Air Waste Manage Assoc 53:1109–1120Google Scholar
  86. Lowenthal DH, Kumar NK (2006) Light scattering from sea-salt aerosols at Interagency Monitoring of Protected Visual Environments (IMPROVE) sites. J Air Waste Manage Assoc 56:636–642Google Scholar
  87. Lowenthal DH, Zielinska B, Mason B, Samy S, Samburova V, Collins D, Spencer C, Taylor N, Allen J, Kumar NK (2009) Aerosol characterization studies at Great Smoky Mountains National Park, summer 2006. J Geophys Res-Atmospheres 114. doi: 10.1029/2008JD011274
  88. Ma CJ, Kasahara M, Tohno S, Hwang KC (2001) Characterization of the winter atmospheric aerosols in Kyoto and Seoul using PIXE, EAS and IC. Atmos Environ 35:747–752Google Scholar
  89. Macias ES, Zwicker JO, Ouimette JR, Hering SV, Friedlander SK, Cahill TA, Kuhlmey GA, Richards LW (1981) Regional haze case studies in the southwestern United States - I. Aerosol chemical composition. Atmos Environ 15:1971–1986Google Scholar
  90. Maenhaut W, Schwarz J, Cafmeyer J, Chi X (2001) Chemical mass closure during the eurotrac-2 aerosol intercomp 2000. J Aerosol Sci 32:1017–1028Google Scholar
  91. Maenhaut W, Schwarz J, Cafmeyer J, Chi XG (2002) Aerosol chemical mass closure during the EUROTRAC-2 AEROSOL Intercomparison 2000. Nucl Instrum Methods Phys Res Sec B: Beam Interac Mater At 189:233–237Google Scholar
  92. Maenhaut W, Raes N, Chi XG, Cafmeyer J, Wang W (2008) Chemical composition and mass closure for PM2.5 and PM10 aerosols at K-puszta, Hungary, in summer 2006. X-Ray Spectrom 37:193–197Google Scholar
  93. Malm WC, Sisler JF, Huffman D, Eldred RA, Cahill TA (1994) Spatial and seasonal trends in particle concentration and optical extinction in the United States. J Geophys Res 99:1347–1370Google Scholar
  94. Malm WC, Pitchford ML, Scruggs M, Sisler JF, Ames RG, Copeland S, Gebhart KA, Day DE (2000) Spatial and seasonal patterns and temporal variability of haze and its constituents in the United States: IMPROVE Report III. ISSN: 0737-5352-47 Cooperative Institute for Research in the Atmosphere. Colorado State University, Ft. CollinsGoogle Scholar
  95. Malm WC, Day DE (2001) Estimates of aerosol species scattering characteristics as a function of relative humidity. Atmos Environ 35:2845–2860Google Scholar
  96. Malm WC, Day DE, Kreidenweis SM, Collett JL, Jr., Lee T (2003) Humidity-dependent optical properties of fine particles during the Big Bend Regional Aerosol and Visibility Observational Study (BRAVO). J Geophys Res-Atmos 108:ACH 13-1-ACH 13-16Google Scholar
  97. Malm WC, Hand JL (2007) An examination of the physical and optical properties of aerosols collected in the IMPROVE program. Atmos Environ 41:3407–3427Google Scholar
  98. Malm WC, Schichtel BA, Pitchford ML (2011) Uncertainties in PM2.5 gravimetric and speciation measurements and what we can learn from them. J Air Waste Manage Assoc 61:1131–1149Google Scholar
  99. Maria SF, Russell LM, Turpin BJ, Porcja RJ (2002) FTIR measurements of functional groups and organic mass in aerosol samples over the Caribbean. Atmos Environ 36:5185–5196Google Scholar
  100. Maria SF, Russell LM, Turpin BJ, Porcja RJ, Campos TL, Weber RJ, Huebert BJ (2003) Source signatures of carbon monoxide and organic functional groups in Asian pacific regional Aerosol Characterization Experiment (ACE-Asia) submicron aerosol types. J Geophys Res-Atmos 108. doi: 10.1029/2003JD003703
  101. Mason B (1966) Principles of geochemistry, 3rd edn. John Wiley & Sons, Inc., New York, NYGoogle Scholar
  102. McDade CE (2008) CASOP 351-2, 258: IMPROVE standard operating procedure. Univerrsity of California, Davis, CA, Crocker Nuclear LaboratoryGoogle Scholar
  103. McInnes IM, Quinn PK, Covert DS, Anderson TI (1996) Gravimetric analysis, ionic composition, and associated water mass of the marine aerosol. Atmos Environ 30:869–884Google Scholar
  104. McMurry PH (2000) A review of atmospheric aerosol measurements. Atmos Environ 34:1959–1999Google Scholar
  105. Mircea M, Facchini MC, Decesari S, Fuzzi S, Charlson RJ (2002) The influence of the organic aerosol component on CCN supersaturation spectra for different aerosol types. Tellus Ser B Chem Phys Meteorol 54:74–81Google Scholar
  106. Mkoma SL, Maenhaut W, Chi XG, Wang W, Raes N (2009) Characterisation of PM10 atmospheric aerosols for the wet season 2005 at two sites in East Africa. Atmos Environ 43:631–639Google Scholar
  107. Mozurkewich M (1993) The dissociation constant of ammonium nitrate and its dependence on temperature, relative humidity and particle size. Atmos Environ 27A:261–270Google Scholar
  108. Murillo JH, Ramos AC, Garcia FA, Jimenez SB, Cardenas B, Mizohata A (2012) Chemical composition of PM2.5 particles in Salamanca, Guanajuato Mexico: Source apportionment with receptor models. Atmos Res 107:31–41Google Scholar
  109. Nejedly Z, Campbell JL, Teesdale WJ, Gielen C (1997) PIXE and PESA aspects of the Guelph visibility and fine particulate monitoring program. Nucl Instrum Methods Phys Res Sec B: Beam Interac Mater At 132:489–500Google Scholar
  110. Ni TR, Li PH, Han B, Bai ZP, Ding X, Wang QW, Huo J, Lu B (2013) Spatial and temporal variation of chemical composition and mass closure of ambient PM10 in Tianjin, China. AAQR 13:1832–1846Google Scholar
  111. Ohta S, Okita T (1994) Measurements of particulate carbon in urban and marine air in Japanese areas. Atmos Environ 18:2439–2445Google Scholar
  112. Pant P, Shukla A, Kohl SD, Chow JC, Watson JG, Harrison RM (2015) Characterization of ambient PM2.5 at a pllution hotspot in New Delhi, India and inference of sources. Atmos Environ 109:178–189Google Scholar
  113. Panteliadis P, Hafkenscheid T, Cary B, Diapouli E, Fischer A, Favez O, Quincey P, Viana M, Hitzenberger R, Vecchi R, Saraga D, Sciare J, Jaffrezo JL, John A, Schwarz J, Giannoni M, Novak J, Karanasiou A, Fermo P, Maenhaut W (2015) ECOC comparison exercise with identical thermal protocols after temperature offset correction—instrument diagnostics by in-depth evaluation of operational parameters. Atmos Meas Tech 8:779–792Google Scholar
  114. Perrino C, Canepari S, Catrambone M (2013) Comparing the performance of Teflon and quartz membrane filters collecting atmospheric PM: influence of atmospheric water. AAQR 13:137–147Google Scholar
  115. Peterson MR, Richards MH (2002) Thermal-optical-transmittance analysis for organic, elemental, carbonate, total carbon, and OCX2 in PM2.5 by the EPA/NIOSH method. In: Winegar ED, Tropp RJ (eds) Proceedings, Symposium on Air Quality Measurement Methods and Technology-2002. Air & Waste Management Association, Pittsburgh, PA, pp 83-1–83-19Google Scholar
  116. Pettijohn FJ (1975) Sedimentary Rocks, 3rd edn. Harper & Brothers, New York, NYGoogle Scholar
  117. Philip S, Martin RV, Pierce JR, Jimenez JL, Zhang Q, Canagaratna MR, Spracklen DV, Nowlan CR, Lamsal LN, Copper MJ, Krotkov NA (2014) Spatially and seasonally resolved estimate of the ratio of organic mass to organic carbon. Atmos Environ 87:34–40Google Scholar
  118. Pilinis C, Seinfeld JH, Grosjean D (1989) Water content of atmospheric aerosols. Atmos Environ 23:1601–1606Google Scholar
  119. Pitchford ML, Malm WC, Schichtel BA, Kumar NK, Lowenthal DH, Hand JL (2007) Revised algorithm for estimating light extinction from IMPROVE particle speciation data. J Air Waste Manage Assoc 57:1326–1336Google Scholar
  120. Pitchford ML, Poirot RL, Schichtel BA, Malm WC (2009) Characterization of the winter midwestern particulate nitrate bulge. J Air Waste Manage Assoc 59:1061–1069Google Scholar
  121. Polidori A, Turpin BJ, Davidson CI, Rodenburg LA, Maimone F (2008) Organic PM2.5: fractionation by polarity, FTIR spectroscopy, and OM/OC ratio for the Pittsburgh aerosol. Aerosol Sci Technol 42:233–246Google Scholar
  122. Putaud JP, VanDingenen R, Mangoni M, Virkkula A, Raes F, Maring H, Prospero JM, Swietlicki E, Berg OH, Hillamo R, Mäkelä T (2000) Chemical mass closure and assessment of the origin of the submicron aerosol in the marine boundary layer and the free troposphere at Tenerife during ACE-2. Tellus 52B:141–168Google Scholar
  123. Puxbaum H, Tenze-Kunit M (2003) Size distribution and seasonal variation of atmospheric cellulose. Atmos Environ 37:3693–3699Google Scholar
  124. Pytkowicz RM, Kester DR (1971) The physical chemistry of sea water. Oceanogr Mar Biol 9:11–60Google Scholar
  125. Querol X, Alastuey A, Rodriguez S, Plana F, Ruiz CR, Cots N, Massague G, Puig O (2001) PM10 and PM2.5 source apportionment in the Barcelona metropolitan area, Catalonia. Spain Atmos Environ 35:6407–6419Google Scholar
  126. Rees SL, Robinson AL, Khlystov A, Stanier CO, Pandis SN (2004) Mass balance closure and the federal reference method for PM2.5 in Pittsburgh. Pennsylvania Atmos Environ 38:3305–3318Google Scholar
  127. Reff A, Turpin BJ, Offenberg JH, Weisel CP, Zhang J, Morandi M, Stock T, Colome S, Winer A (2007) A functional group characterization of organic PM2.5 exposure: results from the RIOPA study. Atmos Environ 41:4585–4598Google Scholar
  128. Reid JS, Koppmann R, Eck TF, Eleuterio DP (2005) A review of biomass burning emissions part II: intensive physical properties of biomass burning particles. Atmos Chem Phys 5:799–825Google Scholar
  129. Remoundaki E, Kassomenos P, Mantas E, Mihalopoulos N, Tsezos M (2013) Composition and mass closure of PM2.5 in urban environment (Athens, Greece). AAQR 13:72–82Google Scholar
  130. Riley JP, Chester R (1971) Introduction to Marine Chemistry. Academic Press, New YorkGoogle Scholar
  131. Robinson AL, Donahue NM, Shrivastava MK, Weitkamp EA, Sage AM, Grieshop AP, Lane TE, Pierce JR, Pandis SN (2007) Rethinking organic aerosols: semivolatile emissions and photochemical aging. Science 315:1259–1262Google Scholar
  132. Robinson AL, Grieshop AP, Donahue NM, Hunt SW (2010) Updating the conceptual model for fine particle mass emissions from combustion systems. J Air Waste Manage Assoc 60:1204–1222Google Scholar
  133. Rogula-Kozlowska W, Klejnowski K, Rogula-Kopiec P, Mathews B, Szopa S (2012) A study on the seasonal mass closure of ambient fine and coarse dusts in Zabrze, Poland. Bull Environ Contam Toxicol 88:722–729Google Scholar
  134. Roy AA, Wagstrom KM, Adams PJ, Pandis SN, Robinson AL (2011) Quantification of the effects of molecular marker oxidation on source apportionment estimates for motor vehicles. Atmos Environ 45:3132–3140Google Scholar
  135. Russell LM (2003) Aerosol organic-mass-to-organic-carbon ratio measurements. Environ Sci Technol 37:2982–2987Google Scholar
  136. Ruthenburg TC, Perlin PC, Liu V, McDade CE, Dillner AM (2014) Determination of organic matter and organic matter to organic carbon ratios by infrared spectroscopy with application to selected sites in the IMPROVE network. Atmos Environ 86:47–57Google Scholar
  137. Sanchez-Ochoa A, Kasper-Giebl A, Puxbaum H, Gelencser A, Legrand M, Pio C (2007) Concentration of atmospheric cellulose: a proxy for plant debris across a west-east transect over Europe. J Geophys Res-Atmos 112. doi: 10.1029/2006JD008180
  138. Saxena P, Hildemann LM, McMurry PH, Seinfeld JH (1995) Organics alter hygroscopic behavior of atmospheric particles. J Geophys Res 100:18755–18770Google Scholar
  139. Saxena P, Hildemann LM (1996) Water-soluble organics in atmospheric particles: a critical review of the literature and application of thermodynamics to identify candidate compounds. Atmos Chem Phys 24:57–109Google Scholar
  140. Saxena P, Hildemann LM (1997) Water absorption by organics: survey of laboratory evidence and evaluation of UNIFAC for estimating water activity. Environ Sci Technol 31:3318–3324Google Scholar
  141. Scheff PA, Valiozis C (1990) Characterization and source identification of respirable particulate matter in Athens. Greece Atmos Environ 24A:203–211Google Scholar
  142. Schmidt MWI, Skjemstad JO, Czimczik CI, Glaser B, Prentice KM, Gelinas Y, Kuhlbusch TK (2001) Comparative analysis of black carbon in soils. Glob Biogeochem Cycles 15:163–167Google Scholar
  143. Sciare J, Cachier H, Oikonomou K, Ausset P, Sarda-Esteve R, Mihalopoulos N (2003) Characterization of carbonaceous aerosols during the MINOS campaign in Crete, July–August 2001: a multi-analytical approach. Atmos Chem Phys 3:1743–1757Google Scholar
  144. Sempéré R, Kawamura K (1994) Comparative distributions of dicarboxylic acids and related polar compounds in snow, rain and aerosols from urban atmosphere. Atmos Environ 28:449–460Google Scholar
  145. Siddique N, Waheed S (2014) Source apportionment using reconstructed mass calculations. J Environ Sci Health-Part A-Tox Hazard Subst Environ Eng 49:463–477Google Scholar
  146. Simon H, Bhave PV, Swall JL, Frank NH, Malm WC (2011) Determining the spatial and seasonal variability in OM/OC ratios across the US using multiple regression. Atmos Chem Phys 11:2933–2949Google Scholar
  147. Simoneit BRT, Schauer JJ, Nolte CG, Oros DR, Elias VO, Fraser MP, Rogge WF, Cass GR (1999) Levoglucosan, a tracer for cellulose in biomass burning and atmospheric particles. Atmos Environ 33:173–182Google Scholar
  148. Solomon PA, Fall T, Salmon LG, Cass GR, Gray HA, Davidson A (1989) Chemical characteristics of PM10 aerosols collected in the Los Angeles area. J Air Pollut Control Assoc 39:154–163Google Scholar
  149. Solomon PA, Crumpler D, Flanagan JB, Jayanty RKM, Rickman EE, McDade CE (2014) US National PM2.5 Chemical Speciation Monitoring Networks-CSN and IMPROVE: description of networks. J Air Waste Manage Assoc 64:1410–1438Google Scholar
  150. Speer RE, Edney EO, Kleindienst TE (2003) Impact of organic compounds on the concentrations of liquid water in ambient PM2.5. J Aerosol Sci 34:63–77Google Scholar
  151. Stelson AW, Friedlander SK, Seinfeld JH (1979) A note on the equilibrium relationship between ammonia and nitric acid and particulate ammonium nitrate. Atmos Environ 13:369–371Google Scholar
  152. Subramanian R, Khlystov AY, Cabada JC, Robinson AL (2004) Positive and negative artifacts in particulate organic carbon measurements with denuded and undenuded sampler configurations. Aerosol Sci Technol 38:27–48Google Scholar
  153. Takahama S, Schwartz RE, Russell LM, Macdonald AM, Sharma S, Leaitch WR (2011) Organic functional groups in aerosol particles from burning and non-burning forest emissions at a high-elevation mountain site. Atmos Chem Phys 11:6367–6386Google Scholar
  154. Tang IN, Munkelwitz HR (1994) Aerosol phase transformation and growth in the atmosphere. J Appl Meteorol 33:791–796Google Scholar
  155. Tang IN, Tridico AC, Fung KH (1997) Thermodynamic and optical properties of sea salt aerosols. J Geophys Res 102:23269–23276Google Scholar
  156. Temesi D, Molnar A, Feczko T, Meszaros E (2001) Diurnal variation in the size distribution of water soluble organic compounds. J Aerosol Sci 32:689–698Google Scholar
  157. ten Brink HM (2004) Artefacts in measuring (the composition of) Particulate Matter in Europe: introducing INTERCOMP2000. Atmos Environ 38:6457Google Scholar
  158. Terzi E, Argyropoulos G, Bougatioti A, Mihalopoulos N, Nikolaou K, Samara C (2010) Chemical composition and mass closure of ambient PM10 at urban sites. Atmos Environ 44:2231–2239Google Scholar
  159. Turpin BJ, Huntzicker JJ, Hering SV (1994) Investigation of organic aerosol sampling artifacts in the Los Angeles Basin. Atmos Environ 28:3061–3071Google Scholar
  160. Turpin BJ, Saxena P, Andrews E (2000) Measuring and simulating particulate organics in the atmosphere: problems and prospects. Atmos Environ 34:2983–3013Google Scholar
  161. Turpin BJ, Lim HJ (2001) Species contributions to PM2.5 mass concentrations: revisiting common assumptions for estimating organic mass. Aerosol Sci Technol 35:602–610Google Scholar
  162. USEPA (1997) National ambient air quality standards for particulate matter: final rule. Fed Regist 62:38651–38760Google Scholar
  163. USEPA (2001) Draft guidance for tracking progress under the regional haze rule. U.S. Environmental Protection Agency, Research Triangle Park, NCGoogle Scholar
  164. USEPA (2006) Modification of carbon procedures in the Speciation Network and FAQs. PM2.5 Speciation Trends Network Newsletter 2-3Google Scholar
  165. USEPA (2015) Chemical speciation. U.S. Environmental Protection Agency, Research Triangle Park, NCGoogle Scholar
  166. van Vaeck L, van Cauwenberghe K (1978) Cascade impactor measurments of the size distribution of the major classes of organic pollutants in atmospheric particulate matter. Atmos Environ 12:2229–2239Google Scholar
  167. Vecchi R, Chiari M, D’Alessandro A, Fermo P, Lucarelli F, Mazzei F, Nava S, Piazzalunga A, Prati P, Silvani F, Valli G (2008) A mass closure and PMF source apportionment study on the sub-micron sized aerosol fraction at urban sites in Italy. Atmos Environ 42:2240–2253Google Scholar
  168. Viana M, Maenhaut W, Chi X, Querol X, Alastuey A (2007) Comparative chemical mass closure of fine and coarse aerosols at two sites in south and west Europe: implications for EU air pollution policies. Atmos Environ 41:315–326Google Scholar
  169. Watson JG, Cooper JA, Huntzicker JJ (1984) The effective variance weighting for least squares calculations applied to the mass balance receptor model. Atmos Environ 18:1347–1355Google Scholar
  170. Watson JG, Chow JC, Frazier CA (1999) X-ray fluorescence analysis of ambient air samples. In: Landsberger S, Creatchman M (eds) Elemental Analysis of Airborne Particles, vol 1. Gordon and Breach Science, Amsterdam, pp 67–96Google Scholar
  171. Watson JG, Chow JC, Bowen JL, Lowenthal DH, Hering SV, Ouchida P, Oslund W (2000) Air quality measurements from the Fresno Supersite. J Air Waste Manage Assoc 50:1321–1334Google Scholar
  172. Watson JG, Turpin BJ, Chow JC (2001) The measurement process: Precision, accuracy, and validity. In: Cohen BS, McCammon CSJ (eds) Air Sampling Instruments for Evaluation of Atmospheric Contaminants, Ninth Edition, 9th edn. American Conference of Governmental Industrial Hygienists, Cincinnati, OH, pp 201–216Google Scholar
  173. Watson JG (2002) Visibility: science and regulation—2002 critical review. J Air Waste Manage Assoc 52:628–713Google Scholar
  174. Watson JG (2004) Protocol for applying and validating the CMB model for PM2.5 and VOC. EPA-451/R-04-001 U.S. Environmental Protection Agency, Research Triangle Park, NCGoogle Scholar
  175. Watson JG, Chow JC, Chen L-WA (2005) Summary of organic and elemental carbon/black carbon analysis methods and intercomparisons. AAQR 5:65–102Google Scholar
  176. Watson JG, Chow JC, Chen L-WA, Frank NH (2009) Methods to assess carbonaceous aerosol sampling artifacts for IMPROVE and other long-term networks. J Air Waste Manage Assoc 59:898–911Google Scholar
  177. Watson JG, Chow JC (2011) Ambient aerosol sampling. In: Kulkarni P, Baron PA, Willeke K (eds) Aerosol measurement: principles, techniques and applications, third edition, 3rd edn. John Wiley & Sons, Inc., Hoboken, NJ, USA, pp 591–614Google Scholar
  178. White WH, Roberts PT (1977) On the nature and origins of visibility-reducing aerosols in the Los Angeles air basin. Atmos Environ 11:803–812Google Scholar
  179. White WH (2008) Chemical markers for sea salt in IMPROVE aerosol data. Atmos Environ 42:261–274Google Scholar
  180. Yan P, Zhang RJ, Huan N, Zhou XJ, Zhang YM, Zhou HG, Zhang LM (2012) Characteristics of aerosols and mass closure study at two WMO GAW regional background stations in eastern China. Atmos Environ 60:121–131Google Scholar
  181. Yao XH, Zhang LM (2012) Chemical processes in sea-salt chloride depletion observed at a Canadian rural coastal site. Atmos Environ 46:189–194Google Scholar
  182. Yu LE, Shulman ML, Kopperud R, Hildemann LM (2005a) Fine organic aerosols collected in a humid, rural location (Great Smoky Mountains, Tennessee, USA): chemical and temporal characteristics. Atmos Environ 39:6037–6050Google Scholar
  183. Yu LE, Shulman ML, Kopperud R, Hildemann LM (2005b) Characterization of organic compounds collected during southeastern aerosol and visibility study: water-soluble organic species. Environ Sci Technol 39:707–715Google Scholar
  184. Yu XY, Lee T, Ayres B, Kreidenweis SM, Collett JL Jr, Malm WC (2005c) Particulate nitrate measurement using nylon filters. J Air Waste Manage Assoc 55:1100–1110Google Scholar
  185. Yu XY, Lee T, Ayres B, Kreidenweis SM, Malm WC, Collett JL Jr (2006) Loss of fine particle ammonium from denuded nylon filters. Atmos Environ 40:4797–4807Google Scholar
  186. Zhang Q, Worsnop DR, Canagaratna MR, Jimenez JL (2005) Hydrocarbon-like and oxygenated organic aerosols in Pittsburgh: insights into sources and processes of organic aerosols. Atmos Chem Phys 5:3289–3311Google Scholar
  187. Zhang XY, Wang YQ, Niu T, Zhang XC, Gong SL, Zhang YM, Sun JY (2012) Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos Chem Phys 12:779–799Google Scholar
  188. Zhang Y, Sartelet K, Zhu S, Wang W, Wu SY, Zhang X, Wang K, Tran P, Seigneur C, Wang ZF (2013) Application of WRF/Chem-MADRID and WRF/Polyphemus in Europe—part 2: evaluation of chemical concentrations and sensitivity simulations. Atmos Chem Phys 13:6845–6875Google Scholar

Copyright information

© The Author(s) 2015

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Judith C. Chow
    • 1
    • 2
    • 3
  • Douglas H. Lowenthal
    • 1
    • 3
  • L.-W. Antony Chen
    • 1
    • 4
  • Xiaoliang Wang
    • 1
    • 3
  • John G. Watson
    • 1
    • 2
    • 3
  1. 1.Division of Atmospheric Sciences, Desert Research InstituteRenoUSA
  2. 2.The State Key Laboratory of Loess and Quaternary Geology, Institute of Earth EnvironmentChinese Academy of SciencesXi’anChina
  3. 3.Graduate FacultyUniversity of NevadaRenoUSA
  4. 4.Department of Environmental and Occupational HealthUniversity of NevadaLas VegasUSA

Personalised recommendations