Scholarly article on topic 'Evolution of biomass burning smoke particles in the dark'

Evolution of biomass burning smoke particles in the dark Academic research paper on "Earth and related environmental sciences"

CC BY-NC-ND
0
0
Share paper
Academic journal
Atmospheric Environment
Keywords
{"Smoke particle" / "Aerosol chamber" / "Dark aging" / "RH effect" / "Effective density" / Hygroscopicity}

Abstract of research paper on Earth and related environmental sciences, author of scientific article — Chunlin Li, Zhen Ma, Jianmin Chen, Xinming Wang, Xingnan Ye, et al.

Abstract The evolution in the dark of physiochemical properties and chemical composition of smoke particles emitted from wheat straw burning, as well as the effect of relative humidity (RH) on these properties, was investigated in an aerosol chamber. The smoke particles are composed primarily of carbonaceous materials and a considerable amount of inorganic salts (∼25 wt.%). During aging, the fraction of inorganic salts in smoke PM1.0 increases, mainly due to the formation of more sulfate and nitrate at the expense of chloride; this heterogeneous conversion is facilitated at high RH. The hygroscopicity parameter κ H of fresh smoke particles is 0.27 and this is estimated to decrease by 0.01 after 4 h dark aging. Both aging and high RH lead to increases of particle size and density. The effective densities of smoke PM2.5 and PM1.0 deduced from concurrent mass and volume concentration measurements gradually increase from about 1.18 to 1.44 g/m3 within 4 h aging at 45%–55% RH, in line with the results obtained both from size-resolved particle density analysis using an aerosol particle mass analyzer (APM) and from estimation using composition-weighted bulk densities. The density of smoke particle is size-, RH-, and aging extent-dependent; the size effect becomes less pronounced with aging.

Academic research paper on topic "Evolution of biomass burning smoke particles in the dark"

Contents lists available at ScienceDirect

Atmospheric Environment

journal homepage: www.elsevier.com/locate/atmosenv

Evolution of biomass burning smoke particles in the dark

Chunlin Li a, Zhen Ma a, Jianmin Chen a' *, Xinming Wang b, Xingnan Ye a, Lin Wang a, Xin Yang a, Haidong Kan a, D.J. Donaldson c' **, Abdelwahid Mellouki d

a Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan Tyndall Center, Department of Environmental Science & Engineering, Fudan University, Shanghai 200433, China

b State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China c Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ont., M5S 3H6, Canada d Institut de Combustion, Aerothermique, Reactivite et Environnement, CNRS, 45071 Orléans Cedex 02, France

HIGHLIGHTS

• Aerosol chamber simulation of agricultural residue burning in the dark.

• Hygroscopicity and effective density measurement using TDMA-APM system.

• RH effect on smoke particle chemical and effective density evolution.

• Density growth with the chemical evolution and morphology changes of smoke particle.

ARTICLE INFO

ABSTRACT

Article history:

Received 1 July 2015

Received in revised form

29 August 2015

Accepted 1 September 2015

Available online 5 September 2015

Keywords: Smoke particle Aerosol chamber Dark aging RH effect Effective density Hygroscopicity

The evolution in the dark of physiochemical properties and chemical composition of smoke particles emitted from wheat straw burning, as well as the effect of relative humidity (RH) on these properties, was investigated in an aerosol chamber. The smoke particles are composed primarily of carbonaceous materials and a considerable amount of inorganic salts (-25 wt.%). During aging, the fraction of inorganic salts in smoke PM10 increases, mainly due to the formation of more sulfate and nitrate at the expense of chloride; this heterogeneous conversion is facilitated at high RH. The hygroscopicity parameter kH of fresh smoke particles is 0.27 and this is estimated to decrease by 0.01 after 4 h dark aging. Both aging and high RH lead to increases of particle size and density. The effective densities of smoke PM25 and PM10 deduced from concurrent mass and volume concentration measurements gradually increase from about 1.18 to 1.44 g/m3 within 4 h aging at 45%—55% RH, in line with the results obtained both from size-resolved particle density analysis using an aerosol particle mass analyzer (APM) and from estimation using composition-weighted bulk densities. The density of smoke particle is size-, RH-, and aging extent-dependent; the size effect becomes less pronounced with aging.

© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

The emission of carbonaceous aerosols from agricultural waste burning contributes greatly to the deterioration of atmospheric quality in some developing countries (Saikawa et al., 2009; Andreae and Merlet, 2001). In China, it was estimated that field burning consumes over 20% (equivalent to 100—120 Tg) of the total

* Corresponding author. Tel.: +86 21 65642298; fax: +86 21 65642080.

** Corresponding author.

E-mail addresses: jmchen@fudan.edu.cn (J. Chen), jdonalds@chem.utoronto.ca (D.J. Donaldson).

agricultural waste each year, generating large amounts of smoke particles consisting of humic-like substances, polycyclic aromatic hydrocarbons (PAHs), and black carbon, which have a significant impact on human health, climate changes, and atmospheric chemistry (Lin et al., 2010; Cao et al., 2011; Li et al., 2007; Zhang et al., 2011). The large-scale emission of smoke particles during harvest degrades the visibility and threatens public health (Kennedy, 2007; Jung et al., 2009). In addition, suspended smoke particles may act as sites for heterogeneous chemical processes, and influence local and regional climate, though direct and indirect aerosol effects. For these reasons, it is of great interest to understand the physiochemical properties and environmental effects of smoke particles, which may certainly change as the particles are

http://dx.doi.org/10.1016/j.atmosenv.2015.09.003

1352-2310/© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

"aged" due to atmospheric chemical processes.

Many studies have focused on the physicochemical properties and environmental/health effects of fresh or photo-oxidized biomass burning particles via ambient investigation, chamber-simulation, remote-sensing, model calculation, or epidemiological research (Zha et al., 2014; Bolling et al., 2009; Hays et al., 2005; Mukherjee et al., 2013; Reid and Hobbs, 1998; Sarnat et al., 2008; Zauscher et al., 2013). Reid et al. (2005) and Zauscher et al. (2013) found that the diameter and chemical compositions of wood fire particles changed rapidly during the daytime. Li (2003) observed that KCl in biomass burning aerosol partly transformed to K2SO4 and KNO3 upon photochemical aging. Grieshop et al. (2009) and Hennigan et al. (2011) found that photochemical oxidation increased organic aerosol emissions from wood fires. Giordano and Asa-Awuku, 2014, Giordano et al. (2013) examined the hygro-scopicity and surface tension properties as a function of photochemical aging of biomass burning aerosol. Engelhart et al. (2012) measured hygroscopicity of different smoke particles and found the cloud condensation nuclei (CCN) activity was impaired by photo-oxidation.

These studies suggest that there can be significant physico-chemical changes associated with the photochemical aging of fresh smoke particles. However, field burning is now happening mostly at night in China to circumvent recent rigorous policy that forbids agricultural fires. Since there is no sunlight during the night and the fluctuation of nighttime relative humidity (RH) is great, it is desirable to study the evolution of smoke particles in the absence of light and as a function of RH. Water vapor may amplify the environmental effects of smoke particles in haze formation and the deterioration of visibility (Shi et al., 2014; Jung et al., 2009), and it may also change the micro-structure and physiochemical properties of aerosol during aging and transport (Rubasinghege and Vicki, 2013; Cocker Iii et al., 2001; Lewis et al., 2009). Nevertheless, the dynamic influence of humidity on the evolution of smoke particles is still undefined in the ambient environment, which limits our overall understanding of the atmospheric process and ultimate impacts of smoke particles.

Here we report the results of an aerosol chamber simulation that aimed to characterize the evolution of some physiochemical properties and chemical composition of agricultural fire smoke particles exposed to different humidities in the dark, and the relationship between the chemical evolution and thermodynamic property changes were also explored. Wheat straw, accounting for about 25% of total agricultural waste yield in China, was chosen as representative of crop residues. This work adds to the knowledge base regarding atmospheric chemistry and climate change effects related to biomass burning.

2. Experimental section

2.1. Setup and general procedures

The effects of relative humidity on smoke particle aging in the dark were investigated by injecting emissions from wheat straw burnings into an aerosol chamber. The experimental system is located in a temperature/RH-controlled room, and includes a specially designed combustion stove, a stainless-steel aerosol chamber (4.504 m3 in volume and 0.3 mm Teflon inner coat), and sampling instruments (Fig. S1 in the Supporting Information: SI). Detailed descriptions can be found elsewhere (Zhang et al., 2011). Before each test, the chamber was scrubbed using aqueous ethanol (50%v/v), flushed with HEPA-filtrated air, and subjected to oxidation by high-concentration ozone (~3 ppm) for 12 h. After cleaning, the chamber was humidified and evacuated to a predefined RH (25%-95% RH) and pressure (90 kPa). Smoke particles formed from

the burning of 2.0 g wheat straw (dehydrated at 100 °C for 24 h) in the sealed combustion stove were introduced to the chamber using a particle-free air supply; this introduction took less than 5 min until the chamber reached ambient pressure, at which time the chamber was sealed and the experiment began. Magnetic fans fixed at the bottom of the chamber ensured mixing of the chamber contents. The evolution of the smoke particles' properties was monitored for 4 h, during which time the chamber temperature and RH were also tracked using a hygroclip monitor (model IM-4, Rotronic). During each test, the chamber RH fluctuated within 5%, and the temperature was around 20 ° C. Experiments were conducted at different humidities, classified into 4 humidity groups (25%-35%, 45%-55%, 65%-75%, 85%-95% RH). At least 4 tests were repeated in each group.

2.2. Modified combustion efficiency (MCE)

To distinguish between flaming and smoldering combustion conditions and to ensure repeatability of agri-fire simulations, the modified combustion efficiency (MCE) of the burning processes was deduced by measuring CO and CO2 concentrations using GC (Model 930, Shanghai Hai Xin Gas Chromatograph Co., LTD) (Reid et al., 2005). More details are given in SI. The MCE was 0.91 ± 0.03 for all tests, indicating that the flaming phase dominated in these experiments, comparable to the result from field burning campaigns (Li et al., 2007).

2.3. Particle concentrations measurement

Particle concentrations were continuously monitored during the 4 h aging. The particle size, number, and volume were directly measured using a combination of wide-range particle spectrometer (WPS, Model 1000XP, TSI Inc., USA) and aerodynamic particle sizer (APS, Model 3321, TSI, Inc., USA). The WPS was operated in a scanning mobility spectrometer (SMS) mode (PMo.5 cutoff - short DMA - CPC, 0.3 L/min, 3 min/loop, 48 channels) covering electrical mobility diameters from 10 to 500 nm; The APS detects particles possessing aerodynamic diameters from 0.5 to 20 mm (1.0 L/min, 3 min/loop, 51 channels). These size distributions were merged by assuming an effective density of spherical smoke particle to be 1.30 g/cm3 (see SI). PM2.5 and PM1.0 volume concentrations were transformed from particle number concentrations. The WPS and APS were calibrated with polystyrene latex sphere particles (PSLs, 0.04, 0.08, 0.22, 0.70, and 1.50 mm, 1.05 g/cm3, Duke Scientific, USA) generated from an atomizer (Model 3076, TSI Inc., USA) before the test.

Real-time smoke particle dry mass concentrations of PM2.5 and PM1.0 were measured using two aerosol monitors (AM510, Side-pak™ Personal Aerosol Monitor, Model 510, TSI, Inc., USA, 1.6 L/min, resolution of 1 mg/m3) with 2.5 and 1.0 mm impact cutoff kits. A dilutor system (dilution ratio 5:1) was applied in front of AM510 to ensure the real mass concentration fell into the detection range (0-20 mg/m3). Zero calibration was conducted before each test, and a humidity effect calibration was made prior to the experiments (Figs. S2-S3, SI).

2.4. Effective particle density measurement

Effective density of smoke particle in dry mobility diameter of 100-400 nm is characterized using a home-fabricated TDMA-APM system which has been described in detail previously (Hu et al., 2011; Zhang et al., 2011). An aerosol mass analyzer (APM, Model 3601, Kanomax Inc.) is combined to classify aerosol particles according to the mass-to-charge ratio. Briefly, smoke particles from the chamber are charged using radiation from a 210Po source, and

subsequently dehydrated by passage through a diffusion dyer and nafion tube, then size-selected by the first DMA (DMA1, Model 3081, TSI Inc.). Afterward, the monodisperse particles are introduced into the APM and a condensation particle counter (CPC, Model 3771, TSI Inc.). Finally, mass distributions are obtained by voltage scanning and particle counting. The effective density can be calculated by the following equation:

_mi_ Pi = Vi =

where pi and mi are size-specific density and mass of particle at mobility diameter Di.

Before measurement, TDMA-APM system was calibrated using PSL (40, 80, and 220 nm, 1.05 g/m3, Duke Scientific) and (NH4)2SO4 particles (1.77 g/m3) produced from an atomizer (Model 3076, TSI, Inc.) purged with high purity N2. The average effective densities of PSL and (NH4)2SO4 particle were measured as 1.07 ± 0.01 and 1.73 ± 0.03 g/m3, which are in good agreement with material densities.

2.5. Hygroscopicity measurement

Measurement of smoke particle hygroscopicity is performed using an H-TDMA system that measures the new size distribution of individual particles after hygroscopic growth. Briefly, dehydrated and charged particles are first selected by DMA1, then introduced into the humidifier (multitube Nafion humidifier, Model PD-70T-24ss, Perma Pure Inc.) at a defined RH to grow to a new diameter when water vapor condenses or is absorbed; the new size distribution is measured by DMA2. The hygroscopic growth factor (GF) of a particle is defined as the ratio of the humidified diameter at a certain RH to the initial dried diameter:

GFi(RH)=DH

where Di is initial dried particle diameter selected from DMA1, DHi is new diameter of humidified particle.

In this study, approximately 2.5 s of exposure is allowed in the humidifier, which is sufficient to obtain hygroscopic equilibrium. The functioning of the DMAs is checked with PSL (40, 80, and 220 nm, Duke Scientific) periodically, and the accuracy of RH control is calibrated by measurement the deliquescence RH (DRH) of (NH4)2SO4 particles prior the test.

2.6. Filter sampling ofPM10 and single particle analysis

PM1.0 was collected from the chamber by a high-volume sampler with a 90 mm nucleopore quartz filter (Tissuquartz, Pall Corp., USA) at 100 L/min (HY-100, Qingdao Hengyuan S.T. Development Co., Ltd, China). During the 4 h evolution of smoke aerosol,

5 samples in one series were collected at the initial time and every hour thereafter; it took 5 min to complete each sampling. In total, 6 series of filters (30 sampling filters and 4 blank filters) were collected (3 series of samples in 45%—50% RH, and 3 series of samples in 75%—80% RH). The filters were prebaked at 450 °C for

6 h to remove organic contaminants before usage. They were conditioned for 24 h (40% RH, 22 °C) before and after sampling, and weighed on a microbalance (Sartorius BP211D, resolution of 10 mg). OC (organic carbon) and EC (elemental carbon) were determined by a thermal-optical aerosol analyzer (Sunset Laboratory Inc., Forest Grove, OR) based on the thermal-optical transmittance (TOT) method with a modified N10SH-5040 (National Institute of Occupational Safety and Health) protocol. Water-soluble organic acids

and ionic species including CH3COOH, MSA, C2H2O4, HCOOH, Cl", SO42", NO3", K+, NH4+, Mg2+, and Ca2+ were quantified by an ion chromatograph (1C, model: 850 Professional 1C; UMetrohm, USA) equipped with separation columns (Metrosep A Supp 7 250/4.0 for anion and organic acids, Metrosep C4 150/4.0 for cations, see S1). The quality of the data was confirmed by standard calibration, recovery and operational blank correction.

To obtain the morphology and mixing state changes of individual particles, fresh and 4 h aged smoke particles at 45%—55% RH were collected onto the 300-mesh copper grids coated with carbon films using a single-stage cascade impactor with a 0.5 mm diameter jet nozzle at a flow rate of 1.0 L/min, then individual particle was imaged using JEOL—2100F transmission electron microscope (TEM). More information about the sampler can be found elsewhere (Fu et al., 2012).

3. Result and discussion

3.1. Evolution of smoke particle size

Fig. 1 displays representative results of the time evolution of the smoke particle distributions measured at 50% RH over the 4-h experiment. Smoke particles from wheat straw burning exhibit a unimodal number size distribution (Fig. 1a), with a peak near 110 nm diameter, and a bimodal volume size distribution (Fig. 1b), with peaks at 0.2—0.3 mm and at 5—7 mm diameter. Although the volumes (assuming spherical particles) of the two modes are similar, there are only a few of the larger diameter particles per cm3 of air, compared to the initial concentration of ~106/cm3 of those in the accumulation mode.

Over the course of the experiment, a pronounced increase of particle size is observed, in line with previous lab research and ambient investigation (Reid and Hobbs, 1998; Zhang et al., 2011). As shown in Fig. 1a, the median diameter of accumulation mode particles grows by almost 100 nm over the 4 h of aging, and the breadth of the distribution shrinks considerably. At the same time as the growth in size, Fig. 1c shows a distinct decrease in both the particle mass and volume concentrations over the time of the experiment. The decrease of PM1.0/PM2.5 ratios from -0.9 to -0.7 indicates a more rapid change in PM10 concentrations (Fig. 1d). These size and concentration changes must be the result of some combination of microstructure changes, particulate coagulation, and gas—particle interactions, including condensation, evaporation, or heterogeneous reactions (Seinfeld and Pandis, 2012). Size-dependent particle loss on the chamber walls will give rise to overall loss of concentration, and may also alter the shape of the particle size distribution (Crump et al., 1982).

According to the hygroscopicity analysis presented in Fig. S4, hygroscopic growth of smoke particles at 50% RH can be neglected. Therefore, the density of PM1.0 and PM2.5 can be directly derived from mass and volume concentrations. As illustrated in Fig. 1d, PM densities increase from 1.18 to 1.45 g/cm3, and PM1.0 density is slight larger and increases more rapidly than that of PM2.5.

Fig. 2 summarizes the growth in size of smoke particles in the chamber under four RH groups: 25%—35% RH (RH1), 45%—55% RH (RH2), 65%—75% RH (RH3), and 85%—95% RH (RH4). The initial geometric mean diameter (GMDN) values in the four RH ranges are 112 ± 3,117 ± 5,128 ± 5, and 166 ± 8 nm, respectively. Note that average diameter at t = 0 varies with RH, because it takes about 5 min to introduce the exhaust into the chamber before size distribution monitoring, which is sufficiently long for smoke particles to reach hygroscopic growth equilibrium. For all RH values, the growth of particle size as a function time is well fitted by an exponential growth rate, with a time constant to (see SI for more details). The inset to Fig. 2 illustrates that t0 shows a linear negative

120 180 240

Elapsed time (min)

60 120 180 240

Elapsed time (min)

60 120 1S0

Elapsed time (min)

(d) PM10/PM2 5 mass ratio

PMi Q/PM2 5 volume ratio

Q- 0.6 ■

PMj q density PMj 5 density

120 180 Elapsed time (min)

Fig. 1. Time profiles of: a) smoke particle number size distribution; b) smoke particle volume size distribution; c) PMi0 and PM25 dry mass and volume concentrations; d) PM density and PM10/PM2 5 ratio in mass and volume [chamber environment: 50 ± 2% RH, 19.7 ± 1.5 °C|.

Fig. 2. Time series of smoke particle geometric mean diameter (GMD in term of number concentration as GMDN) under different humidity conditions during aging in aerosol chamber; exponential decay fitting of GMD changes is performed, the fitting function isy = y0 - Ae The inset graph shows trends of r0 as a function of RH.

correlation with RH, suggesting that water vapor accelerates the particle growth, perhaps by boosting the coagulation or gas-to-particle processes. The index to is used to indicate particle growth rate, the more rapid size growth is in response to smaller t0 (SI). Here t0 is lineally negative correlated with RH in Fig. 2, confirming that water vapor accelerates the particle growth by

boosting the coagulation or gas-to-particle process.

The positive shift of GMD (GMDV in Fig. S5, SI) as a function of RH is consistent with hygroscopic growth of smoke particles; the GMDn increases with an increase in RH, noticeably at RH >65%. As illustrated in Fig. S4, smoke particles are weakly hygroscopic below 60% RH, where growth factors (GFs) are less than 1.05. With an increase of RH, GFs increase to 1.09 and then 1.28 at 70% and 80% RH respectively, and no deliquescence transition is observed. Taking GMDn at RH1 (t = 0) as the initial dry particle diameter, the ratios of GMDn at RH2, RH3, and RH4 divided by GMDN at RH1 are 1.04,1.14, and 1.48, respectively, all larger than the corresponding GFs. This observation also indicates that particulate coagulation or gas-to-particle transformation processes are facilitated by water vapor.

Smoke particle concentration changes should be an integration of wall loss and other mass transfer process. To simplify analysis, first-order loss functions were used to fit the decrease of smoke particle mass and volume concentrations with time (Fig. S6, see SI for details). As discussed in the Supporting Information, the calculated first-order loss coefficient depends on the particle size and also varies lineally with RH, indicating that moisture accelerates the attenuation of smoke particles. Since the wall loss rate is not merely a function of particle size (Crump et al., 1982), but is affected by the degree of polydispersity (Park et al., 2001), poly-dispersed BC particles with size distributions similar to those of smoke particles were used to test the particle wall loss. The results are given in Fig. S10 and Table S1.

3.2. Evolution of smoke particle density associated with RH effect

The evolution of size-resolved (100-400 nm) smoke particle density at 50%-55% and 90%-95% RH was characterized using a

differential mobility diameter analyzer-aerosol particle mass analyzer-condensed particle counter system (DMA-APM-CPC). As shown in Fig. 3, and consistent with Fig. 1d, particle densities increase during aging in the chamber; moreover, there is a significant size- and RH- dependence to the increase. At relative dry condition shown in Fig. 3a, particle densities show a wide, size-dependent range, from 1.23 to 1.27 g/cm3, and undergo rapid changes within the initial 120 min, increasing to 1.31—1.34 g/cm3 before become stable. Under the high humidity conditions illustrated in Fig. 3b, the size-resolved densities start at larger values, show a smaller range and increase by a smaller fractional amount in the first 60 min. The size-dependence of particle densities may result from differences in chemical composition, mixing state, and morphology, but the effect of size is clearly phased out with aging. According to the hygroscopic growth in Fig. S4, an interfacial aqueous phase should form at RH over 90%—95%, which may enhance the coagulation of particles and uptake of trace gases, facilitate heterogeneous reaction, or promote gas-to-particle conversion, any of which may give larger density values to the particles. As well, since, biomass burning particles contain highly agglomerated structures, humidification may collapse their fractal structure; the dehydration of these deliquesced particles by a Nafion tube prior to density measurement may further make the particles more compact, yielding a larger density (Lewis et al., 2009; Zhang et al., 2008).

Ensemble average densities of the smoke over the 4 h experiment are displayed in Fig. 3c. Considering the uncertainty in measurements, the density changes observed at 50%—55% RH via DMA-APM-CPC measurements are similar to derived from the mass and volume concentrations in Fig. 1d, verifying that it is reasonable to apply 1.30 g/cm3 as the effective density for APS data processing.

3.3. Evolution of smoke particle PM10 chemical composition

Samples of PM10 evolved at 45%—50% RH and 75%—80% RH were collected, and PM1.0 mass concentrations and chemical compositions were analyzed. The results are displayed in Fig. 4 (details in S1, Fig. S11). Emission factors of particulate chemical species in fresh smoke PM1.0 were also estimated for all samples and presented in Table 1. As the mass loading of the filter sample was determined after conditioning at 40% RH for 24 h, adsorbed water will evaporate to reach a new equilibrium, and so the calculated PM1.0 mass concentration can be compared with that from AM510 measurements. The results are in good agreement, as is clear by comparing Fig. 4a and c.

Overall, about 61% of the mass of smoke PM10 is composed of organic matter (OM) and approximately 11% is elemental carbon (EC). The OM amount is derived from OC by multiplying a factor to account for the hydrogen, oxygen, and other minor species in or-ganics. 1n the literature, OM/OC ratio in smoke particles is highly uncertain, ranging from 1.2 to 1.8, which would vary with source,

aging level of aerosol, and burning conditions (Turpin and Lim, 2001; Reid et al., 2005). By applying a factor of 1.3 in our study, well "closed" material balances are reached. A significant fraction of the organic compounds identified in the PM1.0 smoke particles are water soluble, including organic acids listed in Table 1 (-2.3 wt.% of PM1.0) The fresh particles also contain a considerable fraction of water-soluble inorganic salts (-23.8 wt.% of PM1.0), which has implications for their the hygroscopic properties and CCN activities (Gunthe et al., 2009; Martins et al., 2009).

The inorganic fractions of PM10 increase by 1—2 wt.% within 4 h evolution, and the aged smoke particles show a significant enrichment of sulfate and nitrate species associated with secondary aerosol production. Fig. 4b and d display the time evolution of the mass fractions of inorganic species under low and high RH conditions. The mass ratio of the inert species K+ and EC in smoke PM1.0 is about 0.69, and does not change over the experiment time; these species can then be used to track changes in other components with aging. The corresponded diagnostic ratios of fresh smoke PM1.0 are summarized in Table 2, which are comparable to the values in many documents. K+/EC ratio should be a more practical parameter to distinguish the pyrogenic pollutants and make source apportionment of biomass burning emissions than K+, levoglucose, or K+/OC, as K+ has additional significant sources such as sea-salts, fertilizers, and mineral dust, while levoglucose and OC represent large uncertainties arising from burning conditions and extend of photooxidation (Gao, 2003; Grieshop et al., 2009; Rastogi et al., 2014).

The stable OC/EC ratio of 4.1 implies a weak gas-to-particle partitioning of carbonaceous gases to the particle phase in the chamber. By contrast, the ratio of Cl"/K+ decreases from 1.42 to 1.01 during evolution, which implies a significant chloride loss from the particles during aging in the dark chamber. Chloride loss may result from decomposition of chlorine salts and/or heterogeneous reactions involving the formation of sulfate and nitrate, as both SO42"/EC and NO3"/SO42" increase, consistent with the results from previous studies of biomass burning in Africa (Li, 2003; Hobbs et al., 2003). Fig. 5 compares the loss of chloride to the gain in charge equivalent of (nitrate + sulfate) under low and high RH conditions (see S1 for details). The close balance between these two suggests that displacement reactions are at least partly responsible for the chloride loss. Conversion of SO2 to particulate sulfate could occur through heterogeneous reactions of SO2, or via aqueous phase oxidation within the particles. Nitrate may form from the oxidation of nitrogen oxides and the subsequent reaction with ammonia through homogenous and heterogeneous reactions (Lelieveld and Heintzenberg, 1992; Kong et al., 2014; Seinfeld and Pandis, 2012). An increase of the NH4+/EC ratio from -0.17 to -0.22 in PM1.0 suggests that gaseous NH3 is also deposited to the particles and is thus available to participate in the reaction. 1nter-estingly, gas-to-particle conversion measured in the chamber

Fig. 3. Time series of a) and b) size-classified smoke particle densities at 50%-55% RH and 90%-95% RH; c) size affected none particle densities at two distinct humidity conditions. 100 nm smoke particles decreased below detection limit, resulting in data missing beyond 30 min evolution.

Fig. 4. Chemical evolutions of smoke PM10 under 45%-50% RH and 75%-80% RH. OM (particulate organic matter) equal to 1.3 fold of OC; others in the graph label mean sum of Na+, Mg2+, and Ca2+. Rc/a is the equivalent charge ratio of total water soluble inorganic cations (Na+, K+, NH4+, Mg2+, Ca2+) to anions (Cl-, SO42 , NO3 ). The rest chemical ratios are mass concentration ratios. RIn/Or is mass ratio of water soluble inorganic salts to organics (OM + EC). Detailed pie-graphs can be found in Fig. S11.

occurs to a lesser extent than that observed in field measurements, which have shown an increase by 3-5 times of particulate inorganic species in aged smoke (Hobbs et al., 2003).

From linear fitting analysis of the chemical compound ratio changes presented in Table S2, moisture obviously accelerates chloride loss and sulfate, nitrate and ammonium gain in the smoke particles. At RH values above 75%, an aqueous phase may form on the smoke particles (as suggested by Fig. S4), whereas below 50% RH, the interface can be considered as solid. At the higher RH, the presence of liquid (or liquid-like) water at the smoke particle surface is expected to facilitate heterogeneous reaction by increasing the uptake of precursor gases and by activating or catalyzing chemical transformations (Rubasinghege and Vicki, 2013). The conversion of chlorides (including sea salts and alkali metal salts) into the corresponding sulfate and nitrate has been demonstrated to occur at interfaces (Gibson et al., 2006; Laskin, 2003). Similar reaction mechanisms are probably involved in the conversion of

chloride in smoke particles. As no light is introduced in the chamber, photochemical oxidation can be excluded, and the proposed reaction mechanisms are shown in the SI.

Further changes in the particles are observed in TEM and EDX images taken of fresh and 4-h aged smoke particles. As shown in Fig. 6, amorphous carbonaceous particles are internally mixed with inorganic salts in fresh samples. In agreement with the chemical analysis discussed above, the salts associated with the smoke particles include KCl, as well as K- and Ca-sulfate and nitrate, mostly coated by organics or coagulated in a "tar ball". Overall, the morphology of the fresh smoke particles is smoother than the fractal microstructure of soot displayed in Fig. S8. After four hours of aging in the chamber, the irregular particles and KCl crystals evolve into coated spheres or collapsed aggregates. More K2SO4 and KNO3 particles can be seen, consistent with the heterogeneous displacement of chloride by sulfate and nitrate.

Elapsed time (h)

Elapsed time (h)

Fig. 5. Time series of equivalent molar charge ratio changes: a) smoke particles evolved at 45%~50% RH; b) smoke particles evolved at 75%—80% RH.

Table 1

Emission factors of particulate species in fresh smoke PM10 from wheat straw burning.

Chemical species (g/kg) Wheat straw

PM1.0 5.46 ± 0.38

OC 2.59 ± 0.14

EC 0.66 ± 0.04

Inorganic ions (g/kg) 1.39 ± 0.11

SO42 0.12 ± 0.06

cr 0.57 ± 0.03

NO3 0.03 ± 0.01

no2 0.01 ± 0.01

nh4+ 0.12 ± 0.03

K+ 0.46 ± 0.08

Ca2+ + Na+ + Mg2+ 0.05 ± 0.01

Organic Acids (mg/kg) 124.31 ± 25.17

CH3COOH 115.79 1 ± 21.94

MSA 6.83 ± 2.03

H2C2O4 1.69 ± 1.20

HCOOH ND

Note: fresh particles from wheat straw burning; emission factor means emitted the amount of species produced from 1 kg wheat straw burning; ND means not detected.

3.4. Relating chemical evolution to changes in particle properties

The results above may be summarized thus: Fresh smoke particles contain about 25—30% by mass of inorganic ions, with the balance comprising organic and elemental carbonaceous material. These particles are hygroscopic, displaying an ensemble growth factor of 1.28 at 80% RH. During the 4-h processing time in the dark,

fresh wheat smoke particles increase both their size and density, in conjunction with the displacement of chloride by nitrate and sulfate and the uptake of ammonia. These effects are stronger at higher relative humidity, consistent with the presence of an aqueous coating on the particles at RH > ~75—80%.

The time evolution of chemical composition may partly explain the observed density increase of smoke particles. The effective density of smoke PM1.0 was estimated with weighted density and proportion of individual chemical species (Fig. S12b, SI). By comparing density profiles in Fig. S12b and Fig. 4c, it is clear that the observed density increase of smoke particles could be simply a consequence of chemical changes, at least at relative dry conditions (45% < RH < 55%). In a more humid environment (RH > 90%), morphology changes from evolution in the chamber and dehydration at measurement should also contribute to any observed particle density increase.

CCN activity of atmospheric particles is not only size-dependent, chemical composition also plays vital role (Dusek et al., 2006). The hygroscopicity parameter was proposed to quantitatively measure water uptake characteristics of smoke particles (Ye et al., 2013; Petters et al., 2009). To assess CCN activity of smoke particles during aging in the dark chamber we calculated for various samples. We measure that fresh smoke particles have a of 0.27 (less-hygroscopic); the of the bulk carbonaceous materials (OM + EC, OE) in smoke particles is estimated to be 0.16 ± 0.07, implying a strong role of organic species in CCN activity of smoke particles (Novakov and Corrigan, 1996). These values are consistent with that from ambient investigations (Rose et al., 2011). Assuming that dark aging does not alter the hygroscopicity of OE, as chloride is displaced by nitrate and sulfate, the of smoke particles should change to reflect the contribution to the total hygroscopicity by these species.

Table 2

Comparison of present measurements with earlier results. Values are expressed as mean.

Samples MCE OC/EC K+/EC Cr/EC SO42 /EC NO3 /EC nh4+/ec Reference

Chamber, AR (dry), PMj.0 (fresh) 0.91 4.08 0.69 1.37 0.15 0.24 0.17 This work

Field, BB, PM3.0 NA 4.30 0.60 0.56 0.64 0.22 0.93 Andreae et al. (1998)

Field, AR, PM2.5 0.91 5.03 1.30 1.80 0.20 0.03 0.48 Li et al. (2007)

Field, BB, TSP NA 8.47 0.62 0.25 3.33 3.51 1.66 Rengarajan et al. (2007)

Field, BB, PM2.5 NA 9.06 0.56 0.29 0.25 0.21 NA Akagiet al. (2011)

Field, BB, TSP NA 8.70 0.52 NA 2.10 0.54 0.53 Rastogi et al. (2014)

Chamber, AR (dry), PM2.5 0.95 4.83 NA 1.45 0.13 0.03 0.40 Hayashi et al. (2014)

Chamber, AR, PM2.5 NA 2.35 0.22 0.76 0.08 0.16 0.20 Sen et al. (2014)

Field, AR, PM2.5 NA 5.86 NA NA 2.88 1.63 NA Bisht et al. (2015)

AR = Agricultural Residues; BB = Biomass burning; NA = Not available.

We estimate the change in kH by forming a weighted sum of the time-dependent contributions from the changing inorganic species in the smoke particle (see SI for details). The kH was estimated to decrease from 0.27 to 0.26 in 4 h aging (Fig. S13 b), indicating that the changes in smoke particle CCN activation are not only driven by photochemical aging of SOA (secondary organic aerosol) production, but also dark aging from S1A (secondary inorganic aerosol) formation (Engelhart et al., 2012; Giordano et al., 2013).

4. Conclusion and atmospheric implication

The nighttime evolution of agricultural waste burning particles, under different conditions of RH, was investigated using an aerosol chamber. The results imply that nocturnal field burning emissions are subject to rapid and extensive physiochemical processes in the atmosphere.

As great contributor to atmospheric pollution, smoke particles will inevitably be involved in nighttime atmospheric processes and make them more complicated. Dynamic changes in particle density and size may increase pollutant mass loadings, while heterogeneous reactions facilitated by high relative humidities may produce more water-soluble inorganic salts including sulfate and nitrate, with consequent potential effects on haze and fog formation and on human health. The transformation of chloride in smoke particles to sulfate and nitrate may alter nighttime behaviors of gaseous pollutants and the environmental effects of emitted smoke particles. In particular, if the chloride is released as photochemically reactive Cl2 or ClNO2, this could have a large impact on early morning oxidation chemistry in the smoke plume (Young et al., 2012; Riedel et al., 2014). Although physiochemical changes of smoke particles are less significant than those upon photo-oxidation, nighttime aging will endow smoke particle properties that are distinctly different from fresh ones before secondary diurnal aging.

Acknowledgment

This work is financially supported by the National Natural Science Foundation of China (Nos. 21077025, 21527814), Cyrus Tang Foundation (No. CTF-FD2014001), Shanghai Science and Technology Commission of Shanghai Municipality (Nos. 13XD1400700, 12DJ1400100), Ministry of Science and Technology of China (2014BAC22B01), Priority Fields for Ph.D. Programs Foundation of Ministry of Education of China (No. 20110071130003), Strategic

Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB05010200) and FP7 (P1RSES-GA-2011).

Appendix A. Supporting information

Supporting information related to this article can be found at http://dx.doi.org/10.1016/j.atmosenv.2015.09.003.

References

Akagi, S.K., Yokelson, R.J., Wiedinmyer, C., Alvarado, M.J., Reid, J.S., Karl, T., Crounse, J.D., et al., 2011. Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys. 9, 4039—4072.

Andreae, M.O., Merlet, P., 2001. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 4, 955—966.

Andreae, M.O., Andreae, T.W., Annegarn, H., Beer, J., Cachier, H., le Canut, P., Elbert, W., et al., 1998. Airborne studies of aerosol emissions from savanna fires in southern Africa: 2. Aerosol chemical composition. J. Geophys. Res. D24, 32119—32128.

Bisht, D.S., Dumka, U.C., Kaskaoutis, D.G., Pipal, A.S., Srivastava, A.K., Soni, V.K., Attri, S.D., et al., 2015. Carbonaceous aerosols and pollutants over Delhi urban environment: temporal evolution, source apportionment and radiative forcing. Sci. Total Environ. 431—445.

Bolling, A.K., Pagels, J., Yttri, K.E., Barregard, L., Sallsten, G., Schwarze, P.E., Boman, C., 2009. Health effects of residential wood smoke particles: the importance of combustion conditions and physicochemical particle properties. Part Fibre Toxicol. 29, 20.

Cao, G., Zhang, X., Gong, S., An, X., Wang, Y., 2011. Emission inventories of primary particles and pollutant gases for China. Chin. Sci. Bull. 8, 781 —788.

Cocker 1ii, D.R., Clegg, S.L., Flagan, R.C., Seinfeld, J.H., 2001. The effect of water on gas—particle partitioning of secondary organic aerosol. Part 1: a-pinene/ozone system,. Atmos. Environ. 35, 6049—6072.

Crump, J.G., Flagan, R.C., Seinfeld, J.H., 1982. Particle wall loss rates in vessels. Aerosol Sci. Tech. 3, 303—309.

Dusek, U., Frank, G.P., Hildebrandt, L., Curtius, J., Schneider, J., Walter, S., Chand, D., et al., 2006. Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science 5778,1375—1378.

Engelhart, G.J., Hennigan, C.J., Miracolo, M.A., Robinson, A.L., Pandis, S.N., 2012. Cloud condensation nuclei activity of fresh primary and aged biomass burning aerosol. Atmos. Chem. Phys. 15, 7285—7293.

Fu, H., Zhang, M., Li, W., Chen, J., Wang, L., Quan, X., Wang, W., 2012. Morphology, composition and mixing state of individual carbonaceous aerosol in urban Shanghai. Atmos. Chem. Phys. 2, 693—707.

Gao, S., 2003. Water-soluble organic components in aerosols associated with savanna fires in southern Africa: identification, evolution, and distribution. J. Geophys. Res. D13.

Gibson, E.R., Hudson, P.K., Grassian, V.H., 2006. Physicochemical properties of nitrate aerosols: implications for the atmosphere. J. Phys. Chem. A 42, 11785—11799.

Giordano, M.R., Asa-Awuku, A., 2014. Rebuttal to correspondence on "changes in droplet surface tension affect the observed hygroscopicity of photochemically aged biomass burning aerosol". Environ. Sci. Technol. 3, 2084—2085.

Giordano, M.R., Short, D.Z., Hosseini, S., Lichtenberg, W., Asa-Awuku, A.A., 2013.

Changes in droplet surface tension affect the observed hygroscopicity of photochemically aged biomass burning aerosol. Environ. Sci. Technol. 19, 10980-10986.

Grieshop, A.P., Logue, J.M., Donahue, N.M., Robinson, A.L., 2009. Laboratory investigation of photochemical oxidation of organic aerosol from wood fires 1: measurement and simulation of organic aerosol evolution. Atmos. Chem. Phys. 4,1263-1277.

Gunthe, S.S., King, S.M., Rose, D., Chen, Q., Roldin, P., Farmer, D.K., Jimenez, J.L., et al., 2009. Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity. Atmos. Chem. Phys. 9, 7551-7575.

Hayashi, K., Ono, K., Kajiura, M., Sudo, S., Yonemura, S., Fushimi, A., Saitoh, K., et al., 2014. Trace gas and particle emissions from open burning of three cereal crop residues: increase in residue moistness enhances emissions of carbon monoxide, methane, and particulate organic carbon. Atmos. Environ. 36-44.

Hays, M.D., Fine, P.M., Geron, C.D., Kleeman, M.J., Gullett, B.K., 2005. Open burning of agricultural biomass: physical and chemical properties of particle-phase emissions. Atmos. Environ. 36, 6747-6764.

Hennigan, C.J., Miracolo, M.A., Engelhart, G.J., May, A.A., Presto, A.A., Lee, T., Sullivan, A.P., et al., 2011. Chemical and physical transformations of organic aerosol from the photo-oxidation of open biomass burning emissions in an environmental chamber. Atmos. Chem. Phys. 15, 7669-7686.

Hobbs, P.V., Sinha, P., Yokelson, R.J., Christian, T.J., Blake, D.R., Gao, S., Kirchstetter, T.W., et al., 2003. Evolution of gases and particles from a savanna fire in South Africa. J. Geophys. Res. Atmos. (1984-2012) D13.

Hu, D., Chen, J., Ye, X., Li, L., Yang, X., 2011. Hygroscopicity and evaporation of ammonium chloride and ammonium nitrate: relative humidity and size effects on the growth factor. Atmos. Environ. 14, 2349-2355.

Jung, J., Lee, H., Kim, Y.J., Liu, X., Zhang, Y., Gu, J., Fan, S., 2009. Aerosol chemistry and the effect of aerosol water content on visibility impairment and radiative forcing in Guangzhou during the 2006 Pearl River Delta campaign. J. Environ. Manag. 11, 3231-3244.

Kennedy, I.M., 2007. The health effects of combustion-generated aerosols. Proc. Combust. Inst. 2, 2757-2770.

Kong, L., Yang, Y., Zhang, S., Zhao, X., Du, H., Fu, H., Zhang, S., et al., 2014. Observations of linear dependence between sulfate and nitrate in atmospheric particles. J. Geophys. Res. Atmos. 1, 341-361.

Laskin, A., 2003. Reactions at interfaces as a source of sulfate formation in sea-salt particles. Science 5631, 340-344.

Lelieveld, J., Heintzenberg, J., 1992. Sulfate cooling effect on climate through in-cloud oxidation of anthropogenic SO2. Science 5079,117-120.

Lewis, K.A., Arnott, W.P., Moosmüller, H., Chakrabarty, R.K., Carrico, C.M., Kreidenweis, S.M., Day, D.E., et al., 2009. Reduction in biomass burning aerosol light absorption upon humidification: roles of inorganically-induced hygro-scopicity, particle collapse, and photoacoustic heat and mass transfer. Atmos. Chem. Phys. 22, 8949-8966.

Li, J., 2003. Individual aerosol particles from biomass burning in southern Africa: 2, compositions and aging of inorganic particles. J. Geophys. Res. D13.

Li, X., Wang, S., Duan, L., Hao, J., Li, C., Chen, Y., Yang, L., 2007. Particulate and trace gas emissions from open burning of wheat straw and corn stover in China. Environ. Sci. Technol. 17, 6052-6058.

Lin, P., Engling, G., Yu, J.Z., 2010. Humic-like substances in fresh emissions of rice straw burning and in ambient aerosols in the Pearl River Delta Region, China. Atmos. Chem. Phys. 14, 6487-6500.

Martins, J.A., Gon^alves, F.L.T., Morales, C.A., Fisch, G.F., Pinheiro, F.G.M., Leal Júnior, J.B.V., Oliveira, C.J., et al., 2009. Cloud condensation nuclei from biomass burning during the Amazonian dry-to-wet transition season. Meteorol. Atmos. Phys. 1-2, 83-93.

Mukherjee, B., Dutta, A., Roychoudhury, S., Ray, M.R., 2013. Chronic inhalation of biomass smoke is associated with DNA damage in airway cells: involvement of particulate pollutants and benzene. J. Appl. Toxicol. 4, 281 -289.

Novakov, T., Corrigan, C.E., 1996. Cloud condensation nucleus activity of the organic component of biomass smoke particles. Geophys. Res. Lett. 16, 2141-2144.

Park, S.H., Kim, H.O., Han, Y.T., Kwon, S.B., Lee, K.W., 2001. Wall loss rate of poly-dispersed aerosols. Aerosol Sci. Technol. 3, 710-717.

Petters, M.D., Carrico, C.M., Kreidenweis, S.M., Prenni, A.J., DeMott, P.J., Collett, J.L., Moosmueller, H., 2009. Cloud condensation nucleation activity of biomass burning aerosol. J. Geophys. Res. Atmos. (1984—2012) D22.

Rastogi, N., Singh, A., Singh, D., Sarin, M.M., 2014. Chemical characteristics of PM2.5 at a source region of biomass burning emissions: evidence for secondary aerosol formation. Environ. Pollut. 563—569.

Reid, J.S., Hobbs, P.V., 1998. Physical and optical properties of young smoke from individual biomass fires in Brazil. J. Geophys. Res. Atmos. (1984—2012) D24, 32013—32030.

Reid, J.S., Koppmann, R., Eck, T.F., Eleuterio, D.P., 2005. A review of biomass burning emissions part 11: intensive physical properties of biomass burning particles,. Atmos. Chem. Phys. 3, 799—825.

Rengarajan, R., Sarin, M.M., Sudheer, A.K., 2007. Carbonaceous and inorganic species in atmospheric aerosols during wintertime over urban and high-altitude sites in North 1ndia. J. Geophys. Res. D21.

Riedel, T.P., Wolfe, G.M., Danas, K.T., Gilman, J.B., Kuster, W.C., Bon, D.M., Vlasenko, A., et al., 2014. An MCM modeling study of nitryl chloride (ClNO2) impacts on oxidation, ozone production and nitrogen oxide partitioning in polluted continental outflow. Atmos. Chem. Phys. 8, 3789—3800.

Rose, D., Gunthe, S.S., Su, H., Garland, R.M., Yang, H., Berghof, M., Cheng, Y.F., et al., 2011. Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China — part 2: size-resolved aerosol chemical composition, diurnal cycles, and externally mixed weakly CCN-active soot particles. Atmos. Chem. Phys. 6, 2817—2836.

Rubasinghege, G., Vicki, G., 2013. Role(s) of adsorbed water in the surface chemistry of environmental interfaces. Chem. Commun. (Camb., Engl.) 3, 371 —394.

Saikawa, E., Naik, V., Horowitz, L.W., Liu, J., Mauzerall, D.L., 2009. Present and potential future contributions of sulfate, black and organic carbon aerosols from China to global air quality, premature mortality and radiative forcing. Atmos. Environ. 17, 2814—2822.

Sarnat, J.A., Marmur, A., Klein, M., Kim, E., Russell, A.G., Sarnat, S.E., Mulholland, J.A., et al., 2008. Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environ. Health Perspect. 4, 459.

Seinfeld, J.H., Pandis, S.N., 2012. Atmospheric Chemistry and Physics: from Air Pollution to Climate Change. John Wiley & Sons.

Sen, A., Mandal, T.K., Sharma, S.K., Saxena, M., Gupta, N.C., Gautam, R., Gupta, A., et al., 2014. Chemical properties of emission from biomass fuels used in the rural sector of the western region of 1ndia. Atmos. Environ. 411—424.

Shi, Y., Chen, J., Hu, D., Wang, L., Yang, X., Wang, X., 2014. Airborne submicron particulate (PM1) pollution in Shanghai, China: chemical variability, formation/ dissociation of associated semi-volatile components and the impacts on visibility. Sci. Total Environ. 199—206.

Turpin, B.J., Lim, H., 2001. Species contributions to PM2. 5 mass concentrations: revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol. 1, 602—610.

Ye, X., Tang, C., Yin, Z., Chen, J., Ma, Z., Kong, L., Yang, X., et al., 2013. Hygroscopic growth of urban aerosol particles during the 2009 Mirage-Shanghai Campaign. Atmos. Environ. 263—269.

Young, C.J., Washenfelder, R.A., Roberts, J.M., Mielke, L.H., Osthoff, H.D., Tsai, C., Pikelnaya, O., et al., 2012. Vertically resolved measurements of nighttime radical reservoirs in Los Angeles and their contribution to the urban radical budget. Environ. Sci. Technol. 20, 10965—10973.

Zauscher, M.D., Wang, Y., Moore, M.J.K., Gaston, C.J., Prather, K.A., 2013. Air quality impact and physicochemical aging of biomass burning aerosols during the 2007 San Diego wildfires. Environ. Sci. Technol. 14, 7633—7643.

Zha, S., Cheng, T., Tao, J., Zhang, R., Chen, J., Zhang, Y., Leng, C., et al., 2014. Characteristics and relevant remote sources of black carbon aerosol in Shanghai. Atmos. Res. 159—171.

Zhang, R., Khalizov, A.F., Pagels, J., Zhang, D., Xue, H., McMurry, P.H., 2008. Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing. Proc. Natl. Acad. Sci. U. S. A. 30,10291—10296.

Zhang, H., Hu, D., Chen, J., Ye, X., Wang, S.X., Hao, J.M., Wang, L., et al., 2011. Particle size distribution and polycyclic aromatic hydrocarbons emissions from agricultural crop residue burning. Environ. Sci. Technol. 13, 5477—5482.