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Aerosol Science and Engineering

, Volume 2, Issue 3, pp 130–140 | Cite as

Emission Characteristics of PM2.5 and Trace Gases from Household Wood Burning in Guanzhong Plain, Northwest China

  • Yong Zhang
  • Jie Tian
  • Zhenxing Shen
  • Wenjie Wang
  • Haiyan Ni
  • Suixin Liu
  • Junji Cao
Original Paper
  • 247 Downloads

Abstract

Considering woods used as the primary fuel in the countryside of Guanzhong Plain and its emission impacts on environment and human health. Five kinds of common wood fuels (Persimmon tree, Pear tree, Apple tree, Jujube, and Peach) were collected and burned in a laboratory combustion chamber with a common stove to identify emission characteristics of PM2.5 and trace gases (i.e., CO2, CO, NOx, and SO2). The average EFs of wood burning were estimated to be 1401 ± 71 g kg−1 for CO2, 53.48 ± 11.83 g kg−1 for CO, 1.48 ± 0.54 g kg−1 for NOx, 0.53 ± 0.19 g kg−1 for SO2, and 3.01 ± 0.72 g kg−1 for PM2.5, respectively. PM2.5 mass reconstruction for the five tree samples demonstrated excellent results, ranged from 80.7 to 98.4%. OC, EC, and water-soluble ions (sum of Na+, NH4+, K+, Mg2+, Ca2+, SO42−, NO3, and Cl) are major constituents of PM2.5, accounting for average abundance of 29.86 ± 2.03, 15.65 ± 1.07, and 17.51 ± 6.24% of the mass, respectively. The average EFs of OC and EC in PM2.5 were 910 ± 279 mg kg−1 and 465 ± 279 mg kg−1, and EC1 was the dominant carbon fraction with average abundance of 44 ± 3% of total carbon. Sodium (Na+), potassium (K+), and chloride (Cl) were the dominant water-soluble ions, with average abundance of 4.69 ± 2.51, 3.81 ± 2.13, and 3.30 ± 2.45% of PM2.5 mass. Similarity measures (i.e., Student’s t test, coefficient of divergence) showed that the five profiles derived in this study were similar for the species measured, which indicates that those profiles could be replaced by each other for PM2.5 source apportionment. Finally, the emission of woods burning in Guanzhong Plain was estimated. The total emission in 2016 was 14,924.6 Gg for CO2, 569.9 Gg for CO, 15.8 Gg for NOx, 5.6 Gg for SO2, and 32.1 Gg for PM2.5, respectively.

Keywords

Source profiles Emission factors PM2.5 Wood burning 

1 Introduction

PM2.5 pollution has drawn an increasing attention from both government and the public because of its significant influence on climate change and public health (Booth et al. 2012; Stevens and Feingold 2009; Lelieveld et al. 2015; Bandowe et al. 2014; Poschl 2005). Solid fuel including woods is one of the primary sources of PM2.5. Though the proportion of solid fuel utilization decreased from 62% in 1980 to 41% in 2010, however, the absolute population using these fuels is still very large. Globally, approximate 2.8 billion people rely on traditional solid fuels (Sophie et al. 2013). The first Chinese Environmental Exposure-Related Human Activity Patterns Survey showed that 43% of investigated households use solid fuels for daily cooking and 41% of them rely on solid fuels for heating (Duan et al. 2014). Burning solid fuels in a closed environment has been recognized as one of the top health risk factors, which lead to over 3.5 million premature deaths in 2010 (Lim et al. 2012). A recent updated report showed about 4.3 million deaths caused by biomass burning in 2012, including 12% of the death trigged by pneumonia, 34% by stroke, 26% by ischemic heart disease, 22% by chronic obstructive pulmonary disease (COPD), and 6% by lung cancer (6%) (WHO 2014). Wood is the most important kind of solid fuels, whose burning emissions have significant impacts on local and regional environment.

Some researches on emission from wood burning have been conducted in Europe and USA (Chandrasekaran et al. 2012; McNamara et al. 2013; Mohr et al. 2013; Nyström et al. 2017; Olave et al. 2017; Wilnhammer et al. 2017). However, limited studies have measured emission characteristics from wood burning in developing countries, especially in northwest China (Gonçalves et al. 2010; Liu et al. 2017; Schmidl et al. 2008; Shen et al. 2013, 2015; Zhang et al. 2012). Given the limited availability of emission factors (EFs) in China, most emission inventories in China (Huang et al. 2012; Streets et al. 2003; Yan et al. 2006) used the EFs reported by Andreae and Merlet (2001) or Akagi et al. (2011). Biomass-burning EFs are aggregated without specifying the fuel types or combustion conditions, which can lead to large uncertainties for emission inventories compilation. Furthermore, these pervious researches mainly focused on the EFs of both particulate matter and gaseous pollutants from wood burning, but lack source profiles of particulate matter. It is well known that particulate pollution in China is very severe. To control atmospheric particulate pollution, the source apportionment of particles needs to be done first. Wood burning as the one of primary source of PM2.5, its source profiles is necessary to be known.

The Guanzhong Plain is situated in the central region of Shaanxi Province (an area of 34,000 km2, with a population of 30 million), and it contains five major cities: Xi’an, Tongchuan, Baoji, Weinan, and Xianyang. Biomass burning is the major source for PM2.5 in Guanzhong area which has been indicated by a number of relevant research studies (Han et al. 2010; Huang et al. 2014; Niu et al. 2016). To gain some insight in the pollutant emission and source contribution from biomass burning, the emission factors of various pollutant (CO, CO2, NOx, and PM) and source profiles of PM from different biomass materials need to be investigated. Woods were preferred fuels for rural areas in Guanzhong Plain owing to a million of hectares orchard which could produce tons of wood residues every year. However, the source profile of wood burning has been studies rarely in Guanzhong plain.

The objective of this study is to quantify EFs for gaseous and particle pollutants (i.e., CO2, CO, PM2.5, OC, and EC) from major fruit woods (i.e., Persimmon tree, Pear tree, Apple tree, Jujube, and Peach) in Guanzhong Plain, using a laboratory combustion chamber. In addition, PM2.5 chemical source profiles containing OC, EC, water-soluble ions, and elements were obtained from these tests and compared with other anthropogenic sources. Similarities and differences among profiles from different wood types were also investigated. Finally, the emission from woods in Guanzhong Plain was estimated.

2 Methods and Materials

2.1 Woods Burning Simulation

Five kinds of fruit woods (Persimmon tree, Pear tree, Apple tree, Jujube and Peach) were collected from Guanzhong Plain in Shaanxi province. All samples were transported to the laboratory and air-dried at ambient temperature (around 20 °C) and humidity (30–50%) for at least 1 month before experiments. For woods burning experiments, all wood samples with approximately 250 g were chopped into chips (~ 10 cm × 1 cm × 1 cm) and loaded in a traditional stove in a combustion chamber to simulate household combustion. The chamber was set up the combustion laboratory at the Institute of Earth Environment, Chinese Academy of Science (IEECAS) in collaboration with the Desert Research Institute (DRI), USA (Tian et al. 2015). The chamber was equipped with a dilution system and a variety of monitoring instruments including CO, NOx, SO2, and CO2 analyzers. Before experiments, the dilution system and monitoring instruments were operated at least 3 min for measuring background value of trace gases; then, the woods were flamed by igniter. Gases emission of woods burning was analyzed by gas analyzer and PM2.5 were collected on filters through dilution system tunnel. The sampling periods for per test typically lasted 30–50 min. All woods were, respectively, burned in the stove with two parallel combustion experiments for per sample. The total of ten sets of tests were conducted.

2.2 Chemical Analysis

All filter samples were weighted at least three times before and after sampling using a microbalance with a ± 1 μg sensitivity (ME 5-F, Sartorius, Göttingen, Germany). Before weighting, filters were conditioned for 24 h at ~ 25 °C and ~ 35% relative humidity. The carbonaceous fractions in PM2.5 were analyzed among quartz filters (Whatman, QM/A, φ47 mm) using a Thermal and Optical Carbon Analyzer (Model 2001, Atmoslytic Inc., Calabasas, CA, USA) with IMPROVE (Inter Interagency Monitoring of Protected Visual Environment) thermal/optical reflectance (TOR) protocol (Chow et al. 2007). The nine water-soluble ions (Na+, NH4+, K+, Mg2+, SO42−, NO2, NO3, and Cl) in PM2.5 were determined by an ion chromatography (IC, Dionex 600, Dionex Corp, Sunnyvale, California, United States) with the pretreatment that one-fourth quartz filter was cut and placed into a 15 ml polypropylene centrifugal tube with 10 mL deionized water, then subjected to ultrasonic vibration for 0.5 h (Zhang et al. 2014). For elements analysis, 37 elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, As, Se, Br, Rb, Sr, Y, Mo, Pd, Ag, Cd, Sn, Cs, Ba, La, W, Hg, Pb, and Tl) were analyzed by energy-dispersive X-ray fluorescence (ED-XRF) spectrometry (Epsilon 5, PANalytical B. V., Almelo, The Netherlands) with Teflon-membrane filter (2 μm pore size, R2PJ047, Pall Life Sciences Ann Arbor, MI, USA) (Cao et al. 2008; Xu et al. 2012).

2.3 Emission Factors’ Calculation

Emission factors are representative values indicating the quantity of pollutants released to ambient air through combustion. That were calculated by dividing the pollutant emission by the mass of the fuel consumed and expressed as grams of emission per kilogram of consumed air-dried fuel (g kg−1) (Andreae and Merlet 2001). For PM2.5 and its chemical components, the EFs were calculated as
$${\text{EF}}_{\text{p}} = \frac{{m_{\text{f}} }}{Q} \times \frac{{V_{\text{C}} }}{m} \times {\text{DR}},$$
where EFp is the EF for particulate pollutant p for the specific fuel type; mf is the mass of pollutant collected on the filter; Vc is the total volume of exhaust flowing through the chimney during the experiment (m3) at standard temperature and pressure; Q is the sampling volume through the filter (m3) at standard temperature and pressure; and m is the mass of the burned fuel. The dilution rate (DR) of the dilution sampler was calculated based on the CO2 concentrations in different positions (Tian et al. 2015; Ni et al. 2015). For gaseous pollutants, including NOx, SO2, CO, and CO2, the EFs were calculated using online monitored concentrations as follows:
$${\text{EF}}_{\text{p}} = \frac{{V_{\text{c}} }}{m} \times \frac{{C_{{x,{\text{D}}}} }}{{V_{x} }} \times M_{x} \times {\text{DR}},$$
where Cx,D is the average concentration (molar fraction) measured in the dilution sampler; Vx is the molar volume of gas at standard temperature and pressure (0.224 m3), and Mx is the molecular weight of species × (g mol−1) (Ni et al. 2015). The combustion status of biogenic fuels is always influenced by fuels characters and oxygen concentration.
The MCE (modified combustion efficiency) has been widely used to define combustion status, and MCE could calculate as following formula:
$${\text{MCE}} = \frac{{[{\text{CO}}_{2} ]}}{{[{\text{CO}}] + [{\text{CO}}_{2} ]}},$$
where [CO2] and [CO] are the excess molar mixing ratios of CO2 and CO, respectively. If the average MCE value is higher than 0.90, then it indicates that wood burning state is flaming (Klason and Bai 2007; Nyström et al. 2017; Olave et al. 2017; Orasche et al. 2012).

2.4 Emission Estimate

Wood burning emission estimates for Guanzhong plain were calculated by multiplying the EFs in this study by the weight of the burned woods:
$$E_{i} = {\text{EF}}_{i} \times W \times N; \quad W = R \times P \times 365,$$
where Ei is total emission of pollutant i (Gg year−1). EFi is average emission factor of pollutant i (g kg−1). W is burning wood weight of one rural household in Guanzhong Plain every year, R is average burning wood weight for one person per day (kg day−1), which was defined 2.05 kg day−1 according questionnaire and field research. P is the average number of people for per household, which was defined 4 according Shaanxi Provincial Bureau of Statistics (2017). N is number of rural household in Guanzhong plain from the fifth census of National Bureau of Statistics (2000).

2.5 Similarities and Differences Among Profiles

Two parameters [the Student’s t test and coefficient of divergence (CD)] are usually used to evaluate similarities and differences among source profiles. The Student’s t test between Fi1 and Fi2 is used to estimate the statistical significance of differences between paired profiles. Subscripts “1” and “2” refer to the two paired profiles. Fi1 and Fi2 are chemical species fractions of PM mass for species i from paired sources 1 and 2. If p > 0.05, it means that two profiles are not significant diffident at 95% confidence level (Ni et al. 2017). The CD, a self-normalizing parameter, is used to compare the similarities and differences between the source profiles:
$${\text{CD}}_{jk} = \sqrt {\frac{1}{p}\sum\limits_{i = 1}^{p} {\left( {\frac{{x_{ij} - x_{ik} }}{{x_{ij} + x_{ik} }}} \right)^{2} } } ,$$
where xij represents the average concentration for a chemical component i from source j; j and k represent two different wood residues; and p is the number of chemical components. For this study, CD < 0.3 is used to indicate similarity between the two profiles (Chow et al. 2003).

3 Results and Discussion

3.1 Mass Reconstruction for PM2.5 from Wood Burning

PM2.5 composition for each burning was reconstructed, and the reconstructed mass (RM) concentration was calculated as the following equation (Chow et al. 2015):
$${\text{RM}} = \left[ {\text{OM}} \right] + \left[ {\text{EC}} \right] + \left[ {\text{Inorganic ions}} \right] + \left[ {\text{GM}} \right] + [{\text{Elements}}],$$
where [OM] (organic matters) is estimated from OC with a multiplier (f). A multiple of f = 1.6 is selected in this study, which is commonly used in the literature for biomass burning (Aiken et al. 2008; Levin et al. 2010; Reid et al. 2005). [EC] is used for EC concentration which is defined operationally, and absolute EC concentrations vary among carbon analysis protocols. [Inorganic ions] is the sum of Na+, NH4+, K+, Mg2+, Cl, NO3, and SO42−. [GM] (geological minerals) are estimated to be the sum of the oxides aluminum (Al), silicon (Si), calcium (Ca), and iron (Fe). These elements are assumed in the common forms of Al2O3, SiO2, CaO, and Fe2O3, which are estimated as 1.89 Al, 2.14Si, 1.4Ca, and 1.43Fe, respectively (Chow et al. 2015). [Elements] are the sum of remaining elements from XRF analysis. However, for K, Cl, S, Na, Mg elements were eliminated water-soluble fraction.
PM2.5 mass reconstruction from wood burning emission is shown in Fig. 1. OM was the primary component which dominated 43.94% (Pear tree) to 52.83% (Apple tree) reconstructed mass lower than 52–96% which presented by Ni et al. (2015) from straws burning experiments. Inorganic ions, EC, and elements accounted for approximately 15, 15, and 5% reconstructed mass, respectively. GM abundance in RM was lower than 1%. As total, RM accounted for 78.13% (Peach) to 98.40% (Jujube) of PM2.5 from woods burning, which indicates that chemical analysis for this study was reasonable (Chow et al. 2015).
Fig. 1

PM2.5 mass closure of major components for each wood burning profile

3.2 Emission Factors

3.2.1 Gaseous Pollutants from Wood Burning

The emission factors of CO2, CO, NOx, SO2, and MCE are measured for all woods, and the results are presented in Table 1. The average MCE value for five wood combustions is all higher than 0.90, which indicated that wood burning states were flaming. The average CO2 EFs of wood burning are 1401 ± 71 g kg−1 with range of 1350 ± 24 g kg−1 for Apple tree to 1512 ± 37 g kg−1 for Persimmon tree. Compared to the previous studies, average CO2 EFs in this study are close to 1445 ± 57 g kg−1 reported by Shen et al. (2015), higher than 1039–1242 g kg−1 reported by Brassard et al. (2014), but lower than 1548–1879 g kg−1 reported by Cereceda-Balic et al. (2017).
Table 1

Summary of EFs from stove burning of wood with the corresponding MCE

Species

Unit

Persimmon tree

Pear tree

Apple tree

Jujube

Peach

Woodsa

MCE

0.93 ± 0

0.9 ± 0.02

0.92 ± 0.02

0.91 ± 0.01

0.93 ± 0.01

0.92 ± 0.02

CO2

g kg−1

1512 ± 37

1360 ± 17

1350 ± 24

1378 ± 92

1407 ± 3

1401 ± 71

CO

g kg−1

38.51 ± 4.42

64.4 ± 9.61

61.31 ± 11.97

58.08 ± 1.24

45.12 ± 3.42

53.48 ± 11.83

NOx

g kg−1

2.1 ± 0.96

1.28 ± 0.03

1.57 ± 0.72

1.37 ± 0.09

1.11 ± 0.05

1.48 ± 0.54

SO2

g kg−1

0.89 ± 0.05

0.45 ± 0.01

0.41 ± 0.05

0.47 ± 0.03

0.43 ± 0.03

0.53 ± 0.19

aAverage value of five woods

The CO EFs of Persimmon tree burning are 38.51 ± 4.42 g kg−1, which are close to 45.12 ± 3.42 g kg−1 of Peach, but lower than three other woods with 64.4 ± 9.61 g kg−1 for Pear tree, 61.31 ± 11.97 g kg−1 for Apple tree, and 58.08 ± 1.24 g kg−1 for Jujube. The average CO EFs is 53.48 ± 11.83 g kg−1 closer to 49.35 ± 7.27 g kg−1 reported by Cereceda-Balic et al. (2017) and 42 ± 4 g kg−1 reported by Tissari et al. (2008), but lower than 112.6–297.3 g kg−1 reported by Chen et al. (2010).

The NOx EFs of woods burning are 2.1 ± 0.96 g kg−1 for Persimmon tree, 1.28 ± 0.03 g kg−1 for Pear tree, 1.57 ± 0.72 g kg−1 for Apple tree, 1.37 ± 0.09 g kg−1 for Jujube, and 1.11 ± 0.05 g kg−1 for Peach, respectively. The average NOx EFs (1.48 ± 0.54 g kg−1) are close to 1.4 ± 0.1 g kg−1 from Tissari et al. (2008).

For SO2 EFs, Pear tree with 0.45 ± 0.01 g kg−1, Apple tree with 0.41 ± 0.05 g kg−1, Jujube with 0.47 ± 0.03 g kg−1, and Peach with 0.43 ± 0.03 g kg−1 are all close to 0.41 ± 0.10 g kg−1 from Cereceda-Balic et al. (2017), but lower than Persimmon tree emission with 0.89 ± 0.05 g kg−1. As shown above, the Persimmon tree burning produces more gaseous pollutant.

Generally speaking, results of gaseous pollution emission factors in this research share a lot similarity with the previous research conducted by different researchers. Nonetheless, there are still some distinct differences in the comparisons above, which could be caused by the divergent combustion details including the method, condition, and different wood samples (Ribeiro et al. 2017; Shen et al. 2013; Tissari et al. 2008).

3.2.2 PM2.5 and Speciated Emission Factors

PM2.5 and speciated EFs of woods burning are shown in Table 2. The PM2.5 EFs in this study are 2.53 ± 0.62 g kg−1 for Persimmon tree, 3.39 ± 0.93 g kg−1 for Pear tree, 3.39 ± 1.20 g kg−1 for Apple tree, 3.14 ± 0.58 g kg−1 for Jujube, and 2.61 ± 0.52 g kg−1 for Peach, respectively. That all are close to 1.6–3.8 g kg−1 from Fine et al. (2002, 2004) and McDonald et al. (2000), higher than 0.84–1.3 g kg−1 reported by Cereceda-Balic et al. (2017), but lower than 5.2–13 g kg−1 reported by Calvo et al. (2015) and Gonçalves et al. (2012). The discrepancy of the EFs of PM2.5 in different researches is likely caused using different wood samples and experiment design. In addition, the summary of the previous researches about biomass burning (Table S1) points that the PM2.5 EFs from straw and leaves burning (8.7–27.1 g kg−1) are distinctively higher than those from wood burning.
Table 2

PM2.5 and speciated EFs from wood burning

Species

Unit

Persimmon tree

Pear tree

Apple tree

Jujube

Peach

Woodsa

PM2.5

g kg−1

2.53 ± 0.62

3.39 ± 0.93

3.39 ± 1.2

3.14 ± 0.58

2.61 ± 0.52

3.01 ± 0.72

OC

mg kg−1

746.06 ± 275.06

929.1 ± 248.78

1138.18 ± 499.87

944.5 ± 261.19

790.77 ± 236.96

909.72 ± 279.13

EC

mg kg−1

436.79 ± 91.87

527.83 ± 157.21

529.49 ± 128.14

460.02 ± 74.1

371.08 ± 68.8

465.04 ± 102.9

Cl

mg kg−1

28.17 ± 6.82

131.67 ± 19.82

20.14 ± 2.09

203.56 ± 17.86

104.16 ± 3.04

97.54 ± 72.48

NO3

mg kg−1

6.61 ± 0.98

12.79 ± 2.88

15.74 ± 7.53

11.37 ± 2.11

9.26 ± 1.45

11.16 ± 4.33

SO42−

mg kg−1

56.27 ± 0.21

82.37 ± 15.75

86.25 ± 3.08

67.94 ± 14.55

32.58 ± 4.7

65.08 ± 21.78

Na+

mg kg−1

156.75 ± 21.15

258.94 ± 128.53

115.82 ± 45.65

147.04 ± 71.48

22.53 ± 1.85

140.21 ± 95.38

NH4+

mg kg−1

5.69 ± 8.05

1.14 ± 3.6

K+

mg kg−1

27.13 ± 19.88

148.34 ± 72.86

159.65 ± 42.97

197.69 ± 39.88

64.17 ± 6.63

119.4 ± 74.15

Mg2+

mg kg−1

2.69 ± 3.81

2.33 ± 3.29

11.66 ± 8.98

13.57 ± 8

1.22 ± 0.36

6.29 ± 7.01

Ca2+

mg kg−1

64.87 ± 91.74

8.36 ± 11.82

81.28 ± 73.61

161.41 ± 70.98

40.02 ± 12.27

71.19 ± 71.15

S

mg kg−1

18.48 ± 3.24

37.67 ± 14.49

59.53 ± 16.41

17.93 ± 3.72

10.1 ± 0.78

28.74 ± 20.27

Cl

mg kg−1

30.14 ± 0.22

243.87 ± 10.11

63.53 ± 21.08

295.28 ± 30.33

121.37 ± 7.81

150.84 ± 108.87

K

mg kg−1

66.86 ± 6.67

293.15 ± 83.5

337.42 ± 145.27

360.04 ± 55.16

154.52 ± 6.31

242.4 ± 132.98

Na

mg kg−1

0.31 ± 0.44

149.95 ± 84.03

11.05 ± 5.71

15.35 ± 7.51

12.36 ± 1.93

37.81 ± 65.7

Mg

mg kg−1

8.82 ± 7.63

3.63 ± 5.13

2.49 ± 4.76

Al

mg kg−1

3.55 ± 1.53

2.21 ± 3.13

0.33 ± 0.46

1.62 ± 1.12

0.07 ± 0.1

1.56 ± 1.82

Si

mg kg−1

0.22 ± 0.26

0.04 ± 0.13

P

mg kg−1

0.16 ± 0.23

0.32 ± 0.45

0.26 ± 0.37

0.07 ± 0.1

0.17 ± 0.15

0.2 ± 0.24

Ca

mg kg−1

0.24 ± 0.11

0.02 ± 0.03

0.64 ± 0.67

0.64 ± 0.91

0.31 ± 0.48

Sc

mg kg−1

0.21 ± 0.04

0.04 ± 0.09

Ti

mg kg−1

0.01 ± 0.01

0.01 ± 0.01

0.1 ± 0.08

0.03 ± 0.04

0.01 ± 0.01

0.03 ± 0.05

V

mg kg−1

0.03 ± 0.04

0.01 ± 0.02

Cr

mg kg−1

0.04 ± 0

0.04 ± 0.05

0.08 ± 0.12

0.03 ± 0.05

Mn

mg kg−1

0.08 ± 0.01

0.17 ± 0.06

0.2 ± 0.21

0.12 ± 0.04

0.51 ± 0.09

0.22 ± 0.18

Fe

mg kg−1

0.16 ± 0.03

0.29 ± 0.41

 

0.09 ± 0.18

Co

mg kg−1

±0.01

0.1 ± 0.14

0.02 ± 0.06

Cu

mg kg−1

0.11 ± 0.16

0.07 ± 0.04

0.07 ± 0.08

0.06 ± 0.07

Zn

mg kg−1

2.3 ± 1.3

1.75 ± 1.09

0.61 ± 0.02

1.51 ± 0.33

3.91 ± 0.72

2.01 ± 1.31

As

mg kg−1

0.03 ± 0.04

0.01 ± 0.02

0.06 ± 0.09

0.02 ± 0.04

Se

mg kg−1

0.13 ± 0.02

0.03 ± 0.05

Br

mg kg−1

0.05 ± 0.07

0.13 ± 0.05

0.04 ± 0.06

Rb

mg kg−1

0.26 ± 0.1

0.15 ± 0.02

0.08 ± 0.12

Sr

mg kg−1

0.02 ± 0.02

0.02 ± 0.02

0.07 ± 0.1

0.23 ± 0.04

0.07 ± 0.1

Y

mg kg−1

0.61 ± 0.56

0.25 ± 0.35

 

0.23 ± 0.32

0.15 ± 0.21

0.25 ± 0.33

Mo

mg kg−1

0.13 ± 0.06

0.02 ± 0.02

0.05 ± 0.07

0.13 ± 0.07

0.02 ± 0.03

0.07 ± 0.07

Pd

mg kg−1

0.1 ± 0.14

0.2 ± 0.06

0.34 ± 0.18

0.44 ± 0.13

0.25 ± 0.02

0.27 ± 0.15

Ag

mg kg−1

0.01 ± 0.02

0.02 ± 0.02

0.05 ± 0.04

0.06 ± 0.06

0.03 ± 0.04

Cd

mg kg−1

0.06 ± 0.08

0.01 ± 0.04

Sn

mg kg−1

0.89 ± 0.98

0.01 ± 0.01

0.12 ± 0.17

0.2 ± 0.49

Sb

mg kg−1

0.13 ± 0.18

0.13 ± 0.08

0.73 ± 1.04

0.2 ± 0.45

Cs

mg kg−1

1.34 ± 1.8

0.5 ± 0.71

1.8 ± 0.73

0.24 ± 0.08

0.11 ± 0.16

0.8 ± 0.98

Ba

mg kg−1

0.54 ± 0.77

0.67 ± 0.94

0.01 ± 0.02

0.24 ± 0.51

La

mg kg−1

0.66 ± 0.93

0.57 ± 0.81

0.3 ± 0.43

1.06 ± 1.5

0.52 ± 0.76

W

mg kg−1

1.12 ± 0.85

0.22 ± 0.55

Hg

mg kg−1

0.64 ± 0.42

1.34 ± 0.12

0.01 ± 0.02

1.21 ± 1.11

0.82 ± 1.16

0.8 ± 0.74

Pb

mg kg−1

1.72 ± 1.34

0.47 ± 0.31

0.72 ± 0.12

0.41 ± 0

0.08 ± 0.12

0.68 ± 0.75

Tl

mg kg−1

‘–’ means no detected

aAverage value of five woods

The highest OC EFs in PM2.5 is 1138.18 ± 499.87 mg kg−1 from Apple-tree burning, which is higher than 746 ± 275 mg kg−1 from Persimmon tree and 790.77 ± 236.96 mg kg−1 form Peach, but close to 929.1 ± 248.78 mg kg−1 from Pear tree and 944.5 ± 261.19 mg kg−1 from Jujube. For EC EFs, Persimmon tree and Jujube produce similar emission with 436.79 ± 91.87 mg kg−1 and 460.02 ± 74.1 mg kg−1, lower than 527.83 ± 157.21 mg kg−1 from Pear tree and 529.49 ± 128.14 mg kg−1 from Apple tree. The lowest EC EFs in PM2.5 are 371.08 ± 68.8 mg kg−1 from Peach burning.

Persimmon tree produces the lowest emission of Cl and K+ among all the tested trees, with 28.17 ± 6.82 mg kg−1 and 27.13 ± 19.88 mg kg−1, respectively. Cl and K+ emissions from Jujube are the highest in the test, with 203.56 ± 17.86 mg kg−1 and 197.69 ± 39.88 mg kg−1, respectively. Apple tree has the most interesting results. The K+ emission of it is eight times higher than its Cl emission (159.65 ± 42.97 mg kg−1 for K+ and 20.14 ± 2.09 for Cl). While Peach burning result demonstrates an inversed result, with 104.16 ± 3.04 mg kg−1 for Cl and 64.17 ± 6.63 for mg kg−1 K+

For SO42−, the average EFs of five woods burning are 65.08 ± 21.78 mg kg−1, where Peach and Apple-tree burnings account for the lowest and highest emissions with 32.58 ± 4.70 mg kg−1 and 86.25 ± 3.08 mg kg−1, respectively. The NO3 EFs of five woods burning are close to each other, with an average of 11.16 ± 4.33 mg kg−1. The average Na+ EFs is 140.21 ± 95.38 mg kg−1; however, there is a huge fluctuation amongst different types of tree burning. As demonstrated in Table 1, the lowest emission is from Peach (22.53 ± 1.85 mg kg−1) which is only-one tenth of the emission from Pear tree burning (258.94 ± 128.53 mg kg−1).

For the rest of water-soluble ions (Mg2+, Ca2+, and NH4+), the EFs range from 0 mg kg−1 to 174.98 mg kg−1.

The main elemental component of PM2.5 is S, Cl, K, and Na, whose average EFs are 28.74 ± 20.27 mg kg−1, 150.84 ± 108.87 mg kg−1, 242.4 ± 132.98 mg kg−1, and 37.81 ± 65.7 mg kg−1, respectively. Other elements average EFs are all lower than 1 mg kg−1 except for Mg (2.49 ± 4.76 mg kg−1), Al (1.56 ± 1.82 mg kg−1), and Zn (2.01 ± 1.31 mg kg−1).

3.3 PM2.5 Source Profiles from Wood Burning

The source profiles of PM2.5 from woods combustion are shown in Fig. 2. Carbonaceous components are main component for PM2.5, and the abundance of OC and EC in PM2.5 is near to 50%. The OC abundance in PM2.5 varies from 27.46 ± 0.20% (Pear tree) to 33.02 ± 3.08% (Apple tree), and EC varies from 14.69 ± 5.58% (Jujube) to 17.32 ± 0.63% (Persimmon tree). The previous PM2.5 source profile researches about wood burning are summarized in Table S2, which all indicate that the carbonaceous component (OC and EC) is the main abundances for PM2.5 from biomass burning (Fine et al. 2002, 2004; Turn et al. 1997; Watson et al. 2001; Zhang et al. 2012).
Fig. 2

Chemical profiles of PM2.5 from woods burning

OC/EC ratio has been used to discriminate among different source emissions. OC/EC ratios in biomass burning ranged from 3 to 10 (Cao et al. 2008; Li et al. 2007, 2009; Sun et al. 2017) usually are higher than those from coal combustion ranged from 1.6 to 3 (Chen et al. 2015; Shen et al. 2012; Zhi et al. 2008) and vehicle emission ranged from 0.23 to 1.71 (Cui et al. 2016; Gillies and Gertler 2011; Sahu et al. 2011). In this study, OC/EC ratios range from 1.68 ± 0.28 (Persimmon tree) to 2.23 ± 1.05(Peach), approximating to 2.60 reported by Fine et al. (2004) from Red Maple burning in filed measurement, but lower than result of straw burning by Ni et al. (2015) with OC/EC ratio of 11.94–20.20 for wheat straws, rice straws, and corn stalks taking open burning simulation on laboratory. The correlation of MCE to PM2.5 EFs and to OC/EC ratios was also plotted, respectively, in Figure S1, which shows an inversed proportional relationship. As MCE increases, PM2.5 EFs and OC/EC ratios decrease. That characteristic is in line with the previous studies (Hosseini et al. 2013; McMeeking et al. 2009). In addition, the eight carbon sub-fractions (OC1, OC2, OC3, OC4, EC1, EC2, and EC3) could be used in the source apportionment, because different source types produce distinctly different abundances of the sub-fractions (Cao et al. 2005, 2006; Chow et al. 2004; Han et al. 2010). As shown in Fig. 3, the OC1 abundance in TC for all woods burning is below 3%, which is lower than results from straw burning (Ni et al. 2015; Sun et al. 2017). The OC2 abundance in TC ranges from 10% (Peach) to 12% (Persimmon and Apple tree), comparable to 11.9 ± 0.11% from vegetative burning by Chow et al. (2004) and lower than motor vehicle and cooking emission. The OC3 abundance in TC ranges from 19% (Persimmon tree) to 24% (Peach), close to OC4 abundance ranging 18% (Persimmon tree and Peach) to 22% (Pear tree and Jujube), which is higher than motor vehicle, vegetative burning, and coal-burning emission (Chow et al. 2004). The EC1 is the most abundance in carbon sub-fraction with the range of 42% (Pear tree) to 48% (Persimmon), higher than straw burning, vehicle, and coal-burning emission. The EC2 and EC3 abundances in TC are near to 0%, and POC abundance ranges from 6% (Pear tree) to 13% (Peach).
Fig. 3

Carbon fractiona composition PM2.5 profiles from different wood combustions. aEight carbon fractions including: OC1 (120 °C), OC2 (250 °C), OC3 (450 °C), and OC4 (550 °C) combusted in a 100% helium atmosphere; and EC1 (550 °C), EC2 (700 °C), and EC3 (800 °C) combusted in a mixture of 2% oxygen and 98% helium atmosphere. A 630 nm helium–neon laser is used to monitor pyrolyzed carbon (POC) based on the IMPROVE thermal/optical reflectance (TOR) carbon analysis protocol (Chow et al. 1993, 2001)

Water-soluble ions account for 10–30% of PM2.5 mass from wood burning in this study. In addition, Na+, Cl, K+, Ca2+, and SO42− account for ~ 99% of the total water-soluble ions. The abundance of Na+ in PM2.5 is from 0.87 ± 0.10% for Peach to 7.40 ± 1.76% for Pear tree. This result is higher than 0.17 ± 0.03% reported by Zhang et al. (2012) and 0.083–0.049% from Turn et al. (1997). The abundance of Cl ranges from 0.62 ± 0.16% (Apple tree) to 6.65 ± 1.80% (Jujube), comparable to 0.45 ± 0.32% reported by Zhang et al. (2012) and 0.12–2.7% from Turn et al. (1997). These levels are higher than 0.26–0.39% from other similar studies (Fine et al. 2002, 2004; Watson et al. 2001). The study found that K+ abundance in PM2.5 varies from 1.00 ± 0.54% (Persimmon tree) to 6.53 ± 2.49% (Jujube). For SO42−, Peach- and Apple-tree burnings result in the lowest and highest abundances of 1.25 ± 0.07 and 2.70 ± 3.22% in PM2.5, respectively. The sum of other water-soluble ions (Mg2+, NO3, and NH4+) account for approximate 1% abundance in PM2.5 from wood burning.

In summary, K+ and Cl were the main water-soluble ions in PM2.5 from wood burning, which is in accordance with the result from the previous straw burning studies (Ni et al. 2015; Sun et al. 2017). In addition, Na+ and SO42− are also the important components in PM2.5 from wood burning in this study.

As regard with the abundance of elements in PM2.5, the highest K and Cl abundance can be found in Jujube (11.84 ± 3.95 and 9.66 ± 2.76%), and the lowest one was found in Persimmon tree (2.75 ± 0.94 and 1.23 ± 0.29%). The highest Na abundance was found in Pear-tree sample (4.24 ± 0.32%), while the lowest one was found in Persimmon tree sample (0.01 ± 0.01%). As for S, burning Pear tree would generate the highest S with 1.78 ± 0.15% of the total PM2.5 abundance, contrasting the lowest one (0.40 ± 0.11%) given by peach burning. For the rest of elements, their abundances in PM2.5 are less than 0.50%.

Moreover, the ratios of main speciated components from woods burning were discussed and compared with other anthropogenic sources in Table S3. The ratios of K+/K range from 0.41 ± 0.03 for Peach to 0.55 ± 0.03 for Jujube, which are lower than 0.73–0.88 reported by Turn et al. (1997) from wood burning and 0.77 ± 0.13 reported by Ni et al. (2015) from straw burning. In addition, K+/EC ratios have been used to distinguish biomass-burning contributions (Srinivas and Sarin 2014). In this study, K+/EC varies from 0.06 ± 0.03 (Persimmon tree) to 0.44 ± 0.16 (Jujube), close to other woods burning result (0.11–0.76) (Turn et al. 1997; Watson et al. 2001; Zhang et al. 2012). However, those ratios are lower than result from straw burning which ranged 0.65–1.14 reported by Turn et al. (1997) and 1.16–3.16 reported by Ni et al. (2015). Besides, K/EC ratios in this study range from 0.16 ± 0.05 (Persimmon tree) to 0.80 ± 0.25 (Jujube), which are close to wood burning results from the literature (Fine et al. 2004; Turn et al. 1997; Watson et al. 2001; Zhang et al. 2012), but lower than 1.56–4.34 (Ni et al. 2015) and 0.73–1.73 (Turn et al. 1997) from straw burning. The ratios of K+/Cl in PM2.5 from biomass burning are also widely reported, which ranged from 0.35 to 2.83 for straw burning (Ni et al. 2015; Turn et al. 1997) to 1.59–9.31 for wood burning (Turn et al., 1997). In this study, K+/Cl ratios are close to unity such as 1.08 ± 0.97 for Persimmon, 1.10 ± 0.39 for Pear tree, 0.97 ± 0.11 for Jujube, and 0.62 ± 0.08 for Peach. Most of them are much lower than 5.93 reported by Zhang et al. (2012) and 5.38 reported by Watson et al. (2001). However, K+/Cl value of Apple-tree sample (7.86 ± 1.32) is consistent with the result reported by Turn et al. (1997).

Similarity and difference for the five PM2.5 source profiles from wood burning in this study were evaluated. As shown in Table 3, P values from different paired profiles are all larger than 0.05 except for the pair of Jujube and Peach. The small value of the Jujube-pear may be caused by abundance of Na+. The CD values from different paired profiles are all lower than 0.3 except for the pair of Apple tree and Peach. The exception is caused by the abundance of Na+ and Cl. P and CD values of most pairs indicate the five profiles above can be replaced by each other in CMB model for PM2.5 source apportionment (Henry 1992; Lowenthal et al. 1992).
Table 3

Similarity statistics for chemical profiles of PM2.5 from different woods

Profile #1

Profile #2

t-statics; p values

CD

Persimmon tree

Pear tree

0.227

0.20

Persimmon tree

Apple tree

0.111

0.21

Persimmon tree

Jujube

0.082

0.23

Persimmon tree

Peach

0.994

0.25

Pear tree

Apple tree

0.920

0.23

Pear tree

Jujube

0.178

0.18

Pear tree

Peach

0.125

0.24

Apple tree

Jujube

0.376

0.21

Apple tree

Peach

0.083

0.37

Jujube

Peach

0.010

0.24

3.4 Estimates of Wood Burning in Guanzhong Plain

The estimated emissions from wood burning in Guanzhong Plain are listed in Table 4. In 2016, the total amounts of woods burned in households are 2.51, 0.33, 2.04, 2.49, and 3.28 Tg in Xi’an, Tongchuan, Baoji, Xianyang, and Weinan, respectively. Emissions from wood burning in Guanzhong Plain were estimated to be 14,924.6 Gg CO2, 569.9 Gg CO, 15.8 Gg NOx, 5.6 Gg SO2, and 32.1 Gg PM2.5. For PM2.5 speciated emission, OC is the dominant pollutant in the wood burning conducted in this research, with 9.7 Gg per year, This amount accounts for 30% of total PM2.5, which is lower than 44% reported by Ni et al. (2015) from biomass straw burning. The total emission of water-soluble ions is 5.5 Gg year−1, accounting for 17% of PM2.5. Cl (1.0 Gg year−1), Na+ (1.5 Gg year−1), and K+ (1.3 Gg year−1) are the top three ions emitted from wood burning, which occupy 19, 27, and 23% of total ions, respectively.
Table 4

Estimates of 2016 annual emissions (Gg) from woods burning in Guanzhong Plain

 

CO2

CO

NOx

SO2

PM2.5

OC

EC

Cl

Na+

K+

Other irons

Cl

K

Other elements

Xi’an

3515.6

134.2

3.7

1.3

7.6

2.3

1.2

0.2

0.4

0.3

0.4

0.4

0.6

0.2

Tongchuan

458.1

17.5

0.5

0.2

1.0

0.3

0.2

0.0

0.0

0.0

0.1

0.0

0.1

0.0

Baoji

2864.6

109.4

3.0

1.1

6.2

1.9

1.0

0.2

0.3

0.2

0.3

0.3

0.5

0.2

Xianyang

3492.9

133.4

3.7

1.3

7.5

2.3

1.2

0.2

0.3

0.3

0.4

0.4

0.6

0.2

Weinan

4593.4

175.4

4.9

1.7

9.9

3.0

1.5

0.3

0.5

0.4

0.5

0.5

0.8

0.3

Total

14,924.6

569.9

15.8

5.6

32.1

9.7

5.0

1.0

1.5

1.3

1.6

1.6

2.6

0.8

4 Conclusion

The EFs of pollutants (CO2, CO, NOx, SO2, and PM2.5) and PM2.5 chemical source profiles from wood burning (Persimmon tree, Pear tree, Apple tree, Jujube, and Peach) in Guanzhong Plain were investigated using a combustion chamber and compared with the previous researches. The average EFs of CO2, CO, NOx, SO2, and PM2.5 are 1401 ± 71 g kg−1, 53.48 ± 11.83 g kg−1, 1.48 ± 0.54 g kg−1, 0.53 ± 0.19 g kg−1, and 3.01 ± 0.72 g kg−1, respectively. OC, EC, and water-soluble ions (sum of Na+, NH4+, K+, Mg2+, SO42−, NO2, NO3, and Cl) are major constituents of PM2.5, which account for average abundance of 29.86 ± 2.03, 15.65 ± 1.07, and 17.51 ± 6.24%, respectively. The OC/EC ratios from woods burning range from 1.68 to 2.23, which can be applied to source identification. K+/EC ratios of wood burning range from 0.06 to 0.44. The PM2.5 source profiles of the studied five tree types are similar enough to replace each other in CMB model for source apportionment. In 2016, emissions from woods burning in Guanzhong Plain were 14,924.6 Gg CO2, 569.9 Gg CO, 15.8 Gg NOx, 5.6 Gg SO2, and 32.1 Gg PM2.5, respectively. To develop effective pollutant control strategies, comprehensive emission inventories including major biomass combustion are needed.

Notes

Acknowledgements

This work was jointly supported by the Ministry of Science and Technology (2013FY112700) and the Key Lab of Aerosol Chemistry and Physics of the Chinese Academy of Sciences.

Supplementary material

41810_2018_30_MOESM1_ESM.docx (101 kb)
Supplementary material 1 (DOCX 100 kb)

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Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2018

Authors and Affiliations

  1. 1.Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth EnvironmentChinese Academy of SciencesXi’anChina
  2. 2.State Key Laboratory of Loess and Quaternary Geology, Institute of Earth EnvironmentChinese Academy of SciencesXi’anChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Department of Environmental Sciences and EngineeringXi’an Jiaotong UniversityXi’anChina
  5. 5.Institute of Global Environmental ChangeXi’an Jiaotong UniversityXi’anChina

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