Environ Sci Technol. Author manuscript; available in PMC 2013 Apr 3.
FutureBioTec (Future Low Emission Biomass Combustion Systems) has. Without increasing other harmful emissions such as fine particulate matter. NOx emissions by primary measures (air staging and air distribution, grate. Data regarding the influence of air staging on NOx and PM1 emissions for fixed bed biomass.
Published in final edited form as:
Published online 2012 Mar 13. doi: 10.1021/es203957u
NIHMSID: NIHMS363859
The publisher's final edited version of this article is available at Environ Sci Technol
See other articles in PMC that cite the published article.
Associated Data
NIHMS363859-supplement-1_si_001.pdf (42K)
Abstract
Published emission factors (EFs) often vary significantly, leading to high uncertainties in emission estimations. There are few reliable EFs from field measurements of residential wood combustion in China. In this study, 17 wood fuels and one bamboo were combusted in a typical residential stove in rural China to measure realistic EFs of particulate matter (PM), organic carbon (OC) and elemental carbon (EC), as well as to investigate the influence of fuel properties and combustion conditions on the EFs. Measured EFs of PM, OC, and EC (EFPM, EFOC, and EFEC, respectively) were in the range of 0.38~6.4, 0.024~3.0 and 0.039~3.9 g/kg (dry basis), with means and standard derivation of 2.2±1.2, 0.62±0.64 and 0.83±0.69 g/kg, respectively. Shrubby biomass combustion produced higher EFs than tree woods, and both species had lower EFs than those of indoor crop residue burning (p<0.05). Significant correlations between EFPM, EFOC and EFEC were expected. By using a nine-stage cascade impactor, it was shown that size distributions of PM emitted from tree biomass combustions were unimodal with peaks at a diameter less than 0.4 µm (PM0.4), much finer than the PM from indoor crop residue burning. Approximately 79.4% of the total PM from tree wood combustion was PM with a diameter less than 2.1µm (PM2.1). PM size distributions for shrubby biomasses were slightly different from those for tree fuels. Based on the measured EFs, total emissions of PM, OC, and EC from residential wood combustion in rural China in 2007 were estimated at about 303, 75.7, and 92.0 Gg.
Introduction
Residential wood combustion is of wide concern due to its adverse impacts on air quality and human health, especially in the many developing countries where wood is often used for residential cooking and heating. Particulate matter (PM), one of many pollutants emitted from wood combustion, contains organic carbon (OC) and elemental carbon (EC, sometimes referred to as black carbon1). PM is harmful to human health, and is considered a climate-relevant pollutant. PM, especially fine PM, can penetrate deep into the bronchial and lung areas, leading to respiratory and cardiovascular diseases and increased rates of mortality–. Sulfates and OC content in PM generally have a cooling effect on climate because they scatter light, while EC can absorb light resulting in a positive radiative forcing 1, 5. It has been reported that PM emitted from combustion in wood stoves is often characterized by small particle size and high levels of organic pollutants; subsequently, it can cause high levels of free radicals, DNA damage, inflammatory, and oxidative stress response in humans who are exposed to it .
An estimated 1324~1615 Tg of wood was used in the residential sector globally in 1995, of which approximately 800~930 Tg was consumed in Asia . Accordingly, 670~820 Gg and 400~470 Gg EC were emitted from wood combustion globally and in Asia, respectively , contributing an important fraction of the total emissions from all sources. In most developing countries, residential biomass combustion, including wood and crop residues, dominates the total emissions of fine PM, EC and OC. It has been calculated that around 28, 72, and 39 % of the total PM, OC, and EC emissions in China in 2005 were from residential biomass combustion 8.
Emissions of primary PM, OC, and EC are usually calculated based on the total quantities of consumed fuel and emission factors (EFPM, EFOC, and EFEC), defined as PM, OC, and EC emitted per mass of fuel consumed. Unfortunately, data on EFs of these pollutants are scarce in China 9– and using the EFs reported for other countries and/or calculated based on EFs of other pollutants in the emission inventories often results in high uncertainties and bias –8, 12–. The measured EFs for biomass burning often varied dramatically due to the differences in fuel properties, like fuel moisture and volatile matter content, and combustion conditions, e.g. fire management, burning temperature, oxygen supply and mixing states in stove chamber 14–24. The impacts of these factors were complicated and sometimes interacted with one another. For example, fuel with high moisture often required additional energy to vaporize the water and hence resulted in low combustion efficiency and high pollutant emissions, while in a stove chamber with limited volume, low moisture fuel may burn too fast resulting in incomplete combustion due to the insufficient air supply 17, 25. To achieve high efficient combustion, adequate air supply and proper mixing status are necessary 26–27. It is accepted that high combustion efficiency leads to relative low emissions of pollutants, and combustion efficiency is usually affected by many factors, like fuel moisture, oxygen supply, and combustion temperature 20–21. In addition, fuel properties and combustion conditions were also reported to influence the size distribution of PM emitted 28–29.
In a previous study, we found that the EFEC for crop residue measured using a traditional cooking stove in China was significantly higher than the EFEC measured in laboratory chambers, and fuel moisture and combustion efficiency were the most important factors affecting the EFPM and EFEC from crop residue burning . To provide firsthand field data for firewood combustion in China, EFPM, EFOC, and EFEC were measured for a wide range of wood fuels used in China in a traditional cooking stove. The influences of fuel properties and burning conditions on the EFs were also investigated. Finally, the size distributions of freshly emitted PM were characterized.
Method
Fuels, Stove and Combustion Experiments
Residential wood combustion experiments were conducted in a simulated rural kitchen, which was built to replicate the layout of kitchens found in rural Northern China. The experimental site is located in a remote area outside of Beijing with no residential or traffic sources nearby. One commonly used brick cooking stove was used in this study. This type of stove is currently used by about 175 million residences in rural areas, following a campaign to disseminate fuel-saving stoves (National Improved Stove Program) during the 1980~1990s in rural China. Detailed information about the kitchen and the stove was published in a previous study . For residential cooking and heating, residents usually burn a mixture of various wood fuels that are available for them, so it is difficult to accurately estimate the consumption of each type of woods burned in the household. This study investigated 17 types of wood, which represent the main tree species used for bio-energy in China 30. These include Chinese white poplar (Populus tomentosa Carr.), water Chinese fir (Metasequoia glyptostroboides), Chinese pine (Pinus tabulaeformis Carr.), cypress (Cupressus funebris Endl.), elm (Ulmus pumila L.), fir (Cunninghamia lanceolata), larch (Larix gmelini (Rupr.) Rupr.), maple (Acer mono Maxim.), oak (Quercus mongolica), paulowonia tomentosa (P.tomentosa (Thunb.) Steud.), toon (Ailanthus altissima), white birch (Betula platyphylla Suk), willow (Salix babylonica), locust (Robinia pseudoacacia L.), bamboo (Phyllostachys heterocycla(Carr.)), lespedeza(Leapedeza bicolor. Turcz), holly (Buxus megistophylla Lévl) and buxus sinica shrub (Buxus sinica (Rehd. et Wils.) Cheng). The properties of these fuels, including density, moisture, elemental contents (C, H, N, O), proximate analysis results (volatile matter, fixed carbon, and ash content), and high heating values, were measured and provided in the Table 1 and Table 2.
Table 1
Density and elemental analysis of C, H, N, O content (%, dry basis) in tested wood fuels. C, H, and N contents were analyzed by the Analytical Instrumentation Center, Peking University (Elementar Vario MICRO CUBE, German) and O content is calculated by the difference.
Fuel type | Density, g/cm3 | C, % | N, % | H, % | O,% | |
---|---|---|---|---|---|---|
Chinese White Poplar | Populus tomentosa Carr. | 0.463 | 47.75 | 0.08 | 6.15 | 46.03 |
Elm | Ulmus pumila L. | 0.536 | 46.89 | 0.84 | 5.89 | 46.40 |
Yellow Locust | Robinia pseudoacacia L. | 1.176 | 45.37 | 1.37 | 6.05 | 47.22 |
Maple | Acer mono Maxim. | 0.949 | 47.73 | 0.18 | 6.10 | 46.00 |
Fir | Cunninghamia lanceolata | 0.427 | 49.83 | 0.13 | 6.18 | 43.87 |
Larch | Larix gmelini (Rupr.) Rupr. | 0.634 | 48.23 | 0.14 | 6.20 | 45.44 |
Water Chinese fir | Metasequoia glyptostroboides | 0.410 | 49.42 | 0.29 | 6.07 | 44.22 |
Cypress | Cupressus funebris Endl. | 0.667 | 50.13 | 0.36 | 6.02 | 43.49 |
Oak | Quercus mongolica | 1.114 | 47.42 | 0.30 | 6.15 | 46.14 |
Chinese Pine | Pinus tabulaeformis Carr. | 0.443 | 49.10 | 0.18 | 6.32 | 44.41 |
Willow | Salix babylonica | 0.551 | 47.39 | 0.23 | 6.14 | 46.25 |
Paulownia tomentosa | P.tomentosa (Thunb.) Steud. | 0.284 | 48.76 | 0.13 | 6.11 | 45.02 |
Toon | Ailanthus altissima | 0.734 | 47.71 | 0.18 | 6.13 | 45.99 |
White Birch | Betula platyphylla Suk | 0.798 | 48.33 | 0.21 | 6.20 | 45.28 |
Lespedeza | Leapedeza bicolor. Turcz | 48.59 | ||||
Buxus sinica | Buxus sinica Cheng | 48.19 | ||||
Holly | Buxus megistophylla Lévl | 45.85 | ||||
Bamboo | Phyllostachys heterocycla | 0.912 | 48.75 | 0.26 | 5.98 | 45.02 |
Table 2
Moisture, volatile matter, ash, fixed carbon content and HHV of tested fuels. Proximate analysis was conducted by the Analytical Center of Chinese Academy of Agricultural Engineering.
Fuel type | Moisture, % | ash, % | VM, % | Fixed carbon, % | HHV, MJ/kg |
---|---|---|---|---|---|
Chinese White Poplar | 5.32 | 0.90 | 81.69 | 17.41 | 18.35 |
Elm | 6.52 | 1.50 | 78.56 | 19.94 | 18.27 |
Yellow Locust | 33.33 | 0.90 | 78.93 | 20.16 | 18.77 |
Maple | 31.41 | 1.80 | 86.12 | 12.08 | 19.03 |
Fir | 9.12 | 0.42 | 82.94 | 16.64 | 18.61 |
Larch | 12.77 | 0.46 | 82.10 | 17.44 | 19.22 |
Water Chinese fir | 12.83 | 0.71 | 79.91 | 19.38 | 19.49 |
Cypress | 12.71 | 1.47 | 76.60 | 21.93 | 20.02 |
Oak | 29.41 | 1.85 | 78.33 | 19.82 | 19.00 |
Chinese Pine | 9.10 | 0.25 | 84.77 | 14.98 | 18.51 |
Willow | 9.92 | 1.41 | 82.84 | 15.75 | 16.99 |
Paulownia tomentosa | 8.69 | 0.30 | 85.03 | 14.67 | 16.00 |
Toon | 7.17 | 1.49 | 82.94 | 15.57 | 17.28 |
White Birch | 32.21 | 0.39 | 87.45 | 12.17 | 20.22 |
Lespedeza | 6.04 | 4.60 | 81.08 | 14.32 | |
Buxus sinica | 6.96 | 5.84 | 78.36 | 15.80 | |
Holly | 6.76 | 8.56 | 77.58 | 13.86 | |
Bamboo | 8.18 | 0.51 | 84.94 | 14.55 | 18.33 |
The combustion experiments were conducted by heating known amounts of water, similar to what has been done in indoor crop residue burning experiments . Pre-weighed (~1.0 kg) quantities of wood fuels were cut into small pieces (about 15~20 cm2 × 20~30 cm in length), ignited at the split wood tips and inserted into the stove chamber, to mimic the pattern of residential wood combustion in rural residents’ daily lives. The shrubby biomass was broke into 20~30 cm sections. The associated flue gas entered a mixing chamber (about 4.5 m3) with a built-in fan. There was no further dilution conducted to avoid the alterations in PM mass loading and size distribution . Measured smoke temperature and relative humidity were 20–35 °C and 40–60% (TM184, Tenmars), respectively. The sampling period covered the whole burning cycle, including the flaming (obvious fire) and smoldering phases (without obvious fire). The sampling started after the initial ignition and stopped when the measured CO and CO2 concentrations dropped to the background levels. The combustion processes lasted for about 40~60 minutes, and ash in the stove was collected and weighed after combustion. It is recognized that combustion conditions and emissions varied in various burning phases and future study using continuous on-line measurements is suggested. This combustion experiment was repeated three times for each type of fuel.
Sampling and Measurement
Sampling work was done throughout the whole burning cycle. Quartz fiber filters (Pall QAT-UP, cut into rounded ones with 25 cm in diameter) were used to collect particulate matter in the flue gas using low-volume pumps (XQC-15E, Tianyue, China) at a flow rate of about 1.0 L/min. Size segregated PM samples were collected using a nine stage cascade impactor (FA-3, Kangjie, China) with glass fiber filters at a flow rate of 28.3 L/min. The cutoff aerodynamic diameters (Da) for each stage were <0.4, 0.4~0.7, 0.7~1.1, 1.1~2.1, 2.1~3.3, 3.3~4.7, 4.7~5.8, 5.8~9.0, and 9.0~10.0 µm, respectively. All filters were baked at 450 °C for 6 hours and equilibrated in a desiccator for 24 hours prior to being weighed. After sampling, particle-loaded filters were folded and packed using aluminum foil. Procedure blanks of PM, OC and EC were also measured using the same protocol, and subtracted from the results.
Gravimetric measurements were conducted using a high precision (0.00001g) digital balance. EC and OC were analyzed using a Sunset EC/OC analyzer (Sunset Lab, USA) . The filter was heated in a pure helium at 600, 840, 550 °C for OC detection, and then at 550, 650, 870 °C in an oxygen/helium atmosphere to determine EC. The carbon results were calculated using methane at the end of each analysis cycle, and pryolyzed OC, produced in inert helium when temperature increased, was subtracted from EC results accordingly the initial laser value. CO2 and CO were measured every 2 seconds with an on-line detector equipped with non-dispersive infrared sensor and measurements were recorded automatically. The equipment (GXH-3051, Technical Institute, China) was calibrated using a span gas before each combustion experiment (CO, 1.00%; CO2, 5.00%).
Data Analysis
EFs of PM, OC, and EC were calculated using the carbon mass balance method by assuming that the carbon in the fuel was completely released in the form of CO2, CO, total gaseous hydrocarbons, and carbonaceous carbon in particulate phase 9. In this study, gaseous total hydrocarbon was not measured which may lead to an estimated error of less than 4%, since most of the released gaseous carbon was in the forms of CO2 or CO .
Modified combustion efficiency (MCE), defined as CO2/(CO2+CO) ratios (molar basis), and fuel burning rate (R) were calculated to quantitatively describe the combustion conditions. Results of calculated MCE and burning rates for each fuel type were listed in Table 3. Since EFs were lognormal distributed , log-transformed EFs were used for comparison and correlation analysis. When other parameters with limited information on frequency distribution were involved, non-parametric tests were applied. Statistical analysis was conducted at a significance level of 0.05. Principal Component Analysis (PCA) was used to investigate the impacts of various factors on EFs of PM, OC and EC from residential wood combustion.
Table 3
Calculated MCE and burning rates of tested residential wood combustion in the whole burning cycle. Data shown are average and standard derivations of triplicate combustion experiments.
Fuel type | MCE, % | R, kg/min | OC, g/kg | EC, g/kg | PM, g/kg |
---|---|---|---|---|---|
Chinese White Poplar | 94.7±0.9 | 0.055±0.006 | 0.66±0.32 | 0.88±0.49 | 1.8±0.3 |
Elm | 95.6±0.1 | 0.064±0.005 | 0.79±0.15 | 1.2±0.3 | 2.2±0.2 |
Yellow Locust | 93.7±0.1 | 0.082±0.011 | 1.9±1.5 | 0.21±0.15 | 1.7±0.6 |
Maple | 95.7±0.2 | 0.065±0.003 | 0.11±0.01 | 0.056±0.004 | 0.8±0.4 |
Fir | 96.0±0.3 | 0.051±0.014 | 0.97±0.88 | 0.95±0.20 | 1.7±0.7 |
Larch | 95.8±0.3 | 0.049±0.000 | 0.14±0.11 | 0.35±0.34 | 0.71±0.36 |
Water Chinese fir | 96.2±0.0 | 0.071±0.001 | 0.36±0.17 | 0.85±0.45 | 1.8±0.2 |
Cypress | 93.5±0.8 | 0.052±0.002 | 0.82±0.45 | 0.71±0.39 | 2.0±0.6 |
Oak | 95.0±0.4 | 0.051±0.008 | 0.54±0.63 | 0.13±0.13 | 1.7±1.0 |
Chinese Pine | 94.1±0.6 | 0.078±0.010 | 0.60±0.35 | 0.94±0.40 | 1.6±0.3 |
Willow | 95.1±0.2 | 0.070±0.006 | 0.23±0.10 | 0.47±0.30 | 0.89±0.21 |
Paulownia tomentosa | 94.8±0.3 | 0.076±0.009 | 0.39±0.12 | 0.94±0.51 | 1.9±0.2 |
Toon | 95.4±0.3 | 0.071±0.008 | 0.19±0.13 | 0.52±0.41 | 1.0±0.4 |
White Birch | 93.7±0.7 | 0.064±0.006 | 0.69±0.32 | 0.67±0.61 | 1.9±0.4 |
Lespedeza | 94.4±0.1 | 0.192±0.052 | 0.21±0.17 | 0.48±0.40 | 3.5±2.5 |
Buxus sinica | 94.8±0.5 | 0.095±0.006 | 1.2±0.1 | 2.5±1.7 | 4.7±0.6 |
Holly | 95.8±0.7 | 0.086±0.004 | 0.73±0.28 | 1.6±0.4 | 3.1±0.1 |
Bamboo | 95.2±0.9 | 0.071±0.021 | 0.13±0.09 | 1.2±0.5 | 2.1±0.5 |
Note: Carbon mass fraction for wood of different types varied due to difference in fuel type and combustion condition. And, adsorption of gaseous organics and systematic error in PM gravimetric measurement may result in the sum percentage larger than 100% in some cases 33.
Results and Discussion
Emission Factors
The measured EFs of PM, OC, and EC for 18 tested wood fuels (dry basis) burned under normal conditions are listed in Table 3. Significant differences (p < 0.05) in EFs of PM, OC, and EC between the tested tree woods (1.5±0.6, 0.62±0.65, and 0.65±0.46 g/kg as means and standard deviations, respectively, n = 42) and shrubs (3.8±1.4, 0.81±0.64, and 1.53±1.10 g/kg, respectively, n = 9) was found. Since there was no significant difference in chemical composition (C, H, N, and O contents) between the tree and shrubby biomasses, and the burning rates of the tree wood fuels (0.065±0.012 kg/min) were significantly lower than those of shrubby biomasses (0.121±0.054 kg/min) (p < 0.05), the relatively higher EFs of shrubby biomass likely resulted from the faster burning which may cause severe oxygen shortage in a stove hearth with limited volume 16, . It was also found that the measured EFs for the woods were significantly lower (p < 0.05) than those for crop residues (8.2±4.3, 1.5±0.6, and 1.4±0.7 g/kg for PM, OC, and EC, respectively), which were measured using the same facility under same conditions (p < 0.05) . The difference may be partly explained by the higher bulk densities and lower moistures of the crop residues , 17.
PM emissions from wood burning in China were reported at 1.17~5.87 g/kg in instant combustion, and 1.51~8.73 g/kg in ultimate combustion 9. In a field measurement on residential wood combustion in rural area, OC, EC, and PM2.5 EFs were 1.14±0.40, 1.49±0.69, and 3.08±0.82 g/kg, respectively . It can be seen that these EFs, and also published data for biomass burning in other countries – were all highly variable depending on fuel types and combustion conditions. It was reported that for the same fuel, EFs of fine PM from cooking stove combustion were significantly lower than those in fireplaces 36–38. Based on the previously published EFs in literature and those measured in this study, EFs of PM, OC, and EC were 2.95±3.04 (0.31~16.3, as range, n=85), 1.10±1.56 (0.02~8.09, n=109), and 0.68±0.64 (0.04~3.77, n=85) g/kg for woodstoves 9,14, 37– and 7.96±4.32 (1.6~20.2, n=61), 4.80±2.06 (1.09~9.17, n=36), and 0.66±1.23 (0.04~6.58, n=36) for fireplaces, respectively 14, 37–42, –. The differences between them were indeed significant (p < 0.05).
In a previous study, we found that the EFs of PM and EC associated with burning crop residues in stoves were significantly higher than those measured in laboratory chambers. For firewood, however, our data were not higher than those (0.2~1.3, 8.0~27.8, and 7~55 g/kg for EC, OC, and PM, respectively) obtained from an open-burning chamber experiment –. More studies, like those focusing on the evaporation of gaseous organics which were largely emitted in crop residue burning, are needed to explain this phenomenon.
It is reasonable to expect that EFs of PM, OC and EC are significantly correlated (p < 0.05) (Figure 1). Similar correlation has been reported for crop residue and coal combustion, . Although CO is also an incomplete combustion product and is occasionally used as a surrogate for the emissions of PM and other pollutants 18, , no significant correlation was found between EFs of CO and PM, EC, or OC in this study (p > 0.05). This complicated relationship between CO and emissions of other pollutants has been mentioned in the literature , 18.
Correlations among EFs of PM, EC, and OC for wood combusted in a residential stove.
The mean ratios of OC/PM and EC/PM were 0.32±0.27 and 0.39±0.26, respectively and there was no significant difference between tested shrubby and tree biomasses (p > 0.05). These ratios were significantly higher (p < 0.05) than those for crop residue burning in the same stove (0.19 ± 0.07 and 0.19 ± 0.07) , suggesting that carbon mass fractions in PM from wood combustion were higher than those produced from crop residue burning. Similar results were reported in another study on carbonaceous aerosol emissions from household biofuel combustion . Future study is necessary to look into the reason of the phenomenon, for example the difference in fuel ash content.
The mean EC/OC ratio obtained in this study was 1.71±1.19. Li et al., reported an EC/OC ratio at 1.41±0.57 for household wood combustion in China. The value was comparable to our result, and both were found to be higher than those reported in the literature 1, 38–. CO/CO2 ratios measured in this study (lower in the flaming phase with obvious fire and increased in the smoldering phase without obvious fire) were lower than 10%, indicating the hot flaming domination in the tested wood combustion , 54, and hence more EC emitted from the relatively high temperature combustion in the flaming phase. Further studies are necessary to investigate the carbon emission dominated in different phases, like EC emitted in the flaming phase and brown carbon produced mainly in the smoldering phase 24, 55–56.
Individually, most of the investigated factors were not significantly correlated with the measured EFs (p>0.05) in this study, which can be partly explained by the fact that the impacts of these factors were often interacted and relatively greater variation of these factors and measured EFs may prevent from seeing the effect of an individual factor . By using PCA (each factor enter individually), four associations were extracted and can explain more than 80% of the total variation. Factor 4 (burning rate and MCE) was the most significant factor identified (p < 0.05) for EFs of OC, EC, and PM (details in the Supporting Information).
PM Size Distribution
Figure 2 shows size distributions of PM emitted from wood combustion. The fuels tested in this study were classified into three categories of bamboo, tree, and shrubby biomass. For comparison, size distribution of PM from crop residue burning under similar conditions is also illustrated. It appears that size distributions of PM from tree, shrubby, and crop residue combustions were different though all of them were unimodal.
Size distributions of PM from the combustion of bamboo (A), tree fuel (B), shrubby biomass (C), and crop residue (D). The last one in Shen et al., (2010) is shown to compare with those obtained in this study.
PM from 14 tested tree woods of different types had very similar size distributions with peaks (24.3±6.1%) at less than 0.4 µm (PM0.4). In general, PM with diameter less than 2.1 µm (PM2.1) composed up to 79.4±7.0% of the total PM. Domination of fine particles in emissions from firewood combustion has been reported before , 42 and it is believed that these fine particles are primarily soot-related , 57. The domination of fine particles from tree wood burning is a health concern for those who cook in a kitchen, since fine particles can penetrate deeper into the lungs and are often associated with many toxic compounds –. For shrubby biomass combustion, PM was dominated by those between 2.1 and 3.3 µm (21.0±9.4%), followed by PM at diameter between 1.1 and 2.1 µm (19.6±4.4%). Overall, PM0.4 and PM2.1 fractions were 14.6±5.4% and 57.7±10.8%, respectively, which were very different from those emitted from tree biomass combustion. To the best of our knowledge, size distribution data on PM emissions from shrubby biomass burning are not found in existing literature, likely due to the fact that shrubs are not used as a biomass fuel in developed countries. However, since shrubby biomass contributes to a significant fraction of the overall firewood consumed in developing countries , the difference between shrubby and tree biomasses cannot be ignored when characterizing PM emission from firewood combustion.
In this study, MCE was found to be negatively correlated with 3 PM fractions with diameters less than 1.1 µm and positively correlated with all other PM fractions (p < 0.05), which means that under higher MCE, the mass median diameters of emitted PM would be larger. A positively linear relationship between MCE and median diameter was previously reported for fresh forest smoke 22. Meanwhile, moisture affects the size distribution in an opposite direction to MCE. It is negatively correlated with coarse PM and positively correlated with fine PM (p < 0.05), indicating that the median mass diameters of PM from lower moisture wood combustions were larger, and this might be partly explained by the oxygen scarce condition 28. The influence of fuel moisture on median diameters of PM from combustion can be either negative or positive since they can also be affected by many other factors 28–29, 57. It was believed that the influence of moisture is related to the combustion temperature and efficiency, and also the change in the relative humidity of the flue gas and the condensation process. Lower moisture may result in a higher temperature which is favorable for formation of small particles 28, 57; however, wood with too low moisture may burn too fast to result in an oxygen limited atmosphere in the stove with relatively small chamber and only natural air ventilation 17, 25, . These, in turn, produce a large number of large particles since lower oxygen levels are expected to promote less intense smoldering conditions producing large particles due to agglomeration and condensation processes 17, 28. Future studies are needed to investigate the factors influencing formation mechanisms of both fine and coarse PM.
Primary Emission off EC, OC, and PM in rural China
Total residential consumption of wood in rural China was 182.17 Tg in 2007 59. Based on the measured EFs, total emissions of PM, OC, and EC from residential wood combustion in rural China were estimated to be 303, 75.7, and 92.0, Gg in 2007. It should be pointed out that, similar to the emissions from many other sources, our estimated emissions are subject to high uncertainty due to uncertainties in both EFs and in the qualities of fuel consumed. One of the sources of uncertainty originates from the difference in the EFs among various woods, which varied dramatically with coefficients of variation of 40~71%. Unfortunately, data on the fractions of various wood fuels consumed in China are unavailable and there is no choice but to average EFs.
The geographical distribution of PM emissions from rural residential wood combustion in mainland China in 2007 is shown in Figure 3 as emission density. The highest emission densities were found in southern China including Guizhou, Guangxi, and Guangdong. Due to a shortage of fossil fuel and relatively high abundance of biomass fuels, wood was widely used for daily cooking in these areas. And, a large rural population in the regions requires large volume of fuels and subsequently leads to high emissions. Although other southern provinces including Fujian, Hainan, and Yunnan and northeastern provinces such as Heilongjiang are also rich in woods, emission densities of firewood burning were relatively low due to either the domination of fossil fuel or lower population density.
Emission density of primary PM from residential wood combustion in Mainland China.
Supplementary Material
1_si_001
Acknowledgment
Funding for this study was provided by the National Natural Science Foundation of China (41001343, 41130754, 40703029), Ministry of Environmental Protection (201209018), Beijing Municipal Government (YB20101000101), and NIEHS (P42 ES016465). We thank Zoë Chafe (University of California, Berkeley) and Marcus Trail (Georgia Institute of Technology) for proof reading.
Footnotes
Supporting Information Available
Analysis of influencing factors using PCA are provided in the supporting information, and available free of charge via the Internet at http://pubs.acs.org
References
1. Chow JC, Watson JG, Lowenthal DH, Antony Chen L-W, Motallebi N. PM2.5 source profiles for black and organic carbon emission inventories. Atmos. Environ. 2011;45:5407–5414.[Google Scholar]
2. Dockery DW, Pope CA, III, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG, Speizer FE. An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med. 1993;329:1753–1759. [PubMed] [Google Scholar]
3. Englert N. Fine particles and human health-a review of epidemiological studies. Toxicol. Lett. 2004;149:235–242. [PubMed] [Google Scholar]
4. Pope CA, III, Ezzati M, Dockey DW. Fine-particulate air pollution and life expectancy in the United States. N. Engl. J. Med. 2009;360:376–386.[PMC free article] [PubMed] [Google Scholar]
5. Jacobson MZ. A physically-based treatment of elemental carbon optics: Implications for global direct forcing of aerosols. Geophys. Res. Lett. 2000;27:217–220.[Google Scholar]
6. Danielsen PH, Møller P, Jensen KA, Sharma AK, Wallin H, Bossi R, Autrup H, Mølhave L, Ravanat J-L, Briedé JJ, de Kok TM, Loft S. Oxidative stress, DNA damage, and inflammation induced by ambient air and wood smoke particulate matter in human A549 and THP-1 cell lines. Chem. Res. Toxicol. 2011;24:168–184. [PubMed] [Google Scholar]
7. Venkataraman C, Habib G, Eiguren- Fernandez A, Miguel AH, Friedlander SK. Residential biofuels in South Asia: carbonaceous aerosol emissions and climate impacts. Science. 2005;307:1454–1456. [PubMed] [Google Scholar]
8. Lei Y, Zhang Q, He KB, Streets DG. Primary anthropogenic aerosol emission trends for China, 1990–2005. Atmos. Chem. Phys. 2011;11:931–954.[Google Scholar]
9. Zhang J, Smith KR, Ma Y, Ye S, Jiang F, Qi W, Liu P, Khalil MAK, Rasmussen RA, Thorneloe SA. Greenhouse gases and other airborne pollutants from household stoves in China: a database for emission factors. Atmos. Environ. 2000;34:4537–4549.[Google Scholar]
10. Chen YJ, Zhi G, Feng Y, Fu J, Feng J, Sheng G, Simoneit BRT. Measurement of emission factors for primary carbonaceous particles from residential raw-coal combustion in China. Geophys. Res. Lett. 2006;33:L20815.[Google Scholar]
11. Li X, Wang S, Duan L, Hao J, Nie Y. Carbonaceous aerosol emissions from household biofuel combustion in China. Environ. Sci. Technol. 2009;43:6076–6081. [PubMed] [Google Scholar]
12. Streets DG, Gupta S, Waldhoff ST, Wang MQ, Bond TC, Yiyun B. Black carbon emissions in China. Atmos. Environ. 2001;35:4281–4296.[Google Scholar]
13. Roden CA, Bond TC, Conway S, Pinel ABO. Emission factors and real-time optical properties of particles emitted from traditional wood burning cookstoves. Environ. Sci. Technol. 2006;40:6750–6757. [PubMed] [Google Scholar]
14. McDonald JD, Zielinska B, Fujita EM, Sagebiel JC, Chow JC, Watson JG. Fine particle and gaseous emission rates from residential wood combustion. Environ. Sci. Technol. 2000;34:2080–2091.[Google Scholar]
15. Shen GF, Yang YF, Wang W, Tao S, Zhu C, Min Y, Xue M, Ding J, Wang B, Wang R, Shen H, Li W, Wang X, Russell AG. Emission factors of particulate matter and elemental carbon for crop residues and coals burned in typical household stoves in China. Environ. Sci. Technol. 2010;44:7157–7162.[PMC free article] [PubMed] [Google Scholar]
16. Jenkins BM, Jones AD, Turn SQ, Williams RB. Emission factors for polycyclic aromatic hydrocarbons from biomass burning. Environ. Sci. Technol. 1996;30:2462–2469.[Google Scholar]
17. Simoneit BRT. Biomass burning- a review of organic tracers for smoke from incomplete combustion. Appl. Geochem. 2002;17:129–162.[Google Scholar]
18. Bignal KL, Langridge S, Zhou JL. Release of polycyclic aromatic hydrocarbons, carbon monoxide and particulate matter form biomass combustion in a wood-fired boiler under varying boiler conditions. Atmos. Environ. 2008;42:8863–8871.[Google Scholar]
19. Chomanee C, Tekasakul S, Tekasakul P, Furuuchi M, Otani Y. Effects of moisture content and burning period on concentration of smoke particles and particle-bound polycyclic aromatic hydrocarbons from rubber wood combustion. Aerosol Air Qual. Res. 2009;9:404–411.[Google Scholar]
20. Dhammapala R, Claiborn C, Corkill J, Gullett B. Particulate emissions from wheat and Kentucky bluegrass stubble burning in eastern Washington and northern Idaho. Atmos. Environ. 2006;40:1007–1015.[Google Scholar]
21. McMeeking GR, Kreidenweis SM, Baker S, Carrico CM, Chow JC, Collett JL, Jr, Hao WM, Holden AS, Kirchstetter TW, Malm WC, Moosmüller H, Sullivan AP, Wold CE. Emission of trace gases and aerosols during the open combustion of biomass in the laboratory. J. Geophys. Res. 2009:114. D19210. [Google Scholar]
22. Janhäll S, Andreae MO, Pöschl U. Biomass burning aerosol emissions from vegetation fires: particle number and mass emission factors and size distributions. Atmos. Chem. Phys. 2010;10:1427–1439.[Google Scholar]
23. Shen G, Wang W, Yang Y, Zhu C, Min Y, Xue M, Ding J, Li W, Wang B, Shen H, Wang R, Wang X, Tao S. Emission factors and particulate matter size distribution of polycyclic aromatic hydrocarbons from residential coal combustions in rural Northern China. Atmos. Environ. 2010;44:5237–5243.[PMC free article] [PubMed] [Google Scholar]
24. Chen L-WA, Verburg P, Shackelford A, Zhu D, Susfalk R, Chow JC, Watson JG. Moisture effects on carbon and nitrogen emission from burning of wildland biomass. Atmos. Chem. Phys. 2010;10:6617–6625.[Google Scholar]
25. Rogge WF, Hildemann LM, Mazurek MA, Cass GR, Simoneit BRT. Sources of fien organic aerosol. 9. pine, oak, and synthetic log combustion in residential fireplaces. Environ. Sci. Technol. 1998;32:13–32.[Google Scholar]
26. Lu H, Zhu L, Zhu N. Polycyclic aromatic hydrocarbon emission from straw burning and the influence of combustion parameters. Atmos. Environ .2009;43:4978–4983.[Google Scholar]
27. Johansson LS, Leckner B, Gustavsson L, Cooper D, Tullin C, Potter A. Emission characteristics of modern and old-type residential boilers fired with wood logs and wood pellets. Atmos. Environ. 2004;38:4183–4195.[Google Scholar]
28. Hays MD, Smith ND, Kinsey J, Dong Y, Kariher P. Polycyclic aromatic hydrocarbon size distributions in aerosols from appliances of residential wood combustion as determined by direct thermal desorption-GC/MS. J. Aerosol. Sci. 2003;34:1061–1084.[Google Scholar]
29. Venkataraman C, Joshi P, Sethi V, Kohli S, Ravi MR. Aerosol and carbon monoxide emissions from low temperature combustion in a sawdust packed-bed stove. Aerosol Sci. Technol. 2004;38:50–61.[Google Scholar]
30. Gao SW, Ma WY. Tree species as main bio-energy resources in China. Beijing, P.R. China: China Forestry Press; 1990. [Google Scholar]
31. Xu S, Liu W, Tao S. Emission of polycyclic aromatic hydrocarbons in China. Environ. Sci. Technol. 2006;40:702–708. [PubMed] [Google Scholar]
32. Zhang Y, Dou H, Chang B, Wei Z, Qiu W, Liu S, Liu W, Tao S. Emission of polycyclic aromatic hydrocarbons form indoor straw burning and emission inventory updating in China. Ann. NY. Acad. Sci. 2008;1140:218–227. [PubMed] [Google Scholar]
33. Fine PM, Cass GR, Simoneit BRT. Chemical characterization of fine particle emissions from fireplace combustion of wood types grown in the Midwestern and western United States. Environ. Eng. Sci. 2004;21:387–409.[Google Scholar]
34. Venkataraman CA, Rao GUM. Emission factors of carbon monoxide and size-resolved aerosols from biofuel combustion. Environ. Sci. Technol. 2001;35:2100–2107. [PubMed] [Google Scholar]
35. Johnson M, Edwards R, Alatorre Frenk C, Masera O. In-field greenhouse gas emissions from cookstoves in rural Mexican households. Atmos. Environ. 2008;42:1206–1222.[Google Scholar]
36. Gonçalves C, Alves C, Fernandes AP, Monteiro C, Tarelho L, Evtyugina M, Pio C. Organic compounds in PM2.5 emitted from fireplace and woodstove combustion of typical Portuguese wood species. Atmos. Environ. 2011;45:4533–4545.[Google Scholar]
37. Alves C, Gonçalves C, Fernandes AP, Tarelho L, Pio C. Fireplace and woodstove fine particle emissions from combustion of western Mediterranean wood types. Atmos. Res. 2011;101:692–700.[Google Scholar]
38. Fine PM, Cass GR, Simoneit BRT. Chemical characterization of fine particle emissions from wood stove combustion of prevalent United States tree species. Environ. Eng. Sci. 2004;21:705–721.[Google Scholar]
39. Schauer JJ, Kleeman MJ, Cass GR, Simoneit BRT. Measurement of emissions from air pollution sources. 3. C1–C29 organic compounds from fireplace combustion of wood. Environ. Sci. Technol. 2001;35:1716–1728. [PubMed] [Google Scholar]
40. Fine PM, Cass GR, Simoneit BRT. Chemical characterization of fine particle emissions from fireplace combustion of woods grown in the Northeastern United States. Environ. Sci. Technol. 2001;35:2665–2675. [PubMed] [Google Scholar]
41. Fine PM, Cass GR, Simoneit BRT. Chemical characterization of fine particle emissions from fireplace combustion of woods grown in the Southern United States. Environ. Sci. Technol. 2002;36:1442–1451. [PubMed] [Google Scholar]
42. Hedberg E, Kristensson A, Ohlsson M, Johansson C, Johansson P, Swietlicki E, Vesely V, Wideqvist U, Westerholm R. Chemical and physical characterization of emissions from birch wood combustion in a wood stove. Atmos. Environ. 2002;36:4823–4837.[Google Scholar]
43. Tissari J, Hytönen K, Lyyränen J, Jokiniemi J. A novel field measurement method for determining fine particle and gas emissions from residential wood combustion. Atmos. Environ. 2007;41:8330–8344.[Google Scholar]
44. Tissari J, Lyyränen J, Hytönen K, Sippula O, Tapper U, Frey A, Saarnio K, Pennanen AS, Hillamo R, Salonen RO, Hirvonen M–R, Jokiniemi J. Fine particle and gaseous emissions from normal and smouldering wood combustion in a conventional masonry heater. Atmos. Environ. 2008;42:7862–7873.[Google Scholar]
45. Gonçalves C, Alves C, Evtyugina M, Mirante F, Pio C, Caseiro A, Schmidl C, Bauer H, Carvalho F. Characterization of PM10 emissions form woodstove combustion of common woods grown in Portugal. Atmos. Environ. 2010;44:4474–4480.[Google Scholar]
46. Lamberg H, Nuutinen K, Tissari J, Russunen J, Yli-Pirilä P, Sippula O, Tapanainen M, Jalava P, Makkonen U, Teinilä K, Saarnio K, Hillamo R, Hirvonen M, Jokiniemi J. Physicochemical characterization of fine particles from small-scale wood combustion. Atmos. Environ. 2011[Google Scholar]
47. Pettersson E, Boman C, Westerholm R, Boström D, Nordin A. Stove performance and emission characteristics in residential wood log and pellet combustion, Part 2: wood stove. Energy & Fuel. 2011;25:315–323.[Google Scholar]
48. Gullett BK, Touati A, Hays MD. PCDD/F, PCD, HxCBz, PAH and PM emission factors for fireplace and woodstove combustion in the San Francisco Bay Region. Environ. Sci. Technol. 2003;37:1758–1765. [PubMed] [Google Scholar]
49. Hildemann LM, Markowski GR, Cass GR. Chemical composition of emissions from urban sources of fine organic aerosol. Environ. Sci. Technol. 1991;25:744–759.[Google Scholar]
50. Hays MD, Geron CC, Linna KJ, Smith ND. Speciation of gas-phase and fine particle emissions from burning of foliar fuels. Environ. Sci. Technol. 2002;36:2281–2295. [PubMed] [Google Scholar]
51. Lee RG, Coleman P, Jones JL, Jones KC, Lohmann R. Emission factors and importance of PCDD/Fs, PCBs, PCNs, PAHs and PM10 from the domestic burning of coal and wood in the U.K. Environ. Sci. Technol. 2005;39:1436–1447. [PubMed] [Google Scholar]
52. Grandesso E, Gullett B, Touati A, Tabor D. Effect of moisture, charge size and chlorine concentration on PCDD/F emissions from simulated open burning of forest biomass. Environ. Sci. Technol. 2011;45:3887–3894. [PubMed] [Google Scholar]
53. Chen Y, Sheng G, Bi X, Feng Y, Mai B, Fu J. Emission factors for carbonaceous particles and polycyclic aromatic hydrocarbons from residential coal combustion in China. Environ. Sci. Technol. 2005;39:1861–1867. [PubMed] [Google Scholar]
54. Hurst DF, Griffith DWT, Carras JN, Williams DJ, Fraser PJ. Measurements of trace gases emitted by Australian savanna fires during the 1990 dry seasons. J. Atmos. Chem. 1994;18:33–56.[Google Scholar]
55. Andreae MO, Gelencser A. Black carbon or brown carbon? The nature of lightabsorbing carbonaceous aerosols. Atmos. Chem. Phys. 2006;6:3131–3148.[Google Scholar]
56. Chakrabarty RK, Moosmüller H, Chen LWA, Lewis K, Arnott WP, Mazzoleni C, Dubey MK, Wold CE, Hao WM, Kreidenweis SM. Brown carbon in tar balls from smoldering biomass combustion. Atmos. Chem. Phys. 2010;10:6363–6370.[Google Scholar]
57. Purvis CR, McCrillis RC, Kariher PH. Fine particulate matter (PM) and organic speciation of fireplace emissions. Environ. Sci. Technol. 2000;34:1653–1658.[Google Scholar]
58. Hu H, Li A. Charactristics of gas release during combustion of main arbor and shrub species in Xiaoxing’ an Mountain. Chin. J. Appl. Ecol. 2008;19:1431–1436. (in Chinese). [PubMed] [Google Scholar]
59. National Bureau of Statistics of China. http://www.stats.gov.cn/tjsj/qtsj/hjtjzl/index.htm.
J Environ Sci (China). Author manuscript; available in PMC 2014 Dec 31.
Published in final edited form as:
J Environ Sci (China). 2013 Mar 1; 25(3): 511–519.
NIHMSID: NIHMS649549
See other articles in PMC that cite the published article.
Abstract
The uncertainty in emission estimation is strongly associated with the variation in emission factor which could be influenced by a variety of factors, like fuel property, stove type, fire management and even methods used in measurements. The impacts of these factors were usually complicated and often interacted with each other. In the present study, controlled burning experiments were conducted to investigate the influence of fuel mass load, air supply and burning rate on the emission of carbonaceous particulate matter (PM) from indoor corn straw burning. Their impacts on PM size distribution were also studied. The results showed that EFs of PM (EFPM), organic carbon (EFOC) and element carbon (EFEC) was independent of the fuel mass load. The differences among them under different burning rates or air supply amounts were also found to be insignificant (p > 0.05) in the tested circumstances. PM from the indoor corn straw burning was dominated by fine PM, and PM with diameter less than 2.1 μm contributed about 86.4±3.9% of the total. The size distribution of PM was also influenced by the burning rate and changed air supply conditions. On average, EFPM, EFOC and EFEC for corn straw burned in a residential cooking stove were 3.84±1.02, 0.846±0.895 and 0.391±0.350 g/kg, respectively. EFPM, EFOC and EFEC were found to be positively correlated with each other, but they were not significantly correlated with EF of co-emitted CO, suggesting a special attention should be paid to the use of CO acting as a surrogate for other incomplete pollutants.
Keywords: indoor corn straw burning, emission factor, size distribution, influencing factor
Introduction
Particulate matter (PM), especially fine PM, has been widely studied due to its adverse effect on air quality, human health and climate change. Increased ambient levels of fine PM were thought to be an important reason for the decrease of visibility in many cities (Che et al., 2007, 2009; ). Fine PM can penetrate deep into the lung area. Exposure to serious fine PM pollution was thought to be associated with the increased risks of various respiratory and cardiovascular diseases and mortality rates (; ; ; ; ; ; ). Though the mechanism of adverse health effects induced by PM exposure was not so clear at this stage, it was thought to be related to the physic-chemical properties of the PM, like particle size and its chemical compositions, and there are often high temporal and spatial variations existing. As a climate-relevant pollutant, it was generally believed that sulfate and organic carbon (OC) contents in PM usually have a cooling effect by scattering the light, while element carbon (EC) may have a positive radiative forcing because of light absorption (Chow et al., 2011; ; Jacobson, 2000).
To study the transport and fate behaviors of a pollutant, and to analyze the environmental impacts and pollution control strategy, it is necessary to develop the emission inventory of the pollutant. An accurate estimation with a high resolution in both regional and global scales is urgently needed (; Chow et al., 2011; ). However, current emission estimations in the literature generally have a high uncertainty and bias because of limited data available and high degree of variation, especially in EFs (Bond et al., 2004; ; ; Chow et al., 2011; ; Li et al., 2007, ; Yan et al., 2006). Emission estimation is usually based on the total fuel consumption and emission factors (EFs), defined as mass of the pollutant per mass of consumed fuel, or per unit energy (Bond et al., 2004; Zhang et al., 2000). EFs for a target can be measured directly or derived from EFs of another surrogate and their relationship. EFs reported in the literature often vary dramatically due to the difference in fuel properties (e.g. bulk density and moisture), stove types (e.g. traditional or improved stoves with a chimney or not), burning conditions (e.g. oxygen supply amount, fuel/air mixing status and fire management pattern), and even experimental methods (e.g. laboratory chamber study, simulated combustion or field measurement) (Bignal et al., 2008; ; Dhammapala et al., 2006, 2007; ; , 2009; McDonald et al., 2000; ). For example, emissions of organic compounds from prescribed burning were much higher than those from the combustion in fireplace and laboratory simulated burning (). It was reported that EFs of PM from residential wood combustion in cooking stoves were about 2-4 times higher in field measurements in comparison to those in laboratory investigations (, 2009). But good comparison between field measurements and laboratory studies was also found in some studies when taking the combustion efficiency into the consideration (Christian et al., 2003; Dhammapala et al., 2007). EFs were generally lower from the combustion in higher combustion efficiencies. The combustion efficiency could be influenced by various factors including fuel moisture, oxygen supply amounts and fuel/air mixing status, and even fire management (Dhammapala et al., 2007; McMeeking et al., 2009; Janhäll et al., 2010). It was accepted that the impacts of fuel property and burning conditions were complicated and sometimes interacted with each other (Lu et al., 2009; Rogge et al., 1998; Simoneit, 2002).
While filed measurements were able to get representative and reliable EFs, laboratory simulation studies were also commonly conducted to repeat the combustion process providing information and also enough samples for emission characterization studies, and to get EFs for various pollutants from different burning scenarios in a relatively short study period. The later also provided the opportunity to investigate the influence of fuel properties and burning conditions under controlled conditions (; Lu et al., 2009; Ryu et al., 2006; Xie et al., 2009). For example, Ryu et al., (2006) investigated the influence of fuel bulk density, size and air flow rate on emissions from biomass combustion in a fixed bed. simulated the open biomass burning in laboratory and studied the effect of moisture (7-50%) and fuel charge size (1-10 kg) on PCDD/F emissions, and found that the total EFs of PCDD/F and toxic equivalency were not significantly influenced by fuel charge size and moisture (). studied the influence of excess air, the degree of air staging and the fuel feeding position on the emissions of gaseous SO2 and NOX from the burning of coal in a bench scale circulating fluidized bed combustor. Lu et al., (2009) investigated the influence of combustion temperature, fuel moisture and oxygen amount on polycyclic aromatic hydrocarbon emissions from crop straw burning in a laboratory chamber under controlled conditions.
Residential biomass burning is a larger emitter of fine PM, OC and EC in China (Bond et al., 2004; Lei et al., 2011; ). It was estimated that the total emissions of PM2.5 (PM with diameter less than 2.5 μm), PM10 (PM with diameter less than 10 μm), total suspended particle (TSP), OC and EC in China were 13.0, 18.8, 34.3, 3.19 and 1.51 Tg in 2005, of which 27.7, 19.8, 11.3, 71.8 and 39.1% were from indoor biomass burning (Lei et al., 2011). In our previous study, EFs of PM, OC and EC from indoor crop straw burning were measured in a rural kitchen. Nine types of crop straw were burned, and the influences of fuel moisture and combustion efficiency on measured EFs were quantitatively analyzed (). The main objective of the present study is to investigate the influence of fuel mass load, burning rate and air supply conditions on the emission of carbonaceous PM from indoor corn straw burning in a residential cooking stove. The burning processes were conducted in a real cooking stove in a rural kitchen, rather than a laboratory chamber. In addition to the total emissions of PM, size distributions of PM in different circumstances were also characterized.
1. Material and Methods
1.1 Combustion experiment
Combustion experiments were conducted in a rural kitchen. The kitchen was built previously to measure the emission factors of pollutants from residential solid fuel combustion in rural Northern China (, ). A commonly used brick cooking stove (80 cm in length, 70 cm in width and 65 cm in height) with one iron pot in the middle was used. The combustion chamber was approximately 0.20 m3, and the grate air inlet area was 0.09 m2. Corn, rice and wheat were three main crops grown in China, and the straw of corn, rice and wheat contributed about 40, 18 and 20% of the total crop residue combusted in rural area (; Zeng et al., 2007). In this study, corn straw with moisture of 7.02% was burned. More detailed information of the used stove and corn straw properties can be found elsewhere ().
To investigate the influence of fuel mass load on the emissions of carbonaceous particulate matter, three different load conditions with fuel mass at 275, 550, and 1100 g, respectively were adopted. Fuels were inserted into the combustion chamber and burned following the routine pattern used by the rural residents in daily lives. To investigate the impact of fuel burning rate, one man-induced slower and another faster burning experiment were done in addition to the combustion conducted in a normal way with a median burning rate. Fuel mass loads in these three burning scenarios were all 550 g. Calculated burning rates were 0.020±0.001, 0.045±0.002 and 0.119±0.016 kg/min, respectively. Air supply rate in the routine corn straw burning with only natural ventilation in the present study was estimated at about 9.0 m3/h (Wei, 2012). To investigate the potential influence of air supply on emission performance, another two combustion scenarios with reduced and enhanced air supply were conducted. To get a higher ventilation rate (∼ 19.0 m3/h) during the corn straw burning, one additional blower was used. A lower air supply amount during the burning was achieved by decreasing the grate air inlet area to about 0.04 m2. Figure 1 shows the experimental design of the present study. In all circumstances, combustion experiments were done in triplicate.
Experimental design (different fuel mass load, air supply condition and burning rate) of each test circumstances in the present study. For each scenario, triplicate combustion experiments were conducted.
1.2 Sampling and measurement
The exhaust from the combustion entered into a mixing chamber (4.5 m3) with a small fan built-in. The sampling port was placed in the center of the mixing chamber, and no further dilution was conducted to avoid potential impacts of dilution ratio and rate on the mass and size distribution of particles emitted (; ). PM was collected on quartz fiber filters using a low-volume pump (XQC-15E, Tianyue, China) at a flow rate of 1.5 L/min. In addition to total PM, size segregated samples were collected using a nine stage cascade impactor (FA-3, Kangjie, China) with the cutoff diameter at < 0.4, 0.4-0.7, 0.7-1.1, 1.1-2.1, 2.1-3.3, 3.3-4.7, 4.7-5.8, 5.8-9.0, and 9.0-10.0 μm at a flow rate of 28.3 L/min.
After sampling, particle-loaded filters were packed with pre-baked aluminum foil and stored in a desiccator for 24 hours prior to weighing. A high precision digital balance (0.00001 g) was used in PM gravimetric measurement. EC and OC compositions in PM were determined with a Sunset EC/OC analyzer using thermal-optical transmission (Sunset Lab, USA). The temperature protocol was 600, 840, and 550 °C in a pure helium atmosphere for OC, and then at 550, 650, and 870 °C in an oxygen/helium atmosphere for EC detection. Pyrolyzed organic carbon was determined when the laser signal returned to the initial value and subtracted from EC. CO2 and CO were measured and recorded automatically every 2 seconds with an on-line detector equipped with non-dispersive infrared sensor (GXH-3051, Technical Institute, China). The equipment was calibrated using a span gas before each experiment and zero checked after. All filters used were pre-baked at 450 °C for 6 hours and equilibrated in a desiccator. Blank samples were also collected and the results were subtracted.
1.3 Data analysis
Emission factors of PM, OC, and EC (EFPM, EFOC and EFEC, respectively) were determined based on the carbon mass balance method (Zhang et al., 2000). It was assumed that total carbon burned was released into the forms of gaseous CO, CO2, total gaseous hydrocarbons (THC) and carbon in PM. In the present study, THC was not measured since most of the gaseous carbon was emitted in the forms of CO or CO2, and the neglect may result in an error in 1-4% (). It was reported EFs calculated using the carbon mass balance method were comparable to those from direct measurements (Dhammapale et al., 2006). Since it was not necessary to collected all the emitted species and the site of sampling port was flexible in the flue gas, the carbon method was widely used in EF measurements (Dhammapale et al., 2006, 2007; , 2009; Zhang et al., 2000). The detailed description of EF calculation can be found elsewhere (Zhang et al., 2000; ).
The combustion efficiency is usually defined as the mass of carbon emitted in the form of CO2 divided by the total amount of carbon released. Since most of the carbon was released in the forms of CO or CO2, modified combustion efficiency (MCE), defined as CO/(CO2+CO) ratio (molar basis), was also commonly calculated and used to describe the combustion condition (Dhammapale et al., 2006; Janhäll et al., 2010). In the present study, MCE was calculated. Non-parametric analysis was applied for data analysis using Statistica at a significant level of 0.05.
2. Results and Discussion
2.1 The influence of fuel mass load
MCEs of the corn straw burning with mass load at 275, 550, and 1100 g were 95.4±0.2, 96.1±0.4 and 96.8±0.3%, respectively (p > 0.05). Measured EFPMs in these three burning circumstances were 3.29±0.11, 4.65±0.07, 3.68±0.55 g/kg, respectively. There was no significant difference in these EFPMs (p > 0.05). Insignificant differences were also found in measured EFOC and EFEC (p > 0.05). Means and standard derivations for EFOC from the burning of 275, 550, and 1100 g corn straw were 0.635±0.257, 0.879±0.367, 0.495±0.322 g/kg, and were 0.149±0.048, 0.401±0.029, 0.905±0.655 g/kg for EFEC, respectively. The ratio of OC and EC, and total carbon mass fraction in PM (TC/PM, and TC=EC+OC) are two useful parameters in carbonaceous particulate matter study, and commonly used in the source apportionment and climate effect analysis (Bond et al., 2004; ). Calculated OC/TC and TC/PM ratios were also independent of the burned fuel mass load. On average, the OC/TC ratio was 0.60±0.20, and total carbon contributed about 29.0% of the PM mass.
Figure 2 shows the size distribution of PM emitted from corn straw burning under different fuel mass load. Generally, fine particles dominated the total PM emission from the indoor corn straw burning. Under low, median and high fuel mass load conditions in the present study, mass fractions of PM2.1 (PM with diameter less than 2.1 μm, the most close to PM2.5 in this study) were 85.9±2.0, 87.9±1.1 and 87.0±1.5% of the total, respectively. PM1.1 (PM with diameter less than 1.1 μm, the most close to PM1.0) made up 77.2±3.2, 77.9±2.8 and 67.7±1.6%, respectively. There were no significant differences among the PM size distributions from the burning of corn straw with different fuel mass load (p > 0.05).
Size distribution of PM from indoor corn straw combustions of different mass loads. Results shown are means and standard derivations of triplicate measurements.
In the literature, a previous laboratory study on pollutant emissions from simulated open biomass burning reported that the emission concentrations of PM and other pollutants did not vary significantly and the variation in measured EFs was thought to be within the same order of magnitude as the experimental error when fuel mass load ranged from 1 to 10 kg (). Without a significant tendency in EFPM, EFOC and EFEC when the mass of burned fuel changed, it was suggested that the effect of mass load on measured EF data was limited. In a field study on the emission and ambient pollution levels of carbonaceous PM from indoor biomass burning in a rural household in Northern China, the consumption of corn straw was documented at about 0.3-0.8 kg/person/day (; Zhong et al., 2012). There was much small day to day variation in the consumption amounts of fuel consumed in rural residents' daily lives since the dietary types and persons living in the household were similar at most of the time.
2.2 The influence of burning rate
In the corn straw burning with relatively slow (0.020±0.001 kg/min), median (0.045±0.002 kg/min) and fast rates (0.119±0.016 kg/min), MCEs were 96.2±0.1, 96.1±0.4 and 95.2±0.6%, respectively, decreasing with the increase of fuel burning rate in general (p > 0.05). Measured EFPM, EFOC and EFEC in the slow burning were 4.54±1.17, 0.636±0.664 and 0.215±0.304 g/kg, respectively. In the fast burning, EFPM, EFOC and EFEC were 3.57±0.82, 0.338±0.338 and 0.342±0.294 g/kg, respectively. The results were not significant from those in corn straw burning at the median rate (p > 0.05). The ratios of OC/TC and TC/PM were also not significantly different among the burning with these three burning rate levels. It was thought that burning too fast could cause an oxygen limited condition that produced high emissions of various pollutants from the incomplete combustion (Jenkins et al., 1996; Simoneit, 2002; McDonald et al., 2000; Rogge et al., 1998). However, relatively large variations in measurements would result in statistically insignificant difference in measured EFs under different burning rates.
Size distributions of the PM emitted from corn straw burning under different burning rates are compared in Figure 3. PM1.1 and PM2.1 fractions were 84.8±1.0 and 91.5±0.9% of the total in the slow burning, were 77.9±2.8 and 87.9±1.1% in corn straw burning at the median burning rate, and were 53.2±6.1 and 85.0±1.0% of the total PM in the fast burning experiment. The contribution of fine particles generally declined with the increase of fuel burning rates. This phenomena could be partly explained by the oxygen deficient conditions formed at a fast burning rate. It was reported that high oxygen could sustain the intense flaming conditions that benefit the emissions of fine particles from biomass burning (Hays et al., 2003). As a result, the formation and emission of fine particles would decrease when fuels burned too fast and an oxygen deficient atmosphere was formed in the combustion chamber (Simoneit, 2002; Rogge et al., 1998).
Size distribution of PM from indoor corn straw burning under different burning rates. Results shown are means and standard derivations of triplicate measurements.
The fuel burning rate in field was mainly determined by the fire care and skill of the residents during the burning process. In rural residents' daily lives, fuel burning rate measured in a previous field study was in the range of 0.029 to 0.064 kg/min (). The rate fell in the slow and median burning rates in the present study. As mentioned, a fast burning could produce high emissions of incomplete pollutants because of oxygen deficient atmosphere formed. However, it was also suggested from this study that under these relatively low burning rates, the yield of fine PM might be higher though the emission of total particles did not change obviously in comparison with the emission from the fast burning.
2.3 The influence of air supply
It is accepted that adequate air supply is required for the stable and high efficient combustion with low emissions of incomplete combustion pollutants (Houshfar et al., 2011; Lu et al., 2009; Ryu et al., 2006; ). Fuel combustion in oxygen deficient conditions usually produced high emissions of pollutants from the incomplete combustion (Jenkins et al., 1996; ), while increased air supply may cool down the combustion temperature and result in the increased emissions of the incomplete pollutants (Johansson et al., 2003; Skreiberg et al., 1997; Houshfar et al., 2011). Excess air ratio, defined as the ratio of actual air quantity to theoretical air requirement, was commonly used to quantitatively describe the air supply condition during the combustion process (Liu et al., 2001; Venkataraman et al., 2004). It was suggested that the optimized excess air ratio should be around 2.0 (Liu et al., 2001). In the present study, calculated excess air ratios in three burning experiments with reduced (∼4.0 m3/h), median (∼9.0 m3/h, a routine situation in rural household biomass burning practice) and enhanced (∼19 m3/h) air supply rates were 1.40±0.04, 2.44±0.09 and 4.52±0.18, respectively.
MCEs for the tested corn straw burning with the low and high air supply rates were 92.7±0.7 and 95.2±1.1%, respectively, which were lower (p < 0.05) than 96.1±0.4% in normal burning condition with median air supply. Measured EFPM, EFOC and EFEC from the combustion with reduced air supply were 3.83±1.04, 2.16±1.88 and 0.277±0.138 g/kg, and were 3.29±2.04, 0.769±0.543 and 0.319±0.224 g/kg in the burning with a high air supply amount, respectively. The results were not significantly different (p > 0.05) from those from the burning in the normal situation with median air supply (EFPM, EFOC and EFEC at 4.65±0.07, 0.879±0.367 and 0.401±0.029 g/kg, respectively), mainly due to large variations in measurements, Also, the ratios of OC/TC and TC/PM were comparable in the burning under different air supply conditions.
In PM emission, PM2.1 contributed 88.5±3.5, 87.9±1.1 and 78.9±1.7% of the total in the burning with low, median and high air supply rates, and the mass fractions of PM1.1 were 53.0±17.4, 77.9±2.8 and 55.3±10.2% of the total in these three burning circumstances, respectively (p > 0.05). Figure 4 compares the size distributions of PM from the corn straw burning under different air supply conditions. Slight difference was found in the PM size distributions. In the burning under routine air supply conditions, the distribution was dominated by fine PM with diameter between 0.4 and 0.7 μm (PM0.4-0.7) and between 0.7 and 1.1 μm (PM0.7-1.1), followed by those with diameter less than 0.4 μm (PM0.4). But in the burning with either reduced or enhanced air supply, the most abundant PM fractions were PM1.1-2.1 and PM0.7-1.1 followed by PM0.4-0.7 and PM0.4. Oxygen deficient conditions formed in the combustion chamber with small air supply amount were responsible for the decreased mass fractions of fine PM0.4 and PM0.4-0.7, since fine particles were preferably formed in intense flaming condition that could be sustained by adequate oxygen amount (Hays et al., 2003). In the burning of a high air supply amount, declined mass fractions of these fine PM0.4 and PM0.4-0.7 might be related to the cooled combustion temperature that was not favorable for the formation of fine particles (Purvis et al., 2000; ; Venkataraman et al., 2004). It seems that the non-linear impacts of air supply not only exist in emission factors, but also on the PM size distribution. The influence was complicated and the mechanism was not very clear at this stage, especially its influence on PM size distribution (Hedberg et al., 2002; Wardoyo et al., 2007). Further study on the influence of air supply amount on both PM EF and size distribution is required.
Size distribution of PM from indoor corn straw burning under different oxygen supply. Data shown are means and standard derivations of triplicate measurements.
2.4 Relationship among co-emitted pollutants
EFPM was positively correlated with EFOC (r = 0.446, p = 0.021) and EFEC (r = 0.480, p = 0.014). The relationship between EFOC and EFEC was also significantly positive (r = 0.481, p = 0.014). Similar results were reported in the previous study (, ). As mentioned above, the impacts of investigated factors, significant or not, were similar on EFPM, EFOC and EFEC. The ratios of OC/TC and TC/PM did no show significant difference in the distinct burning circumstances. Taking all data in this study into consideration, the ratio of OC/TC was 0.67±0.19, and the total carbon mass percent in PM was approximate 27.3%.
It is also interesting to investigate the relationship between carbonaceous particulate matter and CO, which is an important by-product of incomplete combustions. As incomplete pollutants often emitted simultaneously from the burning process, a positive correlation between EFPM and EFCO was sometimes reported in the literature (Bingal et al., 2008; Gupta et al., 1998; ; ). However, a converse tendency showing decreased PM emission with the increase of CO emission was also observed in residential biomass burning (Li et al., 2007; Venkataraman et al., 2004). In the present study, there were no significant correlation found between EF of CO (EFCO) and EFPM (p = 0.378), between EFCO and EFOC (p = 0.122), and between EFCO and EFEC (p = 0.206), as shown in Figure 5. Similar insignificant relationship was also reported in the literature (Roden et al., 2009; ). The complicated relationship between CO and other co-emitted pollutants indicated that the use of CO as a surrogate for other incomplete pollutants in the development of emission factors and emission inventory should be in caution (Ballard-Tremeer and Jawurek, 1996; Venkataraman et al., 2004).
Relationship between EF of CO and EFs of PM, OC, and EC from indoor corn straw burning. Data shown are results from all combustion experiments under different burning conditions.
3. Conclusion
The effects of fuel mass load, burning rate and air supply conditions on the emission and size distribution of carbonaceous particulate matter from indoor corn straw burning were evaluated. Measured EFPM, EFOC and EFEC were in the range of 1.02-5.50, 0.129-4.34 and 0.073-1.29 g/kg with means and standard deviations of 3.84±1.02, 0.846±0.895 and 0.391±0.350 g/kg, respectively. They were independent of fuel mass load. The impacts of burning rate and air supply amount were also found to be insignificant under the given circumstances, though a fast burning or the burning with a low air supply amount could result in oxygen deficient atmosphere, and increased air supply may cool the combustion temperature, both of which could lead to the change in the formation and emission of carbonaceous particulate matter. Relatively high variation in measurements should be partly responsible for the insignificant difference in this study. EFs of PM, OC and EC were positively correlated with each other, but not significantly correlated with co-emitted CO.
Generally, PM from indoor corn straw burning was dominated by fine PM. PM2.1 and PM1.1 made up 86.4±3.9 (77.7-92.1% as range) and 67.0±14.1% (40.7-85.4%) of the total, respectively. Size distribution of PM could be also influenced by the burning conditions, like burning rate and air supply amounts. However, the reason was not very clear currently. Future study on the influencing factors of PM with different sizes is of interest.
Acknowledgments
Funding for this work was supported by the National Natural Science Foundation of China (No. 41130754, 41001343, and 41001343), Beijing Municipal Government (No. YB20101000101), Ministry of Environmental Protection (No. 201209018) and NIEHS (No. P42 ES016465). Part of Guofeng SHEN's work in Jiangsu Academy of Environmental Sciences was support by the Environmental Portection Department of Jiangsu Province. The authors thank the reviewers for their valuable comments and Dr. Rice for polishing the language.
References
- Ballard-Tremeer G, Jawurek H. Comparison of five rural, wood-burning cooking devices: efficiencies and emissions. Biomass and Bioenergy. 1996;11:419–430.[Google Scholar]
- Bignal K, Langridge S, Zhou J. Release of polycyclic aromatic hydrocarbons, carbon monoxide and particulate matter from biomass combustion in a wood-fired boiler under varying boiler conditions. Atmospheric Environment. 2008;42:8863–8871.[Google Scholar]
- Bond T, Streets D, Yarber K, Nelson S, Woo J, Klimont Z. A technology-based global inventory of black and organic carbon emissions from combustion. Journal of Geophysical Research. 2004;109:D14203. doi: 10.1029/2003JD003697. [CrossRef] [Google Scholar]
- Brook R, Rajagopalan S, Pope C, III, Brook J, Bhatnagar A, Diez-Roux A, Holguin F, Hong Y, Luepker R, Mittleman M, Peters A, Siscovick D, Smith S, Jr, Whitsel L, Kaufman J. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Hearth Association. Circulation. 2010;121:2331–2378. 2010. [PubMed] [Google Scholar]
- Cao G, Zhang X, Gong S, Zheng F. Investigation on emission factors of particulate matter and gaseous pollutants from crop residue burning. Journal of Environmental Sciences. 2008;20:50–55. [PubMed] [Google Scholar]
- Cappa C, Onasch T, Massoli P, Worsnop D, Bates T, Cross E, Davidovits P, Hakala J, Hayden K, Jobson B, Kolesar K, Lack D, Lerner B, Li S, Mellon D, Nuaaman I, Olfert J, Petaja T, Quinn P, Song C, Subramanian R, Williams E, Zaveri R. Radiative absorption enhancements due to the mixing state of atmospheric black carbon. Science. 2012;31:1078–1081. [PubMed] [Google Scholar]
- Che H, Zhang X, Li Y, Zhou Z, Qu J. Horizontal visibility trends in China 1981-2005. Geophysical research letters. 2007;34:L24706.[Google Scholar]
- Che H, Zhang X, Li Y, Zhou Z, Qu J, Hao X. Haze trends over the capital cities of 31 provinces in China, 1981-2005. Theoretical and Applied climatology. 2009;97:235–242.[Google Scholar]
- Chen Y, Roden C, Bond T. Characterizing biofuel combustion with patterns of real-time emission data (PaRTED) Environmental Science & Technology. 2012;46:6110–6117. [PubMed] [Google Scholar]
- Cheng Y, Duan F, He K, Zheng M, Du Z, Ma Y, Tan J. Intercomparison of thermal-optical methods for the determination of organic and elemental carbon: influences of aerosol composition and implications. Environmental Science and Technology. 2011;45:10117–10123. [PubMed] [Google Scholar]
- Chow J, Watson J, Lowenthal D, Chen L, Motallebi N. PM2.5 source profiles for black and organic carbon emission inventories. Atmospheric Environment. 2011;45:5407–5414.[Google Scholar]
- Christian T, Kleiss B, Yokelson R, Holzinger R, Crutzen P, Hao W, Saharjo B, Ward D. Comprehensive laboratory measurements of biomass-burning emissions: 1. Emission from Indonesian, African, and other fuels. Journal of Geophysical Research. 2003;108:D234719.[Google Scholar]
- Chung C, Ramanathan V, Decremer D. Proceedings of the National Academy of Sciences of the United States of America. 2012. Observationally constrained estimates of carbonaceous aerosol radiative forcing. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- Dhammapala R, Claiborn C, Corkill J, Gullett B. Particulate emissions from wheat and Kentucky bluegrass stubble burning in eastern Washington and northern Idaho. Atmospheric Environment. 2006;40:1007–1015.[Google Scholar]
- Dhammapala R, Claiborn C, Simpson C, Jimenez J. Emission factors from wheat and Kentucky bluegrass stubble burning: comparison of field and simulated burn experiments. Atmospheric Environment. 2007;41:1512–1520.[Google Scholar]
- Ding J, Zhong J, Yang Y, Li B, Shen G, Su Y, Wang C, Li W, Shen H, Wang B, Wang R, Huang Y, Zhang Y, Cao H, Zhu Y, Simonich S, Tao S. Occurrence and exposure to polycyclic aromatic hydrocarbons and their derivatives in a rural Chinese home through biomass fuelled cooking. Environmental Pollution. 2012;169:160–166.[PMC free article] [PubMed] [Google Scholar]
- Dockery D, Pope C, III, Xu X, Spengler J, Ware J, Fay M, Ferris B, Speizer F. An association between air pollution and mortality in six U.S. cities. The New England Journal of Medicine. 1993;329:1753–1759. [PubMed] [Google Scholar]
- Englert N. Fine particles and human health-a review of epidemiological studies. Toxicology letter. 2004;149:235–242. [PubMed] [Google Scholar]
- Grandesso E, Gullett B, Touati A, Tabor D. Effect of moisture, charge size, and chlorine concentration on PCDD/F emissions from simulated open burning of forest biomass. Environmental Science & Technology. 2011;45:3887–3894. [PubMed] [Google Scholar]
- Gupta S, Saksena S, Shankar V, Joshi V. Emission factors and thermal efficiencies of cooking biofuels from five countries. Biomass and Bioenergy. 1998;14:547–559.[Google Scholar]
- Hays M, Smith N, Kinsey J, Dong Y, Kariher P. Polycyclic aromatic hydrocarbon size distributions in aerosols from appliances of residential wood combustion as determined by direct thermal desorption-GC/MS. Journal of Aerosol Science. 2003;34:1061–1084.[Google Scholar]
- Hedberg E, Kristensson A, Ohlsson M, Johansson C, Johansson P, Swietlicki E, Vesely V, Wideqvist U, Westerholm R. Chemical and physical characterization of emissions from brich wood combustion in a wood stove. Atmospheric Environment. 2002;36:4823–4837.[Google Scholar]
- Houshfar E, Skreiberg Ø, Løvås T, Todorovic D, Sørum L. Effect of excess air ratio and temperature on NOx emission from grate combustion of biomass in the staged air combustion scenario. Energy & Fuels. 2011;25:4643–4654.[Google Scholar]
- Huang W, Zhu T, Pan X, Hu M, Lu S, Lin Y, Wang T, Zhang Y, Tang X. Air pollution and autonomic and vascular dysfunction in patients with cardiovascular disease: interactions of systemic inflammation, overweight and gender. American Journal of epidemiology. 2012;176:117–126.[PMC free article] [PubMed] [Google Scholar]
- Jacobson M. A physically-based treatment of elemental carbon optics: Implications for global direct forcing of aerosols. Geophysical Research Letter. 2000;27:217–220.[Google Scholar]
- Janhäll S, Andreae M, Pöschl U. Biomass burning aerosol emissions from vegetation fires: particle number and mass emission factors and size distributions. Atmospheric chemistry and physics. 2010;10:1427–1439.[Google Scholar]
- Jetter J, Zhao Y, Smith K, Khan B, Yelverton T, DeCarlo P, Hays M. Pollutant emissions and energy efficiency under controlled conditions for household biomass cookstoves and implications for metrics useful in setting international test standards. Environmental Science & Technology. 2012;46:10827–10834. [PubMed] [Google Scholar]
- Jenkins R, Jones A, Turn S, Williams R. Emission factors for polycyclic aromatic hydrocarbons from biomass burning. Environmental Science & Technology. 1996;30:2462–2469.[Google Scholar]
- Johansson L, Tullin C, Leckner B, Sjovall P. Particle emissions from biomass combustion in small combustors. Biomass and Bioenergy. 2003;25:435–446.[Google Scholar]
- Kan H, Chen R, Tong S. Ambient air pollution, climate change, and population health in China. Environ International. 2012;42:10–19. [PubMed] [Google Scholar]
- Lee S, Baumann K, Schauer J, Sheesley R, Naeher L, Meinardi S, Blake D, Edgerton E, Russell A, Clements M. Gaseous and particulate emissions from prescribed burning in Georgia. Environmental Science & Technology. 2005;39:9049–9056. [PubMed] [Google Scholar]
- Lei Y, Zhang Q, He K, Streets D. Primary anthropogenic aerosol emission trends for China, 1990-2005. Atmospheric Chemistry and Physics. 2011;11:931–954.[Google Scholar]
- Li X, Duan L, Wang S, Duan J, Guo X, Yi H, Hu J, Li C, Hao J. Emission characteristics of particulate matter from rural household biofuel combustion in China. Energy & Fuel. 2007;21:845–851.[Google Scholar]
- Li X, Wang S, Duan L, Hao J, Nie Y. Carbonaceous aerosol emissions from household biofuel combustion in China. Environmental Science & Technology. 2009;43:6076–6081. [PubMed] [Google Scholar]
- Liu J, Zhai G, Chen R. Analysis on the characteristics of biomass fuel direct combustion process. Journal of Northeast Agricultural University. 2001;32:290–294. In Chinese. [Google Scholar]
- Lu H, Zhu L, Zhu N. Polycyclic aromatic hydrocarbon emission from straw burning and the influence of combustion parameters. Atmospheric Environment. 2009;43:978–983.[Google Scholar]
- McDonald J, Zielinska B, Fujita E, Sagebiel J, Chow J, Watson J. Fine particle and gaseous emission rates from residential wood combustion. Environmental Science & Technology. 2000;34:2080–2091.[Google Scholar]
- McMeeking G, Kreidenweis S, Baker S, Carrico C, Chow J, Collett J, Jr, Hao W, Holden A, Kirchstetter T, Malm W, Moosmüller H, Sullivan A, Wold C. Emission of trace gases and aerosols during the open combustion of biomass in the laboratory. Journal of geophysical research. 2009;114:D19210. doi: 10.1029/2009JD011836. [CrossRef] [Google Scholar]
- Pope C, III, Ezzati M, Dockey D. Fine-particulate air pollution and life expectancy in the United States. The New England Journal of Medicine. 2009;360:376–386.[PMC free article] [PubMed] [Google Scholar]
- Purvis C, McCrillis R, Kariher P. Fine particulate matter (PM) and organic speciation of fireplace emissions. Environmental Science & Technology. 2000;34:1653–1658.[Google Scholar]
- Rich D, Kipen H, Huang W, Wang G, Wang Y, Zhu P, Ohman-strickland P, Hu M, Philipp C, Diehl S, Lu S, Tong J, Gong J, Thomas D, Zhu T, Zhang J. Association between changes in air pollution levels during the Beijing Olympics and biomarkers of inflammation and thrombosis in healthy young adults. The Journal of the American Medical Association. 2012;307:2068–2078.[PMC free article] [PubMed] [Google Scholar]
- Roden C, Bond T, Conway S, Pinel A. Emission factors and real-time optical properties of particles emitted from traditional wood burning cookstoves. Environmental Science & Technology. 2006;40:6750–6757. [PubMed] [Google Scholar]
- Roden C, Bond T, Conway S, Pinel A, MacCarty N, Still D. Laboratory and field investigation of particulate and carbon monoxide emissions from traditional and improved cookstoves. Atmospheric Environment. 2009;43:1170–1181.[Google Scholar]
- Rogge W, Hildemann L, Mazurek M, Cass G, Simoneit B. Sources of fine organic aerosol. 9. pine, oak, and synthetic log combustion in residential fireplaces. Environmental Sciences & Technology. 1998;32:13–32.[Google Scholar]
- Ryu C, Yang Y, Khor A, Yates N, Sharifi V, Swithenbank J. Effect of fuel properties on biomass combustion: Part I. Experiments-fuel type, equivalence ratio and particle size. Fuel. 2006;85:1039–1046.[Google Scholar]
- Shen G, Yang Y, Wang W, Tao S, Zhu C, Min Y, Xue M, Ding J, Wang B, Wang R, Shen H, Li W, Wang X, Russell A. Emission factors of particulate matter and elemental carbon for crop residues and coals burned in typical household stoves in China. Environmental Science & Technology. 2010;44:7157–7162.[PMC free article] [PubMed] [Google Scholar]
- Shen G, Wang W, Yang Y, Ding J, Xue M, Min Y, Zhu C, Shen H, Li W, Wang B, Wang R, Wang X, Tao S, Russell A. Emissions of PAHs from indoor crop residue burning in a typical rural stove: emission factors, size distributions and gas-particle partitioning. Environmental Science & Technology. 2011;45:1206–1212.[PMC free article] [PubMed] [Google Scholar]
- Shen G, Wei S, Wei W, Zhang Y, Min Y, Wang B, Wang R, Li W, Shen H, Huang Y, Yang Y, Wang W, Wang X, Wang X, Tao S. Emission factors, size distributions and emission inventory of carbonaceous particulate matter from residential wood combustion in rural China. Environmental Science & Technology. 2012;46:4207–4214.[PMC free article] [PubMed] [Google Scholar]
- Simoneit B. Biomass burning- a review of organic tracers for smoke from incomplete combustion. Applied Geochemistry. 2002;17:129–162.[Google Scholar]
- Skreiberg Ø, Glarborg P, Jensen A, Dam-Johansen K. Kinetic NOx modeling and experimental results from single wood particle combustion. Fuel. 1997;76:671–682.[Google Scholar]
- Venkataraman C, Rao G. Emission factors of carbon monoxide and size-resolved aerosols from biofuel combustion. Environmental Science & Technology. 2001;35:2100–2107. [PubMed] [Google Scholar]
- Venkataraman C, Joshi P, Sethi V, Kohli S, Ravi M. Aerosol and carbon monoxide emissions from low temperature combustion in s sawdust packed-bed stove. Aerosol Science and Technology. 2004;38:50–61.[Google Scholar]
- Wang R, Tao S, Wang W, Liu J, Shen H, Shen G, Wang B, Liu X, Li W, Huang Y, Zhang Y, Lu Y, Chen H, Chen Y, Wang C, Zhu D, Wang X, Li B, Liu W, Ma J. Black carbon emissions in China from 1949 to 2050. Environmental Science & Technology. 2012;46:7595–7603. [PubMed] [Google Scholar]
- Wang T, Jiang F, Deng J, Shen Y, Fu Q, Wang Q, Fu Y, Xu J, Zhang D. Urban air quality and regional haze weather forecast for Yangtze River Delta region. Atmospheric Environment. 2012;58:70–83.[Google Scholar]
- Wardoyo A, Morawska L, Ristovski Z, Jamriska M, Carr S, Johanson G. Size distribution of particles emitted from grass fires in the Northern Territory. Australia Atmospheric Environment. 2007;41:8609–8619.[Google Scholar]
- Wei W. Master thesis. Peking University; 2012. Research on mercury emissions from indoor biomass burning in rural China. [Google Scholar]
- Xie J, Yang X, Zhang L, Ding T, Song W, Lin W. Emissions of SO2, NO and N2O in a circulating fluidized bed combustor during co-firing coal and biomass. Journal of Environmental Sciences. 2007;19:109–116. [PubMed] [Google Scholar]
- Yan X, Ohara T, Akimoto H. Bottom-up estimate of biomass burning in mainland China. Atmospheric Environment. 2006;40:5262–5273.[Google Scholar]
- Zeng X, Ma Y, Ma L. Utilization of straw in biomass energy in China. Renewable and sustainable energy reviews. 2007;11:976–987.[Google Scholar]
- Zhang J, Smith K, Ma Y, Ye S, Jiang F, Qi W, Liu P, Khalil M, Rasmussen R, Thorneloe S. Greenhouse gases and other airborne pollutants from household stoves in China: a database for emission factors. Atmospheric Environment. 2000;34:4537–4549.[Google Scholar]
- Zhang Y, Dou H, Chang B, Wei Z, Qiu W, Liu S, Liu W, Tao S. Emission of polycyclic aromatic hydrocarbons from indoor straw burning and emission inventory updating in China. Annals of the New York Academy of Sciences. 2008;1140:218–227. [PubMed] [Google Scholar]
- Zhi G, Peng C, Chen Y, Liu D, Sheng G, Fu J. Deployment of coal briquettes and improved stoves: possibly an option for both environment and climate. Environmental Science & Technology. 2009;43:5586–5591. [PubMed] [Google Scholar]
- Zhong J, Ding J, Su Y, Shen G, Yang Y, Wang C, Simonich S, Cao H, Zhu Y, Tao S. Carbonaceous particulate matter air pollution and human exposure from indoor biomass burning practices. Environmental Engineering Science. 2012 doi: 10.1089/ees.2011.0543. [CrossRef] [Google Scholar]