Environ. Sci. Technol. 2003, 37, 2869-2877
Characterization of Non-methane Hydrocarbons Emitted from Various Cookstoves Used in China STELLA MANCHUN TSAI,† J U N F E N G ( J I M ) Z H A N G , * ,†,‡ KIRK R. SMITH,§ YUQING MA,| R. A. RASMUSSEN,⊥ AND M. A. K. KHALIL# University of Medicine and Dentistry of New Jersey - School of Public Health, Piscataway, New Jersey, Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey, Environmental Health Sciences, University of California, Berkeley, California, Institute for Techno-economics and Energy System Analysis, Tsinghua University, Beijing, China, Department of Environmental Sciences and Engineering, Oregon Graduate Institute, Beaverton, Oregon, and Department of Physics, Portland State University, Portland, Oregon
Emission contributions from cookstoves to indoor, regional, and global air pollution largely depend on stove and fuel types. This paper presents a database on emission factors of speciated non-methane hydrocarbons (NMHCs) for 16 fuel/stove combinations burning 2 types of crop residue, wood, 4 types of coal, kerosene, and 3 types of gaseous fuels. The emission factors are presented both on a fuel mass basis (compound mass per fuel mass) and on a cooking task basis (compound mass per unit energy delivered to the pot). These fuel/stove combinations cover a large spectrum of the cookstoves used in both urban and rural households in China. Up to 54 hydrocarbons were identified, some of which are reactive precursors of photochemical smog. Based on published maximum incremental reactivity (MIR) values for NMHCs, we estimated stove-specific and fuel-specific ozone forming potentials (OFPs). The results indicate that raw coal powder, wood, and crop residues have higher OFP values than the other types of fuels tested. Strikingly, burning the coal briquette and honeycomb coal briquette produced OFP values more than 2 orders of magnitude lower than burning unprocessed (raw) coal, even in the same vented metal stove, for every 1 MJ delivered to the pot.
Methods
Introduction In many parts of developing countries, household cookstoves are the major sources of air pollution exposure (1-3). Elsewhere we have presented results from studies of cookstove emissions, including a pilot study in the Philippines * Corresponding author phone: (732)445-0158; fax: (732)445-0116; e-mail:
[email protected]. Corresponding author address: EOHSI, Room 358, 170 Frelinghuysen Road, Piscataway, NJ 08854. † University of Medicine and Dentistry of New Jersey - School of Public Health. ‡ Environmental and Occupational Health Sciences Institute. § University of California. | Tsinghua University. ⊥ Oregon Graduate Institute. # Portland State University. 10.1021/es026232a CCC: $25.00 Published on Web 06/04/2003
(4), and two reporting the results of more extensive studies in which emissions from 28 fuel/stove combinations were measured in India and in China (5, 6). In both the pilot and the more extensive studies, we measured stove performance and emissions of several important greenhouse gases (GHGs) and other pollutants of health concern, including carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), total non-methane hydrocarbon (TNMHC), total suspended particles (TSP), oxides of nitrogen (NOx) (for Chinese stoves only), and sulfur dioxide (for Chinese stoves only). These studies found that a great fraction of fuel carbon was converted to products of incomplete combustion (PICs), rather than CO2, in simple household stoves and that burning solid fuels generated substantially greater emissions of total PICs than burning liquid and gaseous fuels to deliver the same amount of energy to the pot. The studies also found that the emission of non-CO2 GHGs (e.g., CH4, nitrous oxide, and CO) can be a significant contributor to total global warming potentials for many measured cookstoves (7). In the aforementioned extensive studies of cookstove emissions, a subset of the fuel/stove combinations were also measured for speciated NMHCs, the results of which have not yet been reported. In this paper, we present a database of emission factors of NMHCs for the 16 fuel/stove combinations measured in China. The database for individual NMHCs can provide better information for future study on health risk assessment based on particular toxic compounds emitted from household cookstoves. Respiratory illnesses, including acute respiratory infection, chronic obstructive lung diseases, and lung cancer, have been strongly associated with exposure to indoor smoke from solid-fuel stoves (8). Other effects, including tuberculosis, adverse pregnancy outcomes, aero-digestive cancer, cataracts, and asthma, have also been associated with such exposures, but the evidence base is still small (8-10). Synergistic and antagonistic effects of toxic compounds present in the cookstove smoke may contribute to the observed adverse heath effects. Knowing detailed constituents of the smoke will certainly help in understanding its toxicological properties. In addition to some NMHCs that have direct health concerns, some of the compounds identified are reactive precursors, in the atmosphere, leading to the formation of ozone (O3) and photochemical smog. The database on individual NMHCs, thus, can be useful in modeling local or regional O3. In this paper, we compare stove- and fuel-specific ozone forming potentials (OFPs) using the measured emission factors and the published reactivity indices for NMHCs. The analysis of stove/fuel OFPs may provide useful energy policy implications in China from a stand point of O3 control strategies.
2003 American Chemical Society
Fuel/Stove Combination Measured. The fuels measured included biomass fuels (crop residues and wood), several types of coals, kerosene, liquefied petroleum gas (LPG), coal gas, and natural gas. These selected fuels covered fuels of common use in rural and urban households in China in 19951996 when the measurements were made. The stove selection was based on popular models for each fuel type. Since 1996, usage of gas fuels may have been increased in some major Chinese cities (e.g., Beijing) (11, 12). However, no significant changes in household fuel types have occurred in most areas of China. The stove/fuel combinations tested in this study are still commonly found in China as a whole, although some stove/fuel combinations found in 1995-1996 have been replaced with others in some cities or areas. Detailed VOL. 37, NO. 13, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Description of the Fuel/Stove Combinations Tested in Chinaa description serial no.
symbol fuel/stove
fuel
stove
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Honey-Metal-v Honey-Metal Honey-Imp. CoalBriq-Metal WashCoal-Metal-v Coal-Metal-v Coal-Brick-v Wood-Brick-v Wood-Imp.-v Wheat-Brick-v Maize-Brick-v Maize-Imp.-v Kero-Wick LPG-Trad. CoalGas-Trad. NaturalGas-Trad.
honeycomb coal briquette honeycomb coal briquette honeycomb coal briquette coal briquette washed coal powder unprocessed coal (coal powder) unprocessed coal (coal powder) fuel wood fuel wood wheat residue maize residue maize residue kerosene liquefied petroleum gas coal gas natural gas
metal coal stove with a flue metal coal stove without flue improved metal coal stove without flue metal coal stove without flue metal coal stove with a flue metal coal stove with a flue brick stove with a flue brick stove with a flue improved brick stove with a flue brick stove with a flue brick stove with a flue improved brick stove with a flue kerosene wick stove without flue LPG traditional stove without flue traditional gas stove without flue traditional gas stove without flue
a Flue code: v ) vented, i.e., with flue. The fuel category includes: #1-7 for coal; #8-9 for wood; #10-12 for crop residue; #13 for kerosene; and #14-16 for gaseous fuel. “Coal powder” is raw coal with large pieces being sieved out. Coal power consists of coal particles (pieces) with diameters typically smaller than 1 cm. Coal powder is the most common unprocessed coal used in Chinese households.
information about the fuel sources, fuel compositions, and stove characteristics can be found in a previous published paper (6). Briefly, for the stoves using piped gaseous fuels, the measurements were conducted in actual homes. The emissions of all the other fuel/stove combinations were measured in a simulated village kitchen located in the rural campus of Tsinghua University, Beijing, China. Among the 28 fuel/stove combinations measured primarily for major GHGs, 16 were also successfully measured for speciated NMHCs. These 16 fuel/stove combinations, described in Table 1, represent the most commonly used ones and covers at least one fuel/stove combination within each broad fuel type (coal, wood, crop residue, kerosene, and gas). With the exception of Honey-Metal-v combination, every fuel/stove combination was measured for NMHCs only once for a complete burn cycle, due to the budgetary constraint. However, we were able to make three repeated measurements for the Honey-Metal-v to get a sense about the variation from one burn cycle to another burn cycle. This was evaluated by reporting the coefficient of variation (CV) determined for this particular fuel/stove combination. Uncertainties associated with emission factors for the other tested fuel/stove combinations were discussed based on the CV values of TNMHC reported in the previous paper and an analysis of uncertainty sources (6). Measurement Methods. Additional details on experimental design and sample collection procedures can be found in Zhang et al. (6). Briefly, flue gas samples were collected using a sampling configuration that included, from upstream to downstream of the sampling train, a stainless steel probe, a filter holder, a sampling pump (SKC Inc., U.S.A.), and a clean Tedlar bag (SKC Inc., U.S.A.). The filter holder contained a 37 mm quartz fiber filter that was used to collect TSP. For stoves with flue, the probe was inserted in the flue. For those having no flues, the stoves were placed under a hood built for the test purpose and the probe was placed inside the hood exhaust duct. The flue gas samples had gone through dilution and cooling before they were collected in the Tedlar bags. Thus, the temperature of the flue gas samples was the same as the ambient temperature. The flow rate of the sampling pump, ranging from 0.2 to 2 L/min, was adjusted to fill one or two 80-L Tedlar bags throughout a whole burn cycle (i.e., from fire start to fire extinction). If two Tedlar bags were used, a time-weighted fraction of air from each of the two bags was taken and then mixed in the third Tedlar bag for final sample analysis. Hence, the flue samples collected 2870
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were time-averaged emissions covering a complete burn cycle consisting of different combustion stages. Two ambient air samples were collected nearby the simulated kitchen for background corrections. Aliquots of air samples were taken out of the Tedlar bags for various subsequent chemical analyses. These included the determination of flue gas concentration of CO2, CO, CH4, and TNMHC, and carbon in airborne particulates, all necessary for the construction of a carbon balance model, along with measured carbon mass in the fuel combusted and in combustion residues (for solid fuel only). The carbon balance model was used to determine emission factors of each measured species in the flue gas (6). As described in detail by Zhang et al. (6), the carbon balance approach only requires the measurement of emission ratio, i.e., [species concentration]/[CO2 concentration] of an emitted airborne species. The emission factor of a given species in the flue gas can be calculated from the CO2 emission factor and the emission ratio of the species. Therefore, here we report emission ratios of all speciated NMHCs, which were used to derive emission factors. Emission factors were reported, by convention, on a dry fuel mass basis (i.e., on moisture free basis). The moisture content of each fuel was measured through standard fuel moisture analysis, as described in detail in the previous paper (6). Since different amounts of fuels are needed for the same cooking task using different fuel/stove combinations, taskbased emission factors (pollutant mass per cooking task) rather than the fuel mass based is a better performance index to compare the air pollution potential of different fuel/stove combinations (2, 13). For this reason, the emission factors were also reported as mass of a compound emitted per unit energy, one mega-joule (MJ), delivered to the pot. We chose 1 MJ as unit energy because about 5 MJ of delivered heat is roughly what a typical family meal would require, although this obviously vary largely by meal size, type, and cooking method (5). The conversion between the mass-based and task-based emission factors was achieved using the fuel energy content (also known as calorific value) (MJ/kg) and stove thermal efficiency (%), both of which were measured in the study. Thermal efficiencies [energy effectively received by the pot]/[total energy in the fuel burned] of the stoves were determined using water boiling tests, as described in detail in the previous paper (6). The measurement of flue gas concentrations of speciated NMHCs involved filling a clean evacuated 850-mL stainless
steel canister with the flue gas collected in the large Tedlar bags to 1.5-2 atm pressure using a battery-powered pump. The canisters were precleaned and provided by the laboratory at Oregon Graduate Institute (OGI), Beaverton, OR. The canisters containing flue gas samples were shipped back by air carriers to the OGI laboratory for analysis. Storage time before analysis varied from one batch to another but no more than 30 days. There were no field blank or duplicate samples collected due to budget constraint. The background concentration was determined from two ambient samples collected nearby the simulated kitchen and was used to correct for net flue gas concentrations. The hydrocarbon speciation analysis was made using the procedure established as EPA Compendium Method TO-14A (14). A system of gas chromatography (GC) equipped with a flame ionization detector (FID) and mass-selective detector was used for the analysis. In each analytical run, a small aliquot of air sample was collected from the canister into the analytical system through a dryer (to remove moisture), a chromatographic valve, and then a cryogenic trap. The trap was heated rapidly, and the sample was injected onto an OV-1 capillary column. High purity helium was used as the carrier gas. A standard mixture containing 74 NMHCs (Scott Specialty Gases, Inc., San Bernardino, CA) was used to generate calibration curves for every batch of samples. All calibration curves had linear regression R2 values > 0.99. The detection limit values varied by individual compound but were in sub-ppb ranges. Calculation of Ozone Forming Potential. Among the NMHCs identified in the cookstove flue gas, a total of 44 compounds were selected for ozone forming potential calculation. These compounds are on a target list, which was developed by the U.S. EPA Photochemical Assessment Monitoring Stations (PAMS) program, due to their reactivity in terms of producing tropospheric ozone and photochemical smog. We estimated ozone forming potentials (OFPs) of the 16 fuel/stove combinations using the measured emission factors and values of the Maximum Incremental Reactivity (MIR) scale developed by Carter (15) for most of the NMHCs measured, except o-xylene and sec-butylbenzne. Since only total xylenes (o+m+p) were measured, the published MIR value for m,p-xylenes was applied to total xylenes. Similarly, since 1,2,4-trimethylbenzene and sec-butylbenzene were not separated for the flue gas samples, we applied the MIR value for 1,2,4-trimethylbenzene to the sum of 1,2,4-trimethylbenzene and sec-butylbenzene. The MIR of individual NMHC was defined in terms of grams of ozone formed by photochemical reactions in the atmosphere per gram of carbon (C) in NMHC emitted (g ozone/g C emitted). The total ozone forming potential (OFP) for each fuel/stove combination is the sum of OFPs of individual NMHCs. The OFP from each individual NMHC was calculated as follows. Per kg fuel mass OFP:
OFP (g ozone/kilogram of dry fuel) ) [MIR of a given NMHC (g ozone/g C)] × [emission factor of the NMHC (g C/ kilogram of dry fuel)] Per task (1 MJ energy delivered) OFP:
OFP (g ozone/MJ energy delivered) ) [MIR of a given NMHC (g ozone/ g C)] × [emission factor of individual NMHC (g C/MJ energy delivered)]
Results Molar Emission Ratios. Non-CO2 carbon-containing compounds detected in the flue gas are typically called products of incomplete combustion (PICs). The ratio of an individual
species to CO2 in the flue gas, namely emission ratio, represents relative abundance of the species in the flue gas. Table 2 presents molar emission ratios of 54 speciated NMHCs, among the 74 targeted species, that were detected for any of the 16 fuel/stove combinations tested. The emission ratios were calculated using net concentrations of individual NMHC and net concentrations of CO2. (The net concentration equals flue gas concentration minus background concentration). The values for total speciated NMHC were the sums of the molar emission ratios of all the quantified NMHCs listed in Table 2. The ranking of relative abundance of total speciated NMHC to CO2 is also shown in Table 2. Burning biomass fuels (wheat residue, maize residue, and wood) generally had greater emission ratios than burning gaseous fuels. It is striking that coal type had a profound effect on the emission ratio of the sum of all the quantified NMHCs. Among the 16 tested fuel/stove combinations, the stoves burning unprocessed coal powder and washed coal powder has highest emission ratios while the vented honeycomb coal stove had the lowest emission ratio. The results from the three repeated measurements of Honey-Metal-v showed a large range of CV (8-113%) for the emission factors of the NMHCs detected in the flue gas, providing a rough idea about run-to-run variations in NMHC emissions. By nature, the emission can vary largely from one burn cycle to another cycle. Therefore the uncertainty associated with the single measurement values reported in this paper is expected to be large, which is discussed later in the Discussion Section. Emission Factors. Table 3 presents emission factors of each identified compound, by fuel/stove combination, on a dry-fuel mass basis. The emission factors in Table 3 represent the amount of individual compound in grams (g) generated by burning 1 kg of dry fuel with each fuel/stove combination. That is, for example, burning 1 kg of honeycomb coal briquette using metal coal stove with a flue (Honey-Metal-v) produced 1.98 mg of benzene. The emissions of total speciated NMHC from burning 1 kg of fuel ranged from 11.9 mg to 6.48 g across the fuel/stove combinations measured. Even within a fuel type, the range of total speciated NMHC emission factor can be large for different fuel species and stove types. The coal/stove combinations had a range from 11.9 mg to 6.48 g; the wood stoves had a range from 1.36 and 2.21 g; the crop residues had a range from 0.73 to 2.28 g; and the stoves burning gaseous fuels had a range from 0.024 to 0.42 g. Table 4 presents emission factors on a delivered energy basis. The data on stove efficiency and fuel energy content (measured as low-heat calorific value) are also included in Table 4. These values were used to convert between fuelmass-based and task-based emission factors. Emission factors in Table 4 represent the amount of each compound generated from delivering 1 mega-joule (MJ) heat to the pot by a given fuel/stove combination. These task-based emission factors of total speciated NMHCs also had a large range, from 1.11 mg to 2.41 g per 1 MJ heat delivered. Within-fuel type ranges were 3.28 mg to 2.41 g for coals, 0.57 g to 0.82 g for wood, 0.37 g to 1.52 g for crop residues, 0.02 g for kerosene, and 1.11 mg to 17.5 mg for gaseous fuels. Ozone Forming Potentials. The estimated ozone forming potentials (OFPs) on a dry-fuel mass basis ranged from 9 mg to 11 g ozone per kilogram of dry fuel from 16 fuel-stove combinations tested in this study. The estimated mean OFP was 6.03 g ozone per kilogram of dry fuel for coal (coal powder), 0.03 g for coal briquettes and honeycomb coal briquettes, 1.68 g for wood, 1.75 g for crop residue, 0.74 g for kerosene, 0.14 g for LPG, 0.03 g for coal gas, and 0.06 g for natural gas. The estimated OFPs on a delivered energy basis ranged from 1.41 mg to 4.05 g per 1 MJ of heat delivered to the pot. Figure 1 shows delivering the same amount of heat to the VOL. 37, NO. 13, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Molar Emission Ratios to CO2 (Unit in 10-7)a HoneyMetal-v fuel/stoveb
meanc
Coal Wash Coal Natural CV Honey- Honey- Briq- Coal- Coal- Coal- Wood- Wood- Wheat- Maize- Maize- Kero- LPG- Gas- Gas(%) Metal Imp. Metal Metal-v Metal-v Brick-v Brick-v Imp.-v Brick-v Brick-v Imp.-v Wick Trad. Trad. Trad.
benzene 1,3-butadiene xylenes (o+m+p) styrene ethane ethylene acetylene propane propene i-butane i-butene 1-butene n-butane trans-2-butene 2,2-dimethylpropane cis-2-butene 3-methyl-1-butene i-pentane 1-pentene 2-methyl-1-butene n-pentane trans-2-pentene cis-2-pentene 2-methyl-2-butene cyclopentene 4-methyl-1-pentene cyclopentane 2,3-dimethylbutane 2-methylpentane 3-methylpentane 1-hexene n-hexane methylcyclopentane 2,4-dimethylpentane cyclohexane 2-methylhexane 2,3-dimethylpentane 3-methylhexane 2,2,4-trimethylpentane n-heptane methylcyclohexane 2,3,4-trimethylpentane toluene 2-methylheptane 3-ethylhexane n-octane ethylbenzene n-nonane i-propylbenzene n-propylbenzene p-ethyltoluene m-ethyltoluene 1,2,4-trimethylbenzene and sec-butylbenzene n-decane total speciated NMHC ranking (1 ) highest)
5.91 nd 0.488 nd 4.87 9.30 9.78 0.689 1.16 0.106 0.156 0.073 0.142 nd nd nd nd 0.064 nd nd 0.101 nd nd nd nd nd nd nd 0.075 0.066 nd 0.151 nd nd nd 0.052 nd 0.113 0.671 0.169 0.094 nd 1.17 nd nd 0.157 0.218 0.396 nd nd nd nd 2.43
42 na 60 na 30 16 8 14 12 na 49 na 80 na na na na na na na 38 na na na na na na na 21 88 na 38 na na na 45 na na na 87 na na 57 na na 45 75 64 na na na na na
26.0 nd 3.31 nd 34.3 44.6 34.6 6.18 14.4 1.88 2.91 1.21 1.95 1.03 nd 0.486 nd 2.52 0.486 0.486 1.13 nd nd nd nd nd 0.049 0.198 1.27 0.633 0.324 1.11 0.594 0.680 nd nd nd nd nd 0.793 0.879 nd 6.75 0.030 0.239 1.11 0.855 1.88 nd nd nd nd nd
0.599 113 2.27 36.3 21 197 16 13
29.3 0.061 nd nd 2.92 11.9 3.88 0.597 5.27 nd 1.06 0.352 nd 1.00 nd 0.235 nd nd 0.188 nd 0.183 nd nd nd nd nd nd nd nd nd nd 0.153 nd nd nd nd nd nd nd 0.307 nd nd 4.24 nd nd 0.557 0.145 2.22 nd nd nd nd 2.57
25.8 nd 8.74 nd 63.6 86.0 17.8 14.7 24.5 1.66 2.66 1.89 3.95 1.50 nd 1.37 0.206 1.98 0.481 0.275 1.74 0.481 0.206 0.618 nd nd 0.034 0.140 0.615 0.447 0.573 1.23 0.191 0.144 nd 0.265 0.024 0.417 nd 1.14 0.573 nd 10.9 0.274 0.253 0.914 1.15 2.08 0.581 nd nd nd nd
989 43.0 4.28 nd 6557 8708 2678 1614 2098 94.1 124 352 368 55.8 nd 10.8 22.3 65.7 2.72 20.3 167 2.56 1.78 2.27 nd nd 10.9 15.6 36.9 9.98 56.6 96.1 27.4 nd 0.842 6.65 9.87 6.20 10.1 56.1 11.0 nd 129 6.06 1.06 14.9 nd 1.38 nd nd nd nd nd
2437 71.5 14.0 nd 4193 18474 5725 639 1930 31.1 nd 261 134 83.1 nd nd 5.82 19.2 nd 3.64 60.8 nd nd nd nd nd nd 6.75 10.3 3.01 24.0 34.9 9.18 nd 2.95 1.78 1.82 2.03 5.58 20.3 5.75 nd 316 nd nd 5.95 2.25 1.41 nd nd nd nd nd
78.8 nd 7.59 nd 94.4 467 352 29.0 73.3 3.60 2.58 6.64 7.59 1.95 nd 7.42 0.623 0.914 2.07 nd 2.20 0.131 nd nd nd nd 0.213 nd 0.347 0.160 2.13 1.12 nd nd nd nd nd 0.865 nd 1.00 0.016 nd 18.5 nd nd 0.577 1.59 0.078 nd nd nd nd 2.93
4.37 71.5 15
3.40 285 11
nd nd nd 24488 34536 1167 2 1 9
977 5.16 1.53 nd 177 2652 5591 20.1 154 nd 5.82 13.7 3.81 43.3 nd 2.05 nd nd 1.83 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd 58.5 nd nd nd 1.32 nd nd nd nd nd nd
2225 4.31 nd nd 137.5 6038 9237 34.3 205.5 nd nd 20.8 3.55 46.4 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd 86.6 nd nd nd nd nd nd nd nd nd nd
2122 26.3 3.36 nd 372 8068 10490 55.4 704 1.98 27.2 67.4 5.18 91.6 nd 8.97 4.94 0.620 8.37 4.17 1.64 4.15 2.71 0.224 0.767 0.828 nd nd 0.222 nd 7.22 0.445 nd nd nd nd nd 0.064 1.45 0.203 nd nd 155 0.084 nd 0.010 0.891 nd nd nd nd nd nd
495 nd nd nd 513 3986 3447 44.0 273 2.93 7.52 31.6 5.87 23.8 nd 3.09 1.92 2.22 nd nd 2.93 nd nd nd nd nd nd nd nd nd nd 0.921 nd nd nd nd nd nd nd nd nd nd 10.7 nd nd 1.36 nd nd nd nd nd nd nd
529 nd nd nd 10236 18039 22237 8853 6 4 3 7
949 nd 0.578 nd 371 4539 7846 113 517 4.49 26.3 80.9 14.0 55.2 nd 10.4 3.49 0.176 7.59 1.54 0.249 3.43 2.21 7.34 nd nd nd nd nd nd 5.85 1.10 nd nd nd nd nd nd nd 0.710 nd nd 39.4 nd nd nd 0.515 nd nd nd nd nd nd
81.2 8.16 nd nd 10.5 933 529 4.09 117 3.41 8.99 25.6 0.537 4.72 nd 2.12 1.70 nd 8.94 2.00 nd 1.03 0.616 0.822 1.48 nd nd nd nd nd 7.36 0.084 nd nd nd nd nd nd 3.82 0.851 2.04 0.252 12.4 0.552 0.978 1.50 nd nd nd nd nd nd nd
147 0.458 73.3 243 3.48 4.52 9.10 6.37 18.3 5.97 3.54 5.89 1.33 2.80 3.19 8.25 0.236 nd nd 0.236 nd nd 0.236 0.943 nd nd nd nd nd nd 0.786 nd nd nd 13.6 nd nd nd nd nd nd nd 51.0 nd nd nd 5.03 4.43 nd nd nd nd nd
12.8 nd 3.99 nd 31.0 58.5 5.16 5.72 8.10 0.558 0.899 0.514 1.22 0.289 nd 3.34 0.205 0.283 0.205 0.051 0.300 nd nd nd nd nd 0.077 nd 0.125 nd 0.428 0.125 nd nd nd nd nd 0.060 nd nd nd nd 4.90 nd nd 0.053 0.317 nd 0.015 0.659 0.389 0.060 0.060
81.9 nd 21.2 0.850 33.8 26.1 5.83 12.4 3.02 2.89 0.316 0.158 3.48 2.17 nd 12.8 nd 0.716 2.21 nd 0.982 nd nd nd nd nd nd nd 0.514 nd nd 0.462 0.070 nd nd 0.066 0.243 0.780 nd 0.250 nd nd 24.0 nd nd 0.258 1.89 nd nd nd nd nd nd
nd nd 24.7 0.160 nd 14601 1775 638 141 239 5 8 10 14 12
a Key: nd ) not detected or background level g flue gas concentration; CV (coefficient of variation) ) (standard deviation)/mean; na ) variance calculation cannot be made, only one or no measurement with detected level. b See Table 1 for fuel/stove codes. c Average of 3 burning cycles using Honey-Metal-v combination.
pot by different coal/stove combinations would contribute quite differently to the ozone forming potentials. The results indicated that burning coal powder and biomass fuels would contribute highest ozone forming potential among the 16 tested fuel-stove combinations on a delivered energy basis. Strikingly, burning the coal briquette and honeycomb coal briquette produced OFP values more than 2 orders of magnitude lower than burning unprocessed coal, even in the same vented metal stove. However, the interpretation of these OFP results is very crude because the MIR values used in our estimation were from the database developed for the United States photochemical situation which may be different 2872
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from the Chinese situation due to the possible difference in relative makeup of ozone precursors (NOx and NMHCs). To put these cookstove OFPs into perspective, we compare them with the published vehicular OFPs reported by Chang et al. for gasoline-powered and LPG-powered vehicles representative of the Taipei fleet (16). The OFPs were estimated based on emission factors of some 56 NMHCs and their MIR values: 1.8 g/km-vehicle for (average) gasoline-powered vehicles and 0.97 g/km-vehicle for (average) LPG-powered vehicles. If we use 5 MJ delivered energy as what is roughly needed for cooking a meal (5), cooking one meal would produce OFPs ranging from 0.007 g (the CoalGas-Trad. stove)
TABLE 3. Emission Factorsa (mg of Compound per kg of Dry Fuel, by Fuel/Stove Combination)b fuel/stovec benzene 1,3-butadiene xylenes (o+m+p) styrene ethane ethylene acetylene propane propene i-butane i-butene 1-butene n-butane trans-2-butene 2,2-dimethylpropane cis-2-butene 3-methyl-1-butene i-pentane 1-pentene 2-methyl-1-butene n-pentane trans-2-pentene cis-2-pentene 2-methyl-2-butene cyclopentene 4-methyl-1-pentene cyclopentane 2,3-dimethylbutane 2-methylpentane 3-methylpentane 1-hexene n-hexane methylcyclopentane 2,4-dimethylpentane cyclohexane 2-methylhexane 2,3-dimethylpentane 3-methylhexane 2,2,4-trimethylpentane n-heptane methylcyclohexane 2,3,4-trimethylpentane toluene 2-methylheptane 3-ethylhexane n-octane ethylbenzene n-nonane i-propylbenzene n-propylbenzene p-ethyltoluene m-ethyltoluene 1,2,4-trimethylbenzene and sec-butylbenzene n-decane total speciated NMHC
Coal Wash Honey Honey- Honey- Briq- CoalCoal- Coal- Wood- Wood- Wheat- Maize- Maize- Kero- LPGd Metal-v Metal Imp. Metal Metal-v Metal-v Brick-v Brick-v Imp.-v Brick-v Brick-v Imp.-v Wick Trad.
Coal Natural Gas- GasTrad. Trad.
2.71 nd 0.305 nd 0.857 1.52 1.47 0.177 0.283 0.034 0.050 0.023 0.046 nd nd nd nd 0.027 nd nd 0.042 nd nd nd nd nd nd nd 0.038 0.032 nd 0.076 nd nd nd 0.031 nd 0.069 0.446 0.102 0.056 nd 0.631 nd nd 0.104 0.137 0.304 nd nd nd nd 1.77
11.0 nd 1.91 nd 5.58 6.77 4.88 1.48 3.27 0.590 0.885 0.369 0.615 0.313 nd 0.148 nd 0.984 0.184 0.184 0.443 nd nd nd nd nd 0.018 0.092 0.590 0.295 0.148 0.516 0.270 0.369 nd nd nd nd nd 0.430 0.467 nd 3.37 0.018 0.148 0.688 0.492 1.30 nd nd nd nd nd
14.0 0.020 nd nd 0.538 2.04 0.618 0.161 1.36 nd 0.363 0.121 nd 0.343 nd 0.081 nd nd 0.081 nd 0.081 nd nd nd nd nd nd nd nd nd nd 0.081 nd nd nd nd nd nd nd 0.188 nd nd 2.39 nd nd 0.390 0.094 1.75 nd nd nd nd 1.90
7.40 nd 3.41 nd 7.02 8.87 1.71 2.37 3.79 0.354 0.549 0.390 0.844 0.310 nd 0.283 0.053 0.526 0.124 0.071 0.461 0.124 0.053 0.159 nd nd 0.009 0.044 0.195 0.142 0.177 0.390 0.059 0.053 nd 0.097 0.009 0.154 nd 0.419 0.207 nd 3.71 0.115 0.106 0.384 0.449 0.980 0.257 nd nd nd nd
440 13.3 2.58 nd 1121 1390 397 405 502 31.1 39.7 113 122 17.8 nd 3.44 8.90 27.0 1.09 8.12 68.5 1.02 0.709 0.904 nd nd 4.35 7.66 18.1 4.89 27.1 47.1 13.1 nd 0.403 3.79 5.63 3.54 6.54 32.0 6.13 nd 67.5 3.94 0.689 9.70 nd 1.00 nd nd nd nd nd
1050 21.3 8.22 nd 694 2856 822 155 448 9.97 nd 80.6 42.8 25.7 nd nd 2.25 7.64 nd 1.41 24.2 nd nd nd nd nd nd 3.21 4.91 1.43 11.1 16.6 4.26 nd 1.37 0.983 1.00 1.12 3.51 11.2 3.11 nd 161 nd nd 3.74 1.32 0.995 nd nd nd nd nd
25.8 nd 3.38 nd 11.9 54.9 38.4 5.36 12.9 0.877 0.607 1.56 1.85 0.458 nd 1.74 0.183 0.276 0.607 nd 0.665 0.039 nd nd nd nd 0.063 nd 0.125 0.058 0.752 0.405 nd nd nd nd nd 0.363 nd 0.421 0.006 nd 7.15 nd nd 0.276 0.707 0.042 nd nd nd nd 1.47
264 0.963 0.560 nd 18.3 257 503 3.06 22.3 nd 1.13 2.65 0.763 8.39 nd 0.397 nd nd 0.443 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd 18.6 nd nd nd 0.485 nd nd nd nd nd nd
629 0.843 nd nd 14.9 612 870 5.47 31.3 nd nd 4.22 0.747 9.41 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd 28.9 nd nd nd nd nd nd nd nd nd nd
80.4 0.173 54.3 177 0.731 0.885 1.65 1.96 5.39 2.42 1.39 2.31 0.539 1.10 1.62 3.23 0.115 nd nd 0.115 nd nd 0.115 0.462 nd nd nd nd nd nd 0.462 nd nd nd 7.96 nd nd nd nd nd nd nd 32.8 nd nd nd 3.73 3.96 nd nd nd nd nd
4.19 nd 1.77 nd 3.90 6.87 0.563 1.06 1.43 0.136 0.211 0.121 0.297 0.068 nd 0.784 0.060 0.085 0.060 0.015 0.090 nd nd nd nd nd 0.023 nd 0.045 nd 0.151 0.045 nd nd nd nd nd 0.025 nd nd nd nd 1.89 nd nd 0.025 0.141 nd 0.008 0.332 0.196 0.030 0.030
0.514 11.9
1.75 50.6
3.80 30.4
1.78 48.6
nd 4977
nd 6480
nd 173
260 1361
nd nd 2207 2282
24.5 409
0.096 nd 24.7 121
512 4.40 1.10 nd 34.6 699 844 7.54 91.4 0.355 4.71 11.7 0.929 15.9 nd 1.55 1.07 0.138 1.81 0.904 0.366 0.898 0.586 0.048 0.161 0.215 nd nd 0.059 nd 1.88 0.118 nd nd nd nd nd 0.020 0.511 0.063 nd nd 44.2 0.030 nd 0.004 0.292 nd nd nd nd nd nd
102 nd nd nd 40.8 296 237 5.12 30.3 0.450 1.12 4.68 0.902 3.53 nd 0.458 0.356 0.424 nd nd 0.558 nd nd nd nd nd nd nd nd nd nd 0.210 nd nd nd nd nd nd nd nd nd nd 2.60 nd nd 0.411 nd nd nd nd nd nd nd
194 nd 0.161 nd 29.3 334 536 13.1 57.1 0.683 3.86 11.9 2.14 8.12 nd 1.53 0.642 0.033 1.40 0.283 0.047 0.630 0.407 1.35 nd nd nd nd nd nd 1.29 0.247 nd nd nd nd nd nd nd 0.187 nd nd 9.52 nd nd nd 0.143 nd nd nd nd nd nd
44.9 3.12 nd nd 2.23 185 97.6 1.28 34.7 1.40 3.57 10.2 0.221 1.87 nd 0.842 0.842 nd 4.44 0.995 nd 0.510 0.306 0.408 0.714 nd nd nd nd nd 4.39 0.051 nd nd nd nd nd nd 3.09 0.604 1.42 0.204 8.12 0.446 0.791 1.22 nd nd nd nd nd nd nd
nd 727
nd nd 1208 416
50.0 nd 17.6 0.691 7.92 5.72 1.19 4.28 0.991 1.31 0.138 0.069 1.58 0.950 nd 5.60 nd 0.403 1.21 nd 0.553 nd nd nd nd nd nd nd 0.346 nd nd 0.311 0.046 nd nd 0.052 0.190 0.610 nd 0.196 nd nd 17.3 nd nd 0.230 1.57 nd nd nd nd nd nd
a Zhang et al. (6) presented detailed information on calculation for emission factors using the carbon balance model. b Key: nd ) not detected or background level g flue gas concentration. c See Table 1 for fuel/stove codes. d Average of 3 burning cycles using Honey-Metal-v combination.
to 20.3 g (the Coal-Metal-v stove). At the lower bound of the cookstove OFP range, cooking 257 meals would produce OFP equivalent to driving a gasoline-powered vehicle for 1 km, and cooking 139 meals would produce OFP equivalent to driving a LPG-powered vehicle for 1 km. At the upper bound of the cookstove OFP range, in contrast, cooking 1 meal would produce OFP equivalent to driving a gasoline-powered vehicle for about 11 km or equivalent to driving a LPG-powered vehicle for about 21 km.
Discussion Uncertainties for Emission Factors. Due to the budgetary constraint, only one fuel/stove combination (Honey-Metalv), among the 16 combinations measured for NMHC emissions, was repeatedly measured for three independent burn
cycles (experimental runs). These measurements showed high run-to-run variations (8-113% CV) in emission factors estimated using the carbon balance model for the HoneyMetal-v combination. High CV values were also expected for the emission factors of speciated NMHCs for the other 15 fuel/stove combinations based on the analysis of uncertainty sources of the carbon balance approach. There were two major sources of uncertainty for this approach. One was the variability in emissions from one burn cycle to another, largely associated with variations in fire tending behavior and variations in fuel parameters (e.g., fuel size, fuel “layout” in the stove). This type of variability was obviously greater for the solid fuel stoves than for the LPG and gas stoves. The other source was the measurement errors for all the parameters and concentration values used in the carbon VOL. 37, NO. 13, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 4. Emission Factors (mg Compound/MJ Delivered to the Pot, by Fuel/Stove Combination)a fuel/stove stove efficiency (%) low-heat CV(MJ/kg fuel) benzene 1,3-butadiene xylenes (o+m+p) styrene ethane ethylene acetylene propane propene i-butane i-butene 1-butene n-butane trans-2-butene 2,2-dimethylpropane cis-2-butene 3-methyl-1-butene i-pentane 1-pentene 2-methyl-1-butene n-pentane trans-2-pentene cis-2-pentene 2-methyl-2-butene cyclopentene 4-methyl-1-pentene cyclopentane 2,3-dimethylbutane 2-methylpentane 3-methylpentane 1-hexene n-hexane methylcyclopentane 2,4-dimethylpentane cyclohexane 2-methylhexane 2,3-dimethylpentane 3-methylhexane 2,2,4-trimethylpentane n-heptane methylcyclohexane 2,3,4-trimethylpentane toluene 2-methylheptane 3-ethylhexane n-octane ethylbenzene n-nonane i-propylbenzene n-propylbenzene p-ethyltoluene m-ethyltoluene 1,2,4-trimethylbenzene and sec-butylbenzene n-decane total speciated NMHC a
Coal Wash Coal Natural Honey Honey- Honey- Briq- CoalCoal- Coal- Wood- Wood- Wheat- Maize- Maize- Kero- LPG- Gas- Gasb Metal-v Metal Imp. Metal Metal-v Metal-v Brick-v Brick-v Imp.-v Brick-v Brick-v Imp.-v Wick Trad. Trad. Trad. 16 19.2 0.888 nd 0.100 nd 0.281 0.494 0.476 0.058 0.092 0.010 0.016 0.007 0.014 nd nd nd nd 0.009 nd nd 0.013 nd nd nd nd nd nd nd 0.012 0.010 nd 0.025 nd nd nd 0.010 nd 0.024 0.144 0.034 0.019 nd 0.207 nd nd 0.033 0.046 0.103 nd nd nd nd 0.607
22 19.2 2.57 nd 0.445 nd 1.30 1.58 1.14 0.345 0.764 0.138 0.207 0.086 0.144 0.073 nd 0.034 nd 0.230 0.043 0.043 0.103 nd nd nd nd nd 0.004 0.022 0.138 0.069 0.034 0.121 0.063 0.086 nd nd nd nd nd 0.101 0.109 nd 0.787 0.004 0.034 0.161 0.115 0.305 nd nd nd nd nd
48 19.2 1.51 0.002 nd nd 0.058 0.221 0.067 0.017 0.147 nd 0.039 0.013 nd 0.037 nd 0.009 nd nd 0.009 nd 0.009 nd nd nd nd nd nd nd nd nd nd 0.009 nd nd nd nd nd nd nd 0.020 nd nd 0.258 nd nd 0.042 0.010 0.189 nd nd nd nd 0.205
37 13.9 1.46 nd 0.674 nd 1.39 1.75 0.337 0.469 0.747 0.070 0.108 0.077 0.167 0.061 nd 0.056 0.010 0.104 0.024 0.014 0.091 0.024 0.010 0.031 nd nd 0.002 0.009 0.038 0.028 0.035 0.077 0.012 0.010 nd 0.019 0.002 0.030 nd 0.083 0.041 nd 0.732 0.023 0.021 0.076 0.089 0.194 0.051 nd nd nd nd
10 30.1 146 4.39 0.857 nd 372 461 132 134 167 10.3 13.2 37.3 40.4 5.90 nd 1.14 2.95 8.94 0.360 2.69 22.7 0.338 0.235 0.300 nd nd 1.44 2.54 5.99 1.62 8.99 15.6 4.36 nd 0.134 1.26 1.87 1.17 2.17 10.6 2.03 nd 22.4 1.31 0.229 3.21 nd 0.333 nd nd nd nd nd
10 27.3 390 7.92 3.05 nd 258 1061 305 57.7 166 3.70 nd 30.0 15.9 9.55 nd nd 0.836 2.84 nd 0.522 8.97 nd nd nd nd nd nd 1.19 1.82 0.532 4.14 6.16 1.58 nd 0.508 0.365 0.372 0.416 1.30 4.17 1.16 nd 59.7 nd nd 1.39 0.489 0.370 nd nd nd nd nd
26 27.3 3.71 nd 0.486 nd 1.71 7.90 5.52 0.771 1.86 0.126 0.087 0.225 0.266 0.066 nd 0.251 0.026 0.040 0.087 nd 0.096 0.006 nd nd nd nd 0.009 nd 0.018 0.008 0.108 0.058 nd nd nd nd nd 0.052 nd 0.061 0.001 nd 1.03 nd nd 0.040 0.102 0.006 nd nd nd nd 0.212
10 16.3 159 0.582 0.338 nd 11.1 155 304 1.85 13.5 nd 0.681 1.60 0.461 5.07 nd 0.240 nd nd 0.268 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd 11.2 nd nd nd 0.293 nd nd nd nd nd nd
24 16.3 161 0.216 nd nd 3.83 157 223 1.40 8.01 nd nd 1.08 0.191 2.41 nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd nd 7.39 nd nd nd nd nd nd nd nd nd nd
11 14.0 342 2.94 0.735 nd 23.1 467 564 5.04 61.1 0.237 3.15 7.80 0.620 10.6 nd 1.04 0.715 0.092 1.21 0.604 0.244 0.600 0.392 0.032 0.108 0.144 nd nd 0.040 nd 1.25 0.079 nd nd nd nd nd 0.013 0.341 0.042 nd nd 29.5 0.020 nd 0.002 0.195 nd nd nd nd nd nd
12 16.1 52.2 nd nd nd 20.8 151 121 2.62 15.5 0.230 0.570 2.39 0.461 1.80 nd 0.234 0.182 0.217 nd nd 0.285 nd nd nd nd nd nd nd nd nd nd 0.107 nd nd nd nd nd nd nd nd nd nd 1.33 nd nd 0.210 nd nd nd nd nd nd nd
19 16.1 62.6 nd 0.052 nd 9.42 108 173 4.22 18.4 0.220 1.24 3.83 0.688 2.62 nd 0.491 0.207 0.011 0.450 0.091 0.015 0.203 0.131 0.435 nd nd nd nd nd nd 0.416 0.080 nd nd nd nd nd nd nd 0.060 nd nd 3.07 nd nd nd 0.046 nd nd nd nd nd nd
47 43.3 2.20 0.153 nd nd 0.109 9.06 4.77 0.062 1.70 0.069 0.175 0.498 0.011 0.092 nd 0.041 0.041 nd 0.217 0.049 nd 0.025 0.015 0.020 0.035 nd nd nd nd nd 0.214 0.002 nd nd nd nd nd nd 0.151 0.030 0.069 0.010 0.397 0.022 0.039 0.059 nd nd nd nd nd nd nd
48 49.0 3.44 0.007 2.32 7.56 0.031 0.038 0.071 0.084 0.231 0.104 0.059 0.099 0.023 0.047 0.069 0.138 0.005 nd nd 0.005 nd nd 0.005 0.020 nd nd nd nd nd nd 0.020 nd nd nd 0.341 nd nd nd nd nd nd nd 1.40 nd nd nd 0.160 0.170 nd nd nd nd nd
51 43.8 0.188 nd 0.080 nd 0.175 0.309 0.025 0.047 0.064 0.006 0.009 0.005 0.013 0.003 nd 0.035 0.003 0.004 0.003 0.001 0.004 nd nd nd nd nd 0.001 nd 0.002 nd 0.007 0.002 nd nd nd nd nd 0.001 nd nd nd nd 0.085 nd nd 0.001 0.006 nd 0.000 0.015 0.009 0.001 0.001
0.175 3.91
0.408 0.411 0.351 nd 11.8 3.28 9.60 1650
nd 2407
nd 24.9
157 822
nd 565
nd 1524
nd 371
nd 389
nd 20.3
1.05 17.5
0.004 nd 1.11 4.37
Key: nd ) not detected or background level g flue gas concentration.
balance equations. The measurement errors appeared to be greater for those compounds present at a level below or close to the method detection limit. The gas stoves emitted more compounds at low concentration levels. The estimated CV values of emission factors reflected the overall contribution of these two uncertainty sources. This analysis is supported by large CV values reported for the emission factors of TNMHC for the 28 Chinese fuel/stove combinations including the 16 ones measured for speciated NMHCs (6). The CV values for the TNMHC emission factors ranged from 13% to 173% across the 28 fuel/stove combinations with the highest value being for the Honey-Metal-v. The CV values for the speciated NMHCs appeared to be within the CV value ranges for the TNMHC for the Honey-Metal-v, the only fuel/stove combinations that were repeatedly measured for both speciated 2874
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b
54 51.3 1.81 nd 0.635 0.025 0.286 0.207 0.043 0.155 0.036 0.047 0.005 0.002 0.057 0.034 nd 0.202 nd 0.015 0.044 nd 0.020 nd nd nd nd nd nd nd 0.012 nd nd 0.011 0.002 nd nd 0.002 0.007 0.022 nd 0.007 nd nd 0.625 nd nd 0.008 0.057 nd nd nd nd nd nd
Average of 3 burning cycles using Honey-Metal-v combination.
NMHCs and TNMHC. [The sum of measured individual NMHCs did not account for all the HCs present in the samples. It represents, in theory, a fraction of TNMHC. However, TNMHC and speciated NMHCs were measured using different methods (see ref 6), and thus the sums of speciated NMHCs cannot be directly compared with the concentrations of TNMHC. Measurement precisions for TNMHC and speciated NMHCs were, however, expected to be similar.] Based on these data, we expect the emission factors of speciated NMHCs reported in this paper may have large CV values, but probably smaller than 173%. Another source of uncertainty and reason to do more tests in field settings is that wood fuels seem to have higher emission rates of PICs during the smoldering phase of combustion (17). Since smoldering can continue in the field after a cooking
FIGURE 1. Ozone forming potential (mg ozone/MJ) for the 16 fuel/stove combinations (log-scale). The standard deviation bar for HoneyMetal-v was derived from three repeated measurements. No repeated measurements were made for the other 15 fuel/stove combinations. session is completed, the emissions from the smoldering are difficult to measure in simulated settings. This is only a serious issue with wood fuels, however, as our tests of the Chinese fuel/stove combinations did include smoldering phases for coal and crop residues do not smolder significantly after fueling stops. Gases and liquids, of course, have no such smoldering phases. Speciated NMHCs were also measured in the pilot study in which 6 fuel/stove combinations in the Philippines were tested using the carbon balance approach (2, 4, 18). The 6 fuel/stove combinations tested included the stoves burning LPG, kerosene, charcoal, and wood fuels. As it was typical in Manila, none of the tested stove had flues or chimneys. Compared to the extensive study (5, 6), the pilot study measured fewer parameters that needed to construct the complete carbon balance model and did not use a hood to promote mixing of flue gases. No repeated measurements were made in the pilot study. Hence, the results from the pilot study can be expected to be less “accurate” than those from the extensive study. Nevertheless, both the pilot study and the present study showed that burning wood fuels yielded greater emissions of benzene (0.2-0.4 g as carbon per kg dry fuel) than burning other fuels. 1,3-Butadiene emissions measured in the present study, however, were substantially lower (by 1 or 2 orders of magnitude) than those measured in the pilot study for the similar fuel/stoves tested in both studies. Relatively large differences in other NMHC emissions were also found when the results from the two studies were compared. A very limited number of other studies of cookstove emissions only measured certain specific compounds of interest and usually did not report NMHCs. Therefore, it is necessary to conduct future systematic studies and uncertainty analyses to better understand the natural variability and to reduce the measurement errors associated with emission factors for various fuel-stove systems.
Ozone Concerns. Local and regional pollution of ozone and photochemical smog is common worldwide and occurs in many urban and suburban areas in China. Among the speciated NMHCs identified in this study, some compounds had higher ozone forming potentials than others. Saturated organic compounds such as alkanes usually have lower MIR values than alkenes and aromatic species that are more active in terms of forming ozone. When both MIR values and amount of emissions were considered, ethylene, among the 54 speciated NMHCs, was the major contributor to OFP values for most fuel-stove combination tested in this study. In most fuel/stove combinations, emission of ethylene contributes more than 50% of ozone (ranging from 12% to 85%), followed by acetylene (1-46%), xylenes (0-32%), styrene (0-31%), propene (10-24%), cis-2-butene (0-19%), 1,2,4-trimethylbenzene and sec-butylbenzene (0-18%), and toluene (0-10%). The type and amount of fuels used in Chinese households varies in urban and rural areas (11, 12). In urban areas of China, the major types of fuel used in the residential sector are coal, electricity, LPG, natural gas, and coal gas. In rural areas, biomass fuels (crop residues and wood) are the major types of fuel used in the residential sector. Because groundlevel ozone accumulation results from complex chemical reactions, in the atmosphere, between NMHCs and NOx, the actual ozone formation may be limited by NOx concentrations in rural areas where no significant sources of NOx are present. Therefore, here we only discuss ozone-forming potentials for urban areas. However, some NMHCs emitted from rural household stoves can be transported to urban areas, contributing to the urban ozone pollution. Table 5 shows the estimated total ozone forming potentials from domestic cookstoves in the urban areas of China based on the fuel usage and the specific OFP value for each type of fuel measured in this study. The estimates in Table 5 were VOL. 37, NO. 13, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 5. Estimated Yearly Ozone Forming Potential (OFP) in the Urban Area of China related to household cookstoves
fuel types raw coal coal briquette kerosene LPG coal gas natural gas total
av OFP (g ozone /kg fuel) 6.03 0.03 0.74 0.144 0.031 0.064
a Reference 11. consumed.
b
estimated total amount of consumption OFP per year in the in the urban area of urban area China in 1995 (ton ozone) × 102 (metric ton × 106)a 58.3b 0.25 0.07 5.10 2.37 1.68
3515 0.08 0.52 7.34 0.74 1.07 3525
Total amount of raw coal and washed coal
fuel specific rather than fuel/stove specific, because data on fuel consumption were only fuel specific. We break down the coal consumption data by coal type to separate raw coal and coal briquette (including honeycomb coal briquette) consumptions. Our estimates indicate that the total ozone amount attributable to urban residential NMHC emissions in 1995 were approximately 353 000 tons per year. The results indicated that coal-fired cookstoves would contribute > 99% of total ozone amount attributable to NMHC emissions from all the cookstoves used in urban China. This large coal contribution was mainly driven by the high OFP value for raw coal and the large amount of raw coal consumption. However, reliable data sources of coal briquette consumption or production are uncertain because most coal briquettes were produced in small workshops where no effective reporting or tracking systems were in place to record the production data. We suspect that the production of coal briquettes might be substantially underestimated in the China national energy statistics and, hence, the OFP estimate for coal briquette in Table 5 may be lower than the actual value. Based on the statistical information from the China Energy Yearbook (11, 12), the raw coal usage has decreased gradually yearly and replaced by coal briquettes, kerosene, and gaseous fuels. However, since the amount of raw coal consumption is much larger and raw coal has the highest OFP value among all the fuel tested, the total OFP from the urban residential sector (for cooking) should have changed insignificantly over the past few years. Our analysis shows, for example, in 1997 raw coal still contributes more than 95% of total OFP attributable to household cookstove emissions of NMHCs. This type of OFP analysis using the measured emission factors of NMHCs may provide useful information for energy decision-making, from a standpoint of ozone control strategy, in China or other countries having similar suite of fuel/stove combinations. By switching the fuel/stove combinations, the NMHC contribution to ozone formation can be significantly reduced (see Figure 1). For example, on average, a switch of coal powder to natural gas would lead to a reduction of OFP by 70 times, and a switch of coal powder to coal gas would lead to a 40-fold OFP reduction. Among different types of coal fuels, burning coal briquettes and honeycomb coal briquettes contribute much less OFPs than burning coal powder. However, our estimates were subject to large uncertainties due to the uncertainty associated with the emission factors measured, the uncertainty associated with fuel consumption data, and the uncertainty associated with MIR values (difference in atmospheric NOx/NMHC conditions between the United States and China urban areas). It will probably be necessary to conduct measurements under field conditions to reduce these uncertainties sufficiently to make definitive policy decisions about ozone control policy in China. Other Potential Applications of the Database. The speciated NMHC database can be useful in source ap2876
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portionment studies. Emissions from numerous cooking stoves may be a significant source to particulate matter and volatile organic compounds and thus significantly influence the local and regional air quality including the formation of photochemical smog. Sorting out the relative importance of sources is always attractive to the scientific community and policy makers. Source apportionment techniques have been widely used to estimate relative contributions of various sources to the concentrations of given (target) pollutants measured at the receptor. Using VOCs, similar to what we measured in the study, to do source apportionment has been done in previous studies (19, 20). For example, Anderson et al. (20) applied personal exposure measurements for VOCs to four receptor-oriented source apportionment models. The previous studies used the VOC data collected in the Total Exposure Assessment Methodology (TEAM) studies conducted in New Jersey and California as well as the data collected in the California Indoor Exposure study to evaluate several receptor models. The models evaluated include the Chemical Mass Balance model, Principal Component Analysis/Absolute Principal Component Scores, Positive Matrix Factorization, and Graphical Ratio Analysis for Composition Estimates/Source Apportionment by Factors with Explicit Restriction. The source apportionment results from the four models agreed reasonably well for the New Jersey data but were less consistent for the California data. Model performance varies from several factors such as availability of information on emission composition, number of receptor samples, reactivity of compounds used as tracers, measurement uncertainty, fraction of missing data, fraction of belowdetection-limit data, number of factors determined, factors interpretation for sources, whether all important sources are included, and whether there is collinearity within/among the source profiles used in the model. Nevertheless, this indicates that an accurate source profile is the key to the successful implementation of source apportionment models. A previous analysis by Zhang and Smith (2) evaluated lifetime cancer risks associated with exposure to benzene, 1,3-butadiene, styrene, and xylenes released from the 6 fuel/ stove combinations tested in the Manila pilot study. The analysis found that estimated cancer risk of benzene and that of styrene from the use of the tested wood stove could exceed published risk estimates from all sources of airborne benzene or styrene (excluding active tobacco smoking) in the United States. These kind of important findings based on the limited data of the pilot study should be further evaluated using similar or more sophisticated analysis based on the more extensive data collected in this study. In the future analysis, cancer risks for all the tested fuel/stove combinations can be compared to make quantitative recommendations on fuel or stove switching (2). Additional analyses may be preformed to also assess the noncancer health risks. For example, this database may be used to conduct a chemical mixture risk assessment using groups (or classes) of hydrocarbons based on carbon number fractions (e.g., C5-C8, C9-C16), their fate and transport, and toxicity characteristics. These will be discussed in a more detailed health risk analysis paper to follow.
Acknowledgments We greatly appreciate the effective and timely peer review process of our paper. We appreciate the comments from Dr. Rufus Edwards of University of California at Berkeley and editorial assistance from Dr. Steven Miller of the New Jersey Department of Health and Senior Services and Mr. Robert C. Harrington of Environmental and Occupational Health Sciences Institute, New Jersey. We appreciate the assistance from Dr. Jonathan Sinton of Lawrence Berkeley National Laboratory in providing information on coal consumption in China. The original data collection was funded through
a Cooperative Agreement between U.S. Environmental Protection Agency and the East-West Center (#CR 82024301). However, this manuscript has not gone through official EPA review procedures and thus should not be considered to have EPA official approval. Dr. J. Zhang is supported in part by a NIEHS Center Grant (#ES05022-10).
Literature Cited (1) Smith, K. R. Annu. Rev. Energy Environ. 1993, 18, 529-566. (2) Zhang, J.; Smith, K. R. J. Exposure Anal. Environ. Epidemiol. 1996, 6, 147-161. (3) Zhang, J.; Smith, K. R.; Uma, R.; Ma, Y.; Kishore, V. V. N.; Lata, L.; Khalil, M. A. K.; Rasmussen, R. A.; Thorneloe, S. A. Chemosphere: Global Change Sci. 1999, 1, 367-375. (4) Smith, K. R.; Khalil, M. A. K.; Rasmussen, R. A.; Thorneloe, S. A.; Manegdeg, M.; Apte, M. Chemosphere 1993, 26, 479-505. (5) Smith, K. R.; Uma, R.; Kishore, V. V. N.; Lata, K.; Joshi, V.; Zhang, J.; Rasmussen, R. A.; Khalil, M. A. K. Greenhouse Gases from Small-scale Combustion Devices in Developing Countries, Phase IIa: Household Stoves in India; EPA-600/R-00-052; U.S. Environmental Protection Agency, Office of Research and Development: Washington, DC, 2000. (6) Zhang, J.; Smith, K. R.; Ma, Y.; Ye, S.; Jiang, F.; Qi, W.; Liu, P.; Khalil, M. A. K.; Rasmussen, R. A.; Thorneloe, S. A. Atmos. Environ. 2000, 34, 4537-4549. (7) Smith, K. R.; Uma, R.; Kishore, V. V. N.; Zhang, J.; Joshi, V.; Khalil, M. A. K. Annu. Rev. Energy Environ. 2000, 25, 741-763. (8) Smith, K. R.; Mehta, S.; Feuz, M. Indoor smoke from household solid fuels. In Comparative Quantification of Health Risks: Global and Regional Burden of Disease due to Selected Major Risk Factors; Ezzati, M., Rodgers, A. D., Lopez, A. D., Murray,
C. J. L., Eds.; Geneva: World Health Organization, in press. (9) Bruce, N.; Perez-Padilla, R.; Albalak, R. Bull. World Health Organization 2000, 78, 1078-1092. (10) Smith, K. R. Proc. Natl. Acad. Sci. 2000, 97, 13286-13293. (11) State Statistical Bureau. China Energy Statistical Yearbook (19911996); Statistical Publishing House: Beijing, China, 1998. (12) National Bureau of Statistics. China Energy Statistical Yearbook (1997-1999); China Statistics Press: Beijing, China, 2001. (13) Joshi, V.; Venkataraman, C.; Ahuji, D. R. Environ. Manage. 1989, 13, 763-772. (14) EPA website: http://www.epa.gov/ttn/amtic/airtox.html. (15) Carter, W. P. L. J. Air Waste Manage. Assoc. 1994, 44, 881-899. (16) Chang, C.; Lo, J.; Wang, J. Atmos. Environ. 2001, 35, 6201-6211. (17) Brocard, D.; Lacaux, C.; Lacaux, J. P.; Kouadio, G.; Yoboue, V. Emissions from the combustion of biofuels in Western Africa. In Biomass Burning and Global Change; Levine, J. S., Ed.; MIT Press: Cambridge, 1996; Vol. 1, pp 350-360. (18) Smith, K. R.; Rasmussen, R. A.; Manegdeg, F.; Apte, M. Greenhouse Gases from Small-Scale Combustion in Developing Countries: A Pilot Study in Manila; EPA-600/R-92-005; Global Emissions and Control Division, U.S. EPA: Research Triangle Park, NC, 1992. (19) Edwards, R. D.; Jurvelin, J.; Koistinen, K.; Saarela, K.; Jantunen, M. Atmos. Environ. 2001, 35, 4829-4841. (20) Anderson, M.; Daly, E.; Miller, S. L.; Milford J. Atmos. Environ. 2002, 36, 3643-3658.
Received for review October 10, 2002. Revised manuscript received April 18, 2003. Accepted April 30, 2003. ES026232A
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