Environ. Sci. Technol. 1996, 30, 104-109
Assessment of Particulate Concentrations from Domestic Biomass Combustion in Rural Mexico M I C H A E L B R A U E R , * ,†,‡ KAREN BARTLETT,‡ JUSTINO REGALADO-PINEDA,§ AND ROGELIO PEREZ-PADILLA§ Department of Medicine, Respiratory Division, and Occupational Hygiene Program, University of British Columbia, 2206 East Mall, 3rd Floor, Vancouver, BC, V6T 1Z3 Canada, and Instituto Nacional de Enfermedades Respiratorias, Tlalpan, DF, Mexico
Recent evidence has suggested that woodsmoke exposure in developed countries is associated with acute and chronic health impacts. Accordingly, it is increasingly important to investigate the much higher woodsmoke exposures associated with the use of wood and other biomass for cooking and heating in developing countries. Particulate concentrations were measured in rural Mexican kitchens using biomass combustion for cooking. To investigate differences in indoor particle concentrations between kitchens using different fuels and stove types, measurements were made in eight kitchens using only biomass, six using only liquefied petroleum gas (LPG), six using a combination of biomass and LPG, and three using biomass in ventilated stoves. Outdoor samples were collected at the same time as the indoor samples. PM10 and PM2.5 measurements were made with inertial impactors, and particle light scattering was measured continuously with an integrating nephelometer. Nephelometer and particulate mass measurements were highly correlated (r2 of 0.9 and 0.83 for PM2.5 and PM10, respectively), indicating that the light scattering measurements could be used to estimate shortterm concentrations. PM10 and PM2.5 concentrations (mean concentrations of 768 and 555 µg m-3, respectively) in the kitchens burning only biomass were greater than in all other types (biomass > biomass + LPG > ventilated > LPG > outdoor). A similar trend was evident for the indoor/outdoor concentration ratio. Based on the short-term measurements estimated from the nephelometer data, PM10 and PM2.5 cooking period average and 5-min peak concentrations were significantly higher (p < 0.05) in kitchens using only biomass than in those using LPG, a combination of LPG and biomass, or a ventilated biomass stove.
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Introduction Recent information from urban air pollution studies in developed countries has suggested that exposure to particulates is associated with increased morbidity and mortality (1). While the specific biologically active component(s) of particulate matter have yet to be identified for these epidemiological studies, one common feature is that the particles are produced in combustion processes. One combustion process is the burning of wood for home heating in colder climates. Ambient particulate levels below 100 µg m-3 in areas where a major particulate source was wood smoke have been associated with increased hospital admissions (2), decreased lung function in children (3), and increased risk of acute respiratory infections (ARI) in young children (4). Even in cases where woodsmoke is effectively vented outdoors, indoor/outdoor ratios are high (>0.9), indicating that the fine particles penetrate back indoors (5), resulting in elevated exposures. Due to the evidence relating woodsmoke exposure in developed countries with acute and chronic health impacts, it is increasingly important to investigate the largest source of woodsmoke exposure, the use of wood and other biomass for cooking and heating in developing countries. On a global basis, it is the rural population in developing countries who are most highly exposed to fine particulates (6, 7). Biomass combustion, the main source of heating and cooking fuel in the world (8, 9), is likely the major contributor to fine particle exposure. In Mexico, 48% of homes in 1991 used biomass for cooking with the proportion much higher in rural areas (69%) than in urban areas (0.2%) (10). The potential health effects associated with exposure to biomass combustion products in developing countries are widespread and have recently been reviewed (7). In particular, exposure to biomass combustion products has been identified as a risk factor for ARI. ARI are the leading cause of infant mortality in the developing countries (11). Globally, ARI are estimated to kill between 2 and 4 million children under the age of 5 each year (7, 12). In addition to the risks of infants, the women who are cooking are also at risk for chronic respiratory diseases as well as adverse pregnancy outcomes. Here we report fine particle measurements in the kitchens of homes in rural Mexico in which biomass combustion was used for cooking. In Mexico, one of the dietary staples is the tortilla, which is cooked by heating (typically corn) dough on a flat metal surface that is placed directly over a heat source. For a typical extended family household, tortilla cooking takes place 3-4 days each week, and the preparation lasts 2-4 h each day. During this cooking period, exposures to combustion products may be substantial. The traditional heat source is a simple threestone stove surrounding an open fire. Due to recent restrictions on wood cutting and a severe shortage of wood, a diverse array of biomass fuels are now burned. Most of * Author to whom all correspondence should be addressed; telephone: (604)822-9585; fax: (604)822-9588; e-mail address:
[email protected]. † Department of Medicine, University of British Columbia. ‡ Occupational Hygiene Program, University of British Columbia. § Instituto Nacional de Enfermedades Respiratorias.
0013-936X/96/0930-0104$12.00/0
1995 American Chemical Society
these fuels have a lower energy content and produce more emissions per weight than wood (8), suggesting that exposures may actually be worsening for those using biomass for a cooking fuel. Liquefied petroleum gas (LPG) is also distributed in many rural areas, and cooking may be performed with unvented LPG stoves. Several types of ventilated biomass stoves are also used. We sought to investigate particle exposures in rural Mexican kitchens and to investigate the differences in indoor particle concentrations between kitchens using different cooking fuels and stove types. An additional goal of these measurements was to use a portable nephelometer in conjunction with traditional filter sampling to improve the assessment of fine particle exposure.
Methods During 11 days from April 23 to May 3, 1994, samples were collected inside two different kitchens per day. A single outdoor sample was collected each day immediately outside one of the kitchens in which indoor samples were collected. The sampling location was the village of San Jose´ de Solis (population approximately 1300) in the State of Mexico, approximately 200 km northwest of Mexico City. The region is a high plateau agricultural river valley at an altitude of 2450 m. The community of San Jose´ de Solis lies on the slope of the eastern edge of the valley. Weather during the sampling period was warm and dry with no rain. Homes were typically made of adobe (sometimes covered with cement) with wood-frame and terra-cotta roofs. Typical roof construction allowed for several air ventilation holes in upper corners. Kitchen floors were usually cement or rock, although several homes had dirt floors in the kitchens. The 22 kitchens used a variety of methods and fuels for cooking, including unvented biomass only, unvented LPG, a mixture of unvented biomass and unvented LPG, and vented biomass. None of the homes were occupied by smokers, and there was no smoking during any of the sample collection. PM10 and PM2.5 particulate measurements were made with inertial impactors (13) operated at a flow rate of 4 L min-1. The impactors were designed for high particle loadings and employ dual impaction stages and coated impaction plates to minimize particle bounce. Fine particulates were collected on 41-mm (PTFE) Teflon membranes with a polyolefin ring (Gelman R2POJ41). Membrane filters were equilibrated 48 h prior to pre- and post-sample weighing in a controlled atmosphere of 25 ( 0.5 °C, 39 ( 7% relative humidity. All weighings were made on an analytical filter microbalance (Sartorius M3P) with a resolution of 1 µg and (2 µg sensitivity. Three repeated weighings were made of each filter (samples and field blanks) before and after sampling. For sampling, impactors were placed on tripods to elevate the sampler inlet to the height of the breathing zone. All sampler inlets were placed at least 0.5 m from kitchen walls and were as close as 1 m to the combustion source, although placement varied somewhat due to allowable space and so as to not inconvenience the woman who was cooking. Sampling pump (URG Corp. URG-200Q) flow rates were measured before and after sampling with calibrated rotameters (Matheson, 603). Rotameters were calibrated on-site with a primary standard electronic frictionless piston (BIOS International Corp. DC-1). Sample durations were approximately 9 h (range: 6-11.5 h) and began in early morning (6-8 AM).
All impactor samples were accompanied by light scattering measurements made with a portable integrating nephelometer (Radiance Research, M903). Use of this device in the assessment of indoor particle levels has been described in detail elsewhere (14). The nephelometer measures the particle light scattering extinction coefficient (σsp) with a wavelength defining optical filter (530 nm) and a variable rate flashlamp. The nephelometer optics and electronics allow for measurements of 0.002 km-1 > σsp > 1 km-1. The instrument also has an internal datalogger and a real time clock that stores σsp averages and internal instrument operating parameters. The nephelometer was operated (without heating the inlet airstream) at a flash rate of 2 Hz with a signal time constant of 32 s. Averages (5 min) were recorded for all measurements. Air was drawn into the chamber of the nephelometer with a small fan. For field operation, the nephelometers were connected to 12-V sealed lead-acid batteries, which were recharged after each sampling period. The nephelometers were purged and calibrated on-site after each sampling period with clean, dry air, generated by passing ambient air through a cartridge of silica gel and two 0.2-µm absolute particle filters (Pall Corp. EMFLON II), which were connected in series. Zeroair offsets were applied to all data prior to final processing. Additionally, zero-air and span calibrations were performed in our Vancouver laboratory before and after travel to Mexico, with Freon-12 (Dupont) as a check of span stability. During each sampling period, characteristics of the kitchen (volume of kitchen, location of stove, type of stove) and the cooking method (what was being cooked, fuel used for cooking) were recorded by the sampling technicians or by interviewing the women performing the cooking with a standardized questionnaire (time spent in kitchen, socioeconomic status). Cooking durations were determined by analyzing the nephelometer data. Concentration data were approximately log-normally distributed. Differences between kitchen types were evaluated by ANOVA with a Bonferroni multiple range test. Nonparametric test (Wilcoxon rank sum test) results indicated equivalent results in analyses of differences between kitchen types.
Results A summary of the cooking conditions and household characteristics are presented in Table 1. Unless specifically stated, all cooking was performed without local exhaust ventilation, although many of the kitchens were constructed with gaps between the walls and roof to allow for some potential ventilation. Of the 22 homes in which samples were collected, eight used biomass only for cooking during the sampling period and six homes used a combination of biomass and LPG. Biomass used for cooking usually consisted of dried corn stalks and husks. Cactus leaves, wood, and cow dung were also used in some instances (Table 1). Five homes used LPG exclusively during the sample collection, and three homes had stoves with some form of ventilation, such as a stove with a chimney, that passively vented exhaust from the fuel, but not the food being cooked, outdoors. For all but four samples, the primary food being cooked was tortillas, which were prepared by heating a thin piece of the corn dough on a flat metal surface that was placed directly over the fire. The mean cooking duration for homes burning only biomass (394 min) was greater than that of all the other cooking conditions and significantly different
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TABLE 1
Kitchen and Household Characteristics % of time sitea (kitchen type)
kitchen vol (m3)
fuelb
A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 C6 D1 D2 D3
11.5 28.8 10.4 19.7 42.1 18.4 12.9 20.6 28.4 34.0 25.1 21.3 42.5 21.4 48.1 25.1 16.8 29.0 30.5 31.1 30.7 43.4
1 134 14 34 4 15 1 14 2 2 2 2 2 12 124 12 12 12 12 4 1 12
tortillasc
education (years)
income
1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1
2 3 3 2 0 3 1 0 5 3 1 6 6 1 0 2 2 3 3 3 2 2
400 200 600 500 500 200 200 200 1000 600 200 400 300 200 100 200 400 400 480 600 200 200
a Site: A, biomass; B, LPG; C, biomass + LPG; D, ventilated stove (biomass). dung. c 1, yes; 2, no. d Pesos/month (1 peso ≈ $0.15).
(p < 0.01) from homes using LPG for cooking (127 min when tortillas were cooked). Kitchen volumes were also smaller in homes burning only biomass (mean volume ) 20 m3) than in homes burning LPG (30 m3) or homes with a ventilated stove (35 m3), although the differences were not statistically significant. Socioeconomic status as determined by monthly income or years of education was variable, although we found no association between these factors and the particulate levels in the kitchens. Similarly, there was no association between the kitchen type and socioeconomic status, although homes using LPG exclusively had the highest incomes. Time-activity data (Table 1) indicated that although the women spent more time outdoors than is typical for developed countries or populations in urban areas, more than 60% of their time was spent indoors, primarily in the kitchen. These data refer to the daytime period of sample collection. Although there may have been short periods during cooking when the woman was not in the kitchen, in most instances, particularly when biomass was being burned, the women were in the kitchen working adjacent to the fire. The average mass gain on the filters was 692 (SD ) 789) and 494 (SD ) 728) µg for PM10 and PM2.5, respectively. The average difference in mass of field blanks pre- and postsampling was 23 µg (SD ) 9). The mean standard deviation of repeated weighings of the same filter was 5 µg. The estimated limit of detection, based on 3 SD of the difference in field blanks, was 27 µg. For a 9-h sample collected at 4 L min-1, the detection limit is 12.5 µg m-3. Summary statistics of the PM10 and PM2.5 concentrations are presented in Table 2. While there was a clear trend for both PM10 and PM2.5 mass in the kitchens burning only biomass to be greater than all other types, the only significant differences were between biomass, biomass + LPG or ventilated biomass, and the outdoor concentrations (p < 0.05). Outdoor concentrations were not significantly different than
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b
kitchen
bedroom
other room
outdoors
42 50 82 67 59
11 8 6 7 4
16 33 1 7 22
32 8 12 20 15
37 49 83
25 24 6
13 3 0
25 24 10
47 92 75 57 62 68 65
23 8 13 5 7 1 18
18 0 0 1 21 5 12
12 0 13 38 10 25 6
Fuel: 1, corn cobs and stalks; 2, LPG; 3, cactus; 4, wood; 5, cow
TABLE 2
PM10, PM2.5, and Indoor/Outdoor (I/O) Concentration Ratios for Different Kitchen Typesa mean
max
min
SD
N
PM2.5 (µg m-3) biomass 554.7 1492.9 30.1 492.9 7 biomass + LPG 203.6 475.2 49.9 180.6 6 LPG 69.4 165.0 36.5 54.2 5 ventilated 132.4 295.4 41.9 141.5 3 outdoor 37.4 51.2 22.0 9.9 11 biomass biomass + LPG LPG ventilated outdoor
cook mean
I/O ratio
887.5 12.4 482.0 6.3 324.7 1.8 281.0 4.1
PM10 (µg m-3) 767.9 1654.9 49.3 540.5 8 1142.9 10.0 311.2 612.6 81.2 247.8 6 664.8 4.7 225.5 683.5 42.2 260.8 5 480.0 3.9 282.9 547.5 72.1 242.2 3 427.7 6.2 68.3 140.1 35.7 33.1 11
a Cook mean is the mean concentration estimated for the cooking period.
those in kitchens using LPG for cooking, although they were lower than those using ventilated biomass stoves. Since the indoor/outdoor ratio may provide a clearer representation of the impact of indoor cooking sources on the particle concentrations, we also calculated indoor/outdoor ratios for both PM10 and PM2.5. Again a trend was evident (Table 2) but the only significant differences were for the PM2.5 ratio between LPG and both biomass and biomass + LPG kitchens (p < 0.05). The ratio of PM2.5 to PM1.0 was calculated as an assessment of the impact of the indoor combustion source on the indoor concentrations. Since the size distribution of biomass combustion particulates is approximately 0.05-1 µm in aerodynamic diameter (8), biomass combustion contributes mainly to the PM2.5 fraction. Therefore, a greater ratio would be evident in cases where the biomass combustion was the primary particulate source. This was
TABLE 3
Cooking Period Mean and 5-Min Peak PM2.5 and PM10 Concentrations (µg m-3) As Estimated by Nephelometry PM10 cooking period
FIGURE 1. Relationship between PM2.5 mass concentration and mean particle light scattering coefficient during duration of filter sample collection. All samples included.
the case for the biomass-only kitchens where the mean ratio was 0.76. LPG kitchens and kitchens with ventilated stoves had lower ratios (0.45). Kitchens using a combination of biomass and LPG had an intermediate mean ratio of 0.67. PM10 and PM2.5 were highly correlated (r2 ) 0.85). PM10 and PM2.5 concentrations were compared with the average σsp measured by the nephelometer during each sampling period. Nephelometer averages and both the PM10 and PM2.5 concentrations were highly correlated when all samples (indoor and outdoor) or when just the indoor samples were included. Figure 1 depicts the relationship between the σsp average and the PM2.5 concentration for all samples. In Figure 1, it is apparent that the correlation coefficient is influenced by the presence of one extreme point; however, removing this point does not change the magnitude of the relationship (after removing extreme data point: σsp ) 0.0015 PM2.5 + 0.128, r2 ) 0.77, N ) 30). The slope was higher for the regression of σsp on PM2.5 (σsp ) 0.0015 PM2.5 + 0.127, r2 ) 0.90, N ) 31) than for PM10 (σsp ) 0.0013 PM10, r2 ) 0.83, N ) 32) due to the higher mass associated with the PM10 measure. Since the nephelometer responds primarily to particles of 0.1-1 µm, the correlation coefficient for the regression of σsp on PM10 was slightly lower than that of σsp on PM2.5. Using the relationship described above indicated that a PM2.5 concentration of 100 µg m-3 was approximately equal to a σsp of 0.00028 m-1. Correlation of σsp with PM10 and PM2.5 was not as high for outdoor samples, in part due to the decreased range of concentrations but also due to the variable particle composition (in particular water content) and the larger size range which affected the relationship between aerosol light scattering and mass. To more clearly differentiate the kitchen types, we also estimated mean and peak (5-min average) particle concentrations during the cooking periods (Table 3). These estimates were obtained by calculating short-term concentrations based upon the relationship between the measured particle mass concentrations and the average nephelometer light scattering coefficient for each sampling period. Based on these short-term measurements, clear distinctions were evident between the kitchen types. Both PM10 and PM2.5 cooking period average and 5-min peak concentrations were significantly higher (p < 0.05) in biomass-only kitchens than in those using LPG, a combination of LPG and biomass, or a ventilated biomass stove. Kitchens using LPG only had lower short-term concentrations than did kitchens using a combination of LPG and wood, although the differences were not statistically significant. The nephelometer data (either converted to mass estimates or as σsp values) also allow an examination of the
PM2.5 cooking period
ratio max/mean mean
sitea cooktimeb mean
max
A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 C6 D1 D2 D3
2333 2690 1968 2539 2390 2694 2390 2390 1936 1012 200 1241 405 1983 918 2310 1610
1.9 1.8 2.4 2.4 2.8 1.9 2.5 1.7 1.9 2.7
961 1160 627 809 639 1097 719 1088 801 233
4.3 1.8 3.6 4.3 3.3 3.0
162 103 384 98 507 371
1334 2304 227 750 623 2222 433 1000
1.7 3.3 3.6 2.3
165 520 215 690 570 315 285 210 50 10 25 45 180 120 165 335 160 210 260 55
1228 1470 836 1050 850 1388 944 1377 1041 372 288 219 549 213 694 534
max
ratio max/mean
1899 2210 1590 2075 1948 2207 1948 1948 1562 777 87 972 261 1603 697 1880 1286
2.0 1.9 2.5 2.6 3.0 2.0 2.7 1.8 2.0 3.3
1050 1875 110 555 447 1805 286 767
1.8 5.0 4.0 2.7
6.0 2.5 4.2 7.1 3.7 3.5
a Kitchen type: A, biomass; B, LPG; C, biomass + LPG; D, ventilated stove (biomass). b Cooktime is the duration of cooking (min).
FIGURE 2. Simultaneous particle light scattering extinction coefficients and estimated PM2.5 concentrations inside and outside a kitchen in which both biomass and LPG were used for cooking. Biomass was used in morning for tortilla preparation while the afternoon meal was prepared with LPG.
dynamics of the exposure situation. Figure 2 depicts a time series plot for a kitchen in which biomass was used for tortilla cooking in the morning, but LPG was used to heat the afternoon meal. Although an increase in the concentration was evident during the preparation of the afternoon meal, the magnitude was much lower than in the morning. Figure 3 is for a kitchen using only biomass. As this home supported a larger number of dependents (N ) 17) than in the other example, the tortilla cooking period in the morning was longer and levels were higher than in the other example. Following a short break around noon, the afternoon meal was prepared, and the levels again rose. Another short period of low levels was followed by high concentrations, which correspond to the boiling of corn in preparation of tortillas for the next day. From Figures 2 and 3, it was clear that the kitchens were actually well-ventilated since levels
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FIGURE 3. Simultaneous particle light scattering extinction coefficients and estimated PM2.5 concentrations inside and outside a kitchen in which only biomass was used for cooking. Concentrations were elevated throughout entire sampling period.
decreased rapidly once the fire was put out. Levels also rose rapidly once the fire was lit. During cooking periods, indoor levels were clearly greater than outdoor levels, while the indoor levels decreased to outdoor levels during noncooking periods. With the nephelometer data, we were able to estimate PM10 emission and production rates to assess the impact of kitchen characteristics on these parameters. In many cases, nephelometer σsp increased or decreased so sharply that estimation of the rate of change in concentrations was not possible. For the cases where smooth production or decay curves were evident, estimated continuous PM10 concentrations were converted into logarithms and plotted against time. The slope of the linear portion of these plots was used as an estimate of the first-order decay or production rates. Decay rates ranged from 1.9-11.5 h-1, indicating fast removal of the particles. Since the majority of the biomass combustion-produced particles are small, the dominant mechanism of removal is exfiltration, as opposed to settling. For a typical 0.5-µm particle that rises in the smoke plume to the top of the kitchen (3 m) , the settling time is 111 h. Even if the minimum estimated decay rate is assumed to be the ventilation rate, more than 50 air changes will have occurred before the particle is removed by tranquil settling. Particle decay rates were inversely related to kitchen volume; the kitchens with the greatest volume had the lowest decay rates, suggesting that the effective volume of the kitchens was lower than the total measured volume. Particle production rates were not related to the kitchen volume. In most cases, production rates were higher than decay rates, which is expected if an active indoor source is operating while removal is passive. Although we had only a few samples in which estimates were possible, production rates appeared to be dependent on the source type, as expected. The mean (N ) 6) production rate for biomass was 6.7 h-1, while those of LPG (N ) 1) and ventilated biomass (N ) 1) were 10.5 and 4.7 h-1, respectively.
Discussion A number of studies have shown good agreement between σsp and mass concentration of aerosols (3, 14, 15). Correlation between σsp and PM2.5 is highest for aerosols in which the mass mean diameter is 0.2-1.0 µm. In this size range, the relationship between the light scattering coefficient and the mass concentration is relatively independent
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of particle size, whereas it is highly dependent on mass mean diameter at larger and smaller size ranges (15, 16). Since the majority of biomass combustion particulate is within this size range (8), nephelometer measurements are particularly suitable for this aerosol. Once calibration between gravimetric and light scattering measurements was completed, the portable nephelometer proved to be a reliable, quiet, and straightforward instrument to estimate indoor particle mass concentrations. If a consistent relationship between particle light scattering and particle mass can be demonstrated for particular fuel types and combustion conditions, the fast response, continuous measurement, and datalogging capabilities of this portable nephelometer make it an important measurement tool to be used in conjunction with traditional filter sampling in the assessment of indoor particle levels. Our results indicate that very high particulate levels were associated with cooking on unvented biomass stoves. Although only a few studies (7, 17) have measured sizeselective particle mass and comparisons are difficult due to different samplers and sample durations, levels recorded in this study were somewhat lower than those reported elsewhere. Daytime respirable particulate (approximately corresponding to PM3.5) measurements in China were 2600 µg m-3, which is significantly higher than our mean PM2.5 measurement of 555 µg m-3. In Kenya and Gambia, 24-h respirable particulate measurements were 1300 and 2100 µg m-3, respectively, while in Guatemala 24-h PM10 measurements were 850 µg m-3 (7). The mean PM10 level measured during the day in our study was 768 µg m-3. During cooking periods, measurements in Brazil and Zimbabwe reported respirable concentrations of 1100 and 1300 µg m-3, respectively (7). These concentrations were slightly higher than our mean estimated cooking period PM2.5 measurement of 887 µg m-3, although some difference would be expected due to the different particle size cut of the samplers. In the Mexican homes, we estimated that peak (5-min) PM2.5 concentrations reached 2000 µg m-3 or higher in most of the homes cooking with biomass. Although the concentrations measured in Mexican homes may be somewhat lower than those reported in other developing countries, they are still much greater than PM10 concentrations reported for urban settings in developed countries and well above both the 24-h National Ambient Air Quality Standard of 150 µg m-3 or the California Air Resources Board Standard of 50 µg m-3. In a review of health effects of particulate air pollution, Dockery and Pope report mortality increases associated with increases in PM10 concentration for locations with mean PM10 concentrations of only 28-48 µg m-3 (1). In their review, the authors estimate total mortality increases of 10% for each 100 µg m-3 increase in PM10 concentrations in urban areas (1). In addition to the high concentrations measured in the Mexican homes, exposures will be much higher than those typically seen in developed countries where associations have been observed with increased mortality. This is due to a combination of higher concentrations and the fact that exposure occurs indoors where the majority of time is spent. The EPA’s PTEAM study in Riverside, CA, to date is the most comprehensive study of personal particle exposures and measured daytime mean indoor and outdoor PM10 concentrations of 95 and 94 µg m-3, respectively, while mean daytime personal exposures of 150 µg m-3 were measured (18).
To estimate the daytime (12-h) exposure of the Mexican women, we used the relationship
daytime exposure ) Cout (fout + fbed + fother) + Ccook fkitchen where Cout is the outdoor PM10 concentration; fout, fbed, and fother are the fractions of time spent outdoors, in the bedroom, or in rooms other than the kitchen (Table 1); Ccook is the mean concentration during cooking as estimated from the σsp measurements (Table 3); and fkitchen is the fraction of time spent in the kitchen. The mean estimated daytime exposures were 1521, 1401, 905, and 353 µg m-3 for biomass only, biomass + LPG, ventilated biomass, and LPG kitchens, respectively. While these are conservative estimates since they assume that indoor levels outside of the kitchen are the same as outdoor levels, the estimated exposures are approximately 10 times higher than those measured in the PTEAM study (18). Assuming that 24-h exposures are approximately 40% of the daytime exposure level, and using the mortality relationship described by Dockery and Pope, individuals living in kitchens with only biomass cooking would have mortality risks that were elevated by 40-50% over individuals cooking only with LPG. In the homes in which we made measurements, we found that a variety of fuels were used for cooking. The use of varied biomass fuels is likely to reflect changing socioeconomic and environmental conditions within Mexico and developing countries. For example, individuals with greater incomes may be more likely to use LPG than biomass. Traditional biomass fuels such as wood may be replaced with less efficient fuels, crop residues for example, as deforestation and legal restrictions reduce the use of wood. Consequently, for some segments of the population, exposures may be improving while for others they are likely to be worsening. For this reason, cost-effective control strategies are badly needed. Comparisons between stove types indicated that particulate levels were substantially lower in homes using LPG for cooking. In these homes, particulate levels were similar to outdoor concentrations, indicating that the use of LPG is one effective strategy to reduce particulate exposure. Similar results have been reported for measurements in India (19) and Brazil (20). Since the use of LPG is limited by economics, an alternative strategy to reduce exposure is the use of ventilated stoves. Although the sample size of ventilated stoves was small, largely because the presence of these stoves was limited, our measurements suggested that, when stoves are used for cooking, ventilated stoves can effectively reduce particulate levels provided they are well maintained. In stoves used for space heating, loss of heat due to ventilation may reduce the economic effective-
ness of this strategy. Our results indicate that the kitchens, even those in which high particle concentrations were measured, were well-ventilated and that particle levels decreased rapidly once cooking terminated. Accordingly, control efforts should focus on improvements in cooking stoves and/or fuel substitution rather than alterations to the design of kitchens.
Acknowledgments Supported in part by International Development Research Centre Grant 3-P-91-1048 to the National Institute of Respiratory Diseases, Mexico (R.P.-P.). M.B. acknowledges the British Columbia Lung Association and the Medical Research Council of Canada for his scholar award.
Literature Cited (1) Dockery, D. W.; Pope, C. A. Annu. Rev. Public Health 1994, 15, 107-132. (2) Schwartz, J.; Slater, D.; Larson, T. V.; Pierson, W. E.; Koenig, J. Q. Am. Rev. Respir. Dis. 1993, 147, 826-831. (3) Koenig, J. Q.; Larson, T. V.; Hanley, Q. S.; Rebolledo, V.; Dumler, K.; Checkoway, H.; Wang, S. Z.; Lin, D.; Pierson, W. E. Environ. Res. 1993, 63, 26-38. (4) Heumann, M.; Johnson, L.; Kelly, L. Proceedings of the 84th Annual Meeting of the Air and Waste Management Association, Vancouver, BC, 1991; Air Waste Management Assoc.: Pittsburgh, 1991; Paper 91-136.7. (5) Anuszewski, J.; Larson, T. V.; Koenig, J. Q. Presented at the 11th Annual Meeting of the American Association for Aerosol Research, October 12-16, 1992, Paper 3A.5. (6) Smith, K. R. Environment 1988, 30, 16-35. (7) Smith, K. R. Annu, Rev. Energy Environ. 1993, 18, 529-566. (8) Smith, K. R. Biofuels, air pollution and health: A global review; Plenum Press, New York, 1987. (9) De Koning, H. W.; Smith, K. R.; Last, J. M. Bull. WHO 1985, 63, 11-26. (10) Masera, O.; de Buen, O.; Friedmann, R. In Proceedings Primera reunion internacional sobre energia y medio ambiente en el sector residencial Mexicano; Martinez, J. Q., Ed.; Universidad Nacional Autonoma de Mexico: Mexico City, Mexico, 1992; pp 73-96 (in Spanish). (11) Pandey, M. R.; Smith, K. R.; Boleij, J. M.; Wafula, E. M. Lancet 1989, 1 (8635), 427-429. (12) Chretien, J.; Holland, W.; Macklem, P.; Murray, J.; Woolcock, A. N. Engl. J. Med. 1984, 310, 982-984. (13) Marple, V. A.; Rubow, K. L.; Turner, A.; Spengler, J. D. J. Air Pollut. Control Assoc. 1987, 37, 1303-1307. (14) Brauer, M. J. Exposure Anal. Environ. Epidemiol. 1995, 5, 45-56. (15) Waggoner, A. P.; Weiss, R. E. Atmos. Environ. 1980, 14, 623-626. (16) Thomas, A.; Gebhart, J. Atmos. Environ. 1994, 28, 935-938. (17) Smith, K. R.; Liu, Y.; Rivera, J.; Boy, E.; Leaderer, B.; Johnston, C.; Yanagisawa, Y.; Lee, K. In Proceedings of 6th International Conference on Indoor Air Quality and Climate, Helsinki, July 1993; International conference on Indoor Air Quality and Climate: Helsinki, Finland, 1993; Vol. 1, pp 441-446. (18) Pellizari, E. D.; Thomas, K. W.; Clayton, C. A.; Whitmore, R. W.; Shores, R. C.; Zelon, H. S.; Peritt, R. L. Particle Total Exposure Assessment Methodology (PTEAM): Riverside, California Pilot Study-Volume 1; U.S. EPA Project Summary; U.S. EPA: Washington, DC, 1993. EPA/600/SR-93/050. (19) Raiyani, C. V.; Shah, S. H.; Desai, N. M.; Venkaiah, K.; Patel, J. S.; Parikh, D. J.; Kashyap, S. K. Atmos. Environ. 1993, 27A, 16431655. (20) Hamada, G. S.; Kowalski, L. P.; Murata, Y.; Matsushita, H.; Matsuki, H. Tokai J. Exp. Clin. Med. 1992, 17, 145-153.
Received for review February 27, 1995. Revised manuscript received July 31, 1995. Accepted August 7, 1995.X ES9501272 X
Abstract published in Advance ACS Abstracts, November 1, 1995.
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