.5-.6 ug/m<sup>3</sup> for each increase of 1 ug/m<sup>3</sup> in kitchen pollution. In the low-dust season, however, this effect essentially disappears. The OLS estimate is positive, small and significant, while the IV estimate is negative, small and marginally significant.

For building materials, inter-seasonal results are mixed. Relative to thatch, both mud and tin have negative increments that are significant in both seasons. They are roughly constant in magnitude across seasons for mud, while they are substantially greater in the high-dust season for tin. Brick-related increments are insignificant in both seasons. The effect of tin roofing relative to thatch is significant but contradictory across seasons. It is small in both seasons, but positive during the high-dust season and negative during the low-dust season.

The regressions also measure the effect of operating a fan in the living room. During the high-dust season, this has a modest but significant negative effect on IAP. During the low-dust season, however, the effect disappears.

# **4.  Variations in Exposure Patterns: pD-RAM Results**

Even if total particulate exposure is the same during a 24-hour period, brief, highlyconcentrated exposures may affect health differently than sustained exposures. In this context, our experimental data provide useful information on the relationship between exposure patterns, building materials, fuels and kitchen configurations. Our MiniVols only record total 24-hour exposures, so they cannot track exposure variation. However, our pD-RAM samplers measure pollution at regular intervals over the 24-hour cycle. We use the pD-RAM data for a standardized assessment of kitchen exposure patterns during the mid-day meal preparation period. From each experiment, we draw 150 regularinterval pD-RAM observations covering five mid-day hours, centered on the hour of maximum IAP exposure. We draw inferences about distribution patterns from the mean, maximum, minimum, standard deviation and median for each experiment. Distributions with shorter but more extreme exposures have higher maxima, means (pulled upward by higher maxima), and standard deviations. Distributions with more uniform exposures have higher minima and medians.

Table 5 provides regression results for 101 experiments. Overall regression fits are reasonable, with R<sup>2</sup> ’s around .26. The results suggest that building materials do not have significant effects on exposure patterns. However, wood and dung combustion seem to generate more intense exposures than other biofuels (the excluded fuel variable). Their particulate distributions have higher means, maxima and standard deviations; several estimated parameters are highly significant, and all are at least marginally significant. Attached Kitchen configuration also seems to promote more intense exposures. The means, maxima and standard deviations for Attached kitchen distributions are all much higher than those for the excluded configuration: inside-dwelling, and with very high significance. In the opposite vein, Detached Kitchen configuration seems to promote more sustained exposure. Minimum and median particulate concentrations for Detached Kitchens are significantly higher than those for the excluded (inside-dwelling) configuration.<sup>9</sup> The median result for the high-dust season is also worth noting here. It 9 We found pD-RAMs hard to operate in the open due to high deposition and insect entry into the optical chamber.

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