The amount of light scattered by airborne particles inside an aerosol photometer will vary not only with the mass concentration, but also with particle properties such as size, shape, and composition. This study conducted controlled experiments to compare the measurements of a real-time photometer, the SidePak AM510 monitor (SidePak), with gravimetric mass. PM sources tested were outdoor aerosols, and four indoor combustion sources: cigarettes, incense, wood chips, and toasting bread. The calibration factor for rescaling the SidePak measurements to agree with gravimetric mass was similar for the cigarette and incense sources, but different for burning wood chips and toasting bread. The calibration factors for ambient urban aerosols differed substantially from day to day, due to variations in the sources and composition of outdoor PM. A field evaluation inside a casino with active smokers yielded calibration factors consistent with those obtained in the controlled experiments with cigarette smoke.

BackgroundBiomass fuel is the primary source of domestic fuel in much of rural China. Previous studies have not characterized particle exposure through time–activity diaries or personal monitoring in mainland China.ObjectivesIn this study we characterized indoor and personal particle exposure in six households in northeastern China (three urban, three rural) and explored differences by location, cooking status, activity, and fuel type. Rural homes used biomass. Urban homes used a combination of electricity and natural gas.MethodsStationary monitors measured hourly indoor particulate matter (PM) with an aerodynamic diameter ≤ 10 μm (PM10) for rural and urban kitchens, urban sitting rooms, and outdoors. Personal monitors for PM with an aerodynamic diameter ≤ 2.5 μm (PM2.5) were employed for 10 participants. Time–activity patterns in 30-min intervals were recorded by researchers for each participant.ResultsStationary monitoring results indicate that rural kitchen PM10 levels are three times higher than those in urban kitchens during cooking. PM10 was 6.1 times higher during cooking periods than during noncooking periods for rural kitchens. Personal PM2.5 levels for rural cooks were 2.8–3.6 times higher than for all other participant categories. The highest PM2.5 exposures occurred during cooking periods for urban and rural cooks. However, rural cooks had 5.4 times higher PM2.5 levels during cooking than did urban cooks. Rural cooks spent 2.5 times more hours per day cooking than did their urban counterparts.ConclusionsThese findings indicate that biomass burning for cooking contributes substantially to indoor particulate levels and that this exposure is particularly elevated for cooks. Second-by-second personal PM2.5 exposures revealed differences in exposures by population group and strong temporal heterogeneity that would be obscured by aggregate metrics.

For modeling exposure close to an indoor air pollution source, an isotropic turbulent diffusion coefficient is used to represent the average spread of emissions. However, its magnitude indoors has been difficult to assess experimentally due to limitations in the number of monitors available. We used 30-37 real-time monitors to simultaneously measure CO at different angles and distances from a continuous indoor point source. For 11 experiments involving two houses, with natural ventilation conditions ranging from <0.2 to >5 air changes per h, an eddy diffusion model was used to estimate the turbulent diffusion coefficients, which ranged from 0.001 to 0.013 m² s⁻¹. The model reproduced observed concentrations with reasonable accuracy over radial distances of 0.25-5.0 m. The air change rate, as measured using a SF₆ tracer gas release, showed a significant positive linear correlation with the air mixing rate, defined as the turbulent diffusion coefficient divided by a squared length scale representing the room size. The ability to estimate the indoor turbulent diffusion coefficient using two readily measurable parameters (air change rate and room dimensions) is useful for accurately modeling exposures in close proximity to an indoor pollution source.

Real-time particle monitors are essential for accurately estimating exposure to fine particles indoors. However, many such monitors tend to be prohibitively expensive for some applications, such as a tenant or homeowner curious about the quality of the air in their home. A lower cost version (the Dylos Air Quality Monitor) has recently been introduced, but it requires appropriate calibration to reflect the mass concentration units required for exposure assessment. We conducted a total of 64 experiments with a suite of instruments including a Dylos DC1100, another real-time laser photometer (TSI SidePak™ Model AM-510 Personal Aerosol Monitor), and a gravimetric sampling apparatus to estimate Dylos calibration factors for emissions from 17 different common indoor sources including cigarettes, incense, fried bacon, chicken, and hamburger. Comparison of minute-by-minute data from the Dylos with the gravimetrically calibrated SidePak yielded relationships that enable the conversion of the raw Dylos particle counts less than 2.5 μm (in #/0.01 ft(3)) to estimated PM2.5 mass concentration (e.g. μg m(-3)). The relationship between the exponentially-decaying Dylos particle counts and PM2.5 mass concentration can be described by a theoretically-derived power law with source-specific empirical parameters. A linear relationship (calibration factor) is applicable to fresh or quickly decaying emissions (i.e., before the aerosol has aged and differential decay rates introduce curvature into the relationship). The empirical parameters for the power-law relationships vary greatly both between and within source types, although linear factors appear to have lower uncertainty. The Dylos Air Quality Monitor is likely most useful for providing instantaneous feedback and context on mass particle levels in home and work situations for field-survey or personal awareness applications.