Literature round-up: Improving assessments of indoor exposures to outdoor air pollution

Much of my work over the last few years has been focused on improving methods to assess our indoor exposures to outdoor airborne pollutants. This is driven in part by the fact that we spend so much time indoors (and so much time at home) and that different pollutants can infiltrate indoors in different ways; at the end of the day, much of our exposure to outdoor air pollution actually ends up occurring indoors. Another motivator is that there are wide variations in some of the fundamental drivers of indoor proportions of outdoor pollutants, particularly in homes, that I don’t think have been captured very well to date. Air exchange rates are certainly higher in leakier buildings and we understand how to model these fairly well (although not as well as you might think), but good data on envelope penetration factors in a large number of homes are limited. We also don’t know a ton about HVAC filtration, system runtimes, indoor deposition rates, and the least academic yet probably most difficult of all to assess: window opening behaviors.

With all that said, we’re working on a handful of projects (and have a few related proposals under review) that would work to improve our knowledge of some of these drivers (as well as our predictive ability). Also, I noticed a couple of papers out in the Journal of Exposure Science and Environmental Epidemiology that continue to move this kind of work forward. I thought I’d share them here.

1. Breen et al. (2013) published a review of the models that can be used to estimate air exchange rates in buildings. They review driving forces (e.g., I/O temperature differences, wind speed, and mechanical ventilation) in conjunction with the leaks/openings through which driving forces can force airflow. Then they review a handful of models with varying levels of details and input parameter needs for estimating air exchange rates in buildings. They finish up with a list of advantages and disadvantages of each method and describe ongoing research needs. This is a very helpful paper for understanding how airflow can be assessed and ultimately used to impact exposure assessment for epidemiology.

2. Hodas et al. (2013) published a study where they used existing epidemiological data for myocardial infarction (heart attack) associations with elevated outdoor particulate matter (PM2.5). However, instead of using only outdoor concentrations, they accounted for variations in indoor proportions of PM2.5 across a variety of homes (related to the paper above!), as well as for time spent at home, in order to explore whether or not accounting for indoor exposures altered the outcome (accounting for differences in air exchange rates only). Interestingly, they didn’t observe differences in the exposure-response outcomes for those with different air exchange rates in their homes, but primarily because they used data from a “case-crossover” study whereby occupants are their own controls. That is to say that they track the same occupants in time with different exposures; therefore, it makes sense that indoor proportions of outdoor PM2.5 may be always consistent within a particular group, so building factors are in a sense controlled for in each “case”. They did however observe differences in the relative odds for heart attack for those in leakier homes, as occupants of leakier homes (with higher air exchange rates and thus higher indoor proportions of PM2.5 that would otherwise have not been accounted for using central site data). They conclude with “These ?ndings also illustrate that variability in factors that in?uence the fraction of ambient PM2.5 in indoor air (e.g., AER) can bias health effects estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.” Very much a motivator for the work we’re doing!

3. Baxter et al. (2013) actually preceded the study above by comparing the performance of a few different models for predicting indoor proportions of outdoor PM2.5 in homes — the models largely vary by the way they account for different air exchange rates in homes (related back to the first study here). Again, results suggest that differences in residential exposures may be important for epidemiology studies, and importantly, not captured by outdoor monitors alone. These kinds of studies again motivate me to get out there and develop better ways of assessing some of the inputs to these models (which are not always captured well) with our knowledge of building science to ultimately inform epidemiology studies and improve our decision making for regulatory purposes.


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