“Nothing is so frightening as what’s behind the closed door… The audience holds its breath along with the protagonist as she/he (more often she) approaches that door. The protagonist throws it open, and there is a ten-foot-tall bug. The audience screams, but this particular scream has an oddly relieved sound to it. ‘A bug ten feet tall is pretty horrible’, the audience thinks, ‘but I can deal with a ten-foot-tall bug. I was afraid it might be a hundred feet tall’.”
– Stephen King, Danse Macabre
Right now, we are in the COVID-19 corridor, holding our breath as we approach Stephen King’s closed door. Every day brings news of more cases, more deaths, more restrictions, and more fear.
Using science as their guide, legislators from both parties came together to improve air quality around the country. A major breakthrough came with the Clean Air Act, signed into law by President Nixon in 1970 and strengthened under President Bush in 1990.
In February, just as coronavirus fears were escalating, the Trump Administration released its 2021 budget. It calls for cutting U.S. Environmental Protection Agency funding by 26 percent, a reduction that will severely hamper the agency’s ability to conduct cutting-edge research and implement programs that will save lives and improve the health of millions of Americans.
When the coronavirus threat is defused—as it surely will be—we will still face airborne killers. Our only effective protection against the familiar foe of air pollution, the monster we’ve seen, is prevention, reducing harmful emissions from sources like power plants and vehicles.
Just as with our rush to contain the coronavirus, our lives depend on it.
Acknowledgement: Thank you to my adviser and my lab group for their helpful feedback and discussion related to this post.
Even though I’m pursuing a PhD in environmental toxicology, my dissertation uses methods from environmental epidemiology to answer the question of whether long-term air pollution exposure (specifically, fine particulate matter (PM2.5)) is associated with dementia or dementia pathology in a cohort based in the Seattle area.
When I tell people about my project, they always ask: “how are you measuring exposure to air pollution?”
Different pollutants require different methods of measurement (for example, some are measured internally, in urine or blood), but here I’ll focus on common methods to assess long term exposure to ambient (outdoor) air pollution for use in cohort studies evaluating chronic health effects. Other methods can be used for time-series studies, which are focused on short-term exposures and acute health effects. All of this information exists in the academic literature, but as per the title of this post, my goal is to present a summary so that non-exposure science experts (like me!) can understand it.
A brief review of selected air pollution exposure assessment methods for cohort studies
(1) Monitoring & Simple Interpolation Models
TLDR: We can use information from ground-level monitors, or a weighted average from those monitors, as a basic way to estimate air pollution for a given population. This is easy and cheap, but overly simplistic.
The most consistent, long-term measurements of air pollution come from stationary monitors, such as the Environmental Protection Agency’s (EPA’s) Air Quality System (AQS). A highly simplistic (and inexpensive) approach, then, would be to represent each person’s exposure by the nearest monitor. But, there are several problems with this method. First, as you can see if you click on this map (and choose pollutant monitors under the “select layer” function), the existing network of monitors is quite sparse. Here’s a map of the three active PM2.5 monitors in Seattle:
I live in northwest Seattle (see the blue triangle on the map), so it is highly unlikely that those monitors in south Seattle adequately represent my long-term air pollution exposures – particularly for pollutants linked to specific local sources, like roadways or factories. Second, these monitors usually only take measurements every third or sixth day, so there are a lot of missing data over time. (However, long term averages in the region tend to be fairly stable from day-to-day, so this probably isn’t a huge problem for cohort studies looking at the effects of long-term exposures). And third, since we need some amount of population variability to estimate a meaningful association between an exposure and an outcome, assigning the same monitoring information to everyone in a certain area essentially eliminates (spatial) contrasts and decreases our chances of detecting any effects.
To overcome the lack of spatial coverage from federal regulatory monitors, researchers can set up their own monitors. But, developing a new monitoring network is expensive, and you can only get exposure data for the specific period of time that your monitors are set up (as opposed to being able to look at the cumulative effects of exposures over years in the past, which is what most cohort studies want to do).
We can improve on this with “interpolation,” which is a basic model that assigns more weight (influence) to the monitors that are closer to the individual (such as through inverse distance weighting or kriging). You can think about it like a weighted average. While this is an improvement, it often still does not really provide the type of detailed (spatial) resolution that we need.
Overall, these are fairly basic approaches that were more commonly used in the early days of air pollution epidemiology (ex: the seminal study by Dockery et al.), especially when the research focus was on between-city rather than within-city differences in health outcomes. We can definitely do better now.
(2) Spatial Regression Models
TLDR: We can build regression models based on ground monitors, geography, and population features to predict air pollution at various locations. These models are commonly used in cohort studies, but each one varies in quality.
Land use regression (LUR) and related spatial regression models (e.g., universal kriging) use monitoring data as their foundation, but they also integrate information such as traffic density, population, land use, geographic features, and proximity to known emissions sources. All of these inputs are incorporated into a complex regression model to predict concentrations at sites where no monitoring data exist.
LUR model quality is judged through one or more validation processes, to see how well they can predict concentrations at sites where the true concentration is known. Researchers also look at how much of the total variability in the exposure can be explained by the model (the technical term for this is “R2”).
While LUR models are better than relying on monitors alone, they are only as good as their inputs. So, if the model does not include some crucial feature that substantially affects air pollution concentration (like a local factory) or if the input data are not detailed enough, the final model will be inaccurate. There are also concerns with how measurement/prediction errors affect the health effect analyses, a complicated topic that deserves a post of its own (hopefully forthcoming!).
LUR is one of the most common methods used to estimate exposure in air pollution cohort studies. However, model quality varies greatly. And in reality, there’s no single number or assessment that can truly capture whether a model is “good enough;” it’s complicated and depends more on the model inputs and methods as well as the decisions that the modelers make along the way. In other words, you have to read the “methods” sections of the publication very carefully. (I know that is not very satisfying, especially since the quality of these models can affect how much we can trust the results of the associated health analysis…)
TLDR: We can use physical-chemical models to predict air pollution concentrations. These models don’t use ground-level data, which is good if you want to predict in places where those data don’t exist, but it’s not ideal in the broader sense that we generally trust ground-level information more than theoretical models alone.
As with LUR, these models need to be validated, and model performance seems to vary by pollutant. A strength of these models is that they can provide information in places where we don’t have any monitoring data. But, because they are often very expensive to develop, provide limited spatial detail, and are not anchored to any ground-level data (which we tend to trust more than pure models), they are less commonly used in epidemiologic cohort studies than LUR.
TLDR: We have advanced satellite technology that can estimate air pollution from space, based on light scattering! Cool! But, right now, these models provide limited spatial resolution, so they are best when supplemented with ground-level data.
At a global scale, particularly when there are no ground-level monitors or detailed information on local emissions sources (such as in many low income countries), satellite measures of aerosol optical depth (AOD) can provide good estimates of certain air pollutants.
How does this work? A satellite measures the absorption and scattering of specific wavelengths of light at the same time in the same location each day, and these measures are then converted to ground level concentrations using chemical models such as GEOS-Chem.
Incredibly enough, these models can provide fairly accurate predictions of some ground-level pollutants, such as nitrogen dioxide and PM2.5 (but, modeling based on ground-level monitors is usually still more accurate). Remote sensing works less well for ozone, however, since the high levels of ozone in the stratosphere make it more complicated to accurately extrapolate to the earth’s surface. Other issues include potential interference from clouds, limited temporal characterization (usually just at a single time per day), and limited spatial detail (though spatial resolution continues to improve).
That last point – limited spatial detail – is the main downside of remote sensing right now. One way to mitigate this issue, though, is to incorporate data from LUR or ground level monitors, which can substantially improve accuracy.
Application to my work
I’ve skimmed over many details about each of these methods in the interest of making this post semi-accessible (but even so, I know it’s long!). There are also several other assessment methods as well as some interesting hybrid approaches. (For more details, I recommend Chapter 3 of EPA’s recent Integrated Science Assessment for Particulate Matter).
For my study, we’re relying on a sophisticated version of a LUR model with kriging that incorporates both spatial and temporal variability, similar to what has been done previously for other regions of the country. The inputs come from five federal agency monitoring sources and three different monitoring campaigns that have been implemented by the UW Dept. of Environmental and Occupational Health Sciences over the years, and – like all LURs – the final regression model incorporates different geographic features of the area that influence local pollutant levels. In the end, we will have a model that can estimate air pollution exposure at any location in the Puget Sound area all the way back to the early 1980’s!
The elephant in the room (or, the person in the house)
TLDR: Why is it relevant and meaningful to use measures of outdoor air pollution in our health effect studies? 1) Outdoor air enters the home; 2) Measurement error likely underestimates health effects (so we are not exaggerating our results!); 3) Health effects based on outdoor aircan influence policy.
All of the methods I’ve described so far provide exposure estimates based on a given geographic area or point. Some new, small studies use air pollution sensors to get measurements for each person, but that is too expensive for studies with thousands of people – and completely infeasible for those that were started years ago (like the one I’m using). So, instead, I will get estimates of outdoor air pollution based on each person’s residential address and then look at associations with dementia and related pathology.
There are several reasons why a measure of outdoor air pollution based on a home address might not accurately represent someone’s actual exposure. First, we spend most (apparently, 90%!) of our time inside. Second, most of us spend a majority of our waking hours away from our homes, including commuting (in traffic) and working. (Although, for my study of mostly retired individuals, this should be less of an issue). Additionally, from a toxicology perspective, it’s important to consider that a measure of external exposure is very different from internal dose; what actually gets inside your body depends on your breathing rate, among other factors.
So, how can we trust results from epidemiological studies that use these measures of outdoor air pollution from someone’s home address as the “exposure?” When I first began my research in this area, I was quite bothered by this question and talked extensively with my adviser. Here are a few responses and associated thoughts (some more satisfying than others, I admit):
Outdoor pollutants infiltrate into our homes
While we like to believe that we are protected from outdoor air when inside, there is actually measurable and meaningful infiltration of outdoor pollutants into our homes. I experienced this firsthand during the wildfires in Seattle two summers ago, when I could smell smoke inside my room even though all the windows were closed.
When we measure pollution at people’s homes, we are not capturing the full picture of their exposures. For someone like me, who commutes by bike, a measure of outdoor air pollution at my home is likely an underestimate of my daily exposure.
This concept – when the measured/predicted exposure does not represent the actual exposure – is a technical term referred to as “measurement error.” As I mentioned above, this is a complicated topic that I plan to return to in a future post. For now, a highly simplistic summary is that in most cases, exposure measurement error either 1) attenuates (decreases) the observed association, or 2) has no impact on the observed association (but it could affect the margin of error). (See here and here, for example.) So, in this simplified framework, we assume that the consequence of using these imperfect estimates of ambient air pollution is that our results are underestimated.
[Note: to reiterate…this is very simplified. Research by my own adviser suggests that it could also have the opposite effect.]
[Another note: this simplified summary assumes that the measurement error is “non-differential” (similar for all groups). When measurement error is “differential” (differs between groups), the impact on the health effect estimates could be different than what I described. See, I told you this was complicated!]
Effects linked to outdoor air pollution are policy-relevant
Some people consider personal monitors to be the ideal method for exposure assessment. However, a feature of this approach is that they also capture air pollutants from indoor sources, since they measure everything a person is exposed to throughout the day.
Yet, the Clean Air Act regulates outdoor air pollution, not personal or indoor exposures. Therefore, a study demonstrating that exposure to a certain amount of outdoor air pollution is associated with a specific health outcome provides meaningful information that could inform updates to air pollution standards.
Yes, I’m the first to admit that our methods to assess exposure to air pollution are imperfect. But, they allow us to efficiently quantify exposure for large segments of the population. (We really do need these data on large populations to be able to see evidence of the relatively small increased risks for various health effects.) And, while these exposure estimates might not capture the whole story, they are close enough to allow us to consistently see that air pollution is associated with premature mortality, cardiovascular disease, diabetes, asthma….and more.
In the future, I think we will see exciting refinements to these approaches, such as incorporating infiltration rates (to describe how outdoor pollutants enter indoor environments) or more personal behavior information. We might also be able to assess the specific type of particle (ie: metal vs. carbon-based) rather than just evaluating by size (as is done now, in distinguishing between PM2.5 and. PM10); this is important because different particle types may be associated with different health effects. These additional details will increase the accuracy of our exposure assessment methods.
One final note to try to assuage any remaining doubts about epidemiological studies based on these methods. Take the case of PM2.5 and mortality, for example…. Sure, we might not trust one-off results from a study using a single exposure assessment method on a single population. But science moves forward incrementally, based on the results of many studies rather than a single finding. When we see similar results from many studies, even across different populations and based on different exposure assessment methods, we can have strong confidence that PM2.5 is associated with increased risk of death (for example).
In this way, I think that air pollution epidemiology is strengthened by the use of these various methods, each with their own pros and cons. It’s certainly good to be skeptical, though. I think our work (like all science) could benefit from some healthy questioning of the accepted approaches, which could prompt us to improve our methods.
My friends think that I’m slightly obsessed. I prefer to think of myself as extremely passionate.
I’m getting my PhD in toxicology, but my interests don’t stop when I leave campus. I live and breathe this stuff: I love reading, learning, and writing about all things environmental health.
So, naturally, I’m worried about my own exposures to the overwhelming number of pollutants that we’re surrounded by. Though much is out of my control, I do what I can to minimize my exposures by buying organic (even on a student budget, and sometimes to an extreme that annoys my friends and family), avoiding processed and packaged foods, minimizing my use of plastics, choosing fragrance-free products, obsessively searching for flame–retardant free furniture, etc. The list goes on. My environmental health knowledge and concern for my health (as well as the health of my potential future children?) drive my lifestyle and purchasing decisions.
Yet, there’s one lifestyle choice that I’m not willing to give up, especially while living in Seattle: biking. I bike commute to school almost every day of the year. (I only missed 2 days this winter, when the roads were icy). I love riding to campus; it’s my morning and evening meditation/reflection time and my exercise.
While I do ride on the Burke–Gilman trail for most of the way, with gorgeous views of the water, mountains and city skyline, there are also several segments on roads. Obviously, biking on busy, car-filled streets presents immediate physical dangers, like car-bike collisions (my housemate has been hit twice in 6 months) and getting doored (ouch! Looks so painful). But, there’s also all that disgusting air pollution I inhale as take deep breaths alongside the ever-steady stream of cars. Luckily, at least for now, I’m not affected by asthma or other respiratory conditions. Yet, air pollution has been linked to many health problems, including cardiovascular disease and dementia (the latter is the subject of my PhD dissertation); I can’t help but think about those dangers on my daily rides.
Am I causing more harm than good to myself by commuting by bike? Why am I willing to impose such strict controls on other parts of my life (ie: purchasing decisions), but I allow myself to take deep breaths of noxious miasma every single day? Sure, exercise is good for me – for both my physical health and my brain health. But, do the negatives (pollution, collisions) outweigh the positives (physical and emotional health)?
This report (prepared for the National Institute for Transportation and Communities) has a good summary of the research (as of 2014) about traffic-related air pollution exposure to bicyclists. Since then, more studies (for example, in Salt Lake City, Utah; Minneapolis, Minnesota; and Montreal, Canada (here and here)) have also quantified exposures to city cyclists; others (like this one in New York City) are in process now. All of these assessments are specific to each city, season, time, and route. It will take much more research to develop a body of information that reflects the average and range of possible exposures to cyclists.
Such research with individual level measurements is crucial. We routinely track ambient air pollution across the country with a surprisingly few number of monitors (check out this interactive map to explore your area). These devices, which are often located away from major roadways or pollution sources, would definitely underestimate my own exposure, especially when I’m biking right behind a bus.
Exact quantification pending, what am I breathing in on my morning and evening commutes?
Sulfur dioxide (SO2): Sulfur dioxide is released from industrial facilities and vehicles burning fuel with high sulfur content. It is linked to respiratory problems (like asthma attacks and airway irritation) and can contribute to formation of PM.
Of course, we are all exposed to these air pollutants when we walk outside (and while driving in cars). But, during vigorous exercise, like biking up Seattle’s killer hills, we breathe in at 2-5x higher rates, and also more deeply, than at rest. So, I’m inhaling much more of all this bad stuff when I bike – in traffic – each day.
What are the consequences?
Research on the effects of air pollution exposure during exercise and active transportation (ie: walking and cycling) is beginning to emerge. According to one recent study, walking along busy streets reduced the short term cardiovascular benefits of the exercise compared to walking in a park. In studies of cyclists, researchers have found that biking in traffic is associated with various physiological changes, such as increases in certain inflammatory blood cells, alterations in heart rate variability (see here, here, and here) and other cardiovascular measures, and decreases in lung function. The implications of these changes are still unclear, however. As usual, we need more research on the short- and long-term health effects of cycling in traffic.
Several studies (see selected examples from 2017, 2016, 2015, 2014, and 2010) have tried to examine the overall health trade-offs of cycling in cities. The general conclusion is that the long-term benefits of active transportation (ie: namely, physical activity) outweigh the potential risks from traffic accidents and air pollution. However, I think these assessments are limited in several ways:
Most focus on the impact on mortality only, rather than the other myriad of health effects from air pollution that could lead to decreased quality of life and then, indirectly, mortality.
Most only consider the effects of a single pollutant (usually PM2.5) rather than the effects of combined exposures to multiple traffic-related air pollutants (ie: what happens in the real world).
On a more technical level, they assume a linear dose-response (solid line, below), where the relationship between exposure and outcome is the same across all levels of exposure. However, some evidence suggests that this may not actually be the case for PM2.5. Instead, the curve might be supralinear (dashed line, below), where the risk increases more steeply at lower levels of exposure. In this scenario, there might be greater benefits to health per unit decrease in exposure at lower ends of the spectrum, which would alter the modeling calculations.
The alternative scenario (in epidemiology speak, the “counterfactual”) used in these cost-benefit assessments is decreased physical exercise. In other words, they are roughly comparing: [exercise + pollution] vs. [no exercise + pollution]. Because the benefits of physical activity are so enormous, this equation tips towards the [exercise + pollution] side. However, if I didn’t commute by bike, I would replace this exercise with alternative activities (with less exposure to air pollution, presumably). If my equation is instead [exercise + pollution] vs. [exercise + less pollution], it would likely tip in the other direction.
So, in summary, we don’t fully understand all of the physiological impacts of biking in traffic-related air pollution, and I think that the current cost-benefit analyses may actually underestimate the long-term costs to my health.
Hmmm…. Should I re-evaluate my decision?
Last summer, I bought myself an air pollution mask to wear while biking. But, while I hate to admit it, I don’t use it every day. (It often causes my sunglasses to fog up!). I really should use it – assuming it is as effective as it claims?
Even though Seattle has the reputation of having fairly good air quality, the 2018 American Lung Association State of the Air Report indicates that there is still enormous room for improvement. (And, as I noted above, this city-level ranking, based on ambient air monitors, likely definitely underestimates my exposure while biking).
Everyone makes risk-related choices differently, based on their own calculations and priorities; risk perception and decision-making is complex and not entirely rational. But, in general, people are more likely to accept risks that they perceive as controllable, familiar and natural compared to those they perceive as imposed by others, uncontrollable, and unfamiliar (see image, below).
I’m still trying to understand the calculations that led me to my decision to expose myself to substantial pollution every day. Maybe it is related to the fact that I have control in this situation, since it is my choice to bike? Maybe it is because Seattle appears to have relatively clean air, compared to other places I’ve lived, like Atlanta and Bangkok, where the pollution is more directly visible?
Maybe… maybe… I just love biking too much, and this is where I draw my personal line. While it is definitely important to me to minimize harmful exposures and prioritize my health, I cannot and do not want to live in a complete bubble (though sometimes it seems to others that I already do). Life involves risk, and I’ve somehow decided that this is one I’m willing to take. Biking every day brings me too much happiness to give up (at least for now). Plus, cars are no safe haven; there’s plenty of dirty air inside from both internal and external sources.
While I take this risk, which is perhaps ironic given my PhD research on air pollution and dementia, there are some things I can do to mitigate my exposures. In addition to wearing my mask more consistently, I can check local air quality (like through this pollution app) and avoid riding on particularly bad days (like last summer, when Seattle was choked by horrific wildfire smoke). When bike paths are not available, I can do a better job of altering my route to prioritize low traffic roads, where I will be less exposed than on busier routes.
However, like for all pollutants, individuals only have limited abilities to control their own exposures. In the end, we need systemic, societal changes to make cities safer and healthier for people: stricter controls of vehicle emissions, increased utilization of electric cars and buses, improved public transportation, better bicycling infrastructure (eg: off-street bike paths), more greenspace, etc. The intersection of urban planning and public health definitely intrigues me (my next PhD? No, just kidding).
Ultimately, I hope that my own research can demonstrate the importance of strengthening air quality regulations and help motivate policies to reduce exposures across the population.