Air Quality and Forecasts: Measuring Behavioral Impacts

1. Air Quality and Forecasts: Measuring Behavioral Impacts Douglas S. Noonan School of Public Policy Georgia Institute of Technology

2. Air Quality Advisories • Last time I counted, 357 different cities have some kind of program

3. Air Quality Advisories • A program by any other name …. • Air Awareness • Air Quality Action Day • Air Quality Advisory Day • Air Quality Alert Day • Air Quality Health Advisory • Air Quality Health Alert • AirWatch • Care About Clean Air • Health Advisory • Health Advisory Episode • Ozone Action Day • Ozone Alert • Ozone Watch • Smog Alert • Smog Watch

4. Air Quality Advisories • Last time I counted, 357 different cities have some kind of program • They differ in their: • Context (populations, infrastructure, history, etc.) • Message (content, dissemination) • Incentives (perks, policies, etc.) • Program details (source of forecasts, thresholds, days of operation, etc.)

5. Advisory: Giving Advice • Should I run behind the bus? • Should I run (or walk, or sit on the couch, or…) • Should I take the bus (and wait roadside, or drive, or …) “The best way to protect yourself from the harmful effects of air pollution is to be informed”

6. The Atlanta Journal-Constitution By David A. Markiewicz Published on: March 20, 2008 Exercise die-hards have their own strategies against smog:Some get miles in early on bad summer days For the physically active, outdoor exercise in Atlanta during the summer presents a conundrum: When the smog is high, do you recreate or hibernate? … Greg Masterson, president of the Metro Atlanta Cycling Club, said he's "aware" of smog alert days, "but I go ride anyway, and I will continue to do that." … Masterson said poor air quality, "makes you want to drive to Dahlonega."

7. Message(s) • Reduce pollution (“spare the air”) • Carpool, use public transit, telework • Reduce vehicle trips • Change the time of polluting activities (driving, refueling, lawnmowing, using household chemicals) • Avoid exposure (“protect yourself”) • Limit outdoor exertion, exercise • Jog less, or walk • Limit outdoor activities generally • The alert is triggered on exceedance days • Implying the message doesn’t apply on non-exceedance days?

8. Behavioral Impacts • Impacts of what on behavior? • The trick here is to isolate the effects of the advisory itself on behavior… …not the effects of bad air quality on behavior. • What if the advisory triggers incentives (beyond the info alert)?

9. Behavioral Impacts • Hospital visits fall • Outdoor activities fall • Less biking, walking, (waiting for transit?) • Indoor activities rise? • Transportation changes • More alternative modes • Different timing • Strategic commuters? • Timing of activities change • Different time-of-day, different days

10. What do we know? • A lot of anecdotal evidence • Several studies of awareness • Some studies of impacts • rely on self-reported behaviors • voluntary programs that do not account for selection bias • A few rigorous studies out there… (measuring behavioral responses not as easy as it might seem)

11. What do we know? • Los Angeles(Neidell 2005) • kids’ asthma-related hospital visits  • at-risk groups’ zoo/observatory visits  • visits at major museum  • San Francisco(Cutter & Neidell 2007) • traffic  • BART ridership  • observed ozone levels  • Chicago (Welch et al. 2005) • CTA’s turnstile counts • no impact on total; some shifting within day

12. What about Atlanta? • Atlanta begins alert programs in 1998: • household surveyin 1998 by Henry & Gordon (2003) • Only significant effects for government workers • traffic countsin 1998 by Cummings & Walker (2000) • No significant impacts • I looked at two types of behavior: • driving patterns (2001) • household travel diary data from ARC • activities in Piedmont Park (2005) • observations of park users at various sites, times, days, etc.

13. What about Atlanta? • Impacts depend on the behavior: • No significant effect of alerts on household VMTs, traffic flow in the park • Alerts do affect composition of park flow • Fewer elderly • Fewer exercisers (and, less so, runners)

14. Summary • Average treatment effect on: • Ozone-producing activities (i.e., driving) • mixed (insig. in ATL, CHI; sig. in SFO) • Non-emitting activities (i.e., visits, exercise) • significant • This leaves a lot of unanswered questions

15. What we don’t know • Generalizability • Through space, through time • What are the alternatives? • Exposures of alternatives • Net benefits of alternatives • Linking the responders (& nonresponders) and whether they “got the message”

16. What we don’t know • How do different messages affect responses? • What about coupling messages and incentives? De-coupling? • Crying wolf? Rebound effects? • Impact of alerts, alert programs on AQ • peak AQ, also non-peak AQ • alerts have no discernible effects in SFO • similar method: alerts bring higher O3 in ATL

17. Missing links • Systematic data collection on (targeted) behaviors • collected over multiple seasons, cities • tracking of behavior of message recipients • Data collection on substitute behaviors • track un-targeted behaviors also • health risks, emissions • Rigorous research design aimed at measuring effects of advisories on behavior • It may be a risky question to ask…

18. Speculations • Imagine a simple situation: • People care about 3 things: • their travel time • their exposure to air pollution • “warm glow” from reducing their emissions • They choose their transportation mode (walk, bike, bus, train, drive) • How might this choice change with a red alert? • How do we get drivers to switch?

19. Speculations • Effectiveness of alerts hinges on: • Efficacy/warm-glow of altruistic non-driving • Lowering perceived extra exposure of non-driving • Reducing the higher travel costs of non-driving • Simpler prediction (avoidance) for non-emitting activities

20. Speculations • Advisory (from theory) to get people out of cars: • Get more trains • Message: extra trains on red alert days • Emphasize a bonus “warm glow” • Message: alternative modes are even better on red alert days • De-couple the alerts from AQ / risk levels • Message: no threshold effect in AQI • Advertise personal risks of driving, emitting • Psychology, risk communication have much to contribute also…

21. Speculations • Advisory (from evidence): • Responsiveness is high when: • it is a sensitive population • time is flexible, substitutes are easy • reducing exposure is independent of (or involves reducing) emissions • Responsiveness is low (or adverse) when: • reducing exposure involves increasing emissions • Temporal switching also likely • Be careful: people might be listening!

22. Thanks

23. Ozone forecast effects • Following Nichols (2007), the share of observations jumps by 1.1%*** at the 85ppb cutoff Figure 7: Kernel density of observed (ozact) and forecasted (ozpred) daily ozone levels, 1999-2003

24. Ozone forecast effects • Significant tendency to overpredict up to the threshold, then actual levels jump +9.7ppb to remove the bias Observed ozone levels Figure 8: Local regression fit of ozone forecasts to observed levels, with cutoff at 85ppb (mean at cutoff = 75.3ppb) Forecasted ozone levels