Skip to main content

Do people respond to air pollution forecasts?

In 2010, the House of Commons Environmental Audit Committee published a report on air quality in which they concluded that “poor air quality probably causes more mortality and morbidity than passive smoking, road traffic accidents or obesity”. Concerned that the Government was still not giving air quality a high enough priority, the Committee published another report in 2011. To date, the Committee’s main recommendations have not been implemented. Amidst new evidence on the negative effects of air pollution on health and a court case that found the UK Government guilty of failing to meet EU air quality targets, the Committee published a third report on air quality last week.

One of the Committee’s recommendations is that the Government works more closely with the Met Office, the BBC and other broadcasters to ensure that forecasts of high air pollution episodes are disseminated widely together with advice on what action should be taken. The Committee’s rationale is that information about air pollution allows individuals to take action that reduces exposure. However, avoidance behaviour, such as staying indoors, imposes a cost on individuals that might exceed the perceived gains.

A BBC weather forecast for Bristol showing the commonly
encountered “green” air pollution forecast. 

In a paper published this month in the Journal of Health Economics (Link with free access until 22 January 2015) I investigate responses to air pollution warnings in England. I obtained data on the air pollution forecasts issued by Defra from 2002 to 2008. During this period the daily air pollution forecast was freely available via the internet, a Freephone telephone service, Teletext and with the weather forecast on the BBC website. The forecast was disseminated using traffic light colour-coding, with green indicating low levels of air pollution, amber moderate and red high levels. “Red” forecasts were extremely rare (3% of forecasts) and “green” forecasts very common (70% of forecasts), so a change from “green” to “amber” (27% of forecasts) was akin to an air pollution warning. Hence, I define an “amber” or “red” forecast as an air pollution warning.

Air pollution warnings and hospital emergency admissions

First, I looked at indirect evidence of avoidance behaviour by estimating the relationship between air pollution warnings and hospital emergency admissions for respiratory diseases in children aged 5 to 19 years. I controlled for actual air pollution levels and therefore essentially compared days with a certain level of air pollution for which an air pollution warning was issued with days with the same level of air pollution for which no air pollution warning was issued. If parents and children do respond to air pollution warnings by reducing their exposure or taking other preventive measures, we expect fewer emergency hospital admissions on days for which an air pollution warning was issued compared to days with the same level of air pollution but no warning.
Image from

Looking at all respiratory admissions I found no effect. Looking at a subset of respiratory admissions - admissions for acute respiratory infections such as pneumonia and bronchitis – I also found no effect. Only when I examined another subset of respiratory admissions, namely admissions for asthma, did I find that air pollution warnings reduce hospital emergency admissions, by about 8%.

Presumably, it is less costly for asthmatics to respond to an air pollution warning. Standard advice for asthmatics is to adjust the dose of their reliever medicine and to make sure they carry their inhaler with them. Other types of respiratory disease require far more disruptive preventive measures such as staying indoors, making the cost of responding to air pollution warnings larger than the perceived gains.

Direct evidence of avoidance behaviour: visitors to Bristol Zoo

To find direct evidence of avoidance behaviour, I examined daily visitor counts to Bristol Zoo Gardens. Zoos are attractive destinations for families with children. Even with some animal houses under cover, most people will consider a zoo visit to be an outdoor activity and therefore susceptible individuals might adjust their plans to the air pollution forecast.  I found that lower temperature, more rain and higher wind speed reduced visitor numbers but found no effect of air pollution warnings on visitor numbers. Only when I looked at members – visitors who have an annual membership that entitles them to unlimited visits for a year – did I find that air pollution warnings reduce visits by about 6%. For members it is less costly to respond to air pollution warnings as they tend to be local residents who can just drop in for a quick visit. Thus, the perceived gains from postponing a visit are more likely to exceed the cost of postponing than for day visitors.

This graph shows monthly means of visitors to Bristol Zoo Gardens, daily maximum temperature and monthly total of air pollution warnings. Day visitors (grey bars) are far more responsive to temperature (yellow line) than to air pollution warnings (purple bars). Members’ visits (green bars) seem to be fewer in months with more air pollution warnings (purple bars).
Overall, my results show that whether individuals respond to air quality information depends on the costs and benefits of doing so: where costs are low and the benefits clear, responses are higher. This finding suggests, that wider dissemination of high air pollution forecasts as recommended by the Commons Environmental Audit Committee may not bring about the desired prevention of adverse health effects from air pollution. The Committee’s other recommendations aimed at lowering air pollution levels are more likely to succeed in preventing ill health.
This blog is written by Cabot Institute member Katharina Janke, Research Associate in Applied Microeconomics and Health Economics at the Centre for Market and Public Organisation at the University of Bristol.
Katharina Janke

Popular posts from this blog

Converting probabilities between time-intervals

This is the first in an irregular sequence of snippets about some of the slightly more technical aspects of uncertainty and risk assessment.  If you have a slightly more technical question, then please email me and I will try to answer it with a snippet. Suppose that an event has a probability of 0.015 (or 1.5%) of happening at least once in the next five years. Then the probability of the event happening at least once in the next year is 0.015 / 5 = 0.003 (or 0.3%), and the probability of it happening at least once in the next 20 years is 0.015 * 4 = 0.06 (or 6%). Here is the rule for scaling probabilities to different time intervals: if both probabilities (the original one and the new one) are no larger than 0.1 (or 10%), then simply multiply the original probability by the ratio of the new time-interval to the original time-interval, to find the new probability. This rule is an approximation which breaks down if either of the probabilities is greater than 0.1. For example

1-in-200 year events

You often read or hear references to the ‘1-in-200 year event’, or ‘200-year event’, or ‘event with a return period of 200 years’. Other popular horizons are 1-in-30 years and 1-in-10,000 years. This term applies to hazards which can occur over a range of magnitudes, like volcanic eruptions, earthquakes, tsunamis, space weather, and various hydro-meteorological hazards like floods, storms, hot or cold spells, and droughts. ‘1-in-200 years’ refers to a particular magnitude. In floods this might be represented as a contour on a map, showing an area that is inundated. If this contour is labelled as ‘1-in-200 years’ this means that the current rate of floods at least as large as this is 1/200 /yr, or 0.005 /yr. So if your house is inside the contour, there is currently a 0.005 (0.5%) chance of being flooded in the next year, and a 0.025 (2.5%) chance of being flooded in the next five years. The general definition is this: ‘1-in-200 year magnitude is x’ = ‘the current rate for eve

Coconuts and climate change

Before pursuing an MSc in Climate Change Science and Policy at the University of Bristol, I completed my undergraduate studies in Environmental Science at the University of Colombo, Sri Lanka. During my final year I carried out a research project that explored the impact of extreme weather events on coconut productivity across the three climatic zones of Sri Lanka. A few months ago, I managed to get a paper published and I thought it would be a good idea to share my findings on this platform. Climate change and crop productivity  There has been a growing concern about the impact of extreme weather events on crop production across the globe, Sri Lanka being no exception. Coconut is becoming a rare commodity in the country, due to several reasons including the changing climate. The price hike in coconuts over the last few years is a good indication of how climate change is affecting coconut productivity across the country. Most coconut trees are no longer bearing fruits and thos