Research and analysis

Working paper 2. Lag structures for short-term exposures to air pollution for health outcomes

Published 18 March 2025

Summary

This working paper reviews the lag between short-term exposures to elevated levels of air pollution and subsequent adverse health effects (the ‘lag structure’). It examines the lag structures for different health conditions and outlines possible implications for the advice provided in the Daily Air Quality Index (DAQI).

By reviewing relevant literature we found that, for a range of cardiovascular endpoints, persistent effects extend beyond 24 hours post-exposure (up to 5 days). For respiratory diseases, morbidity effects were reported for at least one week after exposure. For some mortality studies (respiratory and cardiovascular) there is evidence for longer lag periods.

Current advice accompanying the DAQI indicates that symptoms may occur when air pollution is elevated and suggests actions that can be taken by individuals, at the time, to reduce exposure and the risk of effects. The evidence we have reviewed indicates that the risk of a range of adverse health effects persists beyond 24-hours after exposure and supports the need for revision of the advice which accompanies the DAQI. We recommend the advice is modified to encourage individuals in ‘at risk’ groups to monitor their symptoms for at least a week and, if symptoms are experienced, to follow their self-management care plan, including consulting their healthcare professional if necessary.

Our recommendations for future research include greater consideration of lag structures in epidemiological studies. In addition, studies need a more detailed consideration of multipollutant impacts; this could extend to consideration of other environmental risks: for instance, temperature, pollen or viruses. It will also be important to identify how lag structures vary for each pollutant, specific pollutant-effect relationships, and how these may change when combining the effects of different pollutants. Other potentially susceptible groups, in addition to those with pre-existing cardiovascular and respiratory disease, should also be considered.

Introduction

Advice accompanying the Daily Air Quality Index

Air pollution is associated with many adverse health impacts, including cardiovascular and respiratory diseases (Chief Medical Officer’s Annual Report, 2022). Both long-term and short-term exposures have been shown to have adverse effects on health.

The current DAQI focusses on providing advice to vulnerable groups regarding short term peaks in levels of criterion pollutants. This means that air pollution might be described, using the DAQI, as “Low” or “Moderate”, while being above the current annual (long-term) legal limits or World Health Organization (WHO) guideline concentrations, and therefore known to be associated with health effects (see working paper 3). This has the potential to create some confusion in public messaging concerning the health risks of air pollution. Nevertheless, from a purely pragmatic perspective, it remains important to alert vulnerable groups to periods of elevated pollution that may have an immediate, or slightly delayed, effect upon their health, and to provide informed advice to mitigate against these risks. This short report, which focuses on lag structures for short-term exposures to air pollution, addresses whether the current messaging meets this requirement and makes recommendations to improve the integration of air pollution data into disease management.

Air quality information systems assist in providing information to members of the public on levels of air pollution and can offer advice on reducing the risks of experiencing adverse effects due to exposure to air pollution. Defra’s UK-AIR website is a major source of air quality information for the UK. UK-AIR hosts the DAQI, which provides information on current and forecasted levels of air pollution in the UK. The index is calculated based on concentrations of 5 pollutants:

• Nitrogen Dioxide (NO₂)
• Sulphur Dioxide (SO₂)
• Ozone (O₃)
• particles less than 2.5 micrometres (PM_2.5)
• particles less than 10 micrometres (PM₁₀)

The index is communicated via a 10-point scale spread across four bands of Low, Moderate, High and Very High. The DAQI score used to communicate daily air quality is determined by the pollutant with the highest score. The bandings are colour coded for ease of interpretation and are each associated with accompanying health advice.

The DAQI was developed by the Committee on the Medical Effects of Air Pollutants (COMEAP) (COMEAP, 2011) to provide accessible information to those who may be particularly likely to experience adverse effects during short-term episodes of elevated concentrations of air pollutants, as well as the general population. It is intended to enable individuals to make appropriate changes to their behaviour, including their symptom or disease management, to reduce the risk of experiencing adverse health effects. The DAQI currently defines at-risk groups as adults and children with lung problems, adults with heart problems, and older people. The advice given differs depending upon the pollution ‘band’.

The current DAQI banding is accompanied by advice (to the public, and particularly to adults and children with lung problems, and adults with heart problems) to reduce, or avoid, strenuous activity outdoors when air pollution is elevated, and to ensure that they have access to the appropriate medication to manage their symptoms. The advice provided by the current DAQI specifically focuses on the day of the event. However, it is known that the health impacts of short-term exposure to air pollution (such as during a pollution episode) can have a delayed impact. This is referred to in the literature as lags, related to days after the event or cumulative exposures over a longer period. Here we review this evidence to determine whether the health advice provided should be amended to reflect an extended period of vulnerability, and include the information required to allow individuals to control their symptoms.

Lagged effects

Effects of short-term exposure have been investigated extensively in epidemiological time-series and case-crossover studies. These have shown increases in symptoms, medication and health care use (including hospital admissions) and death rates when air pollution is elevated, with adverse effects that either persist over several days or are delayed. When considering this data in the context of making health recommendations, one of the key questions that needs to be addressed is the period an individual remains at risk following an air pollution episode: the lag between exposure and adverse effect (Kim and co-authors, 2019). Some studies in the literature have found that air pollution effects continue for a few days; however, other studies indicate that the effects from air pollution persist over a longer period and may differ by cause-specific mortality and morbidity (Kim and co-authors, 2012; Peel and co-authors, 2005; Zanobetti and co-authors; 2003).

This suggests that greater consideration needs to be given to the issue of delayed, or persistent, effects which will likely impact on the behavioural advice provided to at-risk groups. Importantly, lag structures might be different for different health effects, and this may need to be reflected in the recommendations associated with the DAQI.

The aim of this report is to review the lag structures for different health conditions after exposure to air pollution and outline possible implications for the advice provided within the DAQI to mitigate against adverse health responses. The COMEAP AQIS Sub-group initially focused on asthma, but also considered additional respiratory and cardiovascular diseases. Other effects, or potentially vulnerable groups were not considered in this work; the evidence on these is less well developed (see working paper 1 on susceptibility).

Lags may be classified as single day lags: a daily exposure that has an effect x days later. Lags may also be cumulative, reflecting effects of prolonged exposure in the days preceding the health outcome, for example mortality following exposure due to air pollution episodes which last longer than a day (Chen and co-authors, 2022). Cumulative lags can also be interpreted as the effects, over multiple future days, of air pollution exposure on a given day (Gasparrini, 2011; Gasparrini and Leone, 2014). In this document, single day lags are presented separately followed by a comma (for example, lag0, lag1, lag2,) while cumulative lags are presented with a hyphen between day numbers (for example, lag0 to 2, lag0 to 8).

Method

A literature search was conducted in PubMed using broad search terms, including “air pollution” and “short-term” and “lags”. This was a rapid review, and also drew on Members’ files and previous knowledge of the literature. 24 papers were selected, these papers were chosen because they examined the relationship between short-term air pollution exposures and compared several lags for cardiovascular and respiratory effects. Papers reporting just one lag time were excluded, because we were interested in examining whether there was evidence for effects after multiple time lags and, if so, the size of those lags.

This working paper discusses the evidence related to lags for the effects of short-term exposure to air pollutants. Advice related to exercise and physical activity is discussed in a separate working paper from the AQIS Sub-group (see working paper 4).

Literature examining lag structures

Cardiovascular effects

The lag structures following air pollution exposure, for various cardiovascular events and associated conditions, have been studied, including cardiac admissions (Wong and co-authors, 2002), emergency department visits (Ugalde-Resano and co-authors, 2022), myocardial infarction (Bhaskaran and co-authors, 2011; Liu and co-authors, 2018), out-of-hospital cardiac arrest (Zhao and co-authors, 2020) and inflammatory and thrombotic responses (Chen and co-authors, 2017).

Lag structures varied for different cardiovascular endpoints, and by air pollutant. For cardiac admissions to hospital in London, maximal effect was seen at lag0 for NO₂, and lag0 and lag2 for PM₁₀ (Wong and co-authors, 2002). The lags reported in Hong Kong were different: with increases in cardiac admissions found at lag0, lag1, lag2 and lag3 for NO₂ and PM₁₀, and lag1, lag2 and lag3 for O₃ (Wong and co-authors, 2002). Lag effects for NO₂, PM₁₀ and PM_2.5 were found for lag0 up to lag0 to 4 (NO₂) or lag0 to 5 (PM₁₀ and PM_2.5), when examining emergency department visits for cardiovascular diseases in Mexico City (Ugalde-Resano and co-authors, 2022). In the same study, effects were found at lag0, lag0 to 3, lag0 to 4, and lag0 to 5 for O₃.

Using the UK Myocardial Ischaemia National Audit Project (MINAP) database, hourly lags were explored, and it was found that there was an increased risk of myocardial infarction in the period 1 to 6 hours after exposure to PM₁₀ and NO₂ (using hourly data from monitoring stations in the national monitoring network) (Bhaskaran and co-authors, 2011). However, effect estimates at longer lags (7 to 12, 13 to 18, 19 to 24 and 25 to 72 hours) were negative, so that over 1 to 72 hours there was no overall increased risk. In a study looking at hospital admissions and readmissions for myocardial infarction in 26 Chinese cities, ST-elevation myocardial infarction effects were seen at lag2, 3 and 4, but not lag0 or lag1, and also at lag0 to 5 for PM_2.5 (Liu and co-authors, 2018). In a study in Japan, associations with both all-cause out of hospital cardiac arrest (including: cerebrovascular diseases, respiratory diseases, malignant tumours, external causes, other non-cardiac causes) and cardiac-related out of hospital cardiac arrest were seen at lag0, 1, 2, 3, lag0 to 1 and lag0 to 3 for PM_2.5 (Zhao and co-authors, 2020).

A study looking at exposure to elemental carbon (EC), organic carbon (OC), SO₂, NO₂, and carbon monoxide (CO) found that inflammatory and thrombotic markers increased with 1- to 3- day lagged PM_2.5 components and gaseous pollutant exposures (Iag1, lag2 and lag3) (Chen and co-authors, 2017). Lag0 was not assessed.

Overall, the evidence presented above suggests that there are persistent effects for several cardiovascular endpoints and pollutant-outcome pairs extending beyond 24 hours post exposure with evidence to suggest this period extends up to 5 days.

Respiratory effects

Air pollution exposure and lag structures for several respiratory diseases have been studied, including for chronic obstructive pulmonary disease (COPD) (Ross and co-authors, 2023; Canova and co-authors, 2012), asthma (Galán and co-authors, 2002; Bi and co-authors, 2023; Canova and co-authors, 2012; Schildcrout and co-authors, 2006), and respiratory hospital admissions (including emergency admissions) (Wong and co-authors, 2002; Capraz and co-authors, 2017; Szyszkowicz and co-authors, 2018). Acute changes in lung function have also been examined (Oftedal and co-authors, 2008).

Exposure to air pollution has been linked to Accident and Emergency (A&E) and General Practice (GP) visits for respiratory illness in a study in Bradford, UK. Higher levels of exposure to NO₂ and PM_2.5 were associated with acute (lag0) GP and A&E visits. Lagged effects on GP and A&E visits were reported for NO₂, PM_2.5 and PM₁₀, with cumulative effects peaking at between 35- and 100-days’ lag (Mebrahtu and co-authors, 2023). However, these longer lags would be more usually regarded as reflecting long-term exposure to air pollution.

Note: In its Integrated Science Assessments, the US Environmental Protection Agency (EPA) defines short-term exposures as hours up to 1 month and long-term exposures as 1 month to years.

Anderson and co-authors (1997) investigated COPD hospital admissions in 6 EU cities (Amsterdam, Barcelona, London, Milan, Paris and Rotterdam). Researchers in each city determined the most significant single day lag (same day or lagged up to 3 days; or up to 5 days for ozone) or cumulative lag (effects of mean of same day and up to 3 previous days; or up to 5 previous days for ozone) for inclusion in the combined analysis. For single day lags for black smoke, NO₂ and O₃, different lags (lag0, lag1 or lag 2) were determined as the most significant lag in different cities and for different pollutant outcome pairs. In Canada, Ross and co-authors (2023) found that symptoms in those with mild to moderate COPD were exacerbated in the warm season by NO₂ at lag3 and the cold season by PM_2.5 at lag1. Combined COPD and asthma admissions to a London hospital in relation to PM₁₀ were examined by Canova and co-authors (2012), who reported statistically significant effects at lag0, lag0 to 1 and lag0 to 3.

Galán and co-authors (2002) found statistically significant effects on daily asthma emergency visits to a hospital at lag3 for PM₁₀, lag3 and lag4 for NO₂ and lag0, lag1, lag2, and lag3 for O₃ in Madrid, Spain. In a large study in the US, associations of emergency department visits for asthma in 10 US states with O₃, NO₂, PM_2.5, PM_{10 to 2.5}, EC, OC, nitrate and sulphate were examined. Adverse effects were seen at lag1, lag2, lag3 for O₃; lag0, lag3, lag4, lag5, lag6, lag7 for NO₂; lag0, lag1, lag2, lag3, lag4, lag5 for PM_2.5; all lags from 0 to 7 for PM_{10 to 2.5}; lag0, lag4, lag5, lag6 for EC; lag0, lag1, lag2, lag3, lag4, lag5, lag6 for OC; lag0, lag1, lag2 for sulphate, and none for nitrate or SO₂ (Bi and co-authors, 2023).

In children, lag structures for asthma exacerbations and lung function have also been studied. Schildcrout and co-authors (2006) found effects of NO₂ for symptoms of asthma at lag0, lag2 and the 3-day moving sum. No significant effects were seen for PM₁₀, O₃ or SO₂. Moreover, short-term exposure to NO₂ has been associated with reductions in lung function (peak expiratory flow (PEF) and forced expiratory flow (FEF25)) in 9 to 10-year-olds in Norway, at cumulative lag0 to 3, and also over cumulative lags of a week and a month (Oftedal and co-authors, 2008). In this study, reduced lung function was more pronounced in girls.

In a study comparing admissions to hospital for respiratory diseases in two cities, London and Hong Kong, lags for NO₂, PM₁₀ and O₃ differed. In London, NO₂ effects were seen at lag3; lag0, lag2 and lag3 for PM₁₀ and lag0 and lag1 for O₃. In Hong Kong, NO₂ effects were seen at lag0, lag1, lag2 and lag3; lag0 and lag1 for PM₁₀ and lag1 and lag2 for O₃ (Wong and co-authors, 2002). A study looking at respiratory admissions to hospital in Istanbul found that all lags 0 to 9 were important, however, the effects were highest at lag0 for PM₁₀, and lag4 for PM_2.5 and NO₂ (Capraz and co-authors, 2017).

Szyszkowicz and co-authors (2018) investigated differences, by sex, in lag structures for NO₂, PM_2.5 and O₃ for respiratory emergency department visits in 9 districts in Ontario, Canada. For COPD, there was no increase in risk associated with NO₂ for women, while in men effects were seen at lag3, lag4, lag5 and lag6. Effects of PM_2.5 were also only seen in men, at lag1 to lag8. Conversely, an effect of O₃ was only observed for women, at lag2, lag3 and lag4. Elevated risks of emergency department visits for upper respiratory conditions were associated with NO₂ in both males (at lag5, lag6, lag7 and lag8) and females (at lag8). Associations with O₃ and PM_2.5 were also seen in both males and females (O₃, lag0 to lag6 for males, lag0 to lag8 for females: PM_2.5 lag0 to lag8 for both males and females). Effects of SO₂ were only seen after lag7 and lag8 for men and lag8 for women.

Yorifuji and co-authors, (2014) examined hourly lags for emergency calls to the ambulance service in Okayama, Japan for respiratory diseases. Associations with suspended particulate matter (SPM) (defined as particulate matter with an aerodynamic diameter less than 7 μm (PM₇)) were seen at lags of 24 to 48 and 48 to 72 hours and O₃ at lags of 48 to 72 hours and 72 to 96 hours. Risks associated with NO₂ were not statistically significant. Associations for calls relating specifically to pneumonia and influenza (combined) were not statistically significant except for SO₂ at lags of 12 to 18 hours and 0 to 24 hours).

Overall, the evidence presented above suggests that there are persistent adverse effects extending beyond 24 hours post exposure for respiratory end points, with evidence for some pollutant-outcome pairs that this period extends to at least a week.

Mortality

Zanobetti and co-authors (2003) examined lags between daily PM₁₀ concentrations and mortality in 10 cities in the WHO EURO region included in the APHEA project. Both cardiovascular and respiratory mortality (Zanobetti and co-authors, 2003) were more strongly associated with a 10μg/m³ increase in cumulative exposure to PM₁₀ (lags 0 to 20, 0 to 30, 0 to 40) than with the same increase in the mean of lag0 or 1. For cardiovascular mortality, immediate effects of PM₁₀ were seen for up to a week, followed by a prolonged slight increase in risk above the baseline for the following month. The lag structure for mortality from respiratory disease was different, the immediate increase in risk declined much more slowly over the first two weeks, with a second smaller peak after about a month, and remaining positive until the end of the period studied (40 days). Chen and co-authors (2022) found cumulative effects of PM exposure on cardiovascular mortality can last up to 14 days.

In another study, in all 21 European cities in the APHEA study, Samoli and co-authors (2009) investigated lags of up to 20 days for effects of summer O₃ on mortality (June to August) from cardiovascular and respiratory effects. Respiratory mortality risks were highest in the first few days, reducing to below 0 after about 2 weeks, before increasing again; the cumulative risk over the full 20-day period (lag0 to 20) was higher than for lag0 or average lag0/1. For cardiovascular and all-cause mortality, the immediate increase in risk reduced within a few days, and was not increased overall for cumulative lag0 to 20.

Based on these studies, it can be considered that mortality risks may persist for some time (weeks) following exposure, particularly for respiratory mortality.

Discussion

Cardiovascular and respiratory lags

The evidence examined suggests that there are immediate (within a day) and continued impacts of air pollution exposure on cardiovascular events, for example, cumulative lags up to lag0 to 5 (Bhaskaran and co-authors, 2011; Ugalde-Resano and co-authors, 2022; Wong and co-authors, 2002; Zhao and co-authors, 2020). Studies have shown different lengths of lag periods for apparently the same conditions, which could be due to differences in location, source-specific pollution, clinical disease definitions, healthcare systems and their recording practices, and models or statistics used. Risks extended beyond 24 hours for a range of cardiovascular endpoints, with many studies reporting some effects up to 5 days after exposure.

The apparently shorter lags for cardiovascular, compared with respiratory, effects may reflect a predominance of studies looking at Acute Coronary Syndromes rather than (for example) decompensated heart failure which would be expected to have a longer lag. This is pertinent to the DAQI as patients with known cardiac disease, for instance, heart failure, may use the DAQI to guide behaviour, whereas many of the events reflected in the studies of cardiovascular effects may be for first cardiac events in previously apparently healthy patients. Older people are more likely to have an undiagnosed health condition, which could predispose them to acute effects of air pollution. This is one of the reasons why they are regarded as an at-risk group for the DAQI advice.

A number of studies on respiratory diseases report morbidity effects persisting for at least a week after exposure (Bi and co-authors, 2023; Capraz and co-authors, 2017; Szyszkowicz and co-authors, 2018), however, one study (Mebrahtu and co-authors, 2023) reported effects between 35 and 100 days.

In studies of mortality effects, shorter cumulative lags were found for deaths from cardiovascular than respiratory causes, with prolonged periods (several weeks) of increased respiratory mortality risk reported (Chen and co-authors, 2022; Samoli and co-authors, 2009; Zanobetti and co-authors, 2003).

Other factors affecting lags

Several factors may affect lag structures. Firstly, pollutant concentrations were not considered as part of the current review, and lag structure may differ dependent on the exposure level and the composition of the ambient aerosol. Secondly, consideration of other environmental variables may be relevant, such as pollen levels and circulating viruses, which themselves can act as triggers for certain respiratory conditions and interact with inhaled pollutants (Anenberg and co-authors, 2020). Furthermore, within any disease umbrella (that is, respiratory or cardiovascular disease), there are multiple different pathologies that themselves may have a different lag pattern, and that may have contributed to the reported effects to varying degrees.

Aspects such as meteorology and forecasting, which are covered in other workstreams of the wider AQIS review, become relevant for proactive and preventative management of conditions such as asthma. Some studies found differences in lags in different countries (Wong and co-authors, 2002), which raises the question of whether specific factors concerned with location, such as climate, make a difference. If this was the case, it might be appropriate to focus on UK specific and European studies on lag structures to inform the UK DAQI. Nonetheless, all the selected studies considered meteorology or seasonal effects and added control variables into lag models.

For respiratory diseases, 9 of the selected studies were in Europe, and 6 studies from elsewhere: 4 in North America, one in China and one in Japan. Most studies, from Europe or elsewhere, were consistent in reporting effects from lag0 up to several days. Where lags as long as a week were examined, effects continued to be reported at lag7. This is for a range of pollutants (O₃, PM₁₀, PM_2.5 and NO₂) across different conditions termed under respiratory disease (asthma, lung function, COPD, hospital emergency visits and mortality due to respiratory disease). Two studies undertaken in Europe reported longer lags (Zanobetti and co-authors, 2003; Mebrahtu and co-authors, 2023). One study in China examined cumulative lags from day 0 to 30 but no effect was seen (Chen and co-authors, 2022).

For cardiovascular diseases, only 3 studies out of the 9 selected were in Europe. Of the European studies, one (Bhaskaran and co-authors, 2011) reported effects only in the first 6 hours following exposure, and not over subsequent time periods. Samoli and co-authors (2009) reported the risk of cardiovascular mortality reduced within a few days, whereas the APHEA study reported lagged effects on cardiovascular mortality a month after exposure (Zanobetti and co-authors, 2003). Out of the non-European studies, 5 studies reported lag effects on cardiovascular morbidity over a period of 0 to 5 days. The other study found a cumulative lag of 0 to 14 days for cardiovascular mortality. Collectively, the studies looked at O₃, PM₁₀, PM_2.5 and NO₂ while covering a range of conditions termed under cardiovascular diseases (myocardial infarction, emergency visits related to cardiovascular diseases, cardiac arrest and mortality due to cardiovascular disease).

Ozone concentrations are particularly seasonally variable, increasing in the warm season. Canova and co-authors (2012) highlight that O₃ is generally low in the UK compared with the United States and some parts of Europe. Therefore, this may need to be considered when looking at studies examining lag structures for O₃ for a UK-specific information system.

In addition, consideration may also need to be given to differences in healthcare systems in countries where studies on lag effects have been undertaken. Differences in hospital admissions may occur, perhaps due to differences in healthcare seeking behaviours of patients using universal or private healthcare systems (Abuduxike and co-authors, 2020; Rana and co-authors, 2020; Tynkkynen and Vrangbæk, 2018).

Clinical considerations

It is possible that patients may benefit from an increase in dose and/or frequency of preventative medication before, during and following an air pollution event, to minimise risks from periods of moderate to high pollution, including lagged effects. However, air pollution forecasting, including of personal exposure, is challenging and there is a risk that revision to advice could lead to alert fatigue and distrust in the messages, which could be a barrier to behaviour change. This could be partially offset by more nuanced messaging around the cut-points between the alerting levels in the DAQI. Additionally, there is extremely little clinical evidence that adjustment of preventative medicine at the start of a pollution episode is clinically protective. Such evidence is needed before providing this advice as untoward adverse effects are possible with any medication change, however well-intentioned.

Strengths and limitations of our review

Attention is drawn to the fact that references from this literature search were selective, and literature was commented on where effects were found. The evidence presented is not the result of a comprehensive review and, therefore, the conclusions drawn should be regarded as preliminary and the advice provided precautionary. Nonetheless, it is clear that health effects (focusing on respiratory and cardiovascular endpoints), across a range of disease endpoints and for different pollutants (PM, NO₂, O₃), persist beyond 24-hours after exposure. This supports the need for some revision of the health advice which accompanies the DAQI.

Implications for DAQI advice

Overall, whilst there is currently limited evidence on this topic, the available evidence examined suggests a need to revise the behavioural messaging accompanying the DAQI. We believe that this precautionary advice should be modified to encourage individuals with known respiratory and cardiovascular conditions to monitor symptoms for at least a week after an air pollution event to ensure they are adhering to their pre-arranged care plan in accordance with their clinician’s advice, contacting their healthcare professional if necessary.

Advice needs to be carefully integrated with care plans (for example, asthma management plans) and other triggers so that susceptible individuals focus on optimum self-management over the period of higher air pollution and the week following. Advice should also refer to other triggers and environmental exposures which may exacerbate symptoms, for example exercising indoors could result in exposure to poor indoor air quality. Importantly, if susceptible individuals are advised to consider behavioural modifications for not just the day of high pollution but also the week following, it will be important to ensure that the impact on other aspects of daily life is proportionate (for example, exercise regime) and manageable for high-risk individuals. Exercise and physical activity will be considered in a separate working paper.

As the evidence presented above relates to those with respiratory and cardiovascular diseases, very detailed advice may not be necessary for members of the general population. There needs to be a balance between sensitivity and specificity, and consideration given to the nature of messaging. It should also be considered that more nuanced messaging may be needed for those with cardiovascular and respiratory conditions. The difference in risk associated with the concentrations at the top of one pollution band (for instance “Moderate”) and concentrations at the lower end of the next pollution band (for instance, “High”) is very small, but the use of cut-points in the DAQI means that advice for management of the aforementioned conditions may be different.

Furthermore, the evidence on cumulative lags may suggest that longer durations of exposure to elevated concentrations of air pollution (for example, more prolonged air pollution episodes such as a week), may increase an individual’s risk of adverse health effects. Consideration may need to be given to whether this can be taken into account in the derivation of the DAQI, or whether different messaging is needed for pollution episodes of varying duration. Information on pollution levels on previous days would also need to be made more accessible to AQIS users.

COMEAP’s role has been to consider the scientific evidence and how this might inform the advice that should be given. Further consideration, and research, will be needed regarding how best to word and communicate the advice to the various target audiences.

Recommendations for further research

Additional research might allow further refinement of messaging regarding the public health effects when air pollution is elevated and in the following days.

More consideration of lag structures in epidemiological studies is needed to allow evaluation and refinement of the public health messaging used to mitigate health risks following periods of elevated air pollution. Studies need a more detailed consideration of multipollutant impacts, that also includes consideration of other environmental risks (for instance, temperature, pollen, viruses). In order to inform a UK-focused DAQI, studies undertaken in the UK would be valuable. Research into factors underlying possible geographical differences in lags might also be informative.

The DAQI is currently based on whichever pollutant is highest, issuing the same advice regardless of the pollutant. It is important to identify how lag structures vary for each pollutant and how this may change when combining the effects of different pollutants. Furthermore, it is important that more research is conducted into how multiday day exposures to high pollution may impact on response thresholds and the duration of adverse symptoms.

The evidence suggests that there are prolonged effects of air pollution for various health outcomes; however, separation of different health outcomes in studies is needed to determine specific lag effects for different health conditions. We also found that there was little evidence on lags for other potentially susceptible groups, such as those with diabetes and during pregnancy. Furthermore, there is little evidence on sub-types or phenotypes of a particular disease, whether preceding disease control influences risk and whether medication can decrease the risks associated with pollution.

Ideally, more studies on lags are needed, which should consider pollution concentration, how lag structure varies by pollutant, pollutant-effect relationships, separation of health outcomes, differences in locations and additional susceptible groups. However, it is important to note that, in order to be statistically powerful, these studies would require large population samples, and therefore would be resource intensive to conduct.

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