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Air Pollution

Respiratory Effects of a Reduction in Outdoor Air Pollution Concentrations

Boogaard, Hannaa; Fischer, Paul H.b; Janssen, Nicole A. H.b; Kos, Gerard P. A.c; Weijers, Ernie P.c; Cassee, Flemming R.b; van der Zee, Saskia C.d; de Hartog, Jeroen J.a; Meliefste, Keesa; Wang, Menga; Brunekreef, Berta,e; Hoek, Gerarda

Author Information
doi: 10.1097/EDE.0b013e31829e1639


Over recent decades, numerous studies have documented adverse respiratory health effects of traffic-related air pollution.1 Respiratory effects have been found in vulnerable subgroups including children and elderly, as well as healthy adult populations.2 Most evidence is based on cross-sectional studies and cohort studies that investigated associations between baseline air pollution and changes in health status within persons.2 Only a few studies have specifically investigated the effects of changes in air pollution over time and changes in respiratory health within persons. Downs et al3 reported that a reduction of 10 µg/m3 PM10 over 11 years of follow-up slowed the annual rate of forced expiratory volume (FEV1) decline by 9% in Swiss adults. In a subsample of the Children’s Health Study,4 Avol et al5 showed an improvement in lung function growth within 1 to 3 years among children who moved to cleaner regions.

Standard health impact assessment of air pollution abatement policies6,7 have relied on existing concentration-response functions from observational studies, making implicit assumptions that causal effects will apply “in reverse” when exposure is reduced. Clancy et al8 showed that both air pollution and death rates dropped substantially after the Dublin coal ban. This striking result has prompted increasing interest in intervention types of studies. The US-based Health Effects Institute has promulgated a program to investigate the effects of interventions.9 Studies include the traffic policies taken at the Summer Olympic Games in Atlanta10 and the sale ban of coal in multiple Irish cities.11

Intervention studies can provide more direct evidence than observational studies, and therefore may contribute substantially to the causality debate. It is challenging, however, to disentangle effects of a policy from background trends and to have sufficient contrast in exposure to detect health changes.

We evaluated the air quality and health effects of local traffic policies including low emission zones in several Dutch cities. A detailed description of the policies and effects of local policies on air pollution concentrations has been reported separately.12 In this article, we assess whether changes in air pollution concentrations are related to changes in respiratory health status within 2 years.


Study Design

Measurements of air pollution and respiratory health were conducted at 12 locations in the Netherlands before (2008) and 2 years after the policy implementations (2010). In all inner cities, old heavy duty vehicles were forbidden to enter the low emission zones. In one city (The Hague), we also evaluated a traffic recirculation plan designed to reduce concentrations at hotspots. Air pollution was measured at eight busy urban streets in five Dutch cities, and at four suburban background locations near the selected cities. Suburban background locations were chosen as control areas, unlikely to be affected by the policy under investigation. Respiratory function was assessed twice by spirometry and interrupter airway resistance. In addition, nitric oxide (NO) in exhaled air was measured as a marker for airway inflammation.

We reported previously that, with the exception of one urban street in The Hague (Stille Veerkade) where traffic flow was drastically reduced, there was no difference in 2010–2008 concentration trend at the urban streets compared with the suburban background trend. This suggests no measurable effect from the implementation of the low emission zones.12 Because there was considerable variability in air pollution reductions, we focused on the association of respiratory health changes and concentration changes. We additionally analyzed the street with the large change in air pollution separately.

The study has been approved by the Medical Committee of Utrecht University Medical Centre. Signed informed consent forms were obtained from all adults or (for children) their parents.

Study Population

All residents of the 12 locations were invited to participate by a letter and a reminder. We include children (aged 4 years or more) as well as elderly because these groups may be particularly susceptible. Age of four was chosen as those children would be able to perform at least the airway resistance test adequately.13 For the spirometry test, children had to be at least 6 years of age. No other inclusion or exclusion criteria were used.

Assessment of Exposure to Air Pollution

PM10, PM2.5, soot, NO2, NOx, and elemental composition of PM were measured at 12 locations (eight urban streets, four suburban background locations). An additional background location was selected as a reference location to adjust for temporal variation because measurements were divided in two rounds for logistical reasons. At each location, six 1-week samples were collected, spread over two 6-month periods, with identical equipment and at exactly the same location. Temporally adjusted average concentrations were used in subsequent analyses.12

Briefly, PM10 and PM2.5 were collected gravimetrically on Teflon filters using PM10 personal samplers (MSP Corp., Shoreview, MN) and PM2.5 GK2.05 cyclones (BGI Inc., Waltham, MA). Soot content of all PM10 filters was measured using a Smoke Stain Reflectometer (model M43D; Diffusion Systems, London, UK) and transformed into absorption coefficients. All filters were analyzed with energy dispersive X-ray fluorescence spectrometry (ED-XRF) at Cooper Environmental Services (Portland, OR). We focus in this article on those elements that showed the largest contrast between streets and background locations in the 2008 baseline campaign (Cr, Cu, Fe). NO2 and NOx concentrations were measured with Ogawa passive samplers (Ogawa & Company Inc., FL). Sampling and analysis methods have been published before.14

Exposure was measured on the street where participants lived. The maximal distance between sample location and residents’ home was about 500 meters.

Traffic intensity at the busy streets ranged between 10,000 and 19,000 vehicles per 24 hours. At follow-up, traffic intensity did not differ more than 1000 vehicles per 24 hours, apart from one urban street in The Hague (Stille Veerkade) where traffic was reduced by about 50% (baseline traffic: 17,000 vehicles per 24 hours).12

Respiratory Health Measurements

Baseline and follow-up tests were performed in winter 2009 and 2011, following the majority of the exposure measurements. Tests were performed within 2 to 4 days per location, at the same place and close to where the people lived.

Forced vital capacity (FVC), FEV1, maximum mid-expiratory flow (MMEF), and peak expiratory flow (PEF) were measured using the same EASYONE Spirometer (ndd Medical Technologies, Zurich, Switzerland). The EASYONE Spirometer end-of-test criterion was as follows: a test ends when the volume change during the last 2 seconds is <45 mL, or an inspiratory volume >150 mL is detected. Calibration was checked daily. At least three maneuvers were performed per person, and the best values from the technically corrected maneuvers were selected according to European Respiratory Society criteria.15 The majority of the tests (80%) were carried out by the same technician. The fractional concentration of exhaled NO was measured using the NIOXMINO (Aerocrine AB, Solna, Sweden) as a marker of airway inflammation. Exhaled NO tests were performed before spirometry, following standardized procedures.16 Four devices were used, three for the baseline examination (98% with the same device) and one for the examination at the end of the study. Airway resistance was measured using the MicroRint (Micro Medical Ltd, Kent, UK), which used the interrupter technique.13 Airway resistance was measured during expiration, with randomly occlusion of the airway at peak expiratory flow. Airway resistance values were excluded when instructions were not followed or when interruption was not at the peak of expiratory flow. Median values of five to ten approved maneuvers were used in the analyses. We intended to use one device for all measurements. However, during the baseline campaign, the initial device broke, whereas another device gave unreliable flow check, and therefore, a third (new) device had to be used. For the follow-up, only the latter device was used.

Study participants filled in a questionnaire at baseline and at follow-up on prevalence of respiratory symptoms and on important confounder data including detailed smoking history and housing characteristics. Questionnaires were filled in by a parent for children under the age of 12 at baseline.

Data Analysis

Absolute change in health outcome between the post- and the precampaign was the dependent variable, divided by the exact follow-up time between measurements. Absolute change in average air pollution concentration was the independent variable. We performed separate analyses for all pollutants. We specified two-pollutant models when possible given the often-high correlation between pollutants.

Covariates selected a priori in our basic models included age, sex, amount of cigarettes, level of education, having a cold at the time of examination, and the difference of the exact time of examination to account for diurnal fluctuation in respiratory health. For the latter, we explored various options, but a simple linear term appeared to be sufficient. Under the age of 25, the highest level of education of the parents was used. We used the Akaike Information Criterion (AIC) to assess how to account for different time trends related to age because our study population included both children and adults. For spirometry, the best model fit included age in four categories (≤12, 13–17, 18–29, 30+ years) including difference in height as a continuous variable. For airway resistance, two linear splines over the age intervals ≤12 and older than 12 years gave the best model fit. Adding more splines did not improve model fit. For exhaled NO, age as a continuous variable provided the best model fit. These models were fit without air pollution in the model. In the airway resistance basic models, we corrected for device and technician at follow-up, but not in the spirometry and exhaled NO models. In the latter models, this correction led to unstable results because these variables were not evenly distributed over the locations. In sensitivity analyses, we restricted the analysis to tests performed by one technician (spirometry) or with the same device (exhaled NO).

In more extensive models, we added smoking status (yes, former, never), passive smoking, carpet in the bedroom, animals in the home, gas cooking, mould in the home, and occupational exposure to gases (all 0/1 variables). In addition, we accounted for the difference in temperature and ambient background NO2 concentration between the two examinations, to take into account short-term effects of either weather and air pollution. These two variables were 24-hour averages of the day before the examination and were taken from the nearest fixed-site monitor. For the airway resistance extensive models, we also included technician at baseline. All covariates were derived from the baseline questionnaire. Most covariates did not change much within the 2-year time period. In sensitivity analysis, we excluded 26 participants who changed smoking status between the two examinations. In additional subgroup analyses, we excluded children under the age of 18 because of small sample size (N = 72) and influential observations (1% of observations with the highest Cooks distance). Finally, we conducted separate analyses of one street in The Hague with the largest reduction in air pollution concentration and suburban background locations (N = 79).

We did not include baseline air pollution in the models because the study period was relatively short (2 years) and the correlation between baseline and changes in air pollutants was often high (R up to −0.9).

We report simple regression models in the main analysis instead of mixed-model results because the latter did not give a better model fit based upon AIC. In addition, results of mixed models are given in the Supplement (


In total, 853 people agreed to participate in the study in 2008. The response rate was very low (∼10%) and varied across locations. We have no information about the nonresponders. Seven hundred forty-six (87%) participants completed baseline examination, and of these, 661 participants (89%) were reexamined 2 years after baseline examination. Among the full participants, cleaned data on exhaled NO were available for 640 (97%), on spirometry for 585 (89%), and on airway resistance for 497 (75%).

Eighty-four percent of study participants were above 30 years of age at baseline, 46% were never smokers, 54% had college/university education, and 47% had a paid job (Table 1). Number of participants per location ranged from 12 (Weerdsingel WZ, Utrecht) to 108 (suburban background location, Utrecht). Compared with full participants, those lost to follow-up (11%) were of similar age, more likely smokers (24%), with lower education level (39%), and with a somewhat lower baseline respiratory health status. Table 2 displays baseline and changes in respiratory effects of the study participants. There were large variations in baseline and changes in respiratory effects, as this was a heterogeneous study population with both children and adults.

Baseline Characteristics of Study Participants
Baseline Respiratory Health Measurements and 2010–2008 Respiratory Changes

Changes in Air Pollution Concentrations

Most air pollution concentrations declined in the second half of 2010 compared with 2008 (Table 3). One urban street (Stille Veerkade) in The Hague—where traffic flow was substantially reduced—showed substantially larger reductions in air pollution than the other streets and suburban background locations, especially in soot and NOx (Table 4). With the exception of this street, trends in traffic-related pollutants (eg, soot, NO2, NOx, Cu, and Fe) did not differ substantially between the busy streets and the suburban control locations, as previously reported.12

Baseline Air Pollution Concentrations and Distribution of 2010–2008 Air Pollution Changes (N = 12)
Absolute 2010–2008 Changes in Average Air Pollution Concentrations at the Urban Streets Versus Suburban Background Locations

Spearman correlations were high for changes in the traffic-related air pollutants (R>0.7), preventing an analysis of the independent effect of these pollutants. Correlations of the traffic indicators with PM2.5 were lower (R = 0.20–0.52). There was little or no correlation between traffic indicators changes and PM10 changes (R = −0.19–0.13). Correlation between PM10 and PM2.5 changes was 0.40.

The decrease in air pollution concentration during the study period was also apparent in PM10 and NO2 concentrations from fixed-site monitors (which had continuous data, in contrast to our design) throughout the whole country, in both urban and regional sites (data not shown).

Changes in Respiratory Effects

In regression analyses adjusted for important covariates, reductions in traffic-related air pollutants (soot, NO2, NOx, Cu, and Fe) were associated with statistically significant improvements in FVC (Table 5). An interquartile range (IQR) decline in traffic-related air pollution concentration over the 2-year period was associated with a small improvement (about 1%) in FVC (ie, 40 mL). Differences between unadjusted and adjusted models were relatively small, and effect estimates were stable in both confounder models as well as in various sensitivity and subgroup analyses (excluding smoking status, children, and influential observations). Effect estimates were quite similar when we adjusted for clustering of outcomes within streets in mixed-model analysis (Supplement Table 1, When participants were restricted to those with both tests performed by the same technician, effects reported above were stable for soot (estimate −0.73 per IQR, P value: 0.03), but were less negative for the other traffic-related air pollutants, and lost statistical significance. Weak associations in the expected direction were seen with FEV1 and PEF. In addition, no effects were seen in MMEF (Supplement Table 2, Declines in PM10 and PM2.5 were associated with a significant decrease in airway resistance, although somewhat less consistently. There was a suggestion of an airway resistance effect of Cr, but given the large difference between unadjusted and adjusted models, and the relatively low exposure contrast of Cr (see Table 3), we did not consider this very credible. No associations were seen with exhaled NO (Table 5). See Supplement Table 3 ( for a description of self-reported symptoms at baseline and follow-up. There was insufficient variability in symptom status to allow meaningful analysis of self-reported respiratory symptoms.

Association Between 2010–2008 Changes in Average Air Pollution Concentrations at Street Level and 2010–2008 Changes in FVC, Airway Resistance, and Exhaled NO of Residents

The associations were driven largely by residents at the one street (Stille Veerkade) in The Hague where traffic-related air pollution was drastically reduced. In those residents, FVC improved 6% (ie, 240 mL), and FEV1 improved 3% (ie, 90 mL) compared with the matching control population (Table 6; only basic models reported because of small sample size). Similar effects were found when compared with residents from all four suburban background locations (Supplement Table 4, One technician performed all tests at the Stille Veerkade and did the majority of tests (79%) at the matching suburban location. Effects were stable in the sensitivity/subgroup analyses where both tests were performed by the same technician, when excluding current smokers, children and influential observations. Airway resistance was substantially decreased as well in those residents, but reached statistical significance only in the basic model and when compared with all four suburban background residents.

Changes (%) in Lung Function, Airway Resistance, and Exhaled NO Between 2010–2008; Comparison of the Street with the Largest Reduction in Air Pollution and Residents at the Matching Suburban Background Location with a More Modest Air Pollution Difference

Self-reported respiratory symptoms were reduced at the Stille Veerkade, but this was not formally analyzed (Supplement Table 3, The small group of residents at the Stille Veerkade in The Hague (N = 31) did not differ from the matching suburban population in important confounder variables, although there were more current smokers, as was the case for all street residents (Supplement Table 5,

In two-pollutant models with soot (as a marker for traffic-related air pollution) and PM10, FVC effects remained similar. Airway resistance effects remained as well in the PM10 and PM2.5 models that included soot. In a two-pollutant model with PM10 and PM2.5, separate airway resistance effects remained with both particle sizes, but the model became somewhat unstable (Supplement Table 6, Upon removing the street residents in The Hague and the matching suburban background residents from the analysis, FVC effects largely disappeared or became inconsistent (airway resistance) (Supplement Table 7,


Small improvements in respiratory function (FVC, airway resistance) were found after a modest decrease in outdoor air pollution concentrations within 2 years. No substantial associations were seen for other spirometric measures or exhaled NO. Results were driven largely by the small group of residents living at the one urban street where traffic flow as well as air pollution were drastically reduced. FVC improved 6%, and FEV1 improved 3% in those residents compared with suburban background residents. This was accompanied by a suggestive reduction in airway resistance.

Changes in Respiratory Function at One Urban Street

The improvement of respiratory function in the small group of residents on the one street where both traffic and air pollution was drastically reduced was robust when compared with their matching suburban control, with all suburban background locations, and with other streets in this study. Associations were found both for spirometry (FVC and FEV1) and airway resistance. This study adds to the small number of studies suggesting that a reduction in air pollution concentrations has measurable respiratory health benefits within a relatively short time period. MacNeill et al17 reported small improvements in childhood PEF after the introduction of traffic management policies in Oxford, UK. Avol et al5 showed an improvement in lung function growth within 1 to 3 years after children had moved to cleaner areas. Downs et al3 documented improvement in lung function in adults after relatively small reductions in air pollution exposure.

Our study has several strengths and limitations. One limitation is the small study population that experienced a large reduction in air pollution exposure. There were nevertheless measurable associations because our design compared changes in health within persons. Within-subject variability is small compared with between-subject variability, especially for spirometry indices like FVC and FEV1.15 Lower within-subject variability of FVC and FEV1 compared with PEF, MMEF, airway resistance, and exhaled NO probably explains the smaller standard errors of the effect estimates for FVC and possibly the more consistent associations observed for FVC. In our study, within-subject variability for PEF and MMEF was between 5% and 6%, whereas for FVC and FEV1 it was below 2%.

A larger effect was reported for FVC than for FEV1, with no effects on the flow indicators PEF and MMEF. We did not study the mechanisms underlying these changes, but particulate matter air pollution has been associated with increased airway inflammation, systematic inflammation, and oxidative stress.18 Reductions in transition metals and soot (likely associated with organic components) may have contributed to a reduced oxidative stress response.12 These changes over a short period would more plausibly result in an obstructive rather than a restrictive response. However, previous studies do not provide enough evidence to relate the effects of air pollution conclusively to one or the other specific spirometric indicator,19,20 although there are some indications of larger effects on the smaller airways.3,21 Downs et al3 investigated changes in air pollution over 11 years of follow-up in Switzerland. They showed largest effects of an air pollution reduction in MMEF and improvements in FEV1 to a somewhat lesser extent, whereas no significant effect on FVC was reported. On the other hand, Gauderman et al4 investigated changes in air pollution over an 8-year period and showed quantitatively similar effects of FVC and FEV1 in US adolescents. McCreanor et al22 investigated short-term respiratory effects in asthmatics after walking for 2 hours in either a busy street or in a park in London. They reported effects of FEV1 and FVC of similar size after walking in the busy street, but no effect on MMEF. A cross-sectional analysis of a decline in air pollution in East Germany after reunification showed improvements in FVC in children, but not FEV1.23 Other cross-sectional analysis over longer time periods not related to specific air pollution changes or interventions have reported either larger effect in FVC24 or larger effects in FEV1.25 A large cross-sectional US study found larger decrements in FVC than in FEV1 among adult women.26

Another explanation for a larger effect on FVC than on FEV1 is that this may be an artifact. Participants and technicians were aware of the purpose of the study. At the time of examination, neither the participants nor the technicians performing the tests knew the air pollution concentrations at the street under investigation. However, especially at the Stille Veerkade, participants were aware of the intervention because traffic was notably reduced. FVC is more effort dependent than FEV1, and awareness could have influenced their expiratory efforts (or the effort of the one technician performing the tests). End-of-test criteria were similar in both rounds. Because only one technician performed the tests at the Stille Veerkade, investigator bias is unlikely. The airway resistance response, in contrast, is very unlikely related to technician or participant bias. We did not find any association with exhaled NO, measured as a marker of airway inflammation. We cannot exclude a small inflammatory response in our study, as the standard error of the effect estimate was substantially larger for exhaled NO than for FVC and FEV1. There are also studies that suggest exhaled NO reflects inflammation, particularly in allergic subjects.27

Our study population was heterogeneous (all residents of streets to which the policy applied). Because of the small study population, we could not focus exclusively on sensitive subgroups like children, asthmatics, or the elderly. Response rates were very low and therefore we do not claim that the study population was a representative sample of the general population in the selected streets. We do not have systematic information about the reasons for the high nonresponse rate. Nonresponders tended to be older, of lower social economic class, and with poorer baseline health. One might infer that our effect estimates are underestimates because those categories of people are especially vulnerable to air pollution effects.19 However, the low response has likely not caused bias in our effect estimates as our study design is based upon evaluating changes within persons in time. Population characteristics did not change much within the short study period of 2 years. When we excluded people who changed smoking status, results were similar. Some residual confounding by smoking may be present because people who smoked at baseline may smoke more or fewer cigarettes at follow-up. Results were similar after excluding current smokers.

A strength of our study is that we measured exposure to air pollution at the street where residents lived, and therefore, exposure misclassification will be limited. Most studies have assessed air pollution exposures on a much larger spatial scale. Avol et al5 modeled community-wide exposure based on a few fixed-site monitors. Down et al3 modeled annual average air pollution concentrations for each residence on a 200-meter grid, although only for PM10, and the study was not designed to look at traffic effects. We measured air pollution exposure at each location in six 1-week blocks spread over half a year in 2008 and in 2010. The average, based upon those 6 weeks, was representative for that half-year as demonstrated by a comparison with continuous measurement data from fixed-site monitors throughout the country. Changes in air pollution may happen in the time before that half-year as well. For example, in The Hague, traffic had been drastically reduced since November 2009, whereas the follow-up of the health measurements were performed in February 2011, more than a year later. That improvement in respiratory function can be expected within a relatively short time period after a drastic change in air pollution levels has been shown among children who moved to cleaner regions and in bar workers before and after smoking bans in their place of work.5,28,29

Our study had the advantage of including a control population to account for background trends in air pollution and respiratory function. We selected suburban background populations in the neighborhood of the cities under study. Although we tried to include participants with similar age and socioeconomic status, the participating population characteristics were somewhat different at the control locations. We further adjusted for individual-level confounders. Moreover, the spirometry associations were largely unaffected by various levels of adjustment for confounders.

The observed improvements in respiratory function were small and likely clinically insignificant for healthy subjects.30 However, small shifts in the population mean may result in a more substantial shift in the fraction of subjects with low lung function.31 In the 24-city study, a 3.5% decrement in mean FVC in children associated with particle acidity was accompanied by an odds ratio of 2.5 for FVC below 85% of predicted.32 Similar observations were made when comparing decrements in population mean PEF associated with short-term PM10 exposure and the percent of (clinically more relevant) large decrements in PEF.33 Furthermore, an improvement in respiratory function is an important indicator of future health and life expectancy.34

Full Study Population

We used the detailed information on changes in air pollution for each street and control location to directly study the associations between changes in air pollution and changes in respiratory function in the full study population, rather than restrict ourselves to comparisons of changes in respiratory function between intervention and control locations. These associations should therefore not be interpreted as an effect of the intervention.

Effects of traffic-related components on FVC and effects of both PM10 and PM2.5 on airway resistance largely remained in two-pollutant models. We were not able to disentangle effects of the traffic-related components (soot, NO2, NOx, Cu, and Fe).

After exclusion of the street residents in The Hague and the matching suburban background residents, effects on respiratory function largely disappeared. This was probably because of the lack of sufficient exposure contrast because at the Stille Veerkade, traffic-related components like soot and NOx almost halved, whereas for most other locations decreases were only modest.

In summary, small improvements in respiratory function were found after a modest decrease in outdoor air pollution concentrations within 2 years. Results were driven largely by the small group of residents living at the one urban street where traffic flow as well as air pollution were drastically reduced. Our results suggest that a decline in air pollution leads to positive consequences for public health.


We thank representatives from the local government (eg, Peter Glerum, Peter Segaar, Erik Boons, Marc Pluijgers, Paul Karman, Harry van Bergen) and the Local Municipal Health Services (eg, Jessica Kwekkeboom, Anna van Leeuwen, Wim Ovaa) for providing data and for their active contribution to this study. In addition, we thank Henk Jans and Ellis Franssen for their contribution, in particularly with the set up of this study. We are grateful to all residents who participated in this study and to the field investigators.


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