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Study Protocol

NeuroSmog: Determining the Impact of Air Pollution on the Developing Brain: Project Protocol

by 1, 1,2, 1,3, 4,5, 6,7, 6, 1, 1,8, 1, 1, 1, 1,6, 4, 4, 4, 9, 10 and 1,*
Institute of Psychology, Jagiellonian University, Ingardena 6, 30-060 Krakow, Poland
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London SE5 8AF, UK
European Centre of Environment and Human Health, University of Exeter Medical School, Royal Cornwall Hospital, Truro, Cornwall TR1 3HD, UK
Institute of Environmental Protection-National Research Institute, Krucza 5/11d, 00-548 Warsaw, Poland
Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
Faculty of Management and Social Communication, Institute of Applied Psychology, Jagiellonian University, Łojasiewicza 4, 30-348 Krakow, Poland
Institute of Psychology, University of Gdansk, Bażyńskiego 4, 80-952 Gdansk, Poland
ENIANO GmbH, Schwanthalerstraße 73, 80336 Munich, Germany
Laboratory of Brain Imaging, Nencki Institute for Experimental Biology, Pasteur 3, 02-093 Warsaw, Poland
Brain Imaging Core Facility, Małopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A, 30-387 Krakow, Poland
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(1), 310;
Received: 18 October 2021 / Revised: 8 December 2021 / Accepted: 24 December 2021 / Published: 28 December 2021


Exposure to airborne particulate matter (PM) may affect neurodevelopmental outcomes in children. The mechanisms underlying these relationships are not currently known. We aim to assess whether PM affects the developing brains of schoolchildren in Poland, a country characterized by high levels of PM pollution. Children aged from 10 to 13 years (n = 800) are recruited to participate in this case–control study. Cases (children with attention deficit hyperactivity disorder (ADHD)) are being recruited by field psychologists. Population-based controls are being sampled from schools. The study area comprises 18 towns in southern Poland characterized by wide-ranging levels of PM. Comprehensive psychological assessments are conducted to assess cognitive and social functioning. Participants undergo structural, diffusion-weighted, task, and resting-state magnetic resonance imaging (MRI). PM concentrations are estimated using land use regression models, incorporating information from air monitoring networks, dispersion models, and characteristics of roads and other land cover types. The estimated concentrations will be assigned to the prenatal and postnatal residential and preschool/school addresses of the study participants. We will assess whether long-term exposure to PM affects brain function, structure, and connectivity in healthy children and in those diagnosed with ADHD. This study will provide novel, in-depth understanding of the neurodevelopmental effects of PM pollution.

1. Introduction

Air pollution has received increased global public attention in recent years as presenting a major risk to human health [1]. According to the estimates of the World Health Organization (WHO), ambient air pollution accounts for 4.3 million deaths per year [2]. Exposure to outdoor particulate matter (PM) with an aerodynamic diameter <2.5 µm (PM2.5) is the fifth-ranking risk factor for mortality worldwide [3]. Epidemiological, pathophysiological, and animal model studies provide extensive evidence that short- and long-term exposure to particulate air pollution causes cardiovascular diseases [4,5], contributes to respiratory health deterioration and allergy development [6,7], and can lead to lung cancer [8,9]. Organic compounds and trace metals in PM may harm human health throughout the course of life, beginning in utero [10,11,12,13].
PM has been found incorporated within human brain tissue. Most likely, it enters via the olfactory and the gastrointestinal nerves, and is associated with abnormal protein aggregation in the brainstem [14,15]. In studies investigating the effects of PM using rodent models, inflammation, histopathological changes in the brain [16], and behavioural alterations [17,18] have been attributed to PM exposure. Two recent studies on mice and rats showed that astrocyte function and mitochondrial activity in the cortex were severely affected by PM; larger effects were observed for exposure to smaller particle sizes [19,20]. Some studies have shown that PM can affect the structure and the functioning of the human brain [21], but the results of this research are far from conclusive. For example, prenatal exposure to PM was negatively linked with cortical thickness in children aged between 6 and 10 years and was associated with impairment in inhibitory control [22]. Prenatal exposure to PM is also associated with decreased size of the corpus callosum and higher prevalence of behavioural problems in children aged between 8 and 12 years [23]. Postnatal air pollution exposure has been found to be associated with altered functional connectivity during rest [24]. Another study [17] has reported changes in grey matter surface area, volume, and thickness, but with no effect on cognitive functioning assessed by the comprehensive National Health Institute (NIH) Toolbox Cognitive Battery in children aged 9–10 years. Although exposure to PM has been reported to increase inattention, hyperactivity [12], and impulsivity symptoms [22]; lead to the lower intelligence quotient (IQ) [25]; and impact the prevalence of attention deficit hyperactivity disorder (ADHD) in children [26], the evidence for these effects is far from conclusive. In sum, the effects of PM and air pollution on neurodevelopment merit extensive further study.
Emissions of many air pollutants in the European Union (EU) have decreased over the past three decades, and air quality is gradually improving. In particular, between the years 2000 and 2014, significantly decreasing trends in annual average concentrations of PM with an aerodynamic diameter <10 µm (PM10) and nitrogen dioxide (NO2) were reported [27,28]. Despite these improvements, EU urban populations are exposed to PM levels that exceed the WHO limits for the protection of human health [29]. Between 2000 and 2010, the daily limit value for PM2.5 and PM10 concentrations in the EU were exceeded from 16 to 52% and from 18 to 44% of days, respectively [29]. These exceedances of PM limit values have mostly been observed in the eastern EU countries of Bulgaria, the Czech Republic, Slovakia, and Poland [28,29].
Most of the PM pollution in Poland results from the combustion of coal and other fossil fuels in power and heat generation, and from traffic emissions [30,31]. Estimates of the mortality attributable to PM2.5, based on air quality monitoring data for Poland, have demonstrated that the urban Polish population experiences an unduly high impact from particulate air pollution on their health [32]. Annual deaths attributable to air pollution in Poland were estimated at 39,800 for the year 2000, increasing to 47,300 deaths for the year 2017 [33].
The overall objective of the NeuroSmog study is to assess whether long-term exposure to outdoor PM affects brain function, structure, and connectivity in both healthy children and in those diagnosed with ADHD. Primarily, we will focus on the effect of PM on neural systems for attention and inhibitory control. In addition to neuroimaging, impacts of PM on these systems will be investigated using behavioural tests, including IQ. To meet the study aim, we assembled a multidisciplinary team of experts comprising air pollution modellers, environmental epidemiologists, neuroscientists, and clinical psychologists.

2. Methods

2.1. Study Area

We selected towns in southern Poland, based on their levels of particulate air pollution and population size, that are located within two hours’ drive to the magnetic resonance imaging (MRI) scanning centre in Kraków. The particulate air pollution level of each candidate town (n = 202) was classified as high, medium, or low based on the population-weighted median air pollution level inferred from 1 km × 1 km interpolated PM2.5 data for the year 2015 [34]. We chose small and large towns across different levels of air pollution to minimize urbanicity-related confounding. Towns with more than 90,000 inhabitants were classified as large; others were classified as small. Accordingly, we selected 18 towns (Table 1, Figure 1).
We are recruiting comparable numbers of children (cases and controls) for each combination of air pollution level and town population size (Table 1). Within each such combination, the number of children recruited per town is approximately proportional to the town’s population size.

2.2. Study Design and Study Population

Recruitment of study participants began in October 2020, with the aim of having 800 children complete all study procedures by the end of 2022. We are recruiting one ADHD case (intended number n = 267) per two population controls (intended number n = 533).
Cases with ADHD diagnosis according to the 11th revision of the International Classification of Diseases and Related Health Problems (ICD-11) [35] are recruited by field psychologists in the study towns. Children at risk of ADHD are referred to the psychologists either by cooperative school psychologists or teachers, or by paediatricians, psychologists, psychiatrists, or neurologists from psychological-educational counselling centres and mental health centres. In addition, cases are also recruited from families who voluntarily contacted us after seeing our recruitment advertisements.
We use a two-step model to verify and eliminate ADHD diagnosis in cases and controls, respectively (see Section 2.6). Firstly, a comprehensive psychological assessment is administered and evaluated by field psychologists. Secondly, three consultant clinical psychologists review the assessments to verify the diagnoses according to the ICD-11. Cases that do not meet the ICD-11 criteria are excluded from the study; controls who meet the ADHD diagnostic criteria are recruited into the case arm.
All study participants are fluent Polish speakers, aged between 10 and 13 years, have average or above average intelligence (i.e., attend non-specialised schools), are in grades IV–VI, and attend school in the selected towns. Exclusion criteria for all are diagnosed intellectual disability, neurological or comorbid psychiatric disorders, other serious medical conditions, as well as contraindications to MRI. Children born before the 35th week or after the 40th week of gestation, or with birth weight <2500 g or with an Apgar score <8 are also excluded. As we are interested in the long-term effects of air pollution, children who resided outside of Poland during the last year are also excluded, as are children whose legal guardians are not fluent in Polish.
A team of field psychologists with at least five years of clinical experience was recruited to enlist potential cases and perform psychological evaluation of all children. Prior to beginning their work, all psychologists received accredited training in the use of psychological tests utilised in the study (see Section 2.6). They were also trained in the administration of two behavioural tests (see Section 2.7), as well as in data protection and privacy laws.
Population controls are recruited from randomly selected larger non-specialized primary schools within each of the study towns (one to five schools per town). Lists of candidate schools were retrieved from the Boards of Education (Kuratoria Oświaty). Within each of the selected schools, classes are randomly selected in grades IV–VI. Within each class, one to five pupils are selected at random. The sampling is defined a priori using randomized lists defining which classes in every grade and which pupils in every selected class to approach and in what order. The randomization was implemented using the function in R statistical software [36].
Headteachers of selected schools are contacted and asked to support the study’s recruitment campaign and sign a formal collaborative agreement. Participating schools are promoted on the project website, and are offered workshops on neurodevelopment and on the study results. Consent for contact from the legal guardians is obtained by the schools. Families who give their consent are contacted by telephone by research assistants. The assistants explain the study aims and procedures, confirm study eligibility, and invite the legal guardians to participate in the study.
If a school does not agree to participate, the next school in the randomized list of schools is contacted. If the legal guardians of a selected child do not consent to being contacted, the next family from the randomized list of pupils is contacted. Where insufficient numbers of pupils are recruited in each grade, additional pupils are selected from the next class in the randomized list of classes (for that grade) using the same procedure until the desired number of pupils are recruited.

2.3. Study Timeline Overview and Progress

All psychological and behavioural assessments are performed during three meetings with the field psychologist. Each meeting lasts about one hour and twenty minutes to two hours, and both the child and their legal guardian are present. At the final meeting, the results of the assessments are explained to the child and to their guardian, a formal psychological evaluation is provided. The MRI scan is completed in Kraków within three months of the psychological evaluation. Typical MRI sessions last up to one and a half hours. After the MRI session, families are rewarded with vouchers equal to 270 PLN (about 60 EUR at the time of writing) and are reimbursed for travel expenses. The children receive t-shirts printed with MRI images of their brains.
Figure 2 illustrates the progress in the recruitment of controls at the end of the first study year (June 2021).
A total of 370 children have undergone psychological evaluation, of whom 236 have also completed the MRI scan. Figure 3 depicts the number of cases and controls tested in each town at the end of August 2021.

2.4. Data Management

Behavioural testing is conducted on HP laptops with 15.6” screens, set up identically for each field psychologist. The laptops are encrypted using BitLocker and synchronised with a secure OneDrive cloud folder at the Jagiellonian University that collects raw data from every test performed.
To minimise the impact of the study on the environment and to reduce transcription from paper to computer, the psychological data are—wherever possible—collected digitally using online questionnaires implemented in survey tools developed by Qualtrics (, accessed on 23 December 2021).
The raw data are processed and prepared for import in R and Python. To allow easy data selection and extraction of variables of interest by data analysts, a secure user website will be implemented that will interface with an SQLite database.

2.5. Study-Specific Questionnaires

Two questionnaires have been developed for the study: a General Questionnaire, and an Address Questionnaire. The General Questionnaire is a Qualtrics-based tool filled in by a legal guardian that collects data on confounders, effect modifiers, and additional exposures. Some items were adopted from the existing studies [37,38,39,40], and some of the items were invented for the purpose of NeuroSmog. Briefly, information on socio-demographics, pregnancy and early life, general health, habits, academic performance, home, and neighbourhood environments of the child is being collected (see Supplementary File S1 for a paper-adopted version of the questionnaire). Non-response options are always presented as a variation of “not applicable”, “don’t know”, “don’t wish to respond”, and at the same time, all responses are forced. The General Questionnaire was validated on a small sample of volunteers.
The Address Questionnaire collects full prenatal and postnatal residential history of the study participants, as well as preschool and school addresses, hours spent in preschools and at schools, and the transportation mode used (see Supplementary File S2). It is a pencil-and-paper questionnaire. The collected addresses are being geocoded and will be used to assign air pollution estimates, as well as other geographical exposures.

2.6. Psychological Evaluation

Cognitive functioning is examined using the Stanford-Binet Intelligence Scales, 5th edition (SB5, [41] and tests from the Diagnostic Battery for Cognitive Functions Evaluation PU1, [42]). These tests measure attention, working memory, and executive function, which represent domains relevant to both classroom learning and ADHD [43]. Guardians complete the Polish adaptations of the Third Edition of Conners’ Rating Scales [44] and Child Behaviour Checklist (CBCL) for Ages 6–18 [45], providing information on ADHD and other externalising and internalising behavioural issues. Children complete the Polish adaptation of Youth Self-Report (YSR) [46], which is a child-completed version of the CBCL [45,47].
Social functioning is assessed using two questionnaires. The first is the Polish adaptation of the Family Adaptation and Cohesion Evaluation Scales (FACES-IV) questionnaire (Skala Oceny Rodziny; SOR) [48], which measures family function, communication, and family satisfaction. The SOR is completed by both guardians and children. The second is the Polish Siblings Relationship Questionnaire (Kwestionariusz Relacji z Rodzeństwem W Okresie Adolescencji; KRR) [49], which is completed only by children with siblings.

2.7. Behavioural Tasks

The attention network task (ANT) measures the efficiency of the three attentional networks—orienting, alerting, and executive functioning [50]. The ANT demonstrates high immediate test–retest reliability, and scores are not correlated across individuals, suggesting that each network efficiency can be measured somewhat independently [51]. We use the children’s version of the ANT, with timing and task stimuli operationalised identically to those used in [50] with the exception that, since our subjects are older, cue duration is set to 100 ms (as in the adult version).
The continuous performance test (CPT) is a variant of the Go/NoGo task which allows quantification of deficits in attentiveness, impulsivity, activation/arousal, and vigilance. The CPT has high three-months test–retested reliability [52] and has been validated in children with ADHD and in healthy controls [53]. During the task, geometric figures appear (250 ms) on the computer screen in 18 consecutive blocks of 20 trials (360 trials total). The geometric figures are identical to those used subsequently in the functional MRI (fMRI) variant of the Go/NoGo task. The interstimulus interval (ISI) is set to either 1, 2, or 4 s during each block. Participants are asked to press the spacebar in response to each letter presentation, except for the square and the circle [54]. The proportions of Go to NoGo trials are set at 80% and 20%, respectively [55].
All children complete practice runs of both tests until their performance reaches 90% accuracy.

2.8. Neuroimaging

All neuroimaging data are acquired at the Małopolska Centre of Biotechnology, Jagiellonian University in Kraków, Poland, on a Siemens MAGNETOM Skyra 3T MRI scanner (Erlangen, Germany) using a 64-channel head coil. Participants are familiarised with the scanner and trained to remain still using a mock scanner (, accessed on 23 December 2021). The mock scanning session lasts for 10 min. During the scan, head movements are constrained by inflatable Pearltec pillows (, accessed on 23 December 2021).
The protocol includes T1-weighted (T1-w), T2-weighted (T2-w), and magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) structural images, diffusion-weighted imaging (DWI), two resting-state fMRI (rsfMRI), and two task fMRI runs (see Table 2 for detailed acquisition parameters and order). The scanning protocol, including instructions, lasts for one hour.
The acquisition sequences for T1-w, T2-w, and fMRI neuroimaging data were adopted from the Adolescent Brain Child Development (ABCD) project [56].
The DWI acquisition sequence was adapted from the UK Biobank (, accessed on 23 December 2021) to fit the study scanner and includes multiple b-value volume acquisitions. For the fMRI and DWI acquisitions, we use multi-band accelerated echo planar imaging (EPI) pulse sequences developed by the Center for Magnetic Resonance Research at the University of Minnesota [57,58].
The T1-w sequence provides a better contrast-to-noise ratio in white matter and is used for cortical and subcortical segmentation of the brain [59]. The T2-w sequence allows for the discrimination of structural differences in cerebral fluid-filled regions. The combined use of T1-w/T2-w allows the generation of ratio-based cortical myelin maps [60]. It also provides the anatomical reference for rsMRI and task fMRI data, including anterior–posterior commissure alignment. For improved quality, the T1-w and T2-w protocols include volumetric navigators for prospective motion correction [61]. MP2RAGE achieves a spatially uniform contrast [62] and is used for morphometric analyses of brain anatomy and for construction of cortical myelin maps. DWI enables visualisation and characterisation of white matter tracts [63]. rsfMRI imaging allows for the inference of the intrinsic organization of large-scale brain networks [64].
Throughout the structural, MP2RAGE, and DWI acquisitions, participants watch neutral-valence movies (e.g., nature documentaries on the life of birds); throughout the rsfMRI acquisition, participants are instructed to stay awake, to keep still, and to blink normally while looking at a fixation cross.
Finally, task fMRI analyses can target task-dependent whole-brain activity and functional connectivity [65,66]. Based on our hypotheses, we chose an fMRI task that measures response inhibition as a measure of executive functioning, as inhibitory control is positively associated with cognitive flexibility and problem-solving skills and, thus, represents an important functional domain during brain maturation [67]. In addition, diminished inhibitory control is associated with disorders of impulse control, including ADHD [68,69]. This task is identical to the conditioned approach response inhibition task (CARIT) used in the Human Connectome Project–Development (HCP–D) project [70] and is a variant of the Go/NoGo task. The task allows mapping of differential neuronal activity when response inhibition demands are high (NoGo trials) as compared to free prepotent motor execution (Go trials) [52]. The task uses a rapid event-related fMRI design with jittered inter-trial intervals (1000–4500 mm) and randomized inter-target intervals to optimize statistical efficiency [71]. During each run, participants view shape stimuli (n = 92) and are instructed to press a button as quickly as possible in response to every shape (Go; n = 68) except for a circle and a square (NoGo; n = 24) [70,72].
Region-of-interest measures will be exported from appropriate MRI analysis packages, including FreeSurfer (; accessed on 23 December 2021), Statistical Parametric Mapping (SPM) 2 (, accessed on 23 December 2021), and the Oxford Centre for Functional MRI of the Brain Software Library (FSL;, accessed on 23 December 2021), and analysed in R [36] using logistic regression, with common best statistical practices such as applying corrections for multiple comparisons. Whole-brain measures will be calculated within the MRI analysis packages themselves and statistical tests appropriate to each imaging modality will be applied. For example, for task fMRI data, we will apply a voxel-wise threshold of either p < 0.01 or p < 0.001 and an appropriate cluster-level threshold (p < 0.05, false discovery rate, FDR).

2.9. Air Pollution Exposure Assessment

Three health-relevant air pollutants are considered for this study: PM2.5, PM10, and NO2. Annual and monthly grids of air pollutant concentrations will be created for the study area for the period from 2007 to 2022 using hybrid land use regression (LUR) models, following methods developed by de Hoogh and colleagues [73,74]. Interestingly, the research evaluating the performance of various regression models, regularization techniques, and machine learning methods for air pollution spatial modelling has shown relatively small differences in the prediction of accuracy in air concentration between various algorithms [75].
The LUR models are estimated and validated using measurements from the air quality stations for each of the air pollutants and for each year separately, and will also include monthly LUR models for the prenatal period. Routine measurements of these air pollutants are made by Polish Chief Inspectorate of Environmental Protection via a network of air quality monitoring stations [76]. The locations of the monitoring stations (n = 179) in the study area are illustrated in Figure 1. Measured air pollution concentrations between 2007 and 2019 are depicted in Figure 4.
Predictor variables for the LUR models are summarized in Table 3, and detailed in Supplementary Table S1. We calculate atmospheric dispersion of air pollutants according to meteorological conditions using the methodology described in [77,78], providing 1-h concentrations of pollutants. We use satellite images to inform about land use changes. Data processing is completed with QGIS geographic information system (GIS) software [79].
Land cover predictor variables are calculated for seven girds at the following resolutions: ~4 km × 4 km, 2 km × 2 km, 1 km × 1 km, 500 m × 500 m, 250 m × 250 m, 125 m × 125 m, and 62.5 m × 62.5 m.
Exploratory and correlation analyses of average air pollutant concentrations from the monitoring stations and the predictor variables are performed to assess temporal and spatial distribution patterns. Optimal resolution for each type of variable applied in the LUR air quality modelling is determined based on physical interpretability and supported with statistical analyses, including machine learning. Predictor variables are included in the LUR model only if they adhere to the predefined direction of effect.
Models are validated with out-of-sample and cross-validations methods using data from the monitoring stations. Model assumptions are checked through the analysis of residuals, as well as visual inspection of quantile–quantile plots. Log transformations are tested to minimize the effect of non-normal errors, and p-values are calculated assuming normal distributions of errors. Kriging of air pollution concentrations will be performed, as it is expected to significantly improve model performance of linear regression algorithms for spatial data [75]. All LUR modelling is conducted in R statistical software [36].
Finally, estimates resulting from the LUR models will be assigned to all residential and preschool/school addresses of each study participant. Prenatal, early postnatal, concurrent, and life-long air pollution estimates will be calculated.

2.10. Planned Analyses

The main outcomes resulting from psychological evaluation, behavioural testing, and neuroimaging are listed in Table 4.
We are planning to publish several papers using prenatal, early life, and lifetime PM2.5, PM10, and NO2 as exposures. The papers will be grouped by outcome domain:
  • Structural brain measures
  • Fractional anisotropy
  • Brain measures of inhibitory and attentional functions assessed with task fMRI
  • Resting state connectivity
  • Measures of attention assessed by behavioural tasks
  • ADHD and other externalizing behaviours
  • IQ
Regression analyses will be used to analyse each exposure–outcome pair separately. We will individually assess associations with every pollutant at every exposure window. We will also consider adjusting our models for residuals of co-pollutants, given that PM and NO2 tend to be strongly correlated. Confounder sets will be selected using directed acyclic graphs (DAGs) [80] to avoid overadjustment.

3. Conclusions

The primary objective of the NeuroSmog study is to combine state-of-the-art multimodal neuroimaging, psychological assessment, environmental epidemiology, and air pollution modelling to determine the impact of air pollutants on neurophysiological and behavioural outcomes in healthy children and in atypically developing children diagnosed with ADHD. NeuroSmog is based on an ethnically, racially, and culturally homogeneous Polish population, which reduces potential confounding.
NeuroSmog is the first large-scale study to provide collateral measures of brain structure, function, and connectome, with periodically stratified and lifespan (including prenatal) exposures to air pollutants, and comprehensive child psychological assessments. We use a single MRI scanner and a narrow age range of participants to eliminate multisite acquisition confounding and to reduce epiphenomena related to developmental and hormonal changes in children. Additionally, we have adapted our protocol to fit two other large neuroimaging studies, the ABCD and the HCP-D, and we will make our data openly available to the scientific community providing a rich and easily comparable resource. A particular novelty of NeuroSmog is the inclusion of an fMRI task that directly tests the functioning of neural systems for inhibitory control that are putatively affected by air pollution exposure.
The few neuroimaging air pollution studies conducted to date were done in cities with relatively clean air. Performing this work in southern Poland allows us to investigate the impact of air pollution at concentrations and ranges greater than studied previously, thus increasing the chances of discovering associations, if present. Compared to existing similar studies, the project is unique in terms of its case–control design. Children with ADHD are hypothesized to be more vulnerable to the effects of air pollution. Finally, high-resolution air pollution grids will be produced for the study area using state-of-the-art techniques. We are, thus, confident that the data generated by our project, when combined with appropriate analysis and interpretation, will yield new insights into factors that impact or alter neurodevelopmental trajectories in children.

4. Challenges

The COVID-19 pandemic, and disruptions to work, movement, and education that are associated with it, have greatly complicated the fieldwork component of the study. Even under normal conditions, the logistics of managing fieldwork across multiple centres are challenging. In general, all fieldwork has required more effort than originally anticipated and is taking longer than planned. The pandemic has also impacted negatively on response rates: 50% of contacted schools declined to collaborate and outreach towards the remaining 50% remains challenging.
Use of MRI in research presents additional challenges. Some members of the public distrust MRI and, on occasion, it has been difficult to motivate families of both cases and controls to participate in the study. A mixed attitude towards research involving underaged participants, exacerbated by the recent rise of anti-vaccine sentiment, also poses a challenge.
Although we will be able to assess lifelong exposure to air pollution in our study, we are only able to investigate the associations with a limited number of air pollutants. Our PM measures are gross, and we have no knowledge of its actual composition, which could vary considerably across Poland. There are a host of environmental stressors that could potentially affect neurodevelopment and be associated with air pollutants (and therefore cause confounding). The study would be better able to capture some of the wide array of environmental chemicals which might affect neurodevelopment if we had biological samples. Finally, as with all case–control studies, we anticipate that recall bias may affect our analyses, particularly in terms of early life factors and residences.

Supplementary Materials

The following are available online at, Supplementary File S1: NeuroSmog–General Questionnaire, Supplementary File S2: NeuroSmog–Address Questionnaire; Supplementary Table S1: Predictor variables for the NeuroSmog LUR models.

Author Contributions

I.M. designed the study, conceptualized the fieldwork, and is overseeing it, coordinating and executing the data management, consulting on the air pollution modelling, drafted the first version of the study protocol, revised the draft, and approved the final manuscript. N.O. is coordinating and executing the data management, drafted the first version of the study protocol, revised the draft, and approved the final manuscript. J.G. designed the study and obtained the necessary funds, is consulting on the air pollution modelling, revised the study protocol, and approved the final manuscript. K.K.-M. is responsible for the air pollution modelling, drafted the first version of the study protocol, revised the draft, and approved the final manuscript. M.L. designed the study, conceptualized the fieldwork and is overseeing it, revised the study protocol, and approved the final manuscript. K.S.-W. designed the study, conceptualized the fieldwork and is overseeing it, revised the study protocol, and approved the final manuscript. Y.M. conceptualized the fieldwork and is overseeing it, is coordinating and executing the data management, drafted the first version of the study protocol, revised the draft, and approved the final manuscript. C.B. designed the study, conceptualized the fieldwork and is overseeing it, is coordinating and executing the data management, drafted the first version of the study protocol, revised the draft, and approved the final manuscript. M.W.-Ł. conceptualized the fieldwork and was overseeing it, was coordinating and executing the data management, revised the study protocol, and approved the final manuscript. M.H.S. drafted the first version of the study protocol, revised the draft, and approved the final manuscript. M.C. conceptualized the fieldwork and is overseeing it, is executing the data management, revised the study protocol, and approved the final manuscript. B.I. designed the study, conceptualized the fieldwork and oversees it, revised the study protocol, and approved the final manuscript. K.S. is responsible for the air pollution modelling, revised the study protocol, and approved the final manuscript. A.D. (Anna Degórska) is responsible for the air pollution modelling, revised the study protocol, and approved the final manuscript. J.B. is responsible for the air pollution modelling, revised the study protocol, and approved the final manuscript. B.K. is responsible for the quality control of the MRI data, revised the study protocol, and approved the final manuscript. A.D. (Aleksandra Domagalik) contributed to the design of the MRI acquisition protocol, revised the study protocol, and approved the final manuscript. M.S. is the principal investigator of the study and obtained the necessary funds, designed the study, in particular the behavioural and MRI acquisition protocol, conceptualized the fieldwork and is overseeing it, drafted the first version of the study protocol, revised the draft, and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.


The “NeuroSmog: Determining the impact of air pollution on the developing brain” (Nr. POIR.04.04.00-1763/18-00) project is implemented as part of the TEAM-NET programme of the Foundation for Polish Science, co-financed from EU resources obtained from the European Regional Development Fund under the Smart Growth Operational Programme. The aforementioned funding sources had no involvement in the design of the study, collection, analysis nor interpretation of the data, writing of the report, nor in the decision to submit the manuscript for publication.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethical Committee of the Institute of Psychology, Jagiellonian University, Kraków, Poland (#KE_24042019A). The Clinical Trials Identifier is NCT04574414.

Informed Consent Statement

Written informed consent was obtained from all children involved in the study and their legal guardians.

Data Availability Statement

The data collected in this study will be available on request from the correspondin author. The data are not publicly available due to the local ethical and legal restrictions.


We are very grateful to all of the children and their parents for their participation in the study, partner schools for helping us to reach out to children assigned to the control group, field psychologists for identifying children with ADHD and performing psychological testing on all children, and the study team for technical, logistic, administrative, and communication efforts. We would also like to thank the project’s Advisory Board members—Serena Counsell, Frank J. Kelly, Michał Krzyżanowski, Franck Ramus, Jordi Sunyer, Tomasz Ulanowski, and Wiktor Warchałowski—for their scientific support and advice. Finally, we would also like to thank Kees de Hoogh for assistance with air pollution modelling.

Conflicts of Interest

The authors declare that they have no conflicts of interest.


ABCDAdolescent Brain Child Development study
ACCAnterior cingulate cortex
ADHDAttention deficit hyperactivity disorder
ANTAttention network test
ASDAutism spectrum disorder
BDOT10kBaza Danych Obiektów Topograficznych (Database of Topographic Objects) at accuracy level 1:10,000
BOLDBlood oxygen level dependent effect
CARITConditioned approach response inhibition task
CBCLChild Behaviour Checklist
CIConfidence interval
CPTContinuous performance test
DAGDirected acyclic graph
DPLFCDorsolateral prefrontal cortex
DWIDiffusion-weighted imaging
EPIEcho planar imaging
EUEuropean Union
FDRFalse discovery rate
FOVField of view
GISGeographic information system
fMRIFunctional magnetic resonance imaging
HCP–DHuman Connectome Project–Development
IQIntelligence quotient
ISIInterstimulus interval
KRRKwestionariusz Relacji z Rodzeństwem W Okresie Adolescencji (Syblings Relationship Questionnaire)
LURLand use regression model
MP2RAGEMagnetization-prepared 2 rapid acquisition gradient echo
MRIMagnetic resonance imaging
NIHNational Institute of Health
NO2Nitrogen dioxide
NODDINeurite orientation dispersion and density
PMParticulate matter
PM2.5Particulate matter with aerodynamic diameter <2.5 µm
PM10Particulate matter with aerodynamic diameter <10 µm
PU1Bateria diagnozy funkcji poznawczych PU1: Pamięć—uwaga—funkcje wykonawcze (Diagnostic Battery for Cognitive Functions Evaluation)
rsfMRIResting-state functional magnetic resonance imaging
SB5Stanford-Binet Intelligence Scales, 5th edition
SESSocio-economic status
SMASupplementary motor area
SORSkala Oceny Rodziny (Family Adaptation and Cohesion Evaluation Scales (FACES IV))
SPMStatistical parametric mapping
TIInversion time
TRRepetition time
TEEcho time
WHOWorld Health Organization
YSRYouth Self-Report


  1. Manisalidis, I.; Stavropoulou, E.; Stavropoulos, A.; Bezirtzoglou, E. Environmental and Health Impacts of Air Pollution: A Review. Front. Public Health 2020, 8, 14. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. World Health Organization. Preventing Disease through Health Environments: A Global Assessment of the Burden of Disease from Environmental Risks; Prüss-Üstün, A., Wolf, J., Corvalán, C., Bos, R., Neira, M., Eds.; World Health Organization: Paris, France, 2016; ISBN 978 92 4 156519 6. Available online: (accessed on 23 December 2021).
  3. Cohen, A.J.; Brauer, M.; Burnett, R.; Anderson, H.R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R.; et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907–1918. [Google Scholar] [CrossRef][Green Version]
  4. Al-Kindi, S.G.; Brook, R.D.; Biswal, S.; Rajagopalan, S. Environmental determinants of cardiovascular disease: Lessons learned from air pollution. Nat. Rev. Cardiol. 2020, 17, 656–672. [Google Scholar] [CrossRef]
  5. Rajagopalan, S.; Al-Kindi, S.G.; Brook, R.D. Air Pollution and Cardiovascular Disease: JACC State-of-the-Art Review. J. Am. Coll. Cardiol. 2018, 72, 2054–2070. [Google Scholar] [CrossRef]
  6. Bowatte, G.; Lodge, C.; Lowe, A.J.; Erbas, B.; Perret, J.; Abramson, M.J.; Matheson, M.; Dharmage, S.C. The influence of childhood traffic-related air pollution exposure on asthma, allergy and sensitization: A systematic review and a meta-analysis of birth cohort studies. Allergy 2015, 70, 245–256. [Google Scholar] [CrossRef]
  7. Lu, C.; Norbäck, D.; Li, Y.; Deng, Q. Early-life exposure to air pollution and childhood allergic diseases: An update on the link and its implications. Expert Rev. Clin. Immunol. 2020, 16, 813–827. [Google Scholar] [CrossRef]
  8. Yin, P.; Brauer, M.; Cohen, A.; Burnett, R.T.; Liu, J.; Liu, Y.; Liang, R.; Wang, W.; Qi, J.; Wang, L.; et al. Long-term Fine Particulate Matter Exposure and Nonaccidental and Cause-specific Mortality in a Large National Cohort of Chinese Men. Environ. Health Perspect. 2017, 125, 117002. [Google Scholar] [CrossRef]
  9. Yu, K.; Qiu, G.; Chan, K.H.; Lam, K.B.H.; Kurmi, O.P.; Bennett, D.A.; Yu, C.; Pan, A.; Lv, J.; Guo, Y.; et al. Association of Solid Fuel Use with Risk of Cardiovascular and All-Cause Mortality in Rural China. JAMA 2018, 319, 1351–1361. [Google Scholar] [CrossRef]
  10. Achilleos, S.; Kioumourtzoglou, M.-A.; Wu, C.-D.; Schwartz, J.D.; Koutrakis, P.; Papatheodorou, S.I. Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis. Environ. Int. 2017, 109, 89–100. [Google Scholar] [CrossRef]
  11. Bové, H.; Bongaerts, E.; Slenders, E.; Bijnens, E.M.; Saenen, N.D.; Gyselaers, W.; Van Eyken, P.; Plusquin, M.; Roeffaers, M.B.J.; Ameloot, M.; et al. Ambient black carbon particles reach the fetal side of human placenta. Nat. Commun. 2019, 10, 3866. [Google Scholar] [CrossRef] [PubMed]
  12. Lu, C.; Zhang, W.; Zheng, X.; Sun, J.; Chen, L.; Deng, Q. Combined effects of ambient air pollution and home environmental factors on low birth weight. Chemosphere 2020, 240, 124836. [Google Scholar] [CrossRef] [PubMed]
  13. Myhre, O.; Låg, M.; Villanger, G.D.; Oftedal, B.; Øvrevik, J.; Holme, J.A.; Aase, H.; Paulsen, R.E.; Bal-Price, A.; Dirven, H. Early life exposure to air pollution particulate matter (PM) as risk factor for attention deficit/hyperactivity disorder (ADHD): Need for novel strategies for mechanisms and causalities. Toxicol. Appl. Pharm. 2018, 354, 196–214. [Google Scholar] [CrossRef]
  14. Calderón-Garcidueñas, L.; Torres-Jardón, R.; Kulesza, R.J.; Mansour, Y.; González-González, L.O.; Gónzalez-Maciel, A.; Reynoso-Robles, R.; Mukherjee, P.S. Alzheimer disease starts in childhood in polluted Metropolitan Mexico City. A major health crisis in progress. Environ. Res. 2020, 183, 109137. [Google Scholar] [CrossRef]
  15. Maher, B.A.; Ahmed, I.A.M.; Karloukovski, V.; MacLaren, D.A.; Foulds, P.G.; Allsop, D.; Mann, D.M.A.; Torres-Jardón, R.; Calderon-Garciduenas, L. Magnetite pollution nanoparticles in the human brain. Proc. Natl. Acad. Sci. USA 2016, 113, 10797–10801. [Google Scholar] [CrossRef] [PubMed][Green Version]
  16. Fu, P.; Bai, L.; Cai, Z.; Li, R.; Yung, K.K.L. Fine particulate matter aggravates intestinal and brain injury and affects bacterial community structure of intestine and feces in Alzheimer’s disease transgenic mice. Ecotoxicol. Environ. Saf. 2020, 192, 110325. [Google Scholar] [CrossRef]
  17. Cserbik, D.; Chen, J.-C.; McConnell, R.; Berhane, K.; Sowell, E.R.; Schwartz, J.; Hackman, D.; Kan, E.; Fan, C.C.; Herting, M.M. Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure. Environ. Int. 2020, 143, 105933. [Google Scholar] [CrossRef]
  18. Nephew, B.C.; Nemeth, A.; Hudda, N.; Beamer, G.; Mann, P.; Petitto, J.; Cali, R.; Febo, M.; Kulkarni, P.; Poirier, G.; et al. Traffic-related particulate matter affects behavior, inflammation, and neural integrity in a developmental rodent model. Environ. Res. 2020, 183, 109242. [Google Scholar] [CrossRef]
  19. Araújo, J.E.; Jorge, S.; Santos, H.M.; Chiechi, A.; Galstyan, A.; Lodeiro, C.; Diniz, M.; Kleinman, M.T.; Ljubimova, J.Y.; Capelo, J.L. Proteomic changes driven by urban pollution suggest particulate matter as a deregulator of energy metabolism, mitochondrial activity, and oxidative pathways in the rat brain. Sci. Total Environ. 2019, 687, 839–848. [Google Scholar] [CrossRef] [PubMed]
  20. Di Domenico, M.; Benevenuto, S.G.D.M.; Tomasini, P.P.; Yariwake, V.Y.; Alves, N.D.O.; Rahmeier, F.L.; Fernandes, M.D.C.; Moura, D.J.; Saldiva, P.H.N.; Veras, M.M. Concentrated ambient fine particulate matter (PM2.5) exposure induce brain damage in pre and postnatal exposed mice. Neurotoxicology 2020, 79, 127–141. [Google Scholar] [CrossRef] [PubMed]
  21. Herting, M.M.; Younan, D.; Campbell, C.; Chen, J.-C. Outdoor Air Pollution and Brain Structure and Function from Across Childhood to Young Adulthood: A Methodological Review of Brain MRI Studies. Front. Public Health 2019, 7, 332. [Google Scholar] [CrossRef][Green Version]
  22. Guxens, M.; Lubczyńska, M.J.; Muetzel, R.L.; Dalmau-Bueno, A.; Jaddoe, V.W.; Hoek, G.; van der Lugt, A.; Verhulst, F.C.; White, T.; Brunekreef, B.; et al. Air Pollution Exposure During Fetal Life, Brain Morphology, and Cognitive Function in School-Age Children. Biol. Psychiatry 2018, 84, 295–303. [Google Scholar] [CrossRef] [PubMed][Green Version]
  23. Mortamais, M.; Pujol, J.; Martínez-Vilavella, G.; Fenoll, R.; Reynes, C.; Sabatier, R.; Rivas, I.; Forns, J.; Vilor-Tejedor, N.; Alemany, S.; et al. Effects of prenatal exposure to particulate matter air pollution on corpus callosum and behavioral problems in children. Environ. Res. 2019, 2019178, 108734. [Google Scholar] [CrossRef] [PubMed]
  24. Pujol, J.; Martínez-Vilavella, G.; Macià, D.; Fenoll, R.; Alvarez-Pedrerol, M.; Rivas, I.; Forns, J.; Blanco-Hinojo, L.; Capellades, J.; Querol, X.; et al. Traffic pollution exposure is associated with altered brain connectivity in school children. Neuroimage 2016, 129, 175–184. [Google Scholar] [CrossRef][Green Version]
  25. Loftus, C.T.; Hazlehurst, M.F.; Szpiro, A.A.; Ni, Y.; Tylavsky, F.A.; Bush, N.R.; Sathyanarayana, S.; Carroll, K.N.; Karr, C.J.; LeWinn, K.Z. Prenatal air pollution and childhood IQ: Preliminary evidence of effect modification by folate. Environ. Res. 2019, 176, 108505. [Google Scholar] [CrossRef] [PubMed]
  26. Aghaei, M.; Janjani, H.; Yousefian, F.; Jamal, A.; Yunesian, M. Association between ambient gaseous and particulate air pollutants and attention deficit hyperactivity disorder (ADHD) in children; a systematic review. Environ. Res. 2019, 173, 135–156. [Google Scholar] [CrossRef]
  27. Annesi-Maesano, I. The air of Europe: Where are we going? Eur. Respir. Rev. 2017, 26, 170024. [Google Scholar] [CrossRef][Green Version]
  28. Guerreiro, C.B.; Foltescu, V.; De Leeuw, F. Air quality status and trend in Europe. Atmos. Environ. 2014, 98, 376–384. [Google Scholar] [CrossRef][Green Version]
  29. Sicard, P.; Agathokleous, E.; De Marco, A.; Paoletti, E.; Calatayud, V. Urban population exposure to air pollution in Europe over the last decades. Environ. Sci. Eur. 2021, 33, 28. [Google Scholar] [CrossRef]
  30. Khomenko, S.; Cirach, M.; Pereira-Barboza, E.; Mueller, N.; Barrera-Gómez, J.; Rojas-Rueda, D.; de Hoogh, K.; Hoek, G.; Nieuwenhuijsen, M. Premature mortality due to air pollution in European cities: A health impact assessment. Lancet Planet Health 2021, 5, e121–e134. [Google Scholar] [CrossRef]
  31. Raczyk, P.; Gruszecka-Kosowska, A. The Condition of Air Pollution in Krakow, Poland, in 2005–2020, with Health Risk Assessment. Int. J. Environ. Res. Public Health 2020, 17, 6063. [Google Scholar] [CrossRef]
  32. Badyda, A.J.; Grellier, J.; Dąbrowiecki, P. Ambient PM2.5 Exposure and Mortality Due to Lung Cancer and Cardiopulmonary Diseases in Polish Cities. Adv. Exp. Med. Biol. 2017, 944, 9–17. [Google Scholar] [PubMed]
  33. Holnicki, P.; Tainio, M.; Kałuszko, A.; Nahorski, Z. Burden of Mortality and Disease Attributable to Multiple Air Pollutants in Warsaw, Poland. Int. J. Environ. Res. Public Health 2017, 14, 1359. [Google Scholar] [CrossRef] [PubMed][Green Version]
  34. European Environment Agency. European Air Quality Maps for 2018—PM10, PM2.5, Ozone, NO2 and NOx Spatial Estimates and Their Uncertainties (Eionet Report—ETC/ATNI 2020/10). Available online: https// (accessed on 23 December 2021).
  35. World Health Organization. International Classification of Diseases for Mortality and Morbidity Statistics (11th revision). Available online: (accessed on 23 December 2021).
  36. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013; Available online: https// (accessed on 17 December 2021).
  37. Dzhambov, A.; Hartig, T.; Markevych, I.; Tilov, B.; Dimitrova, D. Urban residential greenspace and mental health in youth: Different approaches to testing multiple pathways yield different conclusions. Environ. Res. 2018, 160, 47–59. [Google Scholar] [CrossRef] [PubMed]
  38. Dzhambov, A.M.; Markevych, I.; Hartig, T.; Tilov, B.; Arabadzhiev, Z.; Stoyanov, D.; Gatseva, P.; Dimitrova, D.D. Multiple pathways link urban green- and bluespace to mental health in young adults. Environ. Res. 2018, 166, 223–233. [Google Scholar] [CrossRef]
  39. Heinrich, J.; Brüske, I.; Cramer, C.; Hoffmann, U.; Schnappinger, M.; Schaaf, B.; Von Berg, A.; Berdel, D.; Krämer, U.; Lehmann, I.; et al. GINIplus and LISAplus—Design and selected results of two German birth cohorts about natural course of atopic diseases and their determinants. Allergol. Sel. 2017, 1, 85–95. [Google Scholar] [CrossRef]
  40. Kuiper, I.N.; Markevych, I.; Accordini, S.; Bertelsen, R.J.; Bråbäck, L.; Christensen, J.H.; Forsberg, B.; Halvorsen, T.; Heinrich, J.; Hertel, O.; et al. Associations of Preconception Exposure to Air Pollution and Greenness with Offspring Asthma and Hay Fever. Int. J. Environ. Res. Public Health 2020, 17, 5828. [Google Scholar] [CrossRef]
  41. Roid, G.H.; Jurek, P.; Olech, P.; Sajewicz-Radtke, U.; Radtke, B.M. Skale Inteligencji Stanford-Binet, Edycja Piąta. Podręcznik Diagnosty. In Stanford-Binet Intelligence Scales, 5th ed.; Diagnostic manual; Pracownia Testów Psychologicznych i Pedagogicznych (PTPiP): Gdańsk, Poland, 2017. [Google Scholar]
  42. Borkowska, A.R.; Sajewicz-Radtke, U.; Lipowska, M.; Kalka, D. Bateria diagnozy funkcji poznawczych PU1. Pamięć—uwaga—funkcje wykonawcze. In Diagnostic Battery for Cognitive Functions Evaluation: Memory—Attention—Executive Functions; Pracownia Testów Psychologicznych i Pedagogicznych (PTPiP): Gdańsk, Poland, 2015. [Google Scholar]
  43. Holmes, J.; Guy, J.; Kievit, R.A.; Bryant, A.; Mareva, S.; Team, C.; Gathercole, S.E. Cognitive dimensions of Learning in Children with Problems in Attention, Learning, and Memory. J. Educ. Psychology 2020, 113, 1454–1480. [Google Scholar] [CrossRef]
  44. Wrocławska-Warchał, E.; Wujcik, R. Conners 3—Questionnaires for ADHD Assessment; Western Psychological Services: Los Angeles, CA, USA, 2018. [Google Scholar]
  45. Achenbach, T.M.; Rescorla, L.A. Multicultural Understanding of Child and Adolescent Psychopathology: Implications for Mental Health Assessment; Guilford Press: New York, NY, USA, 2007. [Google Scholar]
  46. Wolańczyk, T. Zaburzenia Emocjonalne i Behawioralne u Dzieci i Młodzieży Szkolnej w Polsce; Rozprawa Habilitacyjna; Akademia Medyczna w Warszawie: Warsaw, Poland, 2002. [Google Scholar]
  47. Achenbach, T.M. The Achenbach System of Empirically Based Assessment (ASEBA): Development, Findings, Theory, and Applications; University of Vermont Research Center for Children, Youth, and Families: Burlington, VT, USA, 2009. [Google Scholar]
  48. Margasinski, A. The Polish Adaptation of FACES EV-SOR. Pol. J. Appl. Psychol. 2015, 13, 43–66. [Google Scholar] [CrossRef][Green Version]
  49. Lewandowska-Walter, A.; Połomski, P.; Peplińska, A. Kwestionariusz Relacji z Rodzeństwem W Okresie Adolescencji [Questionnaire of Relationships with Siblings for Adolescents]; Pracownia Testów Psychologicznych i Pedagogicznych (PTPiP): Gdańsk, Poland, 2016. [Google Scholar]
  50. Rueda, M.R.; Fan, J.; McCandliss, B.D.; Halparin, J.D.; Gruber, D.B.; Lercari, L.P.; I Posner, M. Development of attentional networks in childhood. Neuropsychologia 2004, 42, 1029–1040. [Google Scholar] [CrossRef]
  51. Fan, J.; McCandliss, B.D.; Sommer, T.; Raz, A.; Posner, M.I. Testing the efficiency and independence of attentional networks. J. Cogn. Neurosci. 2002, 14, 340–347. [Google Scholar] [CrossRef]
  52. Conners, C.K.; Sitarenios, G.; Ayearst, L.E. Conners’ Continuous Performance Test, 3rd ed.; Encyclopedia of Clinical Neuropsychology; Kreutzer, J.S., DeLuca, J., Caplan, B., Eds.; Springer: New York, NY, USA, 2018; Available online: (accessed on 23 December 2021).
  53. Seidel, W.T.; Joschko, M. Evidence of difficulties in sustained attention in children with ADDH. J. Abnorm. Child Psychol. 1990, 18, 217–229. [Google Scholar] [CrossRef] [PubMed]
  54. Conners, C.K.; Epstein, J.N.; Angold, A.; Klaric, J. Continuous performance test performance in a normative epidemiological sample. J. Abnorm. Child Psychol. 2003, 31, 555–562. [Google Scholar] [CrossRef] [PubMed]
  55. Shaked, D.; Faulkner, L.M.D.; Tolle, K.; Wendell, C.R.; Waldstein, S.R.; Spencer, R.J. Reliability and validity of the Conners’ Continuous Performance Test. Appl. Neuropsychol. Adult 2020, 27, 478–487. [Google Scholar] [CrossRef] [PubMed]
  56. Casey, B.j.; Cannonier, T.; Conley, M.I.; Cohen, A.O.; Barch, D.M.; Heitzeg, M.m.; Soules, M.E.; Teslovich, T.; Dellarco, D.V.; Garavan, H.; et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 2018, 32, 43–54. [Google Scholar] [CrossRef]
  57. Xu, J.; Moeller, S.; Auerbach, E.J.; Strupp, J.; Smith, S.M.; Feinberg, D.A.; Yacoub, E.; Uğurbil, K. Evaluation of slice accelerations using multiband echo planar imaging at 3 T. Neuroimage 2013, 83, 991–1001. [Google Scholar] [CrossRef] [PubMed][Green Version]
  58. Xu, J.; Moeller, S.; Auerbach, E.; Strupp, J.; Smith, S.; Feinberg, D.A.; Yacoub, E.; Uğurbil, K. Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS ONE 2010, 5, e15710. [Google Scholar]
  59. van der Kouwe, A.J.; Benner, T.; Salat, D.H.; Fischl, B. Brain morphometry with multiecho MPRAGE. NeuroImage 2008, 40, 559–569. [Google Scholar] [CrossRef][Green Version]
  60. Glasser, M.F.; Van Essen, D.C. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 2011, 31, 11597–11616. [Google Scholar] [CrossRef][Green Version]
  61. Tisdall, M.D.; Hess, A.; Reuter, M.; Meintjes, E.; Fischl, B.; van der Kouwe, A.J.W. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn. Reson Med. 2012, 68, 389–399. [Google Scholar] [CrossRef][Green Version]
  62. Marques, J.P.; Kober, T.; Krueger, G.; van der Zwaag, W.; Van de Moortele, P.-F.; Gruetter, R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 2010, 49, 1271–1281. [Google Scholar] [CrossRef][Green Version]
  63. Assaf, Y.; Pasternak, O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: A review. J. Mol. Neurosci. 2008, 34, 51–61. [Google Scholar] [CrossRef] [PubMed]
  64. Buckner, R.L.; Krienen, F.M.; Yeo, B.T. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci. 2013, 16, 832–837. [Google Scholar] [CrossRef] [PubMed]
  65. Friston, K.J. Modalities, modes, and models in functional neuroimaging. Science 2009, 326, 399–403. [Google Scholar] [CrossRef][Green Version]
  66. Logothetis, N.K. What we can do and what we cannot do with fMRI. Nature 2008, 453, 869–878. [Google Scholar] [CrossRef] [PubMed]
  67. Dowsett, S.M.; Livesey, D.J. The development of inhibitory control in preschool children: Effects of “executive skills” training. Dev. Psychobiol. 2000, 36, 161–174. [Google Scholar] [CrossRef]
  68. Eme, R.F. Sex differences in child-onset, life-course-persistent conduct disorder: A review of biological influences. Clin. Psychol. Rev. 2007, 27, 607–627. [Google Scholar] [CrossRef]
  69. Romer, D.; Betancourt, L.; Giannetta, J.M.; Brodsky, N.L.; Farah, M.; Hurt, H. Executive cognitive functions and impulsivity as correlates of risk taking and problem behavior in preadolescents. Neuropsychologia 2009, 47, 2916–2926. [Google Scholar] [CrossRef] [PubMed][Green Version]
  70. Somerville, L.H.; Bookheimer, S.Y.; Buckner, R.L.; Burgess, G.C.; Curtiss, S.W.; Dapretto, M.; Elam, J.S.; Gaffrey, M.S.; Harms, M.; Hodge, C.; et al. The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds. Neuroimage 2018, 183, 456–468. [Google Scholar] [CrossRef]
  71. Dale, A.M. Optimal experimental design for event-related fMRI. Hum. Brain Mapp. 1999, 8, 109–114. [Google Scholar] [CrossRef]
  72. Newman, S.D.; Hansen, M.T.; Gutierrez, A. Gutierrez. An fMRI Study of the Impact of Block Building and Board Games on Spatial Ability. Front. Psychol. 2016, 7, 1278. [Google Scholar] [CrossRef][Green Version]
  73. De Hoogh, K.; Wang, M.; Adam, M.; Badaloni, C.; Beelen, R.; Birk, M.; Cesaroni, G.; Cirach, M.; Declercq, C.; Dėdelė, A.; et al. Development of land use regression models for particle composition in twenty study areas in Europe. Environ. Sci. Technol. 2013, 47, 5778–5786. [Google Scholar] [CrossRef]
  74. De Hoogh, K.; Chen, J.; Gulliver, J.; Hoffmann, B.; Hertel, O.; Ketzel, M.; Bauwelinck, M.; van Donkelaar, A.; Hvidtfeldt, U.A.; Katsouyanni, K.; et al. Spatial PM2.5, NO2, O3 and BC models for Western Europe—Evaluation of spatiotemporal stability. Environ. Int. 2018, 120, 81–92. [Google Scholar] [CrossRef] [PubMed]
  75. Chen, J.; de Hoogh, K.; Gulliver, J.; Hoffmann, B.; Hertel, O.; Ketzel, M.; Bauwelinck, M.; van Donkelaar, A.; Hvidtfeldt, U.A.; Katsouyanni, K.; et al. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environ. Int. 2019, 130, 104934. [Google Scholar] [CrossRef]
  76. Air Quality Portal. Chief Inspectorate of Environmental Protection. Available online: https// (accessed on 23 December 2021).
  77. Werner, M.; Kryza, M.; Wałaszek, K. Emission projections and limit values of air pollution concentration—A case study using the EMEP4PL model. Int. J. Environ. Pollut. 2019, 65, 164–176. [Google Scholar] [CrossRef]
  78. Werner, M.; Kryza, M.; Wałaszek, K.; Vieno, M.; Dore, A.J. EMEP4PL and WRF-Chem—Evaluation of the Modelling Results. In Air Pollution Modeling and Its Application XXV: ITM 2016 Springer Proceedings in Complexity; Mensink, C., Kallos, G., Eds.; Springer: Cham, Germany, 2018; Available online: (accessed on 23 December 2021).
  79. QGIS Development Team; QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available online: (accessed on 23 December 2021).
  80. Textor, J.B.; van der Zander, M.K. Gilthorpe, M. Liskiewicz; G.T.H. Ellison. Robust causal inference using directed acyclic graphs: The R package ‘dagitty’. Int. J. Epidemiol. 2016, 45, 1887–1894. [Google Scholar] [PubMed][Green Version]
Figure 1. NeuroSmog study towns coloured according to their population-weighted median PM2.5 (2015) levels, as well as the locations of the air-monitoring stations used for air pollution modelling (blue dots), and location of Poland on the map of Europe (study area highlighted by a black square).
Figure 1. NeuroSmog study towns coloured according to their population-weighted median PM2.5 (2015) levels, as well as the locations of the air-monitoring stations used for air pollution modelling (blue dots), and location of Poland on the map of Europe (study area highlighted by a black square).
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Figure 2. Flow chart describing the progress in the recruitment of controls at the end of the Year 1 (from October 2020 to June 2021).
Figure 2. Flow chart describing the progress in the recruitment of controls at the end of the Year 1 (from October 2020 to June 2021).
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Figure 3. Town-level control-case-specific progress of testing children in the project as of 9 September 2021.
Figure 3. Town-level control-case-specific progress of testing children in the project as of 9 September 2021.
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Figure 4. Boxplots illustrating annual means of daily measured NO2, PM10, and PM2.5 air pollutants, grouped by type of station (urban, traffic, rural) from 2007 to 2019, based on monitoring stations in the study area. Between 2007 and 2019, the annual mean values of air pollutant concentrations at the monitoring stations ranged from 4.5 to 73.1 µg/m3 for NO2, from 11.2 to 80.9 µg/m3 for PM10, and from 16.1 to 61.1 µg/m3 for PM2.5.
Figure 4. Boxplots illustrating annual means of daily measured NO2, PM10, and PM2.5 air pollutants, grouped by type of station (urban, traffic, rural) from 2007 to 2019, based on monitoring stations in the study area. Between 2007 and 2019, the annual mean values of air pollutant concentrations at the monitoring stations ranged from 4.5 to 73.1 µg/m3 for NO2, from 11.2 to 80.9 µg/m3 for PM10, and from 16.1 to 61.1 µg/m3 for PM2.5.
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Table 1. NeuroSmog study towns by type.
Table 1. NeuroSmog study towns by type.
Air Pollution LevelPopulation Size
Nowy Targ
Strzelce Opolskie
Table 2. Neuroimaging scanning parameters.
Table 2. Neuroimaging scanning parameters.
SequenceMatrixSlicesFOV% FOV PhaseResolution (mm)TR (ms)TE (ms)TI (ms)Flip Angle (deg)Parallel ImagingMulti Band
Phase Partial FourierDiffusion Directionsb-ValuesAcquisition Time
T1-w256 × 256176256 × 256100%1.0 × 1.0 × 1.025002.8810608OffOffN/AN/A06:09
T2-w256 × 256176256 × 256100%1.0 × 1.0 × 1.03200565N/AVariableOffOffN/AN/A05:34
fMRI90 × 9060216 × 216100%2.4 × 2.4 × 2.480030N/A52Off6OffN/AN/A2 × 4.11 (task fMRI) 2 × 6.08 (rsfMRI)
DWI104 × 10472210 × 210100%2.0 × 2.0 × 2.03800101N/A78Off36/81170 (10 dirs)
500 (18-dirs)
1250 (36-dirs)
2500 (53-dirs)
MP2RAGE256 × 256176256 × 256100%1.0 × 1.0 × 1.05000370043xOffOffN/AN/A08:22
DWI—Diffusion-weighted imaging; fMRI—Functional magnetic resonance imaging; FOV—field of view; MP2RAGE—Magnetization-prepared 2 rapid acquisition gradient echo; rsfMRI—Resting-state functional magnetic resonance imaging; T1-w—T1-weighted; T2-w—T2-weighted; TE—echo time; TI—inversion time; TR—repetition time.
Table 3. Predictor variables for the air quality LUR modelling.
Table 3. Predictor variables for the air quality LUR modelling.
TypeSource Main IndicatorsCharacteristics
EmissionsNational Centre for Emissions Management [76], regional data from the voivodeship authorities, emission sector mapsTraffic emissions of air pollutants
Residential emission of air pollutants
Daily data available, monthly/annual means incorporated in LUR
Land useCorine Land Cover Forest and wooded area
Residential area
Surface water
Vegetation and agricultural area
Area under roads, rail, and airport roads
Unused land, landfill, excavation
Remaining, undeveloped area
Annual data collection years: 2006, 2012, 2018
Road dataDatabase of Topographic Objects, 1:10, 000 scale, nationwide (BDOT10k) Type of road (e.g., highway, expressway, main road, etc.)
Number of traffic lanes within one road
Data collection from years 2019 to 2020
Air qualityAtmospheric dispersion models [74]Estimates from the dispersion modelling Hourly data available,
monthly/yearly means incorporated in LUR
Meteorological conditionsInstitute of Meteorology and Water Management National Research InstituteTemperature
Wind speed and direction
Relative humidity
Atmospheric pressure
Hourly data available, monthly/yearly means incorporated in LUR
BDOT10k—Baza Danych Obiektów Topograficznych (Database of Topographic Objects) at accuracy level 1:10,000; LUR—land use regression model.
Table 4. List of the main outcomes of the study.
Table 4. List of the main outcomes of the study.
ToolMain Outcomes
T1-w and T2-wVolume of subcortical structures
Cortical grey matter thickness and surface
DWIFractional Anisotropy in regions of interest (tractography)
Task fMRIBOLD activation in the NoGo > Go contrast in the CARIT
Amplitude of BOLD signal change between task-related and default-mode-network activations
Resting-state connectivityFunctional connectivity in regions of interest
T1-w, T2-w, and MP2RAGECortical myelin content
Behavioural tasks
CPT and CARITOmission errors
Commission errors
Mean reaction time
Standard deviation of reaction time
ANTMean reaction time
Alerting network
Orienting network
Executive network
Psychological evaluation
CBCL and YSRTotal problems
Internalizing problems
Externalizing problems
Somatic complaints
Social problems
Thought problems
Attention problems
Rule-breaking behaviour
Aggressive behaviour
SB5General IQ
Verbal IQ
Non-verbal IQ
PU1Selective attention
Memory–phonological loop
Visual–spatial memory
Executive functions
Opinions of assessing psychologists, Conners 3, PU1, SB5, CBCL, and validationADHD diagnosis
ADHD—Attention deficit hyperactivity disorder; ANT—attention network test; BOLD—Blood oxygen level dependent effect; CARIT—Conditioned approach response inhibition task; CBCL—Child Behaviour Checklist; CPT—Continuous performance test; DWI—CDiffusion-weighted imaging; fMRI—Functional magnetic resonance imaging; IQ—Intelligence quotient; MP2RAGE—Magnetization-prepared 2 rapid acquisition gradient echo; NODDI—Neurite orientation dispersion and density; PU1—Bateria diagnozy funkcji poznawczych PU1: Pamięć—uwaga—funkcje wykonawcze (Diagnostic Battery for Cognitive Functions Evaluation); SB5—Stanford-Binet Intelligence Scales, 5th edition; T1-w—T1-weighted; T2-w—T2-weighted; YSR—Youth Self-Report.
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Markevych, I.; Orlov, N.; Grellier, J.; Kaczmarek-Majer, K.; Lipowska, M.; Sitnik-Warchulska, K.; Mysak, Y.; Baumbach, C.; Wierzba-Łukaszyk, M.; Soomro, M.H.; et al. NeuroSmog: Determining the Impact of Air Pollution on the Developing Brain: Project Protocol. Int. J. Environ. Res. Public Health 2022, 19, 310.

AMA Style

Markevych I, Orlov N, Grellier J, Kaczmarek-Majer K, Lipowska M, Sitnik-Warchulska K, Mysak Y, Baumbach C, Wierzba-Łukaszyk M, Soomro MH, et al. NeuroSmog: Determining the Impact of Air Pollution on the Developing Brain: Project Protocol. International Journal of Environmental Research and Public Health. 2022; 19(1):310.

Chicago/Turabian Style

Markevych, Iana, Natasza Orlov, James Grellier, Katarzyna Kaczmarek-Majer, Małgorzata Lipowska, Katarzyna Sitnik-Warchulska, Yarema Mysak, Clemens Baumbach, Maja Wierzba-Łukaszyk, Munawar Hussain Soomro, and et al. 2022. "NeuroSmog: Determining the Impact of Air Pollution on the Developing Brain: Project Protocol" International Journal of Environmental Research and Public Health 19, no. 1: 310.

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