Association between Air Quality and Children’s Restorative Experience: A Systematic Review
Abstract
:1. Introduction
1.1. Background
1.2. Objectives
- What past literature has focused on the air pollution exposure on children’s restorative experience, including psychological health outcomes, attention, stress, anxiety, etc.?
- Which tools and assessments have been applied to examine the impact of air pollution exposure on children’s restorative experience?
- What are the barriers in research of air pollution exposure and children’s restorative experience?
2. Previous Literature Related to Children’s Restorative Experience: A Review
3. Materials and Methods
3.1. Analytical Framework
3.2. Paper Selection for Literature Review
3.3. Inclusion and Exclusion Criteria
4. Results
4.1. Outdoor Air Pollutants on Children’s Restorative Experience
4.2. Indoor Air Pollutants on Children’s Restorative Experience
4.3. Children’s Restorative Outcome
4.4. Barriers in Air Quality Studies in Children’s Restorative Experience
4.4.1. Data from the Literature Review
4.4.2. Categories of Barriers
4.4.3. Determining the Relationships between Barriers
4.4.4. Network Modeling
4.4.5. Analysis of Network Model
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author and Year | Methodology | Description | Findings | Outcome Measurements |
---|---|---|---|---|
van den Berg and Van den Berg (2011) [9] | Qualitative and Quantitative | This study examines the influence of contact with nature on children with ADHD comparing natural and built settings. | Nature provides a positive restorative environment for children with ADHD. | Attention, Stress |
Collado and Staats (2016) [18] | Literature review | This article offers an overview of current restorative research with children. | Exposure to nature has restorative benefits to children. | / |
Korpela et al. (2002) [19] | Qualitative and Quantitative | This study examined the role of restorative experience and self-regulation in the formation of children’s place preferences. | Children like to use their favorite places for restoration and emotion-regulation. | Emotion |
Amicone et al. (2018) [10] | Quantitative: Pre- and post-test | This article conducted two field studies to test the hypothesized positive effect of recess time spent in a natural (vs. built) environment on pupils’ cognitive performance and their perceived restorativeness. | There is an increase in sustained attention after the natural environment condition and a decrease after the built environment condition. | Attention |
Bagot (2004) [20] | Quantitative | This study uses school playgrounds and their school library to examine perceived restorative components for children. | School playgrounds have higher restoration potential than school libraries. | Mental health |
Bagot, Allen, and Toukhsati (2015) [21] | Quantitative | This study examines the predictors of restorativeness of children’s school playgrounds, using Attention Restoration Theory. | Level of naturalness impacts children’s restorative experience, which is different from that of adults. | Stress, Restorative |
Collado and Corraliza (2015) [22] | Quantitative | This study explores the relation between exposure to nature and environmental behaviors. | Exposure to nature could improve children’s restorative experience, resulting in pro-environmental behaviors. | Restoration, Attention |
Corraliza et al. (2012) [23] | Quantitative | This study used Perceived Restoration Components Scale for Children (PRCS-C) to analyze the impact of nature in school playgrounds on children’s perceived restoration. | Including nature in school playgrounds has a positive effect on children’s perceived restoration. | Restoration, Attention |
Kelz et al. (2015) [8] | Mixed methods: A pre–post, quasi-experimental design | This study investigated the influence of a redesign (greening) of a schoolyard on pupils’ physiological stress, psychological well-being, and executive functioning. | Students in the renovated schoolyard setting perceived the environment as more restorative following the redesign. | Stress, Well-being |
Mårtensson et al. (2009) [12] | Quantitative | This study uses the outdoor play environment categories (OPEC), the sky view factor, and tools for behavior of attention to assess the restorative potential of green outdoor environments for children in preschool settings. | The restorative potential of green outdoor environments applies to preschool children. Meanwhile, environmental assessment tools, such as OPEC, can be useful. | Attention |
Barbiero et al. (2021) [11] | Quantitative | This study compared a conventional learning environment with two nature-based environments created according to biophilic design to examine the relationship between biophilic design and children’s (1) attentional performance, (2) perceived restorativeness, and (3) affiliation with nature. | Learning environments with biophilic design are perceived as more restorative, supporting children’s attentional performance and connection with nature. | Attention |
Van den Berg et al. (2017) [13] | Quantitative | This study evaluated the restorative impacts of green walls with living plants in classrooms of two elementary schools using a controlled, prospective design. | Empirical support for green walls as a means for restorative classroom design is provided. | Attention, Well-being |
Bernardo et al. (2021) [24] | Quantitative: Pre- and post-test | This study uses pre-and-posttests to examine the effect of introducing greenery to the classroom in children’s cognitive performance. | Children’s sustained attention is increased with the presence of greenery in the classroom which indicates further implication of nature’s role in human–environment interactions. | Cognitive, Attention |
Inclusion Criteria | Exclusion Criteria |
---|---|
Studies related to psychological health | Studies not related to psychological health
|
Studies related to air quality | Studies not related to air quality |
Studies targeting children | Studies not targeting children
|
Studies focusing on the association of air quality and children’s restoration | Studies not focusing on the association of air quality and children’s restoration |
Studies focusing on neuroscience | Studies focusing on neuroscience |
Database | “Children Attention Air” | “Children Stress Air” | “Children Cognitive Air” | “Children Mental Air” | “Children Emotion Air” | Subtotals Combining Keywords | Not Related to Psychological Health | Not Related to Air Quality | Not Targeting Children | Not Focusing on the Association | Focusing on Neuroscience | Literature Review | Remaining Papers |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Web of Science | 3 | 4 | 11 | 4 | 0 | 16 | −3 | −1 | −4 | −2 | / | −2 | 4 |
PubMed | 64 | 101 | 63 | 61 | 28 | 203 | −58 | −19 | −53 | −14 | −37 | / | 22 |
Embase | 1 | 2 | 7 | 4 | 0 | 9 | −2 | / | −1 | −1 | −2 | / | 3 |
subtotal | 29 | ||||||||||||
remaining papers after combining | 18 |
Author and Year | Country | Category | Exposure/Pollutants | Outcome | Barriers and Gaps |
Shier et al. (2019) [25] | The USA | Ambient air pollution | Ambient concentrations of O3, PM2.5,and PM10 | Cognitive outcomes through reading and math tests | Lack of individual exposure to incorporate individual activity patterns and time spent outdoors; difficulties in characterizing cumulative exposure over the entire life-course; difference in accuracy |
Thygesen et al. (2020) [26] | Denmark | Residential air pollution | NO2 and PM2.5 from air-modeling data | Clinical diagnoses of ADHD | Lack of comparison across other urban and rural environments; disregard of genetic factors in influencing mental health; lack of data from daycares where children usually spend time |
Olson (2021) [27] | The USA (Los Angeles) | Ambient air pollution | Concentrations of three air pollutants (NO2, PM2.5, and O3) | Mental health disorder using psychiatric emergency department visits | / |
Yu and Weitzman (2021) [28] | / | / | / | / | Gaps in understanding the links between climate change, air pollution, and mental health |
Junge et al. (2021) [29] | Spain (Barcelona) | Indoor air quality exposure at school | Tª, relative humidity, black carbon (BC), CO2, and PM2.5 | Attention level | / |
Roberts et al. (2019) [30] | The UK (England and Wales) | Residential air pollution | Annualized PM2.5 and NO2 concentrations | Anxiety, depression, conduct disorder, and attention deficit hyperactivity disorder | Lack of comparison across other urban and rural environments; disregard of genetic factors in influencing mental health; lack of information in school areas; inability to control other factors associated with or that vary within urban environments; lack of larger, population-based cohort studies; lack of comprehensive assessments of exposures and outcomes at different developmental stages |
Latham et al. (2021) [31] | The UK | Ambient air pollution | Annual exposure to four pollutants–nitrogen dioxide (NO2), nitrogen oxides (NOX), and particulate matter <2.5 μm (PM2.5) and <10 μm (PM10) | The risk of developing major depressive disorder (MDD) | Lack of monitors to capture individual exposure accurately; inability to control other confounds associated with or that vary within environments; lack of comprehensive assessments of exposures and outcomes at different developmental stages |
Ahmed et al. (2022) [32] | Australia | Ambient air pollution | Annual PM2.5 and NO2 | Emotion and behavior, and developmental delay in communication and gross motor skills in children <13 years | Lack of monitors to capture individual exposure accurately; difficulties in exploring later stages of the children ’s development; inabiltiy to capture exposure during vulnerable periods of the scale of days to weeks; lack of clarification of the differences between indoor and outdoor air quality |
Gignac et al. (2021) [33] | Spain (Barcelona) | Traffic-related air pollutants (TRAP) conducted in classrooms | Average levels of PM2.5, black carbon (BC), CO2, temperature, humidity | Attention level | Lack of measurement of short-term timescales of exposure; lack of comprehensive assessments of exposures and outcomes at different developmental stages and age ranges |
Yolton et al. (2019) [34] | The USA (Cincinnati) | Traffic-related air pollution (TRAP) | Exposure to elemental carbon attributable to traffic (ECAT) including PM2.5, elemental and black carbon (EC/BC), NO2, and UFPs | Self-reported and parent-reported depression and anxiety | Exposure misclassification due to time spent away from the home; disregard of genetic factors in influencing mental health; inability to control other confounds associated with or that vary within environments |
Ni et al. (2021) [35] | China (Shenzhen) | Ambient air pollution | Air pollution, including nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter 2.5 (PM2.5), ozone (O3), etc. | Positive Youth Development and emotional disorders (i.e., anxiety, neuroticism, and withdrawal) | Lack of comparison across other urban and rural environments; lack of assessment tool for air quality exposure; inability to control other confounds associated with or that vary within environments |
Midouhas et al. (2019) [36] | The UK (England and Wales) | Outdoor air pollution and indoor air quality | Outdoor (NO2 and SO2), indoor (damp or condensation and secondhand smoke exposures) | Emotional, conduct, and hyperactivity problems | Inability to control other confounds associated with or that vary within environments; inability to account for outdoor pollutants that may come from adjacent neighborhoods; lack of assessment tool for air quality exposure |
Brunst et al. (2019) [37] | The USA (Cincinnati) | Traffic-related air pollution (TRAP) | TRAP exposure | Anxiety symptoms | Inability to control other confounds associated with or that vary within environments |
Midouhas et al. (2018) [38] | The UK (England and Wales) | Outdoor air quality and indoor air quality | Outdoor (NO2), indoor (damp or condensation and secondhand smoke exposures) | Cognitive ability | Lack of measurement of short-term timescales of exposure |
Markevych et al. (2018) [39] | Germany | Outdoor air pollution and greenspace | Population-weighted mean values of PM10, NO2, and NDVI | ADHD incidence | Lack of monitors to capture individual exposure accurately; lack of consideration of medical and socioeconomic status |
Saadeh et al. (2022) [40] | The USA (Pennsylvania) | Ambient air pollution | Daily measurements of air pollutants (SO2, CO, and PM2.5) | ADHD medication administration (ADHD-MA) | Lack of data from daycares and school areas where children spend a large amount of time; lack of data in different seasons |
Rivas et al. (2019) [41] | Spain (Barcelona) | Residential air pollution | PM2.5 exposure | Working memory, attentiveness, and conflict network | Lack of data from daycare and school areas where children spend a large amount of time; lack of consideration of medical and socioeconomic status |
Miller et al. (2019) [42] | The USA (Northern California) | Residential air pollution | PM2.5 | Autonomic reactivity to social stress in adolescents, anxiety, and depression | Lack of consideration of differences in time spent indoor and outdoor; lack of measurement of short-term timescales of exposure; lack of comparison across other urban and rural environments; lack of improvements in assessment tool for physiological health |
Code | Barrier | Key References |
---|---|---|
B01 | Lack of tools to capture individual level air pollution exposure accurately | [25,31,32,35,36,39] |
B02 | Difference in accuracy of air pollution exposure geographically | [25] |
B03 | Lack of measurement of short-term timescales of air pollution exposure | [33,37,42] |
B04 | Lack of improvements in assessment tool for physiological health | [42] |
B05 | Difficulties in characterizing cumulative exposure at different developmental stages and age ranges or over the entire life-course | [25,30,31,32,33] |
B06 | Lack of larger, population-based cohort studies | [30] |
B07 | Lack of comparison across different geographical areas such as urban and rural environments | [26,30,35,42] |
B08 | Lack of air pollution exposure data from daycares and school areas where children spend a large amount of time | [26,30,34,40,41] |
B09 | Lack of air pollution exposure data in different seasons | [32,40] |
B10 | Inability to account for outdoor pollutants that may come from adjacent neighborhoods | [36] |
B11 | Inadequate consideration in socioeconomic status of children | [25,39,41] |
B12 | Disregard of genetic factors in influencing mental health | [26,30,34] |
B13 | Inability to control other environmental confounds associated with or that vary within environments | [30,31,34,36,37] |
B14 | Gaps in understanding the links among climate change, air pollution, and restoration | [28] |
B15 | Lack of clarification of the differences between indoor and outdoor air quality | [32] |
B16 | Lack of consideration of differences in time spent indoor and outdoor | [42] |
Category | Barrier Code |
---|---|
Assessment tool | B01, B02, B03, B04 |
Research scale | B05, B06 |
Spatial and temporal difficulties | B07, B08, B09, B10 |
Considered confounders | B11, B12, B13 |
Cross-disciplinary studies | B14 |
Indoor and outdoor difference | B15, B16 |
B01 | B02 | B03 | B04 | B05 | B06 | B07 | B08 | B09 | B10 | B11 | B12 | B13 | B14 | B15 | B16 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
B01 | - | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 |
B02 | 0 | - | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
B03 | 1 | 0 | - | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
B04 | 0 | 0 | 0 | - | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
B05 | 0 | 0 | 0 | 0 | - | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
B06 | 0 | 0 | 0 | 0 | 0 | - | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
B07 | 0 | 0 | 0 | 0 | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
B08 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
B09 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | - | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
B10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | - | 0 | 0 | 0 | 0 | 1 | 0 |
B11 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 0 | 0 | 0 | - | 0 | 1 | 0 | 0 | 0 |
B12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | 1 | 0 | 0 | 0 |
B13 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | - | 0 | 1 | 1 |
B14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | 0 | 0 |
B15 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | - | 0 |
B16 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | - |
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Wang, L.; Yang, Q.; Sun, Q. Association between Air Quality and Children’s Restorative Experience: A Systematic Review. Atmosphere 2022, 13, 1815. https://doi.org/10.3390/atmos13111815
Wang L, Yang Q, Sun Q. Association between Air Quality and Children’s Restorative Experience: A Systematic Review. Atmosphere. 2022; 13(11):1815. https://doi.org/10.3390/atmos13111815
Chicago/Turabian StyleWang, Ling, Qiuyi Yang, and Qingqing Sun. 2022. "Association between Air Quality and Children’s Restorative Experience: A Systematic Review" Atmosphere 13, no. 11: 1815. https://doi.org/10.3390/atmos13111815
APA StyleWang, L., Yang, Q., & Sun, Q. (2022). Association between Air Quality and Children’s Restorative Experience: A Systematic Review. Atmosphere, 13(11), 1815. https://doi.org/10.3390/atmos13111815