Clean air captures attention whereas pollution distracts: evidence from brain activities

doi:10.1007/s11783-024-1801-x

Front. Environ. Sci. Eng.

2024, Vol. 18

Issue (4) : 41 https://doi.org/10.1007/s11783-024-1801-x

RESEARCH ARTICLE

Clean air captures attention whereas pollution distracts: evidence from brain activities

Jianxun Yang^1,², Yunqi Liu³, Berry van den Berg⁴, Susie Wang⁵, Lele Chen⁶, Miaomiao Liu^1,²(

), Jun Bi^1,²(

)

¹. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
². Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, China
³. Nanjing Foreign Language School, Nanjing 210008, China
⁴. Department of Experimental Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, The Netherlands
⁵. Department of Social Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, The Netherlands
⁶. School of Education Science, Nantong University, Nantong 226019, China

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Abstract

● We find air pollution distracts attention and reveal the neurocognitive mechanisms.

● Clean air captures more attention and evokes larger N300 amplitudes in all trials.

● Pollution causes lower accuracy and larger P300 wave in attention-holding trials.

● Pollution causes higher accuracy and lower P300 wave in attention-shifting trials.

Awareness of the adverse impact of air pollution on attention-related performance such as learning and driving is rapidly growing. However, there is still little known about the underlying neurocognitive mechanisms. Using an adapted dot-probe task paradigm and event-related potential (ERP) technique, we investigated how visual stimuli of air pollution influence the attentional allocation process. Participants were required to make responses to the onset of a target presented at the left or right visual field. The probable location of the target was forewarned by a cue (pollution or clean air images), appearing at either the target location (attention-holding trials) or the opposite location (attention-shifting trials). Behavioral measures showed that when cued by pollution images, subjects had higher response accuracy in attention-shifting trials. ERP analysis results revealed that after the cue onset, pollution images evoked lower N300 amplitudes, indicating less attention-capturing effects of dirty air. After the target onset, pollution cues were correlated with the higher P300 amplitudes in attention-holding trials but lower amplitudes in attention-shifting trials. It indicates that after visual exposure to air pollution, people need more neurocognitive resources to maintain attention but less effort to shift attention away. The findings provide the first neuroscientific evidence for the distracting effect of air pollution. We conclude with several practical implications and suggest the ERP technique as a promising tool to understand human responses to environmental stressors.

Keywords Air pollution Attention Disengagement Performance Event-related potential

Corresponding Author(s): Miaomiao Liu,Jun Bi

Issue Date: 18 December 2023

Cite this article:

Jianxun Yang,Yunqi Liu,Berry van den Berg, et al. Clean air captures attention whereas pollution distracts: evidence from brain activities[J]. Front. Environ. Sci. Eng., 2024, 18(4): 41.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1801-x
https://academic.hep.com.cn/fese/EN/Y2024/V18/I4/41

Fig.1 Example trial sequences in the experiment. The attention-holding trial on the left side shows an example of using PM pollution pictures as the cue, and the attention-shifting trial on the right side shows an example of using good air pictures as the cue.

Tab.1 Four research hypotheses and related behavioral and EEG indicators

Fig.2 Behavioral performance per cue type and trial type. Red and blue dots represent the least-squares means of pollution cues and clean air cues of (a) response accuracy and (b) reaction time, respectively. Error bars reflect standard errors of the mean (SEM).

Fig.3 ERP comparison between pollution and clean air cues in attention-holding trials. (a) Topographic plots of ERP waves show the brain activities in attention-holding trials per 200 ms with pollution images (the first panel) and clean air images (the second panel) as the cue. The third panel shows the ERP difference waves between pollution and clean air conditions. (b) Mean ERP waves for the channels of N300 ROI. (c) Mean ERP waves for the channels of P300 ROI. Gray rectangles indicate time windows of interest.

Fig.4 ERP comparison between pollution and clean air cues in attention-shifting trials. (a) Topographic plots of ERP waves show the brain activities in attention-holding trials per 200 ms with pollution images (the first panel) and clean air images (the second panel) as the cue. The third panel shows the ERP difference waves between pollution and clean air conditions. (b) Mean ERP waves for the channels of N300 ROI. (c) Mean ERP waves for the channels of P300 ROI. Gray rectangles indicate time windows of interest.

1	J L Allen, C Klocke, K Morris-Schaffer, K Conrad, M Sobolewski, D A Cory-Slechta. (2017). Cognitive effects of air pollution exposures and potential mechanistic underpinnings. Current Environmental Health Reports, 4(2): 180–191 https://doi.org/10.1007/s40572-017-0134-3
2	E Berdica, A B M Gerdes, F Bublatzky, A J White, G W Alpers. (2018). Threat vs. threat: attention to fear-related animals and threatening faces. Frontiers in Psychology, 9: 1154 https://doi.org/10.3389/fpsyg.2018.01154
3	A T Biggs, R D Kreager, B S Gibson, M Villano, C R Crowell. (2012). Semantic and affective salience: the role of meaning and preference in attentional capture and disengagement. Journal of Experimental Psychology. Human Perception and Performance, 38(2): 531–541 https://doi.org/10.1037/a0027394
4	M M Bradley, M Codispoti, B N Cuthbert, P J Lang. (2001). Emotion and motivation I: defensive and appetitive reactions in picture processing. Emotion (Washington, D C), 1(3): 276–298 https://doi.org/10.1037/1528-3542.1.3.276
5	M M Bradley, P J Lang (2007). Handbook of Emotion Elicitation and Assessment. New York: Oxford University Press, 29–46
6	N Camgöz, C Yener, D Güvenç. (2004). Effects of hue, saturation, and brightness: Part 2. Attention. Color Research and Application, 29(1): 20–28 https://doi.org/10.1002/col.10214
7	L Carretié, J Iglesias, T García, M Ballesteros. (1997). N300, P300 and the emotional processing of visual stimuli. Electroencephalography and Clinical Neurophysiology, 103(2): 298–303 https://doi.org/10.1016/S0013-4694(96)96565-7
8	R J Compton. (2003). The interface between emotion and attention: a review of evidence from psychology and neuroscience. Behavioral and Cognitive Neuroscience Reviews, 2(2): 115–129 https://doi.org/10.1177/1534582303002002003
9	J He, H Liu, A Salvo. (2019). Severe air pollution and labor productivity: evidence from industrial towns in China. American Economic Journal: Applied Economics, 11(1): 173–201 https://doi.org/10.1257/app.20170286
10	M L Hidayetoglu, K Yildirim, A Akalin. (2012). The effects of color and light on indoor wayfinding and the evaluation of the perceived environment. Journal of Environmental Psychology, 32(1): 50–58 https://doi.org/10.1016/j.jenvp.2011.09.001
11	A S H Huang, Y J Lin. (2020). The effect of landscape colour, complexity and preference on viewing behaviour. Landscape Research, 45(2): 214–227 https://doi.org/10.1080/01426397.2019.1593336
12	J Huang, N Xu, H Yu (2020). Pollution and performance: Do investors make worse trades on hazy days? Management Science, 66(10): 4455–4476 https://doi.org/10.1287/mnsc.2019.3402
13	L Huang, C Rao, T J van der Kuijp, J Bi, Y Liu. (2017). A comparison of individual exposure, perception, and acceptable levels of PM2.5 with air pollution policy objectives in China. Environmental Research, 157: 78–86 https://doi.org/10.1016/j.envres.2017.05.012
14	N P Hyslop. (2009). Impaired visibility: the air pollution people see. Atmospheric Environment, 43(1): 182–195 https://doi.org/10.1016/j.atmosenv.2008.09.067
15	N Jacobs, N Roman, R Pless (2007). Consistent temporal variations in many outdoor scenes. In: Proceedings of IEEE Conference on Computer Vision & Pattern Recognition. IEEE: Piscataway
16	L T E Kessels, R A Ruiter, B M Jansma. (2010). Increased attention but more efficient disengagement: neuroscientific evidence for defensive processing of threatening health information. Health Psychology, 29(4): 346–354 https://doi.org/10.1037/a0019372
17	M Kumar, K D Federmeier, D M Beck. (2021). The N300: an index for predictive coding of complex visual objects and scenes. Cerebral Cortex Communications, 2(2): tgab030 https://doi.org/10.1093/texcom/tgab030
18	J J Lee, F Gino, B R Staats (2014). Rainmakers: Why bad weather means good productivity? Journal of Applied Psychology, 99(3): 504–513 https://doi.org/10.1037/a0035559 pmid: 24417552
19	M Li, J Li, G Zhang, W Fan, H Li, Y Zhong. (2023). Social distance modulates the influence of social observation on pro-environmental behavior: an event-related potential (ERP) study. Biological Psychology, 178: 108519 https://doi.org/10.1016/j.biopsycho.2023.108519
20	T Li, Y Zhang, J Wang, D Xu, Z Yin, H Chen, Y Lv, J Luo, Y Zeng, Y Liu. et al.. (2018). All-cause mortality risk associated with long-term exposure to ambient PM2.5 in China: a cohort study. The Lancet Public Health, 3(10): e470–e477 https://doi.org/10.1016/S2468-2667(18)30144-0
21	Y Li, D Guan, Y Yu, S Westland, D Wang, J Meng, X Wang, K He, S Tao. (2019). A psychophysical measurement on subjective well-being and air pollution. Nature Communications, 10(1): 5473 https://doi.org/10.1038/s41467-019-13459-w
22	X Liu, S Chen, X Guo, H Fu. (2022). Can social norms promote recycled water use on campus? The evidence from event-related potentials. Frontiers in Psychology, 13: 818292 https://doi.org/10.3389/fpsyg.2022.818292
23	S J Luck, E S Kappenman (2012). The Oxford Handbook of Event-Related Potential Components. New York: Oxford University Press
24	Q Ma, X Bai, G Pei, Z Xu. (2018). The hazard perception for the surrounding shape of warning signs: evidence from an event-related potentials study. Frontiers in Neuroscience, 12: 824 https://doi.org/10.3389/fnins.2018.00824
25	K Mogg, B P Bradley. (2016). Anxiety and attention to threat: cognitive mechanisms and treatment with attention bias modification. Behaviour Research and Therapy, 87: 76–108 https://doi.org/10.1016/j.brat.2016.08.001
26	L Sager. (2019). Estimating the effect of air pollution on road safety using atmospheric temperature inversions. Journal of Environmental Economics and Management, 98: 102250 https://doi.org/10.1016/j.jeem.2019.102250
27	J Sunyer, M Esnaola, M Alvarez-Pedrerol, J Forns, I Rivas, M López-Vicente, E Suades-González, M Foraster, R Garcia-Esteban, X Basagaña. et al.. (2015). Association between traffic-related air pollution in schools and cognitive development in primary school children: a prospective cohort study. PLoS Medicine, 12(3): e1001792 https://doi.org/10.1371/journal.pmed.1001792
28	J Sunyer, E Suades-González, R García-Esteban, I Rivas, J Pujol, M Alvarez-Pedrerol, J Forns, X Querol, X Basagaña. (2017). Traffic-related air pollution and attention in primary school children: short-term association. Epidemiology, 28(2): 181–189 https://doi.org/10.1097/EDE.0000000000000603
29	G F Woodman. (2010). A brief introduction to the use of event-related potentials in studies of perception and attention. Attention, Perception & Psychophysics, 72(8): 2031–2046 https://doi.org/10.3758/BF03196680
30	Y Xie, H Zhong, Z Weng, X Guo, S E Kim, S Wu. (2023). PM2.5 concentration declining saves health expenditure in China. Frontiers of Environmental Science & Engineering, 17(7): 90 https://doi.org/10.1007/s11783-023-1690-4
31	J Yang, Q Gao, M Liu, J S Ji, J Bi. (2023). Same stimuli, different responses: a pilot study assessing air pollution visibility impacts on emotional well-being in a controlled environment. Frontiers of Environmental Science & Engineering, 17(2): 20
32	J Yang, S Qu, M Liu, X Liu, Q Gao, W He, J S Ji, J Bi. (2021). Gray cityscape caused by particulate matter pollution hampers human stress recovery. Journal of Cleaner Production, 279: 123215 https://doi.org/10.1016/j.jclepro.2020.123215
33	J Yang, Q Zhou, X Liu, M Liu, S Qu, J Bi (2018). Biased perception misguided by affect: how does emotional experience lead to incorrect judgments about environmental quality? Global Environmental Change, 53: 104–113 https://doi.org/10.1016/j.gloenvcha.2018.09.007
34	X Zhang, X Chen, X Zhang. (2018). The impact of exposure to air pollution on cognitive performance. Proceedings of the National Academy of Sciences of the United States of America, 115(37): 9193–9197 https://doi.org/10.1073/pnas.1809474115
35	J G Zivin, M Neidell. (2012). The impact of pollution on worker productivity. American Economic Review, 102(7): 3652–3673 https://doi.org/10.1257/aer.102.7.3652

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