Environmental Research Letters (Jan 2020)

What matters in public perception and awareness of air quality? Quantitative assessment using internet search volume data

  • Young-Hee Ryu,
  • Seung-Ki Min

DOI
https://doi.org/10.1088/1748-9326/ab9fb0
Journal volume & issue
Vol. 15, no. 9
p. 0940b4

Abstract

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Recently, the issue of air quality in South Korea reached an unprecedented level of social concern regarding public health, quality of life, and environmental policies, even as the level of particulate matter less than 10 μ m (PM _10 ) showed a decreasing trend. Why have social concerns emerged in recent years, specifically after 2013–2014? This study aims to understand how people perceive air quality apart from the measured levels of airborne pollutants using internet search volume data from Google and NAVER. An empirical model that simulates the air quality perception index (AQPI) is developed by employing the decay theory of forgetting and is trained by PM _10 , visibility, and internet search volume data. The results show that the memory decay exponent and the accumulation of past memory traces, which represent the weighted sum of past perceived air quality, play key roles in explaining the public’s perception of air quality. A severe haze event with an extremely long duration that occurred in the year 2013–2014 increased public awareness of air quality, acting as a turning point. Before the turning point, AQPI is more influenced by sensory information (visibility) due to the low awareness level, but after the turning point it is more influenced by PM _10 and people slowly forget about air quality. The retrospective AQPI analysis under a low level of awareness confirms that perceived air quality is indeed worst in the year 2013–2014. Our results provide a better understanding of public perception of air quality, and will contribute to the creation of more effective regulatory policies. It should be noted, however, that the proposed model is primarily meant to diagnose historic public perception and that more sophisticated models are needed to reliably predict perception of air quality.

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