East Asia, including the Korean peninsula, is affected by dust storms originating from the deserts of Mongolia and China, especially in spring. These are called Asian dust storm (ADS) events. ADS events frequently generate ambient dust particles less than 10 µm in diameter. Particles of this size are known to be associated with adverse health effects. The aim of this study is to investigate the possible adverse effects of ADS on the asthma symptoms of residents of Seoul, South Korea, during the period 2005–2008. A paired t-test was used to compare daily medical treatment counts for asthma disease on ADS events with those on the comparison days. ‘Comparison days’ is defined as the 7 days before and after the ADS episodes, referred to as index days in this study. The estimated percentage increase in the rate of asthma treatments after the ADS event, using 4–6 day moving means, was about 18%. This shows a statistically significant association between ADS events and asthma treatment counts 4–6 days after the ADS event. It was also found that the percentage increase in asthma treatments on index days with high levels of PM₁₀ concentration was about 22% (P < 0.05) in cases with a 6 day lag. This study generates evidence that ADS events are significantly associated with asthma symptoms and that increased PM₁₀ levels may aggravate asthma disease. Copyright © 2013 Royal Meteorological Society

This study investigated how the public awareness of air quality affects people’s decisions to participate in outdoor activities. Given that the keyword search volume of particulate matter (PM) in Seoul, South Korea started to grow dramatically only after November 2013, we defined two periods (low and high public awareness of PM) and conducted a series of comparative analyses to investigate the impact of public awareness of air quality on the relationship between PM level and people’s outdoor activities. In the low public awareness period, people’s outdoor activities measured by the number of daily subway passengers did not significantly vary over PM levels, even in the ’unhealthy’ range (80 < PM₁₀ < = 150 µg/m³). On the contrary, during the high awareness period, people’s activities were significantly affected by the PM level, even in the ’moderate’ range. Specifically, the perceived safety threshold of PM₁₀ level that people use to decide for engaging outdoor activities has decreased from 120 to 70 µg/m³. These results suggest that public awareness of air quality and its harmful ramifications on health is a key determinant of outdoor activities rather than PM₁₀ concentration itself. Thus, this study alludes to a need for more timely and effective dissemination of air quality information to the public as much as for curbing anthropogenic emissions.

The Korea Meteorological Administration has limited understanding of how users interpret probabilistic forecast information and use it in their decision-making processes. Thus, a survey was conducted among users to find out at which probability threshold for adverse weather they would react and take protective action to minimize the losses. According to previous studies, probability threshold depends on the type of users but they are in general higher than a 50% forecasts chance of adverse weather, even though research has shown that the forecast value is maximized when actions are taken for probability of adverse weather lower than 50%. A collective value score model, defined as a weighted sum of user's satisfaction and threshold distribution in a group of users, is introduced as a measure of total satisfaction of a user group. The collective value score model is applied to a set of precipitation probabilistic forecasts of Seoul, South Korea during 2002–2011. The results show that the collective value score can be improved if the range of users' probability thresholds is widened rather than improving the forecast accuracy.

The weather forecast service industry needs to understand customers’ opinions of the weather forecast to enhance sustainable communication between forecast providers and recipients particularly influenced by inherent uncertainty in the forecast itself and cultural factors. This study aims to investigate the potential for using social media data to analyze users’ opinions of the wrong weather forecast. Twitter data from Korea in 2014 are analyzed using textual analysis and association rule mining to extract meaningful emotions or behaviors from weather forecast users. The results of textual analysis show that the frequency of negative opinions is considerably high compared to positive opinions. More than half of the tweets mention precipitation forecasts among the meteorological phenomena, implying that most Koreans are sensitive to rain events. Moreover, association rules extracted from the negative tweets reveal a pattern of user criticism according to the seasons and types of forecast errors such as a “false alarm” or “miss” error. This study shows that social media data can provide valuable information on the actual opinion of the forecast users in almost real time, enabling the weather forecast providers to communicate effectively with the public.