Introduction

Numerous epidemiological studies have been conducted on the effects of particulate matter (PM) with a diameter of less than 2.5 µm (PM2.5) on human health (Dockery et al. 1993; Hart et al. 2015; Li et al. 2018). The literature, consequently, is replete with evidence of its negative effects on human beings (Burgan et al. 2010; Cesaroni et al. 2014; Atkinson et al. 2014; Yuan et al. 2019).

However, during recent years, general public concern—and interest—regarding ultrafine particles (UFPs), which are PM with a diameter of less than 100 nm, has also increased. The characteristics of PM depend on its size and particle origin (Mühlfeld et al. 2008; Morawska et al. 2008), and there are four distinguishing characteristics of UFPs. First, they constitute less than 20% of the total mass concentration of particles, but more than 90% of the total number concentration of particles, compared to PM with a diameter of less than 10 µm (PM10) and PM2.5 (Kittelson 1998; Kumar et al. 2009). Second, UFPs have a high share in direct emissions from anthropogenic sources, such as road transportation and power plants, whereas PM2.5 has a high share in secondary sources, that is, through chemical processes in the atmosphere (Kittelson 1998; Morawska et al. 2008; Liang et al. 2016). UFPs emitted from road transportation account for over 60% of the total air pollution, as compared to non-road transportation (19%) and domestic combustion (13%) (Kumar et al. 2014). These differences in particle origin cause differences in the components of PM2.5 and UFPs. Both PM2.5 and UFPs have the highest proportion of total carbon (TC), including organic carbon and elemental carbon, but for UFPs, TC accounted for 80%, while for PM2.5, TC accounted for 56%, and SO42− and NH4+ accounted for 21% and 9%, respectively (Kim et al. 2016; Huyen et al. 2021). Third, various natural factors, such as wind direction, wind speed, and breathability, affect UFPs concentration (Buccolieri et al. 2010; Chen et al. 2016). In urban areas, the vortex effect caused by dense traffic concentration and the temperature gradient (Kumar et al. 2008; Marini et al. 2015) results in UFPs—emitted by vehicular fuel combustion—hanging in the atmosphere for a long time, at high concentration levels. Kumar et al. (2014) compared UFP concentration in Asian countries, such as China and India, and European countries. They found that outdoor average UFP concentration in Asian cities is about four times higher than in European cities. According to them, understanding the variability of UFPs is the key to designing effective monitoring strategies and estimating the relationships between UFPs and health effects in urban areas.

Similar to PM10 and PM2.5 studies, there are studies on health effects of UFP exposure. These particles are too small for the human nose and bronchioles to effectively filter out, resulting in their deep absorption into the alveoli or the membranes (HEI 2013). According to Chen et al. (2016), about 50% of all PM deposited on the alveoli is of the size of 20 nm and 10–20% from 100 nm to 2.5 µm. Stafoggia et al. (2017) analyzed the relationship between short-term exposure to UFP concentration and mortality in eight European countries, in which 12 monitoring stations measured at least 3 years of daily UFPs data between 1999 and 2013. They estimated the health effects of each country using a Poisson regression model and combined the results using random-effects meta-analysis. The results showed a 0.35% increase in non-accidental mortality as the number of UFPs increased by 10,000 particles/m3. Liu et al. (2018) estimated the relationship between maximum blood pressure, minimum blood pressure, high sensitivity-C-reactive protein, and UFP concentrations for 100 non-smokers in Taiwan. They collected UFP data by installing three UFP samplers in participants’ apartments and used mixed-effects models. Consequently, when the UFP concentration increased by 0.97 µg/m3, maximum blood pressure, minimum blood pressure, and high sensitivity-C-reactive protein increased by 6.3%, 5.6%, and 8.5%, respectively—higher than the effects of PM10 and PM2.5. Downward et al. (2018) estimated the relationship between UFPs and the incidence of cardiovascular and cerebrovascular diseases (CVD) in the Netherlands. They developed land-use regression models for the concentration of UFPs and used Cox proportional hazard models. They found that the risk was elevated by 18%, 34%, and 76% for all CVD, myocardial infarction, and heart failure per 10,000 particles/m3 increase in UFPs, respectively. Lammers et al. (2020) estimated the health effects of UFPs at airports on cardiopulmonary disease in the Netherlands. They recruited 21 healthy non-smokers aged 18–35 years and exposed them to ambient air 2–5 times for 5 h at a time in the chamber of the laboratory near the airport. They measured UFPs for 5 h in the laboratory and calculated the mean value for every exposure day. Using linear mixed models, they found that the 125,400 particles/m3 increase in UFP concentration, especially less than 20 nm (mainly from aviation) decreased lung and cardiac function. In Korea, Song et al. (2011) analyzed the effect of UFP concentration on respiratory system functions in 41 children aged 8–12 years with atopic dermatitis. They examined an elementary school in Incheon, a metropolitan city in Korea, and measured daily UFP data from the rooftop of a four-story elementary school building. They found a 3.1% increase in itch symptom score in children with atopic dermatitis as the number concentrations of UFPs increased by the interquartile range using linear mixed models. Although a few studies suggest the high potential of adverse health effects due to UFP exposure, more studies are required to prove the same with greater reliability (Ohlwein et al. 2019; Schraufnagel 2020).

The current PM measurement methods in most countries focus on measuring mass concentration, and the main purpose of current PM policy is to reduce its mass concentration. In South Korea, currently using the β-ray absorption method to measure the PM concentration, it is an appropriate method to measure the mass concentration (ME 2021a). However, as mentioned above, UFPs have different characteristics from existing air pollutant—PM10 or PM2.5—so it is difficult to identify whether the current policies are effective in reducing the UFP concentration, which means we need to manage them separately. Consequently, the European Union has number concentration emission standards for vehicles, for example, the Euro 5 and Euro 6 standards limit the number concentration emission of compression-ignition diesel vehicles to 6.0 \(\times\) 1011 per test-cycle (Directive  2008). In addition, the UK included UFPs in its National Atmospheric Emissions Inventory (Lewis et al. 2018). However, several other countries, including Korea, are not discussing about adequate standards and regulatory limits for UFPs. Therefore, establishment of a future policy on air pollutants, including UFPs, necessitates a wide range of UFP studies including their components, characteristics, and health effects. Additionally, prevention of potential UFP risks requires disclosure of information to the public so as to facilitate voluntary avoidance of UFP exposure. To discover scientifically whether a new UFP policy is necessary, the measurement of widely available and reliable UFP data should be performed. However, traditional monitoring systems are suitable for measuring mass concentrations, so they cannot measure the number concentration of UFPs. Therefore, to collect and disseminate this information, a new UFP monitoring and reporting system—different from traditional systems—is required, which will raise the financial burden on the government and people. Moreover, research on public perception of UFPs and the economic value of its information remains insufficient.

This study has two main aims: to analyze the economic value of UFP information using the contingent valuation method (CVM) to estimate the willingness-to-pay (WTP) for building a UFP monitoring and reporting system and to derive policy implications for the government to increase public acceptance of the UFPs monitoring and reporting systems. Previous studies on UFPs have mainly focused on investigating the characteristics of UFPs or analyzing the relationship between exposure and human health using measured UFPs data for individual studies. However, this study estimated the economic value of the monitoring system for measuring UFP data. The remainder of this paper is organized as follows: the “Methods and materials” section explains the model and data used in this study. The “Results” section presents survey and estimation results, while the “Discussion” section discusses the policy implications. The “Conclusions” section provides the summary of the outstanding conclusions and limitations of this study.

Methods and materials

Contingent valuation method

This study used the CVM to estimate the WTP for building a UFP monitoring and reporting system, to evaluate the economic value of UFP information. The CVM has been widely used for economic valuation of non-market goods, especially environmental goods, and consumer perception analysis (Harrison 1992; Han et al. 2011; Mwebaze et al. 2018). To estimate the WTP for non-market goods using the CVM, it is important to design a questionnaire that facilitates respondents’ comprehension of issues and obtains their WTP information. Generally, payment card, open-ended, and dichotomous choice (DC) formats are employed to elicit respondents’ WTP data through the CVM. In the DC format, pre-set bids are presented to the respondents with a “Yes” or “No” choice, and econometric analysis—the probit or logit model—is used to estimate the WTP. We designed the questionnaire using the DC format because of its following strengths: easy to answer, low biases, and low likelihood of estimating unreliable WTP (Hanemann 1984; Oerlemans et al. 2016).

The DC format is divided into the single-bounded dichotomous choice (SBDC) that asks a single bid (Bishop and Heberlein 1979), and the double-bounded dichotomous choice (DBDC) that asks once more for double or half of the first bid, depending on the respondent’s first answer (Hanemann 1985). The SBDC is convenient because it asks each respondent only one bid; however, it has the disadvantage of being statistically inefficient. The DBDC, in contrast, has high efficiency, but one disadvantage: the responses of the first and second bids may not be consistent (Cameron and Quiggin 1994; DeShazo 2002). Respondents are likely to accept or reject the second bid, regardless of their actual WTP, out of guilt for unwillingness to pay for the second bid, which is lower than the first bid, or out of repulsion that the interviewer is trying to make at least some money by offering a lower bid. To overcome SBDC’s inefficiency and DBDC’s bias, Cooper et al. (2002) suggested the one-and-one-half-bounded dichotomous choice (OOHBDC). OOHBDC divides respondents into two groups and randomly presents an initial lower bid to one group and an initial upper bid to the other group. If respondents answer “No” to the lower bid or “Yes” to the upper bid, the survey ends. However, if they answer “Yes” to the lower bid, then the upper bid is presented as the second bid, and if they answer “No” to the upper bid, then the lower bid is presented as the second bid. This study used the OOHBDC method to reduce SBDC’s inefficiency and DBDC’s bias. In the OOHBDC method, if respondents answer “No” to the lower bid or “No–No” to the upper bid, then their WTP may be zero or between zero and the lower bid. It is important to identify whether the respondent’s WTP is actually zero or between zero and the lower bid, so as to estimate their accurate WTP; we used the spike model to solve this problem (Kriström 1997). The OOHBDC spike model asks an additional question if respondents answer “No” to the lower bid or “No–No” to the upper bid: “Are you willing to pay at least KRW 1 each year for the next 5 years from the income tax paid by your household to build the UFP monitoring and reporting system?” Fig. 1 shows the structure of the OOHBDC spike format and its possible responses.

Fig. 1
figure 1

The structure of the OOHBDC spike format and possible responses

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Willingness-to-pay estimation

This study applied the utility difference model suggested by Hanemann (1984) to estimate the WTP using the OOHBDC spike model. Each respondent has the indirect utility function \(\left(j,m;S\right)\), where \(j\) is the status of the UFP monitoring and reporting system: when \(j\) is equal to 1, it means the UFP monitoring and reporting system is presented; otherwise \(j\) is 0. \(m\) is the respondent’s income, and \(S\) is the vector of respondent’s socio-economic and cognition characteristics. The indirect utility function can be expressed as the observable deterministic part, \(v\left(j,m;S\right)\), and unobservable stochastic part, \({\varepsilon }_{j}\), as follows:

$$u\left(j,m;S\right)=v\left(j,m;S\right)+{\varepsilon }_{j}$$
(1)

where \({\varepsilon }_{j}\) is an independently and identically distributed (i.i.d.) variable with a zero mean.

When the respondent answers “Yes” to the question “Are you willing to pay A to build the UFPs monitoring and reporting system?,” to maximize his/her utility, the probability of answering “Yes” is expressed as follows:

$$\mathrm{Pr}{\{}^{\prime\prime}{yes}^{\prime\prime}=\mathrm{Pr}\{\Delta v(A)\ge \eta \}\equiv {F}_{\eta }[\Delta v(A)]$$
(2)

where \(\eta\) is the difference of error terms,\({\varepsilon }_{0}-{\varepsilon }_{1}\), and \({F}_{\eta }\left(\cdot \right)\) is the cumulative distribution function (CDF) of \(\eta\). Meanwhile, if the WTP (denoted as \(W\)) of the respondent is greater than or equal to \(A\), the respondent will answer “Yes,” otherwise “No.” The probability that respondent answers “Yes” can also be expressed as follows:

$$\mathrm{Pr}{\{}^{\prime\prime}{yes}^{\prime\prime}\}=\mathrm{Pr}\{W\ge A\}\equiv 1-{G}_{W}(A)$$
(3)

where \({G}_{W}(A)\) is CDF of \(W\). When we consider Eqs. (2) and (3) together, we can derive \(1-{G}_{W}(A)\equiv {F}_{\eta }[\Delta v(A)]\).

We assume that \({A}_{i}\) is an initial bid presented to the respondent \(i\), and \({A}_{i}^{L}\) and \({A}_{i}^{U}\) represent lower and upper initial bids, respectively. There are eight possible outcomes in the OOHBDC spike model:

$$I\left({A}_{i}^{U}\right)=\left\{\begin{array}{l}\begin{array}{ll}{I}_{i}^{Y}=1& \left(respondents\;i\;{answers\;}^{\prime\prime}{yes}^{\prime\prime}\right), where\;{A}_{i}^{U}<W<\infty \end{array}\\ \begin{array}{ll}{I}_{i}^{NY}=1& \left(respondents\;i\;{answers\;}^{\prime\prime}{{{No}}-{{Yes}}}^{\prime\prime} \right),\;where\;{A}_{i}^{L}<W<{A}_{i}^{U}\end{array}\\ \begin{array}{l}\begin{array}{ll}{I}_{i}^{NNY}=1& \left(respondents\;i\;{answers\;}^{\prime\prime}{{{No}}-{{Yes}}-No}^{\prime\prime} \right),\;where\;0<W<{A}_{i}^{L}\end{array}\\ \begin{array}{ll}{I}_{i}^{NNN}=1& \left(respondents\;i\;{answers\;}^{\prime\prime}{{No}}-{{No}}-{No}^{\prime\prime}\right),\;where\;W=0\end{array}\end{array}\end{array}\right.,$$
(4)

and

$$I\left({A}_{i}^{L}\right)=\left\{\begin{array}{l}\begin{array}{ll}{I}_{i}^{YY}=1& (respondents\;i\;{answers\;}^{\prime\prime}Yes-Ye{s}^{\prime\prime}),\;where\;{A}_{i}^{U}<W<\infty \end{array}\\ \begin{array}{ll}{I}_{i}^{YN}=1& \left(respondents\;i\;{answers\;}^{\prime\prime} {{Yes}}-{No}^{\prime\prime}\right),\;where\;{A}_{i}^{L}<W<{A}_{i}^{U}\end{array}\\ \begin{array}{l}\begin{array}{ll}{I}_{i}^{NY}=1& \left(respondents\;i\;{answers\;}^{\prime\prime} {{No}}-{Yes}^{\prime\prime}\right),\;where\;0<W<{A}_{i}^{L}\end{array}\\ \begin{array}{ll}{I}_{i}^{NN}=1& \left(respondens\;i\;{answers\;}^{\prime\prime} {{No}}-{No}^{\prime\prime}\right),\;where\;W=0\end{array}\end{array}\end{array}\right.,$$
(5)

where the indicator function \(I(\cdot )\) has a value of 1 if the proposition is true; otherwise, it is 0. Using eight indicator functions, the log-likelihood function for the OOHBDC spike model is expressed as follows:

$$\mathrm{ln}L=\sum_{i=1}^{N}\left\{\begin{array}{c}\left({I}_{i}^{Y}+{I}_{i}^{YY}\right)\mathrm{ln}\left[1-{G}_{W}\left({A}_{i}^{U}\right)\right]+\left({I}_{i}^{YN}+{I}_{i}^{NY}\right)\mathrm{ln}[{G}_{W}({A}_{i}^{U})-{G}_{W}({A}_{i}^{L})]\\ +\left({I}_{i}^{NNY}+{I}_{i}^{NY}\right)\mathrm{ln}\left[{G}_{W}\left({A}_{i}^{L}\right)-{G}_{W}\left(0\right)\right]+\left({I}_{i}^{NNN}+{I}_{i}^{NN}\right)\mathrm{ln}[{G}_{W}(0)]\end{array}\right.$$
(6)

Assuming that the respondent’s WTP has a logistic CDF, the spike model of \({G}_{W}(A)\) with parameter \(a\), \(b\) is:

$$G_W\left(A\right)=\left\{\begin{array}{l}\begin{array}{lc}{\lbrack1+\exp(a-bA)\rbrack}^{-1}&if\;A>0\end{array}\\\begin{array}{lc}{\lbrack1+\exp(a)\rbrack}^{-1}&\;\;\;\;\;\;\;\;\;if\;A=0\end{array}\\\begin{array}{lc}0&\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;if\;A<0\end{array}\end{array}\right.$$
(7)

Here, the spike is defined as \({[1+\mathrm{exp}\left(a\right)]}^{-1}\), and the mean WTP is calculated as \(\left(\frac{1}{b}\right)\mathrm{ln}[1+\mathrm{exp}(a)]\).

Survey design and data

We designed the survey to estimate the WTP for building a UFP monitoring and reporting system. Initially, we planned a face-to-face survey; however, it was difficult to meet the respondents owing to COVID-19. Therefore, the survey was conducted online. At the beginning of the questionnaire, we explained the definitions and characteristics of UFPs, and the differences between UFPs and PM2.5, including their effects on human health. We explained that UFPs penetrate deeper into the body compared to PM2.5. Thereafter, we described the kinds of information that the UFP monitoring and reporting system will provide to the public: (1) 1-h average data, presented online in real time, (2) monthly/annual reports, and (3) predicted UFP concentration data and warning alert issuance in case of high concentration. We also explained how to use UFP information, based on policy suggestions of previous studies (Hu et al. 2009; Choi et al. 2012, 2018; Lewis et al. 2018).

In this survey, we selected an additional payment of income tax as the payment vehicle for building the UFP monitoring and reporting system. Additionally, we explained to the respondents that they would have to pay over the next 5 years, considering 90% of PM2.5 monitoring stations in Korea are installed for 5-year terms.Footnote 1 Consequently, we presented the question: “Are you willing to pay an additional KRW [bid] each year for the next five years from the income tax paid by your household to build the UFPs monitoring and reporting system?”.

Gallup Korea, a specialized market research company, conducted the survey from February 4 to 9, 2021. This study conducted a national level survey, of which respondents were stratified by region, age, and sex to reflect the Korean population. Before the actual survey, we conducted a pilot survey with 455 respondents to determine the initial bid sets to be presented to the respondents. The respondents of the pilot survey were also stratified by region, age, and sex at the national level. Based on the results of the pilot survey, we excluded the upper and lower 5% of responses to reduce bias, considering them as outliers. Then, we determined 10 sets of initial bids for the actual survey: KRW (1000; 2000), (2000; 3000), (3000; 4000), (4000; 5000), (5000; 7000), (7000; 9000), (9000; 11,000), (11,000; 14,000), (14,000; 17,000), (17,000, 20,000).Footnote 2 In the actual survey, 1042 respondents aged between 20 and 69 years were selected, and an online survey was completed. We excluded two respondents who did not answer the additional questions and used a total of 1040 respondents in the analysis.

The characteristics of the respondents in the context of the total Korean population are described in Table 1. The proportions of sex, age, region, and average monthly income per household are similar between survey respondents and the total population. However, the education level of the respondents is relatively higher than the actual population—this could be due to the inclusion of internet-savvy individuals in the survey, who generally have a high level of education (Ünver 2014). We compared the average monthly expense for anti-dust products with another survey sample of Min (2019) in Table 1. The proportion of respondents who spend over KRW 50,000 was lower, but the level of spending under KRW 50,000 was higher. This reflected the increase in the sale of face mask—a representative anti-dust product—due to COVID-19 after 2019 (Wu et al. 2020).

Table 1 Demographic characteristics of survey respondents
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Results

Survey results

During the survey, 20 sets of bids were allocated to 1040 respondents in a similar ratio. Table 2 presents the distribution of respondents by initial bid and their answers. A total of 118 people (21.7%) responded “No–No-No” to the upper initial bid, and 131 people (26.5%) responded “No–No” to the lower initial bid. From this, we found that 249 out of all respondents (23.9%) have zero WTP for building a UFP monitoring and reporting system.

Table 2 Distribution of respondents in the survey
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Respondents’ intention for zero WTP can either be a case of their WTP being true zero or a protest response to the survey. A protest response is observed when respondents are dissatisfied with the questionnaire or think there is insufficient information about the survey issue. In this case, their WTP may not be true zero, and, therefore, these answers are most probably a protest response to the survey. In this survey, we presented an additional question to zero WTP respondents to ascertain the reason for their zero WTP, so we could distinguish the protest WTP respondents. Table 3 shows the reasons mentioned for zero WTP. We can consider the respondents who responded with “Not enough information to judge” as protest WTP respondents (Tolunay and Başsüllü 2015). Previous studies suggested two methods for treating protest WTP respondents. First, these respondents were excluded from the CVM analysis because their responses did not reflect their actual preferences (Jorgensen and Syme 2000; Meyerhoff and Liebe 2010). Second, these responses were included in the analysis because analysis excluding them could lead to the overestimated mean WTP (Strazzera et al. 2003; Fonta et al. 2010). In this study, there were 38 (3.65%) protest WTP respondents, and we included their WTP as zero in the main analysis to avoid the overestimation bias; we also presented the estimated WTP, excluding the protest WTP respondents for comparison.

Table 3 Reasons for zero WTP
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We presented four additional questions to confirm the cognition level of respondents regarding PM: seriousness, adverse health effect, reliability of current real-time PM data, and perception of UFPs. Table 4 shows the results for the 1040 respondents. Most people are sensitive to PM, and 932 people (89.62%) responded that the PM problem in Korea is serious. Additionally, 518 people (49.81%) responded that the real-time PM data currently provided by the government is reliable. However, people’s perception of UFPs is low, whereas their interest in PM is considerably high.

Table 4 Respondents’ cognition regarding particulate matter
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Estimation results

Table 5 presents the estimation results of the three OOHBDC spike models. Model 1 did not include covariates. Model 2 included socio-economic covariates, such as sex, age, household income, and anti-dust expense. Model 3 included cognition covariates as well as socio-economic covariates. The estimated WTP can be interpreted as the household’s WTP because we presented the question based on the “income tax paid by respondent’s household” in the survey. The null hypothesis is that all coefficients of the estimated equation are zero, and the Wald statistic is generally used to test this. According to the results of the Wald statistic, this hypothesis was rejected at the 1% significant level in all three models. This means that the estimated equation is statistically appropriate.

Table 5 Estimation results of three models
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First, in the model 1 results, all estimates are statistically significant at the 1% level. The estimated mean WTP is KRW 7222.55 (USD 6.45) per household per year and is statistically significant at the 1% level. Second, in model 2 results, sex and income are not statistically significant. In contrast, age and anti-dust expense are statistically significant at the 1% level, and the estimated covariates of these variables are positive. This indicates that respondents who are older or spend more money on anti-dust expenses are willing to pay more for building a UFP monitoring and reporting system. The estimated mean WTP derived from model 2 is KRW 7196.33 (USD 6.43) per household per year and statistically significant at the 1% level. Third, in the results of model 3, age, seriousness, and adverse health effects are statistically significant at the 5% level, and the reliability of real-time PM data and perception of UFPs are statistically significant at the 1% level. The estimated coefficients of these variables are positive, which indicates that respondents who are older, who take PM concentration in Korea seriously, who think that PM has a negative impact on their health, or who possess deeper background knowledge of UFPs are willing to pay more. Particularly, in model 3, the coefficient of the reliability of real-time PM data is the highest, which can be interpreted as follows: the more reliable the PM information provided by the government, the greater the WTP for building a UFP monitoring and reporting system. The estimated mean WTP is KRW 6958.55 (USD 6.22) per household per year and statistically significant at the 1% level. Excluding protest WTP respondents, mean WTPs were KRW 7459.89, 7429.49, and 7214.91 for the three models, respectively. These estimated WTPs were increased by 3.29%, 3.24%, and 3.68%, respectively, compared to the results including protest WTP respondents.

Additionally, we conducted the Monte Carlo simulation, which is a parametric bootstrapping technique, to calculate the 95% and 99% confidence intervals of the estimated WTP to reflect the uncertainty (Krinsky and Robb 1986; Lee and Cho 2020). Based on the estimated coefficients and their variance–covariance matrix, we generated a multivariate normal distribution from 5000 replications and calculated 5000 mean WTP values. Thereafter, we listed the calculated mean WTPs in order of magnitude and excluded 2.5% and 0.5% at both ends to obtain 95% and 99% confidence intervals, respectively. The last two rows of Table 5 present 95% and 99% confidence intervals of the WTP, respectively, derived from the Monte Carlo simulation. Figure 2 shows the 95% confidence interval plots of the mean WTP by models according to whether the protest WTP responses were included.

Fig. 2
figure 2

95% confidence interval of mean WTP by models [USD]

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Expanding the household-level WTP to the total population could be helpful in estimating the national level economic value of UFP information. As the total number of households was 23,093,108 in 2020 (MOIS 2021), the national level annual WTP is KRW 166.79 billion (USD 148.95 million). We presented 5 years for the payment period in our survey scenario, so the total WTP to receive UFP information nationwide is approximately KRW 833.96 billion (USD 744.75 million).

Discussion

The negative health effects of PM have been demonstrated by several studies, and Korea—like numerous other countries—allocates enormous annual budgets to reduce PM emission and improve air quality. Korea’s Ministry of Environment (ME)’s 2021 budget for atmospheric environment is KRW 2922.71 trillion (USD 2.60 trillion) (ME 2021b). Of late, concerns about UFPs beyond PM2.5 have increased in Korea; however, studies that can reliably demonstrate their potential risks are insufficient. Moreover, the current measurement methods are not suitable for UFPs that have characteristics different from other air pollutants. Obtaining accurate information about UFPs is necessary to conduct studies on their emission sources, characteristics, and health effects. Therefore, we estimated the economic value of UFP information using the CVM, and the scenario was building a new UFP monitoring and reporting system.

The mean WTP was estimated as KRW 6958.55–7222.55 (USD 6.22–6.45) per household per year, applying three models. Expanding the household-level WTP to the total population, the national level annual WTP is KRW 166.79 billion (USD 148.95 million). In 2021, Korea allocated a budget of about KRW 70 billion (USD 62.51 million) for the establishment and operation of an air pollution monitoring system in 2021 (ME 2021b). This means that, in the future, the feasibility of the project can be secured when the government implements the building of the UFPs monitoring and reporting system. People are willing to pay more than the budget allocated for current installation and operating of monitoring systems because they want air pollutant information (Beaumont et al. 1999; Veloz et al. 2020). If the collected UFP data is disclosed equivalently to the current air pollutant data, the government will be able to obtain public consent to expand the UFP monitoring and reporting system nationwide. In addition, we analyzed the cognition level of respondents regarding PM on the WTP. We identified that the cognitive level regarding PM rather than income had a positive effect on the WTP. In other words, people who are already experiencing inconveniences from high PM, such as increased expenditure on anti-dust products and deteriorating health, desire UFP information. People perform various anti-dust activities, such as wearing face masks, refraining from outdoor activities, or using air purifiers, to avoid exposure when PM concentration is high (Wells et al. 2012; Saberian et al. 2017; Cho and Kim 2019). They use real-time PM information to decide upon such behaviors. Consequently, they believe that accurate information is important to prevent potential risks from PM exposure, and therefore, their WTP for UFP monitoring and reporting system is high. Additionally, we found that the more reliable the PM information provided by the government, the higher the WTP for a UFP monitoring and reporting system. Thus, it is important to accurately measure PM concentration and provide reliable PM information to increase public acceptance of UFP monitoring and reporting systems. Furthermore, we identified that the higher the level of perception of UFPs, the higher the WTP. Most of our respondents had a low perception of UFPs, but it is expected that the need for UFP information will increase in future as public perception of UFP increases. Therefore, policymakers should continuously monitor the public’s perception of UFPs and allocate budgets and establish policies so that the UFPs monitoring and reporting system can be implemented at an appropriate time.

To the best of our knowledge, there are no studies on the economic value of UFP information, but a few studies have estimated the economic value of air quality improvement. Table 6 shows previous studies that used the CVM to estimate the WTP for air quality improvement. The different scenarios used in previous studies to explain specific methods for air quality improvement include strengthening policies (Wang and Zhang 2009; Kim et al. 2018), reducing air pollutants (Wang et al. 2006; Akhtar et al. 2017), and reducing the risk of mortality due to air pollution (Lee et al. 2011; Vlachokostas et al. 2011; Istamto et al. 2014; Sun et al. 2016; Ligus 2018). Direct comparison of previous studies’ WTP with our result is difficult because there are differences as regards survey year, scenarios, payment vehicle, and survey unit (per household, per person). However, in general, the WTP derived from the scenario of avoiding death, estimated in Western countries, is higher than those derived from the scenario of improving air quality, estimated in Asian countries. In terms of payment vehicle, studies analyzing the WTP for air quality improvement mainly selected tax increases and voluntary payments. Kim et al. (2018) estimated the WTP for strengthening the PM2.5 concentration reduction policy in Korea using the OOHBDC. They asked respondents about their WTP for policy enhancement, such as tightening the overall regulation of the PM2.5 sources, expanding the concentration monitoring station, and improving the management of deteriorated diesel vehicles. They used an annual income tax increase as the payment vehicle and found that the mean WTP for the enforcement of the PM2.5 concentration reduction policy was USD 4.97 (USD 5.09 in 2020) per household per year. Wang and Zhang (2009) estimated the WTP for improving air quality through enforcing a strict national air quality standard in Jinan—a Chinese city with the poorest air quality. They asked respondents how much they were willing to pay voluntarily and found that the WTP was CNY 100 (USD 22.29 in 2020) per person per year.

Table 6 Summary of the scenario and estimated WTP of the previous studies using the contingent valuation method
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Conclusions

This study estimated the economic value of UFP information. Since data collection must be preceded to provide UFP information to people, we assumed to build the UFP monitoring and reporting system and estimated the annual WTP—income tax—of respondents using CVM. The estimated WTP is KRW 6958.55–7222.55 (USD 6.22–6.45) per household per year, and the national level WTP is KRW 166.79 billion (USD 148.95 million) per year. In addition, we analyzed the cognition level of respondents regarding PM on the WTP. Based in the estimation results, in order to increase the acceptance of UFPs monitoring and reporting system, it is important to accurately measure PM concentration and provide reliable PM information.

As mentioned in the “Introduction” section, previous studies on UFPs have mainly focused on characterizing the components and analyzing their effects on health using temporarily measured UFP data; however, this study is meaningful in that it is the first study to estimate the economic value of the monitoring system for measuring UFP data, which is the basis for studies searching for scientific evidence. However, it also has several limitations. First, the survey was online, which means only the internet savvy could participate. As shown in Table 1, the distribution of sex, age, and income for the respondents was similar to that of the total population, but the respondents’ education level was slightly higher than that of the general population. This could be because the internet-literate generally have a relatively higher education level. In general, the WTP among the highly educated is high (Wang et al. 2006; Wang and Zhang 2009; Kim et al. 2018). We employed an online survey because of COVID-19, but a face-to-face survey should be conducted in future to overcome this limitation and to obtain results that are more accurate. Second, we cannot compare the estimated results of this study with previous studies because to the best of our knowledge, there are no similar studies on the subject and scenarios. Given that concerns about UFPs are increasing, more studies are required to estimate their potential risks and social costs. Third, our respondents had generally low perception of UFPs. We found that respondents with pre-survey knowledge of UFPs placed a higher value on UFP information. Therefore, there is a possibility that the WTP will change as the perception of UFPs changes in the future; therefore, a follow-up research is imperative.