Quantitative Analysis of Health Risk Perception ... - ACS Publications

Article Cite This: Environ. Sci. Technol. 2018, 52, 13824−13833

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Quantitative Analysis of Health Risk Perception, Exposure Levels, and Willingness to Pay/Accept of PM2.5 during the 2014 Nanjing Youth Olympic Games Lei Huang,*,†,‡ Jie Li,† Ruoying He,† Chao Rao,† Tsering J. van der Kuijp,§ and Jun Bi*,†

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†

State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China ‡ Lamont-Doherty Earth Observatory, Columbia University, P.O. Box 1000, 61 Route 9W, Palisades, New York 10964, United States § Department of Environmental Science and Public Policy, Harvard University, Cambridge, Massachusetts 02138, United States S Supporting Information *

ABSTRACT: Local governments in China regularly implement short-term emission control measures to improve air quality during important sporting events. As a condition for hosting the 2014 Youth Olympic Games (YOG), the Nanjing government agreed to temporarily and substantially improve air quality. Regression analysis, Spearman correlation analysis, χ2 test, and the contingent valuation method were used to explore the effects of robust, short-term air pollution control measures on risk perception, daily exposure to PM2.5, risk acceptance levels, and willingness to pay/accept (WTP/ WTA) for reductions in air pollution for the benefit of reducing health risks. Postimplementation, the respondents’ risk perception levels presented the following changes: during the YOG, the respondents perceived the lowest effects of haze pollution while after the YOG, they perceived the highest effects. The changes in risk acceptance levels showed the same tendency. Furthermore, after the YOG, the respondents asked for the most economic compensation, and their willingness to pay for risk reduction also reached the highest level. This study reveals the need to increase the public’s understanding of the health risks of air pollution, protect those populations most exposed to high levels of PM2.5, and take more effective long-term measures to meet local residents’ demands for improved air quality.

1. INTRODUCTION China has been suffering from hazardous air pollution due to the rapid development of its economy and heavy industry over the past several decades. Haze pollution accompanied by high concentrations of particulate matter (particularly PM2.5, which has an aerodynamic particle diameter of less than 2.5 μm) in the atmosphere have come under increased regulatory scrutiny due to their adverse impacts on the environment and public health.1−4 Severe haze pollution occurs frequently in cityclusters such as the Beijing-Tianjin-Hebei Region, the Yangtze River Delta (YRD), and the Pearl River Delta. Nanjing is one of the fastest growing megacities in the YRD, as a result, continued industrialization has increased air pollution and induced frequent haze in Nanjing. Previous studies have linked reductions in national or regional air pollution to governmental policies, national political realignments, and even large-scale sporting events.5−7 Robust, short-term emission control measures have frequently been implemented to improve air quality during important sporting events in China, most notably the 2008 Beijing Olympic Games, the 2010 Guangzhou Asian Games, and the 2010 Shanghai World Expo.8−11 As a condition for hosting the © 2018 American Chemical Society

2014 YOG, the local government of Nanjing agreed to temporarily and substantially improve air quality in Nanjing for the YOG. During the YOG from August 16−28 of 2014, Nanjing met its commitment to holding a “green youth Olympics”, and the number of days achieving the rate of “good” air quality accounted for 93.5% in YOG period. “Good” means that the air pollution index (API) is 0−50 based on the laws promulgated by the Ministry of environmental protection of China in 2012. In this case, the air quality is suitable for all segments of a population to perform outdoor activities. After the YOG, the Nanjing municipal government also made efforts to attain the air pollution control goals of the Action Plan issued by the Chinese State Council. Assessments of the effects of these short-term intervening measures can provide critical insights into the main sources of air pollution as well as how to design the most effective environmental policies.12−16 Received: Revised: Accepted: Published: 13824

March 27, 2018 October 14, 2018 October 16, 2018 October 16, 2018 DOI: 10.1021/acs.est.8b01634 Environ. Sci. Technol. 2018, 52, 13824−13833

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Environmental Science & Technology

Figure 1. Study routes and framework.

Figure 2. Location of study areas and sample size.

focused on the effects of powerful, short-term air pollution control measures on health risk perception, risk acceptance levels, willingness to pay (WTP) for the measures of helping reducing health risks to the population and willingness to accept (WTA) compensation for the health loss. Effective intervention strategies that seek to promote certain behaviors must first address individuals’ subjective judgments regarding air pollution and its related health risks.24 Therefore, the unusually low levels of pollutants that arose postimplementation and favorable weather conditions during the YOG offered a unique opportunity to combine an assessment of the YOG

Numerous health studies have indicated that the impacts of air quality improvements during large-scale sporting events have been associated with beneficial physiological health effects, including reductions in acute myocardial infarction rates17 and systematic inflammation,18,19 as well as improvements in cardio-respiratory health.20 Other studies have found that changes in certain behaviors were mostly affected by individual ’s varying perception of smog pollution.21,22 Individuals with higher perceived concerns, greater knowledge of smog pollution, and higher perceptions of its health risks were more likely to view air pollution as an unacceptable risk.23 However, to the best of our knowledge, no studies have 13825

DOI: 10.1021/acs.est.8b01634 Environ. Sci. Technol. 2018, 52, 13824−13833

Article


short-term air pollution control measures on health risk perception and daily exposure to PM2.5. In addition, the contingent valuation method was used to assess the amount that respondents were willing to pay for air quality improvements and willing to accept in compensation for the excess deaths caused by air pollution. 2.4.1. Risk Perception Analysis. Risk perception factors comprised three categories defined as Ef fect, Familiarity, and Trust. The domain in questions 3−7 corresponds to Ef fect, the domain in questions 1 and 2 corresponds to Familiarity, and the domain in questions 8−11 corresponds to Trust. For example, the items of domain “Ef fect” are as follows: Are the effects of the risk associated with haze immediate or will they take place in the future? Is the risk associated with haze a common risk or a terrible risk? Are you familiar with health risks associated with haze? How severe is the haze in your residence? How severely will haze impact your health? The other items are detailed in the SI. Confirmatory factor analysis was conducted using Lisrel 8.70 software, and the independent-samples t tests conducted using SPSS 22.0 software were used for comparative analysis of risk perception factors before, during, and after the YOG. 2.4.2. Regression Analysis. Regression models were employed to explore what factors influence the public’s perception of air pollution effects. The independent variables included individual characteristics. Age was defined as a continuous variable. Education was divided into six groups: no formal education = 1, primary school = 2, middle school = 3, high school = 4, college = 5, and postgraduate and above = 6. Monthly income was divided into nine ranges (Chinese Yuan, or CNY): < 4000 = 1, 4000−12 000 = 2, 12 000−20 000 = 3, 20 000−100 000 = 4, and >100 000 = 5. Other binary variables included gender, smoking, marriage, experience with severe haze, harm experienced due to haze, and chronic diseases. 2.4.3. Daily PM2.5 Exposure. The respondents’ daily PM2.5 exposure levels were calculated using eq 1, which is recommended by the U.S. Environmental Protection Agency (U.S. EPA).30 The equation links time-activity patterns to the exposure medium concentration normalized by weight.

emission reduction measures with a quantitative analysis of affected residents’ perceptions and behaviors. This paper was set to explore changes in public attitudes, including their health risk perception, acceptable risk levels of air pollution, and their willingness to pay/accept for reductions in air pollution for the benefit of reducing health risks before, during, and after the YOG. Previous studies have established the correlation between people’s behaviors and their opinions;25 we therefore set out to determine whether this correlation exists between people’s perception of the health risks of air pollution and their average daily PM2.5 exposure. Moreover, socioeconomic backgrounds can also alter respondents’ cognition of air pollution and their behaviors.26 Thus, the influencing factors of health perception were also explored. The conceptual framework of our study is shown in Figure 1.

2. METHODOLOGY 2.1. Study Site. As the capital of Jiangsu Province, Nanjing encompasses approximately 6600 km2, with a population of more than 8.2 million in 2013. The study area and sampling site of Nanjing are depicted in Figure 2. 2.2. Sample Selection. The respondents were recruited in public places of residential area, such as parks and markets by a stratified random sampling of those living in seven districts in Nanjing. Senior students in Nanjing University who had been well trained in survey techniques interviewed all respondents face-to-face. The research was approved for human subjects by institutional review board of Nanjing University. Three surveys each administered to 250 adults were conducted before, during (August 16−28, 2014), and after the YOG in Nanjing in 2014. The first round of surveys was administered in January 2014, with a total of 218 questionnaires returned (87.2% response rate). The second round of surveys was conducted in August 2014, with 228 questionnaires returned (86.8% response rate). The third round of surveys was administered in October 2014, with 217 questionnaires returned (87.6% response rate). 2.3. Questionnaire Design. The questionnaire was designed based on psychometric paradigm methods,27−29 with minor modifications based on Chinese residents’ circumstances. The questionnaire comprised five parts (see Supporting Information, SI). The first part included 11 questions to measure health risk perception regarding air pollution. The response to each question was ranked on a 5point Likert-type scale ranging from “1 = minimum” to “5 = maximum”. The second part included an introduction to the Action Plan, pictures showing the city landscape under different PM2.5 concentrations, and questions measuring the respondents’ acceptance of the PM2.5 concentration reductions. The third part investigated the respondents’ daily time-activity patterns. They were asked to recall their activities over the past 24 h in detail. The fourth part was a payment card to investigate the respondents’ willingness to pay/accept in response to the haze pollution. The last part of the questionnaire was designed to collect the respondents’ demographic characteristics, including body weight (BW), age, gender, education, income, marital status, smoking status, previous experience with severe haze pollution (referenced to SI Picture S3), preventative actions taken, and sources of air pollution information. 2.4. Data Analysis. Principal component analysis and independent-samples t tests were used for comparative analysis of risk perception factors. Regression analysis and Spearman correlation analysis were used to explore the effects of robust,

ADD = (C1 × IR1 × EF/24 + C2 × R × IR 2 × EF2 /24)/BW 1

(1)

where ADD is the average daily PM2.5 exposure (μg/kg·d); C1 is the average ambient PM2.5 concentration (μg/m3) of each outdoor activity time; C2 is the average ambient PM2.5 concentration (μg/m3) of each indoor activity time; IR1 is the respiratory rate of the outdoor activities (m3/d); IR2 is the respiratory rate of the indoor activities (m3/d); EF1 is the outdoor exposure time (h); EF2 is the indoor exposure time (h); R is the ratio of indoor to outdoor PM2.5 concentrations; and BW is the body weight of the respondent (kg). The respiratory rates of outdoor and indoor activities for males are 15.8m3/d and 15.1m3/d, respectively, and 14.1m3/d and 13.5m3/d, respectively, for females.31 The ratio of indoor and outdoor PM2.5 concentrations ranges from 1.01 to 1.08 according to Morawska et al.’s study, which has been frequently referenced in other studies.32 The real-time ambient PM2.5 concentrations were collected from the China Environmental Monitoring Centre (CEMC) (http://113.108.142. 147:20035/emcpublish/), while outdoor and indoor exposure times were collected from the questionnaire surveys. On the basis of these variables, we estimated individual ADD levels, and the average estimated ADD for all respondents represents the general public’s ADD. We then stratified individual 13826


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Environmental Science & Technology characteristics by gender, age, education, and income to examine the variations in PM2.5 exposure among different groups of respondents. One-way ANOVA analysis was used to do so. 2.4.4. Public Risk Acceptance Levels. A series of questions were set to evaluate the respondents’ opinions on acceptable levels of PM2.5. Three pictures showed the city landscape under the following scenarios: clean day (PM2.5 < 30 μg/m3), hazy day (PM2.5 = 30−90 μg/m3), and severe hazy day (PM2.5 = 90−210 μg/m3). The respondents were asked to rate their acceptance at five levels: “fully accept,” “easy to accept,” “basically accept,” “hard to accept,” and “do not accept” (see SI). According to the Action Plan issued by the Chinese State Council, which required PM2.5 concentrations to be decreased by 20% in 2017 compared with the mean level in the YRD in 2012 (67 μg/m3), we created two scenarios (2017 and 2022) and seven different degrees of PM2.5 concentration reductions for both scenarios. A scatter diagram with function fitting was employed to calculate the median (50%) of the respondents’ acceptance levels based on the seven degrees of PM2.5 concentration reductions. The results were analyzed using Sigma Plot 10.0 (Sigma Plot Software Inc.). 2.4.5. Respondents’ Willingness to Pay and Willingness to Accept Compensation in Response to Haze Pollution. A payment card was designed to investigate the respondents’ willingness to pay and willingness to accept. The respondents were asked to choose their maximum willingness to pay for three kinds of interventions that could protect their health from air pollution as well as their willingness to accept compensation for losses caused by air pollution. (See SI Part SIV). The contingent valuation method was used to assess the amount that people were willing to pay for air quality improvements and willing to accept in compensation for the excess deaths caused by air pollution. The contingent valuation method is based on constructing a hypothetical scenario that investigates how people respond to changes in environmental quality and how much they may pay for those changes, or their willingness to accept compensation for the loss of this good.33−36 The number of deaths caused by air pollution and the corresponding PM2.5 concentrations were calculated according to the following eq 2:37 case total = (caseap/((RR − 1) × E)) + caseap

(2)

where casetotal is the total number of deaths of the target population; caseap is the number of deaths caused by air pollution; RR is the relative risk caused by the unit concentration changes of pollutants; E is the target population’s exposure concentration to PM 2.5. The casetotal is the total number of nonaccidental deaths in 2012 in Nanjing, which is calculated based on the baseline mortality rate of nonaccidental death according to Jiangsu Provincial Center for Disease Control and Prevention and the total number of death according to Nanjing Statistical Yearbook in 2012. The RR value was set to 1.0036 according to Xiaochuan et al.37

Figure 3. Regression analysis of influencing factors on health perception (β is the regression coefficient for the relevant variables; *p < 0.05, **p < 0.01).

than the rest of the city, likely attributable to the fact that the undereducated are more likely to have difficulty understanding survey questions and completing a questionnaire easily. 3.2. Comparative Analysis and Determining Factors of Health Risk Perception. Figures S1, S2, and S3 present the path diagram of the revised final confirmatory factor analysis model on health risk perception of air pollution before, during, and after the YOG. The goodness-of-fit result of the confirmatory factor analysis can be seen in Table S2. The values of χ2/df, GFI, AGFI, IFI, and CFI largely aligned with the reference standard, indicating that the model aligned well with the date.

3. RESULTS AND DISCUSSION 3.1. Demographics. As shown in Table S1, the demographic characteristics of the respondents were similar to that of the local population in Nanjing. These characteristics include gender, age, and monthly income, but exclude education. The respondents were slightly more educated 13827


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Figure 4. ADD levels during three periods.

Table 1. Demographic Stratification Analysis of ADD one-way ANOVA demographic characteristics gender age

education

monthly income (yuan)

mean ADD μg/kg·d

stratification male female 60 below high school high school college postgraduate 100 000

F(gag)

before YOG

during YOG

after YOG

before YOG

during YOG

after YOG

34.16 35.72 36.85 35.53 33.06 33.77 34.57 31.81 37.77 33.37 35.23 31.35 35.13 34.84 34.06 33.47 34.30

4.67 5.26 4.88 5.24 5.20 5.09 4.22 3.44 5.27 5.07 4.83 5.16 4.79 5.04 5.33 5.11 5.29

16.54 19.73 17.43 18.37 19.68 16.99 18.00 13.86 19.33 18.56 17.32 16.26 19.50 16.96 16.67 15.59 15.18

1.616 (0.205) 1.262 (0.282)

18.258 (0.000) 18.971 (0.000)

56.171 (0.000) 11.362 (0.000)

2.396 (0.069)

1.717 (0.164)

4.338 (0.005)

0.180 (0.948)

1.162 (0.329)

11.127 (0.000)

YOG: p = 0.003; after YOG: p = 0.000). Residents became more and more familiar with haze pollution over the course of the study. These results reflect the public’s heightened risk perception levels to PM2.5 after the government implemented intervention and emission reduction measures during the YOG. On the basis of these heightened levels, it becomes clear that the short-term emission reductions do call the attention of the public, which may increase support for long-term action. Regression models were employed in our study to analyze the determining factors for health risk perception of air pollution, as shown in Figure 3a−c. The variable definition of the regression analysis can be seen in S-II. First, before the YOG, the selected variables had no significant effect on perceived Ef fect. Ef fect during the YOG

Public risk perception toward PM2.5 is shown in Table S3. In addition, we compared the differences in health risk perception during the three periods through an independent-sample t-test analysis. The results indicated that Ef fect during the YOG was the lowest during the three periods due to strong and effective emission reduction measures (before the YOG: p = 0.000; after the YOG: p = 0.001), while residents perceived the highest level of air pollution effects after the YOG. Meanwhile, Trust before and after the YOG were both significantly lower than Trust during the YOG (before the YOG: p = 0.000; after the YOG: p = 0.000). Trust after the YOG was higher than before the YOG, but there is no significant difference between them (p = 0.120). However, Familiarity during and after the YOG were both significantly higher than before the YOG (during 13828


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Environmental Science & Technology Table 2. Spearman Correlation Analysis among Public Risk Perception Factors, PM2.5 Concentrations, and ADDa before YOG

1

2

3

4

5

1. Effect 2. Familiarity 3. Trust 4. PM2.5 concentration 5. ADD during YOG 1. Effect 2. Familiarity 3. Trust 4. PM2.5 concentration 5. ADD after YOG 1. Effect 2. Familiarity 3. Trust 4. PM2.5 concentration 5. ADD

1.000

0.150* 1.000

0.082 −0.375** 1.000

−0.015 −0.116 0.119 1.000

−0.037 −0.144* 0.097 0.747** 1.000

1.000

0.079 1.000

0.056 −0.153* 1.000

0.012 −0.081 −0.079 1.000

0.016 −0.035 −0.085 0.661** 1.000

1.000

0.369** 1.000

0.024 −0.243** 1.000

−0.056 0.101 0.004 1.000

−0.137* 0.083 −0.042 0.466** 1.000

(**. p < 0.01 *. p < 0.05).

a

related to income (β = 0.177, p = 0.001), experience with severe haze (β = −0.530, p = 0001), and chronic diseases (β = −0.242, p = 0.033), suggesting that residents who have higher income levels, have experienced severe haze pollution, and/or have suffered from chronic diseases may be more sensitive to Ef fect. Second, Familiarity before the YOG was significantly influenced by gender (β = 0.201, p = 0.010) and experience with severe haze pollution (β = −0.247, p = 0.004), indicating that women and residents who had experienced severe haze pollution were more familiar with air pollution in general. Familiarity during the YOG was significantly influenced only by education (β = 0.122, p = 0.023), which indicates that the more educated one is, the more knowledge of haze pollution one tends to hold. Familiarity after the YOG was significantly influenced only by chronic diseases (β = −0.773, p = 0.000), suggesting that people who had suffered from chronic diseases were more familiar with air pollution. Third, the selected variables had no significant effect on perceived Trust before and during the YOG. Trust after the YOG was significantly influenced by smoking (β = 0.308, p = 0.020), education (β = −0.169, p = 0.015), and income levels (β = 0.098, p = 0.040), which indicates that smokers hold less trust in government. Moreover, the lower-educated and higherincome respondents displayed greater trust in the government. Previous researchers have discovered that the perception of health risks from atmospheric pollution could be influenced by many factors, such as gender, age, education level, income level, and environmental behavior.38−40 Our study also confirmed that differences in socio-demographic status may cause individuals to hold different perceptions of and responses to air pollution before, during, and after the YOG. Although the influencing variables are not consistent across time periods, the changes of these variables still emerged some regularity: Before and during the YOG, the significant influencing factors were less than those after the YOG, which indicated that the personal difference of the risk perception factors was not obvious during these two periods. While after the YOG, the crowd disparity was more obvious since there were more significant factors of risk perception of air pollution emerging under the strong policy interventions, which implies that

Figure 5. Public risk acceptable levels of air pollution reduction

Figure 6. Public willingness to accept compensation (WTA) for air pollution related deaths.

was significantly affected only by gender (β = −0.433, p = 0.000), which indicates that men may be more sensitive to the effects of haze pollution. Ef fect after the YOG was significantly 13829


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Figure 7. Comparison of risk perception, ADD, risk acceptance levels, and willingness to pay (WTP) and willingness to accept compensation (WTA) during the three periods.

lowest levels of PM2.5. These results are consistent with several previous studies, which confirmed that age, occupation, income level, and education level are associated with levels of exposure to toxic air pollution.42,43 Although residents over 60 are especially vulnerable to the adverse effects of haze pollution,44 they are exposed to the lowest ambient concentrations of PM2.5 because of reduced outdoor activities. The government’s attention is urgently needed to take active measures to help those aged 20−39, who face the highest level of PM2.5 exposure due to extended periods of outdoor activity. For example, notifications and warnings concerning haze days must be provided and the target population ought to be informed to reduce outgoing activities. 3.4. Correlation Analysis between Health Risk Perception Factors and ADD. Spearman correlation analysis was conducted to examine the relationship between public health perception and ADD among the three periods. As shown in Table 2, Familiarity before the YOG was significantly and negatively correlated with ADD, while Ef fect after the YOG was significantly and negatively correlated with ADD. The health perception of air pollution during the YOG had no significant correlation with ADD and PM2.5 concentrations. As shown in Table 2, respondents who perceived the higher Ef fect had the lower PM2.5 exposure after the YOG, which may indicated that respondents tended to be more sensitive and they were more likely to take protective measures to reduce the personal exposure. Cole-Hunter et al. have discovered that the public health risk perception of air pollution was positively associated with the estimated level.45 However, Dorizas et al. concluded that there was no significant correlation between perception of air pollution and measured pollutant concentrations.46 According to our study, respondents who were more familiar with and more sensitive to air pollution were exposed to less air pollution before and after the YOG. We can also see from Table 2 that Familiarity negatively correlates with Trust during the three periods, because residents usually learn about air pollution from all kinds of social medias, which has been demonstrated a negative influence on political trust if they know more about air pollution in previous studies.47,48 It

sensitive groups appeared in increasing numbers after the intervention policies. Generally speaking, in addition to traditionally sensitive groups such as residents with chronic diseases,41 (1) men, (2) residents with higher income levels, and (3) residents who experienced a severe haze pollution perceived a greater risk of haze pollution in our study. People (1) with different income, (2) education levels, as well as (3) smoking status have significant changes of trust after YOG. In addition, the government should prioritize its communications and messaging regarding air pollution mitigation toward (1) men, and (2) residents with lower education. 3.3. Comparative Analysis and One-Way ANOVA of ADD. The mean values of the individual daily PM2.5 exposures before, during, and after the YOG were 36.46 μg/kg·d, 4.95 μg/kg·d, and 17.92 μg/kg·d, respectively. As seen in Figure 4a−c, before the YOG, the public had the highest levels of PM2.5 exposure, while they had the lowest levels during the YOG. After the YOG, residents’ ADD levels increased but remained lower than those before the YOG. As shown in Table 1, according to our calculations and oneway ANOVA of ADD during the three periods, there were significant disparities among certain demographic characteristics, indicating that these characteristics may influence the respondents’ actual ADD levels. No significant association was discovered between the public’s ADD and demographic characteristics before the YOG in this study. The public’s ADD during the YOG was significantly associated with gender (F = 18.258, p = 0.000) and age (F = 18.971, p = 0.000), demonstrating that women had higher levels of PM 2.5 exposure. In addition, people at the 20−29 age group had the highest levels of PM2.5 exposure, while those older than 60 were exposed to the lowest levels of PM2.5. The public’s ADD after the YOG was significantly associated with gender, age, education level, and income level (F = 56.171, p = 0.000; F = 11.362, p = 0.000; F = 4.338, p = 0.005; F = 11.127, p = 0.000). These phenomena reveal that after the YOG, women and residents with lower levels of education and income had higher levels of PM2.5 exposure. Moreover, the results also indicate that the residents aged 30−39 had the highest levels of PM2.5 exposure, while those over 60 were exposed to the 13830


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and providing an air purifier were the highest during the periods, and the proportion of respondents willing to pay for measure 1: setting up an early warning system for extreme weather took the second place. Therefore, a reminder of severe haze weather and health protection measures should be provided especially for the residents aged 20−39, who were exposed to the highest level of PM2.5 concentrations. Besides, the information concerning health risks from haze pollution must be provided in a timely manner through education and risk communication. We found that the proportion of respondents willing to pay for these three protective methods did not exceed 50% during the YOG, and more than one-half of the respondents refused to pay at all. Of these unwilling respondents, the majority thought it was the government’s responsibility to pay for protective measures in response to haze pollution. The values for the proportion of respondents willing to pay, the per capita payment, and the total willingness to pay for the three measures all increased after the YOG compared to those during the YOG. We concluded that after the government implemented effective emission reduction measures, residents became more willing to take measures to protect themselves from the health risks of haze pollution. 3.7. Estimates of Willingness to Accept Compensation. Table S9 contains a statistical summary of the compensation that people are willing to accept for each scenario of risk damage. The cost of air pollution-induced health-related losses could be described as a quadratic equation of the PM2.5 concentrations (see Figure 6). We calculated the total compensation that Nanjing residents were willing to accept in accordance with a PM2.5 concentration of 74 μg/m3. The value of health costs for the total population in Nanjing was 123.62, 80.71, and 138.97 billion CNY before, during, and after the YOG, respectively. In summary, before the YOG, residents’ willingness to accept compensation was higher than that during the YOG. However, after the YOG, residents’ willingness to accept compensation was the highest of the three periods. After the YOG, residents required higher compensation for healthrelated losses than before and during the YOG. We concluded that after the YOG, residents realized that the government had the capability as well as responsibility to take effective measures to reduce PM2.5. According to our study, the mean values for willingness to accept compensation before, during, and after the YOG obtained in this study were 200 times greater than the mean values for willingness to pay (360.18 million, 282.53 million, and 542.82 million CNY, respectively). Willingness to pay and willingness to accept can be explained as the income expenditure in support of environmental improvement and the willingness to accept compensation after the occurrence of environmental damage.49 A number of studies have found that the value of willingness to accept compensation is far greater than that of willingness to pay, which is consistent with our results.50−52 The disparity between willingness to pay and willingness to accept compensation has great importance for environment management; the welfare loss caused by destroying or polluting the environment is much greater than the welfare income generated by protecting and improving the environment. Therefore, the government should take proactive measures to prevent or mitigate environmental and health losses. For example, the government can established an early

indicated that this intervention policy did not effect the relationship between Familiarity and Trust. Therefore, the government should actively communicate with the public through social media and let them know more about emission reduction measures implemented to improve public trust gradually. What’s more, measures that promote risk communication and awareness can in turn reduce people’s exposure to air pollution, as knowledge of its effects can induce people to take active steps to protect themselves. 3.5. Analysis of Public Risk Acceptable Levels (PRAL) of Air Pollution. The specific proportion of public attitudes toward different air pollution control policies before, during, and after the YOG is exhibited in Tables S4−S6. The average PM2.5 levels during these three periods are shown in Table S7. The scatter diagram with function fitting in Figure 5 reveals the relationship between the reduction rate of PM2.5 concentrations and public acceptance levels for the two scenarios before and during the YOG. As expected, risk acceptance increased with rising reductions in PM2.5 concentrations in each scenario. Before the YOG, 50% of the investigated respondents considered it acceptable to have a PM2.5 concentration reduction to 57.58 μg/m3 by 2017 (a 9.22 μg/ m3 reduction since 2014). Furthermore, this concentration must decrease to 52.08 μg/m3 by 2022 for 50% of the respondents (a 14.92 μg/m3 reduction since 2014), which requires the PM2.5 concentration reductions to follow a continuous trend and for more mitigation measures regarding air pollution to be developed. The average PM2.5 level in Nanjing in 2017 was about 67 μg/m3. During the YOG, 68.54% of the respondents considered that level to be acceptable even if the PM2.5 concentrations did not decline. However, according to Figure 5, the acceptable PM2.5 concentration should decrease to 59.2 μg/m3 by 2022 (a 7.80 μg/m3 reduction since 2014) in order to satisfy 50% of the respondents. After the YOG, 50% of the investigated respondents considered it acceptable to have a PM2.5 concentration reduction to 47.69 μg/m3 by 2017(a 19.31 μg/m3 reduction since 2014). The acceptable PM2.5 concentration should decrease to 37.50 μg/m3 (a 29.50 μg/m3 reduction since 2014) for 50% of the respondents by 2022. During the YOG, residents’ risk acceptance level of PM2.5 was at its highest. The PRAL of air pollution after the YOG required a higher PM2.5 concentration reduction rate than the reduction rate required before the YOG by 2017. This same tendency was found according to the second landscape shown to the study respondents: a higher PM2.5 concentration reduction rate needs to be attained after the YOG than before the YOG by 2022. The results indicate that residents usually hope to maintain the current good air quality status and will even raise their standards for stricter environmental controls after experiencing better air quality. 3.6. Estimates of Willingness to Pay. The aggregate statistics of the willingness to pay distribution (see Table S8) reveal the following insights: during the YOG, the proportion of people willing to pay for early warning systems and protective measures decreased, but the per capita payment increased compared to that before the YOG, while the situation for measure 3 showed different results. After the YOG, the proportion of residents willing to pay for the three measures all increased, as did the per capita payment. Moreover, the results showed that the proportion of respondents willing to pay for measure 2: giving out masks 13831



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ACKNOWLEDGMENTS We thank Wenjie Kuang, Can Zhang, Linli Liu, Qianqi Yang, and Penghui Liu for questionnaire distribution assistance and advisory services. This work was supported by the Chinese Natural Science Foundation (41822709, 41571475, and 21637002) and the Fundamental Research Funds for the Central Universities.

warning system for extreme weather, distribute masks during haze-polluted days, etc. 3.8. Implications. As seen in Figure 7, great differences in health perception levels, ADD, public acceptance levels, and willingness to pay and willingness to accept compensation for air pollution before, during, and after the YOG were revealed. During the YOG, the public was exposed to the lowest concentrations of PM2.5 and was most accepting of the risks posed by haze. After the YOG, however, residents became more sensitive to haze pollution and demanded higher air quality as the government implemented strong policy interventions to cut emissions. The resulting change was characterized by a greater willingness to pay for risk reduction measures and a greater willingness to accept compensation for health-related losses. In order to meet the stronger demand for clean air, longterm and effective measures should be taken, as opposed to short-term interventions such as those taken during the YOG. In addition to traditionally sensitive groups such as residents with chronic diseases, (1) women, (2) the middle-aged, (3) people with lower levels of education, and (4) lower-income individuals also require special protections. Furthermore, there was a significant negative correlation between ADD and Trust before the YOG. To remedy the situation, targeted communications and messaging aimed at these sensitive groups should be employed to validate their perception of air pollution risks and encourage them to proactively protect themselves. Although our results are slightly more applicable to a population with higher overall education levels, they can help local governments gain a better understanding of risk management boundaries and develop tailored health risk management strategies suitable for people of varying socioeconomic characteristics. This study would help local governments identify the most at-risk populations, improve awareness of health risks, and influence citizens to take initiative and protective measures to mitigate the effects of air pollution.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b01634. Questionnaires and the variable definition of the regression analysis; tables showing demographic characteristics of respondents, goodness-of-fit statistics, public risk perception of PM2.5 concentrations, the average daily PM2.5 concentrations, public risk acceptance of different PM2.5 concentrations, and public willingness to pay/accept during the three periods; and figures showing path diagram of the revised final confirmatory factor analysis model (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*Tel: 025-89680535; e-mail: [email protected] (L.H.). *Tel: 025-89680535; e-mail: [email protected] (J.B.) . ORCID

Jie Li: 0000-0002-2739-9280 Notes

The authors declare no competing financial interest. 13832


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Quantitative Analysis of Health Risk Perception ... - ACS Publications

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