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. 2020 Sep 3;18(2):1029–1035. doi: 10.1007/s40201-020-00524-6

Relationship between international tourism and concentrations of PM 2.5: an ecological study based on WHO data

Farhad Hemmati 1, Fatemeh Dabbaghi 1,✉, Ghahraman Mahmoudi 1
PMCID: PMC7721785  PMID: 33312621

Abstract

Tourism is regarded as a major global industry. Given the importance of identifying factors affecting the tourism industry and attracting international tourists, the present ecological study explored the impact of environmental pollution on the number of international tourists arrival using concentrations of PM2.5 (particulate matter 2.5 μm or less in size) in a multivariate framework under the context of 190 countries. Using panel data from 190 countries, the author explored the data on the number of international tourists arriving in countries in 2017 extracted from the World Bank (WB) website, and obtained the information about the concentrations of PM2.5 from the World Health Organization (WHO) website. Pearson’s correlation coefficient and linear regression analysis were used to examine the correlation of the number of tourists with the variables of daily concentrations of PM2.5, societal safety, international conflict, and the relationship of tourist arrival with the studied variables, respectively. The number of countries with low, moderate, and high concentrations of PM2.5 in urban areas was 33, 116, and 41, respectively. This numbers for rural areas was 47, 102, and 42 countries, respectively. The mean concentrations of PM2.5 in the surveyed countries was 23.90 ± 15.81 and 25.69 ± 16.76 for rural and urban areas, respectively. The estimation results revealed that there was a significant correlation between the number of tourists with the concentrations of PM2.5 in the rural areas (p = 0.01). There was also a significant relationship between the human development index (HDI) and the concentration of PM2.5. A significant relationship was observed in the results of univariate linear regression analysis between tourist arrival with rural concentrations of PM2.5 (p = 0.02) and societal safety (p = 0.003). After adjusting the effect of societal safety variables, domestic and international conflict, the relationship between tourist arrivals and concentrations of PM2.5 in rural area remained significant (p = 0.02). The results imply that by reducing the concentration of PM2.5 the positive attitude of tourists for traveling to countries with healthy air can be earned.

Keywords: PM2.5, Tourism, Societal safety, International conflict, Ecological study

Introduction

Today, tourism is regarded as one of the largest and most important industries in the world, causing many economic, social, and environmental changes [1, 2]. International tourism worldwide has grown by 4% in 2019 compared to 2018. The Middle East witnessed a 9% increase in tourist arrivals this year. Moreover, Asia, the Pacific, and Africa experienced 5%, Europe 3%, and the US 2% tourist growth [3]. The arrival of tourists to countries has advantages such as economic prosperity [4]. Tourism development leads to increased household incomes and direct and indirect government revenues. According to the literature, 10% spending for foreign tourists results in a 4% increase in gross domestic product (GDP) per capita [5, 6]. Medical tourism is another aspect of tourist attraction that has been growing in recent years. The benefits of medical tourism include the profitability of health systems in destination countries [7–9]. Many factors affect tourist attraction, including domestic insecurity, tourist security, and social, political, cultural, GDP, and economic policies [1, 2]. One of the factors affecting tourist attraction is the air quality status of destination countries [10–12]. Air quality, as an important component of weather and climate, is an external factor in tourism that can strongly influence tourism demand, travelers’ participation in activities, and the overall tourism experience [13]. Many tourism destinations are plagued by increasing air pollution such as haze; Cairo, Beijing, Naples, and Mexico City are among the top 50 most polluted cities worldwide [14]. Besides posing threats to destinations, air pollution has been acknowledged as an increasingly serious health risk to individuals’ physical well-being, affecting 90% of the world’s population. According to WHO’s official website, air pollution is a common cause of health problems such as lung cancer, stroke, and ischemic heart disease [15]. It not only harms the health of tourists, but also reduces air visibility, which directly influences tourists’ travel experiences. In recent years, several studies from various parts of the world have highlighted the subject of air pollution problem and tourism. A study was conducted by Becken et al. [16] on US and Australian residents, and they result showed that the perceived air quality risk in China has a significant negative impact on destination image and visit intention. Also, Peng and Xiao [17] examined the effect of smog on the domestic travel demand to Beijing, and found a significant indirect impact of travel risk perception on avoidance tendency, mediated by travel dissatisfaction and a negative destination image. Deng et al. [18] reported that industrial waste gas emissions in provinces of China have a significant negative effect. Air quality affects people’s willingness to travel to countries. Tourists are often suspicious of local service providers in areas with air pollution and, thus, travel is restricted to such areas. It is thought that tourist destinations with less natural beauty will be affected by air pollution. However, studies have demonstrated that many of these areas are also exposed to air pollution [12].

Previous studies have reported that PM2.5 and PM10 are two of the most frequently used air pollution indicators on the tourism industry. Different studies noticed that other air pollutants, such as SO2 and NO2, have received less attention in relation to tourism. Therefore, there was no consensus on the selection of air pollution indicators in previous studies. Researchers such as Zhou et al. have examined these factors and reported that only PM10 had a statistically significant impact. [19–21]. although PM is not the only component of air pollution, it is considered as a common proxy indicator for air pollution because it affects more people than do other pollutants [22, 23]. Its main components include sulfate, nitrates, ammonia, sodium chloride, black carbon, mineral dust, and water [22]. Overall, the selection of the air pollution indicator mostly depends on the degree of convenience in data collection.

In this study, we considered two major categories of factors, namely the air (environment) quality factor such as PM2.5 concentrations, and social condition such as security and ongoing domestic and international conflict. Given the importance of attracting tourists for the economic boom of 190 countries and the impact of PM2.5 concentration on it, and since there had not been an ecological study on countries, this study aimed to determine the relationship between the numbers of international tourist arrivals with the concentrations of PM2.5.

Method

This was an ecological study conducted on 190 countries. Countries with the required information were included in the study, and countries without information were excluded. Our study was based on the data provided by the World Health Organization (WHO) in 2016 and the World Bank website in 2017.

Air pollution factor

Based on WHO reports and international and national studies on chronic diseases, some important air pollutants are ozone, fine particles (PM2.5), and household air pollution. Information regarding measures of air pollution was obtained for 190 countries [24]. For particulate matter with diameter < 2.5 μm (PM2.5), countries were classified using the Air Quality Index website (https://aqicn.org) into three groups of low (<12), moderate (12–35.4), and high (>35.4).

Number of arrivals

Information regarding the number of arrivals of international tourists was extracted from the World Bank website in 2017 [25].

Confounding variables

To compare the effect of other variables related to tourist arrival, the indicators of societal safety and security and ongoing domestic and international conflict were also investigated. A score was reported for both of these indicators. The most peaceful countries were given the lowest score and the least peaceful countries received the highest scores. The information on these two variables was obtained using the data reported by the Institute for Economics and Peace (IEP) [26]. Also, the information about the human development index (HDI) was acquired from the United Nations Development Programmed (UNDP) websites [27]. Because the information on the number of tourist arrivals was available for 2017 only, the 2017 information was also used for HDI, societal safety, and domestic and international conflict. Although countries were different in size and population, the crude number of tourists was considered as the dependent variable since tourist arrivals are not necessarily influenced by the population or size of the destination country.

Statistical analysis

We analyzed the data with EXCEL, SPSS, and STATA software programs in several steps. We used the Pearson correlation coefficient to assess the correlation between tourist arrival and PM2.5 concentration, societal safety, and domestic and international conflict. Then, the tourist arrival variable was entered in the univariate linear regression model as a dependent variable, and its relationship with PM2.5 concentration, societal safety, and domestic and international conflict was investigated in three separate analyses.

To control the effect of confounding variables, societal safety and domestic and international conflict along with the concentration of PM2.5 were entered in the multivariate linear regression model.

Yijk is the tourist arrival variable, Ui is the concentration of PM2.5, and Cj and sk are societal safety and domestic and international conflict, respectively. Also, eijk is the residual effect and α is the sample estimate for the vertical intercept of the regression line.

Results

PM2.5 concentrations in different countries

The number of countries with low, moderate, and high concentrations of PM2.5 in urban areas was 33, 116, and 41, respectively. This numbers for rural areas was 47, 102, and 42, respectively (Table 1). The mean PM2.5 in the surveyed countries was 23.90 ± 15.81 and 25.69 ± 16.76 for rural and urban areas, in that order.

Table 1.

Concentration of PM2.5 in urban and rural areas

Concentration PM2.5 Urban
Number (%)		 Rural
Number (%)
Low 33 (17) 47 (24.2)
Moderate 116 (59.8) 102 (52.6)
High 41 (21.1) 42 (21.6)
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Figure 1 shows the concentration of PM2.5 pollutants in different countries in 2016. New Zealand had the lowest concentration of PM2.5 (5.7 μg/m3), followed by Brunei (5.8 μg/m3) and Finland (5.9 μg/m3 (. On the other hand, Nepal (94.3 μg/m3), Qatar (90.3 μg/m3), and Egypt (79.3 μg/m3) had the highest level of PM2.5, respectively.

Fig. 1.

Fig. 1

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Global map of the modeled annual median concentration of PM2.5 in μg/m3, WHO

Countries with low human development had the highest concentration of PM2.5 (mean PM2.5 = 35.05). Moreover, the lowest concentration of PM2.5 belonged to countries with very high human development (mean PM2.5 = 18.53). There was a significant relationship between the concentration of PM2.5 and the human development index (p > 0.0) (Table 2).

Table 2.

Mean PM2.5 concentration according to the human development index

concentration of PM2.5
Human development index Mean (SD) † Minimum Maximum p value
Low human development 35.05 (13.33) 10.9 70.80 <0.001
Medium human development 33.16 (20.42) 9.4 94.3
High human development 21.55 (9.33) 7.6 49.2
Very high human development 18.53 (17.08) 5.7 90.3
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†Standard deviation

Correlation coefficient

There was a statistically significant correlation between the tourist arrivals with PM2.5 in rural areas (Fig. 2). In other words, with an increase in air pollution in the rural areas, the number of international tourists to the countries decreased. This correlation was not significant for urban air pollution. The correlation between tourist arrivals and societal safety was −0.24, which was statistically significant (Fig. 3). However, the correlation between tourist arrival and domestic and international conflict was not statistically significant (Table 3).

Fig. 2.

Fig. 2

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Correlation between the number of tourist arrivals and PM2.5 concentration

Fig. 3.

Fig. 3

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Correlation between the number of tourist arrivals and societal safety and security

Table 3.

Correlation between the number of tourist arrivals and variables

Tourist arrival r† P value
Pm‡2.5)Rural( −0.18 0.01
PM2.5 (Urban) −0.13 0.07
Societal safety −0.24 0.003
Domestic and International Conflict −0.12 0.1
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†Pearson correlation coefficient

‡Particulate matter

Univariate regression analysis

Based on the results of univariate linear regression analysis, a significant relationship was found between tourist arrival and rural air pollution (p = 0.01). Nevertheless, this relationship was not significant for urban air pollution (p = 0.07). Accordingly, the average number of tourist arrivals per country was reduced by ~159 individuals per one unit of increase in rural areas by PM2.5. Moreover, tourist arrival was significantly associated with the societal safety index, but this relationship was not significant for indices of urban air pollution and domestic and international conflict (Table 4).

Table 4.

Univariate regression analysis on the relationship between the number of tourist arrivals and the investigated indexes

tourist arrival B† CI‡ P value
PM2.5¥ (Rural) −159.16 (−288.18,-30.15) 0.01
PM2.5 (Urban) −112.54 (−236.29,11.21) 0.07
societal safety −5806.05 (−9663.732,-1948.36) 0.003
Domestic and International Conflict −2870.86 (−6769.77,1028.0.3) 0.1
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†regression coefficient

‡Confidence interval

¥Particulate matter

Multivariate regression analysis

In the next step, the relationship between tourist arrival and rural air pollution was investigated. After adjusting for the effects of societal safety and domestic and international conflict variables, the relationship between tourist arrival and rural air pollution remained significant (p = 0.02). The results were similar for societal safety, and the relationship between tourist arrival and societal safety was significant (p = 0.01). However, this relationship was not significant for the domestic and international conflict variable (p = 0.1) (Table 5).

Table 5.

Multivariate regression analysis on the relationship between the number of tourist arrivals and the investigated indexes

B† CI‡ P value
PM¥2.5(Rural) −197.06 (−368.1757,-25.95) 0.02
societal safety −6800.45 (−12,429.79,-11.71.10) 0.01
Domestic and International Conflict 4750.59 (−1034.66,10,535.86) 0.1
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†Regression coefficient

‡Confidence interval

¥Particulate matter

Discussion

The analyses showed three important findings. First, it was confirmed that countries with low and high human development had the highest and lowest concentration of PM2.5 with 35.05 and 18.53 μg/m3, respectively. Also, New Zealand, Brunei, and Finland had the lowest concentration of PM2.5 (5.7–5.9 μg/m3), whereas Nepal, Qatar, and Egypt had the highest level of PM2.5 (79.3–94.3 μg/m3).

In rural areas particulate matters are emitted from the natural sources such as deserts in sub-Saharan Africa, Saudi Arabia, China and North America while in urban areas, particulate matters are emitted from a wide-range of sources including transportation and industrial activities and their source and concentration are varied compared to urban areas [28].

Jaffari et la reported the high daily concentrations of PM in the dusty days were observed at the rural site as compared to the urban site. The high amount of PM during the dust storm is attributed to the large deserts located at the west and south of the country and they are known as the major sources of dust storms in Iran [29].

Another finding was that there was a significant relationship between HDI and the concentration of PM2.5. The highest concentration of PM2.5 in this study was in countries with low HDI. Also, with increasing the HDI, the concentration of PM2.5 decreased. A study by Santra et al. showed that the quality of air is reflected through HDI for developing countries [30]. The relationship observed in this study can be due to the use of coal and biomass in the form of wood, dung and crop residues in this countries [31].

The concentration of PM2.5 in urban areas was ~2 units (μg/m3) higher than in rural areas. This small difference is not surprising. In areas such as Africa and the Eastern Mediterranean in 2016, even the average concentration of PM2.5 was higher in rural areas than in urban areas. Rural environments are mainly more exposed to dust due to soil-covered lands and unpaved roads, and this dust is transported by the wind. Pesticides/insecticide sprays, biomass fuels, coal fires, and contaminants drawn from animals, plants, and land resources are other sources of air pollution and PM2.5 concentration [32].

Secondly, it was confirmed that air pollution, measured by PM2.5 concentration, exerts a harmful effect on tourist arrival in rural areas. There was a statistically significant correlation between tourist arrivals and PM2.5 in the rural areas. Therefore, with the increase in air pollution in the rural areas, the number of international tourists to the countries decreased. This finding is consistent with that of previous studies (e.g., [33–35]).

Xiaowei et al. explored the impacts of different air pollutants on domestic and inbound tourism in China. Their study were based on a sample of panel data covering 337 Chinese cities for the period between 2007 and 2016, and four pollutant indicators were investigated: PM2.5, PM10, SO2, and NO2. The results demonstrated that PM2.5 had a significantly negative impact on both domestic and inbound tourist arrivals. Regarding the other three pollutant indicators, except for the negative influence of NO2 on inbound tourist arrivals, no statistically significant impactwas found [36].

A study conducted by Dong et al. in China showed that increasing PM10 reduces tourist arrivals in this country [11]. Deng et al. concluded that tourist arrivals in Chinese provinces correlated with the air pollution index [18]. In Wang’s study, a significant relationship was observed between the number of domestic and foreign tourists’ arrival with the concentration of PM2.5 [37]. A study by Law et al. showed that air quality was associated with international visitors [38]. Furthermore, Zhang et al.’s study indicated that people were less likely to travel to countries with high air pollution [12].

In contrast, in a study conducted by Gani et al. in New Zealand, there was no significant relationship between the arrival of international tourists and air pollution [39]. This result is inconsistent with our finding.

The reluctance of tourists to enter countries with air pollution can be due to people’s lack of confidence in the local service system of countries with high air pollution. Therefore, people refuse to travel to these areas because of their suspicion of the overall situation in the country [12]. In this study, there was no significant relationship between the concentration of PM2.5 in the urban areas and the number of tourists entering the countries. The existing studies have not examined this relationship for urban and rural areas separately. Since many trips to cities are due to business and jobs, the lack of association can be because unlike traveling to rural areas with good weather, some trips to the city are made with business purposes regardless of the weather.

The present study did not show a high correlation between tourist attraction and the concentration of PM2.5. This may be because tourist arrivals to countries are affected by many factors and is a multidimensional issue, with air pollution being one of them [1, 2, 10].

Third, the finding of this study proved that there was a significant relationship between the number of international tourists and the societal safety index. This finding is consistent with the results of the study conducted by Michalko et al., showing that security is an important factor in the successful growth of the tourism industry [40]. Shin et al. concluded that safety, security, and peace are related to the tourism industry [41]. Basically, any insecurity in an area causes tourists to change their minds about traveling there. The presence of tourists creates peace, while the arrival of tourists to a country requires security in that country, so that the tourists enter the country with a sense of security [41, 42]. The third finding was that there was no significant relationship between the number of international tourists entering the countries and domestic and international conflict. This result is inconsistent with that of other studies.

Harb et al. showed that war and terrorism had an impact on the Lebanese tourism industry [43]. A study by Bandara et al. also found that the civil war had an adverse effect on the tourism industry [44]. Moreover, based on the study by Asongu et al., violent demonstrations and murder had a negative effect on tourist arrivals [45]. A study by Pratt et al. on 111 countries concluded that there was a relationship between peace and tourism in middle-income and high-income countries, but not in low-income countries [42].

The discrepancy of the results of this study with some of the mentioned studies could be due to the examination of the domestic and international conflict index in this study which was not explored in previous studies. The contrast may also be due to the tourist attractions of some of the countries surveyed in this study, despite the internal conflicts.

Limitations of this study

This was an ecological study and could be affected by ecologic fallacy. There are also several factors that contribute to the entry of tourists to the countries besides air pollution. We attempted to resolve this limitation by adjusting the effects of social safety and conflicts. This study was restricted by several limitations, which actually provide promising directions for future research. Firstly, this study suffered from the lack of information for some countries. Despite these limitations, this was the first study to examine the relationship between the studied indicators and tourist arrival in 190 countries. Secondly, some other air pollutants, such as CO (carbon monoxide) and O3 (ozone), SO2 and NO2, were not investigated due to the limitation of data availability.

Conclusion

This study investigated the relationship between the concentration of PM2.5 and tourist arrival. The results demonstrated that the concentration of PM2.5 and the societal safety index played a dominant role in influencing tourist arrival in rural areas. Therefore, it seems that reducing air pollution, as well as increasing safety in countries, should have an important impact on tourist attraction. On the other hand, this study found that countries with a low human development index have a high concentration of PM2.5. Therefore, it is necessary to implement air pollution control plans to reduce air pollution in these countries.

Compliance with ethical standards

Conflict of interests

The authors of this article declare that they have no conflict of interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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