Next Article in Journal
Abdominal Cutaneous Thermography and Perfusion Mapping after Caesarean Section: A Scoping Review
Next Article in Special Issue
Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia
Previous Article in Journal
Quality of Life and Concerns in Parent Caregivers of Adult Children Diagnosed with Intellectual Disability: A Qualitative Study
Previous Article in Special Issue
Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan

1
Department of Civil Engineering, National Central University, Taoyuan 32001, Taiwan
2
Research Center for Hazard Mitigation and Prevention, National Central University, Taoyuan 32001, Taiwan
3
Fire Bureau, Hsinchu County Government, Hsinchu County 30295, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(22), 8691; https://doi.org/10.3390/ijerph17228691
Submission received: 28 October 2020 / Revised: 17 November 2020 / Accepted: 19 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

Abstract

:
In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced in 2017, “suspended particulate matter” has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area.

1. Introduction

Air pollution is a topic of concern worldwide; it affects the atmospheric and ecological environment and poses a serious threat to the health of humans. Owing to the rapid development of the modern industrial society, global climate change, and increasing environmental awareness of people, air pollution has received increasing attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced on 22 November 2017, “suspended particulate matter” has officially been acknowledged as a disaster-causing hazard.
Haze is caused by extremely small dry particles in the air, which impair visibility. Suspended particulate matter can be classified according to the particle diameter. Particles with sizes of less than 10 μm are PM10, and those with sizes of less than 2.5 μm are PM2.5. The different particle sizes have different effects on the human body; PM2.5 is smaller than PM10 and can therefore penetrate the human cilia and mucus, reach the bronchi and alveoli and then the walls of the bronchioles, and finally interfere with the gas exchange in the lungs. In addition, PM2.5 is more easily suspend in air, does not settle easily, and interacts with other air pollutants [1,2]. Once inhaled by a human, PM2.5 can reach the depth of the lungs and even penetrate the alveoli and enter the cardiovascular system. As blood circulates throughout the entire body, the harm to human health and ecology is more severe than that from other suspended particulate matter [3,4,5,6]. Many researchers have further pointed out that airborne fine particulate matter can directly or indirectly lead to chronic respiratory diseases, cardiovascular diseases, cancer, neurotoxicity, and even dementia diseases [7,8,9,10,11]. In addition, the long-term exposure to high-concentrations of air pollutants is even more harmful [12,13,14]. Therefore, analyzing the long-term spatial distributions of air pollution hazards (particularly PM2.5) will provide valuable information for protecting the health and safety of residents.
Over the past few years, China has repeatedly experienced extremely hazardous PM2.5 concentrations [15,16]. For example, on 19 October 2016, 11 provinces in China were severely affected by air pollutants. Moreover, many cities in western Taiwan are affected by both transboundary and local pollutants, and their air quality is very poor. As Taiwan’s geographical location is close to the southeast of China, it is also the main route for the cold high pressure traveling from China in winter; the transboundary pollutants from China may affect Taiwan’s air quality and the atmospheric circulation. In addition, local or regional sources of pollutants, such as transportation vehicles and factories, produce airborne particulate matter [17,18,19].
The spatial analysis of disaster potentials is a very important part of risk assessments. As assessing the risk of hazards is crucial for a timely evacuation, the analysis of disaster potentials has become very common. The Water Resources Agency of the Ministry of Economic Affairs of Taiwan and researchers have published and applied several generations of flood potential maps for many years [20,21]. In addition, in the Taiwan Central Geological Survey, researchers developed and applied soil liquefaction potential maps [22,23,24]. However, potential disasters caused by suspended particulate matter and the spatial characteristics of air pollution in the past have not been comprehensively investigated; i.e., no potential map of PM2.5 has been drawn before. In addition, a pollution potential map of various regions would provide valuable information.
Tourist attractions are important gathering places for people, particularly on holidays. Most visitors wish to relax and expect high air quality. Many researchers have studied the relationship between areas of interest and air quality; in particular, they have investigated the integration of low-cost air quality monitoring Internet of Things systems and air quality big data models [25,26,27,28,29,30]. Over the past few years, some Chinese researchers have analyzed the air pollution characteristics of certain specific tourist attractions [31,32]. However, the relationship between the overall tourist attractions and air quality has not been studied.
Hsinchu County in Taiwan has diversified industry, with equal emphasis on agriculture, industry, technology, businesses, and leisure tourism. In addition, Hsinchu County is adjacent to Hsinchu City and Hsinchu Science Park. The population and industry are developing rapidly, and large numbers of people enter Hsinchu County’s major tourism and recreation areas every holiday season. Therefore, high air quality around tourist attractions is very important. A previous study of the characteristics of air pollutants in Hsinchu has shown that the PM2.5, total PAHs (Polycyclic Aromatic Hydrocarbons), and BaPeq (benzo(a)pyrene equivalent) mass concentrations during the seasons had the following order: winter > autumn > spring > summer with significant seasonal variations [33]. Some early studies focused on the impacts of the large and dense high-tech industries in the Hsinchu Science Park on health and the environment [34,35,36]; in addition, the researchers considered the emissions of toxic compounds such as VOCs (Volatile Organic Compounds) and arsenical emissions; however, there have been few relevant studies in the past decade.
Therefore, the objective of this study was to investigate the exposure risks of tourist attractions based on the potential map of PM2.5 calculated by the exceedance probability and spatial estimation methods. In this study, the Hsinchu County area was taken as an example. Historical data of PM2.5 concentrations from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze spatially the air pollution hazard potential of PM2.5 concentrations in Hsinchu County based on geographic information system (GIS) statistics. The potential threat of PM2.5 concentrations exceeding a certain standard was spatially investigated with the exceedance probability method; furthermore, the air pollution risk levels of areas with tourist attractions in Hsinchu County were determined. The analysis results of this study can be directly applied to other countries worldwide; they provide references for tourists, tourism resource management, and air quality management, and important information on the long-term health risks for local residents in the study area.

2. Materials and Methods

2.1. Study Area

The terrain of Hsinchu County is mainly composed of flat land, hills, and mountains. There are 13 administrative districts (towns and cities), and its development industries are diverse; they can be mainly classified into agriculture, industry (including science and high technology), commerce, and leisure tourism (Figure 1). Zhubei City is an important town in terms of commerce, economics, and politics; its industries develop high-tech electronics (such as in the Taiyuan Science and Technology Park). The inflow of industrial capital into Zhudong town comprises real estate capital and high-tech manufacturing capital. Like those of Zhubei City, its industries comprise mainly commerce and industry (such as the Industrial Technology Research Institute). The Hukou and Baoshan Townships have the most developed industries and are the production bases for the technology and manufacturing industries, such as the Hsinchu Industrial Park of the Industrial Development Bureau, the Ministry of Economic Affairs in Hukou Township, and high-tech companies such as Taiwan Semiconductor Manufacturing Company and other high-tech factories in Baoshan Township. Baoshan Reservoir and Baoshan Second Reservoir are important water resources for the Hsinchu Science Park. Moreover, the Emei and Wufeng Townships focus on agriculture (tea, oranges, peaches, and sweet persimmons). The Xinfeng, Xinpu, Qionglin, and Beipu Townships exhibit agricultural activities and the establishment of regional industrial zones for the industrial development. Guanxi town, Jianshi Township, and Hengshan Township focus mainly on agriculture and the development of tourism and leisure industries (such as Guanxi Grass, Neiwan Old Street, orchard sightseeing, and visits to the Taiwanese aboriginal people).
To minimize the impacts of disasters, the disaster characteristics of local key industries are studied based on disaster potential data. The results should be sent to the local governmental agencies and key industries (such as the industrial and agricultural management units) in Hsinchu County as an important reference for disaster prevention. More importantly, improvements in areas with higher risks should be prioritized. The main disaster types faced in Hsinchu County can be roughly distinguished according to the topography. The administrative areas on flat land, such as the Zhudong, Hukou, and Xinpu Townships, may experience floods and droughts, and the mountainous administrative areas, such as the Jianshi and Wufeng Townships, can predominantly suffer from landslides or mudflows; in addition, the area close to the sea may face tsunamis. Owing to the development of industrial areas, the Hukou, Baoshan, and Qionglin Townships may suffer man-made disasters caused by toxic chemicals and air pollutants. The industrial characteristics and major and minor risks in the 13 towns and cities in Hsinchu County are summarized in Table 1.

2.2. Framework of Risk Analysis

The potential refers to the frequency or probability of the occurrence of disasters in an area; the determined potential can be used as a reference for future risk assessments. In this study, the hourly data of PM2.5 concentrations measured by the TWEPA in Taiwan in 2017 were used, and spatial interpolation was applied to estimate the hourly PM2.5 concentration of each grid point in the county. Subsequently, the probability that the PM2.5 concentration of each grid point exceeds the standard value statistically was calculated. The air quality index that corresponds to the unhealthy PM2.5 concentration for sensitive groups (35.4 μg/m3) was used as the concentration standard. This probability can be represented based on the exceedance probability of older data, which represents the spatial distribution of the potential of PM2.5. The analysis process is shown in Figure 2.
After determining the spatial distribution of the air pollution potentials, the air pollution risk levels in various tourist areas in Hsinchu County were examined. As shown in Figure 2, the PM2.5 concentrations are based on data from the TWEPA’s Taiwan-wide air quality-monitoring stations from 2017; in addition, radial basis function (RBF) spatial interpolation was used to estimate the grid-like PM2.5 concentrations in the Hsinchu County area, and the exceedance probability method was applied to calculate the probability that the PM2.5 concentration of each grid point exceeds the standard. Finally, the potential air pollution risks in the areas of the major tourist attractions in Hsinchu County were examined. The PM2.5 concentration standard used in this study is based on the air quality index, which considers six levels: good, normal, unhealthy for sensitive groups, unhealthy for all people, very unhealthy, and hazardous. When the “unhealthy for sensitive groups” degree has been reached, it is generally recommended that residents reduce outdoor activities and prolonged vigorous exercise. Therefore, the PM2.5 concentration standard (35.4 μg/m3) corresponding to the “unhealthy air quality for sensitive groups” degree was used as the threshold. In this study, the analysis results of the probability that the PM2.5 concentration exceeds the standard were classified into eight levels. In addition, most areas of the Jianshi and Wufeng Townships are too far from the TWEPA’s air quality monitoring station (15 km from the monitoring station) and mainly in high mountainous terrain; thus, they were not included in the calculations.

2.3. Data Collection

First, the hourly PM2.5 concentrations collected by 76 air quality-monitoring stations of the TWEPA in Taiwan in 2017 were collected. The 327 datasets from areas with tourist attractions originate from the official open data website of the Hsinchu County Government, which were collected in 2019 (https://www.hsinchu.gov.tw/OpenDataDetail.aspx?n=902&s=272).

2.4. Spatial Analysis of Data

The PM2.5 concentrations throughout Taiwan were estimated with the data from the monitoring stations and RBF spatial interpolation method [37,38,39]. The RBF interpolation is one of the most precise interpolation methods. The interpolation function must pass through the observation value of each station and generate a smooth surface. RBF interpolation is a mesh-free method, constructing high-order accurate interpolants of unstructured data. It takes the form of a weighted sum of radial basis functions. In addition, the RBF interpolation method uses a symmetric function centered at each observation point and calculates the change in the distance from the observation point to obtain the weight of each function:
[ φ ( x 0 x 0 ) φ ( x n x 0 ) φ ( x 0 x n ) φ ( x n x n ) ] [ w 0 w n ] = [ f ( x 0 ) f ( x n ) ]
where φ is a centrosymmetric function and w n the weight of each function; the interpolation function f ( x n ) can be obtained by solving the equations.
The RBF interpolation method has a good effect on flat surfaces (for concentration diffusion, for instance). In this study, Taiwan was divided into approximately 32,000 grid points, the hourly PM2.5 concentration of each grid point was estimated with the RBF interpolation method, and the probabilities that the grid points exceed the concentration standard were determined; and finally, we cut and selected the study area of Hsinchu County; ESRI ArcGIS was used to calculate and draw the exceedance probability map. In this study, the exceedance probability of the hourly air pollution concentration was defined as the probability that the hourly data (of an entire year) exceed a certain concentration standard. The air quality index that corresponds to the unhealthy PM2.5 concentration for sensitive groups (35.4 μg/m3) was used as the concentration standard:
P E = N E N a l l
where P E is the exceedance probability, N E the number of times in which the hourly data exceed a certain concentration standard in one year, and N a l l the total number of hourly data of one year. The research data were analyzed with Python and ESRI ArcGIS.

3. Results

3.1. Analysis of Air Pollution Potential

The PM2.5 concentration is greatly affected by meteorological factors; therefore, the data were investigated according to the different seasons (spring: March–May; summer: June–August; autumn: September–November; winter: December–February). The results are shown in Figure 3. The gray area is too far from the air quality station and was therefore excluded. The analysis results show that the pollution potential in spring (Figure 3a) and winter (Figure 3d) is higher; the probability that the standard concentration in all towns and cities is exceeded is 9.5%, particularly in spring when the Xinfeng and Hukou Townships have probabilities of more than 18%; the probability decreases from the northwest plain area to the southeast mountainous area. The pollution potential in summer and autumn is relatively low; the probability that the standard is exceeded in autumn is generally only approximately 5%. The potential in the northern area of Hsinchu County adjacent to Taoyuan City is higher. In summer, the probability does not exceed 1%, and the probability of pollution in the area near Zhudong Station is slightly higher. Figure 4 and Table 2 show the detailed boxplots and basic statistics of the exceedance probabilities of the 13 townships and cities in Hsinchu County, respectively.

3.2. Risk Analysis of Areas with Tourist Attractions

The spatial distribution map of the PM2.5 potential was overlaid on a map of the various tourist areas in Hsinchu County; the most severe spring PM2.5 potential was chosen, as shown in Figure 5, Table 3 and Table 4. The results show that the probability that the standard is exceeded is greater than 18%; the areas with the most severe air pollution potential level (level 6) have three important tourist attractions: the Caixiang Trail, Xiansheng Temple, and Hukou Armored New Village (Village B). The areas of level 5 (16% to 18% chance of exceeding the standard) and level 4 (14% to 16% chance of exceeding the standard) potential—slightly higher potential—have 11 and 7 tourist attractions, respectively. The 11 tourist attractions with level 5 potential are Rongyuanpu Farm, Laohukou Catholic Church Cultural Center, Renhe Trail, Yao Art Street and Bicycle Taro, Hanqing Trail, Hukou Old Street, Xinfeng Sanyuan Temple, Yongning Temple, Chifu Wangye Temple, Hongmaogang Ecological Recreation Area, and Xinfengpuyuan Temple. Another 114 tourist areas are at level 3 (exceeding rates of 12% to 14%), and 124 tourist areas are at level 2 (exceeding rates of 10% to 12%); these locations still exhibit rates greater than 10% in spring (Table A1). These areas encounter a higher risk of air pollution with excessive PM2.5 concentrations. The highest air pollution potentials of the tourist attractions with levels 5 and 6 in Hsinchu County are shown in Table 4; they are located in the Hukou and Xinfeng Townships. As many tourist areas in Hsinchu County are located in hilly or mountainous areas, they are less exposed to PM2.5. Only the scenic spots in the Hukou and Xinfeng Townships experience relatively high PM2.5 concentrations. The detailed PM2.5 air pollution potential of each tourist attraction in Hsinchu County is shown in Appendix A.

3.3. Analysis of Population Density and Air Pollution Exposure Risk

Moreover, the PM2.5 potential spatial distribution map was investigated based on the population density of each township in Hsinchu County (Table 5) to analyze the long-term air pollution exposure risks for residents. According to Figure 6, the population density is correlated with the PM2.5 potential distribution. The Pearson correlation coefficient between the PM2.5 potential and population density in towns throughout the year is 0.44. If it is explored according to the season, the correlation coefficients between the PM2.5 potential and population density in spring, summer, autumn, and winter are 0.36, −0.46, 0.34, and 0.64, respectively. Zhubei City (3885.10 persons per square kilometer), Zhudong town (1811.10 persons per square kilometer), Hukou Township (1325.41 persons per square kilometer), and Xinfeng Township (1226.25 persons per square kilometer) have higher population densities than the remaining areas and therefore higher PM2.5 potentials. A high population density reflects the degree of development and traffic in the city. According to Figure 7, the main industrial areas of Hsinchu County are mostly concentrated in these towns and villages and the main source of pollution. Owing to the prevailing northeast monsoon conditions in winter, these areas have higher pollution risks. Although many tourist attractions are not located in the areas with high air pollution potentials, many residents live in areas with relatively high air pollution potentials for a long time.

4. Discussion

The change in and accumulation, diffusion, and transmission of PM2.5 concentrations are greatly affected by the meteorological conditions or weather patterns [40,41,42]. The analysis results of the air pollution potentials in Figure 3 are consistent with the general air pollution season in Taiwan (winter and spring). The main reason is that the main prevailing wind in Taiwan in winter and spring is the northeast monsoon; thus, the western half is not affected because of the mountains. The leeward places are likely to experience accumulations of pollutants, particularly central and southwestern Taiwan [41,43,44]. Furthermore, the northeast monsoon tends to bring foreign pollutants from west China into this area [45]. Therefore, the Xinfeng and Hukou areas in Hsinchu County have the highest pollution potentials in winter and spring. In addition, Hsinchu Industrial Park lies in the Xinfeng and Hukou area, and the northern region is close to major stationary pollution sources, such as Taoyuan Youth Industrial Park, Pingjhen Industrial Park, and Yongan Industrial Park (Figure 7). Zhubei City and Hsinchu Science Park in the south are densely populated areas with long-term traffic congestion and are the main sources of mobile pollution in Hsinchu County and Hsinchu City [34,35,36,46]. Both spring and winter are high-pollution seasons, but spring exhibits more evident pollution sources (Figure 3).
In order to further compare the PM2.5 potential distribution in different years, in addition to Figure 5 showing 2017, Figure 8 shows the dynamic distributions of PM2.5 potential in tourist areas in Hsinchu County in spring in 2018 and 2019. They show spatial distributions similar to 2017, and Xinfeng and Hukou also have the highest potential. However, it is obvious that the overall probability of PM2.5 exceeding the standard has been declining in the entire region in recent years. In addition to the influences of meteorological conditions in different years, it may be due to the implementation of government policies and the increase in people’s awareness of environmental protection.
Moreover, Xinpu, Guanxi, Qionglin, Baoshan, Emei, and Beipu are dominated by hilly land; this less densely populated area exhibits agricultural, industrial, and touristic activities; thus, the air quality is evidently better than in other areas in all seasons. The Hengshan, Jianshi, and Wufeng Townships have mostly mountainous terrain, and the populations are sparser; consequently, they have the best air quality. In addition, because the west side of Hsinchu is adjacent to the sea and the east side exhibits mostly hilly terrain, the topographical effect is affected by the prevailing wind and major sources of emissions in the air pollution season [47]. Therefore, air pollutants in Hsinchu accumulate easily in the relatively flat plains, such as in Xinfeng and Hukou, which is consistent with the results of this study. Some researchers have investigated the impacts of terrain effects on air pollution [48], particularly the basin effects [49,50]; some researchers have used geostatistical models to estimate the PM2.5 concentrations [51]. Fortunately, most of the tourist areas in Hsinchu County are located in areas with lower PM2.5 air pollution potentials, and the areas with higher air pollution potentials are mostly those with industrial and technological activities. Nevertheless, the areas with high pollution potentials have higher population densities. A high population density leads to more emission sources. Some researchers have used the spatial econometric model to investigate the relationship between the population density and air pollution in Chinese cities; they have discovered a significant positive correlation between the population density and PM2.5 concentration [52,53], which is consistent with the results of this study.

5. Conclusions

In this study, an air pollution potential map was constructed. The results show that the potentials of different seasons are quite different. The most severe air pollution seasons are spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships in Hsinchu County have the highest potential (approximately 18%). Hukou Old Street, which is the most famous tourist attraction, has a relatively high pollution risk. The population density is positively correlated with the PM2.5 potential distribution in most seasons, except for summer. In this study, the hazard potential levels of PM2.5 concentrations exceeding a certain standard were investigated; the exceedance probability and the air pollution potential levels of various tourist areas in Hsinchu County were examined. However, the information on tourist attractions considered in this research study is limited and based on only few important attractions. The air pollution potential map can be combined with more detailed tourist attraction maps in the future. In addition, the map can be applied to investigate the impacts of pollution on schools, elderly people, hospitals, and nurseries to determine their potential long-term exposure risks. Although the study area in Hsinchu County has only three important tourist attractions with the most severe air pollution potential levels (level 6), there are still many schools and residents in these areas.
In the future, a map for the entire country will be constructed; the proposed framework can be directly applied to other countries worldwide. In addition, the spatial and temporal changes in the air pollution potential during different years can be analyzed, and the air pollution data of one year can be expanded to more than five or ten years. In addition to reducing the possibility of being more extreme in certain years, understanding the temporal changes in the spatial distribution of the pollution potentials is more effective for assessing dynamic risks. In addition to providing a reference for tourists, the results provide information on the long-term health risks for local residents in the study area.

Author Contributions

Conceptualization, Y.-C.L.; methodology, Y.-C.L.; software, C.-Y.L. and H.-S.S.; validation, Y.-C.L. and H.-S.S.; formal analysis, C.-Y.L. and H.-S.S.; investigation, Y.-C.L.; resources, Y.-C.L. and J.-K.T.; data curation, C.-Y.L.; writing—original draft preparation, Y.-C.L. and C.-Y.L.; writing—review and editing, Y.-C.L.; visualization, C.-Y.L. and H.-S.S.; supervision, Y.-C.L.; project administration, Y.-C.L. and J.-K.T.; funding acquisition, Y.-C.L. and J.-K.T. All authors have read and agreed to the published version of the manuscript.

Funding

We are grateful for the support funded by the Ministry of Science and Technology (Taiwan): project numbers MOST 108-2119-M-008-003, MOST 108-2636-E-008-004 (Young Scholar Fellowship Program), and MOST 108-2638-E-008-001-MY2 (Shackleton Program Grant).

Acknowledgments

In addition, we are thankful for the cooperation of the Research Center for Hazard Mitigation and Prevention of the National Central University, the Fire Bureau, Hsinchu County Government, and the National Science and Technology Center for Disaster Reduction (NCDR). The ESRI ArcGIS tool and Python and its modules served as powerful tools in our data analysis.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Detailed air pollution potential of each tourist attraction in Hsinchu County.
Table A1. Detailed air pollution potential of each tourist attraction in Hsinchu County.
NumberNameDistrictLongitudeLatitudeThe Level of Air Pollution Potential
1Caixiang trailHukou Township121.0202824.8912216
2Xiansheng TempleHukou Township121.04798924.9028926
3Hukou Armored New Village (Village B)Hukou Township121.04780824.9044836
4Rongyuanpu FarmHukou Township121.044224.87545
5Laohukou Catholic Church Cultural CenterHukou Township121.0551624.876575
6Renhe TrailHukou Township121.05849724.8770325
7Yao Art Street and Bicycle TaroHukou Township121.057524.87735
8Hanqing TrailHukou Township121.0519224.8773995
9Hukou Old StreetHukou Township121.05261224.8777425
10Xinfeng Sanyuan TempleXinfeng Township120.997924.89995
11Yongning TempleXinfeng Township120.98526524.902485
12Chifu Wangye TempleXinfeng Township120.976424.91025
13Hongmaogang Ecological Recreation AreaXinfeng Township120.97636524.9102295
14Xinfengpuyuan TempleXinfeng Township120.97759924.9249165
15Golden World Leisure FarmXinpu Township121.02211524.8531934
16Pinewood Brick and Tile Exhibition HallXinfeng Township120.99075724.8689834
17Hukou Tourist Tea GardenHukou Township121.077924.87294
18Xinfeng Golf CourseXinfeng Township120.97649624.8824964
19Zaixing Golf CourseHukou Township121.09100824.8836794
20Xinfeng WetlandXinfeng Township120.971924.90724
21Xinfeng SeawallXinfeng Township120.9724.90754
22Yuquanshan Puzhao TempleZhudong Township121.08293924.7328353
23Luliaokeng TrailQionglin Township121.11689824.7337263
24Forest Park TrailZhudong Township121.08449524.734573
25Tree Qilin Cultural CenterZhudong Township121.09578924.7356233
26Touqianxi Ecological ParkZhudong Township121.09978724.7360333
27Five Harmony TempleQionglin Township121.120124.73643
28Zhudong Central MarketZhudong Township121.09148224.7368093
29Ruanqiao Rainbow VillageZhudong Township121.09148224.7368093
30Zhudong Forestry Exhibition HallZhudong Township121.09331424.7368513
31Zhudong Forestry Exhibition HallZhudong Township121.09331424.7368513
32Ue Pine Wood Bamboo East Branch OfficeZhudong Township121.093224.73733
33Draw a new pageZhudong Township121.09402624.7379283
34Zhudong Railway StationZhudong Township121.09483124.7381773
35Zhudong City Bike PathZhudong Township121.09474224.7382453
36Flower World Play Cloth WorkshopZhudong Township121.0861324.7385223
37Xiao Rusong Art ParkZhudong Township121.08820124.7394253
38Xiao Rusong Former Residence ComplexZhudong Township121.08820124.7394253
39Huangcheng Bamboo Curtain Cultural CenterZhudong Township121.09175524.7396753
40Ganlu TempleZhudong Township121.08082324.7445483
41JuqingZhudong Township121.085624.7452653
42Mingguan Art MuseumZhudong Township121.08011924.7455293
43Duanmu Shiitake Mushroom FarmQionglin Township121.14015724.7474893
44Luliaokeng Mushroom FarmQionglin Township121.140224.74753
45Zhubei. Zhudongtou Qianxi Bicycle PathQionglin Township121.09463524.7490053
46Jiujiu Health Tomato MuseumQionglin Township121.09541924.7518023
47Xionglin Luliaokeng Bell RoomQionglin Township121.14064824.7582953
48Fulin FarmQionglin Township121.09006424.7617443
49Feifeng WenchangQionglin Township121.09129424.7623083
50Shiming Tomato FarmGuanxi Township121.17318824.7655123
51Wenlin CourtQionglin Township121.082624.77333
52Deng Yuxian Music and Culture Memorial ParkQionglin Township121.08512624.7733263
53Zhiliaowo Papermaking WorkshopQionglin Township121.08262424.7802823
54Jin Yong DIY Tomato FarmGuanxi Township121.18074524.7821493
55Jin Guangfu MansionGuanxi Township121.17684124.786573
56Luo Wu CollegeGuanxi Township121.17565824.7879313
57Guanxi Windward MuseumGuanxi Township121.18370824.7885573
58Guanxi Taiwan Black Tea CompanyGuanxi Township121.17575324.7913053
59Taiwan Red Tea Cultural CenterGuanxi Township121.17575324.7913053
60Guanxi DonganqiaoGuanxi Township121.17817424.7915123
61Instant burned grass, natural ancient flavor [Agricultural good companion 1. Guanxi Town Farmers’ Association Tour]Guanxi Township121.17682924.7916343
62Guanxi Niulan River Bicycle PathGuanxi Township121.18086224.7922483
63Guanxi Catholic ChurchGuanxi Township121.17641924.7943293
64Xinbao Tourist OrchardXinpu Township121.08547724.7969863
65Pinglin Hiking TrailGuanxi Township121.1406624.801153
66Mingdeng Ancient RoadGuanxi Township121.18724.8023
67Guanxi Town Farmers’ Association Xiancao Processing FactoryGuanxi Township121.16253524.80293
68Yuanhe TempleGuanxi Township121.13564524.8035153
69Daluo Strawberry FarmGuanxi Township121.16060624.8038213
70Fukuda Strawberry FarmGuanxi Township121.16009924.8042353
71Gaoping Tomato FarmGuanxi Township121.15252724.8057343
72Gillian Strawberry FarmGuanxi Township121.14499924.8059493
73Lu Ji FarmGuanxi Township121.14499924.8059493
74Shiquan FarmGuanxi Township121.14499924.8059493
75Da Asah Valley Orchid FarmGuanxi Township121.14034524.8090453
76Xiangzhangyuan Leisure FarmXinpu Township121.08015224.8101433
77Agen Strawberry FarmGuanxi Township121.11608524.8168143
78Shuangyuan Leisure FarmZhubei City121.035724.82393
79Leofoo Village Theme ParkGuanxi Township121.18072824.8246793
80Yunhai Leisure Tea FactoryGuanxi Township121.16226824.8248913
81Xiaolixi Bicycle PathXinpu Township121.08792424.8250683
82Yuanxin PersimmonGuanxi Township121.116824.82543
83Guannanyangtang Tang HouseGuanxi Township121.11412324.826373
84Xinpu Liu Family Ancestral HallXinpu Township121.07509324.8272713
85Xinpu Zhu Family TempleXinpu Township121.07635124.8273563
86Xinpu Pan HouseXinpu Township121.07598224.8275843
87Sky, People, Things, I-Whole People XinpuXinpu Township121.07124.8283
88Zhaomen Agricultural Recreation AreaXinpu Township121.07127824.8280423
89Xinpu Elementary School Principal DormitoryXinpu Township121.07922324.8280813
90Happy childhoodXinpu Township121.07913824.8281263
91Zhu Jincheng StudioXinpu Township121.03675424.828263
92Wow, delicious persimmon!Xinpu Township121.07493524.8283823
93Xinpu Chen’s Ancestral HallXinpu Township121.07645124.8283933
94Xinpu Fan Family TempleXinpu Township121.0759724.8285413
95Xinpu Lin Family TempleXinpu Township121.076124.82923
96Comic Art SquareXinpu Township121.07319624.8293433
97Yiyuan Hakka CuisineGuanxi Township121.12272824.8308013
98New farmers marketXinpu Township121.08769824.8311143
99Sansheng TempleXinpu Township121.09879924.8314323
100Flying Dragon Hiking TrailXinpu Township121.09879924.8314323
101Persimmon Dyeing WorkshopXinpu Township121.07914224.8338943
102Zhubei Tianhou TempleZhubei City121.01123124.8356943
103Shaotanwo Old RoadXinpu Township121.04918624.8378013
104Wu Zhuoliu’s Former ResidenceXinpu Township121.10983124.8380113
105Xinpu Shangfangliao Liu HouseXinpu Township121.0494924.8380823
106Chunhe FarmXinpu Township121.03952724.8381793
107The happy persimmon feeling blown by the windXinpu Township121.07666324.8409593
108Barbarian’s Fortune LandZhubei City120.99742324.8414933
109Fengshanxi Fangliao Village Bicycle PathXinpu Township121.044224.84163
110Zhubei Citizen FarmZhubei City120.99801124.8420513
111Xinpu Baozhong PavilionXinpu Township121.03627124.8433543
112Jinhan Dried Persimmons, Arrow Bamboo Nest, Orchard, Zhulan Garden” Rural Regeneration Tour of Daping Community, Xinpu 1Xinpu Township121.07857924.8442583
113Drying Persimmon in Jinhan FarmXinpu Township121.07857824.844263
114Shangpinxiang OrchardXinpu Township121.06902124.844373
115Li Village FarmXinpu Township121.071524.847383
116Zhaomen Trail Group-Huaizu TrailXinpu Township121.10526424.8483893
117Fuming New FarmXinpu Township121.10137824.8496393
118Lin Family OrchardXinpu Township121.10175924.8514763
119Gou Bei Kiln StudioZhubei City120.98513324.8521553
120Nanping, Beipingli Bicycle PathXinpu Township121.086424.85253
121Bamboo GardenXinpu Township121.10196324.8529723
122Crossing the Borders and Traveling in the North Country Scenery ~ Winter’s Jingu FarmXinpu Township121.10557424.8544413
123Fuxiang Cactus Succulent Botanical GardenXinpu Township121.09219124.8556233
124Liujiazhuang Braised ChickenXinpu Township121.10566224.8567423
125Zhoujiazhuang Sightseeing Farm (Recreation Inn)Xinpu Township121.104224.8573743
126Red Dragon Fruit Sightseeing OrchardZhubei City120.9612824.8587063
127Chenjia FarmXinpu Township121.10373124.8608943
128Zhaomen Trail Group-Guannan TrailXinpu Township121.103724.86093
129Wind movement, Jinghai, XiangeZhubei City120.96396624.8614733
130Zhubei‧Binhai Recreation AreaZhubei City120.94633324.8652343
131Tiande TempleXinfeng Township120.979724.86753
132Zhubei Coastal Forest Conservation AreaZhubei City120.9514324.870453
133Lianhua TempleZhubei City120.96116424.8765553
134Fengqi SunsetZhubei City120.96116424.8765553
135Zhubei Lotus Temple WetlandZhubei City120.961224.87663
136Sakura Forest Leisure FarmWufeng Township121.09220724.632462
137Liangshan TribeWufeng Township121.123624.64512
138Shangrui Orange GardenZhudong Township121.115124.66262
139Beipu Cold SpringBeipu Township121.07281124.6630562
140Shangping Old StreetZhudong Township121.09398624.663592
141Youdian Grass Ecological FarmBeipu Township121.0532924.673972
142Huisen Natural Leisure FarmBeipu Township121.054524.67442
143Riding a DragonHengshan Township121.15059424.682512
144Dashanbei Leisure FarmHengshan Township121.15059424.682512
145Emei Catholic ChurchEmei Township121.02172224.6881622
146Emei Catholic ChurchEmei Township121.02172224.6881622
147Mingsheng Ecological Leisure FarmBeipu Township121.04191124.6883092
148Emei Lake Scenic AreaEmei Township121.01958624.6887692
149Dangui TempleEmei Township121.02152724.6888462
150Emei Elementary SchoolEmei Township121.02010924.6889532
151Dahu Mountain ForestBeipu Township121.08771824.6902792
152Bamboo Yucha Reed Sweet PotatoBeipu Township121.04167524.6922112
153Summer Garden Organic FarmZhudong Township121.10242824.692762
154King Kong TempleBeipu Township121.04424.69282
155North Point Suspension BridgeJianshi Township121.20265224.6966842
156Maike Tianyuan Leisure FarmBeipu Township121.04226824.6970662
157Beipu Jiang Family TempleBeipu Township121.05650124.6977332
158Xiaomi decorative artworkJianshi Township121.20504624.6981662
159Deng Nanguang Image Memorial HallBeipu Township121.05803824.6985372
160Deng Nanguang Image Memorial HallBeipu Township121.05803824.6985372
161Beipu Zhongshu ChurchBeipu Township121.05787924.6988512
162Dashanbei LeshantangHengshan Township121.13944724.6993272
163Chen Yongbin Woodworking DIY StudioBeipu Township121.0436124.6994732
164Xiuluan ParkBeipu Township121.060124.69962
165Beipu Old Street, Nanpu Village Bicycle PathBeipu Township121.05739224.69972
166Green World Leisure FarmBeipu Township121.07264824.6997122
167Beipu Citian TempleBeipu Township121.05844924.6997392
168Beipu Township “Farmers Direct Sales Station”Beipu Township121.05540224.700792
169Erliao ShenmuBeipu Township121.05638924.7020382
170Wuzhi Shan Scenic AreaBeipu Township121.05638924.7020382
171Neiwan Old StreetHengshan Township121.132224.70252
172Sharing and gloryJianshi Township121.19939324.703432
173Huazhouyuan Puppet TheaterHengshan Township121.18084224.7045012
174JianshiyanJianshi Township121.20125124.7050952
175Da Ba JianshanJianshi Township121.20125124.7050952
176Aboriginal Cultural Relics Museum of Jianshi TownshipJianshi Township121.20125124.7050952
177Neiwan StationHengshan Township121.18227724.7053312
178Xiaojiao’s Cheering ParadiseHengshan Township121.18227724.7053312
179Riverbank Hot SpringsHengshan Township121.17572824.7054832
180Water Moon Bay WonderlandHengshan Township121.18000224.7059152
181Neiwan Police StationHengshan Township121.18245324.7062542
182Neiwan Catholic ChurchHengshan Township121.1806724.7063362
183Guangji TempleHengshan Township121.18178224.7064582
184Jack and the Magic BeanHengshan Township121.16988924.7066192
185Inner Bay Suspension BridgeHengshan Township121.18046924.7068372
186Ancient Trojan Horse RoadHengshan Township121.18302824.7070952
187Tenren Rock HouseHengshan Township121.166524.71052
188Toyota Village, Baishi Lake Bicycle PathHengshan Township121.16647224.7105472
189Watermelon Manor Cultural Education ParkBeipu Township121.05906324.7151612
190Watermelon ManorBeipu Township121.05906324.7151612
191Fengxiang Waterfall Recreation AreaHengshan Township121.14227724.7157782
192Youluo ValleyHengshan Township121.14227724.7157782
193Hexin, Hexing, everyone agreesHengshan Township121.1535324.7167952
194Hexing StationHengshan Township121.1535324.7167952
195Fugui StationHengshan Township121.1534624.7172442
196Inspiration Pumping TruckHengshan Township121.12178224.7175732
197CihuitangZhudong Township121.07472324.7213292
198Boss Leisure FarmHengshan Township121.13142424.7267492
199Shishang Hot SpringJianshi Township121.22279124.7301722
200Baoshan Golf CourseBaoshan Township120.94358224.730832
201Wax Candle Art HouseBaoshan Township120.96050624.7309992
202Jianshih Lavender CottageJianshi Township121.23395724.7332882
203Fusha Osaki TrailHengshan Township121.165824.7352992
204Petite Teresa ChurchBaoshan Township120.968924.73562
205Baoshan Sugar Factory Bicycle Road LineBaoshan Township120.97023624.7359872
206Wetland farmQionglin Township121.1453924.7366952
207Songtao Tianyuan Leisure FarmBaoshan Township121.02053424.7369612
208Baoshan Reservoir and Baoshan Second ReservoirBaoshan Township121.03885624.7389622
209Nine Dragon TempleBaoshan Township120.97449124.7472972
210Xuyang Golf CourseGuanxi Township121.18355324.7475652
211Shahuli Art VillageBaoshan Township121.04463524.7501222
212Lord Guanxi Golf CourseGuanxi Township121.19732924.7523412
213Blonde Pitaya FarmGuanxi Township121.16952324.7528772
214Double-vitality-hopeZhudong Township121.05534724.7650242
215Goyulang TribeGuanxi Township121.24199824.7660272
216Huashan Leisure FarmGuanxi Township121.17874824.7669152
217Mountain Creek Golf CourseGuanxi Township121.21163624.7673792
218Zhudong DazhenZhudong Township121.05681524.7674882
219Jin Geum Shan Yimin TempleGuanxi Township121.2243624.7677022
220Guanxi Bat CaveGuanxi Township121.22421124.7679592
221Shenjing Village Tea Garden DistrictBaoshan Township120.99939424.768482
222Baohu Suspension Bridge. Bihu Suspension BridgeBaoshan Township120.99939424.768482
223Geumsan Shiitake FarmGuanxi Township121.22927724.7705712
224Two monuments at ZhudongtouZhudong Township121.02986724.7808192
225Sleepy bearZhudong Township121.03068324.7812572
226Li Yi Golf CourseGuanxi Township121.19036624.7837182
227Jin Guangcheng Cultural CenterGuanxi Township121.21245624.7884572
228Xionglin. Six bicycle lanesQionglin Township121.07469224.7901572
229Shiniu Mountain TrailGuanxi Township121.25332224.7935162
230Mercy FarmGuanxi Township121.23125824.7981992
231Lonely OdobyZhubei City121.0393324.8075682
232The birth of new GilaZhubei City121.0393324.8075682
233Hsinchu High Speed Rail StationZhubei City121.04022624.8081962
234Zhubei TongdetangZhubei City121.04761624.8091732
235Zhubei Liuzhanglilin Family ShrineZhubei City121.022224.81072
236Zhubei Six Zhangli DoctorZhubei City121.0244424.8108872
237Four-sided viewZhubei City121.03514624.8110172
238Xinwawu Hakka Culture Preservation AreaZhubei City121.02694324.8116672
239Zhubei Liuzhangli Zhongxiao Hall (No. 13 Dongpingli)Zhubei City121.02520424.8117532
240Zhubei Liuzhangli asked the auditoriumZhubei City121.0251124.8117912
241Chubei Quanzhou Chuo Fenyang HallZhubei City121.01700824.8166852
242Bodhi LoveZhubei City121.03199824.8209342
243Zhubei StadiumZhubei City121.02267324.8212732
244Litou Mountain TrailXinpu Township121.04558624.8213012
245Zhubei Liuzhangli Zhongxiao Hall (No. 18, Dongpingli)Zhubei City121.014224.82212
246Zhubei County FuyuanZhubei City121.01514624.8246722
247Lianhua TempleZhubei City121.02564324.8252712
248Zhubei Lianhua TempleZhubei City121.02564324.8252712
249Time storyZhubei City121.0107324.8262672
250Hsinchu County GovernmentZhubei City121.012924.82692
251Zhubei Guangming Commercial DistrictZhubei City121.01957224.8289182
252Collection, FenghuaZhubei City121.01249624.8300962
253Hsinchu County Art MuseumZhubei City121.01249624.8300962
254Hsinchu County History MuseumZhubei City121.01249624.8300962
255Hsinchu County History MuseumZhubei City121.01249624.8300962
256Dingfeng Bee FarmZhubei City120.99290824.8337972
257Li Longquan Multi-art SpaceZhubei City120.98665624.8362622
258Niupu Creek‧Mangrove Scenic AreaZhubei City120.94854324.8512472
259Tokai Organic Lime GardenZhubei City120.94740124.8531972
260Guize Mountain TrailWufeng Township121.12305724.6141471
261Wufeng Liangshan Camping AreaWufeng Township121.10235724.6157321
262Saixia Basdaai FestivalWufeng Township121.099424.62251
263Guyan WaterfallWufeng Township121.1240324.6248021
264Bamboo Forest Health Village CooperativeWufeng Township121.12055924.6256331
265Maibari tribeWufeng Township121.12067224.6259331
266Fairy Lake Camping AreaWufeng Township121.11631324.6265491
267Shengying Farm and Aboriginal Rattan WeavingWufeng Township121.14384524.6310321
268Qingquan Scenery AreaWufeng Township121.11963224.6320651
269Bailan TribeWufeng Township121.11963224.6320651
270Heping Tribe Recreational Agriculture AreaWufeng Township121.11963224.6320651
271Saixia Dwarf Spirit FestivalWufeng Township121.11963224.6320651
272Meihouman WaterfallWufeng Township121.15747524.6496651
273Wan Fo AnEmei Township121.0228724.651991
274Shuilian Bridge TrailEmei Township121.02444724.6555571
275Lion Mountain TrailEmei Township121.02444724.6555571
276Tianhu FarmJianshi Township121.17996524.6687141
277Song Yunxuan Coffee HouseEmei Township120.99169324.6687661
278Plum Blossom VillaJianshi Township121.19518924.6740831
279Shiliiao Leisure Agricultural ParkEmei Township120.98603124.6750631
280Emei Lake, Twelve Liao, Shishan Visitor Center Bicycle PathEmei Township120.98531924.67941
281Little Raindrop Art SpaceEmei Township120.97440724.6881011
282Emei Fuxing Tea Factory (including the House of Lu Kingdom and Zeng Zhengzhang)Emei Township120.97171124.6881611
283Shen Dongning StudioEmei Township121.009424.69091
284Fuxing Tea Exhibition CenterEmei Township120.98601924.697161
285Dance of YouthEmei Township120.99806324.7153191
286Fengcheng Charcoal Kiln (House of Charcoal)Baoshan Township120.99701624.7212361
287Dongkeng Xinfeng TempleBaoshan Township120.98024724.7227261
288Sanfeng Farmers’ OrchardBaoshan Township120.99752224.7246631
289Dongkeng Bogong TempleBaoshan Township120.98580424.7317561
290Nun templeBaoshan Township120.97781924.7505161
291Baosheng TempleBaoshan Township121.00925824.7505181
292Sunfull TempleBaoshan Township120.99030324.7653951
293Baoshan Ecological Farm PondBaoshan Township120.99127424.7655251
294Baxian WaterfallWufeng Township121.09528924.5344440
295Cinsbus Giant TreesJianshi Township121.29608724.540630
296Town West Fort ChurchJianshi Township121.302424.57310
297Huang Guanglai Greenhouse Honey Peach Garden (Duanmu Mushroom Garden)Jianshi Township121.30158524.5737820
298Sanmao ResidenceWufeng Township121.10580824.5739310
299Qingquan Hot SpringWufeng Township121.10556424.5744730
300Taoshan Elementary SchoolWufeng Township121.10618224.575140
301Leha Mountain Farm Camping AreaWufeng Township121.079924.57530
302Guanwu National Forest Recreation AreaWufeng Township121.11375624.5754890
303Qingquan Catholic ChurchWufeng Township121.1038124.5769760
304Yuanyang Lake Natural Ecological Conservation AreaJianshi Township121.40622124.5776520
305People have sculpture parkWufeng Township121.10749324.5794010
306Bailan Leisure Agriculture AreaWufeng Township121.08745624.5794570
307Xinguang TribeJianshi Township121.303224.57990
308Xiweng WaterfallWufeng Township121.148132324.59153940
309Taoshan TunnelWufeng Township121.10827224.6009230
310Tianyue FarmWufeng Township121.09596624.6035350
311Shanshang Renjia Leisure FarmWufeng Township121.08903724.6046240
312Liying Mountain TrailJianshi Township121.333824.65260
313Jianshi TAPUNG Castle (Li Wei Aiyong Supervision Office)Jianshi Township121.32280524.6606410
314Jinmei Suspension BridgeJianshi Township121.20777524.6703040
315Natural Valley Hot SpringJianshi Township121.269624.67180
316Secret Garden Coffee GardenJianshi Township121.25172424.6777930
317Shanqing Leisure FarmJianshi Township121.2103724.6788770
318Naluowan Leisure FarmJianshi Township121.24362324.6792720
319Luoxing Trout Leisure FarmJianshi Township121.23660424.6798050
320Hengshan and Ulao Bicycle PathsJianshi Township121.24722924.6803250
321Jinping ChurchJianshi Township121.228724.69770
322Jinping ParkJianshi Township121.21863924.6984430
323Linghai Mountain Forest Leisure FarmJianshi Township121.283124.70650
324Bu Lao Ju Leisure FarmJianshi Township121.269324.71350
325Lao Liu Orchard in Bawu MountainJianshi Township121.27933824.7167840
326Bali Forest Hot Spring ResortJianshi Township121.23567624.7219370
327Paddy field campJianshi Township121.25934524.7349870

References

  1. Kim, K.-H.; Kabir, E.; Kabir, S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015, 74, 136–143. [Google Scholar] [CrossRef] [PubMed]
  2. Cheriyan, D.; Hyun, K.Y.; Jaegoo, H.; Choi, J.-H. Assessing the distributional characteristics of PM10, PM2.5, and PM1 exposure profile produced and propagated from a construction activity. J. Clean. Prod. 2020, 276, 124335. [Google Scholar] [CrossRef]
  3. Yu, H.-L.; Lin, Y.-C.; Kuo, Y.-M. A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions. Chemosphere 2015, 134, 571–580. [Google Scholar] [CrossRef] [PubMed]
  4. Yu, H.-L.; Lin, Y.-C.; Sivakumar, B.; Kuo, Y.-M. A study of the temporal dynamics of ambient particulate matter using stochastic and chaotic techniques. Atmospheric Environ. 2013, 69, 37–45. [Google Scholar] [CrossRef]
  5. Wu, Y.; Lin, Y.; Yu, H.; Chen, J.; Chen, T.; Sun, Y.; Wen, L.; Yip, P.; Chu, Y.; Chen, Y. Association between air pollutants and dementia risk in the elderly. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2015, 1, 220–228. [Google Scholar] [CrossRef] [Green Version]
  6. Lippmann, M. Toxicological and epidemiological studies of cardiovascular effects of ambient air fine particulate matter (PM2.5) and its chemical components: Coherence and public health implications. Crit. Rev. Toxicol. 2014, 44, 299–347. [Google Scholar] [CrossRef]
  7. WHO Ambient (Outdoor) Air Pollution. Available online: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (accessed on 23 November 2020).
  8. Chen, J.; Wang, X.; ScD, G.A.W.; Serre, M.L.; Driscoll, I.; Casanova, R.; McArdle, J.J.; Manson, J.E.; Chui, H.C.; Espeland, M.A. Ambient air pollution and neurotoxicity on brain structure: Evidence from women’s health initiative memory study. Ann. Neurol. 2015, 78, 466–476. [Google Scholar] [CrossRef] [Green Version]
  9. Kioumourtzoglou, M.-A.; Schwartz, J.D.; Weisskopf, M.G.; Melly, S.J.; Wang, Y.; Dominici, F.; Zanobetti, A. Long-term PM 2.5 Exposure and Neurological HospitalAdmissions in the Northeastern United States. Environ. Heal. Perspect. 2016, 124, 23–29. [Google Scholar] [CrossRef] [Green Version]
  10. Chen, H.; Kwong, J.C.; Copes, R.; Hystad, P.; Van Donkelaar, A.; Tu, K.; Brook, J.R.; Goldberg, M.S.; Martin, R.V.; Murray, B.J.; et al. Exposure to ambient air pollution and the incidence of dementia: A population-based cohort study. Environ. Int. 2017, 108, 271–277. [Google Scholar] [CrossRef] [PubMed]
  11. Peters, R.; Ee, N.; Peters, J.; Booth, A.; Mudway, I.; Anstey, K.J. Air Pollution and Dementia: A Systematic Review. J. Alzheimer’s Dis. 2019, 70, S145–S163. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Li, T.; Zhang, Y.; Wang, J.; Xu, D.; Yin, Z.; Chen, H.-S.; Lv, Y.; Luo, J.; Zeng, Y.; Liu, Y.; et al. All-cause mortality risk associated with long-term exposure to ambient PM2·5 in China: a cohort study. Lancet Public Heal. 2018, 3, e470–e477. [Google Scholar] [CrossRef] [Green Version]
  13. Amoatey, P.; Sicard, P.; De Marco, A.; Khaniabadi, Y.O. Long-term exposure to ambient PM2.5 and impacts on health in Rome, Italy. Clin. Epidemiology Glob. Heal. 2020, 8, 531–535. [Google Scholar] [CrossRef] [Green Version]
  14. Faridi, S.; Shamsipour, M.; Krzyzanowski, M.; Künzli, N.; Amini, H.; Azimi, F.; Malkawi, M.; Momeniha, F.; Gholampour, A.; Hassanvand, M.S.; et al. Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 2006–2015. Environ. Int. 2018, 114, 37–49. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, S.; Zhou, C.; Wang, Z.; Feng, K.; Hubacek, K. The characteristics and drivers of fine particulate matter (PM2.5) distribution in China. J. Clean. Prod. 2017, 142, 1800–1809. [Google Scholar] [CrossRef]
  16. Liang, F.; Xiao, Q.; Gu, D.; Xu, M.; Tian, L.; Guo, Q.; Wu, Z.; Pan, X.; Liu, Y. Satellite-based short- and long-term exposure to PM2.5 and adult mortality in urban Beijing, China. Environ. Pollut. 2018, 242, 492–499. [Google Scholar] [CrossRef] [PubMed]
  17. Chen, Y.-C.; Chiang, H.-C.; Hsu, C.-Y.; Yang, T.-T.; Lin, T.-Y.; Chen, M.-J.; Chen, N.-T.; Wu, Y.-S. Ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Changhua County, central Taiwan: Seasonal variation, source apportionment and cancer risk assessment. Environ. Pollut. 2016, 218, 372–382. [Google Scholar] [CrossRef]
  18. Tseng, Y.-L.; Yuan, C.-S.; Bagtasa, G.; Chuang, H.-L.; Li, T.-C. Inter-correlation of Chemical Compositions, Transport Routes, and Source Apportionment Results of Atmospheric PM2.5 in Southern Taiwan and the Northern Philippines. Aerosol Air Qual. Res. 2019, 9, 2645–2661. [Google Scholar] [CrossRef]
  19. Lu, H.-Y.; Wu, Y.-L.; Mutuku, J.K.; Chang, K.-H. Various Sources of PM2.5 and their Impact on the Air Quality in Tainan City, Taiwan. Aerosol Air Qual. Res. 2019, 19, 601–619. [Google Scholar] [CrossRef] [Green Version]
  20. Flood vulnerability and risk maps in Taipei City, Taiwan. Compr. Flood Risk Manag. 2012. [CrossRef]
  21. Doong, D.-J.; Lo, W.; Vojinovic, Z.; Lee, W.-L.; Lee, S.-P. Development of a New Generation of Flood Inundation Maps—A Case Study of the Coastal City of Tainan, Taiwan. Water 2016, 8, 521. [Google Scholar] [CrossRef] [Green Version]
  22. Lee, D.-H.; Ku, C.-S.; Yuan, H. A study of the liquefaction risk potential at Yuanlin, Taiwan. Eng. Geol. 2004, 71, 97–117. [Google Scholar] [CrossRef]
  23. Wang, M.-H.; Chen, M.-H.; Loh, C.-H. Liquefaction Potential Study of Taiwan. In Proceedings of the World Conferences on Earthquake Engineering, Auckland, New Zeland, 30 January–4 February 2000. [Google Scholar]
  24. Hsiao, D.H.; Zheng, Z.-Y. Simplified Empirical Method for Predicting Liquefaction Potential and Its Application to Kaohsiung Areas in Taiwan. Int. J. Geotech. Geolog. Eng. 2019, 13, 482–490. [Google Scholar]
  25. Zheng, Y.; Chen, X.; Jin, Q.; Chen, Y.; Qu, X.; Liu, X.; Chang, E.; Ma, W.-Y.; Rui, Y.; Sun, W. A cloud-based knowledge discovery system for monitoring fine-grained air quality. Available online: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/UAir20Demo.pdf (accessed on 23 November 2020).
  26. Hsieh, H.-P.; Lin, S.-D.; Zheng, Y. Inferring Air Quality for Station Location Recommendation Based on Urban Big Data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10–15 August 2015; pp. 437–446. [Google Scholar]
  27. Yuan, M.; Song, Y.; Huang, Y.; Hong, S.; Huang, L. Exploring the Association between Urban Form and Air Quality in China. J. Plan. Educ. Res. 2017, 38, 413–426. [Google Scholar] [CrossRef]
  28. Zheng, Y.; Liu, F.; Hsieh, H.-P. U-Air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, 11–14 August 2013; pp. 1436–1444. [Google Scholar]
  29. Yu, R.; Yang, Y.; Yang, L.; Han, G.; Move, O.A. RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems. Sensors 2016, 16, 86. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Gao, Y.; Dong, W.; Guo, K.; Liu, X.; Chen, Y.; Liu, X.; Bu, J.; Chen, C. Mosaic: A low-cost mobile sensing system for urban air quality monitoring. In Proceedings of the IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, 10–14 April 2016; pp. 1–9. [Google Scholar]
  31. Wu, Z.; Liu, F.; Fan, W. Characteristics of PM10 and PM2.5 at Mount Wutai Buddhism Scenic Spot, Shanxi, China. Atmosphere 2015, 6, 1195–1210. [Google Scholar] [CrossRef] [Green Version]
  32. Shi, J.; Gao, H.; Cheng, H.; Sun, H.; Huang, D. Study on the exposure risk based on the PM2.5 pollution characteristics of POIs and their attractiveness to the crowd. Hum. Ecol. Risk Assess. Int. J. 2020, 1–19. [Google Scholar] [CrossRef]
  33. Yang, T.-T.; Hsu, C.-Y.; Chen, Y.-C.; Young, L.-H.; Huang, C.-H.; Ku, C.-H. Characteristics, Sources, and Health Risks of Atmospheric PM2.5-Bound Polycyclic Aromatic Hydrocarbons in Hsinchu, Taiwan. Aerosol Air Qual. Res. 2017, 17, 563–573. [Google Scholar] [CrossRef]
  34. Chang, S.; Tu, W.; Chiu, H.-M. The silence of silicon lambs: speaking out health and environmental impacts within Taiwan’s hsinchu science-based industrial park. In Proceedings of the IEEE International Symposium on Electronics and the Environment, 2004. Conference Record. 2004, Scottsdale, AZ, USA, 10–13 May 2004; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2004; pp. 10–13. [Google Scholar]
  35. Nian, H.-C.; Liu, H.-W.; Wu, B.-Z.; Chang, C.-C.; Chiu, K.-H.; Lo, J.-G. Impact of inclement weather on the characteristics of volatile organic compounds in ambient air at the Hsinchu Science Park in Taiwan. Sci. Total. Environ. 2008, 399, 41–49. [Google Scholar] [CrossRef]
  36. Chein, H.; Hsu, Y.-D.; Aggarwal, S.G.; Chen, T.-M.; Huang, C.-C. Evaluation of arsenical emission from semiconductor and opto-electronics facilities in Hsinchu, Taiwan. Atmos. Environ. 2006, 40, 1901–1907. [Google Scholar] [CrossRef]
  37. Chen, S.; Cowan, C.; Grant, P. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 1991, 2, 302–309. [Google Scholar] [CrossRef] [Green Version]
  38. Orr, M.J. Introduction to Radial Basis Function Networks; Center for Cognitive Science, University of Edinburgh: Edinburgh, Scotland, 1996. [Google Scholar]
  39. Park, J.; Sandberg, I.W. Approximation and Radial-Basis-Function Networks. Neural Comput. 1993, 5, 305–316. [Google Scholar] [CrossRef]
  40. Chuang, M.-T.; Chou, C.C.-K.; Lin, N.-H.; Takami, A.; Hsiao, T.-C.; Lin, T.-H.; Fu, J.S.; Pani, S.K.; Lu, Y.-R.; Yang, T.-Y. A Simulation Study on PM2.5 Sources and Meteorological Characteristics at the Northern tip of Taiwan in the Early Stage of the Asian Haze Period. Aerosol Air Qual. Res. 2017, 17, 3166–3178. [Google Scholar] [CrossRef] [Green Version]
  41. Cheng, F.-Y.; Hsu, C.-H. Long-term variations in PM2.5 concentrations under changing meteorological conditions in Taiwan. Sci. Rep. 2019, 9, 1–12. [Google Scholar] [CrossRef] [PubMed]
  42. Hsu, C.-H.; Cheng, F.-Y. Classification of weather patterns to study the influence of meteorological characteristics on PM2.5 concentrations in Yunlin County, Taiwan. Atmos. Environ. 2016, 144, 397–408. [Google Scholar] [CrossRef]
  43. Lu, H.-C.; Chang, C.-L.; Hsieh, J.-C. Classification of PM10 distributions in Taiwan. Atmos. Environ. 2006, 40, 1452–1463. [Google Scholar] [CrossRef]
  44. Hsu, C.-H.; Cheng, F.-Y. Synoptic Weather Patterns and Associated Air Pollution in Taiwan. Aerosol Air Qual. Res. 2019, 19, 1139–1151. [Google Scholar] [CrossRef] [Green Version]
  45. Lin, C.; Liu, S.; Chou, C.; Huang, S.; Liu, C.; Kuo, C.; Young, C. Long-range transport of aerosols and their impact on the air quality of Taiwan. Atmos. Environ. 2005, 39, 6066–6076. [Google Scholar] [CrossRef]
  46. Fang, S.-H.; Chen, H.-W. Air quality and pollution control in Taiwan. Atmos. Environ. 1996, 30, 735–741. [Google Scholar] [CrossRef]
  47. Lai, H.-C.; Lin, M.-C. Characteristics of the upstream flow patterns during PM2.5 pollution events over a complex island topography. Atmos. Environ. 2020, 227, 117418. [Google Scholar] [CrossRef]
  48. Lee, M.; Lin, L.; Chen, C.-Y.; Tsao, Y.; Yao, T.-H.; Fei, M.-H.; Fang, S.-H. Forecasting Air Quality in Taiwan by Using Machine Learning. Sci. Rep. 2020, 10, 1–13. [Google Scholar] [CrossRef]
  49. Kikaj, D.; Chambers, S.D.; Kobal, M.; Crawford, J.; Vaupotič, J. Characterizing atmospheric controls on winter urban pollution in a topographic basin setting using Radon-222. Atmos. Res. 2020, 237, 104838. [Google Scholar] [CrossRef]
  50. Zhang, L.; Guo, X.; Zhao, T.; Gong, S.; Xu, X.; Li, Y.; Luo, L.; Gui, K.; Wang, H.; Zheng, Y.; et al. A modelling study of the terrain effects on haze pollution in the Sichuan Basin. Atmos. Environ. 2019, 196, 77–85. [Google Scholar] [CrossRef]
  51. Yang, Q.; Yuan, Q.; Yue, L.; Li, T. Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression. Environ. Pollut. 2020, 262, 114257. [Google Scholar] [CrossRef] [PubMed]
  52. Han, S.; Sun, B. Impact of population density on PM2.5 concentrations: A case study in Shanghai, China. Sustainability 2019, 11, 1968. [Google Scholar] [CrossRef] [Green Version]
  53. Han, S.; Sun, B.; Zhang, T. Mono- and polycentric urban spatial structure and PM2.5 concentrations: Regarding the dependence on population density. Habitat Int. 2020, 104, 102257. [Google Scholar] [CrossRef]
Figure 1. Main industry types of 13 townships and cities in Hsinchu County.
Figure 1. Main industry types of 13 townships and cities in Hsinchu County.
Ijerph 17 08691 g001
Figure 2. Flow chart of the construction of an air pollution potential map and risk analysis.
Figure 2. Flow chart of the construction of an air pollution potential map and risk analysis.
Ijerph 17 08691 g002
Figure 3. Distribution of PM2.5 potential in the study area in Hsinchu County in different seasons. Overall, 76 air quality-monitoring stations of the Taiwan Environmental Protection Administration (TWEPA) across the whole of Taiwan were used for spatial estimation, and we extracted the region of Hsinchu County for further analysis. (a) PM2.5 potential map in spring. (b) PM2.5 potential map in summer. (c) PM2.5 potential map in fall. (d) PM2.5 potential map in winter.
Figure 3. Distribution of PM2.5 potential in the study area in Hsinchu County in different seasons. Overall, 76 air quality-monitoring stations of the Taiwan Environmental Protection Administration (TWEPA) across the whole of Taiwan were used for spatial estimation, and we extracted the region of Hsinchu County for further analysis. (a) PM2.5 potential map in spring. (b) PM2.5 potential map in summer. (c) PM2.5 potential map in fall. (d) PM2.5 potential map in winter.
Ijerph 17 08691 g003
Figure 4. Boxplot of exceedance probabilities of 13 townships and cities in Hsinchu County.
Figure 4. Boxplot of exceedance probabilities of 13 townships and cities in Hsinchu County.
Ijerph 17 08691 g004
Figure 5. Distribution of PM2.5 potential in tourist areas in Hsinchu County in spring in 2017.
Figure 5. Distribution of PM2.5 potential in tourist areas in Hsinchu County in spring in 2017.
Ijerph 17 08691 g005
Figure 6. PM2.5 potential distribution and population density of each township in Hsinchu County.
Figure 6. PM2.5 potential distribution and population density of each township in Hsinchu County.
Ijerph 17 08691 g006
Figure 7. Map of industrial areas and air quality stations.
Figure 7. Map of industrial areas and air quality stations.
Ijerph 17 08691 g007
Figure 8. Dynamic distribution of PM2.5 potential in tourist areas in Hsinchu County in spring in (a) 2018 (b) 2019.
Figure 8. Dynamic distribution of PM2.5 potential in tourist areas in Hsinchu County in spring in (a) 2018 (b) 2019.
Ijerph 17 08691 g008
Table 1. Main industrial characteristics and potential disaster risks of 13 townships and cities in Hsinchu County.
Table 1. Main industrial characteristics and potential disaster risks of 13 townships and cities in Hsinchu County.
DistrictIndustry TypeMajor RiskMinor RiskOther Risks
Zhubei CityCommerce and industryFloods and droughtsToxic chemicalsTsunamis
Zhudong TownshipCommerce and industryFloods and droughtsLandslides/mudflows
Hukou TownshipIndustryFloods and droughtsToxic chemicalsAir pollution
Baoshan TownshipIndustryLandslides/mudflowsToxic chemicalsFloods and droughts
Emei TownshipAgricultureLandslides/mudflowsFloods and droughts
Wufeng TownshipAgricultureLandslides/mudflowsFloods and droughts
Xinfeng TownshipAgriculture and industryFloods and droughtsToxic chemicalsAir pollution or tsunamis
Qionglin TownshipAgriculture and industryFloods and droughtsLandslides/mudflowsToxic chemicals
Beipu TownshipAgriculture and industryLandslides/mudflowsFloods and droughts
Xinpu TownshipAgriculture and industryFloods and droughtsLandslides/mudflowsToxic chemicals
Guanxi TownshipAgriculture and leisure tourismFloods and droughtsLandslides/mudflows
Jianshi TownshipAgriculture and leisure tourismLandslides/mudflowsFloods and droughts
Hengshan TownshipAgriculture and leisure tourismLandslides/mudflowsFloods and droughts
Table 2. Basic statistics of exceedance probabilities of 13 townships and cities in Hsinchu County.
Table 2. Basic statistics of exceedance probabilities of 13 townships and cities in Hsinchu County.
DistrictEntire YearSpringSummerFallWinter
Mean ± Standard Deviation (%)
Zhubei City7.6 ± 0.412.1 ± 0.80.1 ± 0.14.9 ± 0.613.2 ± 0.6
Zhudong Township6.8 ± 0.311.4 ± 0.70.5 ± 0.34.4 ± 0.311.1 ± 0.6
Hukou Township8.9 ± 0.616.8 ± 1.70.3 ± 0.15.8 ± 0.512.9 ± 0.3
Baoshan Township6.5 ± 0.210.2 ± 0.40.2 ± 0.14.2 ± 0.311.7 ± 0.5
Emei Township6.4 ± 0.19.9 ± 0.20.3 ± 0.14.5 ± 0.210.9 ± 0.4
Wufeng Township5.9 ± 0.29.6 ± 0.40.4 ± 0.14.2 ± 0.29.5 ± 0.3
Xinfeng Township9.0 ± 0.416.6 ± 1.50.2 ± 0.16.3 ± 0.413.1 ± 0.3
Qionglin Township7.2 ± 0.112.1 ± 0.30.7 ± 0.24.6 ± 0.211.6 ± 0.3
Beipu Township6.5 ± 0.310.6 ± 0.60.5 ± 0.14.5 ± 0.110.5 ± 0.4
Xinpu Township7.7 ± 0.213.2 ± 0.70.3 ± 0.14.8 ± 0.412.5 ± 0.2
Guanxi Township7.3 ± 0.411.8 ± 0.80.7 ± 0.15.0 ± 0.211.8 ± 0.5
Jianshi Township6.2 ± 0.29.8 ± 0.40.5 ± 0.14.4 ± 0.310.1 ± 0.3
Hengshan Township6.8 ± 0.211.2 ± 0.50.8 ± 0.14.6 ± 0.110.8 ± 0.3
Table 3. Levels of air pollution potential and numbers of affected tourist attractions.
Table 3. Levels of air pollution potential and numbers of affected tourist attractions.
Level of Air Pollution PotentialExceedance ProbabilityNumber of Tourist Attractions
0 (mild)Below 5%34
15% to 10%34
210% to 12%124
312% to 14%114
414% to 16%7
516% to 18%11
618% to 20%3
7 (severe)More than 20%0
Table 4. Highest air pollution potentials of tourist attractions—levels 5 and 6—in Hsinchu County.
Table 4. Highest air pollution potentials of tourist attractions—levels 5 and 6—in Hsinchu County.
Number Name District Longitude Latitude Level of Air Pollution Potential
1 Caixiang TrailHukou Township121.0202824.8912216
2 Xiansheng TempleHukou Township121.04798924.9028926
3 Hukou Armored New Village (Village B)Hukou Township121.04780824.9044836
4 Rongyuanpu FarmHukou Township121.044224.87545
5 Laohukou Catholic Church Cultural CenterHukou Township121.0551624.876575
6 Renhe TrailHukou Township121.05849724.8770325
7 Yao Art Street and Bicycle TaroHukou Township121.057524.87735
8 Hanqing TrailHukou Township121.0519224.8773995
9 Hukou Old StreetHukou Township121.05261224.8777425
10 Xinfeng Sanyuan TempleXinfeng Township120.997924.89995
11 Yongning TempleXinfeng Township120.98526524.902485
12 Chifu Wangye TempleXinfeng Township120.976424.91025
13 Hongmaogang Ecological Recreation AreaXinfeng Township120.97636524.9102295
14 Xinfengpuyuan TempleXinfeng Township120.97759924.9249165
Table 5. Population density of each township in Hsinchu County in 2020.
Table 5. Population density of each township in Hsinchu County in 2020.
DistrictPopulation Density (Persons/km2)
Zhubei City3885.10
Zhudong Township1811.10
Hukou Township1325.41
Baoshan Township224.58
Emei Township118.33
Wufeng Township20.02
Xinfeng Township1226.25
Qionglin Township491.86
Beipu Township185.33
Xinpu Township462.87
Guanxi Township230.21
Jianshi Township18.09
Hengshan Township196.89
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lin, Y.-C.; Shih, H.-S.; Lai, C.-Y.; Tai, J.-K. Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan. Int. J. Environ. Res. Public Health 2020, 17, 8691. https://doi.org/10.3390/ijerph17228691

AMA Style

Lin Y-C, Shih H-S, Lai C-Y, Tai J-K. Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan. International Journal of Environmental Research and Public Health. 2020; 17(22):8691. https://doi.org/10.3390/ijerph17228691

Chicago/Turabian Style

Lin, Yuan-Chien, Hua-San Shih, Chun-Yeh Lai, and Jen-Kuo Tai. 2020. "Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan" International Journal of Environmental Research and Public Health 17, no. 22: 8691. https://doi.org/10.3390/ijerph17228691

APA Style

Lin, Y. -C., Shih, H. -S., Lai, C. -Y., & Tai, J. -K. (2020). Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan. International Journal of Environmental Research and Public Health, 17(22), 8691. https://doi.org/10.3390/ijerph17228691

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop