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Article

Health Risk Assessment of Inhalable Particulate Matter in Beijing Based on the Thermal Environment

State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(12), 12368-12388; https://doi.org/10.3390/ijerph111212368
Received: 5 July 2014 / Revised: 18 November 2014 / Accepted: 19 November 2014 / Published: 28 November 2014
(This article belongs to the Special Issue Air Pollution Modeling)

Abstract

Inhalable particulate matter (PM10) is a primary air pollutant closely related to public health, and an especially serious problem in urban areas. The urban heat island (UHI) effect has made the urban PM10 pollution situation more complex and severe. In this study, we established a health risk assessment system utilizing an epidemiological method taking the thermal environment effects into consideration. We utilized a remote sensing method to retrieve the PM10 concentration, UHI, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). With the correlation between difference vegetation index (DVI) and PM10 concentration, we utilized the established model between PM10 and thermal environmental indicators to evaluate the PM10 health risks based on the epidemiological study. Additionally, with the regulation of UHI, NDVI and NDWI, we aimed at regulating the PM10 health risks and thermal environment simultaneously. This study attempted to accomplish concurrent thermal environment regulation and elimination of PM10 health risks through control of UHI intensity. The results indicate that urban Beijing has a higher PM10 health risk than rural areas; PM10 health risk based on the thermal environment is 1.145, which is similar to the health risk calculated (1.144) from the PM10 concentration inversion; according to the regulation results, regulation of UHI and NDVI is effective and helpful for mitigation of PM10 health risk in functional zones.
Keywords: PM10; urban heat island (UHI); remote sensing; health risk PM10; urban heat island (UHI); remote sensing; health risk

1. Introduction

1.1. Background

Owing to the continuous development of the social economy and industrialization in China, urban regions are facing numerous environmental pollution problems, among which air pollution has become one of the most common. This is especially true for inhalable particulate matter (PM10), which represents a primary air pollutant that is detrimental to human health and has therefore received great attention from urban residents and governments. According to “The Key Environmental Air Quality Protection Cities in The First Half Year of 2012” [1] data published by the Chinese Ministry of Environmental Protection, the average PM10 concentration of 113 key environmental protection cities is 0.086 mg/m3, which exceeds the new air quality secondary standard (0.070 mg/m3) by 22.86%. Additionally, more than half of the cities in China, most of which are in northern China, did not pass the standards.
As a typical northern China city, Beijing is facing a serious problem of inhalable particulate matter pollution owing to increasing growth, construction, industrial production and the car population, coupled with the impact of external dust and specific climatic conditions. According to the Beijing Municipal Environmental Protection Bureau Beijing City 2011 Environmental Status Bulletin, the annual average concentration of PM10 is 0.121 mg/m3 in Beijing, which exceeds the new secondary standard by 72.86% and is 32.97% higher than the average concentration of the 113 key environmental protection cities in China [2].
With the recent increase in urbanization and the continuous expansion of city sizes, urban thermal environments are undergoing profound changes. As a result of this phenomenon, the strength and range of the heat island effect is expanding. In Beijing, climatic warming has been occurring at a rate of about 0.48 °C/decade during the last few decades (1977–2006) based on monitoring at 18 stations [3].

1.2. Study Review

When conducting health risk assessments most researchers reference the United States National Academy of Sciences (NAS) methodology, which mainly consists of four steps: hazard identification, dose response assessment, exposure assessment and risk characterization. Many studies have focused on toxic and harmful substances in inhalable particles health risk evaluation, including polycyclic aromatic hydrocarbons (PAH) [4] and other inorganic matter [5] and heavy metals [6]. Some researchers use epidemiological studies of PM10 health impacts as references, such as the relationship between exposure and response, to elucidate the relationship between the pollution level of inhalable particles and human health effects [7,8,9,10,11].
Under the effects of urban heat islands, urban areas suffer increasingly frequent extreme climatic events, such as heavy rain and heat waves. Additionally, air pollution in metropolitan areas is generally more serious, and has greater potential to affect human health and the ecological environment. These urban heat island (UHI) effects lead to changes in air quality [12] and increased concentrations of ozone [13] and fine particulate matter (PM2.5) or haze [14]. Studies have shown that there is a correlation between urban heat island intensity and the concentration distribution of inhalable particles [15,16,17]. In 1968, researchers found that the winds produced by urban heat island effects tend to sharpen pollution gradients between urban and rural areas [18]. One study in Paris indicated that UHI had an important impact on the primary and secondary regional pollutants [19]. Agarwal and Tandon in their study pointed out that the mesoscale wind produced by urban heat island help the pollutants to circulate and move in upward direction, thus making the problem of air pollution more severe in urban areas [20]. The poor air quality was associated with the greater frequency of a more intense UHI effect during the summer time, which was pronounced during the nighttime than the daytime [21]. Urban heat island can directly affect health because high temperatures place an added stress on human physiology [22]. Researches showed that excessive exposure to high heat was associated with increased rates of heat stress, heat stroke, and premature death [23]. The UHI effect could enhance health risks leading to higher mortality rates in cities compared to rural areas [24]. Moreover, the health risks associated with inhalable particulate matter are greatly influenced by the concentration, making it necessary to focus on the effects of UHI on the health risks of inhalable particulate matter.
Although many studies have been conducted to assess the health risk associated with inhalable particulate matter, few have investigated the regulation of inhalable particulate matter. Lichtenberg and Zilberman reported that an efficient health risk regulation model should be practical and useful for decision makers [17]. A range of health, safety, and environmental risk regulations have been implemented in both Europe and the United States during the last five decades [25], but these have mainly focused on certain toxic chemicals or hazardous materials [26]. The regulation is mostly conducted by the government and expressed as laws or through the political system, which seems to have powerful executive force. Toxicity studies have generally indicated that health risk regulation should first require an in-depth examination of the nature of the toxic risk problems themselves [27]. Accordingly, in a study of inhalable particles, health risk should be based on reasonable and accurate health risk analysis. Since no effective PM10 health risk regulation based on urban heat island effect has been established to date, the double-way regulation method established in this study is meaningful for urban environmental management.
Based on studies conducted in recent decades, it is essential to combine urban heat island effects with any PM10 health risk analysis system, which can be utilized for UHI effect mitigation and inhalable particulate matter reduction at the same time to promote urban sustainable development.

2. Methodology

In this study, we established a PM10 health risk assessment system based on the urban heat island effect. We utilized an established PM10 concentration-thermal environment model to integrate PM10 health risk assessment with urban heat island effect in different functional zones of Beijing. Comparisons between monitoring PM10 concentration/health risk and results based on thermal environment were made to make sure the model accuracy. Additionally, we adjusted the thermal environment indicators to regulate the health risk results in order to decrease the health risks and control the UHI effect simultaneously.

2.1. Study Area

Beijing is the capital of China, and one of the most populous cities in the world. The western, northern and northeastern portions of Beijing are surrounded by mountains, while the southeast is bordered by plains. The unique topography and climatic conditions of Beijing further aggravate the inhalable particulate matter pollution in the city by preventing particulate diffusion.
To promote sustainable economic and social development and optimize the overall function of the capital, Beijing has implemented a functional plan pertaining to its 14 urban and suburban districts and two rural counties (Figure 1). In this plan, districts are divided into four functional regions: core functional zone (Dongcheng, Xicheng districts), new urban expanding urban functional zone (Chaoyang, Fengtai, Shijingshan and Haidian districts), new urban development zone (Fangshan, Tongzhou, Shunyi, Changping, and Daxing districts) and ecological conservation development zone (Mentougou, Huairou, and Pinggu districts and Miyun, Yanqing counties).
Figure 1. Study area and functional regionalization distribution.
Figure 1. Study area and functional regionalization distribution.
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Because of the different functional zones with various population levels, structures of energy consumption and regional GDP, the PM10 health risk assessment of different functional zones in Beijing is more applicable than direct evaluation of the entire city for urban atmospheric environmental management and planning; therefore, this study focused on functional regions and illustrates the reasons for high risk level in certain districts.

2.2. Remote Sensing Data

Landsat 5 Thematic Mapper (TM) data were developed by the National Aeronautics and Space Administration (NASA). The satellite, launched in March 1984 [28], is one of the longest running and widely used satellites today. The repeat interval of Landsat 5 is 16 days, which means that we can obtain data from 2–3 TM images in a month. As a result of this, it is difficult to obtain high quality data in one season. In this study, we utilized the Landsat 5 TM image retrieval method to estimate the PM10 concentration. At present, TM images are available from The Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS) [29].
Higher particle concentration during the heating period in Beijing is due to the coal-burning infrastructure, and always shows a very high incidence of epidemic disease during the spring season; consequently, health risks associated with inhalable particulate matter are more serious during this period. Therefore, it is dramatically imperative to pay attention to this season and mitigate the high health risks due to the PM10 pollution. Based on this consideration, the TM image of Beijing on 14 March 2009 was acquired on a clear-sky day as the basic data for PM10 health risk analysis.
The inhalable particulate matter increase will cause the transmissivity of visible light and near infrared light to decrease, moreover, the transmissivity of near infrared light drops faster than that of the visible light [30]. Therefore, it is feasible to adopt the difference of visible light and near infrared light transmissivity with dual channel technology to establish the difference vegetation index (DVI). With the help of DVI index, we established a correlation between PM10 concentration and DVI index in Beijing. We also obtained the daily average concentration of inhalable particulate matter data from the Beijing environmental protection monitoring center [31] to establish the correlation between DVI and PM10 concentration.

2.3. PM10 Health Risk Assessment

In this study, we utilized PM10 remote sensing inversion and monitoring data to analyze heath risk based on an epidemiological study. Moreover, another health risk assessment was conducted based on thermal environment, which is meaningful for PM10 health risk control and management.

2.3.1. PM10 Health Impact Identification

Inhalable particles cause various respiratory and cardiovascular diseases and increase the number of inpatients, outpatients and mortality [32,33,34,35]. Inhalable particulate matter health impacts are divided into three categories according to their degree of harm: death, including chronic death and acute death (referred to as all-cause mortality); disease, including asthma, chronic bronchitis and acute bronchitis; and hospitalization, including respiratory system disease in the hospital and cardiovascular hospitalization [36] (see Table 1).
Table 1. Exposure-response relationship coefficients of different diameters of PM10i).
Table 1. Exposure-response relationship coefficients of different diameters of PM10i).
Hazard LevelHealth Impact Types (i)βi (PM10)Reference Information
Average95% Confidence Interval
DeathAll causes mortality0.00038(0.00035, 0.00042) [37]Meta analysis based on Chinese studies, 2009
Chronic mortality0.00192 *(0.000494, 0.00328) * [38]Meta analysis based on Chinese studies, 2013
Acute mortality0.00026 *(0.000124, 0.000403) * [38]Meta analysis based on Chinese studies, 2013
MorbidityAsthma0.00190(0.00145, 0.00235) [37]Meta analysis based on Chinese studies, 2009
Chronic bronchitis0.00656 *(0.00238, 0.01013) * [38]Meta analysis based on Chinese studies, 2013
Acute bronchitis0.00550(0.00189, 0.00911) [39]Study in Pearl River Delta in China, 2006
HospitalizationRespiratory system disease0.00124(0.00084, 0.00162) [39]Study in Pearl River Delta in China, 2006
Cardiovascular disease0.00066(0.00036, 0.00095) [39]Study in Pearl River Delta in China, 2006
Notes: * indicates that the data were converted by PM2.5/PM10 = 0.65, all data were collected from recent studies.

2.3.2. PM10 Exposure-Response Assessment

Epidemiological studies have revealed the correlation coefficient of the changes of some health effects caused by variations in inhalable particulate matter concentration, namely the exposure-response coefficient. The health impacts of inhalable particulate matter are closely related to the physical status of local residents and climate conditions, so exposure response relationship factors should be selected as references from domestic epidemiological studies whenever possible, and data from other areas should be considered when appropriate.
This study investigated the studies of exposure-response coefficients of PM10 in China; however, the dataset used for this analysis was incomplete. Most domestic epidemiological research includes analysis of health impacts and the exposure-response relationship of domestic PM10 and PM2.5 based on the meta-analysis method. Such analysis showed that the Pearl River Delta Region were subject to inhalable particulate matter pollution at levels that caused severe health impacts [37]. Additionally, the association between ambient air pollutants and increased hospital emergency room visits for cardiovascular diseases in Beijing, China were investigated [40]. Moreover, some studies have evaluated PM2.5 exposure-response relationship coefficients in some cities in China [9,38]. Recent studies showed that PM2.5/PM10 showed a certain proportion in Beijing. One study showed that the annual PM2.5/PM10 mass ratio was 0.71 in Beijing [41]. Another research showed that the PM2.5/PM10 ratios at the surface sites ranged from 37.5% to 85.1% with noticeably higher average values of 56.1%–66.5% at urban and elevated sites [42]. And long-term monitoring of PM2.5/PM10 concentration study pointed out that the proportion of PM2.5/PM10 was about 61.5% from 2001–2006 [43]. Therefore, based on the studies in China, we assume that the PM2.5/PM10 is 0.65 in general to obtain the exposure-response coefficients [37]. The exposure-response coefficients of relative health impacts are shown in Table 1.

2.3.3. PM10 Health Risk Characterization

This study employed a relative risk model based on Poisson Regression [7,9,11], which is commonly used in epidemiological studies of air pollution to calculate the relative risk of inhalable particulates with certain health impacts. We then adopted the average relative risk of all health impacts to represent the health risk of inhalable particulate matter using the following equations:
TR i = R i R 0 i = e β i × ( C C 0 )
TR = 1 n i = 1 n TR i
where, TR is the health risk of inhalable particulate matter; TRi is the relative risk caused by the ith health impact, i = 1,2,3,…,7 (see Table 1); Ri is the actual risk of the ith health impact; R0i is the reference risk value of the ith health impact; βi is the exposure-response coefficient; C is the actual concentration of inhalable particulate matter; C0 is the reference concentration in the risk assessment based on the average year guiding value of inhalable particulate matter set by the WHO, i.e., PM10 is 20 μg/m3; n is the number of health impact types caused by inhalable particulate matter.

2.3.4. PM10 Health Risk Assessment Based on Thermal Environment

In this study, we utilized infrared temperature to retrieval the surface temperature, and then obtained the UHI, NDVI, NDWI and DVI indicators according to the following equations.
UHI indicator calculation:
L b = L min +   max L min DN max × DN
where Lb means the radiation brightness; L max and L min refer to the maximum and minimum radiation intensities; DN represents the gray value of band 6; L min = 0.1238 mW·cm−2·sr−1·μm−1, L max = 1.56 mW·cm−2·sr−1·μm−1, and DN max = 255.
T b = K 2 ln ( K 1 / L b + 1 )
where Tb brightness temperature; K1 and K2 are constants (K1 = 60.776 mW·cm−2·sr−1·μm−1, K2 = 1260.56 K).
T R =   T i T a T a
where TR is the relative brightness temperature, which represents the UHI index in this study; Ti refers to certain point (i) brightness temperature (Tb), and Ta means the average brightness temperature.
NDVI reflects the vegetation coverage and growth state from space [44]. NDVI indicator calculation:
NDVI =   NIR R NIR + R
where R and NIR represent red (λ~0.6 μm) and near infrared (λ~0.8 μm) reflectivity.
NDWI refers to the differences of water surface content [45]. NDWI indicator calculation:
NDWI = NIR MIR NIR + MIR
where NIR and MIR represent the near infrared (λ~0.8 μm) and middle infrared (λ~1.65 μm) reflectivity, respectively.
DVI indicator calculation:
DVI = NIR R
where R and NIR represent red (λ~0.6 μm) and near infrared (NIR) (λ~0.8 μm) reflectivity.
The PM10 health risk assessment model is considered to adopt the PM10 concentration equations (Equation (9)) generated by Xu et al. (2013), which is based on the correlation between the PM10 concentration and thermal environmental indicators (UHI, NDVI, and NDWI) [46]. We then utilized the concentration formula to calculate PM10 health risk (Equation (10)) with epidemiological method from Equation (1):
{ Core   functional   zone : y   =   4 . 885 x 1 2 . 370 Expanding   urban   functional   zone : y =   1 . 391 x 1 + 82 . 246 x 2 + 0 . 164 x 3 + 0 . 132 New   urban   development   zone : y = 0 . 917 x 1 + 90 . 329 x 2 + 0 . 0215 Ecological   conservation   development   zone : y = 0 . 0401 x 1 + 62 . 470 x 2 + 0 . 620
TR i = e β i × ( F ( DVI ) C 0 ) = e β i × ( F ( f ( UHI ,  NDVI ,  NDWI ) ) C 0 )
where, f (UHI,NDVI,NDWI) refers to the PM10 concentration calculation formulas in Equation (7); y is the value of DVI (Difference vegetation index) in Equation (8); x1, x2, and x3 represent UHI, NDVI and NDWI respectively Equations (3)–(7). Other parameters are explained in Equation (1).

2.4. PM10 Health Risk Regulation

This study utilized the PM10 health risk analysis model combined with thermal environment indicators to regulate PM10 health risk by adjusting the UHI intensity. To illustrate the PM10 health risk regulation effect, we set three scenarios by regulating UHI, NDVI and NDWI to illustrate which indicator influences the PM10 health risk most significantly. Scenario 1 is the UHI regulation, in which we adjust the UHI by 0.1, and then analyzed the PM10 health risk spatial changes in different districts and counties of Beijing utilizing the Zonal Statistics function in ArcGIS. In Scenario 2, we regulated UHI and NDVI together to figure out NDVI influences on the PM10 health risk. In Scenario 3, we added NDWI indicators into Scenario 2, and then compared the three scenarios and analyze the differences among them.

3. Results and Discussion

3.1. Remote Sensing Inversion of PM10 Concentration in Beijing

The DVI index is built due to the different influence of inhalable particle pollutants on the transmissivity of the visible channel and near infrared channel of the NOAA satellite [30]. The DVI (difference vegetation index) was used to determine the inverse spatial distribution of inhalable particulate matter. Recent studies indicate that there is a linear correlation between DVI and PM10 [30,47]. We used the PM10 synchronous monitoring data collected from 17 Beijing ground stations taken when the Landsat Satellite transited Beijing (see Figure 2). The DVI values were then extracted according to the geographic coordinates of the stations. To diminish impacts on the final results due to location errors, the average DVI values of 3 × 3 pixels around the monitoring station were used. SPSS software analysis of the linear correlation of the monitoring data of PM10 and DVI values generated a correlation coefficient of −0.9683. The linear regression equation describing the relationship between PM10 concentration and DVI was then established and the following regression equation of the PM10 concentration and the DVI values based on the TM images in 2009 was generated (Equation (11)):
y   =   8 . 533 x   +   97 . 94   2   = 0 . 937
where y is the concentration of PM10 (μg/m3) and x is the DVI.
Figure 2. Relationship between DVI and PM10 concentration in March 2009.
Figure 2. Relationship between DVI and PM10 concentration in March 2009.
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The Spatial Analyst tool in ArcGIS was used to establish the inverse model based on the regression equation to give the inverse PM10 spatial distribution for Beijing in 2009 (Figure 3). Due to the fact that water surface has a very low reflection; therefore, the DVI values are influenced by this and has a much lower values than the other areas. Thus it is illustrated clear that large water surface areas all have a relative high PM10 inversion concentration. Therefore, the PM10 concentration reversion results of Miyun reservoir and other large water surface areas should not be taken into consideration. To make the study results accurate, we have deleted the PM10 concentration of the water surface areas in the study area. The PM10 concentration retrieval method is not suitable for the water surface; therefore, the Miyun reservoir PM10 concentration reversion results could not be taken into consideration. Except for some unique areas such as the Miyun reservoir, the spatial distribution of the inverse PM10 concentration from the TM images in 2009 were generally in line with the spatial distribution characteristics of inhalable particulate matter in Beijing, with PM10 concentrations in urban areas being larger than in suburbs and southwestern PM10 concentrations being larger than those in the northeast. The statistical analysis function also revealed that the average PM10 concentration in Beijing is 81.507 μg/m3, while the west area of the city had the largest PM10 concentration of 125.958 μg/m3, and that of the Huairou district had the lowest PM10 concentration of 66.464 μg/m3.
Figure 3. PM10 TM image inversion results in March 2009.
Figure 3. PM10 TM image inversion results in March 2009.
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Validation samples were selected at random based on the regression equations for accuracy verification using Equation (12) to acquire the results shown in Table 2. We excluded the largest and smallest error rates during statistical analysis to obtain reliable results. The results of the 2009 PM10 inversion of the TM image had a smaller error and higher precision. The final average error rate was 8.44%, indicating that the error of the PM10 concentration inversion results in 2009 was relatively small and authentic:
E R = | C i C j | C j
where ER is the error rate of the PM10 concentration based on thermal environment, Ci is the value of the PM10 concentration based on thermal environment, Cj is the actual value of the PM10 concentration.
Table 2. Accuracy verification results of PM10 TM image inversion in March 2009.
Table 2. Accuracy verification results of PM10 TM image inversion in March 2009.
Sample Serial Number1234567
Error rate (%)3.9816.685.6614.390.0511.766.41
Average error rate (%)8.44

3.2. PM10 Health Risk Assessment in Beijing

According to the inhalable particulate matter risk assessment method, we used the remote sensing inversion of PM10 spatial distribution to calculate the corresponding relative risk (TRi) to the certain health impact (i) of inhalable particulate matter (Equation (1)), after which we calculated the inhalable particulate matter health risk assessment (TR) according to Equation (2). The calculation results are shown in Figure 4.
Figure 4. PM10 health risk assessment results in Beijing in March 2009.
Figure 4. PM10 health risk assessment results in Beijing in March 2009.
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The spatial distribution of the PM10 health risk assessment results is basically the same as the inhalable particulate matter spatial distribution in Beijing in 2009; with a higher health risk in urban areas than rural areas and southwest regions than northeast regions (Figure 4). Additionally; we excluded specific regions such as the Miyun reservoir and obtained an average health risk value of 1.144. Statistical analysis revealed a descending health risk in central areas of the city; including the Dongcheng; Xicheng; Chaoyang; Fengtai; Shijingshan and Haidian districts; as well as in the new urban development zone; which comprises the Fangshan; Changping; Tongzhou; Shunyi and Daxing districts. However; in the ecological conservation development zone; the PM10 health risk was increasing from the Pinggu; Mentougou; and Huairou districts to Miyun and Yanqing counties.
When Miyun reservoir and other special areas are excluded, the health risk associated with PM10 in Beijing was 1.144. The results indicated that health risks associated with inhalable particulate matter occurred in the following order: Dongcheng > Xicheng > Chaoyang > Fengtai > Shijingshan > Haidian districts, as well as: Fangshan > Changping > Tongzhou > Shunyi > Daxing districts in the new urban development area and Pinggu > Mentougou > Huairou > Miyun > Yanqing in the ecological conservation area.

3.3. PM10 Health Risk Assessment Based on Thermal Environment in Beijing

We calculated the average UHI, NDVI and NDWI and utilized these indicators to compute the PM10 concentration values in different districts or counties in Beijing in March 2009 (Table 3). The calculation equations of UHI, NDVI and NDWI have been conducted and published by Xu et al. [46].
Table 3. UHI, NDVI and NDWI and PM10 concentration of different districts/counties in March 2009.
Table 3. UHI, NDVI and NDWI and PM10 concentration of different districts/counties in March 2009.
Function ZoneDistricts/CountiesUHINDVINDWIPM10
Core functional zoneDongcheng0.0949−0.03402.1858122.1179
Xicheng0.1032−0.04792.26298122.4633
Expanding urban functional zoneChaoyang0.1347−0.01101.82502103.6067
Fengtai0.2059−0.00871.63885103.0618
Shijingshan0.1738−0.00271.5155898.6612
Haidian0.12950.00751.4640891.0722
New urban development zoneFangshan0.08210.01421.8800486.1678
Tongzhou0.16300.02401.6470477.9883
Shunyi0.11590.02331.0496178.8774
Changping0.17180.02011.1688980.9161
Daxing0.19800.01691.1265983.1933
Ecological conservation development zoneMentougou−0.02940.02461.5984379.5299
Huairou0.03140.04821.8759166.9317
Pinggu−0.21410.04232.1113670.1956
Miyun−0.15430.03251.7968675.3956
Yanqing−0.15620.03071.7503876.3468
Note: “PM10” represents the average PM10 concentration (μg/m3) in different districts or counties.
According to the PM10 concentration calculated based on the thermal environment, we obtained the health risks of Beijing in March 2009. The results indicated that the health risk results based on thermal environment were similar to the previous assessment results calculated from PM10 remote sensing inversion, which was with an average variance ratio of 0.38% and the largest variance ratio being 1.05% (Table 4). These findings indicate that the PM10 health risk assessment method based on thermal environment can present PM10 health risks in the region with relatively good precision.
To compare the PM10 risk assessment results based on thermal environment with the previous results in part 3.3, the Zonal Statistics function in the ArcGIS software was used to analyze the statistical results. It was indicated that the analysis of PM10 health risks based on thermal environment was roughly the same as the PM10 spatial distribution in Beijing (Figure 5). The assessment results showed that the health risk of urban areas was higher than the health risk of rural areas and the southwest region had a higher risk than northeast regions. After excluding some unique regions such as the Miyun reservoir, we obtained the average health risk associated with PM10 of 1.145.
Table 4. PM10 health risk assessment results comparison in Beijing in March 2009.
Table 4. PM10 health risk assessment results comparison in Beijing in March 2009.
Function ZoneDistrict/CountyPM10 Health Risk Assessment
Results 1 (TRa) CI (95%)Results 2 (TRb) CI (95%)Variance Ratio (%)
Core functional zoneDongcheng1.2876 (1.1052, 1.5196)1.3012 (1.1094, 1.5482)1.0593
Xicheng1.3157 (1.1139, 1.5789)1.3025 (1.1098, 1.5509)1.0025
Expanding urban functional zoneChaoyang1.2355 (1.0883, 1.4138)1.2351 (1.0882, 1.4131)0.0291
Fengtai1.2351 (1.0882, 1.4130)1.2333 (1.0876, 1.4094)0.1451
Shijingshan1.2254 (1.0850, 1.3940)1.2185 (1.0827, 1.3803)0.5707
Haidian1.1962 (1.0751, 1.3374)1.1937 (1.0742, 1.3326)0.2104
New urban development zoneFangshan1.1754 (1.0679, 1.2982)1.1781 (1.0599, 1.2568)0.2273
Tongzhou1.1469 (1.0577, 1.2457)1.1530 (1.0609, 1.2617)0.5301
Shunyi1.1514 (1.0594, 1.2539)1.1557 (1.0631, 1.2731)0.3682
Changping1.1694 (1.0658, 1.2870)1.1619 (1.0656, 1.2860)0.6436
Daxing1.1657 (1.0644,1.2870)1.1689 (1.0616, 1.2653)0.2727
Ecological conservation development zoneMentougou1.1583 (1.0618, 1.2666)1.1576 (1.0688, 1.3032)0.0588
Huairou1.1192 (1.0476, 1.1962)1.1205 (1.0481, 1.1986)0.1192
Pinggu1.1248 (1.0496, 1.2061)1.1299 (1.0515, 1.2153)0.4590
Miyun1.1429 (1.0563, 1.2385)1.1452 (1.0571, 1.2427)0.2026
Yanqing1.1509 (1.0592, 1.2530)1.1481 (1.0581, 1.2478)0.2499
Average value1.18751.18770.3843
Notes: “Results 1” means the health risk results calculated based on the PM10 inversion of remote sensing, “Results 2” represents the health risk assessment results based on thermal environment. Variance ratio (%) = |TRa − TRb|/TRa × 100.

3.4. PM10 Health Risk Regulation in Beijing

There is a certain relationship between UHI and NDVI, -which means the increase of NDVI may cause the temperature mitigation or UHI intensity reduction. Due to the fact that complex processes are involved in determining the cooling effect of vegetation on daytime air and surface temperature [32], there is no authentic correlation of the two indicators obtained from recent studies.
Figure 5. PM10 health risks based on the thermal environment in Beijing in March 2009.
Figure 5. PM10 health risks based on the thermal environment in Beijing in March 2009.
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There is also no accurate relationship between UHI intensity and NDWI. In this study, we assume that UHI, NDVI and NDWI indicators are relatively independent indicators to set three regulation scenarios:
Scenario 1: UHI regulation. To promote the urban atmospheric environment management, in this study, we decreased UHI indicator for the value of 0.1 and analyzed the variation of PM10 health risk in Beijing in March 2009. Results showed that the core functional zone and expanding urban functional zone were found to have positive regulation effects, with average regulation effects of 0.0152 and 0.0069 (Table 5). Additionally, after reducing UHI indicator of 0.1, the inhalable particulate matter health risk decreased by 1.52% and 0.69% in the two zones. Conversely, the new urban development zone and ecological conservation development zone regulation effects were negative, that was, and the reduction of UHI intensity value leads to the increase of PM10 health risk.
From the health risk assessment results (Table 5), it is claimed that the health risks in Core functional zone (average 1.3016) and Expanding urban functional zone (average 1.2230) are higher than the New urban development zone (average 1.1618) and Ecological conservation development zone (average 1.1392). Therefore, the UHI regulation could be more effective with higher health risks, whereas, the health risk regulation could be adverse with lower health risk in certain circumstances. It is illustrated that UHI regulation can be effective in relative high-risk areas while can be adverse in some low health risk regions.
Table 5. Beijing PM10 health risk regulation results analysis (UHI-0.1).
Table 5. Beijing PM10 health risk regulation results analysis (UHI-0.1).
Function ZoneDistrict/CountyAssessment ResultsRegulation ResultsRegulation Effects
Core functional zoneDongcheng1.28761.28580.0018
Xicheng1.31571.28700.0286
Average1.30161.28640.0152
Expanding urban functional zoneChaoyang1.23551.23110.0044
Fengtai1.23511.22920.0058
Shijingshan1.22541.21450.0109
Haidian1.19621.18990.0063
Average1.22301.21620.0069
New urban development zoneFangshan1.17541.1806−0.0051
Tongzhou1.14691.1553−0.0084
Shunyi1.15141.1580−0.0066
Changping1.16941.16430.0051
Daxing1.16571.1713−0.0056
Average1.16181.1659−0.0041
Ecological conservation development zoneMentougou1.15831.15770.0006
Huairou1.11921.1206−0.0014
Pinggu1.12481.1300−0.0053
Miyun1.14291.1453−0.0024
Yanqing1.15091.14820.0028
Average1.13921.1404−0.0012
Note: regulation effect = assessment result-regulation result.
Scenario 2: UHI and NDVI regulation. Based on Scenario 1, we increased the NDVI indicator by 0.1 to figure out the variation of regulation effects. Table 6 shows the regulation effects after the adjustment of UHI and NDVI in different functional zones.
It is obvious that Scenario 2 has better regulation effects than Scenario 1, and in expanding urban functional zone, new urban development zone and ecological conservation development zone, the health risks decline by 20.48%, 19.48% and 13.82% respectively, while the health risk is the consistent with Scenario1 in core functional zone.
Scenario 3: UHI, NDVI and NDWI regulation. In this scenario, we decreased UHI by 0.1 and increased NDVI and NDWI by 0.1 respectively to analyze the health risk in different districts or counties in Beijing. The calculation results compared with the health risk assessment results are listed in Table 7. As the results illustrated, only in expanding urban functional zone there is a little improvement (0.0003) in regulation effects, while the other zones have the same results compared with Scenario 2 (Table 7). The results may be due to the fact that the NDVI and NDWI show little correlation with the DVI indicator in core functional zone (Equation (3)). Therefore, the increase of NDVI and NDWI does not reduce the health risk of core functional zone obviously. Moreover, NDWI is directly correlated with DVI in expanding urban functional zone only, as a result of this, the regulation of NDWI influences little on the PM10 health risks in the other functional zones.
Table 6. Beijing PM10 health risk regulation results analysis (UHI-0.1, NDVI + 0.1).
Table 6. Beijing PM10 health risk regulation results analysis (UHI-0.1, NDVI + 0.1).
Function ZoneDistrict/CountyRegulation ResultsRegulation Effects
Core functional zoneDongcheng1.28580.0018
Xicheng1.28700.0286
Average1.28640.0152
Expanding urban functional zoneChaoyang1.02900.2065
Fengtai1.02760.2074
Shijingshan1.01710.2084
Haidian0.99930.1969
Average1.01830.2048
New urban development zoneFangshan0.97720.1982
Tongzhou0.95950.1874
Shunyi0.96140.1900
Changping0.96580.2036
Daxing0.97070.1950
Average0.96690.1948
Ecological conservation development zoneMentougou1.01460.1437
Huairou0.98560.1336
Pinggu0.99300.1318
Miyun1.00490.1380
Yanqing1.00710.1438
Average1.00100.1382
Table 7. Beijing PM10 health risk regulation results analysis (UHI-0.1, NDVI + 0.1, NDWI + 0.1).
Table 7. Beijing PM10 health risk regulation results analysis (UHI-0.1, NDVI + 0.1, NDWI + 0.1).
Function ZoneDistrict/CountyRegulation ResultsRegulation Effects
Core functional zoneDongcheng1.28580.0018
Xicheng1.28700.0286
Average1.28640.0152
Expanding urban functional zoneChaoyang1.02860.2068
Fengtai1.02730.2077
Shijingshan1.01670.2087
Haidian0.99900.1972
Average1.01790.2051
New urban development zoneFangshan0.97720.1982
Tongzhou0.95950.1874
Shunyi0.96140.1900
Changping0.96580.2036
Daxing0.97070.1950
Average0.96690.1948
Ecological conservation development zoneMentougou1.01460.1437
Huairou0.98560.1336
Pinggu0.99300.1318
Miyun1.00490.1380
Yanqing1.00710.1438
Average1.00100.1382
However, we must admit that the correlation equations show the main oriented correlation types, which means that NDVI and NDWI still influence the concentration of inhalable particulate matter in core functional zone. To achieve the goal of PM10 health risk mitigation of Beijing in March 2009, Scenario 2 and Scenario 3, which can control the UHI effect and improve the vegetation coverage in urban areas are very acceptable and effective, although for environmental management and control, Scenario 2 is more practicable than the other scenarios.

4. Conclusions

This study established a PM10 health risk assessment system based on the urban thermal environment utilizing the epidemiological method combined with remote sensing inversion and monitoring techniques to provide a proposal for urban inhalable particulate matter regulation and management. The PM10 health risk of Beijing showed two distribution aspects in March 2009; namely, PM10 health risk in urban areas was higher than in rural areas and the southwest than in the northeast portion of the city and different functional regions showed spatial variation. Utilizing the PM10 health risk assessment model based on the thermal environment, the PM10 health risk in Beijing was determined to be 1.145, which is close to the health risk assessment results (1.144) derived from the PM10 concentration inversion with remote sensing method. These findings illustrate that the PM10 health risk assessment system based on thermal environment is acceptable and meaningful for urban environment management as well as UHI effect and PM10 health risk control. According to the health risk regulation of UHI, NDVI and NDWI, it is very effective to control the UHI and NDVI indicators for urban PM10 health risk management. Therefore, for urban heat island effect control and PM10 mitigation, the regulation of the UHI and NDVI together is meaningful and useful. In this research, although have attempted to give general study conclusions at best, there are still some uncertainties that need to be considered The remote sensing data obtained in this study could be limited, while we obtained the TM image on a typical weather condition day, which could reflect the general health risk situation at certain extent. Moreover, the comprehensive health risk is based on the health endpoints selected in this study that may not cover all the health endpoints due to the PM10 pollution or have some overlap among them. Whereas, the health endpoints here are selected in three levels, which could be relative authentic and appropriate for the health risk assessment. As a whole, this study proposes a general solution to mitigate the urban heat island effect as well as the PM10 health risk in urban areas, which could give suggestions for urban management.

Acknowledgments

This work was financially supported by the Fund for Innovative Research Group of the National Natural Science Foundation of China (No. 51121003) and the National Natural Science Foundation of China (No. 41271105).

Author Contributions

Linyu Xu had the original idea for the study and, with all co-authors carried out the design. Linyu Xu was responsible for recruitment and follow-up of study participants. Hao Yin and Xiaodong Xie was responsible for data cleaning and carried out the analyses. Hao Yin drafted the manuscript, which was revised by all authors. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. The Key Environmental Air Quality Protection Cities in the First Half Year of 2012. Ministry of Environmental Protection of the People’s Republic of China. Available online: http://www.zhb.gov.cn/gkml/hbb/bgg/201208/t20120823_235126.htm (accessed on 24 November 2014).
  2. Beijing Environmental Statement 2011; Beijing Municipal Environmental Protection Bureau: Beijing, China, 2012.
  3. Yan, Z.; Li, Z.; li, Q.; Jones, P. Effects of site change and urbanisation in the beijing temperature series 1977–2006. Int. J. Climatol. 2010, 30, 1226–1234. [Google Scholar] [CrossRef]
  4. Kaushik, C.; Sangwan, P.; Haritash, A. Association of polycyclic aromatic hydrocarbons (PAHS) with different sizes of atmospheric particulate in hisar city and its health aspects. Polycycl. Aromat. Compd. 2012, 32, 626–642. [Google Scholar] [CrossRef]
  5. Schlesinger, R.B.; Cassee, F. Atmospheric secondary inorganic particulate matter: The toxicological perspective as a basis for health effects risk assessment. Inhal. Toxicol. 2003, 15, 197–235. [Google Scholar] [CrossRef] [PubMed]
  6. Hu, X.; Zhang, Y.; Ding, Z.; Wang, T.; Lian, H.; Sun, Y.; Wu, J. Bioaccessibility and health risk of arsenic and heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn and Mn) in TSP and PM2.5 in Nanjing, China. Atmos. Environ. 2012, 57, 146–152. [Google Scholar] [CrossRef]
  7. Künzli, N.; Kaiser, R.; Medina, S.; Studnicka, M.; Chanel, O.; Filliger, P.; Herry, M.; Horak, F., Jr.; Puybonnieux-Texier, V.; Quénel, P.; et al. Public-health impact of outdoor and traffic-related air pollution: A European assessment. Lancet 2000, 356, 795–801. [Google Scholar] [CrossRef] [PubMed]
  8. Quah, E.; Boon, T.L. The economic cost of particulate air pollution on health in Singapore. J. Asian Econ. 2003, 14, 73–90. [Google Scholar] [CrossRef]
  9. Kan, H.; Chen, B. Particulate air pollution in urban areas of Shanghai, China: Health-based economic assessment. Sci. Total Environ. 2004, 322, 71–79. [Google Scholar] [CrossRef] [PubMed]
  10. Zhang, M.; Song, Y.; Cai, X. A health-based assessment of particulate air pollution in urban areas of Beijing in 2000–2004. Sci. Total Environ. 2007, 376, 100–108. [Google Scholar] [CrossRef] [PubMed]
  11. Yaduma, N.; Kortelainen, M.; Wossink, A. Estimating mortality and economic costs of particulate air pollution in developing countries: The case of Nigeria. Environ. Resour. Econ. 2013, 54, 361–387. [Google Scholar] [CrossRef]
  12. Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef]
  13. Bloomer, B.J.; Stehr, J.W.; Piety, C.A.; Salawitch, R.J.; Dickerson, R.R. Observed relationships of ozone air pollution with temperature and emissions. Geophys. Res. Lett. 2009, 36. [Google Scholar] [CrossRef]
  14. Zhang, D.; Shou, Y.; Dickerson, R.R. Upstream urbanization exacerbates urban heat island effects. Geophys. Res. Lett. 2009, 36. [Google Scholar] [CrossRef]
  15. Lingjun, L.; Ying, W.; Qiang, Z.; Tong, Y.; Yue, Z.; Jun, J. Spatial distribution of aerosol pollution based on modis data over Beijing, China. J. Environ. Sci. 2007, 19, 955–960. [Google Scholar] [CrossRef]
  16. Pandey, P.; Kumar, D.; Prakash, A.; Masih, J.; Singh, M.; Kumar, S.; Jain, V.K.; Kumar, K. A study of urban heat island and its association with particulate matter during winter months over delhi. Sci. Total Environ. 2012, 414, 494–507. [Google Scholar] [CrossRef] [PubMed]
  17. Jin, M.S.; Kessomkiat, W.; Pereira, G. Satellite-observed urbanization characters in Shanghai, China: Aerosols, urban heat island effect, and land-atmosphere interactions. Remote Sens. 2011, 3, 83–99. [Google Scholar] [CrossRef]
  18. Chandler, T.J. Discussion of the paper by marsh and foster. The bearing of the urban temperature field upon urban pollution patterns. Atmos. Environ. 1968, 2, 619–620. [Google Scholar]
  19. Sarrat, C.; Lemonsu, A.; Masson, V.; Guedalia, D. Impact of urban heat island on regional atmospheric pollution. Atmos. Environ. 2006, 40, 1743–1758. [Google Scholar]
  20. Agarwal, M.; Tandon, A. Modeling of the urban heat island in the form of mesoscale wind and of its effect on air pollution dispersal. Appl. Math. Model. 2010, 34, 2520–2530. [Google Scholar] [CrossRef]
  21. Poupkou, A.; Nastos, P.; Melas, D.; Zerefos, C. Climatology of discomfort index and air quality index in a large urban mediterranean agglomeration. Water Air Soil Pollut. 2011, 222, 163–183. [Google Scholar] [CrossRef]
  22. Shahmohamadi, P.; Che-Ani, A.I.; Etessam, I.; Maulud, K.N.A.; Tawil, N.M. Healthy environment: The need to mitigate urban heat island effects on human health. Procedia Eng. 2011, 20, 61–70. [Google Scholar] [CrossRef]
  23. O’Neill, M.S.; Ebi, K.L. Temperature extremes and health: Impacts of climate variability and change in the United States. J. Occup. Environ. Med. 2009, 51, 13–25. [Google Scholar] [CrossRef] [PubMed]
  24. Conti, S.; Meli, P.; Minelli, G.; Solimini, R.; Toccaceli, V.; Vichi, M.; Beltrano, C.; Perini, L. Epidemiologic study of mortality during the summer 2003 heat wave in Italy. Environ. Res. 2005, 98, 390–399. [Google Scholar] [CrossRef] [PubMed]
  25. Vogel, D. The Transatlantic Shift in Health, Safety, and Environmental Risk Regulation, 1960–2010; (APSA 2011 Annual Meeting Paper); APSA: Washington, DC, USA, 2011. [Google Scholar]
  26. Seaton, A.; Tran, L.; Aitken, R.; Donaldson, K. Nanoparticles, human health hazard and regulation. J. R. Soc. Interface 2010, 7, S119–S129. [Google Scholar] [CrossRef] [PubMed]
  27. Wagner, W.E. The science charade in toxic risk regulation. Columbia Law Rev. 1995, 95, 1613–1723. [Google Scholar]
  28. Chander, G.; Markham, B. Revised landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2674–2677. [Google Scholar] [CrossRef]
  29. Chinese Academy of Sciences. Available online: http://ids.ceode.ac.cn/ (accessed on 18 October 2014).
  30. Zimu, Y.; Hongmei, Z.; Youfei, Z. Study on distribution of urban particle pollution by remote sensing and GIS. J. Nat. Disasters 2004, 13, 58–64. (In Chinese) [Google Scholar]
  31. Beijing Environmental Protection Monitoring Cente. Available online: http://www.bjmemc.com.cn/ (accessed on 18 October 2014).
  32. Holland, W.W.; Bennett, A.; Cameron, I.; Florey, C.d.V.; Leeder, S.; Schilling, R.; Swan, A.; Waller, R. Health effects of particulate pollution: Reappraising the evidence. Amer. J. Epidemiol. 1979, 110, 527–527. [Google Scholar]
  33. Just, J.; Segala, C.; Sahraoui, F.; Priol, G.; Grimfeld, A.; Neukirch, F. Short-term health effects of particulate and photochemical air pollution in asthmatic children. Eur. Respir. J. 2002, 20, 899–906. [Google Scholar] [CrossRef] [PubMed]
  34. Forastiere, F.; Stafoggia, M.; Picciotto, S.; Bellander, T.; D’Ippoliti, D.; Lanki, T.; von Klot, S.; Nyberg, F.; Paatero, P.; Peters, A. A case-crossover analysis of out-of-hospital coronary deaths and air pollution in Rome, Italy. Amer. J. Respir. Crit. Care Med. 2005, 172, 1549–1555. [Google Scholar] [CrossRef]
  35. Simkhovich, B.Z.; Kleinman, M.T.; Kloner, R.A. Air pollution and cardiovascular injury epidemiology, toxicology, and mechanisms. J. Amer. Coll. Cardiol. 2008, 52, 719–726. [Google Scholar] [CrossRef]
  36. Van Leeuwen, F.R. A European perspective on hazardous air pollutants. Toxicology 2002, 181, 355–359. [Google Scholar] [CrossRef] [PubMed]
  37. Peng, X.; Xiaoyun, L.; Zhaorong, L.; Tiantian, L.; Yuhua, B. Exposure-response functions for health effects of ambient particulate matter pollution applicable for China. China Environ. Sci. 2009, 29, 1034–1040. (In Chinese) [Google Scholar]
  38. Guo, Y.; Jia, Y.; Pan, X.; Liu, L.; Wichmann, H. The association between fine particulate air pollution and hospital emergency room visits for cardiovascular diseases in Beijing, China. Sci. Total Environ. 2009, 407, 4826–4830. (In Chinese) [Google Scholar] [CrossRef] [PubMed]
  39. Huang, D.; Zhang, S. Health benefit evaluation for PM2.5 pollution control in Beijing-Tianjin-Hebei region of China. China Environ. Sci. 2013, 33, 166–174. (In Chinese) [Google Scholar]
  40. Wang, Y.; Zhuang, G.; Chen, S.; An, Z.; Zheng, A. Characteristics and sources of formic, acetic and oxalic acids in PM2.5 and PM10 aerosols in Beijing, China. Atmos. Res. 2007, 84, 169–181. [Google Scholar] [CrossRef]
  41. Chan, C.Y.; Xu, X.D.; Li, Y.S.; Wong, K.H.; Ding, G.A.; Chan, L.Y.; Cheng, X.H. Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in Beijing. Atmos. Environ. 2005, 39, 5113–5124. [Google Scholar] [CrossRef]
  42. Wang, H.; Zhuang, Y.; Wang, Y.; Sun, Y.; Yuan, H.; Zhuang, G.; Hao, Z. Long-term monitoring and source apportionment of PM2.5/PM10 in Beijing, China. J. Environ. Sci. 2008, 20, 1323–1327. [Google Scholar] [CrossRef]
  43. Liu, X.; Xie, P.; Liu, Z.; Li, T.; Zhong, L.; Xiang, Y. Economic assessment of acute health impact due to inhalable particulate air pollution in the Pearl River Delta. J. Peking Univ. Nat. Sci. Ed. 2010, 46, 829–834. (In Chinese) [Google Scholar]
  44. Chen, T.; Niu, R.-Q.; Wang, Y.; Zhang, L.-P.; Du, B. Percentage of vegetation cover change monitoring in Wuhan region based on remote sensing. Procedia Environ. Sci. 2011, 10, 1466–1472. [Google Scholar] [CrossRef]
  45. Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
  46. Linyu, X.; Xiaodong, X.; Shun, L. Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing. Environ. Pollut. 2013, 178, 102–114. [Google Scholar] [CrossRef] [PubMed]
  47. Ming, T.; Wenji, Z.; Wenhui, Z. Retrieving of inhalable particulate matter based on spot image. Remote Sens. Land Resour. 2011, 23, 62–65. [Google Scholar]
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