- Email: [email protected]
Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China
Atmospheric Pollution Research xxx (2017) 1e8
H O S T E D BY
Contents lists available at ScienceDirect
Atmospheric Pollution Research journal homepage: http://www.journals.elsevier.com/locate/apr
Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China Zhi-Hong Zhang a, b, Mao-Gui Hu b, e, *, Jing Ren b, Zi-Yin Zhang c, George Christakos d, Jin-Feng Wang b, ** a
Center for Environmental Risk and Damage Assessment, Chinese Academy for Environmental Planning, Beijing, China State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China c Beijing Meteorological Bureau, Beijing, China d Department of Geography, San Diego State University, San Diego, CA, USA e The Key Laboratory of Surveillance and Early Warnings on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 November 2016 Received in revised form 17 April 2017 Accepted 19 April 2017 Available online xxx
Air pollution has become more serious in many developing countries. Heavy particulate matter (PM) air pollution is a major threat to people's respiratory and cardiopulmonary health. It is an important problem for public health research to accurately estimate the spatial distribution of high PM concentrations from a limited number of monitoring stations. In this study, a maximum entropy (MaxEnt) model was adopted to obtain the probability distribution map of high PM10 concentrations. Daily PM10 concentration data from 19 air quality monitoring stations from the years 2008e2011 were collected. Land cover, road density, and meteorological data were selected as explanatory variables entered in the model. A receiver operating characteristic (ROC) analysis was used to evaluate the performance of the MaxEnt model. The area under the ROC curve (AUC) shows that the MaxEnt model fits well in the four year period. AUC is 0.78 in 2008, 0.79 in 2009, 0.81 in 2010, and 0.80 in 2011. A probability distribution map of high PM10 concentration indicates high human health risks in regions of Beijing in areas with dense roads and buildings. During the entire research period from 2008 to 2011, the distribution of high PM10 concentration is relatively stable and it indicates that the trend of high concentration has not changed significantly during the four years. Traffic and land cover are the two most important factors that can explain more than 80% variance of PM10 from 2008 to 2011. © 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Keywords: PM10 Probability distribution of high concentrations Road density Maximum entropy Public health
1. Introduction Air pollution is a major problem in urban areas with high levels of urbanization. Pollution seriously affects public health and the environment, and, in this sense, Beijing is no different than other metropolises worldwide (Wang et al., 2013). Air pollution consists mainly of aerosol and gas pollutants, e.g., sulfur dioxide, nitric
* Corresponding author. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. ** Corresponding author. E-mail addresses: [email protected] (M.-G. Hu), [email protected] (J.-F. Wang). Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
oxide, ozone, and particulate matter (PM). PM is a pollutant that includes PM10 and PM2.5, i.e., particles with aerodynamic diameters of less than 10 and 2.5 mm, respectively (Bayraktar et al., 2008; Wiwanitkit, 2007). Concentrations can be routinely obtained via PM measurement at ground stations. Considerable research has demonstrated that PM10 and PM2.5 pollution is a very serious problem in Beijing (Wang et al., 2013). Many air quality control strategies were followed during the Beijing 2008 Olympic Games, for example, shutting down heavily polluting industries and installing FGD (Flue Gas Desulfurization) to coalfired plants. As a result, the pollution caused by coal burning has been reduced a great deal (Li et al., 2010; MEP, 2011). Meanwhile, the past few years have seen a large increase in car ownership in Beijing. Research reveals that the dominant air pollution source has changed from coal burning to a mix of coal burning and vehicle
http://dx.doi.org/10.1016/j.apr.2017.04.006 1309-1042/© 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
2
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
emissions (Wang and Hao, 2012; Wu et al., 2011). PM10 concentrations have been assessed by regression models that show automobile and industrial pollution and incomplete combustion of coal greatly contribute to PM pollution (Ernst et al., 2012; Henderson et al., 2007; Ul-Saufie et al., 2013; Vanagas, 2004). Vehicle emissions have become a dominant source of air pollutants, including volatile organic compounds, nitrogen oxides, PM, carbon monoxide, and carbon dioxide. Traffic pollution potentially increases pollutant emissions and degrades air quality, particularly near roads (Zhang and Batterman, 2013). According to chemical analyses, industrial pollution is another atmospheric contamination source in Beijing, and emissions from boiler combustion are another PM source during the winter heating period (Wang et al., 2005). Aerosol optical thickness from satellite remote sensing has been found to be a good indicator of PM concentration, and the correlation between aerosol optical thickness and PM concentrations can reach 0.7 (Engel-Cox et al., 2004). Research involving aerosol optical thickness and surface-level PM concentration showed serious PM pollution in Beijing (Wang et al., 2010; An et al., 2007; Xen et al., 2011). Because of PM10's characteristic small size and large specific surface area, PM10 is a toxic substance in the air (Ibadan et al., 2013) and can cause acute and chronic bronchitis, asthma, pneumonia, lung cancer, and other respiratory and cardiovascular diseases (Chen et al., 2004; Martinelli et al., 2013; Peters, 2005; Stern et al., 2013). There is strong evidence that short-term exposure to PM is associated with an increased risk of mortality (Chen et al., 2013). According to the World Health Organization's revised air quality guidelines, three interim targets are defined for PM10. The Interim target-1 (IT-1) concentration threshold for PM10 is 70 mg/m3. This concentration is thought to have serious effects on public health, e.g., it is closely linked to significant mortality levels in developed countries. In particular, people exposed to PM10 at the 70 mg/m3 threshold have approximately a 15% higher long-term risk of mortality relative to the Air Quality Guidelines (AQG) level. The Interim target-2 (IT-2) concentration is 50 mg/m3. Compared to the IT-1, these levels reduce the risk of premature mortality by approximately 6%; the Interim target-3 (IT-3) concentration is 30 mg/m3. These levels bring down the mortality risk about 6% relative to the IT-2 level (WHO, 2006). Accurate spatial distribution of the PM risk is important to both public health and government policy, but the limited number of air quality monitoring stations is not sufficient to cover such a large region. Although many regression and interpolation models have been proposed to map the spatial distribution of PM, a large number of monitoring stations are usually required due to the nonlinear and complex relationship between PM concentration and explanatory variables. However, in some underdeveloped regions and countries, there are not enough stations installed to reduce financial inputs. In this paper, we introduce a machine learning model, MaxEnt, which is relatively insensitive to a small number of samples (Phillips and Dudik, 2008) to assess the probability of high PM concentrations with a rather small number of samples. We collected the daily air quality monitoring data from 19 stations in Beijing, a city of about 16,000 square kilometers. Because the monitoring density (monitoring station per unit area) is rather low and the air pollution could be expanding, it is difficult to understand the distribution of the air pollution particulate matter in a large and continuous area using this monitoring data. The MaxEnt model is a machine learning method which can use present-only data and environmental data to predict the present maximum probability distribution pattern. MaxEnt has been used extensively in many domains, e.g., species distribution prediction and epidemic disease occurrence probability prediction. Besides its low sensitivity in a small sample size, the model is particularly well-suited to
noisy or sparse information (Fourcade et al., 2014; Slater and Michael, 2012). The MaxEnt principles relevant to modeling environmental conditions and suitable for predicting factors are based on three standards: quantification of fit, tuning of program settings, and acquiring independent evaluation data (Radosavljevic and Anderson, 2014). In this study, we used MaxEnt to estimate the concentration of PM pollutants. The main aims were as follows. (a) Establish the probability distribution of high PM10 concentrations and assess the spatial pattern of the probability distribution of heavy pollution throughout Beijing. (b) Analyze the trend of annual average concentration from 2008 to 2011.
2. Data and method 2.1. Data The research area was urban Beijing. Four types of data were involved: PM10 concentration, meteorological data, principal roads, and land cover. PM10 concentration data were obtained from air pollution monitoring stations installed and operated by the Beijing Environmental Protection Bureau. This station network covers urban and suburban Beijing. The stations record daily concentrations of PM10 and other air pollutants. PM10 concentrations were calculated from the reported daily air pollution index for air pollution levels (Hu et al., 2013). We collected the data from a total of 19 pollution monitoring stations from January 1, 2008 to December 31, 2011. Daily meteorological data, including precipitation, maximum, minimum, and average temperatures, average and minimum humidity, pressure, water vapor, average wind speed, wind direction, and solar radiation were obtained from 11 meteorological stations in urban Beijing from 2008 to 2011. The meteorological station network records weather in different parts of the city (Fig. 1). Beijing has a large road network, and the traffic road data used in the study were vector data of the entire network, classified according to road grade (highways, first class, ring roads, secondary roads, and tertiary roads). Land-cover data were also included, which were downloaded from the GlobCover portal (Bontemps et al., 2011). This portal provides access to the results of the GlobCover project, which was initiated in 2005 by the European Space Agency and its partners. This is a 300 m resolution land-cover product, consisting of 22 land-cover types derived from the automated classification of medium-resolution imaging spectrometer data. Different landcover types reflect the diverse levels of PM pollutants' emission. 2.2. Data processing Because of varying data formats, some pretreatment work was necessary (Fig. 2). First, daily PM10 concentration data were transformed into annual averaged concentrations over the period of 2008e2011. The annual average PM10 concentrations greater than or equal to 70 mg/ m3 (WHO IT-1 level) were considered high in the study, and areas experiencing these concentrations were considered areas with a potential high human health risk. Second, daily meteorological data from the observation stations (precipitation, maximum, minimum, and average temperature, median humidity, pressure, and water vapor) were converted to annual averages. The data were then interpolated onto a 1000 1000 m grid covering the Beijing area, using kriging techniques in ESRI ArcGIS 10.3. These interpolated complete-coverage
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
3
Fig. 1. (a) Air pollution monitoring and meteorological observation stations in the Beijing area; (b) land cover and road network in Beijing.
Fig. 2. Data processing flow chart.
meteorological data were independent variables for predicting air pollutants' heavy concentrations by the model. All data were subjected to the same projection. Third, land-cover data labeled GlobCover 2009 were masked for the urban region of Beijing, upscaled to a 1000 1000 m grid, and ultimately saved as an ASCII file. Twenty-two land types were merged into 5 types, i.e., landcover1 (vegetation area), landcover2 (low vegetation area), landcover3 (bare area), landcover4 (building area), and landcover5 (water area), according to the amount of vegetation and the level of PM emission. The 5 land types were entered into the MaxEnt model as independent variables. Fourth, the road data were classified into 3 groups. Highways were labeled “road_a,” first class and ring roads as “road_b,” and secondary and tertiary roads as “road_c.” There were more roads and a large number of vehicles traveling in the road_b and road_c groups. Road_b and road_c had the same or even greater effect on PM pollution among the 3 groups. Therefore, we used the GIS software to merge roads within the three classes, and then calculate road densities in each 1000 1000 m grid with an ArcGIS line density tool. Road density was the sum of different road lengths in the grid divided by the grid area. The road density variables considered traffic factors to generate “roaddensity_a,”
“roaddensity_b,” and “roaddensity_c” data.
2.3. MaxEnt model The MaxEnt model is based on the Shannon information entropy concept (Banavar et al., 2010). Its core idea is to deduce an unknown probability distribution based strictly on available information. The study used MaxEnt in an interdisciplinary manner to predict the PM distribution in areas of air pollutant concentration. According to MaxEnt, the derived probability form is the one that most faithfully describes the available data, and any other probability form would use information that is not actually available (Leshowitz, 1969). To calculate the probability form, we defined the central Beijing area as a large collection X, which consists of pixels x1 ; x2 ; x3 ; …; xm . The probability p of each pixel is unknown. To derive the probability value nearest the actual, we built functions f1 ; f2 ; …; fm (linear, quadratic, product, threshold, and hinge features) that represent the available information about pollutant distribution. Then, the probability distribution sought is obtained in terms of the f-functions, i.e., p½f , which is such that the corresponding mean entropy function p½f lnp½f is maximized (Christakos, 2000; Jaynes, 2003).
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
4
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
Fig. 3. Distribution of high concentrations of PM10 (2008e2011).
The receiver operating characteristic (ROC) curve was used to compare model performance. The curve provides a single measure of model performance (Pietra et al., 1997) and it is one of the most commonly used evaluation indices for diagnostic tests (Vanagas, 2004; Yang et al., 2013a, b). The ROC curve is built by plotting y and x axes for all possible thresholds. The y-axis of the curve is the actual positive rate for which the actual probability distribution is the same as the predicted positive probability. The x-axis is the false positive rate for which there is no similarity in the distribution of the actual and predicted positive probability. The area under the curve (AUC) is used to reflect model fit performance of the data. The AUC is between 0.5 and 1.0; the closer the value is to 1, the better the results. There are several ROC curve evaluation criteria: for an AUC of 0.5e0.6, the model fails; 0.6e0.7, the model is poor; 0.7e0.8, the model is fair; 0.8e0.9, the model is good; 0.9e1.0, the model is excellent (Engler et al., 2004). A jackknife test was performed to ascertain a variable's importance in the MaxEnt model (Roberto et al., 2011). It is a technique of statistical hypothesis and confidence interval estimation that uses observation values as a sample and performs an unbiased estimation of unknown parameters. The jackknife concept focuses on rejecting one value at a time from the original sample, and the remaining data are defined as the jackknife sample. The MaxEnt model uses jackknife to generate a number of models. Each variable is excluded in turn and a model is created with the remaining variables. A model is then created using each variable alone. In addition, a model is created using all variables. All these models are then compared, and the most important variables are identified.
3. Results and discussion Probability distribution maps of PM10 concentration generated using the MaxEnt model are shown in Fig. 3. These maps cover areas with high PM10 concentration from 2008 to 2011 in Beijing. Deep blue shading denotes a low probability of high PM10 concentration and red shading indicates a high probability. The probability of the red areas is close to one. There are high values in the northeast, southwest, and northwest of central Beijing, which are related to county centers in Daxing, Fangshan, Changping, and Shunyi counties. It is seen that dark red areas of higher probability are in central urban Beijing, coinciding with the complex transportation network and heavily built-upon land. Generally, the light blue shading outside central Beijing represents lighter pollution than in the former area, revealing geographic variation in emission levels. A comparison of the PM10 distribution over the 4 years shows no significant change across the Beijing urban area. However, there is a small difference shown by the deep blue areas outside central Beijing in 2008, which indicates that PM10-related air pollution in that region was not as serious as in other years. AUC values calculated by MaxEnt for each year are shown in Fig. 4. Values for the years 2008 through 2011 were 0.78, 0.79, 0.81, and 0.80, respectively. Blue shading represents the average AUC from 19 model runs, and the red curve is the final ROC curve. To identify the importance of environmental factors in the MaxEnt model, estimates of their relative contributions are displayed in Table 1. It is seen that the principal variables in the model are land cover and road density. The contribution of land cover variables is 10e15%, that of road traffic density variables is 80e86%,
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
5
Fig. 4. Average AUC of each calculation by MaxEnt model (2008e2011).
and meteorological variables are less than 10%. To explore each variable's contribution, the jackknife technique was used to identify their relative importance in MaxEnt (Fig. 5). The deep blue bars indicate the selected variable, and light blue bars represent the remaining variables. Road density (roaddensity_b and roaddensity_c) and land cover have the two longest bars. This means that the variables “road density” and “land cover” are very important in the model, and are core factors for explaining the distribution of high PM10 concentrations. Meteorological data also have some effect on the PM10 distribution. In the chart for 2008, 2009, 2010, and 2011 (Fig. 5), values of roaddensity_b and roaddensity_c exceed 70%, and those of land cover exceed 40%. In the chart for 2008, values of temperature, roaddensity_a, vapor, and wind speed all exceed 5%. In the chart for 2009, values of humidity, vapor, wind speed, roaddensity_a, and precipitation are greater than 5%. In the chart for 2010, the value of pressure is 15% and those of humidity, wind speed, roaddensity_a, temperature, and precipitation exceed
Table 1 Percentage contribution of environmental variables (2008e2011). Variable
2008
2009
2010
2011
land cover roaddensity_b roaddensity_c pressure wind speed temperature humidity vapor roaddensity_a precipitation
11.8 31.1 55.1 0 0.2 0.7 1.4 0.2 0 0.1
11.7 30.5 54.3 0.1 0.2 0 0.1 1.1 0 2
11.3 29.5 51.9 6.8 0.2 0 0.1 0.3 0 0
11.9 31.1 55.1 0 0.8 0 0 0.2 0 0.8
5%. In the chart for 2011, values of roaddensity_a and precipitation exceed 5%. The above results reveal that the distribution of high PM10 concentration is focused on the center of Beijing, and consequently high PM10 concentrations are linked to episodes of heavy air pollution. The analysis permitted identification of the relative contributions of the variables in MaxEnt modeling from 2008 to 2011 (Table 1). Road density and land cover were the two most important environmental variables, suggesting that the distribution of high PM10 concentration probability is attributable to traffic and built-up land (Fig. 6). Fig. 5 shows the importance of these variables from 2008 to 2011 via blue bars, which are more informative than Table 1. The land cover and road density bars are larger, suggesting that these are the main variables in the forecast model for PM10 pollution (the same as shown in Table 1). Based on the jackknife test, meteorological variables also contribute in part to heavy air pollution episodes. The meteorological data account for a small fraction of the distribution relative to environmental factors. Three values of meteorological factors, e.g., maximum, minimum, and average values of temperature, wind speed, and humidity are strongly correlated, and the model's AUC is very small (not shown). Therefore, these maximum and minimum values' variables were removed from the MaxEnt model. Fig. 6 shows that each variable affects the probability distribution of high PM10 concentrations in 2010. Roaddensity_b and roaddensity_c have a greater contribution compared to other factors. The more intensive the road network is, the greater the potential for air pollution episodes. In addition, much research has revealed that urban air pollutants vary with the urban area function. PM concentration varies with types of urban functions, with
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
6
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
Fig. 5. Contribution of each variable in the jackknife model (2008e2011).
the traffic function as the most serious (Li and Ma, 2005). When vapor, humidity, and temperature reached some threshold values, the greatest concentration probability occurred. This might be related to secondary particles converted by gaseous air pollutants during certain weather conditions. Pressure and wind have a combined influence on the dispersion of PM, so wind speed and high PM10 concentration have a negative correlation. Land cover 4 is a proxy variable of built-up areas, which contain various types of industrial land, living areas, infrastructure, and so on. By comparing PM concentration before, during, and after the 2008 Olympic Games in Beijing, industrial emissions were found to be another important source of PM10 pollution in Beijing (Shen et al., 2011). Meanwhile, tall buildings and certain weather conditions have a mutual influence on PM pollution because such pollutants are not easily diffused to other areas. Boiler combustion emissions are also a significant source of pollution during the heating period in the built-up area of Beijing (Zhang et al., 2002), which might be another reason that built land areas had high PM10 concentrations. PM10 comes from motor vehicle exhaust emissions, urban construction, smoke from heating, industrial pollution, and smoke from coalfired boilers. All these factors (exhaust emission, industrial production, meteorological conditions, terrain, and human activities) have a synergistic effect on the occurrence of high PM10 concentration areas. Terrain is also an important factor. Beijing is at the northwest edge of the North China Plain. There are many mountains to the west, north, and northeast, which means that the terrain is not conducive to the dispersion of atmospheric pollutants (Yang et al., 2013a, b). Furthermore, there is some remote PM10 transmission from the south of the city, which produces heavy PM10 pollution in the center. PM10 primarily originates from local road traffic and industrial areas, except for that from long-distance transmission of industrial emissions in other regions (Tan et al., 2004). Other research studies based on aerosol optical depth have produced
PM10 concentration maps revealing the same result, i.e., high PM10 concentration focused on the center of urban Beijing (Wang et al., 2010; Xen et al., 2011). Given the high PM10 concentration density in these areas, most residents are at risk of negative effects from PM10 pollution (Zhang et al., 2007). Although current research found that traffic and built-up areas are two of the most important factors contributing to a high probability of heavy PM10 pollution (which is consistent with other recent research, e.g., Shen et al., 2011; Wang and Xie, 2009), one limitation of this study is that we have not separated the contribution of industrial and residential emissions. Detailed land use data and an industrial emission inventory might be extremely helpful to improving the model.
4. Conclusion The MaxEnt model can handle nonlinear relationships and can effectively map the high PM concentration distribution. Built with land cover, traffic, and meteorological variables, the model successfully explains large-scale variability in PM10 concentrations. It is effective in modeling the probability distribution of high PM10 concentrations. The highest PM10 concentrations were over the complex road network and urban building land, and roads and land cover area were the principal variables in the PM10 prediction model. Traffic, industrial, and human emissions are the related emissions from primary particulate pollution sources. Meteorological factors also have an important contribution to PM10 formation and diffusion. Heavy pollution and human activity concentration areas show a strong consistency. The large area of high PM10 concentration indicates that it is a very serious pollution problem in Beijing, especially at the center of urban areas.
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
7
Fig. 6. Response curves between environmental variables and prediction probability.
Acknowledgments This study was supported by grants from The National Environmental Protection Public Welfare Science and Technology Research Program of China (201509027) and the National Natural Science Foundation of China (41301425; 41531179). References An, X., Zhu, T., Wang, Z., Li, C., Wang, Y., 2007. A modeling analysis of a heavy air pollution episode occurred in Beijing. Atmos. Chem. Phys. 7, 3103e3114. Banavar, J.R., Maritan, A., Volkov, I., 2010. Applications of the principle of maximum
entropy: from physics to ecology. J. Phys. 22 (6), 063101. Bayraktar, H., Turalioglu, F.S., Tuncel, G., 2008. Average mass concentrations of TSP, PM10 and PM2.5 in Erzurum urban atmosphere, Turkey. Stoch. Environ. Res. Risk Assess. 24, 57e65. Bontemps, S., Defourny, P., Bogaert, E., Kalogirou, V., Perez, Z., 2011. GLOBCOVER 2009 products description and validation report. ESA Bull. 136, 53. Chen, B.H., Hong, C.J., Kan, H.D., 2004. Methodological research on the health-based risk assessment on air pollution. J. Environ. Health 21, 112e113. Chen, R.J., Peng, D.R., Meng, X., Zhou, Z.J., Chen, B.H., Kan, H.D., 2013. Seasonal variation in the acute effect of particulate air pollution on mortality in the China Air Pollution and Health Effects Study (CAPES). Sci. Total Environ. 450e451, 259e265. Christakos, G., 2000. Modern Spatiotemporal Geostatistics. Oxford University Press. Engel-Cox, J.A., Holloman, C.H., Coutant, B.W., Hoff, R.M., 2004. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
8
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
scale air quality. Atmos. Environ. 38, 2495e2509. Engler, R., Antoine, G., Luca, R., 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudoabsence data. J. Appl. Ecol. 41 (2), 263e274. € dder, D., Secondi, J., 2014. Mapping species distributions Fourcade, Y., Engler, J.O., Ro with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9, e97122. Henderson, S.B., Beckerman, B., Jerrett, M., Brauer, M., 2007. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ. Sci. Technol. 41, 2422e2428. Hu, M.G., Jia, L., Wang, J.F., Pan, Y., 2013. Spatial and temporal characteristics of particulate matter in Beijing, China using the empirical mode decomposition method. Sci. Total Environ. 458e460, 70e80. Ibadan, N., Ana, G., Adeniji, B., Ige, O., Oluwole, O., Olopade, C., 2013. Exposure to emissions from firewood cooking stove and the pulmonary health of women in Olorunda community. Air Qual. Atmos. Health 6, 465e471. Jaynes, E.T., 2003. Probability Theory: the Logic of Science. Cambridge University Press. Leshowitz, B., 1969. Comparison of ROC curves from one- and two-interval ratingscale procedures. J. Acoust. Soc. Am. 46, 399e402. Li, H.W., Ma, Y.D., 2005. Study on distribution regularity of air pollution in Fuxin City. J. Saf. Environ. 5 (6), 76e78. Martinelli, N., Olivieri, O., Girelli, D., 2013. Air particulate matter and cardiovascular disease: a narrative review. Eur. J. Intern. Med. 24, 295e302. MEP, 2011. Report on Environmental Quality in China, vol. 2010. Peters, A., 2005. Particulate matter and heart disease: evidence from epidemiological studies. Toxicol. Appl. Pharmacol. 207, 477e482. Phillips, S., Dudik, M., 2008. Presence-only Modeling with Maxent, vol. 2006. Pietra, S.D., Pietra, V.D., Lafferty, J., 1997. Inducing features of random fields. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1e13. Radosavljevic, A., Anderson, R.P., 2014. Making better MAXENT models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629e643. lica, V., Miguel, A.H., Roberto, M., Ricardo, Z., Juan, R.M., Ange 2011. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using maximum entropy (Maxent). Ecol. Inf. 6, 364e370. Shen, J., Tang, A., Liu, X., Kopsch, J., Fangmeier, A., Goulding, K., 2011. Impacts of pollution controls on air quality in Beijing during the 2008 Olympic Games. J. Environ. Qual. 40, 37e45. Slater, H., Michael, E., 2012. Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling. PLoS One 7, e32202. Stern, G., Latzin, P., Roosli, M., Fuchs, O., Proietti, E., Kuehni, C., Frey, U., 2013. A prospective study of the impact of air pollution on respiratory symptoms and infections in infants. Am. J. Respir. Crit. Care Med. 187, 1341e1348. Ul-Saufie, A.Z., Yahaya, A.S., Ramli, N.A., Rosaida, N., Hamid, H.A., 2013. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmos. Environ. 77, 621e630. Vanagas, G., 2004. Receiver operating characteristic curves and comparison of cardiac surgery risk stratification systems. Interact. Cardiovasc. Thorac. Surg. 3,
319e322. Wang, S.X., Hao, J.M., 2012. Air quality management in China: issues, challenges, and options. J. Environ. Sci. 24, 2e13. Wang, T., Xie, S.D., 2009. Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period. Atmos. Environ. 43, 5682e5690. Wang, Y., Zhuang, G.S., Tang, A.H., Yuan, H., Sun, Y.L., Chen, S., 2005. The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771e3784. Wang, Z., Chen, L., Tao, J., Zhang, Y., Su, L., 2010. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sens. Environ. 114, 50e63. Wang, J.F., Hu, M.G., Xu, C.D., Christakos, G., Zhao, Y., 2013. Estimation of citywide air pollution in Beijing. PLoS One 8, e53400. WHO, 2006. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen, Dioxide and Sulfur Dioxide. World health organization. Wiwanitkit, V., 2007. PM10 in the atmosphere and incidence of respiratory illness in Chiangmai during the smoggy pollution. Stoch. Environ. Res. Risk Assess. 22, 437e440. Wu, Y., Wang, R., Zhou, Y., Lin, B., Fu, L., He, K., 2011. On-road vehicle emission control in Beijing: past, present, and future. Environ. Sci. Technol. 45, 147e153. Xen, Q.Y., Mazlan, H., Maged, M., 2011. Retrieval of PM 10 Concentration from Moderate Resolution Imaging Spectroradiometer (MODIS)derived AOD in Peninsular Malaysia. IGARSS, pp. 4022e4025. Yang, W., Zhao, W.J., Gong, Z.N., Zhao, W.H., Tang, T., 2013a. Spatial distribution of inhalable particulate and association with respiratory disease in Beijing city. Environ. Sci. 34, 233e243. Yang, X.Q., Kushwaha, S., Saran, S., Xu, J.C., Roy, P.S., 2013b. Maxent modeling for predicting the potential distribution of medicinal plant,Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 51, 83e87. Zhang, K., Batterman, S., 2013. Air pollution and health risks due to vehicle traffic. Sci. Total Environ. 450e451, 307e316. Zhang, R.J., Wang, M.X., Hu, F., Xu, Y.F., 2002. Elemental concentrations and distributions of atmospheric particles before and in heating period in Beijing. J. Graduate Sch. Chin. Acad. Sci. 19 (1), 75e81. Zhang, M.S., Song, Y., Cai, X.H., 2007. A health-based assessment of particulate air pollution in urban areas of Beijing in 2000e2004. Sci. Total Environ. 376, 100e105.
Further reading , Z., Michalek, J., 2012. Forecasting of daily PM10 concenStadlober, E., Hübnerova trations in Brno and Graz by different regression approaches. Austrian J. Stat. 41, 287e310. Zhang, Q., Krotkov, N., 2010. Recent large reduction in sulfur dioxide emissions from Chinese power plants observed by the Ozone Monitoring Instrument. Geophys. Res. Lett. 37, L08807. Zhang, Y.M., Tan, R.H., Zheng, Y.F., Lou, W.Y., 2004. Pollution research on PM2.5 in typical urban resident area in Shanghai. J. Labour Med. 21 (3), 226e229.
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
H O S T E D BY
Contents lists available at ScienceDirect
Atmospheric Pollution Research journal homepage: http://www.journals.elsevier.com/locate/apr
Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China Zhi-Hong Zhang a, b, Mao-Gui Hu b, e, *, Jing Ren b, Zi-Yin Zhang c, George Christakos d, Jin-Feng Wang b, ** a
Center for Environmental Risk and Damage Assessment, Chinese Academy for Environmental Planning, Beijing, China State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China c Beijing Meteorological Bureau, Beijing, China d Department of Geography, San Diego State University, San Diego, CA, USA e The Key Laboratory of Surveillance and Early Warnings on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 November 2016 Received in revised form 17 April 2017 Accepted 19 April 2017 Available online xxx
Air pollution has become more serious in many developing countries. Heavy particulate matter (PM) air pollution is a major threat to people's respiratory and cardiopulmonary health. It is an important problem for public health research to accurately estimate the spatial distribution of high PM concentrations from a limited number of monitoring stations. In this study, a maximum entropy (MaxEnt) model was adopted to obtain the probability distribution map of high PM10 concentrations. Daily PM10 concentration data from 19 air quality monitoring stations from the years 2008e2011 were collected. Land cover, road density, and meteorological data were selected as explanatory variables entered in the model. A receiver operating characteristic (ROC) analysis was used to evaluate the performance of the MaxEnt model. The area under the ROC curve (AUC) shows that the MaxEnt model fits well in the four year period. AUC is 0.78 in 2008, 0.79 in 2009, 0.81 in 2010, and 0.80 in 2011. A probability distribution map of high PM10 concentration indicates high human health risks in regions of Beijing in areas with dense roads and buildings. During the entire research period from 2008 to 2011, the distribution of high PM10 concentration is relatively stable and it indicates that the trend of high concentration has not changed significantly during the four years. Traffic and land cover are the two most important factors that can explain more than 80% variance of PM10 from 2008 to 2011. © 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Keywords: PM10 Probability distribution of high concentrations Road density Maximum entropy Public health
1. Introduction Air pollution is a major problem in urban areas with high levels of urbanization. Pollution seriously affects public health and the environment, and, in this sense, Beijing is no different than other metropolises worldwide (Wang et al., 2013). Air pollution consists mainly of aerosol and gas pollutants, e.g., sulfur dioxide, nitric
* Corresponding author. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. ** Corresponding author. E-mail addresses: [email protected] (M.-G. Hu), [email protected] (J.-F. Wang). Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
oxide, ozone, and particulate matter (PM). PM is a pollutant that includes PM10 and PM2.5, i.e., particles with aerodynamic diameters of less than 10 and 2.5 mm, respectively (Bayraktar et al., 2008; Wiwanitkit, 2007). Concentrations can be routinely obtained via PM measurement at ground stations. Considerable research has demonstrated that PM10 and PM2.5 pollution is a very serious problem in Beijing (Wang et al., 2013). Many air quality control strategies were followed during the Beijing 2008 Olympic Games, for example, shutting down heavily polluting industries and installing FGD (Flue Gas Desulfurization) to coalfired plants. As a result, the pollution caused by coal burning has been reduced a great deal (Li et al., 2010; MEP, 2011). Meanwhile, the past few years have seen a large increase in car ownership in Beijing. Research reveals that the dominant air pollution source has changed from coal burning to a mix of coal burning and vehicle
http://dx.doi.org/10.1016/j.apr.2017.04.006 1309-1042/© 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
2
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
emissions (Wang and Hao, 2012; Wu et al., 2011). PM10 concentrations have been assessed by regression models that show automobile and industrial pollution and incomplete combustion of coal greatly contribute to PM pollution (Ernst et al., 2012; Henderson et al., 2007; Ul-Saufie et al., 2013; Vanagas, 2004). Vehicle emissions have become a dominant source of air pollutants, including volatile organic compounds, nitrogen oxides, PM, carbon monoxide, and carbon dioxide. Traffic pollution potentially increases pollutant emissions and degrades air quality, particularly near roads (Zhang and Batterman, 2013). According to chemical analyses, industrial pollution is another atmospheric contamination source in Beijing, and emissions from boiler combustion are another PM source during the winter heating period (Wang et al., 2005). Aerosol optical thickness from satellite remote sensing has been found to be a good indicator of PM concentration, and the correlation between aerosol optical thickness and PM concentrations can reach 0.7 (Engel-Cox et al., 2004). Research involving aerosol optical thickness and surface-level PM concentration showed serious PM pollution in Beijing (Wang et al., 2010; An et al., 2007; Xen et al., 2011). Because of PM10's characteristic small size and large specific surface area, PM10 is a toxic substance in the air (Ibadan et al., 2013) and can cause acute and chronic bronchitis, asthma, pneumonia, lung cancer, and other respiratory and cardiovascular diseases (Chen et al., 2004; Martinelli et al., 2013; Peters, 2005; Stern et al., 2013). There is strong evidence that short-term exposure to PM is associated with an increased risk of mortality (Chen et al., 2013). According to the World Health Organization's revised air quality guidelines, three interim targets are defined for PM10. The Interim target-1 (IT-1) concentration threshold for PM10 is 70 mg/m3. This concentration is thought to have serious effects on public health, e.g., it is closely linked to significant mortality levels in developed countries. In particular, people exposed to PM10 at the 70 mg/m3 threshold have approximately a 15% higher long-term risk of mortality relative to the Air Quality Guidelines (AQG) level. The Interim target-2 (IT-2) concentration is 50 mg/m3. Compared to the IT-1, these levels reduce the risk of premature mortality by approximately 6%; the Interim target-3 (IT-3) concentration is 30 mg/m3. These levels bring down the mortality risk about 6% relative to the IT-2 level (WHO, 2006). Accurate spatial distribution of the PM risk is important to both public health and government policy, but the limited number of air quality monitoring stations is not sufficient to cover such a large region. Although many regression and interpolation models have been proposed to map the spatial distribution of PM, a large number of monitoring stations are usually required due to the nonlinear and complex relationship between PM concentration and explanatory variables. However, in some underdeveloped regions and countries, there are not enough stations installed to reduce financial inputs. In this paper, we introduce a machine learning model, MaxEnt, which is relatively insensitive to a small number of samples (Phillips and Dudik, 2008) to assess the probability of high PM concentrations with a rather small number of samples. We collected the daily air quality monitoring data from 19 stations in Beijing, a city of about 16,000 square kilometers. Because the monitoring density (monitoring station per unit area) is rather low and the air pollution could be expanding, it is difficult to understand the distribution of the air pollution particulate matter in a large and continuous area using this monitoring data. The MaxEnt model is a machine learning method which can use present-only data and environmental data to predict the present maximum probability distribution pattern. MaxEnt has been used extensively in many domains, e.g., species distribution prediction and epidemic disease occurrence probability prediction. Besides its low sensitivity in a small sample size, the model is particularly well-suited to
noisy or sparse information (Fourcade et al., 2014; Slater and Michael, 2012). The MaxEnt principles relevant to modeling environmental conditions and suitable for predicting factors are based on three standards: quantification of fit, tuning of program settings, and acquiring independent evaluation data (Radosavljevic and Anderson, 2014). In this study, we used MaxEnt to estimate the concentration of PM pollutants. The main aims were as follows. (a) Establish the probability distribution of high PM10 concentrations and assess the spatial pattern of the probability distribution of heavy pollution throughout Beijing. (b) Analyze the trend of annual average concentration from 2008 to 2011.
2. Data and method 2.1. Data The research area was urban Beijing. Four types of data were involved: PM10 concentration, meteorological data, principal roads, and land cover. PM10 concentration data were obtained from air pollution monitoring stations installed and operated by the Beijing Environmental Protection Bureau. This station network covers urban and suburban Beijing. The stations record daily concentrations of PM10 and other air pollutants. PM10 concentrations were calculated from the reported daily air pollution index for air pollution levels (Hu et al., 2013). We collected the data from a total of 19 pollution monitoring stations from January 1, 2008 to December 31, 2011. Daily meteorological data, including precipitation, maximum, minimum, and average temperatures, average and minimum humidity, pressure, water vapor, average wind speed, wind direction, and solar radiation were obtained from 11 meteorological stations in urban Beijing from 2008 to 2011. The meteorological station network records weather in different parts of the city (Fig. 1). Beijing has a large road network, and the traffic road data used in the study were vector data of the entire network, classified according to road grade (highways, first class, ring roads, secondary roads, and tertiary roads). Land-cover data were also included, which were downloaded from the GlobCover portal (Bontemps et al., 2011). This portal provides access to the results of the GlobCover project, which was initiated in 2005 by the European Space Agency and its partners. This is a 300 m resolution land-cover product, consisting of 22 land-cover types derived from the automated classification of medium-resolution imaging spectrometer data. Different landcover types reflect the diverse levels of PM pollutants' emission. 2.2. Data processing Because of varying data formats, some pretreatment work was necessary (Fig. 2). First, daily PM10 concentration data were transformed into annual averaged concentrations over the period of 2008e2011. The annual average PM10 concentrations greater than or equal to 70 mg/ m3 (WHO IT-1 level) were considered high in the study, and areas experiencing these concentrations were considered areas with a potential high human health risk. Second, daily meteorological data from the observation stations (precipitation, maximum, minimum, and average temperature, median humidity, pressure, and water vapor) were converted to annual averages. The data were then interpolated onto a 1000 1000 m grid covering the Beijing area, using kriging techniques in ESRI ArcGIS 10.3. These interpolated complete-coverage
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
3
Fig. 1. (a) Air pollution monitoring and meteorological observation stations in the Beijing area; (b) land cover and road network in Beijing.
Fig. 2. Data processing flow chart.
meteorological data were independent variables for predicting air pollutants' heavy concentrations by the model. All data were subjected to the same projection. Third, land-cover data labeled GlobCover 2009 were masked for the urban region of Beijing, upscaled to a 1000 1000 m grid, and ultimately saved as an ASCII file. Twenty-two land types were merged into 5 types, i.e., landcover1 (vegetation area), landcover2 (low vegetation area), landcover3 (bare area), landcover4 (building area), and landcover5 (water area), according to the amount of vegetation and the level of PM emission. The 5 land types were entered into the MaxEnt model as independent variables. Fourth, the road data were classified into 3 groups. Highways were labeled “road_a,” first class and ring roads as “road_b,” and secondary and tertiary roads as “road_c.” There were more roads and a large number of vehicles traveling in the road_b and road_c groups. Road_b and road_c had the same or even greater effect on PM pollution among the 3 groups. Therefore, we used the GIS software to merge roads within the three classes, and then calculate road densities in each 1000 1000 m grid with an ArcGIS line density tool. Road density was the sum of different road lengths in the grid divided by the grid area. The road density variables considered traffic factors to generate “roaddensity_a,”
“roaddensity_b,” and “roaddensity_c” data.
2.3. MaxEnt model The MaxEnt model is based on the Shannon information entropy concept (Banavar et al., 2010). Its core idea is to deduce an unknown probability distribution based strictly on available information. The study used MaxEnt in an interdisciplinary manner to predict the PM distribution in areas of air pollutant concentration. According to MaxEnt, the derived probability form is the one that most faithfully describes the available data, and any other probability form would use information that is not actually available (Leshowitz, 1969). To calculate the probability form, we defined the central Beijing area as a large collection X, which consists of pixels x1 ; x2 ; x3 ; …; xm . The probability p of each pixel is unknown. To derive the probability value nearest the actual, we built functions f1 ; f2 ; …; fm (linear, quadratic, product, threshold, and hinge features) that represent the available information about pollutant distribution. Then, the probability distribution sought is obtained in terms of the f-functions, i.e., p½f , which is such that the corresponding mean entropy function p½f lnp½f is maximized (Christakos, 2000; Jaynes, 2003).
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
4
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
Fig. 3. Distribution of high concentrations of PM10 (2008e2011).
The receiver operating characteristic (ROC) curve was used to compare model performance. The curve provides a single measure of model performance (Pietra et al., 1997) and it is one of the most commonly used evaluation indices for diagnostic tests (Vanagas, 2004; Yang et al., 2013a, b). The ROC curve is built by plotting y and x axes for all possible thresholds. The y-axis of the curve is the actual positive rate for which the actual probability distribution is the same as the predicted positive probability. The x-axis is the false positive rate for which there is no similarity in the distribution of the actual and predicted positive probability. The area under the curve (AUC) is used to reflect model fit performance of the data. The AUC is between 0.5 and 1.0; the closer the value is to 1, the better the results. There are several ROC curve evaluation criteria: for an AUC of 0.5e0.6, the model fails; 0.6e0.7, the model is poor; 0.7e0.8, the model is fair; 0.8e0.9, the model is good; 0.9e1.0, the model is excellent (Engler et al., 2004). A jackknife test was performed to ascertain a variable's importance in the MaxEnt model (Roberto et al., 2011). It is a technique of statistical hypothesis and confidence interval estimation that uses observation values as a sample and performs an unbiased estimation of unknown parameters. The jackknife concept focuses on rejecting one value at a time from the original sample, and the remaining data are defined as the jackknife sample. The MaxEnt model uses jackknife to generate a number of models. Each variable is excluded in turn and a model is created with the remaining variables. A model is then created using each variable alone. In addition, a model is created using all variables. All these models are then compared, and the most important variables are identified.
3. Results and discussion Probability distribution maps of PM10 concentration generated using the MaxEnt model are shown in Fig. 3. These maps cover areas with high PM10 concentration from 2008 to 2011 in Beijing. Deep blue shading denotes a low probability of high PM10 concentration and red shading indicates a high probability. The probability of the red areas is close to one. There are high values in the northeast, southwest, and northwest of central Beijing, which are related to county centers in Daxing, Fangshan, Changping, and Shunyi counties. It is seen that dark red areas of higher probability are in central urban Beijing, coinciding with the complex transportation network and heavily built-upon land. Generally, the light blue shading outside central Beijing represents lighter pollution than in the former area, revealing geographic variation in emission levels. A comparison of the PM10 distribution over the 4 years shows no significant change across the Beijing urban area. However, there is a small difference shown by the deep blue areas outside central Beijing in 2008, which indicates that PM10-related air pollution in that region was not as serious as in other years. AUC values calculated by MaxEnt for each year are shown in Fig. 4. Values for the years 2008 through 2011 were 0.78, 0.79, 0.81, and 0.80, respectively. Blue shading represents the average AUC from 19 model runs, and the red curve is the final ROC curve. To identify the importance of environmental factors in the MaxEnt model, estimates of their relative contributions are displayed in Table 1. It is seen that the principal variables in the model are land cover and road density. The contribution of land cover variables is 10e15%, that of road traffic density variables is 80e86%,
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
5
Fig. 4. Average AUC of each calculation by MaxEnt model (2008e2011).
and meteorological variables are less than 10%. To explore each variable's contribution, the jackknife technique was used to identify their relative importance in MaxEnt (Fig. 5). The deep blue bars indicate the selected variable, and light blue bars represent the remaining variables. Road density (roaddensity_b and roaddensity_c) and land cover have the two longest bars. This means that the variables “road density” and “land cover” are very important in the model, and are core factors for explaining the distribution of high PM10 concentrations. Meteorological data also have some effect on the PM10 distribution. In the chart for 2008, 2009, 2010, and 2011 (Fig. 5), values of roaddensity_b and roaddensity_c exceed 70%, and those of land cover exceed 40%. In the chart for 2008, values of temperature, roaddensity_a, vapor, and wind speed all exceed 5%. In the chart for 2009, values of humidity, vapor, wind speed, roaddensity_a, and precipitation are greater than 5%. In the chart for 2010, the value of pressure is 15% and those of humidity, wind speed, roaddensity_a, temperature, and precipitation exceed
Table 1 Percentage contribution of environmental variables (2008e2011). Variable
2008
2009
2010
2011
land cover roaddensity_b roaddensity_c pressure wind speed temperature humidity vapor roaddensity_a precipitation
11.8 31.1 55.1 0 0.2 0.7 1.4 0.2 0 0.1
11.7 30.5 54.3 0.1 0.2 0 0.1 1.1 0 2
11.3 29.5 51.9 6.8 0.2 0 0.1 0.3 0 0
11.9 31.1 55.1 0 0.8 0 0 0.2 0 0.8
5%. In the chart for 2011, values of roaddensity_a and precipitation exceed 5%. The above results reveal that the distribution of high PM10 concentration is focused on the center of Beijing, and consequently high PM10 concentrations are linked to episodes of heavy air pollution. The analysis permitted identification of the relative contributions of the variables in MaxEnt modeling from 2008 to 2011 (Table 1). Road density and land cover were the two most important environmental variables, suggesting that the distribution of high PM10 concentration probability is attributable to traffic and built-up land (Fig. 6). Fig. 5 shows the importance of these variables from 2008 to 2011 via blue bars, which are more informative than Table 1. The land cover and road density bars are larger, suggesting that these are the main variables in the forecast model for PM10 pollution (the same as shown in Table 1). Based on the jackknife test, meteorological variables also contribute in part to heavy air pollution episodes. The meteorological data account for a small fraction of the distribution relative to environmental factors. Three values of meteorological factors, e.g., maximum, minimum, and average values of temperature, wind speed, and humidity are strongly correlated, and the model's AUC is very small (not shown). Therefore, these maximum and minimum values' variables were removed from the MaxEnt model. Fig. 6 shows that each variable affects the probability distribution of high PM10 concentrations in 2010. Roaddensity_b and roaddensity_c have a greater contribution compared to other factors. The more intensive the road network is, the greater the potential for air pollution episodes. In addition, much research has revealed that urban air pollutants vary with the urban area function. PM concentration varies with types of urban functions, with
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
6
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
Fig. 5. Contribution of each variable in the jackknife model (2008e2011).
the traffic function as the most serious (Li and Ma, 2005). When vapor, humidity, and temperature reached some threshold values, the greatest concentration probability occurred. This might be related to secondary particles converted by gaseous air pollutants during certain weather conditions. Pressure and wind have a combined influence on the dispersion of PM, so wind speed and high PM10 concentration have a negative correlation. Land cover 4 is a proxy variable of built-up areas, which contain various types of industrial land, living areas, infrastructure, and so on. By comparing PM concentration before, during, and after the 2008 Olympic Games in Beijing, industrial emissions were found to be another important source of PM10 pollution in Beijing (Shen et al., 2011). Meanwhile, tall buildings and certain weather conditions have a mutual influence on PM pollution because such pollutants are not easily diffused to other areas. Boiler combustion emissions are also a significant source of pollution during the heating period in the built-up area of Beijing (Zhang et al., 2002), which might be another reason that built land areas had high PM10 concentrations. PM10 comes from motor vehicle exhaust emissions, urban construction, smoke from heating, industrial pollution, and smoke from coalfired boilers. All these factors (exhaust emission, industrial production, meteorological conditions, terrain, and human activities) have a synergistic effect on the occurrence of high PM10 concentration areas. Terrain is also an important factor. Beijing is at the northwest edge of the North China Plain. There are many mountains to the west, north, and northeast, which means that the terrain is not conducive to the dispersion of atmospheric pollutants (Yang et al., 2013a, b). Furthermore, there is some remote PM10 transmission from the south of the city, which produces heavy PM10 pollution in the center. PM10 primarily originates from local road traffic and industrial areas, except for that from long-distance transmission of industrial emissions in other regions (Tan et al., 2004). Other research studies based on aerosol optical depth have produced
PM10 concentration maps revealing the same result, i.e., high PM10 concentration focused on the center of urban Beijing (Wang et al., 2010; Xen et al., 2011). Given the high PM10 concentration density in these areas, most residents are at risk of negative effects from PM10 pollution (Zhang et al., 2007). Although current research found that traffic and built-up areas are two of the most important factors contributing to a high probability of heavy PM10 pollution (which is consistent with other recent research, e.g., Shen et al., 2011; Wang and Xie, 2009), one limitation of this study is that we have not separated the contribution of industrial and residential emissions. Detailed land use data and an industrial emission inventory might be extremely helpful to improving the model.
4. Conclusion The MaxEnt model can handle nonlinear relationships and can effectively map the high PM concentration distribution. Built with land cover, traffic, and meteorological variables, the model successfully explains large-scale variability in PM10 concentrations. It is effective in modeling the probability distribution of high PM10 concentrations. The highest PM10 concentrations were over the complex road network and urban building land, and roads and land cover area were the principal variables in the PM10 prediction model. Traffic, industrial, and human emissions are the related emissions from primary particulate pollution sources. Meteorological factors also have an important contribution to PM10 formation and diffusion. Heavy pollution and human activity concentration areas show a strong consistency. The large area of high PM10 concentration indicates that it is a very serious pollution problem in Beijing, especially at the center of urban areas.
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
7
Fig. 6. Response curves between environmental variables and prediction probability.
Acknowledgments This study was supported by grants from The National Environmental Protection Public Welfare Science and Technology Research Program of China (201509027) and the National Natural Science Foundation of China (41301425; 41531179). References An, X., Zhu, T., Wang, Z., Li, C., Wang, Y., 2007. A modeling analysis of a heavy air pollution episode occurred in Beijing. Atmos. Chem. Phys. 7, 3103e3114. Banavar, J.R., Maritan, A., Volkov, I., 2010. Applications of the principle of maximum
entropy: from physics to ecology. J. Phys. 22 (6), 063101. Bayraktar, H., Turalioglu, F.S., Tuncel, G., 2008. Average mass concentrations of TSP, PM10 and PM2.5 in Erzurum urban atmosphere, Turkey. Stoch. Environ. Res. Risk Assess. 24, 57e65. Bontemps, S., Defourny, P., Bogaert, E., Kalogirou, V., Perez, Z., 2011. GLOBCOVER 2009 products description and validation report. ESA Bull. 136, 53. Chen, B.H., Hong, C.J., Kan, H.D., 2004. Methodological research on the health-based risk assessment on air pollution. J. Environ. Health 21, 112e113. Chen, R.J., Peng, D.R., Meng, X., Zhou, Z.J., Chen, B.H., Kan, H.D., 2013. Seasonal variation in the acute effect of particulate air pollution on mortality in the China Air Pollution and Health Effects Study (CAPES). Sci. Total Environ. 450e451, 259e265. Christakos, G., 2000. Modern Spatiotemporal Geostatistics. Oxford University Press. Engel-Cox, J.A., Holloman, C.H., Coutant, B.W., Hoff, R.M., 2004. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006
8
Z.-H. Zhang et al. / Atmospheric Pollution Research xxx (2017) 1e8
scale air quality. Atmos. Environ. 38, 2495e2509. Engler, R., Antoine, G., Luca, R., 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudoabsence data. J. Appl. Ecol. 41 (2), 263e274. € dder, D., Secondi, J., 2014. Mapping species distributions Fourcade, Y., Engler, J.O., Ro with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9, e97122. Henderson, S.B., Beckerman, B., Jerrett, M., Brauer, M., 2007. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ. Sci. Technol. 41, 2422e2428. Hu, M.G., Jia, L., Wang, J.F., Pan, Y., 2013. Spatial and temporal characteristics of particulate matter in Beijing, China using the empirical mode decomposition method. Sci. Total Environ. 458e460, 70e80. Ibadan, N., Ana, G., Adeniji, B., Ige, O., Oluwole, O., Olopade, C., 2013. Exposure to emissions from firewood cooking stove and the pulmonary health of women in Olorunda community. Air Qual. Atmos. Health 6, 465e471. Jaynes, E.T., 2003. Probability Theory: the Logic of Science. Cambridge University Press. Leshowitz, B., 1969. Comparison of ROC curves from one- and two-interval ratingscale procedures. J. Acoust. Soc. Am. 46, 399e402. Li, H.W., Ma, Y.D., 2005. Study on distribution regularity of air pollution in Fuxin City. J. Saf. Environ. 5 (6), 76e78. Martinelli, N., Olivieri, O., Girelli, D., 2013. Air particulate matter and cardiovascular disease: a narrative review. Eur. J. Intern. Med. 24, 295e302. MEP, 2011. Report on Environmental Quality in China, vol. 2010. Peters, A., 2005. Particulate matter and heart disease: evidence from epidemiological studies. Toxicol. Appl. Pharmacol. 207, 477e482. Phillips, S., Dudik, M., 2008. Presence-only Modeling with Maxent, vol. 2006. Pietra, S.D., Pietra, V.D., Lafferty, J., 1997. Inducing features of random fields. IEEE Trans. Pattern Anal. Mach. Intell. 19, 1e13. Radosavljevic, A., Anderson, R.P., 2014. Making better MAXENT models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629e643. lica, V., Miguel, A.H., Roberto, M., Ricardo, Z., Juan, R.M., Ange 2011. Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using maximum entropy (Maxent). Ecol. Inf. 6, 364e370. Shen, J., Tang, A., Liu, X., Kopsch, J., Fangmeier, A., Goulding, K., 2011. Impacts of pollution controls on air quality in Beijing during the 2008 Olympic Games. J. Environ. Qual. 40, 37e45. Slater, H., Michael, E., 2012. Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling. PLoS One 7, e32202. Stern, G., Latzin, P., Roosli, M., Fuchs, O., Proietti, E., Kuehni, C., Frey, U., 2013. A prospective study of the impact of air pollution on respiratory symptoms and infections in infants. Am. J. Respir. Crit. Care Med. 187, 1341e1348. Ul-Saufie, A.Z., Yahaya, A.S., Ramli, N.A., Rosaida, N., Hamid, H.A., 2013. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmos. Environ. 77, 621e630. Vanagas, G., 2004. Receiver operating characteristic curves and comparison of cardiac surgery risk stratification systems. Interact. Cardiovasc. Thorac. Surg. 3,
319e322. Wang, S.X., Hao, J.M., 2012. Air quality management in China: issues, challenges, and options. J. Environ. Sci. 24, 2e13. Wang, T., Xie, S.D., 2009. Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period. Atmos. Environ. 43, 5682e5690. Wang, Y., Zhuang, G.S., Tang, A.H., Yuan, H., Sun, Y.L., Chen, S., 2005. The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmos. Environ. 39, 3771e3784. Wang, Z., Chen, L., Tao, J., Zhang, Y., Su, L., 2010. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sens. Environ. 114, 50e63. Wang, J.F., Hu, M.G., Xu, C.D., Christakos, G., Zhao, Y., 2013. Estimation of citywide air pollution in Beijing. PLoS One 8, e53400. WHO, 2006. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen, Dioxide and Sulfur Dioxide. World health organization. Wiwanitkit, V., 2007. PM10 in the atmosphere and incidence of respiratory illness in Chiangmai during the smoggy pollution. Stoch. Environ. Res. Risk Assess. 22, 437e440. Wu, Y., Wang, R., Zhou, Y., Lin, B., Fu, L., He, K., 2011. On-road vehicle emission control in Beijing: past, present, and future. Environ. Sci. Technol. 45, 147e153. Xen, Q.Y., Mazlan, H., Maged, M., 2011. Retrieval of PM 10 Concentration from Moderate Resolution Imaging Spectroradiometer (MODIS)derived AOD in Peninsular Malaysia. IGARSS, pp. 4022e4025. Yang, W., Zhao, W.J., Gong, Z.N., Zhao, W.H., Tang, T., 2013a. Spatial distribution of inhalable particulate and association with respiratory disease in Beijing city. Environ. Sci. 34, 233e243. Yang, X.Q., Kushwaha, S., Saran, S., Xu, J.C., Roy, P.S., 2013b. Maxent modeling for predicting the potential distribution of medicinal plant,Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 51, 83e87. Zhang, K., Batterman, S., 2013. Air pollution and health risks due to vehicle traffic. Sci. Total Environ. 450e451, 307e316. Zhang, R.J., Wang, M.X., Hu, F., Xu, Y.F., 2002. Elemental concentrations and distributions of atmospheric particles before and in heating period in Beijing. J. Graduate Sch. Chin. Acad. Sci. 19 (1), 75e81. Zhang, M.S., Song, Y., Cai, X.H., 2007. A health-based assessment of particulate air pollution in urban areas of Beijing in 2000e2004. Sci. Total Environ. 376, 100e105.
Further reading , Z., Michalek, J., 2012. Forecasting of daily PM10 concenStadlober, E., Hübnerova trations in Brno and Graz by different regression approaches. Austrian J. Stat. 41, 287e310. Zhang, Q., Krotkov, N., 2010. Recent large reduction in sulfur dioxide emissions from Chinese power plants observed by the Ozone Monitoring Instrument. Geophys. Res. Lett. 37, L08807. Zhang, Y.M., Tan, R.H., Zheng, Y.F., Lou, W.Y., 2004. Pollution research on PM2.5 in typical urban resident area in Shanghai. J. Labour Med. 21 (3), 226e229.
Please cite this article in press as: Zhang, Z.-H., et al., Probabilistic assessment of high concentrations of particulate matter (PM10) in Beijing, China, Atmospheric Pollution Research (2017), http://dx.doi.org/10.1016/j.apr.2017.04.006