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Vertical characteristics of PM2.5 during the heating season in Tianjin, China
Science of the Total Environment 523 (2015) 152–160
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Vertical characteristics of PM2.5 during the heating season in Tianjin, China Hong Wu a, Yu-fen Zhang a,⁎, Su-qin Han b,⁎, Jian-hui Wu a, Xiao-hui Bi a, Guo-liang Shi a, Jiao Wang a, Qing Yao b, Zi-ying Cai b, Jing-le Liu b, Yin-chang Feng a a State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China b Research Institute of Meteorological Science, Tianjin, 300074, China
H I G H L I G H T S • • • •
Vertical variabilities in the concentrations of PM2.5 and its species were studied. Diurnal distribution of concentrations of PM2.5 and its species was discussed. Characteristics of PM2.5 on clear days and heavy pollution days were analyzed. Vertical characteristics of PM2.5 pollution sources were discussed.
a r t i c l e
i n f o
Article history: Received 25 January 2015 Received in revised form 25 March 2015 Accepted 27 March 2015 Available online xxxx Editor: Xuexi Tie Keywords: Vertical variability Diurnal distribute Clear day Heavy pollution day Chemical mass balance Pollution sources
a b s t r a c t In this study, PM2.5 samples were collected at four heights (10 m, 40 m, 120 m and 220 m) at a meteorological tower in the daytime and nighttime during the heating season in Tianjin, China. The vertical variation and diurnal variability of the concentrations of PM2.5 and main chemical compositions were analyzed in clear days and heavy pollution days. Generally, mass concentrations of PM2.5 and the chemical compositions showed a decreasing − trend with increasing height, while mass percentages of SO2− 4 , NO3 and OC showed an increasing trend with increasing height. Concentrations of ion species and carbon compound in PM2.5 samples in the daytime were higher than those collected at night, which was due to intense human activities and suitable meteorological 2− and OC/EC were also considered, and we have observed that condition in the daytime. The ratios of NO− 3 /SO4 their levels on heavy pollution days were higher than those on clear days. In addition, source apportionments were identified quantitatively using the CMB-iteration model. The results indicated that contributions of secondary ion species increased with increasing height, while contributions of other pollutant sources decreased, and contributions of vehicle exhaust were relatively high on clear days. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Atmospheric particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) has been found to be an important pollutant in most megacities (Janssen et al., 2013; Huang et al., 2012); it plays an important role in environmental quality and human health (Ma et al., 2014; Pui et al., 2014; L.J. Han et al., 2014), and draws considerable attention from governments and scientific communities (Tie and Cao, 2009; Agarwal et al., 2012; Perrone et al., 2012). In recent years, PM2.5 pollution has become an extremely serious issue in the Beijing–Tianjin– Hebei region of China, often exceeding the National Ambient Air Quality Standards of China (75 mg/m3 for 24 h average) (Quan et al., 2014; He ⁎ Corresponding authors. E-mail addresses: [email protected] (Y. Zhang), [email protected] (S. Han).
http://dx.doi.org/10.1016/j.scitotenv.2015.03.119 0048-9697/© 2015 Elsevier B.V. All rights reserved.
et al., in press). Many studies have been conducted to evaluate the mass concentration of particles and their chemical compositions (Sahu et al., 2011; Salameh et al., 2015), but the vertical variations of PM2.5 are rarely researched (Sun et al., 2013; Xiao et al., 2012). The vertical distribution characteristics of PM2.5 at different heights could reflect the air pollution in varying scale. Sites at heights near the ground surface (5–10 m) are influenced extensively by human activities, so data collected at these sites could represent the street scale. The impact by local disturbance weakens gradually with increases in height, and observations at greater heights could represent larger horizontal scales. When the height increases to the top of the urban atmospheric boundary layer, observations can represent urban scales. The heights above the urban boundary layer could reflect the characteristics of regional scales to some extent. Thus, discussing the vertical distributions of PM2.5 in the daytime and nighttime and on clear days and pollution
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days is helpful in revealing the possible sources of fine particles so that we can clearly understand the behavior of PM2.5. In addition, PM2.5 is increasingly attracting people's attention because of its impact on human health (Basagana et al., 2015; Zhang et al., 2015). The scope of people's lives and work in recent years has been extending to vertical space, so we should pay more attention to the variation characteristics of pollutants, especially PM2.5. In this study, the mass concentrations of PM2.5 and some chemical components were measured at four heights (10 m, 40 m, 120 m and 220 m) at a meteorological tower in Tianjin, China. Based on the measurements, the variation characteristics of concentrations and chemical components of PM2.5 were analyzed on both clear days and heavy pollution days and during both the daytime and the nighttime. In addition, source apportionments were identified at different heights. The vertical characteristics of PM2.5 will provide helpful information for air management. 2. Experimental method 2.1. Sample site and methods Tianjin (38°34′N–40°15′N, 116°43′E–118°04′E), one of the core cities in the Beijing–Tianjin–Hebei area, which is the most polluted area of China, is an international megacity with a gross domestic product (GDP) of 1.3 × 107 million Yuan, a population of approximately 12 million, total energy consumption of 8 × 107 t of SCE, and more than 1.5 million automobiles. In this study, PM2.5 samples were collected on a 225-m-high meteorological tower in north Tianjin (39°04′29.4″N, 117°12′20.1″E). A residential and commercial area surrounds the tower and there are no direct industrial sources of atmospheric pollutants located nearby. Filter-based medium-volume PM2.5 samplers were used and were set to sample at heights of 10 m, 40 m, 120 m and 220 m on the tower (Quan et al., 2013; S.Q. Han et al., 2014). The PM2.5 samples were collected during two periods on each day (8:00 am to 18:00 pm and 18:00 pm to 8:00 am on the next day) for 25 consecutive days from Dec 23, 2013, to Jan 16, 2014. Two samplers were used at each height to collect airborne particles on quartz-fiber filters for subsequent ionic/carbon component analysis and on polypropylene-fiber filters for elemental analysis. A total of 380 samples were measured. The meteorological parameters, including wind speed, relative humidity, and temperature, were measured every 10 s with an automatic weather station installed in this meteorological tower, at 10 platform heights (5, 10, 40, 60, 100,120, 140, 180, 200 and 220 m) from Dec 23, 2013, to Jan 16, 2014. And 24-hourly averaged data were analyzed. 2.2. Chemical analysis Elements (Na, Mg, Al, Si, Ca, Mn, Fe, Cu, Zn and Pb) were extracted from polypropylene-fiber filters at the laboratory and analyzed by inductively coupled plasma-mass spectrometry (ICP-AES) (IRIS Intrepid − II, Thermo Electron). The water-soluble ions (K+, Cl−, NH+ 4 , NO3 and SO24 −) were analyzed by ion chromatography (DX-120, Dionex Ltd., USA) after extraction from the quartz-fiber filters using an ultrasonic extraction system (AS3120, AutoScience). Desert Research Institute/ Oregon Graduate Center (DRI/OGC) carbon analysis (DRI-2001A TOR) was used to analyze the OC (organic carbon) and EC (elemental carbon), a technique based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance (TOR) protocol. The background contamination was routinely monitored by performing blank tests. An adequate number of blank tests were conducted and used to validate and correct the corresponding data. The certified reference materials (CRM, produced by National Research Center for Certified Reference Materials, China) were used for quality assurance and quality control. Further details about the sampling,
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treatment and analysis of the ambient samples were reported in our previous study (Bi et al., 2007; Shi et al., 2009; Xue et al., 2010; Zhao et al., 2013; Zhang et al., 2011). 3. Results and discussion 3.1. PM2.5 mass concentration characteristics All samples were collected during the heating season in Tianjin, which is the time of the year when air pollution is highest. In this study, a clear day was defined as a day in which mass concentrations of PM2.5 were less than 75 μg/m3, a polluted aerosol day was defined as a day with PM2.5 mass concentrations between 75 μg/m3 and 150 μg/m3, and a heavy pollution aerosol day was defined as a day with PM2.5 mass concentrations exceeding 150 μg/m3, in accordance with the Technical Regulation on Ambient Air Quality Index (HJ633-2012). During the sampling period, there were only five clear days and eleven heavy pollution days, with the mass concentrations of PM2.5 exceeding 250 μg/m3. The time-series of PM2.5 mass concentrations at different heights is shown in Fig. 1 and indicates that the average mass concentrations were 0.172 mg/m3, 0.189 mg/m3, 0.167 mg/m3 and 0.145 mg/m3 at 10 m, 40 m, 120 m, 220 m, respectively. Overall, PM2.5 concentrations exhibited a general decreasing trend with increasing height. It can be seen that the 220 m site had the lowest PM2.5 concentrations, while the 40 m site had the highest PM2.5 concentrations. This result is similar to the researches in Beijing (Chan et al., 2005) and Guangzhou (Deng et al., in press). Fig. 2 showed the time-series of temperature, humidity, and wind at four heights (10 m, 40 m, 120 m and 220 m). In conjunction with Fig. 1, it can be seen that the heavy pollution day occurred in the meteorological conditions of weak horizontal wind velocity, strong inversion layer, and high relative humidity in the surface layer. Generally, the PM2.5 concentrations measured in daytime were higher than those measured at night, with an average level of 0.174 mg/m3 at 10 m, 0.198 mg/m3 at 40 m, 0.181 mg/m3 at 120 m and 0.162 mg/m3 at 220 m in the daytime and 0.170 mg/m3 at 10 m, 0.180 mg/m3 at 40 m, 0.154 mg/m3 at 120 m and 0.129 mg/m3 at 220 m at night. The difference may be due to the more intensive human activities near the ground surface and more intensive vertical diffusion in the mixing layer during the day. The correlation coefficients for the concentrations measured at different heights were calculated by SPSS 16.0. The daytime correlations were 0.98 between 10 m and 120 m and 0.97 between 10 m and 220 m, and the nighttime correlations were 0.90 between 10 m and 120 m and 0.82 between 10 m and 220 m. The results of the calculations indicated that the daytime height-to-height correlations were noticeably high, while the nighttime 10–220 m correlations were relatively low. The nocturnal planetary boundary layer (NPBL) height is between 100 m and 120 m (Han et al., 2009, in press; Tian et al., 2013). Due to the dynamic stability of the NPBL, air pollutants in the surface layer are normally trapped inside the NPBL. The decreasing trend of PM2.5 mass concentrations with increasing height was observed both on clear days and on heavy pollution days. The following ratio defines the rate of decrease for the PM2.5 concentrations, which was 33% and 20% on clear days and heavy pollution days, respectively: DR ¼ ðx10 −x220 Þ=x10
ð1Þ
where DR is the rate of decrease for the PM2.5 concentrations; and x 10 and x220 are PM2.5 mass concentrations in 10 m and 220 m, respectively. The result was related to the accumulation of PM2.5 concentrations on heavy pollution days. In addition, the difference between daytime and nighttime mass concentrations of PM2.5 was evaluated for both clear days and heavy pollution days. The results indicated 0.015 mg/m3 at 10 m, 0.027 mg/m3 at 40 m, 0.017 mg/m3 at 120 m and 0.020 mg/m3 at
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Fig. 1. PM2.5 mass concentration at each height (10 m, 40 m, 120 m and 220 m) during the sampling period.
220 m for clear days, and 0.049 mg/m3 at 10 m, 0.066 mg/m3 at 40 m, 0.077 mg/m3 at 120 m and 0.063 mg/m3 at 220 m on heavy pollution days, which suggests that the difference between daytime and nighttime PM2.5 mass concentrations was smaller on clear days.
3.2. Element characteristics Element species such as Si, Al, Ca, Mg, Fe and Zn were the primary components identified in PM2.5 with composition percentages varying
Fig. 2. Temperature, humidity and wind speed during the sampling period.
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in the range of 8%–16%. Fig. 3 shows the species concentrations at each height and indicates that the higher mass concentrations of elements, as well as higher mass percentage of elements, were measured at sampling sites at lower heights. The percentage of each element exhibited a different decreasing percentage with increasing sampling height. Among the elements, Si indicated the largest decreasing percentage with increasing height, followed by Al and Zn. This result demonstrated that the contributions of pollution sources varied with different heights. A slight increase in the element mass percentage was observed at night compared with those during the day at each height. The maximum difference in mass percentage between daytime and nighttime was exhibited for Si, with 1.4% increase at 10 m, 1.3% increase at 40 m, 1.2% increase at 120 m, and 1.0% increase at 220 m, indicating that the influence of fugitive dust was relatively stronger at night, which is likely due to the fact that truck transportation is only allowed at night. Element percentages measured on heavy pollution days were higher than those measured on clear days, with increases in percentage ranging from 8.1% to 16.4%. Hierarchical clustering analysis (HCA) using complete linkage and Euclidean distances was applied to evaluate the relationships among the mass concentrations of elements for both clear days and heavy pollution days (Tian et al., 2013). The datasets for the four heights were used for the HCA after being standardized: 0 zi j ¼ ci j −ci =sdi
ð2Þ
where zij is the standardized concentration of the ith element species in the jth sample, cij is the original concentration of the ith element species, c0i is the average concentration of the ith element species, and sdi is the standardized deviation of the ith element species. The HCA results for clear days and heavy pollution days are shown in Fig. 4. Three groups were identified: Si was in group 1; Na, Al and Ca were included in group 2; and Mg, Fe, Zn, Pb, Mn and Cu were included in group 3 (among them, Mg and Fe had a closer relationship). The species were clustered into groups, suggesting that they may come from a common source (Garcia et al., 2004; Watson et al., 2011). There are similar sources of pollution from the elements at each height on both clear days and heavy pollution days. Among those elements, the percentage of Si varied relatively significantly on heavy pollution days, with an approximate 3.5% increase, followed by Al, Na and Ca, which indicated a combined total increase of more than 3.5%, and Fe, Zn, Pb, Mn and Cu, with a combined total increase of less than 4%, comparing with those in clear daya. The source
Fig. 3. Average mass concentrations of elements in PM2.5 at each height (10 m, 40 m, 120 m and 220 m) during the sampling period.
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of Si may be from crustal dust, and the sources of Al, Na, Ca, Mn, Fe, Pb, Cu and Zn may be from traffic and combustion (Zheng et al., 2014). The increase of these elements in the composition of PM2.5 indicates that crustal dust, traffic and combustion should not be ignored factors for heavy pollution days during the heating season. In addition, the element concentrations were found to decrease with increasing sampling height on both clear days and heavy pollution days, with larger decreases on clear days. 3.3. Ionic species characteristics Water-soluble ions had a percentage of more than 40% of the particulate matter, with sulfate showing the highest mass concentrations, followed by nitrate. The mass concentrations of water-soluble ionic species in PM2.5 at each height are shown in Fig. 5, which indicates − + − that higher concentrations of SO2− 4 , NO3 , NH4 and Cl were measured at lower heights. There was also a slight decrease in the mass concentra+ tions of SO24 −, NO− 3 and NH4 at lower heights. In contrast, higher − , NO and NH+ percentages of SO2− 4 3 4 were measured at greater heights. 2− In addition, higher NO− 3 /SO4 mass concentration ratios were exhibited in the higher levels, with values of 0.66 at 10 m, 0.70 at 40 m, 0.73 at 120 m and 0.86 at 220 m. To evaluate the differences in the mass concentrations of ionic species at different heights, the coefficients of divergence (CD) was used to analyze the concentration variability at two adjacent heights (Massoud et al., 2011; Zhang and Friedlander, 2000). The CD is defined as:
CD jk
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !ffi u p u1 X xi j −xik 2 ¼t p i¼1 xi j þ xik
ð3Þ
where x ij and xik are mass concentrations at the jth and kth sites, respectively, and p is the number of sampling days. The small CD values imply similarities in the ionic species concentrations for the two sampling sites, while CD values approaching 1 indicate significant differences. The results of CD analysis for ionic species at different sampling heights are listed in Table 1. According to the previously reported analyses, CD b0.20 was defined as a relatively homogeneous distribution (Krudysz et al., 2008). For individual ionic species, the height-to-height CD values and NO− of SO2− 4 3 were generally less than 0.20, which indicates a more and NO− similar temporal variation at each height for SO2− 4 3 , which may be due to these ions being mainly from secondary sources, while other ions were mainly derived from primary sources, and their changes are linked to more complex factors (Tian et al., 2013). The mass concentrations of water-soluble ionic species were higher during the day than those measured at night, which indicates that there is a larger contribution of ionic species in the daytime from various human activities and suitable meteorological conditions (light, temperature, humidity and vertical diffusion). Cl− is usually considered a major component of a sea salt source, but it is also released from coal combustion (Sun et al., 2006). Thus, the elevated percentages of Cl− and SO2− 4 at night may suggest the contribution from coal combustion source 2− increased at night. In addition, the mean mass ratios of NO− 3 /SO4 were considered, which has been widely applied as an indicator of the relationship of mobile sources to stationary sources (Gao et al., 2011). 2− was higher in the daytime, indicating that the The ratio of NO− 3 /SO4 mobile pollution sources were more pronounced in the daytime. 2− Furthermore, the mass ratios of NO− 3 /SO4 were compared for clear days and heavy pollution days: 0.66 at 10 m, 0.60 at 40 m, 0.63 at 120 m and 0.77 at 220 m on clear days, and 0.73 at 10 m, 0.75 at 40 m, 0.81 at 120 m and 0.91 at 220 m on heavy pollution days. The higher mean 2− was obtained on heavy pollution days. Thus, mass ratio of NO− 3 /SO4 it can be seen that combustion sources such as power plants, industrial boilers, heating boilers and civil coal fire facilities play important roles, but the increasing vehicle pollution is still considered a contributing
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Fig. 4. HCA of elements using the combined datasets for four heights (10 m, 40 m, 120 m and 220 m).
factor and should not be neglected in evaluations of rapidly developing cities (Ji et al., 2012). During heavy pollution days, the mass percentages 2− of NO− 3 and SO4 to PM2.5 ranged from 14% to 19% and from 19% to 21%, respectively, and there was an indication of an increasing range of 7 to 10% in the study area. These results demonstrate an important role for the secondary ions during heavy pollution days. Similar results can be found in other literatures. The mass ratio of SO24 − and NO− 3 to PM2.5 reached 25% on heavy pollution days in Yangtze River Delta, China (Fu et al., 2008), and SO24 − and NO− 3 compose 20.7% and 11.9% of PM2.5, respectively, on heavy pollution days in Beijing, China (Ji et al., 2014). 3.4. Carbon characteristics Typically, EC is emitted from combustion sources, while OC can be emitted from primary emission sources and could also be generated
from secondary chemical reaction (Kim et al., 1999). Fig. 6 shows the average concentrations of OC, EC and the OC/EC ratios at each sampling height. According to Fig. 6, it can be seen that the average OC concentrations show an obvious decreasing trend with increasing height: 33.02 μg/m3 (40 m) N 30.53 μg/m3 (10 m) N 29.75 μg/m3 (120 m) N 26.20 μg/m3 (220 m). The concentrations of EC exhibited a similar order, with 8.21 μg/m3 (10 m) N 8.26 μg/m3 (40 m) N 7.19 μg/m3 (120 m) N 5.78 μg/m3 (220 m). The mass percentage of EC decreased with height, while the percentage of OC showed a slight increase, and the OC/EC ratio increased gradually from 3.7 at 10 m to 4.5 at 220 m, which showed that the contribution of secondary pollution increased along with increasing height. Fig. 7 shows the scatter plot between OC and EC mass concentrations during the study period (r2 = 0.20–0.34). A significant correlation between OC and EC usually suggested common sources such as combustion and traffic (Sharma et al., 2014). In contrast,
Fig. 5. Ion mass concentrations and fractions in PM2.5 at each height (10 m, 40 m, 120 m, and 220 m) during the sampling period.
H. Wu et al. / Science of the Total Environment 523 (2015) 152–160 Table 1 The results of height-to-height correlations.
Cl− NO− 3 SO2− 4 NH+ 4
10–40 m
40–120 m
120–220 m
10–220 m
0.21 0.17 0.18 0.22
0.22 0.12 0.14 0.11
0.27 0.11 0.13 0.09
0.42 0.20 0.20 0.26
a poor correlation between OC and EC indicated the formation of secondary aerosol through a photochemical reaction in the atmosphere. Additionally, the concentrations were higher in the daytime than at night for both EC and OC at each height. However, the difference in concentrations of OC between daytime and nighttime was much greater than that for EC, with a value of 8.4 μg/m3 for OC and only 0.5 μg/m3 for EC at 220 m. This was probably due to the formation of secondary organic carbon (SOC) by photochemical reactions in daytime. The SOC concentration is estimated using the following equations (Castro et al., 1999): SOC ¼ OC−POC
ð4Þ
POC ¼ EC ðOC=ECÞpri
ð5Þ
where (OC/EC)pri is the primary carbon ratio, and POC is the primary organic carbon. Unfortunately, the (OC/EC)pri exhibits significant changes for different sources, so it is difficult to obtain a representative value for (OC/EC)pri. The minimum OC/EC ratio can replace the (OC/EC)pri in the equation and assumptions regarding the use of this procedure were discussed in details (Ji et al., 2014). The concentrations of SOC, estimated through the minimum OC/EC ratio method were 15.33 μg/m3 (10 m), 16.67 μg/m3 (40 m), 12.99 μg/m3 (120 m) and 11.54 μg/m3 (220 m) at night and 15.75 μg/m3 (10 m), 22.52 μg/m3 (40 m), 20.90 μg/m3 (120 m) and 20.93 μg/m3 (220 m) during daytime. The concentrations of SOC are higher in the daytime than at night. The concentrations of OC were 9.2 μg/m3 at 10 m, 9.3 μg/m3 at 40 m, 7.5 μg/m3 at 120 m and 7.8 μg/m3 at 220 m on clear days and 41.8 μg/m3 at 10 m, 43.1 μg/m3 at 40 m, 36.0 μg/m3 at 120 m and 30.4 μg/m3 at 220 m on heavy pollution days. At the same time, the concentrations of EC were 2.7 μg/m3 at 10 m, 2.9 μg/m3 at 40 m, 2.2 μg/m3 at 120 m and 2.1 μg/m3 at 220 m on clear days and 9.1 μg/m3 at 10 m, 9.6 μg/ m3 at 40 m, 8.7 μg/m3 at 120 m and 6.4 μg/m3 at 220 m on heavy pollution days. Compared with the mass concentrations of OC and EC on clear days, an obvious increase in OC and EC concentrations was observed on
50
6.0
EC OC
45
OC/EC 40
5.5
35
4.5
25
OC/EC
Concentration µg/m
3
5.0 30
20 4.0 15 10
3.5
5 3.0
0 10m
40m
120m
220m
Height
Fig. 6. Carbon mass concentrations in PM2.5 and the fraction of OC/EC at each height (10 m, 40 m, 120 m, and 220 m) during the sampling period.
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heavy pollution days, whereas the mass percentage decreased on heavy pollution days, especially for OC, which was 8%–10% lower. This indicates that the accumulation of OC concentration on heavy pollution days was relatively lower compared with other compositions. This result is different with the one in Fu's research (Fu et al., 2008). In that research, the mass percentage of OC has not shown obvious differences between the heavy pollution days and clear days. It was observed in this study that the value of OC/EC increases with increases in height and is higher on heavy pollution days (4.2–4.8 for all sampling sites) than on clear days (3.2–3.8 for all sampling sites), which might be due to the accumulated process of SOC on heavy pollution days. 3.5. Source appointment The CMB model has been widely used in the relative studies (Chow et al., 1992, 1996; Feng et al., 2002; Lee et al., 2008; Zhang et al., 2011; Shi et al., 2009). In this study, the contributions of potential sources at each sampling site for both clean days and heavy pollution days were evaluated using CMB-iteration model, which can estimate the SOC and POC concentrations in the ambient receptor by using the iterative method, without introducing the SOC profile into the CMB model. The calculation procedure and detailed descriptions of model were shown in our previous study (Shi et al., 2011, 2012). According to the investigation of the emission inventory, the crustal dust, coal combustion, cement dust, vehicle exhaust, secondary sulfate, secondary nitrate and SOC were the prominent pollution source categories in Tianjin. The source profiles are described in Fig. 8 and the source appointment results are shown in the following Fig. 9. The contributions of the crustal dust, coal combustion, cement dust, secondary sulfate and secondary nitrate and vehicle exhaust were closely related to the height. An obvious decrease in the contributions of the crustal dust, coal combustion and cement dust was found with increasing height, especially for the crustal dust, which showed a decrease of 6–8%. The larger contribution of primary pollution was found at the lower height. On the contrary, greater contributions of secondary sulfate and nitrate were observed with increasing heights. The vertical variability of the pollution sources was more pronounced on clear days, while on heavy pollution days, the differences among contributions of secondary nitrate, vehicle exhaust and SOC were not obvious at four heights. The results also showed a comparison of the different contributions of pollution sources for clear days and heavy pollution days. The influence of crustal dust, coal combustion, secondary sulfate and nitrate on heavy pollution days was significantly greater than their influence on clear days, while the contributions of vehicle exhaust and SOC as pollution sources were greater on clear days than on heavy pollution days. Vehicle exhaust and coal combustion had prominent contribution to ambient PM2.5 in clear days. While in heavy pollution days, main source categories had considerable contribution, which can be concluded that pollution control aimed at only one or two categories of sources is difficult to obtain obvious environmental effect, and multiple-source control should be implemented. 4. Conclusions Overall, the mass concentrations of PM2.5 and chemical compositions decrease with increasing height. However, the opposite is true − for the mass percentage of SO2− 4 , NO3 and OC. The measured mass concentrations of PM2.5, ion species and carbon component were higher during the day than at night, with high production of SOC during the daytime. In contrast, the difference in mass concentrations at four heights (10 m, 40 m, 120 m and 220 m) was not obvious for the elemental components (Si, Al, Ca, Mg, Fe and Zn), but higher percentage of Si was observed at night. The levels of PM2.5 concentration and the chemical species mass percentages measured on heavy pollution days were much higher than those measured on clear days, except
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Fig. 7. Scatter plot between OC and EC at each height (10 m, 40 m, 120 m, and 220 m) during the sampling period.
2− for OC and EC. In addition, higher levels of NO− and OC/EC 3 /SO4 were observed on heavy pollution days. Source apportionment by the CMB-iteration model shows that larger contributions of crustal dust,
30 20 10 0 30 20
combustion dust and cement dust as pollution sources were observed at sampling sites at lower heights, while the opposite was true for contributions of secondary ion sources. The contributions of crustal
CD
CC
Fraction / %
10 0 50 40 30 20 10 0 100 75 50 25 0 100 75 50 25 0 100 75 50 25 0
CE
VE
SS
SN
Na Mg Al Si
2K Ca Ti V Cr Mn Fe Ni Cu Zn Pb SO4 NO3 TC OC
Species Fig. 8. Source profiles of PM2.5 (CD is crustal dust; CC is coal combustion; VE is vehicle exhaust; CE is cement dust; SS is secondary sulfate; SN is secondary nitrate).
H. Wu et al. / Science of the Total Environment 523 (2015) 152–160
VE 26.99%
SS 11.47%
40m
VE 23.97%
SS 12.72% SN 9.67%
SN 7.71%
SOC 15.12%
CE 3.86% CC 12.58%
CC 18.96%
SS 14.61%
CD 10.91%
VE 20.62%
120m SS 17.71%
SOC 13.83%
220m
SOC 16.94% SN 14.31% CE 2.25% CC 10.92%
CD 8.05%
CC 16.11%
SOC 12.91%
CE 2.33%
CD 13.8%
VE 22.78%
SN 11.1% CE 2.53%
10m
159
CD 5.79%
A SS 18.86%
SN 15.25%
10m
CE 2.77%
VE 12.2%
CC 19.57%
SOC 6.66%
SOC 6.4%
CC 18.34% CD 16.97%
120m
SS 21.19%
SN 20.55%
VE 12.65%
SN 17.99%
VE 12.62%
CE 2.17%
CD 16.74%
SS 20.29%
40m
SS 19.93%
SN 16.49%
220m
VE 12.7%
CE 2.23% SOC 6.2%
CE 2.78% CC 18.54%
CC 18.73%
CD 13.02%
SOC 6.29% CD 10.76%
B Fig. 9. Estimated source contributions (%) for four height on clear days (A) and heavy pollution days (B), by CMB model.(CD is crustal dust; CC is coal combustion; VE is vehicle exhaust; CE is cement dust; SS is secondary sulfate; SN is secondary nitrate; SOC is secondary organic carbon).
dust, combustion dust and secondary ion species as pollution sources were higher on heavy pollution days, while contribution of vehicle exhaust was greater on clear days. Contributions of secondary ion species increased with increasing height, while contributions of other pollutant
sources decreased. The results of this work help to improve the understanding of vertical variations and diurnal variability of PM2.5 in clear days and heavy pollution days, and also provide a scientific basis for PM2.5 pollution control and prevention.
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Acknowledgments This work was funded by the Tianjin Science and Technology projects (14JCYBJC22200), the Science and Technology Support Program (13ZCZDSF02100), and the National Natural Science Foundation of China (NSFC) under Grant No.41205089, No.21207069 and No.21407081.
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Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Vertical characteristics of PM2.5 during the heating season in Tianjin, China Hong Wu a, Yu-fen Zhang a,⁎, Su-qin Han b,⁎, Jian-hui Wu a, Xiao-hui Bi a, Guo-liang Shi a, Jiao Wang a, Qing Yao b, Zi-ying Cai b, Jing-le Liu b, Yin-chang Feng a a State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China b Research Institute of Meteorological Science, Tianjin, 300074, China
H I G H L I G H T S • • • •
Vertical variabilities in the concentrations of PM2.5 and its species were studied. Diurnal distribution of concentrations of PM2.5 and its species was discussed. Characteristics of PM2.5 on clear days and heavy pollution days were analyzed. Vertical characteristics of PM2.5 pollution sources were discussed.
a r t i c l e
i n f o
Article history: Received 25 January 2015 Received in revised form 25 March 2015 Accepted 27 March 2015 Available online xxxx Editor: Xuexi Tie Keywords: Vertical variability Diurnal distribute Clear day Heavy pollution day Chemical mass balance Pollution sources
a b s t r a c t In this study, PM2.5 samples were collected at four heights (10 m, 40 m, 120 m and 220 m) at a meteorological tower in the daytime and nighttime during the heating season in Tianjin, China. The vertical variation and diurnal variability of the concentrations of PM2.5 and main chemical compositions were analyzed in clear days and heavy pollution days. Generally, mass concentrations of PM2.5 and the chemical compositions showed a decreasing − trend with increasing height, while mass percentages of SO2− 4 , NO3 and OC showed an increasing trend with increasing height. Concentrations of ion species and carbon compound in PM2.5 samples in the daytime were higher than those collected at night, which was due to intense human activities and suitable meteorological 2− and OC/EC were also considered, and we have observed that condition in the daytime. The ratios of NO− 3 /SO4 their levels on heavy pollution days were higher than those on clear days. In addition, source apportionments were identified quantitatively using the CMB-iteration model. The results indicated that contributions of secondary ion species increased with increasing height, while contributions of other pollutant sources decreased, and contributions of vehicle exhaust were relatively high on clear days. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Atmospheric particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) has been found to be an important pollutant in most megacities (Janssen et al., 2013; Huang et al., 2012); it plays an important role in environmental quality and human health (Ma et al., 2014; Pui et al., 2014; L.J. Han et al., 2014), and draws considerable attention from governments and scientific communities (Tie and Cao, 2009; Agarwal et al., 2012; Perrone et al., 2012). In recent years, PM2.5 pollution has become an extremely serious issue in the Beijing–Tianjin– Hebei region of China, often exceeding the National Ambient Air Quality Standards of China (75 mg/m3 for 24 h average) (Quan et al., 2014; He ⁎ Corresponding authors. E-mail addresses: [email protected] (Y. Zhang), [email protected] (S. Han).
http://dx.doi.org/10.1016/j.scitotenv.2015.03.119 0048-9697/© 2015 Elsevier B.V. All rights reserved.
et al., in press). Many studies have been conducted to evaluate the mass concentration of particles and their chemical compositions (Sahu et al., 2011; Salameh et al., 2015), but the vertical variations of PM2.5 are rarely researched (Sun et al., 2013; Xiao et al., 2012). The vertical distribution characteristics of PM2.5 at different heights could reflect the air pollution in varying scale. Sites at heights near the ground surface (5–10 m) are influenced extensively by human activities, so data collected at these sites could represent the street scale. The impact by local disturbance weakens gradually with increases in height, and observations at greater heights could represent larger horizontal scales. When the height increases to the top of the urban atmospheric boundary layer, observations can represent urban scales. The heights above the urban boundary layer could reflect the characteristics of regional scales to some extent. Thus, discussing the vertical distributions of PM2.5 in the daytime and nighttime and on clear days and pollution
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days is helpful in revealing the possible sources of fine particles so that we can clearly understand the behavior of PM2.5. In addition, PM2.5 is increasingly attracting people's attention because of its impact on human health (Basagana et al., 2015; Zhang et al., 2015). The scope of people's lives and work in recent years has been extending to vertical space, so we should pay more attention to the variation characteristics of pollutants, especially PM2.5. In this study, the mass concentrations of PM2.5 and some chemical components were measured at four heights (10 m, 40 m, 120 m and 220 m) at a meteorological tower in Tianjin, China. Based on the measurements, the variation characteristics of concentrations and chemical components of PM2.5 were analyzed on both clear days and heavy pollution days and during both the daytime and the nighttime. In addition, source apportionments were identified at different heights. The vertical characteristics of PM2.5 will provide helpful information for air management. 2. Experimental method 2.1. Sample site and methods Tianjin (38°34′N–40°15′N, 116°43′E–118°04′E), one of the core cities in the Beijing–Tianjin–Hebei area, which is the most polluted area of China, is an international megacity with a gross domestic product (GDP) of 1.3 × 107 million Yuan, a population of approximately 12 million, total energy consumption of 8 × 107 t of SCE, and more than 1.5 million automobiles. In this study, PM2.5 samples were collected on a 225-m-high meteorological tower in north Tianjin (39°04′29.4″N, 117°12′20.1″E). A residential and commercial area surrounds the tower and there are no direct industrial sources of atmospheric pollutants located nearby. Filter-based medium-volume PM2.5 samplers were used and were set to sample at heights of 10 m, 40 m, 120 m and 220 m on the tower (Quan et al., 2013; S.Q. Han et al., 2014). The PM2.5 samples were collected during two periods on each day (8:00 am to 18:00 pm and 18:00 pm to 8:00 am on the next day) for 25 consecutive days from Dec 23, 2013, to Jan 16, 2014. Two samplers were used at each height to collect airborne particles on quartz-fiber filters for subsequent ionic/carbon component analysis and on polypropylene-fiber filters for elemental analysis. A total of 380 samples were measured. The meteorological parameters, including wind speed, relative humidity, and temperature, were measured every 10 s with an automatic weather station installed in this meteorological tower, at 10 platform heights (5, 10, 40, 60, 100,120, 140, 180, 200 and 220 m) from Dec 23, 2013, to Jan 16, 2014. And 24-hourly averaged data were analyzed. 2.2. Chemical analysis Elements (Na, Mg, Al, Si, Ca, Mn, Fe, Cu, Zn and Pb) were extracted from polypropylene-fiber filters at the laboratory and analyzed by inductively coupled plasma-mass spectrometry (ICP-AES) (IRIS Intrepid − II, Thermo Electron). The water-soluble ions (K+, Cl−, NH+ 4 , NO3 and SO24 −) were analyzed by ion chromatography (DX-120, Dionex Ltd., USA) after extraction from the quartz-fiber filters using an ultrasonic extraction system (AS3120, AutoScience). Desert Research Institute/ Oregon Graduate Center (DRI/OGC) carbon analysis (DRI-2001A TOR) was used to analyze the OC (organic carbon) and EC (elemental carbon), a technique based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance (TOR) protocol. The background contamination was routinely monitored by performing blank tests. An adequate number of blank tests were conducted and used to validate and correct the corresponding data. The certified reference materials (CRM, produced by National Research Center for Certified Reference Materials, China) were used for quality assurance and quality control. Further details about the sampling,
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treatment and analysis of the ambient samples were reported in our previous study (Bi et al., 2007; Shi et al., 2009; Xue et al., 2010; Zhao et al., 2013; Zhang et al., 2011). 3. Results and discussion 3.1. PM2.5 mass concentration characteristics All samples were collected during the heating season in Tianjin, which is the time of the year when air pollution is highest. In this study, a clear day was defined as a day in which mass concentrations of PM2.5 were less than 75 μg/m3, a polluted aerosol day was defined as a day with PM2.5 mass concentrations between 75 μg/m3 and 150 μg/m3, and a heavy pollution aerosol day was defined as a day with PM2.5 mass concentrations exceeding 150 μg/m3, in accordance with the Technical Regulation on Ambient Air Quality Index (HJ633-2012). During the sampling period, there were only five clear days and eleven heavy pollution days, with the mass concentrations of PM2.5 exceeding 250 μg/m3. The time-series of PM2.5 mass concentrations at different heights is shown in Fig. 1 and indicates that the average mass concentrations were 0.172 mg/m3, 0.189 mg/m3, 0.167 mg/m3 and 0.145 mg/m3 at 10 m, 40 m, 120 m, 220 m, respectively. Overall, PM2.5 concentrations exhibited a general decreasing trend with increasing height. It can be seen that the 220 m site had the lowest PM2.5 concentrations, while the 40 m site had the highest PM2.5 concentrations. This result is similar to the researches in Beijing (Chan et al., 2005) and Guangzhou (Deng et al., in press). Fig. 2 showed the time-series of temperature, humidity, and wind at four heights (10 m, 40 m, 120 m and 220 m). In conjunction with Fig. 1, it can be seen that the heavy pollution day occurred in the meteorological conditions of weak horizontal wind velocity, strong inversion layer, and high relative humidity in the surface layer. Generally, the PM2.5 concentrations measured in daytime were higher than those measured at night, with an average level of 0.174 mg/m3 at 10 m, 0.198 mg/m3 at 40 m, 0.181 mg/m3 at 120 m and 0.162 mg/m3 at 220 m in the daytime and 0.170 mg/m3 at 10 m, 0.180 mg/m3 at 40 m, 0.154 mg/m3 at 120 m and 0.129 mg/m3 at 220 m at night. The difference may be due to the more intensive human activities near the ground surface and more intensive vertical diffusion in the mixing layer during the day. The correlation coefficients for the concentrations measured at different heights were calculated by SPSS 16.0. The daytime correlations were 0.98 between 10 m and 120 m and 0.97 between 10 m and 220 m, and the nighttime correlations were 0.90 between 10 m and 120 m and 0.82 between 10 m and 220 m. The results of the calculations indicated that the daytime height-to-height correlations were noticeably high, while the nighttime 10–220 m correlations were relatively low. The nocturnal planetary boundary layer (NPBL) height is between 100 m and 120 m (Han et al., 2009, in press; Tian et al., 2013). Due to the dynamic stability of the NPBL, air pollutants in the surface layer are normally trapped inside the NPBL. The decreasing trend of PM2.5 mass concentrations with increasing height was observed both on clear days and on heavy pollution days. The following ratio defines the rate of decrease for the PM2.5 concentrations, which was 33% and 20% on clear days and heavy pollution days, respectively: DR ¼ ðx10 −x220 Þ=x10
ð1Þ
where DR is the rate of decrease for the PM2.5 concentrations; and x 10 and x220 are PM2.5 mass concentrations in 10 m and 220 m, respectively. The result was related to the accumulation of PM2.5 concentrations on heavy pollution days. In addition, the difference between daytime and nighttime mass concentrations of PM2.5 was evaluated for both clear days and heavy pollution days. The results indicated 0.015 mg/m3 at 10 m, 0.027 mg/m3 at 40 m, 0.017 mg/m3 at 120 m and 0.020 mg/m3 at
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Fig. 1. PM2.5 mass concentration at each height (10 m, 40 m, 120 m and 220 m) during the sampling period.
220 m for clear days, and 0.049 mg/m3 at 10 m, 0.066 mg/m3 at 40 m, 0.077 mg/m3 at 120 m and 0.063 mg/m3 at 220 m on heavy pollution days, which suggests that the difference between daytime and nighttime PM2.5 mass concentrations was smaller on clear days.
3.2. Element characteristics Element species such as Si, Al, Ca, Mg, Fe and Zn were the primary components identified in PM2.5 with composition percentages varying
Fig. 2. Temperature, humidity and wind speed during the sampling period.
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in the range of 8%–16%. Fig. 3 shows the species concentrations at each height and indicates that the higher mass concentrations of elements, as well as higher mass percentage of elements, were measured at sampling sites at lower heights. The percentage of each element exhibited a different decreasing percentage with increasing sampling height. Among the elements, Si indicated the largest decreasing percentage with increasing height, followed by Al and Zn. This result demonstrated that the contributions of pollution sources varied with different heights. A slight increase in the element mass percentage was observed at night compared with those during the day at each height. The maximum difference in mass percentage between daytime and nighttime was exhibited for Si, with 1.4% increase at 10 m, 1.3% increase at 40 m, 1.2% increase at 120 m, and 1.0% increase at 220 m, indicating that the influence of fugitive dust was relatively stronger at night, which is likely due to the fact that truck transportation is only allowed at night. Element percentages measured on heavy pollution days were higher than those measured on clear days, with increases in percentage ranging from 8.1% to 16.4%. Hierarchical clustering analysis (HCA) using complete linkage and Euclidean distances was applied to evaluate the relationships among the mass concentrations of elements for both clear days and heavy pollution days (Tian et al., 2013). The datasets for the four heights were used for the HCA after being standardized: 0 zi j ¼ ci j −ci =sdi
ð2Þ
where zij is the standardized concentration of the ith element species in the jth sample, cij is the original concentration of the ith element species, c0i is the average concentration of the ith element species, and sdi is the standardized deviation of the ith element species. The HCA results for clear days and heavy pollution days are shown in Fig. 4. Three groups were identified: Si was in group 1; Na, Al and Ca were included in group 2; and Mg, Fe, Zn, Pb, Mn and Cu were included in group 3 (among them, Mg and Fe had a closer relationship). The species were clustered into groups, suggesting that they may come from a common source (Garcia et al., 2004; Watson et al., 2011). There are similar sources of pollution from the elements at each height on both clear days and heavy pollution days. Among those elements, the percentage of Si varied relatively significantly on heavy pollution days, with an approximate 3.5% increase, followed by Al, Na and Ca, which indicated a combined total increase of more than 3.5%, and Fe, Zn, Pb, Mn and Cu, with a combined total increase of less than 4%, comparing with those in clear daya. The source
Fig. 3. Average mass concentrations of elements in PM2.5 at each height (10 m, 40 m, 120 m and 220 m) during the sampling period.
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of Si may be from crustal dust, and the sources of Al, Na, Ca, Mn, Fe, Pb, Cu and Zn may be from traffic and combustion (Zheng et al., 2014). The increase of these elements in the composition of PM2.5 indicates that crustal dust, traffic and combustion should not be ignored factors for heavy pollution days during the heating season. In addition, the element concentrations were found to decrease with increasing sampling height on both clear days and heavy pollution days, with larger decreases on clear days. 3.3. Ionic species characteristics Water-soluble ions had a percentage of more than 40% of the particulate matter, with sulfate showing the highest mass concentrations, followed by nitrate. The mass concentrations of water-soluble ionic species in PM2.5 at each height are shown in Fig. 5, which indicates − + − that higher concentrations of SO2− 4 , NO3 , NH4 and Cl were measured at lower heights. There was also a slight decrease in the mass concentra+ tions of SO24 −, NO− 3 and NH4 at lower heights. In contrast, higher − , NO and NH+ percentages of SO2− 4 3 4 were measured at greater heights. 2− In addition, higher NO− 3 /SO4 mass concentration ratios were exhibited in the higher levels, with values of 0.66 at 10 m, 0.70 at 40 m, 0.73 at 120 m and 0.86 at 220 m. To evaluate the differences in the mass concentrations of ionic species at different heights, the coefficients of divergence (CD) was used to analyze the concentration variability at two adjacent heights (Massoud et al., 2011; Zhang and Friedlander, 2000). The CD is defined as:
CD jk
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi !ffi u p u1 X xi j −xik 2 ¼t p i¼1 xi j þ xik
ð3Þ
where x ij and xik are mass concentrations at the jth and kth sites, respectively, and p is the number of sampling days. The small CD values imply similarities in the ionic species concentrations for the two sampling sites, while CD values approaching 1 indicate significant differences. The results of CD analysis for ionic species at different sampling heights are listed in Table 1. According to the previously reported analyses, CD b0.20 was defined as a relatively homogeneous distribution (Krudysz et al., 2008). For individual ionic species, the height-to-height CD values and NO− of SO2− 4 3 were generally less than 0.20, which indicates a more and NO− similar temporal variation at each height for SO2− 4 3 , which may be due to these ions being mainly from secondary sources, while other ions were mainly derived from primary sources, and their changes are linked to more complex factors (Tian et al., 2013). The mass concentrations of water-soluble ionic species were higher during the day than those measured at night, which indicates that there is a larger contribution of ionic species in the daytime from various human activities and suitable meteorological conditions (light, temperature, humidity and vertical diffusion). Cl− is usually considered a major component of a sea salt source, but it is also released from coal combustion (Sun et al., 2006). Thus, the elevated percentages of Cl− and SO2− 4 at night may suggest the contribution from coal combustion source 2− increased at night. In addition, the mean mass ratios of NO− 3 /SO4 were considered, which has been widely applied as an indicator of the relationship of mobile sources to stationary sources (Gao et al., 2011). 2− was higher in the daytime, indicating that the The ratio of NO− 3 /SO4 mobile pollution sources were more pronounced in the daytime. 2− Furthermore, the mass ratios of NO− 3 /SO4 were compared for clear days and heavy pollution days: 0.66 at 10 m, 0.60 at 40 m, 0.63 at 120 m and 0.77 at 220 m on clear days, and 0.73 at 10 m, 0.75 at 40 m, 0.81 at 120 m and 0.91 at 220 m on heavy pollution days. The higher mean 2− was obtained on heavy pollution days. Thus, mass ratio of NO− 3 /SO4 it can be seen that combustion sources such as power plants, industrial boilers, heating boilers and civil coal fire facilities play important roles, but the increasing vehicle pollution is still considered a contributing
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Fig. 4. HCA of elements using the combined datasets for four heights (10 m, 40 m, 120 m and 220 m).
factor and should not be neglected in evaluations of rapidly developing cities (Ji et al., 2012). During heavy pollution days, the mass percentages 2− of NO− 3 and SO4 to PM2.5 ranged from 14% to 19% and from 19% to 21%, respectively, and there was an indication of an increasing range of 7 to 10% in the study area. These results demonstrate an important role for the secondary ions during heavy pollution days. Similar results can be found in other literatures. The mass ratio of SO24 − and NO− 3 to PM2.5 reached 25% on heavy pollution days in Yangtze River Delta, China (Fu et al., 2008), and SO24 − and NO− 3 compose 20.7% and 11.9% of PM2.5, respectively, on heavy pollution days in Beijing, China (Ji et al., 2014). 3.4. Carbon characteristics Typically, EC is emitted from combustion sources, while OC can be emitted from primary emission sources and could also be generated
from secondary chemical reaction (Kim et al., 1999). Fig. 6 shows the average concentrations of OC, EC and the OC/EC ratios at each sampling height. According to Fig. 6, it can be seen that the average OC concentrations show an obvious decreasing trend with increasing height: 33.02 μg/m3 (40 m) N 30.53 μg/m3 (10 m) N 29.75 μg/m3 (120 m) N 26.20 μg/m3 (220 m). The concentrations of EC exhibited a similar order, with 8.21 μg/m3 (10 m) N 8.26 μg/m3 (40 m) N 7.19 μg/m3 (120 m) N 5.78 μg/m3 (220 m). The mass percentage of EC decreased with height, while the percentage of OC showed a slight increase, and the OC/EC ratio increased gradually from 3.7 at 10 m to 4.5 at 220 m, which showed that the contribution of secondary pollution increased along with increasing height. Fig. 7 shows the scatter plot between OC and EC mass concentrations during the study period (r2 = 0.20–0.34). A significant correlation between OC and EC usually suggested common sources such as combustion and traffic (Sharma et al., 2014). In contrast,
Fig. 5. Ion mass concentrations and fractions in PM2.5 at each height (10 m, 40 m, 120 m, and 220 m) during the sampling period.
H. Wu et al. / Science of the Total Environment 523 (2015) 152–160 Table 1 The results of height-to-height correlations.
Cl− NO− 3 SO2− 4 NH+ 4
10–40 m
40–120 m
120–220 m
10–220 m
0.21 0.17 0.18 0.22
0.22 0.12 0.14 0.11
0.27 0.11 0.13 0.09
0.42 0.20 0.20 0.26
a poor correlation between OC and EC indicated the formation of secondary aerosol through a photochemical reaction in the atmosphere. Additionally, the concentrations were higher in the daytime than at night for both EC and OC at each height. However, the difference in concentrations of OC between daytime and nighttime was much greater than that for EC, with a value of 8.4 μg/m3 for OC and only 0.5 μg/m3 for EC at 220 m. This was probably due to the formation of secondary organic carbon (SOC) by photochemical reactions in daytime. The SOC concentration is estimated using the following equations (Castro et al., 1999): SOC ¼ OC−POC
ð4Þ
POC ¼ EC ðOC=ECÞpri
ð5Þ
where (OC/EC)pri is the primary carbon ratio, and POC is the primary organic carbon. Unfortunately, the (OC/EC)pri exhibits significant changes for different sources, so it is difficult to obtain a representative value for (OC/EC)pri. The minimum OC/EC ratio can replace the (OC/EC)pri in the equation and assumptions regarding the use of this procedure were discussed in details (Ji et al., 2014). The concentrations of SOC, estimated through the minimum OC/EC ratio method were 15.33 μg/m3 (10 m), 16.67 μg/m3 (40 m), 12.99 μg/m3 (120 m) and 11.54 μg/m3 (220 m) at night and 15.75 μg/m3 (10 m), 22.52 μg/m3 (40 m), 20.90 μg/m3 (120 m) and 20.93 μg/m3 (220 m) during daytime. The concentrations of SOC are higher in the daytime than at night. The concentrations of OC were 9.2 μg/m3 at 10 m, 9.3 μg/m3 at 40 m, 7.5 μg/m3 at 120 m and 7.8 μg/m3 at 220 m on clear days and 41.8 μg/m3 at 10 m, 43.1 μg/m3 at 40 m, 36.0 μg/m3 at 120 m and 30.4 μg/m3 at 220 m on heavy pollution days. At the same time, the concentrations of EC were 2.7 μg/m3 at 10 m, 2.9 μg/m3 at 40 m, 2.2 μg/m3 at 120 m and 2.1 μg/m3 at 220 m on clear days and 9.1 μg/m3 at 10 m, 9.6 μg/ m3 at 40 m, 8.7 μg/m3 at 120 m and 6.4 μg/m3 at 220 m on heavy pollution days. Compared with the mass concentrations of OC and EC on clear days, an obvious increase in OC and EC concentrations was observed on
50
6.0
EC OC
45
OC/EC 40
5.5
35
4.5
25
OC/EC
Concentration µg/m
3
5.0 30
20 4.0 15 10
3.5
5 3.0
0 10m
40m
120m
220m
Height
Fig. 6. Carbon mass concentrations in PM2.5 and the fraction of OC/EC at each height (10 m, 40 m, 120 m, and 220 m) during the sampling period.
157
heavy pollution days, whereas the mass percentage decreased on heavy pollution days, especially for OC, which was 8%–10% lower. This indicates that the accumulation of OC concentration on heavy pollution days was relatively lower compared with other compositions. This result is different with the one in Fu's research (Fu et al., 2008). In that research, the mass percentage of OC has not shown obvious differences between the heavy pollution days and clear days. It was observed in this study that the value of OC/EC increases with increases in height and is higher on heavy pollution days (4.2–4.8 for all sampling sites) than on clear days (3.2–3.8 for all sampling sites), which might be due to the accumulated process of SOC on heavy pollution days. 3.5. Source appointment The CMB model has been widely used in the relative studies (Chow et al., 1992, 1996; Feng et al., 2002; Lee et al., 2008; Zhang et al., 2011; Shi et al., 2009). In this study, the contributions of potential sources at each sampling site for both clean days and heavy pollution days were evaluated using CMB-iteration model, which can estimate the SOC and POC concentrations in the ambient receptor by using the iterative method, without introducing the SOC profile into the CMB model. The calculation procedure and detailed descriptions of model were shown in our previous study (Shi et al., 2011, 2012). According to the investigation of the emission inventory, the crustal dust, coal combustion, cement dust, vehicle exhaust, secondary sulfate, secondary nitrate and SOC were the prominent pollution source categories in Tianjin. The source profiles are described in Fig. 8 and the source appointment results are shown in the following Fig. 9. The contributions of the crustal dust, coal combustion, cement dust, secondary sulfate and secondary nitrate and vehicle exhaust were closely related to the height. An obvious decrease in the contributions of the crustal dust, coal combustion and cement dust was found with increasing height, especially for the crustal dust, which showed a decrease of 6–8%. The larger contribution of primary pollution was found at the lower height. On the contrary, greater contributions of secondary sulfate and nitrate were observed with increasing heights. The vertical variability of the pollution sources was more pronounced on clear days, while on heavy pollution days, the differences among contributions of secondary nitrate, vehicle exhaust and SOC were not obvious at four heights. The results also showed a comparison of the different contributions of pollution sources for clear days and heavy pollution days. The influence of crustal dust, coal combustion, secondary sulfate and nitrate on heavy pollution days was significantly greater than their influence on clear days, while the contributions of vehicle exhaust and SOC as pollution sources were greater on clear days than on heavy pollution days. Vehicle exhaust and coal combustion had prominent contribution to ambient PM2.5 in clear days. While in heavy pollution days, main source categories had considerable contribution, which can be concluded that pollution control aimed at only one or two categories of sources is difficult to obtain obvious environmental effect, and multiple-source control should be implemented. 4. Conclusions Overall, the mass concentrations of PM2.5 and chemical compositions decrease with increasing height. However, the opposite is true − for the mass percentage of SO2− 4 , NO3 and OC. The measured mass concentrations of PM2.5, ion species and carbon component were higher during the day than at night, with high production of SOC during the daytime. In contrast, the difference in mass concentrations at four heights (10 m, 40 m, 120 m and 220 m) was not obvious for the elemental components (Si, Al, Ca, Mg, Fe and Zn), but higher percentage of Si was observed at night. The levels of PM2.5 concentration and the chemical species mass percentages measured on heavy pollution days were much higher than those measured on clear days, except
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Fig. 7. Scatter plot between OC and EC at each height (10 m, 40 m, 120 m, and 220 m) during the sampling period.
2− for OC and EC. In addition, higher levels of NO− and OC/EC 3 /SO4 were observed on heavy pollution days. Source apportionment by the CMB-iteration model shows that larger contributions of crustal dust,
30 20 10 0 30 20
combustion dust and cement dust as pollution sources were observed at sampling sites at lower heights, while the opposite was true for contributions of secondary ion sources. The contributions of crustal
CD
CC
Fraction / %
10 0 50 40 30 20 10 0 100 75 50 25 0 100 75 50 25 0 100 75 50 25 0
CE
VE
SS
SN
Na Mg Al Si
2K Ca Ti V Cr Mn Fe Ni Cu Zn Pb SO4 NO3 TC OC
Species Fig. 8. Source profiles of PM2.5 (CD is crustal dust; CC is coal combustion; VE is vehicle exhaust; CE is cement dust; SS is secondary sulfate; SN is secondary nitrate).
H. Wu et al. / Science of the Total Environment 523 (2015) 152–160
VE 26.99%
SS 11.47%
40m
VE 23.97%
SS 12.72% SN 9.67%
SN 7.71%
SOC 15.12%
CE 3.86% CC 12.58%
CC 18.96%
SS 14.61%
CD 10.91%
VE 20.62%
120m SS 17.71%
SOC 13.83%
220m
SOC 16.94% SN 14.31% CE 2.25% CC 10.92%
CD 8.05%
CC 16.11%
SOC 12.91%
CE 2.33%
CD 13.8%
VE 22.78%
SN 11.1% CE 2.53%
10m
159
CD 5.79%
A SS 18.86%
SN 15.25%
10m
CE 2.77%
VE 12.2%
CC 19.57%
SOC 6.66%
SOC 6.4%
CC 18.34% CD 16.97%
120m
SS 21.19%
SN 20.55%
VE 12.65%
SN 17.99%
VE 12.62%
CE 2.17%
CD 16.74%
SS 20.29%
40m
SS 19.93%
SN 16.49%
220m
VE 12.7%
CE 2.23% SOC 6.2%
CE 2.78% CC 18.54%
CC 18.73%
CD 13.02%
SOC 6.29% CD 10.76%
B Fig. 9. Estimated source contributions (%) for four height on clear days (A) and heavy pollution days (B), by CMB model.(CD is crustal dust; CC is coal combustion; VE is vehicle exhaust; CE is cement dust; SS is secondary sulfate; SN is secondary nitrate; SOC is secondary organic carbon).
dust, combustion dust and secondary ion species as pollution sources were higher on heavy pollution days, while contribution of vehicle exhaust was greater on clear days. Contributions of secondary ion species increased with increasing height, while contributions of other pollutant
sources decreased. The results of this work help to improve the understanding of vertical variations and diurnal variability of PM2.5 in clear days and heavy pollution days, and also provide a scientific basis for PM2.5 pollution control and prevention.
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Acknowledgments This work was funded by the Tianjin Science and Technology projects (14JCYBJC22200), the Science and Technology Support Program (13ZCZDSF02100), and the National Natural Science Foundation of China (NSFC) under Grant No.41205089, No.21207069 and No.21407081.
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