Impact of Air Humidity Fluctuation on the Rise of PM Mass Concentration Based on the High-Resolution Monitoring Data

Liyuan Zhang1, Yan Cheng1, Yue Zhang1, Yuanping He1, Zhaolin Gu1*, Chuck Yu1,2

1 School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China 2 International Society of the Built Environment (ISBE), Milton Keynes, UK

ABSTRACT

Hourly particulate matter mass concentrations and meteorological parameters, recorded by the China National Environmental Monitoring Center (CNEMC) and China Observatory, between June 2013 and March 2016 in Beijing, Xi’an, Shanghai and Guangzhou were examined to explore correlations. Characteristics of a rapid (abrupt) rise in PM2.5 mass concentration during early stage of a serious urban PM pollution event were examined and compared with pollution events with a gradual (accumulative) rise in PM2.5 mass concentration. The accumulative rise in air pollution is characterized –3 –1 by a prolonged slow PM2.5 growth rate (3–5 µg m h ), and could eventually lead to middle level pollution (ambient –3 PM2.5 mass concentration of about 150 µg m ), accompanied with an uncertain temporal variation in SO2, NO2, O3 and CO concentrations. The abrupt rise process is characterized by a short-term high aerosol growth rate (> 10 µg m–3 h–1), and –3 could eventually form severe air pollution (PM2.5 mass concentration exceeds 250 µg m ), with a constant increase in gaseous pollutants concentrations. The average relative humidity (RH) was observed to have a less impact on the rise of PM2.5 concentration, but the fluctuation in RH was found to have a strong correlation with the rise in PM2.5 concentration. Further analysis has indicated that both abrupt and accumulative rise in PM2.5 mass concentration could be due to the RH fluctuation in atmosphere. The RH variation is important for the study of the fine-particle growth and for prediction of PM pollution episodes.

Keywords: Relative humidity (RH); Fluctuation of RH; Aerosol concentration; PM abrupt rise; PM accumulative rise.

INTRODUCTION to the different urban economic developments, traffic conditions, industrial activities and meteorological factors Research on airborne particulate matter (PM) has become (Zhang et al., 2011; Zhang and Gu, 2013; Huang et al., one of the most important subject areas for the understanding 2014; Rohde and Muller, 2015), however, the characteristics of the urban air pollution (Chan and Yao, 2008; Kan et al., of the atmospheric conditions on the formation of haze 2012; Lu et al., 2015). The serious air pollution has been episodes have not been sufficiently studied. threatening the environmental heath of many mega or The sudden formation of severe haze has become a medium-sized cities in many countries, and is believed to common problem in developing countries, especially in have contributed to the deaths of more people than AIDS, China, India, Brazil and other industrial countries, due to malaria and breast cancer all over the world (WHO, 2012). rapid urbanization (Reid et al., 1998; Pachauri et al., 2013; The PM pollution has been estimated to have caused 3 to 7 Wang et al., 2014a). An interesting feature is the soaring million deaths every year due to their adverse impact on PM2.5 concentrations that occurred during the early stage worsening cardiorespiratory diseases (Pope et al., 2002; Hoek of severe hazes (Friedlander and Marlow, 1977; Du et al., et al., 2013; Yang et al., 2013; WHO, 2014). Meanwhile, the 2012; Wang et al., 2014b), which is called the abrupt rise seriously high concentrations of PM2.5 in ambient air is period. Abrupt rise of PM2.5 haze was also observed during contributing to the ash haze weather in various cities Beijing winter hazes by Zheng et al. (2015), which was (Friedlander and Marlow, 1977; Du et al., 2012). Various presented as dramatic hourly fluctuation with a maximum –3 –1 studies have shown that the mass concentration and chemical mass growth rate of up to 351.8 µg m h and PM2.5 compositions of aerosols in each city are very different due concentrations rose in excess of 400 µg m–3 within 1–3 hours during the episode in January 2013. The abrupt rise of PM2.5 concentrations could only be observed with high-resolution monitoring data, which is the reason that most previous * Corresponding author. studies failed to focus on the temporal change in particle E-mail address: [email protected] mass. What happened during the early stage of haze episodes A

544 Zhang et al., Aerosol and Air Quality Research, 17: 543–552, 2017 is the key to understand the formation of high pollutions. average concentrations of all kinds of gaseous pollutants Previous studies have demonstrated that wind speed and have been recorded. This is a typical consequence of rapid atmospheric relative humidity (RH) are the two most industrialization of developing countries which is being important external meteorological factors affecting the mass experienced all over the world. concentrations of aerosol (Sun et al., 2006; Tian et al., We studied the ambient air pollution in various cities in 2014; Zhang et al., 2016). In the stable atmosphere, high China and monitored atmospheric PM concentrations in 4 atmospheric RH could enhance fine particulate accumulation megacities and areas around: Beijing, Xi’an, Shanghai and and therefore formation of haze, and could also induce a Guangzhou. The relative locations of these cities in China 2– rise in concentrations of water soluble inorganic ions (SO4 , are shown in Fig. 1. – + NO3 , NH4 ) in the atmosphere, and reduce the visibility in urban areas (Yan et al., 2009; Ding and Liu, 2014; Gao et Data Sources al., 2015; Guo et al., 2014; Lin et al., 2015). During haze The original data was mainly collected from the China episodes, high atmospheric RH could accelerate the formation National Air Quality Network, which has 945 fast- of secondary particles, causing the aggravation of atmospheric responding and real-time monitoring sites at 160 cities in pollution in ambient air (Sun et al., 2013). The components China, providing the high precision concentrations of PM2.5, and size distribution of atmospheric PM could determine PM10, SO2, NO2, O3 and CO in the ambient atmosphere in the deliquescence point, growth rate and the light scattering these cities. However, most archived observations are not coefficient of fine particles (Wang et al., 2003; Gysel et al., publicly available. To compensate, real-time data was 2007; Cao et al., 2012; Wang et al., 2012). Air humidity is downloaded during a 33 month interval from June 2013 to also a significant factor that could determine the movement March 2016. Due to the download restrictions on the of particles in troposphere due to the distribution of electrical official report, two different third-party sources (PM25.in charges on particles (Wei and Gu, 2015; Zhang et al., 2016). and AQICN.org) were used. PM25 in is a direct mirror of High RH might not necessary be a direct impact factor on data from the China’s national network, while AQICN.org the growth in PM2.5 concentration. For instance, the abrupt provides a large amount of the world’s real-time air quality –3 rise in PM2.5 concentration, rising from 140 to 318 µg m data and included many additional sites in China and in 5 hours, happened with an average RH as low as 50% in surrounding areas. Thus we have 38 stations in Beijing, 14 Beijing during January 17th, 2010 (Zhao et al., 2013). Similar stations in Xi’an, 13 stations in Shanghai and 17 stations in phenomenon was also observed in Los Angeles in 1969 Guangzhou. Logging interval at these stations was set at 1 (Husar et al., 1972). On the other hand, the average humidity hour. According to the Chinese national standard (HJ/T as high as 85% did not result in further rise in PM2.5 193-2005), the sampling pedestals of automatic monitoring concentration during this high concentration phase, the PM2.5 instruments were placed 3–15 m above the ground or 1m concentration was ranging from 302 to 447 µg m–3 during above the top of buildings. In each city the monitoring the October 9th to 11th, 2014. stations were located in different downtown and suburb Furthermore, regardless of the average humidity was areas. The average PM2.5 concentrations were monitored at high or not, stronger fluctuation of relative humidity was these downtown monitoring sites to serve as the regional recorded before or at the beginning of each abrupt rise PM2.5 data for our study. event of PM2.5 concentration (Yang et al., 2011; Zhao et The ambient temperature, relative humidity, atmospheric al., 2013; Wang et al., 2014b). For instance, the abrupt rise pressure, wind scale and wind direction were recorded –3 in PM2.5 concentration, rising from 50 to 600 µg m in 20 together with the concentrations of ambient air pollutants hours, happened with a strong RH fluctuation from 38% to by local official meteorological stations at China Observatory. 87% in Shanghai during December 5th, 2013 (Leng et al., The logging interval of these data were set at one hour. 2016). The purpose of this study is to examine if the RH fluctuation is related to PM2.5 abrupt rise event. The RH Data Quality Control variation in 6 hours (Var-RH6) would be presented by an Automated quality control checks were performed on each index to analyse the influence of RH on the abrupt rise in PM air pollutant to remove the repeated values and implausible concentration. Using the hourly high-resolution atmospheric zeroes, common signs of missing measurements being pollution data, the effect of air humidity on the process of replaced with a placeholder. Collectively, these quality control abrupt rise in urban PM concentration was analysed to checks excluded 7% of the reported values from China. The illustrate the significance of atmospheric water vapour most common exclusion criterion (54% of exclusions) was fluctuation. This could provide an important indicator in a for continuous non-zero concentration of pollutants reported warning system for prediction of an onset of a serious haze for several hours in a row. Due to natural variability, episode and the abrupt growth of pollution aerosols to instruments will give the same value in a row occasionally. form part of a development of amelioration strategy for If the data is the same for more than 5 hours, it is more urban management of atmospheric pollution. likely that the instrument has stopped monitoring and the reporting system is simply repeating the last data received. METHODS Repeated values once continued every hour for as long as 3 weeks. China is facing serious ambient air pollutions in world’s In addition, a regional consistency check has been history (Chan and Yao, 2008; Kan et al., 2012), where high performed for every pollutant where the value at each A

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Fig. 1. The locations of the four monitoring cities: Beijing, Xi’an, Guangzhou and Shanghai in China. location was compared to the average value at nearby March 2016 and with 16468 hours of data in Shanghai, and stations at the same time. The least consistent 0.6% of 15025 hours in Guangzhou, and 16334 hours in Beijing. observations were excluded. In many cases, the observations The effective data moment (t0) was defined as below: –1 removed are more than double what one would expect When t ∈ (t0 ± 5h), if wind speed < 3.3 m s , precipitation based on the average of their neighbours. Data removed as J = 0, then t0 is accepted the effective data. a result of this test is not necessarily inaccurate, but may The duration of effective data in this analysis: 12668 reflect a pollution source in the immediate vicinity of the hours in Xi’an, 9007 hours in Shanghai, 10297 hours in monitoring site that is not representative of conditions in Guangzhou is and 8275 hours in Beijing. the rest of the city or region. As we wish to focus on large- The variance of six-hours RH was adopted to measure scale patterns, it is desirable to remove such local outliers. the strength of fluctuations in the atmospheric humidity Additional steps to compensate for local noise and outliers based on the long-time monitoring in the four cities. The were built into the interpolation scheme described below. variance is a typical index to represent the fluctuation of In addition to these quality control steps, data gaps in a data and 6 hours is 1/4 a period of fluctuation, which is easy station record lasting two hours or less were filled by linear to compare because this is always stable in different times. interpolation to help reduce the noise in the reconstruction So we adopt the natural logarithm of the variance value of due to missing stations. six-hours RH, as shown in Eq. (1), to measure the strength Similar to other scholars’ results (Karar et al., 2006; Tai, of the atmospheric humidity fluctuation, the calculation et al., 2010), PM concentration is inversely correlated with equation is as follows: both of the strong wind and precipitation, which are determined by severe convections (the correlation coefficient 6 2 2  RH RH   i=1t-i t of strong wind and PM2.5 concentration is R = –0.86). ln Var RH ln  , (1) Occasionally in spring, strong wind often brings dust t  6  storms covering the northern cities, causing a sharp rise in   the PM10 concentration and small increase in the PM2.5 concentration; hence causing serious PM10 pollution. Because RESULTS AND DISCUSSION the purpose of this research is focusing on the process of rapid growth of the fine particles (PM2.5), the time of Valid Data Analysis—Air pollution Condition of Study strong wind and precipitation weather were excluded from Areas the analyses of raw data. Through the raw data analysis, excluding the invalid The monitoring period in Xi’an was from June 2013 to data, the annual main air pollutants and meteorological March 2016 with 19258 hours of total raw data. The elements of four typical cities are shown in Fig. 2. The monitoring period in other cities was from January 2014 to analysis of various air gaseous pollutants indicated that the A

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Fig. 2. The annual average temperature and RH and the concentration of PM and several main air pollutants in Xi’an, Beijing, Shanghai and Guangzhou.

–3 –3 concentration of NO2 is similar (43.9–47.0 µg m ), the (PM2.5 mass concentration > 250 µg m ). In contrast, the –3 concentrations of SO2 is highest in Xi’an (21.8 µg m ) and rise in PM2.5 concentration is nearly linear during the –3 –3 the concentration of O3 is highest in Shanghai (169 µg m ). formation period of normal aerosol pollution (115 µg m < –3 –3 PM2.5 pollution is most severe in Beijing (74.3 µg m ) and PM2.5 mass concentration < 200 µg m ). This period is –3 PM10 pollution is most severe in Xi’an (136.1 µg m ). defined as “accumulative rise” of PM2.5 concentration. PM2.5 and PM10 concentrations of inland cities such as The duration of the accumulative rise period can exceed Xi’an and Beijing are much higher than coastal cities such as 24 hours (sometimes in excess of 48 hours) and the average Shanghai and Guangzhou. All these four cities’ people concentration growth was 3–5 µg m–3 h–1, in which would –3 experience the unhealthy air condition (PM2.5 > 35 µg m ) not cause a serious PM2.5 pollution. A typical event in Xi’an (GB 3095-2012, 2012). The northern cities (Beijing and is shown in Fig. 3(b). Meanwhile, during the accumulative Xi’an) are more polluted than the southern cities (Shanghai rise period, the gaseous pollutants concentration varied and Guangzhou), but certainly not as humid. irregularly and the relative humidity fluctuated within a tight range during a 24-hour cycle. However, during the Effective Data Analysis—Formation Period of Severe abrupt rise period, the concentration of various gaseous Haze Period pollutants would rise (except O3) simultaneously with a During the monitoring period, a lot of extremely severe rise in PM2.5 concentration, and the relative humidity was PM2.5 pollution events (PM2.5 mass concentration > 250 shown to exhibit extreme volatility prior to this period. –3 µg m , lasting for a period for over 3h, PM2.5 is the main Fig. 4 illustrates 3 severe haze episodes recorded under pollutant) have been observed which exceeded the highest the stable atmosphere during October 1st to 30th in Beijing. threshold of Chinese national standard, WHO recommend During this period, the increase of PM could be distinguished and US EPA standard (HJ 633-2012; GB 3095-2012; WHO, into two modes, the abrupt rise (yellow rectangle) and 2006; EPA, 2014) in four cities. Further analysing the early accumulative rise (light blue rectangle). Fig. 4 illustrated period of haze in the effective data, the growth rate of two modes of PM increase. –3 –1 PM2.5 concentration was above 10 µg m h , accompanied Pollution emissions are always localized during these with stable atmospheric condition (wind speed < 3.3 m s–1, abrupt rise events, especially in these mega cities because planetary boundary layer is lower than 500 m, no most of the largest emissions sources are located in or near precipitation). This phenomenon is defined as “abrupt rise” urban districts. As shown in the 3 haze episodes of Fig. 4, of PM2.5 concentration. under the stable atmosphere in this region, although there Fig. 3(a) shows a typical abrupt rise event in Beijing. was little migration of PM in the atmosphere, we observed The duration of abrupt rise period in Beijing was always similar deviation in the curves of the PM2.5 concentration within 12 hours and the average concentration growth was in three cities close to each other (260 km apart): Beijing –3 –1 above 10 µg m h , leading to a serious PM2.5 pollution (BJ), Shijiazhuang (SJZ) and Langfang (LF), illustrating A

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Fig. 3. (a): An abrupt rise event in Beijing and (b): an accumulative rise event in Xi’an. (a1) and (b1) shows the variation in the concentrations of PM2.5, PM10 and RH value, (a2) and (b2) shows the variation in the concentrations of SO2 and O3, (a3) and (b3) shows the concentrations of NO2 and CO.

the character of localization of PM pollution. Therefore, to the rise in PM2.5 concentration (shown in Fig. 6). The during the abrupt rise event, the migration is not the main relative humidity was divided into eight sectors namely cause in the rise in PM2.5 mass concentration. 0%–24%, 25%–34%, 35%–44%, 45%–54%, 55%–64%, Further analysing the effective data, it is worth to mention 65%–74%, 75%–84%, 85%–100%. The results of our that before each abrupt rise period, Ln(Var-RH) is larger analyses indicated that: low humidity conditions are not than 6, referring that the fluctuation of RH is stronger. In conducive to the growth of PM2.5 concentrations, when RH the accumulative rise period and severe haze period, < 25%, the concentration of PM2.5 has generally a downward Ln(Var-RH) is lower than 6. declining trend. When RH is between 45% and 84%, the Fig. 5 demonstrate the abrupt rise events in Xi’an (a), concentration of PM2.5 is generally on the rise and the Guangzhou (b) and Shanghai (c). Under the stable growth rate is 0.67–1.13 µg m–3 h–1; when RH > 85%, the –3 –1 atmosphere, the growth rate of all these three events is growth rate of PM2.5 concentration is only 0.39 µg m h . In more than 10 µg m–3 h–1, and finally result severe haze fact, neither too low nor too high relative humidity conditions –3 (PM2.5 concentration is above 250 µg m ). Values of Var- would be conducive to the rapid rise in the PM2.5 RH were also higher before each abrupt rise period. concentration. However, the peak growth rate within the scope of the entire humidity sections is only 1.13 µg m–3 h–1, Effective Data Analysis—Average RH Influence which is obviously not enough to support the abrupt rise As discussed in the introduction, RH is an important process (minimum growth rate is 10 µg m–3 h–1). Thus the external meteorological factors affecting the mass average humidity is not the most significant condition concentrations of aerosol. RH do not influence the increase leading to the abrupt rise of urban PM2.5 concentration. of PM directly. In the effective data, the average growth rate In addition, neither of sunlight nor high humidity of PM2.5 concentration under different relatively humidity conditions would be the essential condition that cause the sectors of our four monitoring cities in the stable atmosphere process of abrupt rise in PM2.5 concentration, as shown in was calculated to determine the direct effect of air humidity Fig. 3(a1), during the period of 10:00–17:00, 9th December, A

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Fig. 4. The high-resolution observation result of regional PM2.5, atmospheric pressure, RH, variance of RH in 6 hours (Var- RH), wind speed and precipitation from October 1st to 30th in Beijing. the urban air relative humidity was only 17% to 23%, were also shown in Figs. 4 and 5. In contrast, in Fig. 3(b), which is a typical low humidity condition. Events of the the process of accumulative rise, relative humidity was abrupt rise process occurring at night, were repeatedly kept to 35% to 65% and the cycle was stable. Even during observed, suggesting that the sunshine could also not an this slow incremental rise process, before a small jump in essential factor for the abrupt rise in PM2.5 concentration. PM2.5 concentration, there was a relatively strong fluctuation in the atmospheric RH. Effective Data Analysis—Fluctuation RH Influence Fig. 7(a) demonstrate the classification of RH fluctuation, The fluctuation of RH is the key to analyse the role (or the normal fluctuation range is 2–4 (54.8% of the total time), effect) of RH on the occurrence of a sudden heavy haze the weak fluctuation range is 0–2 (18.5% of the total time) event. The violent fluctuation of RH was commonly observed and the strong fluctuation range is above 4 (26.8% of the prior to every abrupt rise in PM2.5 concentration, however total time). we are still do not clearly understand the effect in the lab Statistical analysis of RH fluctuation shows highly study. The period of RH fluctuation in the atmosphere is correlation with the increase of PM concentration under the usually 24 hours which is usually accompanied with a daily stable atmosphere. When ln(Var-RH) is lower than 2, the fluctuation in temperature. However, in different cities and fluctuation of RH is weak, PM concentration has no clear during different seasons, the difference in the average trend; When ln(Var-RH) is between 2 and 4, the fluctuation of atmospheric humidity and the fluctuation range of relative RH is normal, PM concentration always rise accumulatively humidity is very large. In fact, the daily fluctuation range (growth rate < 3 µg m–3 h–1); When ln(Var-RH) is between of RH is usually 30% to 60%, but in Fig. 3(a), prior to the 4 and 6, the fluctuation of RH is strong, PM concentration –3 –1 abrupt rise in PM2.5 concentration, there was an obvious always rise rapidly (growth rate is from 3 to 10 µg m h ); RH jump, from 85% to 17% in only six hours, and the When ln(Var-RH) is above 6, the fluctuation of RH is fluctuation range of RH before and after the abrupt rise violent, PM concentration always rise abruptly (growth period was very stable (within 30%). Similar phenomenon rate is above 10 µg m–3 h–1). A

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Fig. 5. The high-resolution observation result of PM2.5 concentration, Var-RH, wind speed and precipitation of several typical abrupt rise events in Xi’an (a), Guangzhou (b) and Shanghai (c).

Fig. 6. Average increment of PM2.5 concentration per hour in the different RH range.

Analysis results show that the strong humidity fluctuations concentration appearing in 10 hours is as high as 89% would have a strong influence on the growth of PM2.5 (average RH > 80%), 73% (average RH < 70%). When the concentration. Actually, the strong RH fluctuation was shown fluctuation in RH is weak (ln(Var-RH) < 2), the probability to appear prior to every abrupt rise in PM2.5 concentrations. of an abrupt rise in PM2.5 mass concentration appearing in In order to evaluate the function of RH fluctuation, the 10 hours is as low as 27% (average RH > 80%), 15% abrupt rise in PM2.5 concentration is defined as the growth (average RH < 70%). rate over 5 µg m–3 h–1, with a duration of over 5 hours, which Based on the study results, further research will be focusing would have a more rapid rise than accumulative rise. Fig. 7(b) on the mechanism of humidity fluctuations that could affect indicates that when the fluctuation in RH is strong (ln(Var- the aggregation and growth in the PM concentration, RH) > 4), the probability of a abrupt rise in PM2.5 mass especially the growth in the secondary pollutants in urban A

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Fig. 7. (a): The distribution of ln(Var-RH) to show the weak, normal, strong and violent RH fluctuation; (b): the probability of rapid growth in PM2.5 concentration. air. In addition, because of the large regional differences in essential condition for the 32 times observed process of this research, more precision chemical composition would abrupt rise in PM concentration. The probability of be needed and specific changes in meteorological data abrupt rise in PM2.5 concentration is as high as more would also be required to enable the quantitative analysis of than 80% due to the aftereffect of strong RH fluctuation, the interaction between the growth of atmospheric aerosols which is also significant in the process of accumulative and the fluctuation of air humidity. rise in PM2.5 concentration.

CONCLUSIONS ACKNOWLEDGEMENTS

In this study, we collected the long period air pollutant This research was supported by the National Science concentration and meteorological data from June 2013 to Foundation of China (No. 11572242) and the Key Scientific March 2016 in four typical mega cities. The differences in Research Innovation Team Project of Shaanxi Province meteorological conditions and pollution sources between (under grant No. 2016KCT-14). The authors wish to thank cities would determine the different aerosol pollution, but environment protection agencies and meteorological stations there are always a direct correlation between the occurrence in the study areas for providing precious data. of serious aerosol pollution and the strong fluctuation of air humidity. Our main research conclusions are as follows: REFERENCES 1. The process of abrupt rise, occurs at the early stage of the severe PM pollution, remain underappreciated. The Cao, J.J., Wang, Q.Y., Chow, J.C., Watson, J.G., Tie, X.X., early stage of PM pollution process can be distinguished Shen, Z.X., Wang, P. and An, Z.S. (2012). Impacts of into two modes, the accumulative rise and the abrupt rise, aerosol compositions on visibility impairment in Xi'an, due to their respective growth rate and duration. In the China. Atmos. Environ. 59: 559–566. process of abrupt rise, gaseous pollutants concentration Chan, C.K. and Yao, X. (2008). Air pollution in mega rise in step with PM2.5 concentration, determined by the cities in China. Atmos. Environ. 42: 1–42. combined effects of physical and chemical process that China (2012). Ambient Air Quality Standards (in Chinese). lead to the extremely severe pollution. The mechanism GB 3095–2012. and the process of accumulative rise, and the subsequent Ding, Y. and Liu, Y. (2014). Analysis of long-term concentrations are very different from the abrupt rise. variations of fog and haze in China in recent 50 years 2. The analysis results indicate that under the stable and their relations with atmospheric humidity. Sci. atmospheric environment, the humidity levels are China Earth Sci. 57: 36–46. significant to low visibility, but have little effect on the Du, J., Cheng, T., Zhang, M., Chen, J., He, Q., Wang, X., rise in PM concentration. When RH is in 35%–85%, Zhang, R., Tao, J., Huang, G. and Li, X. (2012). Aerosol the growth rate of PM2.5 concentration was only 0.67– size spectra and particle formation events at urban 1.13 µg m–3 h–1. Extremely high or low RH could not Shanghai in Eastern china. Aerosol Air Qual. Res. 12: have an effect on the rise in PM concentration. 1362–1372. 3. Under the stable atmospheric environment, the fluctuation Environmental Protection Agency (EPA) (2014). Air of air humidity would have a strong correlation with Quality Index: A Guide to Air Quality and Your Health. the process of accumulative rise and abrupt rise in PM U.S. EPA Report EPA-456/F-14-002. concentration. The strong fluctuation in RH is an Friedlander, S.K. and Marlow, W.H. (1977). Smoke, dust A

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