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Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing
Atmospheric Environment 81 (2013) 158e165
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing Jizhi Wang a, Yaqiang Wang a, Hua Liu b, *, Yuanqin Yang a, Xiaoye Zhang a, d, Yi Li a, Yangmei Zhang a, Guo Deng c a
Atmospheric Composition Observing & Service Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China China Meteorological Administration Training Centre, Beijing 100081, China National Meteorological Centre, Beijing 100081, China d National Programme 973 of Chinese Atmospheric Aerosol and Its Climatic Effect, China b c
h i g h l i g h t s Correlation between b and PM2.5. Sensibility of condensing function to PM2.5 concentration. Condensation is good for accumulation of PM2.5 concentration. Restricting impact of meteorological condition parameter on PM2.5. Analysis on AQ identifying index under meteorological condition.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 May 2013 Received in revised form 10 August 2013 Accepted 19 August 2013
Using daily PM2.5 concentration data from Beijing, surface observations and upper-air sounding data from regional weather stations in Beijing and North China from 2007 to 2008, 5-min AWS (automatic weather station) observations and hourly AMS (aerosol mass spectrum) data from July 2008, we analysed sensitive meteorological parameters and conditions that affect the concentration of PM2.5. We also diagnosed and identified the impact of meteorological conditions on air quality (AQ). The results show that the condensation function fc is a sensitive and significant parameter for PM2.5 concentration, favourable for generation of secondary aerosol particles. Statistical analysis of a large sample of PM2.5 and meteorological observation data indicates that adaptive weight parameter b is of great value in diagnosing changes in PM2.5 concentrations. When Beijing and North China experience dry, cold winters with a low fc, the parameter b will be large, creating conditions that are conducive to suspended fine particles. In moist, hot summers, the high temperature and humidity increase the fc, but b plays a much weaker role than in winter. b and fc influence and restrict each other, and their impacts on the changes in PM2.5 concentrations are consistent with the observed seasonal changes in meteorological elements and PM2.5 concentrations. In addition, a good correlation exists between the 24-h forecast of the I index and the PM2.5 observations in Beijing, which will prove useful in diagnosing, identifying and predicting the influence of meteorological conditions on AQ based on PM2.5 concentrations. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 Diagnosis Condensation function Adaptive weight parameter Meteorological condition
1. Introduction Atmospheric environment and air quality (AQ) parameters that are closely related to human health and living conditions have
* Corresponding author. No. 46 Zhongguancun South Street, Haidian Dist., Beijing 100081, China. Tel.: þ86 10 68409221; fax: þ86 10 68409225. E-mail addresses: [email protected] (J. Wang), [email protected] (H. Liu), [email protected] (X. Zhang). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.08.033
become popular scientific issues in recent years. With the increase of PM2.5 pollution in the atmosphere, the concentration of inhalable fine particles (PM2.5) has increased correspondingly, increasing the rates of disease (Li et al., 2010; Shi et al., 2004; Liu et al., 2012; Friedman et al., 2001). Scientific studies that focus on AQ have investigated many parameters, but the impact of meteorological conditions on regional air quality and its quantification are still an important topic for study. Studying and recognising atmospheric conditions that contribute to changes in the concentrations of air pollutants,
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including the impact of weather conditions on the accumulation or dilution of harmful pollutants in the atmosphere and their quantitative identification in urban CBDs (central business districts), is an increasingly important and applicable scientific pursuit (Neil et al., 2013; Koren et al., 2005). Recent studies have made progress in this field, especially concerning the changes in the concentration of PM2.5 atmospheric aerosol fine particles (Lu and Guo, 2012; Johnson and Donald, 1997; Zhang et al., 2005; Anders and Ekman, 2010). The static-stable air environment can cause aerosol concentrations to increase drastically at low levels in summer (Zhang et al., 2009). In winter season, the local accumulation of aerosols has a significant influence on the occurrence of continuous heavy snow, fog and haze (Dominé and Shepson, 2002; Wang et al., 2011). The atmospheric condensing process and the concentration of CCN (cloud condensation nuclei) are likely to be important signals connecting aerosols to atmospheric circulation and meteorological conditions (Polissar et al., 2001). How to analyse the interactions between multiple meteorological elements, sensitive meteorological factors and large-scale weather conditions? How to investigate the trigger conditions for micro-scale atmospheric condensation and evaporation effects under changing aerosol concentrations to improve our ability of diagnosing and predicting AQ parameters based on PM2.5? To solve these questions, one of the important scientific issues is to quantify the effects of meteorological conditions on the variation in the intensity of pollutant emissions (Gong et al., 2003). Research to date has resulted in the development of three dimensional, numerical, chemical models for quantitative global AQ prediction that have progressed to different degrees (McKeen, 2007; Moran, 2009). However, the chemical prediction model has encountered a problem; the forecasting capability of the model is limited because the emission data always lags behind the requirements for real-time quantitative identification. Research about AQ parameterisation and identification has progressed in recent years using analyses of observational data for atmospheric aerosol particles and the physical features of sensitive meteorological parameters. During the 2008 Beijing Olympic Games, for example, a parameterised identification method that linked AQ to weather conditions was used to anticipate the AQ in Beijing, obtaining reasonable results (Zhang et al., 2009). Rigby and Toumi (2008) used significant sensitive meteorological parameters to develop an identification method for an ANN (artificial neural network) PM concentration that was based on atmospheric diffusion features. Kassomenos et al. (2008) developed an AQ risk prediction model based on the effect of meteorological conditions. In addition, the application of AQ parameter identification based on weather conditions has been under development. Li et al. (2010) analysed the relationships between changes in ozone and PM concentrations and AQ meteorological condition indices. In studies that identify the impact of meteorological conditions, the process by which pollutant concentrations influence PM2.5 concentration is quite complex. Due to the mutual restrictions and influences of multiple meteorological variables, the contribution of meteorological conditions to PM2.5 pollution can result in significantly different results. The relationship among multiple meteorological variables and the representativeness and sensitivity of the response analysis on pollutant accumulation or elimination also requires further study. Based on these latest studies (Metwally et al., 2010; Wang et al., 2010) that have investigated the direct and feedback influences of meteorological conditions on aerosol PM2.5 concentrations, we are to focus on discussing sensitive meteorological parameters and emission factors as well as their interactions and effects in this study. Besides, we will discuss how to increase the ability to
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synthetically recognise the parameterised diagnosis and identification, providing meaningful references for research about the diagnosis, analysis and identification of PM2.5 concentration changes in large cities under the influence of meteorological conditions. 2. Observational data and parameterised identification 2.1. Observational data We used daily observation data for PM2.5 concentrations in Beijing during 2007e2008 as well as dense surface observations and sounding data from meteorological stations in Beijing and the North China region to analyse characteristics of the PM2.5 observations. We studied the sensitive meteorological elements and variables to determine the effect of meteorological conditions on changes in PM2.5 aerosol fine particles. Synchronous observations were also used, which included 5-min, high resolution meteorological data (pressure, temperature, humidity, wind, etc.) from the AWS (AUTO-weather station) at the Beijing Meteorological Observatory from 1 to 31 July 2008 and hourly concentration data for sulphate, nitrate ammonium salt and organic matter from the AMS (aerosol mass spectrum) from 20 to 31 July. Based on the real-time observational features of the AMS data as well as tests for the quality and representativeness of the data (Li, 2010; Zhang et al., 2011), synthetic diagnoses and analyses of the real observations were conducted. PM2.5 observations: Tapered element oscillating microbalances (TEOM, model 1400a, Rupprecht and Patashnick; Thermo Electron, East Greenbush, NY) operated at a controlled flow rate of 4 L min1 were used to continuously record the PM10 and PM2.5 mass concentrations, averaged over 5 min periods. All of the data were subjected to a quality control analysis, which was used to evaluate the percentages on the basis of annual average deviations. An analysis of variance and a t-test were used to determine the daily air pollution, meteorological factors and related index errors for passing credibility. The significance level was defined as P < 0.05 (Li et al., 2010). The diameters of the air pollution particles each day were less than 2.5 mm (PM2.5), and the data were collected from observation stations near the Third Ring Road in urban areas (39.8 N, 116.5 E) of Beijing. Using these observations, we calculated the correlation between the sensitive parameter meteorological conditions and the PM2.5 observational features, calculated the atmospheric condensation function, fc, and moist equivalent potential temperature, qe, obtained the adaptive weighting parameter, b, that influences the meteorological conditions of PM2.5, and established the forecasting model for the meteorological condition influence index for air quality based on PM2.5. 2.2. Identifying an influence index for the meteorological condition on AQ based on PM2.5 The parameterised identification research about the impact of meteorological conditions on AQ has attracted the attention of scientists worldwide. Studies by Li et al. (2010) and Zhang et al. (2009) suggest that meteorological conditions, such as pressure, temperature, humidity, condensation, etc., and emission factors are influential factors that affect changes in PM10 concentrations. Compared to the formation process of PM10, the formation conditions for fine particles, PM2.5, are affected by meteorological conditions more significantly, although the processes and mechanisms are quite different. To explore the diagnostic characteristics of the effect of meteorological conditions on aerosol PM2.5, sensitive meteorological parameters and atmospheric physical processes under meteorological conditions as well as the feedback influence
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of pollutant emissions should be taken into account. Observations have indicated that when PM2.5 is high and AQ is poor (such as before and after fog and haze), the concentration of sulphur dioxide and nitrogen dioxide in the atmosphere has a peak value, indicating that atmospheric condensation is the key element for diagnosing and identifying the air quality related to fog and haze in cities (Zhang et al., 2005). Based on the latest research results, which focused on the direct and feedback effects of related weather conditions on aerosol fine particle PM2.5 concentration, we analysed the PM2.5 features in Beijing, discussing the diagnostic identification of the influence of meteorological conditions on the variability of Beijing’s PM2.5 concentrations. Accordingly, the identification parameter (I) can be classified into two stages. Initially, they are independent, but they gradually affect each other over a limited period, resulting in a joint function of their contribution factors, i.e., the interactions between the initial meteorological conditions a(m) related to atmospheric pressure, temperature, humidity, condensation, etc. and the contribution of the pollutant emission factor b(Q) in the atmosphere. Therefore, the identification parameter can be expressed by Equation (1):
I ¼ aðmÞ bðQ Þ
(1)
When b(Q) of the pollution particles that originate in nature or from anthropogenic emissions meets the environmental atmosphere a(m) in the beginning stage, they are independent of each other, but, after a period of physical and chemical activities, they interact and become coagulated and mixed together. The emitted pollutants, including the precursor gas emissions, turn into atmospheric aerosols through the processes of hydration, condensation, solidification, etc. in the atmosphere. Atmospheric conditions and the processes of humidification, condensation, cloud formation and precipitation, etc. cause the pollutant concentration to increase or decrease, and PM2.5 particles diffuse and settle, leaving the atmospheric layer. Over a limited period of time, particulate matter develops from a new generation, matures, and enters senescence. This limited period represents the lifetime of pollution particles in the atmosphere. During this sustaining and interacting period with meteorological conditions (temperature, pressure, humidity, wind, etc.), b(Q) and a(m) have complex physical and chemical actions and interactions. The important processes that influence human health and climate all occur during this period. Therefore, we analysed the PM2.5 observational data from Beijing, studied the possible mechanisms for interactions between b(Q) and a(m), and presented these diagnoses and identification characteristics based on the daily PM2.5 concentration data from Beijing during 2007e2008, the 5-min, high resolution AWS data, the hourly resolution AMS data, and other related information.
Table 1 Source strength characteristics, lifetime, AOD etc. of the main aerosol types (Textor et al., 2006; Mian et al., 2009). Aerosol type
Mean value (change range) Sulphate 190 (100e230) Black charcoal 11 (8e20) Organic 100 (50e140) microparticles Sand dust 1600 (700e4000) Sea salt 6000 (2000e120,000) Total
3.1. Initial meteorological condition parameter a(m) Johnson and Donald (1997) noted that atmospheric processes under neutral, stable conditions can be explained with the moist adiabatic process, i.e., with the normal ‘static-stable’ meteorological conditions in which the stable, neutral air facilitates the mixing process in the atmosphere. Dominated by ‘static-stable’ atmospheric systems, the dry and moist adiabatic processes are the major processes in the atmosphere, and the moist equivalent temperature, qe, is an important conserved physical quantity. Therefore, focussing on the analysis of meteorological conditions
AOD (550 nm)
Mean value Mean value (change range) (change range) 4.1 (2.6e5.4) 6.5 (5.3e15) 6.2 (4.3e11)
0.034 (0.0015e0.051) 0.004 (0.002e0.009) 0.019 (0.006e0.030)
4.0 (1.3e7) 0.032 (0.012e0.054) 0.4(0.03e1.1) 0.030 (0.020e0.067) 0.13 (0.065e0.15)
Note: Tg (teragram) ¼ 1012 g.
on physical processes and tracking the individual changes in the moist equivalent temperature, qe, the possible relationships between meteorological factors and aerosol concentration changes under moist adiabatic conditions can be observed. The meteorological condition’s initial parameter, a(m), can be described by the individual change, dqe/dt, in the moist equivalent potential temperature, qe. During the atmospheric moist adiabatic process, condensation and the moist equivalent temperature, qe, are both important physical quantities that can be tracked and computed. The condensation latent heat of adiabatic changes is entirely used to warm the atmosphere (Gao and Zhou, 2005):
cp T=qe ðdqe =dtÞ ¼ fc;
(2)
a(m) is described by the individual change, dqe/dt, in the moist equivalent temperature, qe, which is defined by Equation (3):
aðmÞ ¼ dqe =dt ¼ qe
fc Cp T
(3)
where fc represents the condensation function, qe is the moist equivalent temperature, T refers to temperature and Cp is the specific heat at a constant pressure. a(m) reflects the impact of meteorological conditions on PM concentrations, calculated as the dimensionless number. The related variables are expressed separately in the following equations:
" fc ¼ fcd =
L vqs 1þ Cp vT
# (4) p
where Cp is the heat capacity of air, L is the latent heat for condensation or evaporation of water vapour and qs is the specific humidity. fcd is the dry condensation function defined as:
fcd ¼ 3. Parameterisation of the initial meteorological conditions and emission influence
Source strengths total Lifetime (day) (Tg y1)
vqs vqs þ gp vP T vT p
(5)
where the dry adiabatic lapse rate is computed by:
gp ¼
Rd T Cp P
(6)
where Rd is the gas constant. 3.2. Contribution factor for pollution aerosol emission: b(Q) Many elements affect the aerosol microparticle PM2.5. Different meteorological conditions provide different environments for the aerosol fine particles that have blown into the air to develop,
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sustain, and drift in the atmosphere. Some environments are favourable for particles to sustain, while others provide unfavourable conditions that cause particles to quickly diffuse or decrease. Different types of aerosols have different characteristics in the atmosphere. Textor et al. (2006) stated that to compare assessments of various types of aerosol constituent impacts under the influence of atmospheric conditions, it is reasonable to adopt one relative diagnostic parameter method. They presented the basic statistical features for such a method, including the total mass of the main components of the aerosols in the global atmosphere, the suspended lifetime of aerosols in air, AOD550 (aerosol optical depth), etc., and also comprehensively compared and analysed the results (Table 1). In order to quantify the impact of weather conditions on air pollution emissions and eliminate the effect of total aerosol concentration change, a ratio of the initial weather conditions to the observed PM concentrations is introduced as the relative value comparison of emissions and meteorological condition parameter. m is used to express the pollution emitting contribution, expressed by the following equation:
m ¼ aðmÞ=Q 100 ½%; :
(7)
Q is a unitless value, expressing the value in units of volume and mass of PM2.5 (m g m3), and m is a unitless parameter, expressed by percentage. m means the influence of emission parameters, representing the relative value comparison of emissions and meteorological condition. The initial contribution of meteorological conditions, a(m), is divided by Q, the observed value of the unit PM2.5 concentration, and then multiplied by 100% to convert it into a percentage (%). If m 1, the meteorological condition is favourable for the dilution of pollutants, but if m < 1, the dilution capability of the atmosphere is poor (Tang et al., 2006). There exists a sharp seasonal variation in meteorological parameters. For example, the average temperature in Beijing can range from 28 C in summer to 8 C in winter, and relative humidity also varies a lot from summer to winter. The degree of the variation for a(m) with seasons is larger than that for PM10 observations. In order to derive a parameter applicable for a wider range of conditions (Yang et al., 2011), an adaptive function dividing the meteorological influence scales into three categories and expressing the adaptive contribution of meteorological conditions (favourable or unfavourable), b, is introduced:
b¼
ð1 mÞi1
m
(8)
where several cases exist, depending on the size of the relative parameter value in this spectrum: if m 1, i ¼ 1: Favourable meteorological conditions for pollutants either to be diffused or to be maintained; if 0.5 m < 1, i ¼ 2: More adjustable weather conditions for pollutants to be accumulated; if m < 0.5, i ¼ 3: The weather conditions for pollutants to be accumulated.
b is an adaptive weight contribution parameter, which expresses the sustainability of pollution emitting that favours or doesn’t favour suspended particles under the meteorological conditions. Table 1 provides the distribution of the source strength characteristics, lifetime and AOD of the main types of aerosols. Comparing different types of aerosols around the world, the sulphate component has a longer lifetime, intensity and influence among the fine particles in the atmosphere with respect to its
Fig. 1. Correlation of PM2.5 and b in 2007e2008.
intensity, lifetime and the integrated influence of AOD. Some studies have noted that in Beijing and its surrounding areas, sulphate is also the primary pollutant that accounts for a higher proportion of the PM2.5 (Zhang et al., 2009; Zhang et al., 2011), in accordance with the lifetime (4e5 days) of the air masses that are ‘calm and stable’ and less altered in North China. This result is essentially consistent with the indices displayed in Table 1, which indicate that the lifetime of sulphate in air is 4e5 days. 4. Results and discussion 4.1. Analysis of the correlation between b and PM2.5 To test the contribution of the adaptive weight parameter, b, under the impact of meteorological conditions, we adopted a large number of daily PM2.5 observation values (731 data points) during the 24 months from January 2007 to December 2008 and obtained the b values from the daily meteorological elements, calculating the correlations between PM2.5 and b (Fig. 1). Fig. 1 indicates that: 1) Significant correlations exist between b and PM2.5. The correlation coefficients for the linear (R2) and exponential (e) correlations are 0.2 and 0.1116, respectively, which both exceed the 0.001 significance level. 2) The numerical test and statistical analysis from Table 1 indicate that the lifetime of various aerosols in air is 0.03e15 days. The sulphate lifetime is 2.6e5.4 days, and the average is 4.1 days. The calculated results from Fig. 1 and the observations indicate that under the influence of meteorological conditions, the main distribution of the adaptive weight parameter, b, is approximately 0.5e20. Surprisingly, this value conforms numerically with the lifetime of various PMs in the atmosphere that were presented in Table 1, which suggests that b can describe the characteristics of PM that confer an adaptive survival ability in the air under the influence of meteorological conditions. 3) Based on the large-number sample test and related analyses, the dependency relationship between the aerosol fine particles (PM2.5 concentration) and the adaptive weight parameter (b) under the influence of weather conditions fits an e-exponential correlation (red line in the figure):
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the b value is useful for diagnosing changes in the meteorological conditions, e.g., for analysing the sustainable period of ‘static-stable’ weather and identifying local changes in aerosol concentrations (e.g., the aggregate level of intensification or weakening). 4.2. Sensitivity of condensing features to changes in the PM2.5 concentration We calculated the correlations between the condensation function, fc, and RH using the 366 data groups of daily 00:00Z (8:00 am BT, Beijing Meteorological Observatory) observations from January to December 2008. Fig. 2 suggests the following conclusions:
Fig. 2. Correlation of fc and RH in Jan.eDec., 2008.
pðPM2:5 Þ ¼ aekb
(9)
where a and k are constants that vary with the region. 4) The change in PM2.5 concentration and b value satisfies the distribution rules for the e exponential analysis. According to the related analysis for the real data in Fig. 1, when b < 0.5, PM2.5 concentrations are generally distributed from 20 to 60 mg m3, i.e., less than 70 mg m3; when the b value is between 0.5 and 10, the PM2.5 concentration ranges from 60 to 280 mg m3. The probability of b > 10 is low, smaller than 11%, although it still exists. The PM2.5 concentration may occasionally reach 250e 300 mg m3 or more. The complexity of the influence of meteorological conditions can be conveyed from the statistical distribution of the large number of observed data points. Therefore,
1) Under the various changes in weather conditions over the four seasons of spring, summer, autumn and winter, the condensation function and RH have a nonlinear, increasing relationship, and the correlation coefficient (R2) reaches 0.6091, with the significance level exceeding 0.001. 2) The condensation rate is obviously related to RH non-linearly. When RH < 60%, the condensation function increases slowly along with the increase in RH, varying between 0.1 and 0.4. The condensation process is slow, and the condensation humidity ratio (fc/RH) is 0.66. 3) When RH > 60%, RH changes within the range of 60e80%, and the condensation function increases rapidly from 0.4 to 1.4 g kg1 hPa 102. The condensation humidity ratio (fc/RH) reaches 5.0, with a condensation contribution that is 7.6 times greater. This result indicates that during the formation of the secondary aerosol particles in the atmosphere, the observed phenomenon of a ‘critical humidity’ that has a high frequency is actually the faster increment of the condensation function. Therefore, fc is more sensitive and meaningful for recognition and diagnosis, especially for the identification of the related features of the variation in fine particle aerosol concentrations. 4.3. Contribution of condensation to the cumulative process for PM2.5 concentration The variation in atmospheric concentration is significantly affected by meteorological conditions. As mentioned above, Ackerman et al. (2004) highlighted the formation of secondary aerosol particles in the atmosphere as one of the main processes
Fig. 3. a. Hourly distribution of PM2.5 and SO4 2 in 25e28 July, 2008. b. Analysis of PM2.5 and SO4 2 correlation in 20e31 July, 2008.
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Fig. 4. a. Correlations between b value and PM2.5 in winter (blue dots) and summer (red dots) during 2007e2008. b. Correlations between fc and PM2.5 concentration in winter (blue dots) and summer (red dots) during 2007e2008. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
that cause PM to increase, and this process relies strongly on RH. A RH of 80% is the deliquescence relative humidity (DRH) for SO4 2 to form and fine particles (PM2.5) to increase (Brendan et al., 2008), providing beneficial conditions for the accelerating formation of secondary aerosol particles. However, the incremental change in aerosol concentrations and the contribution of atmospheric condensation processes needs further study. Fig. 3a illustrates the temporal distribution of PM2.5 and SO4 2 and was drawn using the hourly data from the Beijing Aerosol Mass Spectrum (AMS) from 25 to 28 July 2007. During 24e27 July, the observed SO4 2 concentration in Beijing increased greatly but then rapidly decreased near 0 by the early morning of the 28th. During the increasing period for the SO4 2 concentration, the increase in the accumulating process for PM2.5 occurred simultaneously. For the 5 days from 24 to 28 July, the observed haze and fog occurred continuously in Beijing. Furthermore, the analysis of the Beijing AMS data further proved that during the 5-d when the SO4 2 concentration increased, the accumulating process for the PM2.5 concentration was increasing as well. This finding is concordant with the result presented in Section 3.1; the adaptive weight parameter, b, has a lifetime of approximately 4e5 days under the influence of meteorological conditions. It also coincides with the frequency of westerlies dominating Beijing. Fig. 3b displays the analyses of the correlation between PM2.5 and SO4 2 , according to the hourly AMS data from 20 to 31 July 2008. The variations in the PM2.5 and SO4 2 concentrations are highly correlated, with the correlation coefficient (R2) reaching 0.86 and the significance level above 0.001. Analysing Fig. 3a and b comprehensively, it is apparent that the concentrations of PM2.5 and SO4 2 increase synchronously, creating new aerosol fine particles (Zhang et al., 2011). This observation reveals that favourable meteorological conditions, i.e., the combined action of atmospheric condensation, microphysical processes and atmospheric thermodynamicedynamic processes, cause the secondary accumulation and local increase in PM2.5 pollutant concentrations.
4.4. The restricting and adjusting influences of the meteorological condition parameter on changes in PM2.5 concentrations There are many elements that impact aerosol fine particles (PM2.5). After pollutants become airborne, meteorological conditions provide the necessary environment for new aerosol particles to form, maintain and float in the air. To diagnose the manner in which weather conditions affect the variation in PM2.5 concentrations, we accounted for the characteristics of the interaction between the emission parameter b(Q), and the meteorological condition a(m). Using the observations from weather element fields and the PM2.5 data from 2007 to 2008, we calculated the correlation between PM2.5 and the condensation function fc, which is the major parametric variable for the adaptive weight parameter b, and meteorological field a(m). Furthermore, we analysed the joint impact of b and fc on PM2.5 as well as their restriction of and adjustment to PM2.5. Further analyses were completed on the seasonal differences between winter and summer in Beijing and North China. Winter here refers to November, December, January and February, when the relative humidity and temperature are quite low, and summer refers to July and August, the two months in which it rains abundantly, with the highest temperatures of the whole year. Fig. 4a reveals the correlation analysis for the b value and PM2.5 in the winter and the rainy summer. From the figure, we draw the following conclusions: 1) Under the influence of meteorological conditions during Beijing’s winter, PM2.5 and the adaptive weight parameter, b, meet the distribution rules for the e exponential function. The major distribution of the b value is approximately 5e15 (see the blue dotted line in the figure), and occasionally a small chance greater than 15 occurs as well. 2) In the summer in Beijing and North China, atmospheric condensation and precipitation occur frequently. Influenced by the emission of air pollutants, the contribution of weather
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conditions to the b value of the aerosols decreases below than the mean value of 4, ranging from 0.1 to 2. However, PM2.5 increases rapidly with the increase of b value, which is depicted in the form of the e exponential function. This result is related to the condensation conditions of the ‘sauna’ weather in the hot, humid mid-summer, causing the local aerosols to increase swiftly and accumulate in large quantities. This finding is in agreement with the observed data. In Fig. 4a, a cluster of red points below the fitting line is seen, which shows that during the summer rainy season in Beijing and North China, frequent rainfall makes pollution concentrations rapidly decline. In such cases, the calculated b value is relatively higher while the observed values of PM2.5 are lower. As in Section 4.2 and Fig. 2, the contribution of the condensation function, which is one of the important parameters for meteorological conditions, is significant. However, condensation performs quite differently in the dry and rainy seasons. Using Eq. (4), we calculated the correlation between the condensation function, fc, and the PM2.5 concentration in the winter and summer of 2007e 2008. Fig. 4b displays the correlation analysis for the fc and PM2.5 concentrations in the winter and summer of 2007e2008. According to the real-time observation analysis, displayed in Fig. 4b, that the following statements can be made: 1) Significant correlations exist between PM2.5 concentrations and the variation in the atmospheric condensation function in both summer and winter. The correlation coefficients are R2 ¼ 0.1895 and R2 ¼ 0.1395, respectively, both exceeding the 0.001 significance level. With the increase in the condensation function, the PM2.5 concentration increases in the form of an e exponential function. 2) In winter, the air in Beijing is dry and cold with low relative humidity. The condensation function varies between 0.0 and 0.5 (g kg1 hPa 102). Due to the variation in the condensation function, the slope of the e exponential curve is steep, and the PM2.5 concentration grows very quickly, increasing quickly from <50 to 250 mg m3 or more. 3) In summer, the situation in a typical rainy season is completely different. The basic weather conditions during this period are high humidity, high temperature and high condensation. The condensation function ranges between 0.8 and 1.25 (g kg1 hPa 102), reflecting the characteristics of high humidity and high condensation in summer. With the increase in fc, the PM2.5 concentration also increases in the form of an e exponential function, but more slowly than in winter time. This phenomenon reveals the complexity of the impact of seasonal weather conditions on the variation in the PM2.5 concentration. Integrating Fig. 4a and b, one meaningful result is noted. The winter in Beijing and North China is the typical dry and cold season. Therefore, condensation plays a weak role, but the adaptive weight, b, which is good for the survival of suspended particles, has a remarkable effect. Conversely, July and August constitute the representative rainy season in Beijing and North China. The summers’ high temperature and humidity makes the condensation very effective, but, influenced by frequent rainfalls and convection events, etc., the effect of b is obviously weaker than that in winter. Therefore, we conclude that the adaptive weight parameter, b, and the condensation function, fc, have significant restriction and adjustment effects on each other under the influence of changes in the PM2.5 concentration. This type of restriction and adjustment is consistent with the seasonal difference, atmospheric temperature and humidity difference and other meteorological conditions as
Fig. 5. Correlation between I index 24-h forecast and PM2.5 during 2007e2008.
well as the variation in the meteorological parameters and the observed PM2.5 concentration changes. 4.5. Related analysis of an AQ identifying index under meteorological conditions, based on PM2.5 The main objective of this study is to discuss how changes in meteorological conditions impact PM2.5 concentration, conducive to pollution dispersion or accumulation of pollution concentration. This is what the public and researchers concerned about. So, even if the pollutant concentration data (PM2.5) is available, for I index, diagnosis and prognosis is still in need very much. The 24-h forecast of the diagnostic identifying parameter I is given as an influence index for AQ, based on PM2.5 concentrations. A correlation analysis between this index and the PM2.5 concentrations was conducted. Fig. 5 displays the correlation analysis between the 24-h forecast of I over the 731 days from 1 January 2007 to the 31 December 2008 and the PM2.5 observations (indicated by the solid dotted line) in Beijing. The horizontal ordinate represents I using a 24-h forecast, while the vertical ordinate refers to PM2.5, which has units of mg m3. The I index 24-h forecast is well correlated with PM2.5 in Beijing, and the correlation coefficient (R2) is 0.4832, exceeding the 0.001 significance level. This result indicates that the AQ meteorological condition index, I, 24-h prediction correlates well with observed PM2.5 concentrations in Beijing. 5. Conclusions Based on the analyses of sensitive meteorological parameters that influence the variation in PM2.5 concentrations, we diagnosed and identified the impact of meteorological conditions on changes in the PM2.5 concentration in Beijing. The study results indicated the following conclusions: 1) The condensation function, fc, of fine particle pollutants in the atmosphere increases rapidly in the form of an e exponential
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function when there are suitable weather conditions and reaches the deliquescence relative humidity (DRH), which is generated with a high frequency, resulting in a remarkable increase in the PM2.5 concentration. Therefore, compared to RH, the fc parameter is more sensitive and meaningful for diagnosis. However, fc is the more sensitive factor for indicating a fast accumulation of PM2.5. Significant discrepancies were determined between the typical dry, cold winter season and the humid, hot, rainy summer season in Beijing. 2) Meteorological conditions drastically impact the variation in atmospheric aerosol concentrations. The interaction between the polluting emission b(Q) and the initial meteorological condition a(m) factor affects the variability of the PM2.5 concentration. The adaptive weight parameter, b, for the conditions (favourable or unfavourable for the formation and longevity of suspended particles) is presented. The changes in the PM2.5 concentrations vary with b and fit the distribution of an e exponential function, suggesting that b is a very meaningful parameter for PM2.5 fine pollutant particles to adapt, grow and exist in air under the influence of weather conditions. 3) In the dry, cold winter in Beijing and North China, the contribution of condensation is weak but the adaptive weight parameter, b, that is good for the survival of suspended fine particles contributes significantly. By comparison, in July and August, the typical rainy season in Beijing and North China, the high temperature, humidity and condensation are highly noticeable, but the b value is clearly less effective than in the winter. The adaptive weight parameter, b, and condensation function, fc, have noticeable restricting and adjusting effects due to the influence of changes in the PM2.5 concentration, and these restrictions and adjustments reflect the seasonal differences in the atmospheric temperature and humidity and the variation in the meteorological parameter and the observed PM2.5 concentration changes. 4) I index 24-h prediction was calculated using daily observation data for 731 days in 2007e2008. There is a significant correlation between the I index 24-h forecast and the PM2.5 observations in Beijing. This result is meaningful for analysing, diagnosing and forecasting the impact of meteorological conditions on AQ based on PM2.5. Acknowledgements This study is supported jointly by the National Key Basic Research Development Program of China under Grant No. 2011CB403401 and No. 2011CB403404 as well as the National Natural Science Foundation of China under Grant No. 41275167 and No. 41075079. In addition, authors sincerely appreciate the editors, reviewers for their instructions and suggestions to this paper. References Ackerman, A.S., Kirkpatrick, M.P., Stevens, D.E., Toon, O.B., 2004. The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature 432, 1014e1017. http://dx.doi.org/10.1038/nature03174. Anders, E., Ekman, Annica M.L., 2010. Impact of meteorological factors on the correlation between aerosol optical depth and cloud fraction. Geophys. Res. Lett. 37 (18). http://dx.doi.org/10.1029/2010GL044361. Brendan, M.M., Ann, M.M., Timothy, B.O., 2008. Collection efficiencies in an aerodyne aerosol mass spectrometer as a function of particle phase for laboratory generated aerosols. Aerosol Sci. Technol. 42 (11), 884e898. Dominé, F., Shepson, P.B., 2002. Airesnow interactions and atmospheric chemistry. Science 297, 1506e1510. Friedman, M.S., Powell, K.E., Hutwagner, L., Graham LeRoy, M., Teague, G.W., 2001. Impact of changes in transportation and commuting behaviors during the 1996 Summer Olympic Games in Atlanta on air quality and childhood Asthma. J. Am. Medical Assoc. 285 (7), 897e905.
165
Gao, S.T., Zhou, Y.S., 2005. Analyses on warm and humid weather in summer in Beijing and its dynamical identification. Sci. China 48 (Suppl. II), 128e137. Gong, S.L., Barrie, A., Blanchet, J.P., Salzen, K. n, Lohmann, U., Lesins, G., Spacek, L., Zhang, L.M., Girard, E., Lin, H., Leaitch, R., Leighton, H., Chylek, P., Huang, P., 2003. Canadian aerosol module: a size-segregated simulation of atmospheric aerosol processes for climate and air quality models 1. Module development. J. Geophys. Res. 108, 4007. Johnson, A.B., Donald, G.B., 1997. Climatology of diffusion potential classes for MinneapoliseSt Paul. J. Appl. Meteorol. 36 (12), 1620e1628. Kassomenos, P., Papaloukas, C., Petrakis, M., Karakitsios, S., 2008. Assessment and prediction of short term hospital admissions: the case of Athens, Greece. Atmos. Environ. 42 (30), 7078e7086. Koren, I., Kaufman, Y.J., Rosenfeld, D., Remer, L.A., Rudich, Y., 2005. Aerosol invigoration and restructuring of Atlantic convective clouds. Geophys. Res. Lett. 32, LI4828. http://dx.doi.org/10.1029/2005GL023187. Li, Y., 2010. Chemical characteristics of precipitation at three Chinese regional background stations from 2006 to 2007. Atmos. Res.. http://dx.doi.org/10.1016/ j.atmosres.2009.12.011. Li, Y., Wang, W., Wang, J.Z., Zhang, X.L., Lin, W.L., Yang, Y.Q., 2010. Impact of air pollution control measures and weather conditions on asthma during the 2008 Summer Olympic Games in Beijing. Int. J. Biometeorol.. http://dx.doi.org/ 10.1007/s00484-010-0373-6. Liu, Q., Sun, Y., Hu, B., Liu, Z., Akio, S., Wang, Y.S., 2012. In situ measurement of PM1 organic aerosol in Beijing winter using a high-resolution aerosol mass spectrometer. Chin. Sci. Bull. 57, 819e826. http://dx.doi.org/10.1007/s11434-0114886-0. Lu, G.X., Guo, X.L., 2012. Distribution and origin of aerosol and its transform relationship with CCN derived from the spring multi-aircraft measurements of Beijing Cloud Experiment (BCE). Chin. Sci. Bull. 57. http://dx.doi.org/10.1007/ s11434-012-5136-9. McKeen, S., 2007. Evaluation of several real-time PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study. J. Geophys. Res. 112, D10S20. Metwally, M.E., Alfaro, S.C., Abdel, M.M., Zakey, A.S., Chatenet, B., 2010. Seasonal and inter-annual variability of the aerosol content in Cairo (Egypt) as deduced from the comparison of MODIS aerosol retrievals with direct AERONET measurements. Atmos. Res. 97, 14. Mian, C., Ralph, A.K., Lorraine, A.R., 2009. Atmospheric Aerosol Properties and Climate Impacts, Synthesis and Assessment Product 2.3. US Climate Change Science Program and the Subcommittee on Global Change Research, 1717 Pennsylvania Avenue, NW Suite 250, Washington, D.C., p. 116 Moran, M.D., 2009. In 30th NATO/SPS ITM on Air Pollution Modeling and Its Application. (San Francisco). Neil, P.L., Erik Crosman, C., David, W., John, D.H., Sebastian, W.H., William, O.J.B., Thomas, W.H., 2013. The persistent cold-air pool study. Bull. Am. Meteorol. Soc. 94, 51e63. http://dx.doi.org/10.1175/BAMS-D-11-00255.1. Polissar, A.V., Hopke, P.K., Harris, J.M., 2001. Source regions for atmospheric aerosol measured at Barrow, Alaska. Environ. Sci. Technol. 35, 4214e4226. Rigby, M., Toumi, R., 2008. London air pollution climatology: indirect evidence for urban boundary layer height and wind speed enhancement. Atmos. Environ. 42 (20), 4932e4947. Shi, Z.B., Shao, L.Y., Jones, T.P., Whittaker, A.G., Richards, R.J., Zhang, P.F., 2004. Oxidative injury onto the plasmid DNA by urban air inhalable particulate matter. Chin. Sci. Bull. 49 (7), 673e678. Tang, X.Y., Zhang, Y.H., Shao, M., 2006. Atmospheric Environment Chemistry. Higher Education Press, Beijing, p. 739. Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S., Berntsen, T., Berglen, T., Boucher, O., Chin Dentener, F., Diehl, T., Easter, R., Feichter, H., Fillmore, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I., Iversen, I., Kloster, S., Koch, D., Kirkevåg, A., Kristjansson, J.E., Krol, M., Lauer, A., Lamarque, J.F., Liu, X., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., Tie, X., 2006. Analyses and quantification of the diversities of aerosol life cycles within AEROCOM. Atmos. Chem. Phys. 6, 1777e1813. Wang, J.Z., Yang, Y.Q., Zhang, G.Z., Yu, S.Q., 2010. Climatic trend of cloud amount related to the aerosol characteristics in Beijing during1950-2005. Acta Meteorol. Sin. 24 (6), 762e775. Wang, J.Z., Li, D., Yang, Y.Q., 2011. Study on the annual variation of winter precipitation and the characteristics of atmospheric aerosol distribution in Northern China. J. Meteorol. Environ. 27 (6), 66e71. Yang, Y.Q., Wang, J.Z., Hou, Q., Li, Y., Zhou, C.H., 2011. Discriminant Genetic Algorithm Extended (DGAE) model for seasonal sand and dust storm prediction. Sci. China (Ser. D) 54 (1), 10e18. Zhang, G.Z., Bian, L.G., Wang, J.Z., Yang, Y.Q., Yao, W.Q., Xu, X.D., 2005. The boundary layer characteristics in the heavy fog formation process over Beijing and its adjacent areas. Sci. China Ser. D 48 (Suppl. II), 80e101. Zhang, X.Y., Wang, Y.Q., Lin, W.L., Zhang, Y.M., Zhang, X.C., Gong, S., Zhao, P., Yang, Y.Q., Wang, J.Z., hou, Q., Zhang, X.L., Che, h. Z., Guo, J.P., Li, Y., 2009. Changes of atmospheric composition and optical properties over Beijing 2008 Olympic Monitoring Campaign. Bull. Am. Meteorol. Soc. 90 (11), 1633e1649. Zhang, Y.M., Zhang, X.Y., Sun, J.Y., Lin, W.L., Gong, S.L., Shen, X.J., Yang, S., 2011. Characterization of new particle and secondary aerosol formation during summertime in Beijing, China. Tellus 63B, 382e394. http://dx.doi.org/10.1111/ j.1600-0889.2011.00533.x.
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing Jizhi Wang a, Yaqiang Wang a, Hua Liu b, *, Yuanqin Yang a, Xiaoye Zhang a, d, Yi Li a, Yangmei Zhang a, Guo Deng c a
Atmospheric Composition Observing & Service Center, Chinese Academy of Meteorological Sciences, Beijing 100081, China China Meteorological Administration Training Centre, Beijing 100081, China National Meteorological Centre, Beijing 100081, China d National Programme 973 of Chinese Atmospheric Aerosol and Its Climatic Effect, China b c
h i g h l i g h t s Correlation between b and PM2.5. Sensibility of condensing function to PM2.5 concentration. Condensation is good for accumulation of PM2.5 concentration. Restricting impact of meteorological condition parameter on PM2.5. Analysis on AQ identifying index under meteorological condition.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 20 May 2013 Received in revised form 10 August 2013 Accepted 19 August 2013
Using daily PM2.5 concentration data from Beijing, surface observations and upper-air sounding data from regional weather stations in Beijing and North China from 2007 to 2008, 5-min AWS (automatic weather station) observations and hourly AMS (aerosol mass spectrum) data from July 2008, we analysed sensitive meteorological parameters and conditions that affect the concentration of PM2.5. We also diagnosed and identified the impact of meteorological conditions on air quality (AQ). The results show that the condensation function fc is a sensitive and significant parameter for PM2.5 concentration, favourable for generation of secondary aerosol particles. Statistical analysis of a large sample of PM2.5 and meteorological observation data indicates that adaptive weight parameter b is of great value in diagnosing changes in PM2.5 concentrations. When Beijing and North China experience dry, cold winters with a low fc, the parameter b will be large, creating conditions that are conducive to suspended fine particles. In moist, hot summers, the high temperature and humidity increase the fc, but b plays a much weaker role than in winter. b and fc influence and restrict each other, and their impacts on the changes in PM2.5 concentrations are consistent with the observed seasonal changes in meteorological elements and PM2.5 concentrations. In addition, a good correlation exists between the 24-h forecast of the I index and the PM2.5 observations in Beijing, which will prove useful in diagnosing, identifying and predicting the influence of meteorological conditions on AQ based on PM2.5 concentrations. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: PM2.5 Diagnosis Condensation function Adaptive weight parameter Meteorological condition
1. Introduction Atmospheric environment and air quality (AQ) parameters that are closely related to human health and living conditions have
* Corresponding author. No. 46 Zhongguancun South Street, Haidian Dist., Beijing 100081, China. Tel.: þ86 10 68409221; fax: þ86 10 68409225. E-mail addresses: [email protected] (J. Wang), [email protected] (H. Liu), [email protected] (X. Zhang). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.08.033
become popular scientific issues in recent years. With the increase of PM2.5 pollution in the atmosphere, the concentration of inhalable fine particles (PM2.5) has increased correspondingly, increasing the rates of disease (Li et al., 2010; Shi et al., 2004; Liu et al., 2012; Friedman et al., 2001). Scientific studies that focus on AQ have investigated many parameters, but the impact of meteorological conditions on regional air quality and its quantification are still an important topic for study. Studying and recognising atmospheric conditions that contribute to changes in the concentrations of air pollutants,
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including the impact of weather conditions on the accumulation or dilution of harmful pollutants in the atmosphere and their quantitative identification in urban CBDs (central business districts), is an increasingly important and applicable scientific pursuit (Neil et al., 2013; Koren et al., 2005). Recent studies have made progress in this field, especially concerning the changes in the concentration of PM2.5 atmospheric aerosol fine particles (Lu and Guo, 2012; Johnson and Donald, 1997; Zhang et al., 2005; Anders and Ekman, 2010). The static-stable air environment can cause aerosol concentrations to increase drastically at low levels in summer (Zhang et al., 2009). In winter season, the local accumulation of aerosols has a significant influence on the occurrence of continuous heavy snow, fog and haze (Dominé and Shepson, 2002; Wang et al., 2011). The atmospheric condensing process and the concentration of CCN (cloud condensation nuclei) are likely to be important signals connecting aerosols to atmospheric circulation and meteorological conditions (Polissar et al., 2001). How to analyse the interactions between multiple meteorological elements, sensitive meteorological factors and large-scale weather conditions? How to investigate the trigger conditions for micro-scale atmospheric condensation and evaporation effects under changing aerosol concentrations to improve our ability of diagnosing and predicting AQ parameters based on PM2.5? To solve these questions, one of the important scientific issues is to quantify the effects of meteorological conditions on the variation in the intensity of pollutant emissions (Gong et al., 2003). Research to date has resulted in the development of three dimensional, numerical, chemical models for quantitative global AQ prediction that have progressed to different degrees (McKeen, 2007; Moran, 2009). However, the chemical prediction model has encountered a problem; the forecasting capability of the model is limited because the emission data always lags behind the requirements for real-time quantitative identification. Research about AQ parameterisation and identification has progressed in recent years using analyses of observational data for atmospheric aerosol particles and the physical features of sensitive meteorological parameters. During the 2008 Beijing Olympic Games, for example, a parameterised identification method that linked AQ to weather conditions was used to anticipate the AQ in Beijing, obtaining reasonable results (Zhang et al., 2009). Rigby and Toumi (2008) used significant sensitive meteorological parameters to develop an identification method for an ANN (artificial neural network) PM concentration that was based on atmospheric diffusion features. Kassomenos et al. (2008) developed an AQ risk prediction model based on the effect of meteorological conditions. In addition, the application of AQ parameter identification based on weather conditions has been under development. Li et al. (2010) analysed the relationships between changes in ozone and PM concentrations and AQ meteorological condition indices. In studies that identify the impact of meteorological conditions, the process by which pollutant concentrations influence PM2.5 concentration is quite complex. Due to the mutual restrictions and influences of multiple meteorological variables, the contribution of meteorological conditions to PM2.5 pollution can result in significantly different results. The relationship among multiple meteorological variables and the representativeness and sensitivity of the response analysis on pollutant accumulation or elimination also requires further study. Based on these latest studies (Metwally et al., 2010; Wang et al., 2010) that have investigated the direct and feedback influences of meteorological conditions on aerosol PM2.5 concentrations, we are to focus on discussing sensitive meteorological parameters and emission factors as well as their interactions and effects in this study. Besides, we will discuss how to increase the ability to
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synthetically recognise the parameterised diagnosis and identification, providing meaningful references for research about the diagnosis, analysis and identification of PM2.5 concentration changes in large cities under the influence of meteorological conditions. 2. Observational data and parameterised identification 2.1. Observational data We used daily observation data for PM2.5 concentrations in Beijing during 2007e2008 as well as dense surface observations and sounding data from meteorological stations in Beijing and the North China region to analyse characteristics of the PM2.5 observations. We studied the sensitive meteorological elements and variables to determine the effect of meteorological conditions on changes in PM2.5 aerosol fine particles. Synchronous observations were also used, which included 5-min, high resolution meteorological data (pressure, temperature, humidity, wind, etc.) from the AWS (AUTO-weather station) at the Beijing Meteorological Observatory from 1 to 31 July 2008 and hourly concentration data for sulphate, nitrate ammonium salt and organic matter from the AMS (aerosol mass spectrum) from 20 to 31 July. Based on the real-time observational features of the AMS data as well as tests for the quality and representativeness of the data (Li, 2010; Zhang et al., 2011), synthetic diagnoses and analyses of the real observations were conducted. PM2.5 observations: Tapered element oscillating microbalances (TEOM, model 1400a, Rupprecht and Patashnick; Thermo Electron, East Greenbush, NY) operated at a controlled flow rate of 4 L min1 were used to continuously record the PM10 and PM2.5 mass concentrations, averaged over 5 min periods. All of the data were subjected to a quality control analysis, which was used to evaluate the percentages on the basis of annual average deviations. An analysis of variance and a t-test were used to determine the daily air pollution, meteorological factors and related index errors for passing credibility. The significance level was defined as P < 0.05 (Li et al., 2010). The diameters of the air pollution particles each day were less than 2.5 mm (PM2.5), and the data were collected from observation stations near the Third Ring Road in urban areas (39.8 N, 116.5 E) of Beijing. Using these observations, we calculated the correlation between the sensitive parameter meteorological conditions and the PM2.5 observational features, calculated the atmospheric condensation function, fc, and moist equivalent potential temperature, qe, obtained the adaptive weighting parameter, b, that influences the meteorological conditions of PM2.5, and established the forecasting model for the meteorological condition influence index for air quality based on PM2.5. 2.2. Identifying an influence index for the meteorological condition on AQ based on PM2.5 The parameterised identification research about the impact of meteorological conditions on AQ has attracted the attention of scientists worldwide. Studies by Li et al. (2010) and Zhang et al. (2009) suggest that meteorological conditions, such as pressure, temperature, humidity, condensation, etc., and emission factors are influential factors that affect changes in PM10 concentrations. Compared to the formation process of PM10, the formation conditions for fine particles, PM2.5, are affected by meteorological conditions more significantly, although the processes and mechanisms are quite different. To explore the diagnostic characteristics of the effect of meteorological conditions on aerosol PM2.5, sensitive meteorological parameters and atmospheric physical processes under meteorological conditions as well as the feedback influence
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of pollutant emissions should be taken into account. Observations have indicated that when PM2.5 is high and AQ is poor (such as before and after fog and haze), the concentration of sulphur dioxide and nitrogen dioxide in the atmosphere has a peak value, indicating that atmospheric condensation is the key element for diagnosing and identifying the air quality related to fog and haze in cities (Zhang et al., 2005). Based on the latest research results, which focused on the direct and feedback effects of related weather conditions on aerosol fine particle PM2.5 concentration, we analysed the PM2.5 features in Beijing, discussing the diagnostic identification of the influence of meteorological conditions on the variability of Beijing’s PM2.5 concentrations. Accordingly, the identification parameter (I) can be classified into two stages. Initially, they are independent, but they gradually affect each other over a limited period, resulting in a joint function of their contribution factors, i.e., the interactions between the initial meteorological conditions a(m) related to atmospheric pressure, temperature, humidity, condensation, etc. and the contribution of the pollutant emission factor b(Q) in the atmosphere. Therefore, the identification parameter can be expressed by Equation (1):
I ¼ aðmÞ bðQ Þ
(1)
When b(Q) of the pollution particles that originate in nature or from anthropogenic emissions meets the environmental atmosphere a(m) in the beginning stage, they are independent of each other, but, after a period of physical and chemical activities, they interact and become coagulated and mixed together. The emitted pollutants, including the precursor gas emissions, turn into atmospheric aerosols through the processes of hydration, condensation, solidification, etc. in the atmosphere. Atmospheric conditions and the processes of humidification, condensation, cloud formation and precipitation, etc. cause the pollutant concentration to increase or decrease, and PM2.5 particles diffuse and settle, leaving the atmospheric layer. Over a limited period of time, particulate matter develops from a new generation, matures, and enters senescence. This limited period represents the lifetime of pollution particles in the atmosphere. During this sustaining and interacting period with meteorological conditions (temperature, pressure, humidity, wind, etc.), b(Q) and a(m) have complex physical and chemical actions and interactions. The important processes that influence human health and climate all occur during this period. Therefore, we analysed the PM2.5 observational data from Beijing, studied the possible mechanisms for interactions between b(Q) and a(m), and presented these diagnoses and identification characteristics based on the daily PM2.5 concentration data from Beijing during 2007e2008, the 5-min, high resolution AWS data, the hourly resolution AMS data, and other related information.
Table 1 Source strength characteristics, lifetime, AOD etc. of the main aerosol types (Textor et al., 2006; Mian et al., 2009). Aerosol type
Mean value (change range) Sulphate 190 (100e230) Black charcoal 11 (8e20) Organic 100 (50e140) microparticles Sand dust 1600 (700e4000) Sea salt 6000 (2000e120,000) Total
3.1. Initial meteorological condition parameter a(m) Johnson and Donald (1997) noted that atmospheric processes under neutral, stable conditions can be explained with the moist adiabatic process, i.e., with the normal ‘static-stable’ meteorological conditions in which the stable, neutral air facilitates the mixing process in the atmosphere. Dominated by ‘static-stable’ atmospheric systems, the dry and moist adiabatic processes are the major processes in the atmosphere, and the moist equivalent temperature, qe, is an important conserved physical quantity. Therefore, focussing on the analysis of meteorological conditions
AOD (550 nm)
Mean value Mean value (change range) (change range) 4.1 (2.6e5.4) 6.5 (5.3e15) 6.2 (4.3e11)
0.034 (0.0015e0.051) 0.004 (0.002e0.009) 0.019 (0.006e0.030)
4.0 (1.3e7) 0.032 (0.012e0.054) 0.4(0.03e1.1) 0.030 (0.020e0.067) 0.13 (0.065e0.15)
Note: Tg (teragram) ¼ 1012 g.
on physical processes and tracking the individual changes in the moist equivalent temperature, qe, the possible relationships between meteorological factors and aerosol concentration changes under moist adiabatic conditions can be observed. The meteorological condition’s initial parameter, a(m), can be described by the individual change, dqe/dt, in the moist equivalent potential temperature, qe. During the atmospheric moist adiabatic process, condensation and the moist equivalent temperature, qe, are both important physical quantities that can be tracked and computed. The condensation latent heat of adiabatic changes is entirely used to warm the atmosphere (Gao and Zhou, 2005):
cp T=qe ðdqe =dtÞ ¼ fc;
(2)
a(m) is described by the individual change, dqe/dt, in the moist equivalent temperature, qe, which is defined by Equation (3):
aðmÞ ¼ dqe =dt ¼ qe
fc Cp T
(3)
where fc represents the condensation function, qe is the moist equivalent temperature, T refers to temperature and Cp is the specific heat at a constant pressure. a(m) reflects the impact of meteorological conditions on PM concentrations, calculated as the dimensionless number. The related variables are expressed separately in the following equations:
" fc ¼ fcd =
L vqs 1þ Cp vT
# (4) p
where Cp is the heat capacity of air, L is the latent heat for condensation or evaporation of water vapour and qs is the specific humidity. fcd is the dry condensation function defined as:
fcd ¼ 3. Parameterisation of the initial meteorological conditions and emission influence
Source strengths total Lifetime (day) (Tg y1)
vqs vqs þ gp vP T vT p
(5)
where the dry adiabatic lapse rate is computed by:
gp ¼
Rd T Cp P
(6)
where Rd is the gas constant. 3.2. Contribution factor for pollution aerosol emission: b(Q) Many elements affect the aerosol microparticle PM2.5. Different meteorological conditions provide different environments for the aerosol fine particles that have blown into the air to develop,
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sustain, and drift in the atmosphere. Some environments are favourable for particles to sustain, while others provide unfavourable conditions that cause particles to quickly diffuse or decrease. Different types of aerosols have different characteristics in the atmosphere. Textor et al. (2006) stated that to compare assessments of various types of aerosol constituent impacts under the influence of atmospheric conditions, it is reasonable to adopt one relative diagnostic parameter method. They presented the basic statistical features for such a method, including the total mass of the main components of the aerosols in the global atmosphere, the suspended lifetime of aerosols in air, AOD550 (aerosol optical depth), etc., and also comprehensively compared and analysed the results (Table 1). In order to quantify the impact of weather conditions on air pollution emissions and eliminate the effect of total aerosol concentration change, a ratio of the initial weather conditions to the observed PM concentrations is introduced as the relative value comparison of emissions and meteorological condition parameter. m is used to express the pollution emitting contribution, expressed by the following equation:
m ¼ aðmÞ=Q 100 ½%; :
(7)
Q is a unitless value, expressing the value in units of volume and mass of PM2.5 (m g m3), and m is a unitless parameter, expressed by percentage. m means the influence of emission parameters, representing the relative value comparison of emissions and meteorological condition. The initial contribution of meteorological conditions, a(m), is divided by Q, the observed value of the unit PM2.5 concentration, and then multiplied by 100% to convert it into a percentage (%). If m 1, the meteorological condition is favourable for the dilution of pollutants, but if m < 1, the dilution capability of the atmosphere is poor (Tang et al., 2006). There exists a sharp seasonal variation in meteorological parameters. For example, the average temperature in Beijing can range from 28 C in summer to 8 C in winter, and relative humidity also varies a lot from summer to winter. The degree of the variation for a(m) with seasons is larger than that for PM10 observations. In order to derive a parameter applicable for a wider range of conditions (Yang et al., 2011), an adaptive function dividing the meteorological influence scales into three categories and expressing the adaptive contribution of meteorological conditions (favourable or unfavourable), b, is introduced:
b¼
ð1 mÞi1
m
(8)
where several cases exist, depending on the size of the relative parameter value in this spectrum: if m 1, i ¼ 1: Favourable meteorological conditions for pollutants either to be diffused or to be maintained; if 0.5 m < 1, i ¼ 2: More adjustable weather conditions for pollutants to be accumulated; if m < 0.5, i ¼ 3: The weather conditions for pollutants to be accumulated.
b is an adaptive weight contribution parameter, which expresses the sustainability of pollution emitting that favours or doesn’t favour suspended particles under the meteorological conditions. Table 1 provides the distribution of the source strength characteristics, lifetime and AOD of the main types of aerosols. Comparing different types of aerosols around the world, the sulphate component has a longer lifetime, intensity and influence among the fine particles in the atmosphere with respect to its
Fig. 1. Correlation of PM2.5 and b in 2007e2008.
intensity, lifetime and the integrated influence of AOD. Some studies have noted that in Beijing and its surrounding areas, sulphate is also the primary pollutant that accounts for a higher proportion of the PM2.5 (Zhang et al., 2009; Zhang et al., 2011), in accordance with the lifetime (4e5 days) of the air masses that are ‘calm and stable’ and less altered in North China. This result is essentially consistent with the indices displayed in Table 1, which indicate that the lifetime of sulphate in air is 4e5 days. 4. Results and discussion 4.1. Analysis of the correlation between b and PM2.5 To test the contribution of the adaptive weight parameter, b, under the impact of meteorological conditions, we adopted a large number of daily PM2.5 observation values (731 data points) during the 24 months from January 2007 to December 2008 and obtained the b values from the daily meteorological elements, calculating the correlations between PM2.5 and b (Fig. 1). Fig. 1 indicates that: 1) Significant correlations exist between b and PM2.5. The correlation coefficients for the linear (R2) and exponential (e) correlations are 0.2 and 0.1116, respectively, which both exceed the 0.001 significance level. 2) The numerical test and statistical analysis from Table 1 indicate that the lifetime of various aerosols in air is 0.03e15 days. The sulphate lifetime is 2.6e5.4 days, and the average is 4.1 days. The calculated results from Fig. 1 and the observations indicate that under the influence of meteorological conditions, the main distribution of the adaptive weight parameter, b, is approximately 0.5e20. Surprisingly, this value conforms numerically with the lifetime of various PMs in the atmosphere that were presented in Table 1, which suggests that b can describe the characteristics of PM that confer an adaptive survival ability in the air under the influence of meteorological conditions. 3) Based on the large-number sample test and related analyses, the dependency relationship between the aerosol fine particles (PM2.5 concentration) and the adaptive weight parameter (b) under the influence of weather conditions fits an e-exponential correlation (red line in the figure):
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the b value is useful for diagnosing changes in the meteorological conditions, e.g., for analysing the sustainable period of ‘static-stable’ weather and identifying local changes in aerosol concentrations (e.g., the aggregate level of intensification or weakening). 4.2. Sensitivity of condensing features to changes in the PM2.5 concentration We calculated the correlations between the condensation function, fc, and RH using the 366 data groups of daily 00:00Z (8:00 am BT, Beijing Meteorological Observatory) observations from January to December 2008. Fig. 2 suggests the following conclusions:
Fig. 2. Correlation of fc and RH in Jan.eDec., 2008.
pðPM2:5 Þ ¼ aekb
(9)
where a and k are constants that vary with the region. 4) The change in PM2.5 concentration and b value satisfies the distribution rules for the e exponential analysis. According to the related analysis for the real data in Fig. 1, when b < 0.5, PM2.5 concentrations are generally distributed from 20 to 60 mg m3, i.e., less than 70 mg m3; when the b value is between 0.5 and 10, the PM2.5 concentration ranges from 60 to 280 mg m3. The probability of b > 10 is low, smaller than 11%, although it still exists. The PM2.5 concentration may occasionally reach 250e 300 mg m3 or more. The complexity of the influence of meteorological conditions can be conveyed from the statistical distribution of the large number of observed data points. Therefore,
1) Under the various changes in weather conditions over the four seasons of spring, summer, autumn and winter, the condensation function and RH have a nonlinear, increasing relationship, and the correlation coefficient (R2) reaches 0.6091, with the significance level exceeding 0.001. 2) The condensation rate is obviously related to RH non-linearly. When RH < 60%, the condensation function increases slowly along with the increase in RH, varying between 0.1 and 0.4. The condensation process is slow, and the condensation humidity ratio (fc/RH) is 0.66. 3) When RH > 60%, RH changes within the range of 60e80%, and the condensation function increases rapidly from 0.4 to 1.4 g kg1 hPa 102. The condensation humidity ratio (fc/RH) reaches 5.0, with a condensation contribution that is 7.6 times greater. This result indicates that during the formation of the secondary aerosol particles in the atmosphere, the observed phenomenon of a ‘critical humidity’ that has a high frequency is actually the faster increment of the condensation function. Therefore, fc is more sensitive and meaningful for recognition and diagnosis, especially for the identification of the related features of the variation in fine particle aerosol concentrations. 4.3. Contribution of condensation to the cumulative process for PM2.5 concentration The variation in atmospheric concentration is significantly affected by meteorological conditions. As mentioned above, Ackerman et al. (2004) highlighted the formation of secondary aerosol particles in the atmosphere as one of the main processes
Fig. 3. a. Hourly distribution of PM2.5 and SO4 2 in 25e28 July, 2008. b. Analysis of PM2.5 and SO4 2 correlation in 20e31 July, 2008.
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Fig. 4. a. Correlations between b value and PM2.5 in winter (blue dots) and summer (red dots) during 2007e2008. b. Correlations between fc and PM2.5 concentration in winter (blue dots) and summer (red dots) during 2007e2008. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
that cause PM to increase, and this process relies strongly on RH. A RH of 80% is the deliquescence relative humidity (DRH) for SO4 2 to form and fine particles (PM2.5) to increase (Brendan et al., 2008), providing beneficial conditions for the accelerating formation of secondary aerosol particles. However, the incremental change in aerosol concentrations and the contribution of atmospheric condensation processes needs further study. Fig. 3a illustrates the temporal distribution of PM2.5 and SO4 2 and was drawn using the hourly data from the Beijing Aerosol Mass Spectrum (AMS) from 25 to 28 July 2007. During 24e27 July, the observed SO4 2 concentration in Beijing increased greatly but then rapidly decreased near 0 by the early morning of the 28th. During the increasing period for the SO4 2 concentration, the increase in the accumulating process for PM2.5 occurred simultaneously. For the 5 days from 24 to 28 July, the observed haze and fog occurred continuously in Beijing. Furthermore, the analysis of the Beijing AMS data further proved that during the 5-d when the SO4 2 concentration increased, the accumulating process for the PM2.5 concentration was increasing as well. This finding is concordant with the result presented in Section 3.1; the adaptive weight parameter, b, has a lifetime of approximately 4e5 days under the influence of meteorological conditions. It also coincides with the frequency of westerlies dominating Beijing. Fig. 3b displays the analyses of the correlation between PM2.5 and SO4 2 , according to the hourly AMS data from 20 to 31 July 2008. The variations in the PM2.5 and SO4 2 concentrations are highly correlated, with the correlation coefficient (R2) reaching 0.86 and the significance level above 0.001. Analysing Fig. 3a and b comprehensively, it is apparent that the concentrations of PM2.5 and SO4 2 increase synchronously, creating new aerosol fine particles (Zhang et al., 2011). This observation reveals that favourable meteorological conditions, i.e., the combined action of atmospheric condensation, microphysical processes and atmospheric thermodynamicedynamic processes, cause the secondary accumulation and local increase in PM2.5 pollutant concentrations.
4.4. The restricting and adjusting influences of the meteorological condition parameter on changes in PM2.5 concentrations There are many elements that impact aerosol fine particles (PM2.5). After pollutants become airborne, meteorological conditions provide the necessary environment for new aerosol particles to form, maintain and float in the air. To diagnose the manner in which weather conditions affect the variation in PM2.5 concentrations, we accounted for the characteristics of the interaction between the emission parameter b(Q), and the meteorological condition a(m). Using the observations from weather element fields and the PM2.5 data from 2007 to 2008, we calculated the correlation between PM2.5 and the condensation function fc, which is the major parametric variable for the adaptive weight parameter b, and meteorological field a(m). Furthermore, we analysed the joint impact of b and fc on PM2.5 as well as their restriction of and adjustment to PM2.5. Further analyses were completed on the seasonal differences between winter and summer in Beijing and North China. Winter here refers to November, December, January and February, when the relative humidity and temperature are quite low, and summer refers to July and August, the two months in which it rains abundantly, with the highest temperatures of the whole year. Fig. 4a reveals the correlation analysis for the b value and PM2.5 in the winter and the rainy summer. From the figure, we draw the following conclusions: 1) Under the influence of meteorological conditions during Beijing’s winter, PM2.5 and the adaptive weight parameter, b, meet the distribution rules for the e exponential function. The major distribution of the b value is approximately 5e15 (see the blue dotted line in the figure), and occasionally a small chance greater than 15 occurs as well. 2) In the summer in Beijing and North China, atmospheric condensation and precipitation occur frequently. Influenced by the emission of air pollutants, the contribution of weather
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conditions to the b value of the aerosols decreases below than the mean value of 4, ranging from 0.1 to 2. However, PM2.5 increases rapidly with the increase of b value, which is depicted in the form of the e exponential function. This result is related to the condensation conditions of the ‘sauna’ weather in the hot, humid mid-summer, causing the local aerosols to increase swiftly and accumulate in large quantities. This finding is in agreement with the observed data. In Fig. 4a, a cluster of red points below the fitting line is seen, which shows that during the summer rainy season in Beijing and North China, frequent rainfall makes pollution concentrations rapidly decline. In such cases, the calculated b value is relatively higher while the observed values of PM2.5 are lower. As in Section 4.2 and Fig. 2, the contribution of the condensation function, which is one of the important parameters for meteorological conditions, is significant. However, condensation performs quite differently in the dry and rainy seasons. Using Eq. (4), we calculated the correlation between the condensation function, fc, and the PM2.5 concentration in the winter and summer of 2007e 2008. Fig. 4b displays the correlation analysis for the fc and PM2.5 concentrations in the winter and summer of 2007e2008. According to the real-time observation analysis, displayed in Fig. 4b, that the following statements can be made: 1) Significant correlations exist between PM2.5 concentrations and the variation in the atmospheric condensation function in both summer and winter. The correlation coefficients are R2 ¼ 0.1895 and R2 ¼ 0.1395, respectively, both exceeding the 0.001 significance level. With the increase in the condensation function, the PM2.5 concentration increases in the form of an e exponential function. 2) In winter, the air in Beijing is dry and cold with low relative humidity. The condensation function varies between 0.0 and 0.5 (g kg1 hPa 102). Due to the variation in the condensation function, the slope of the e exponential curve is steep, and the PM2.5 concentration grows very quickly, increasing quickly from <50 to 250 mg m3 or more. 3) In summer, the situation in a typical rainy season is completely different. The basic weather conditions during this period are high humidity, high temperature and high condensation. The condensation function ranges between 0.8 and 1.25 (g kg1 hPa 102), reflecting the characteristics of high humidity and high condensation in summer. With the increase in fc, the PM2.5 concentration also increases in the form of an e exponential function, but more slowly than in winter time. This phenomenon reveals the complexity of the impact of seasonal weather conditions on the variation in the PM2.5 concentration. Integrating Fig. 4a and b, one meaningful result is noted. The winter in Beijing and North China is the typical dry and cold season. Therefore, condensation plays a weak role, but the adaptive weight, b, which is good for the survival of suspended particles, has a remarkable effect. Conversely, July and August constitute the representative rainy season in Beijing and North China. The summers’ high temperature and humidity makes the condensation very effective, but, influenced by frequent rainfalls and convection events, etc., the effect of b is obviously weaker than that in winter. Therefore, we conclude that the adaptive weight parameter, b, and the condensation function, fc, have significant restriction and adjustment effects on each other under the influence of changes in the PM2.5 concentration. This type of restriction and adjustment is consistent with the seasonal difference, atmospheric temperature and humidity difference and other meteorological conditions as
Fig. 5. Correlation between I index 24-h forecast and PM2.5 during 2007e2008.
well as the variation in the meteorological parameters and the observed PM2.5 concentration changes. 4.5. Related analysis of an AQ identifying index under meteorological conditions, based on PM2.5 The main objective of this study is to discuss how changes in meteorological conditions impact PM2.5 concentration, conducive to pollution dispersion or accumulation of pollution concentration. This is what the public and researchers concerned about. So, even if the pollutant concentration data (PM2.5) is available, for I index, diagnosis and prognosis is still in need very much. The 24-h forecast of the diagnostic identifying parameter I is given as an influence index for AQ, based on PM2.5 concentrations. A correlation analysis between this index and the PM2.5 concentrations was conducted. Fig. 5 displays the correlation analysis between the 24-h forecast of I over the 731 days from 1 January 2007 to the 31 December 2008 and the PM2.5 observations (indicated by the solid dotted line) in Beijing. The horizontal ordinate represents I using a 24-h forecast, while the vertical ordinate refers to PM2.5, which has units of mg m3. The I index 24-h forecast is well correlated with PM2.5 in Beijing, and the correlation coefficient (R2) is 0.4832, exceeding the 0.001 significance level. This result indicates that the AQ meteorological condition index, I, 24-h prediction correlates well with observed PM2.5 concentrations in Beijing. 5. Conclusions Based on the analyses of sensitive meteorological parameters that influence the variation in PM2.5 concentrations, we diagnosed and identified the impact of meteorological conditions on changes in the PM2.5 concentration in Beijing. The study results indicated the following conclusions: 1) The condensation function, fc, of fine particle pollutants in the atmosphere increases rapidly in the form of an e exponential
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function when there are suitable weather conditions and reaches the deliquescence relative humidity (DRH), which is generated with a high frequency, resulting in a remarkable increase in the PM2.5 concentration. Therefore, compared to RH, the fc parameter is more sensitive and meaningful for diagnosis. However, fc is the more sensitive factor for indicating a fast accumulation of PM2.5. Significant discrepancies were determined between the typical dry, cold winter season and the humid, hot, rainy summer season in Beijing. 2) Meteorological conditions drastically impact the variation in atmospheric aerosol concentrations. The interaction between the polluting emission b(Q) and the initial meteorological condition a(m) factor affects the variability of the PM2.5 concentration. The adaptive weight parameter, b, for the conditions (favourable or unfavourable for the formation and longevity of suspended particles) is presented. The changes in the PM2.5 concentrations vary with b and fit the distribution of an e exponential function, suggesting that b is a very meaningful parameter for PM2.5 fine pollutant particles to adapt, grow and exist in air under the influence of weather conditions. 3) In the dry, cold winter in Beijing and North China, the contribution of condensation is weak but the adaptive weight parameter, b, that is good for the survival of suspended fine particles contributes significantly. By comparison, in July and August, the typical rainy season in Beijing and North China, the high temperature, humidity and condensation are highly noticeable, but the b value is clearly less effective than in the winter. The adaptive weight parameter, b, and condensation function, fc, have noticeable restricting and adjusting effects due to the influence of changes in the PM2.5 concentration, and these restrictions and adjustments reflect the seasonal differences in the atmospheric temperature and humidity and the variation in the meteorological parameter and the observed PM2.5 concentration changes. 4) I index 24-h prediction was calculated using daily observation data for 731 days in 2007e2008. There is a significant correlation between the I index 24-h forecast and the PM2.5 observations in Beijing. This result is meaningful for analysing, diagnosing and forecasting the impact of meteorological conditions on AQ based on PM2.5. Acknowledgements This study is supported jointly by the National Key Basic Research Development Program of China under Grant No. 2011CB403401 and No. 2011CB403404 as well as the National Natural Science Foundation of China under Grant No. 41275167 and No. 41075079. In addition, authors sincerely appreciate the editors, reviewers for their instructions and suggestions to this paper. References Ackerman, A.S., Kirkpatrick, M.P., Stevens, D.E., Toon, O.B., 2004. The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature 432, 1014e1017. http://dx.doi.org/10.1038/nature03174. Anders, E., Ekman, Annica M.L., 2010. Impact of meteorological factors on the correlation between aerosol optical depth and cloud fraction. Geophys. Res. Lett. 37 (18). http://dx.doi.org/10.1029/2010GL044361. Brendan, M.M., Ann, M.M., Timothy, B.O., 2008. Collection efficiencies in an aerodyne aerosol mass spectrometer as a function of particle phase for laboratory generated aerosols. Aerosol Sci. Technol. 42 (11), 884e898. Dominé, F., Shepson, P.B., 2002. Airesnow interactions and atmospheric chemistry. Science 297, 1506e1510. Friedman, M.S., Powell, K.E., Hutwagner, L., Graham LeRoy, M., Teague, G.W., 2001. Impact of changes in transportation and commuting behaviors during the 1996 Summer Olympic Games in Atlanta on air quality and childhood Asthma. J. Am. Medical Assoc. 285 (7), 897e905.
165
Gao, S.T., Zhou, Y.S., 2005. Analyses on warm and humid weather in summer in Beijing and its dynamical identification. Sci. China 48 (Suppl. II), 128e137. Gong, S.L., Barrie, A., Blanchet, J.P., Salzen, K. n, Lohmann, U., Lesins, G., Spacek, L., Zhang, L.M., Girard, E., Lin, H., Leaitch, R., Leighton, H., Chylek, P., Huang, P., 2003. Canadian aerosol module: a size-segregated simulation of atmospheric aerosol processes for climate and air quality models 1. Module development. J. Geophys. Res. 108, 4007. Johnson, A.B., Donald, G.B., 1997. Climatology of diffusion potential classes for MinneapoliseSt Paul. J. Appl. Meteorol. 36 (12), 1620e1628. Kassomenos, P., Papaloukas, C., Petrakis, M., Karakitsios, S., 2008. Assessment and prediction of short term hospital admissions: the case of Athens, Greece. Atmos. Environ. 42 (30), 7078e7086. Koren, I., Kaufman, Y.J., Rosenfeld, D., Remer, L.A., Rudich, Y., 2005. Aerosol invigoration and restructuring of Atlantic convective clouds. Geophys. Res. Lett. 32, LI4828. http://dx.doi.org/10.1029/2005GL023187. Li, Y., 2010. Chemical characteristics of precipitation at three Chinese regional background stations from 2006 to 2007. Atmos. Res.. http://dx.doi.org/10.1016/ j.atmosres.2009.12.011. Li, Y., Wang, W., Wang, J.Z., Zhang, X.L., Lin, W.L., Yang, Y.Q., 2010. Impact of air pollution control measures and weather conditions on asthma during the 2008 Summer Olympic Games in Beijing. Int. J. Biometeorol.. http://dx.doi.org/ 10.1007/s00484-010-0373-6. Liu, Q., Sun, Y., Hu, B., Liu, Z., Akio, S., Wang, Y.S., 2012. In situ measurement of PM1 organic aerosol in Beijing winter using a high-resolution aerosol mass spectrometer. Chin. Sci. Bull. 57, 819e826. http://dx.doi.org/10.1007/s11434-0114886-0. Lu, G.X., Guo, X.L., 2012. Distribution and origin of aerosol and its transform relationship with CCN derived from the spring multi-aircraft measurements of Beijing Cloud Experiment (BCE). Chin. Sci. Bull. 57. http://dx.doi.org/10.1007/ s11434-012-5136-9. McKeen, S., 2007. Evaluation of several real-time PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study. J. Geophys. Res. 112, D10S20. Metwally, M.E., Alfaro, S.C., Abdel, M.M., Zakey, A.S., Chatenet, B., 2010. Seasonal and inter-annual variability of the aerosol content in Cairo (Egypt) as deduced from the comparison of MODIS aerosol retrievals with direct AERONET measurements. Atmos. Res. 97, 14. Mian, C., Ralph, A.K., Lorraine, A.R., 2009. Atmospheric Aerosol Properties and Climate Impacts, Synthesis and Assessment Product 2.3. US Climate Change Science Program and the Subcommittee on Global Change Research, 1717 Pennsylvania Avenue, NW Suite 250, Washington, D.C., p. 116 Moran, M.D., 2009. In 30th NATO/SPS ITM on Air Pollution Modeling and Its Application. (San Francisco). Neil, P.L., Erik Crosman, C., David, W., John, D.H., Sebastian, W.H., William, O.J.B., Thomas, W.H., 2013. The persistent cold-air pool study. Bull. Am. Meteorol. Soc. 94, 51e63. http://dx.doi.org/10.1175/BAMS-D-11-00255.1. Polissar, A.V., Hopke, P.K., Harris, J.M., 2001. Source regions for atmospheric aerosol measured at Barrow, Alaska. Environ. Sci. Technol. 35, 4214e4226. Rigby, M., Toumi, R., 2008. London air pollution climatology: indirect evidence for urban boundary layer height and wind speed enhancement. Atmos. Environ. 42 (20), 4932e4947. Shi, Z.B., Shao, L.Y., Jones, T.P., Whittaker, A.G., Richards, R.J., Zhang, P.F., 2004. Oxidative injury onto the plasmid DNA by urban air inhalable particulate matter. Chin. Sci. Bull. 49 (7), 673e678. Tang, X.Y., Zhang, Y.H., Shao, M., 2006. Atmospheric Environment Chemistry. Higher Education Press, Beijing, p. 739. Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S., Berntsen, T., Berglen, T., Boucher, O., Chin Dentener, F., Diehl, T., Easter, R., Feichter, H., Fillmore, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I., Iversen, I., Kloster, S., Koch, D., Kirkevåg, A., Kristjansson, J.E., Krol, M., Lauer, A., Lamarque, J.F., Liu, X., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., Tie, X., 2006. Analyses and quantification of the diversities of aerosol life cycles within AEROCOM. Atmos. Chem. Phys. 6, 1777e1813. Wang, J.Z., Yang, Y.Q., Zhang, G.Z., Yu, S.Q., 2010. Climatic trend of cloud amount related to the aerosol characteristics in Beijing during1950-2005. Acta Meteorol. Sin. 24 (6), 762e775. Wang, J.Z., Li, D., Yang, Y.Q., 2011. Study on the annual variation of winter precipitation and the characteristics of atmospheric aerosol distribution in Northern China. J. Meteorol. Environ. 27 (6), 66e71. Yang, Y.Q., Wang, J.Z., Hou, Q., Li, Y., Zhou, C.H., 2011. Discriminant Genetic Algorithm Extended (DGAE) model for seasonal sand and dust storm prediction. Sci. China (Ser. D) 54 (1), 10e18. Zhang, G.Z., Bian, L.G., Wang, J.Z., Yang, Y.Q., Yao, W.Q., Xu, X.D., 2005. The boundary layer characteristics in the heavy fog formation process over Beijing and its adjacent areas. Sci. China Ser. D 48 (Suppl. II), 80e101. Zhang, X.Y., Wang, Y.Q., Lin, W.L., Zhang, Y.M., Zhang, X.C., Gong, S., Zhao, P., Yang, Y.Q., Wang, J.Z., hou, Q., Zhang, X.L., Che, h. Z., Guo, J.P., Li, Y., 2009. Changes of atmospheric composition and optical properties over Beijing 2008 Olympic Monitoring Campaign. Bull. Am. Meteorol. Soc. 90 (11), 1633e1649. Zhang, Y.M., Zhang, X.Y., Sun, J.Y., Lin, W.L., Gong, S.L., Shen, X.J., Yang, S., 2011. Characterization of new particle and secondary aerosol formation during summertime in Beijing, China. Tellus 63B, 382e394. http://dx.doi.org/10.1111/ j.1600-0889.2011.00533.x.