Spectral analysis of weekly variation in PM10 mass concentration and meteorological conditions over China

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Atmospheric Environment 42 (2008) 655–666 www.elsevier.com/locate/atmosenv

Spectral analysis of weekly variation in PM10 mass concentration and meteorological conditions over China Yong-Sang Choia, Chang-Hoi Hoa,, Deliang Chenb, Yeon-Hee Noha, Chang-Keun Songc a

School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea b Earth Sciences Centre, Go¨teborg University, Go¨teborg, Sweden c Global Environment Research Center, National Institute of Environmental Research, Incheon, Republic of Korea Received 4 May 2007; received in revised form 28 September 2007; accepted 28 September 2007

Abstract This study investigates the region-dependent anthropogenic weekly variation in air pollutants and its relationship with the meteorological conditions over China for the summers of 2001–2005. Spectral analysis was applied to the local daily observations of PM10 (aerosol particulate matter with a diameter o10 mm) mass concentrations and precipitation from 31 ground stations, reanalysis estimates of regional atmospheric variables, and satellite retrievals of clouds. Our analysis shows that the 6–8-day variance of PM10 concentrations from the periodogram is closely correlated with the mean PM10 concentration, which may depend on the size (population) and geographical setting of a city, its prevailing climatic conditions, and the type/degree of human activities. We define normalized variance as the ratio of the 6–8-day to 2–14-day variance of PM10 concentrations, possibly indicating the relative anthropogenic signal to the noise of natural weather variability. The normalized variance of PM10 concentrations has a distinct regional rainfall distribution from that of the mean PM10 concentration in China. As compared to regions with lower normalized variance of PM10 concentrations, the regions with higher normalized variance generally show higher normalized variance of rainfall events, 1000 hPa wind speeds, sea-level pressure, size spectrum and phase of cloud particles, cloud optical depth, and cloud top pressure. Our results confirm the presence of the interaction between PM10 and the meteorological conditions in the boundary layer, and suggest a possible link of cloud formation to PM10 on a weekly scale. r 2007 Elsevier Ltd. All rights reserved. Keywords: PM10; Air pollution index; Weekly variation; Spectral analysis; China

1. Introduction In many countries, the weekly variation in the concentration of atmospheric aerosols and meteorCorresponding author. Tel.: +82 2 880 8861; fax: +82 2 876 6795. E-mail address: [email protected] (C.-H. Ho).

1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.09.075

ological variables in urban areas has been reported as evidence of anthropogenic influence on the weather system. Most previous studies were carried out on a case-by-case basis by using satelliteretrieved aerosol data (Delene and Ogren, 2002; Beirle et al., 2003) and records of near-surface gas or aerosol components such as carbon oxide (Hies et al., 2000; Cerveny and Coakley, 2002), nitrogen

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oxide (Marr and Harley, 2002), and particulate matter with diameters o2.5 and 10 mm (PM2.5 and PM10, respectively) (Jin et al., 2005; Gong et al., 2007). Although the relationship between the variation in aerosol concentration and weather variables has not been clearly understood, many studies have revealed significant weekday–weekend differences in weather variables such as the diurnal temperature range (Forster and Solomon, 2003; Gong et al., 2006), and clouds and precipitation (Cerveny and Balling, 1998; Jin et al., 2005). When it comes to regions that witness weekly variations in the concentration of aerosol particles and meteorological variables, China is one of the major air pollutant source regions in the world (Qian et al., 2006; Gong et al., 2007). Recently, the weekly interaction between aerosols and the weather system in China was studied (Gong et al., 2007). This study revealed that the aerosols accumulated in the air until Wednesday induced low-level atmospheric motion due to radiative heating, and the resulting air motion in turn diluted the aerosols by dynamic mixing. Although Gong et al. (2007) suggested a new viewpoint with regard to the aerosol–meteorology relation, they presented an overall characteristic that is based on the averaged states of PM10 concentrations and weather variables in China. In fact, the aerosol concentrations are largely controlled by a combination of wet/dry scavenging, atmospheric mixing, chemical transformation, and emission (Garrett et al., 2006). Moreover, nonlinear weather systems are strongly coupled with aerosol concentrations, and the weekly variation cannot be completely separated from weather-induced noise (Jin et al., 2005). Accordingly, there would be regional differences in the weekly variations, and these differences may not be uniform even at a certain location. However, the general criteria that can justify this regionality are yet to be clearly established. The present study attempts to classify regions with different weekly variations in the aerosol concentration normalized by weather noises on the basis of the spectral analysis. For this study, daily surface observations of the PM10 concentrations were freshly compiled at 31 stations in China for the summers (June–August) of 5 years (2001–2005). From these 31 stations, precipitation records for the same period were also available. It is known that the concentration of PM10 is closely related to human activities since it comprises fugitive dust and smoke from domestic and industrial combustion, primary

particles from automobiles, and organic and secondary aerosols from anthropogenic emission (USEPA, 2005). Further, this study examines the weekly variations in meteorological variables over the PM10 stations and discusses their relationship with PM10 variations. This paper is organized as follows. Section 2 describes the data and the spectral analysis method used in this study. Section 3 shows the results obtained from the spectral analyses of PM10 concentrations and atmospheric variables. Section 4 discusses the implications of the results through the weekly phase of the mean variable values. Finally, the summary is provided in Section 5.

2. Data and methods 2.1. Data The State Environmental Protection Administration of China has been monitoring the daily air pollution index (API) of China’s major cities since 2000 (available online at http://www.sepa.gov.cn/ quality/air.php3). The API is defined as the highest index among the sub-indices of three leading pollutants: PM10, SO2, and NO2. It is given by the formula API ¼ max(IPM10, I SO2 , I NO2 ) (Gong et al., 2007). The sub-indices—IPM10, I SO2 , and I NO2 —are the values converted from the observed mass concentrations (C) in mg m3 of the leading pollutants (Table 1). I PM10;SO2 ;NO2 ¼ (IUIL)(CCL)/(CUCL)+IL, where IU and IL (CU and CL) are the upper and lower standard index (concentration) values, respectively. It should be noted that the day on which the API is o50 (i.e., PM10o50 mg m3, SO2o50 mg m3, and NO2o 80 mg m3) is defined as ‘‘clean,’’ and it is inferred that no pollutant type was recorded on this day. Hence, we Table 1 National standards of ambient air quality in China Index

50 100 200 300 400 500

Concentration (mg m3) SO2 (daily mean)

NO2 (daily mean)

PM10 (daily mean)

50 150 800 1600 2100 2620

80 120 280 565 750 940

50 150 350 420 500 600

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derive the concentration of the pollutant via the inverse formula: C ¼ CL+(IIL)(CUCL)/(IUIL). The daily API dataset contains two parameters: the final API values and the pollutant types. Thus, on a given day, the origin of the calculated concentration is necessarily a single pollutant type. In this study, we considered the API obtained in summers only because dust storms, which lead to anomalously high PM10 concentrations of natural origin (largely mineral dusts), are less frequent in summer (Gong et al., 2007). Further, a continuous dataset of daily aerosol concentrations for spectral analysis is obtained in summer because the number of PM10-polluted days and clean days is large (nearly 96% of the total days). In fact, with regard to the different characteristics of the monsoon circulations in the two seasons, the features of the weekly variations in the diurnal temperature range in summer and winter are in opposition (Gong et al., 2006). Therefore, focusing only on the summer season may diminish the complexity of the weekly variations in China. The National Center for Environmental Prediction/ Department of Energy (NCEP/DOE) Reanalysis-2 data (R-2) (Kanamitsu et al., 2002) have been used as reliable sources of meteorological data while studying aerosols (Jacobson and Kaufman, 2006; Gong et al., 2006, 2007). We used the R-2 data to examine the weekly variations in PM10 concentrations in relation to meteorological variables including 1000 hPa wind and sea-level pressure. The 2.51 R-2 grids closest to the 31 API stations were selected by assuming that the R-2 variables are reasonably representative of the meteorology around the APIobserved stations. We also examined the regional characteristics of the weekly variations in the cloud properties from the moderate resolution imaging spectroradiometer (MODIS)/Terra level-3 daily atmospheric product (Platnick et al., 2003). The cloud properties used in this study include the cloud phase (water, mixed, and ice phases), effective particle radius (the ratio of the third moment of size distribution to the second moment), and cloud top pressure. The cloud properties have spatial resolutions of 11  11; therefore, we used grids selected in the same manner as that used for the R-2 data. 2.2. Spectral analysis Spectral analysis has been widely applied in meteorology. This method has been used in some

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studies on weekly variations in aerosols (e.g., Hies et al., 2000; Marr and Harley, 2002). The foregoing variables are in the form of a 92-day time series for each year. Since our focus is on daily to approximately 2-week timescales, it is useful to calculate the spectra of the power densities (periodogram) for many successive overlapping (by 28 days) 42-day segments of the dataset (cf., Wheeler and Kiladis, 1999). Accordingly, we discarded the last eight data points for each year to create an 84-day time series. By averaging the periodograms of 20 (4 per year  5 years) segments, the signals over the frequency range except those around 1–0.07 day1 (period of 1–14 days) become smooth. We calculated the discrete Fourier transform, X, of the time series by using the fast Fourier transform (FFT) algorithm. The periodogram for a finite time series was then calculated as the squared magnitude of X: 2    1 N1 X   Fðnk Þ ¼ jX ðkÞj2 ¼ pffiffiffiffiffi xt expf2pink tg (1)   N t¼0 where k ¼ 0, 1, y, (N1). N is the number of observations; xt is the segment time series; and nk ¼ k/N. We set N to 42. When k is 6, nk corresponds to approximately 0.14 day1 (exactly 7 days). The periodogram indicates the strength of the signal as a function of frequency, and its spectra over the frequency range corresponds to the variance of the time series data (Marr and Harley, 2002). The spectrum F(nk) contains a background value F0(nk) determined by a ‘‘red noise’’ fit to the spectrum (Gilman et al., 1963) as well as the spectral peaks of the periodic cycles in the time series. We used the power spectrum with a red noise background in our analysis because most climatic and other geophysical time series tend to have larger power at lower frequencies (Ghil et al., 2002). The significance of the spectral peaks in a periodogram has been usually assessed by comparing F(nk) to F0(nk) under the null hypothesis of a non-periodic time series. Therefore, we used the ratio F(nk)/F0(nk) to compare the spectral peaks in the periodogram with those of other regions. The ratios for the 20 segments were finally averaged for each station, and we thus obtained the final periodograms Fðnk Þ=F0 ðnk Þ, where the upper bar denotes the average of 20 segments. Since we are interested in the regional differences of the spectral peaks with a 7-day frequency, which

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may be related to anthropogenic influences such as the weekend–weekday difference in the amount of air pollutant emission, the amplitude of such peaks needs to be further quantified for regional comparisons. The variance—the periodogram integrated over the frequency range—is conventionally used to indicate the amplitude of the spectral peaks. However, the atmospheric time series includes inevitable synoptic weather noise on approximately a few days to 14 days (Jin et al., 2005), a period that includes the 7-day period of our interest. Moreover, the 7-day period of the periodic variations itself may fluctuate by 1 or 2 days before and after via contamination from aerosol by-product formation and aerosol loading. For these reasons, we define normalized variance (L) as the ratio of the 6–8-day variance to the 2–14-day variance as follows: P7 Fðnk Þ=F0 ðnk Þ L ¼ Pk¼5 , (2) 21 k¼3 Fðnk Þ=F0 ðnk Þ where k values 3, 5, 7, and 21 correspond to 0.07, 0.12, 0.17, and 0.5 day1 (14, 8, 6, and 2 days), respectively. We calculate the value of L in Eq. (2) for each station by using all the data used in this study for the analysis period. L is therefore assumed to be directly proportional to the human-influenced spectral peaks, but inversely proportional to the weather noise. This indicates that the value of L is notably large only in those regions where anthropogenic impact plays a relatively more important role than the natural weather variations. Accordingly, the periodogram in those regions appears to have a distinct 6–8-day spectral peak.

Table 2 API monitoring stations, summer means of PM10 concentrations, normalized variances of PM10, and populations in 2005 No.

Name

Population (10,000)

PM10 average (mg m3)

PM10 L (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Taiyuan Beijing Jinan Chongqing Shenyang Xian Lanzhou Xining Nanjing Changsha Tianjin Chengdu Wuhan Zhengzhou Hangzhou Hohhot Yinchuan Harbin Hefei Guangzhou Changchun Kunming Urumqi Guiyang Dalian Fuzhou Wenzhou Nanning Xiamen Lhasa Haikou

340.4 1180.7 597.4 3169.2 698.6 741.7 311.7 209.9 595.8 620.9 939.3 1082.0 801.4 679.7 660.5 213.5 140.6 974.8 455.7 750.5 731.5 508.5 194.2 350.7 565.3 614.8 746.0 659.5 153.2 44.0 147.3

93.4 87.9 86.4 85.0 84.6 83.2 82.3 78.4 78.2 78.0 77.5 75.4 73.0 72.6 71.2 65.0 63.9 63.2 63.0 62.7 58.6 56.5 55.7 55.6 55.0 54.1 53.5 45.4 38.4 38.1 23.6

23.7a 18.9 18.2 24.1a 19.0a 21.2a 15.1b 18.6 11.5b 19.5a 20.8a 22.1a 15.5b 14.2b 15.1b 16.3 21.0a 12.7b 13.5b 19.1a 13.3b 17.3 15.1b 20.4a 14.2b 18.5 12.6b 22.3a 24.4a 12.7b 17.6

a

12 stations with high normalized 6–8-day variance. 12 stations with low normalized 6–8-day variance.

b

3. Results 3.1. Weekly variation in PM10 concentration over China In order to clarify the relationship between the mean concentration and weekly variations in aerosols as well as their spatial distribution, we compared the stations in terms of the summermean of the PM10 concentration and the normalized variance L of the PM10 concentration (hereafter LPM10) (Eq. (2)). It is convenient that the stations are numbered from 1 to 31 in the descending order according to the summer-mean PM10 concentrations (Table 2). The summer-mean PM10 concentrations appear to be somewhat related to the population, but both are not exactly

proportional. In terms of the mean PM10 concentrations, Taiyuan, Beijing, Jinan, and Chongqing top the list (PM10X85 mg m3). Taiyuan, Beijing, and Jinan are geographically located in northern China within 35–401N and 110–1201E (Fig. 1) where the climate is dry. Taiyuan has a relatively small population, but it is well known for its largescale coal mining. Chongqing has a population of 430 million, and it is the largest city among the analysis stations. On the contrary, stations 28–31 indicate that the cities—Nanning, Xiamen, Lhasa, and Haikou, respectively—are relatively unpolluted (PM10o50 mg m3). All these cities, with the exception of Lhasa, are located in southern China (20–251N), where the climate is humid and the vegetation cover is considerably greater than that in

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50N

18 45N

23

21 5

Yellow 16 17 River 1

40N

8 Tibetan plateau

7

12 30

30N

4

Yellow Sea

14

Yangtze River

13 10

24 22

25N

25

3 6

35N

211

19 9 15 27 26 29

20

28

East China Sea

31 20N

15N 85E

90E

95E

100E

105E

110E

115E

120E

125E

130E

Fig. 1. The regional distribution of the normalized 68-day variances of the PM10 concentration at the stations is indicated by circles with proportional sizes. The station number corresponds to the rank of the summer-mean PM10 concentration, as shown in Table 2. The gray (black) circles indicate the 12 stations with high (low) values of the normalized 6–8-day variances.

the northern stations. Lhasa on the other hand is located high (approximately 3700 m above the level) on the Tibetan plateau in southwestern China (Fig. 1). Besides, stations 25, 26, and 27 with relatively small PM10 averages (55 mg m3) are located in Dalian, Fuzhou, and Wenzhou, respectively, all of which are located along the eastern coast. The regional distributions of the mean PM10 concentrations cannot be attributed to the degree of emission alone. Emissions of anthropogenic pollutants are generally higher in northeastern China than in southern and inland China. In fact, other factors such as wet scavenging and dynamic mixing may also affect the distributions of the PM10 concentrations (Garrett et al., 2006; Gong et al., 2007). In summer, the frequency and amount of rainfall in northern China are lower than that in southern China (Gong and Ho, 2002; Ho et al., 2005). Precipitation is one of the most important processes that reduce atmospheric aerosols; therefore, weaker wet scavenging due to lower precipitation in northern China can to some extent account for the higher PM10 averages in northern China than in southern China (stations 1–3 versus stations 28–31). The concentration of anthropogenic aerosols can be decreased by dynamic mixing with

cleaner air. From this point of view, the reduction in aerosols over open coastal regions would be more effective than that over a basin or an inland area. Moreover, raindrops initiated by large-sized sea salt can cleanse the air as well (Rosenfeld et al., 2002). Therefore, such oceanic influences may reduce the PM10 average near the coastal regions (stations 25–29 and station 31). Fig. 1 shows the regional distribution of the normalized variance LPM10. The LPM10 values are proportional to the size of the circles in this figure. Among the 31 stations in this study, 12 stations with relatively high LPM10 and 12 with relatively low LPM10 are selected (gray and black circles, respectively). High-LPM10 stations (LPM10X19.0%) are stations 1, 4, 5, 6, 10–12, 17, 20, 24, 28, and 29, while stations 7, 9, 13–15, 18, 19, 21, 23, 25, 27, and 30 (these stations are denoted by superscripts ‘‘a’’ and ‘‘b’’ in Table 2, respectively) are low-LPM10 stations (LPM10o16.0%). It is noteworthy that the regional distribution of LPM10 is different from that of the PM10 average. To interpret the above-mentioned regional distribution of LPM10, Fig. 2 depicts (a) the 6–8-day variance, (b) 2–14-day variance, and (c) LPM10 as a function of the PM10 average. The numbers

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660

6−8-day variance (μg/m3)

200 8

2

150 7 100

31 0 20

6

24

29

50

3 28

30

1

4

10

5 20

40 60 80 PM10 average (μg/m3)

100

2−14-day variance (μg/m3)

1600 8

1200

7

800 20

10 4

6

400 29 31

30

1

24 28

5

3

0 20

40 60 80 PM10 average (μg/m3)

100

25 Normalized variance (%)

29 28 20

4 6

24

19.0%.

52

10 20

8

31 15

1

3 7

15.5% 30

10 20

40

60

80

100

PM10 average (μg/m3) Fig. 2. The relationship of the summer-mean PM10 concentrations with the 6–8-day variance (a), 2–14-day variance (b), and 68-day variance normalized by the 2–15 variance of the daily PM10 concentrations (c) at the 31 stations in China. The gray and black circles correspond to the gray and black stations in Fig. 1, respectively. The solid (dashed) line in (a) and (b) indicates the linear regression line (95% confidence interval).

indicate the station numbers in the figure. Based on the result of LPM10 in Fig. 2c, the values corresponding to high- and low-LPM10 stations are

indicated by gray and black circles, respectively, to enable a comparison with Fig. 1. Fig. 2a shows that the PM10 averages are correlated with the 6–8-day variance (correlation coefficient, R ¼ 0.66, significant at a 99% confidence level by t-test), which includes the numerator of LPM10 in Eq. (2). This 6–8-day variance is also closely correlated with the 2–14-day variance and the total data variance (R40.8, significant at a 99% confidence level by ttest) (figure not shown); this can be deduced from Fig. 2a and b. However, no notable relationship between the PM10 averages and LPM10 is observed (Fig. 2c); therefore, a higher value of LPM10 does not indicate higher PM10 averages. Among the high-LPM10 stations, stations 1, 4, 6, and 10 have both a high PM10 average and a strong 6–8-day variance (Fig. 2a). These may be due to both basin-like geographical characteristics and large anthropogenic weekly activities. Stations 5, 20, 28, and 29 also have high LPM10 values, but a relatively small 6–8-day variance (o45 mg m3). The four stations have a low 2–14-day variance, resulting in a high LPM10 value (Fig. 2b). The natural weather variability corresponding to the 2–14-day variance in these regions may not be strong enough to contaminate the 6–8-day variance, perhaps due to the large heat capacity of the ocean that leads to small weather variability in general. Thus, the 6–8day spectral peak in the periodogram for the four stations is conspicuous, although the 6–8-day variance itself is small. The remaining high-LPM10 stations—stations 11, 12, 17, and 24—can be also explained by the relatively higher 6–8-day variance than the 2–14-day variance. On the other hand, the low-LPM10 stations in western China include stations 7, 23, and 30. These regions are very arid (annual precipitation o500 mm) and are located around deserts and plateaus. Natural dust blown from such arid surfaces may result in large variations due to varying surface winds (Qian et al., 2002). Station 7, in particular, has a large 6–8-day variance, but this value is small when compared with the 214-day variance (compare Fig. 2a and b). The low LPM10 values in eastern China (stations 9, 13–15, 19, and 27) may result from other reasons. In this region, the 6–8-day variances are relatively small. Plain topography may be favorable for transporting anthropogenic aerosols from emission sources, and the prevailing weather noise may obscure the weekly variation in the regions. The remaining northern stations—stations 18, 21, and

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25—may also be in a similar condition since they have low LPM10 values.

3.3. Relationship with weekly variation in atmospheric variables Anthropogenic aerosols influence important atmospheric physical processes such as radiation, temperature, stability, wind, clouds, and precipitation (Ramanathan et al., 2001; Jacobson and Kaufman, 2006). Therefore, the meteorological conditions in regions with a more evident weekly variation in aerosol concentration would be more

14

8 7 6

4.7 (Station 5)

2

1.8 Φ()/Φ0()

We simplified the diverse regional characteristics of the weekly variation in the concentration of PM10 pollutants by averaging the periodogram Fðnk Þ=F0 ðnk Þ over the high- and low-LPM10 stations separately (Fig. 3). In this figure, the averages (maximum and minimum) of the 12 periodograms for each frequency are calculated and indicated by solid (dashed) lines. Here, the periodogram is flat with respect to frequency because it is the value divided by the red noise background F0(nk). The figure clearly shows that, on an average, there exists a 7-day spectral peak for the high-LPM10 stations (Fig. 3a). This is contrary to the periodogram for the low-LPM10 stations; the averaged spectrum at all frequencies is around a noise level of 1.0 (Fig. 3b). The maximum of the spectra for the high-LPM10 stations exhibits another strong peak at a frequency of 0.21 day1 (4.7 days). This spectral peak originates from station 5. The region in which station 5 is located must have distinct local characteristics that cause such a peak at an unknown frequency. Anthropogenic effects indicated by both the PM10 average and the 6–8-day variance are high in this region, but they are possibly contaminated by nonlinear weather noise or by the by-products of anthropogenic aerosols. Moreover, the maximum of the spectra for the low-LPM10 stations exhibits another strong peak at a frequency of 0.29 day1 (3.5 days); this peak originates from station 25. Although station 25 is located close to station 5 geographically, it is interesting that this station seems to detect a different system that may operate at a shorter day interval with low values of PM10 average, 6–8-day variance, and LPM10.

2.2

Period (day) 1.4

1.0

0.6

0.2 0.0

0.1

0.2

0.3

0.4

0.5

Frequency (1/day) 2.2 14

8 7 6

3.5 (Station 25)

2

1.8 Φ()/Φ0()

3.2. Power spectra of PM10 concentration for highand low-LPM10 stations

661

1.4

1.0

0.6

0.2 0.0

0.1

0.2

0.3

0.4

0.5

Frequency (1/day)

Fig. 3. The average (solid line), maximum, and minimum (dashed line) of the power spectra of the PM10 concentrations divided by a background value for 12 stations with higher normalized variances (gray stations in Fig. 1) (a) and for 12 stations with lower normalized variances (black stations in Fig. 1) (b). The number indicates the daily periodicity and the corresponding station number in parenthesis.

likely indicators of the weekly variation in the weather as well. To examine this possible aerosol–meteorology relationship, we calculated L for various standard meteorological variables (hereafter Lmet). We examined the statistical distribution of Lmet for the high- and low-LPM10 stations classified in the previous section (Fig. 4). The distributions indicated by bar graphs show that the high-LPM10 stations generally have higher Lmet values, which are to a large extent identified by the median and to some extent by the mean. Bearing in mind that our sample size is small—12 stations per distribution— we consider that Lmet can take a wide range of

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35

Normalized variance (%)

Rainfall event

Cloud ice phase

1000-hPa wind

Effective particle radii

30

Cloud top pressure Maximum 95% 75% Mean Median 25% 5% Minimum

25

20

15 Sea-level pressure

10 High

Low

High

Low

High

Low

High

Low

High

Low

High

Low

High- versus low-ΛPM10 stations Fig. 4. Box plots summarizing the distribution, mean, median, and variability of the normalized variances of five atmospheric variables. The box plots are separated by the normalized variances of PM10 concentrations: higher 12 stations versus lower 12 stations.

values and that most of the discrepancy is statistically weak. There appears to be a relation between the high Lmet of the rainfall events and the high LPM10, indicating the presence of an interaction between aerosols and precipitation (or clouds) on a weekly timescale (Fig. 4). The medians (means) of Lmet of the rainfall events are 18.5% and 14.4% (18.3% and 15.8%) for the high- and low-LPM10 stations, respectively. The null hypothesis of no difference between the two medians (or means) of Lmet of the rainfall events were rejected by a paired t-test with a 99% confidence level. This result indicates that there is a significant difference between the Lmet of rainfall events in the high- and low-LPM10 stations. Lmet of the cloud ice phase, the effective radius, and the cloud top pressure are consistently higher, albeit marginally, in the high-LPM10 stations than in the low-LPM10 stations. The difference between the medians (means) of Lmet of the three cloud properties in the high- and low-LPM10 stations are relatively small, possibly because of spaceborne observations with low temporal resolution and/or downwind aerosol transport. Similar results were obtained for 1000 hPa wind speeds: the higher the Lmet of the wind speeds, the higher is the LPM10. The medians (means) of Lmet of the wind speed are 21.6% and 17.2% (20.5% and 17.3%) for the high- and low-LPM10 stations, respectively. A paired t-test indicated that the difference between Lmet of wind speeds of the highand low-LPM10 stations is also significant at a 99%

confidence level. A possible effect of aerosol particles on near-surface wind speeds is the wind reduction induced by atmospheric stabilization below aerosol particles (Jacobson and Kaufman, 2006). The reduced wind speed reduces the vertical or horizontal transport of aerosols. On the contrary, black carbon aerosols may heat up the local atmosphere via solar radiation absorption, resulting in the destabilization of the middle to lower troposphere and wind generation over China (Gong et al., 2007). In any case, the change in air motion can alter the ventilation condition. Assuming the result shown in Fig. 4, such effects may operate at a weekly interval and may be more effective in regions with stronger weekly variations in the PM10 concentrations. The sea-level pressure field has a higher Lmet for the high-LPM10 stations, a feature that is largely responsible for the higher Lmet of wind speeds. We note that a wide range of Lmet of wind and pressure may be due to the smoothly varying R-2 data on a large scale, contrary to the surface observations. 4. Discussion The human-induced weekly cycle may only be recognized by the consistent weekend–weekday difference that cannot be inferred by spectral analysis alone. For clarification, we depicted the averaged anomaly values by the days of the week (Fig. 5). The anomalies at each station are first calculated as departures from the 5-year

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4 2

2 Rainy days

PM10 concentrations (μg/m3)

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0

-2

0

-2 High-ΛPM10 stations HighLow- PM10 stations Low-Λ

-4

-4 Mon Tue Wed Thu

Fri

Sat

Sun

Mon Tue Wed Thu

Fri

Sat

Sun

Mon Tue Wed Thu

Fri

Sat

Sun

Sea-level pressure (hPa)

Wind speed (m/s)

0.3 0.2 0.1 0.0 -0.1 -0.2

0.4 0.2 0.0 -0.2 -0.4 -0.6

-0.3 Mon Tue Wed Thu

Fri

Sat

Sun

Fig. 5. Averaged anomalies of four variables by the day of the week over 12 higher LPM10 stations (solid circles) and 12 lower LPM10 stations (opened circle); the error bars indicate the standard error of the value (71s). The anomaly originates from the 5-year summer mean for each station.

summer mean. They are then averaged separately over the 12 high-LPM10 stations and the 12 lowLPM10 stations. A robust and steadily periodic cycle of any variable yields a high L value. The weekly change in PM10 concentrations for the high-LPM10 stations clearly reveals high and low values for weekdays and weekends, respectively, whereas this is not clear for the low-LPM10 stations (Fig. 5a). The weekly change in the rainfall events for the high-LPM10 stations shows an opposite signal: low events for weekdays and high events for weekends (Fig. 5b). The result for the high-LPM10 stations is consistent with Fig. 9 of Gong et al. (2006). Low rainfall events on Wednesday and Thursday can be related to the high PM10 concentration on those days of the week owing to the low wetscavenging effect. The low rainfall events on weekdays may originate from the enhanced reduction in the precipitation efficiency of low clouds due to increased indirect aerosol effects (Rosenfeld, 1999; Ramanathan et al., 2001). The results obtained from spectral analysis and the weekly change consistently show that such an aerosol–precipitation interaction should be more intimate for a high LPM10 than for a low LPM10.

Similarly, the weekly change in the 1000 hPa wind speed appears to be considerably larger for the highLPM10 stations than for the low-LPM10 stations; this may be the reason for the different values of Lmet. However, the weekly change in the sea-level pressure for the high-LPM10 stations is not very different from that of the low-LPM10 stations (Fig. 5d). The negligible difference in the pressure, Lmet, between the high- and low-LPM10 stations may have induced the wide range of distributions of Lmet shown in Fig. 4. The anomalies in the pressure are very small (o1 hPa), which indicates a relatively small contribution of anthropogenic aerosols to the pressure at the surface. The weekly changes in cloud properties are shown in Fig. 6. The relative fraction of cloud ice phase for an equal cloud amount increases continuously from Monday through Friday (Fig. 6a). Since an ice crystal is generally much larger than a water droplet, an increase in the relative fraction of ice clouds may result in an increase in the averaged effective radius of the cloud particles (Fig. 6b). From Monday through Friday, the averaged cloud top pressures decrease (Figs. 6c). Further analysis of cloudiness in terms of different cloud phases reveals that the ice clouds increase by approximately 6%

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0.4 Effective particle radius (μm)

4

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stations PM10 stations PM10

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Fig. 6. Same as Fig. 5 except for the spaceborne cloud properties: relative fraction of cloud ice phase (a), effective particle radius (b), cloud top pressure (c), and ice and water cloud cover (d and e).

from Monday through Saturday, whereas water clouds behave differently within 71.5%, i.e., maximum and minimum on Tuesday and Friday, respectively (Fig. 6d and e). The weekend–weekday difference is clearer in the high-LPM10 stations than in the low-LPM10 stations. This regional discrepancy may be responsible for the different distribution of Lmet in Fig. 4. To relate the cloud properties and precipitation with PM10 concentrations, we consider the MODIS/Terra scanning time that is, between 10:30 and 11:30 a.m. local time. In other words, the MODIS cloud properties observed the next morning should be compared with the PM10 concentrations measured the day before. Thus, the increase in PM10 concentrations from Sunday

through Thursday can be related to the increases in ice clouds and cloud tops from Monday through Friday (or Saturday), or the initial increase in water clouds from Monday through Wednesday (compare Fig. 5a with Fig. 6). Previous studies have demonstrated that increased aerosols can increase the fractional cloudiness of tropical low-level clouds and reduce warm-rain efficiency by the second indirect effect (Rosenfeld, 1999; Ramanathan et al., 2001). On the contrary, increased aerosols can hinder low-level cloud formation by the semi-direct effect (Koren et al., 2004). These effects may operate together in the Chinese summer, resulting in the initial increase in water clouds and the decrease in rainfall events in conjunction with an increase in PM10 concentrations from Monday through Thursday.

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On the other hand, the resulting increases in ice clouds and cloud tops shown in Fig. 6 indicate that cloud formation may also occur in the mid-troposphere when the freezing level is considered (600 hPa) in the Chinese summer. Presumably, the mid-level cloud formation occurs via the ice nucleation (Demott et al., 2003) and/or the initial delay in warm-rain processes (Khain et al., 2005). The invigoration of mid-level cloud formation must be related to the enhanced cold-rain processes (Lin et al., 2006). However, this effect is not shown in our calculation, perhaps because light rainfall (o5 mm day1) related to warm-rain processes dominates the rainfall events in the Chinese summer. The detailed mechanism of the aerosol– meteorology interaction responsible for the weekly change observed in this study (Figs. 5 and 6), although beyond the scope of this paper, deserves further study, and it is desirable that a modeling approach be formulated to deal with this issue.

fore, the weekly cycles of aerosol concentrations and meteorological variables should be coupled. These results confirm the previous findings on the interaction between aerosols and the meteorological conditions in the boundary layers and can even indicate a possible relationship between cloud formation and aerosols.

5. Summary

References

We have presented the summertime weekly variation in PM10 concentrations over 31 aerosol monitoring stations in China by means of spectral analysis. The mean PM10 concentrations have a regional distribution that depends on the size (population) and geographical setting of a city, its prevailing climatic conditions, and the type/degree of human activities. Assuming that the 6–8-day variance of PM10 is mainly influenced by human activities, the 6–8-day variance normalized by the 2–14-day variance (L) is used to minimize the influence of weather noise on the 6–8-day variance of PM10 and meteorological conditions; as a result, a prominent human activity induced weekly signal of any given time series emerges only when the variance for the human-related period (around 7 days) is stronger than that for the period of natural weather variability (in 2 weeks). Accordingly, the averaged periodogram for high-LPM10 stations shows a conspicuous weekly power peak, contrary to that for the low-LPM10 stations. Due to different geographical settings, emission characteristics, and local weather variability on varying time scales, the LPM10 values are distributed inhomogeneously. Furthermore, the high LPM10 values appear to be related to the high L values of the associated atmospheric processes including air motion, clouds, and precipitation. The high L values are induced by a robust and steadily periodic weekly cycle; there-

Beirle, S., Platt, U., Wenig, M., Wagner, T., 2003. Weekly cycle of NO2 by GOME measurements: a signature of anthropogenic sources. Atmospheric Chemistry and Physics 3, 2225–2232. Cerveny, R.S., Balling Jr., R.C., 1998. Weekly cycles of air pollutants, precipitation and tropical cyclones in the coastal NW Atlantic region. Nature 394, 561–563. Cerveny, R.S., Coakley, K.J., 2002. A weekly cycle in atmospheric carbon dioxide. Geophysical Research Letters 29, 15. Delene, D.J., Ogren, J.A., 2002. Variability of aerosol optical properties at four North American surface monitoring sites. Journal of the Atmospheric Sciences 59, 1135–1150. DeMott, P.J., Sassen, K., Poellot, M.R., Baumgardner, D., Rogers, D.C., Brooks, S.D., Prenni, A.J., Kreidenweis, S.M., 2003. African dust aerosols as atmospheric ice nuclei. Geophysical Research Letters 30, 1732. Forster, P.M., Solomon, S., 2003. Observations of a ‘‘weekend effect’’ in diurnal temperature range. Proceedings of the National Academy of Sciences of the United States of America 100, 11,225–11,230. Garrett, T.J., Avey, L., Palmer, P.I., Stohl, A., Neuman, J.A., Brock, C.A., Ryerson, T.B., Holloway, J.S., 2006. Quantifying wet scavenging processes in aircraft observations of nitric acid and cloud condensation nuclei. Journal of Geophysical Research 111, D23S51. Ghil, M., Allen, M.R., Dettinger, M.D., Ide, K., Kondrashov, D., Mann, M.E., Robertson, A.W., Saunders, A., Tian, Y., Varadi, F., Yiou, P., 2002. Advanced spectral methods for climatic time series. Reviews of Geophysics 40 (1), 1003. Gilman, D.L., Fuglister, F.J., Mitchel Jr., J.M., 1963. On the power spectrum of red noise. Journal of the Atmospheric Sciences 20 (2), 182–184. Gong, D.-Y., Ho, C.-H., 2002. Shift in the summer rainfall over the Yangtze River valley in the late 1970s. Geophysical Research letters 29, 10, doi:10.1029/2001GL014523.

Acknowledgments This research was supported by CATER 2006–4204. The authors Yong-Sang Choi and Yeon-Hee Noh were supported by the BK21 project. The authors wish to thank Prof. Dao-Yi Gong for carefully reviewing this document. The MODIS data were obtained from the Atmospheric Sciences Data Center at NASA’s Langley Research Center.

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Gong, D.-Y., Guo, D., Ho, C.-H., 2006. Weekend effect in diurnal temperature range in China: opposite signals between winter and summer. Journal of Geophysical Research 111, D18113. Gong, D.-Y., Ho, C.-H., Chen, D., Qian, Y., Choi, Y.-S., Kim, J., 2007. Weekly cycle of aerosol–meteorology interaction over China. Journal of Geophysical Research 112, in press, doi:10.1029/2007JD008888. Hies, T., Treffeisen, R., Sebald, L., Reimer, E., 2000. Spectral analysis of air pollutants. Part 1: elemental carbon time series. Atmospheric Environment 34, 3495–3502. Ho, C.-H., Kim, J.-H., Lau, K.-M., Kim, K.-M., Gong, D., Lee, Y.-B., 2005. Interdecadal changes in heavy rainfall in China during the northern summer. Terrestrial Atmospheric and Oceanic Sciences 16, 1163–1176. Jacobson, M.Z., Kaufman, Y.J., 2006. Wind reduction by aerosol particles. Geophysical Research Letters 33, L24814. Jin, M., Shepherd, J.M., King, M.D., 2005. Urban aerosols and their variations with clouds and rainfall: a case study for New York and Houston. Journal of Geophysical Research 110, D10S20. Kanamitsu, M., Ebisuzaki, W., Woolen, J., Potter, J., Fiorino, M., 2002. NCEP/DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society 83, 1631–1643. Khain, A., Rosenfeld, D., Pokrovsky, A., 2005. Aerosol impact on the dynamics and microphysics of deep convective clouds. Quarterly Journal of the Royal Meteorological Society 131, 2639–2663. Koren, I., Kaufmann, Y.J., Remer, L.A., Martins, J.V., 2004. Measurement of the effect of Amazon smoke on inhibition of cloud formation. Science 303, 1342–1345. Lin, J.C., Matsui, T., Pielke Sr., R.A., Kummerow, C., 2006. Effects of biomass-burning-derived aerosols on precipitation

and clouds in the Amazon Basin: a satellite-based empirical study. Journal of Geophysical Research 111, D19204. Marr, L.C., Harley, R.A., 2002. Spectral analysis of weekday–weekend differences in ambient ozone, nitrogen oxide, and non-methane hydrocarbon time series in California. Atmospheric Environment 36, 2327–2335. Platnick, S., King, M.D., Ackerman, S.A., Menzel, W.P., Baum, B.A., Riedi, J.C., Frey, R.A., 2003. The MODIS cloud products: algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing 41, 459–473. Qian, W., Quan, L., Shi, S., 2002. Variations of the dust storm in China and its climatic control. Journal of Climate 15, 1216–1229. Qian, Y., Kaiser, D.P., Leung, R., Xu, M., 2006. More frequent cloud-free sky and less surface solar radiation in China from 1955 to 2000. Geophysical Research Letter 33, L01812. Ramanathan, V., Crutzen, P.J., Kiehl, J.T., Rosenfeld, D., 2001. Aerosols, climate, and the hydrological cycle. Science 294, 2119–2124. Rosenfeld, D., 1999. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall. Geophysical Research Letters 26, 3105–3108. Rosenfeld, D., Lahav, R., Khain, A., Pinsky, M., 2002. The role of sea spray in cleansing air pollution over ocean via cloud processes. Science 297, 1667–1670. USEPA, 2005. Emission Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Air Quality Standard (NAAQS) and Regional Haze Regulations. USEPA, Research Triangle Park, North Carolina, August 2005. Wheeler, M., Kiladis, G.N., 1999. Convectively coupled equatorial waves: analysis of clouds and temperature in the wavenumber–frequency domain. Journal of the Atmospheric Sciences 56, 374–399.