Open access peer-reviewed chapter - ONLINE FIRST

Linkage between Urban Aerosols Distribution and Large-Scale Circulation

Written By

Yassin Mbululo

Submitted: November 2nd, 2021 Reviewed: February 7th, 2022 Published: March 18th, 2022

DOI: 10.5772/intechopen.103099

IntechOpen
Urban Aerosols - From Emission Sources to Health Impacts Edited by Chung-Shin Yuan

From the Edited Volume

Urban Aerosols - From Emission Sources to Health Impacts [Working Title]

Prof. Chung-Shin Yuan and Dr. Iau-Ren Ie

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Abstract

This chapter analyzed the dynamics of the atmospheric boundary layer structure (ABLS), Antarctic Oscillation Index (AAOI), and its relationship with air pollution. With regard to the linkage between Antarctic Oscillation (AAO) and pollutants distribution, AAOI was correlated with the dust surface mass concentration of PM2.5 over the mainland China, whereby the boreal summer (June and July) AAO signals (JJ–AAOI) was selected as the determinant factor in establishing a relationship with pollutants during boreal winter. It was found that the average of JJ–AAOI has a significant correlation with the dust surface mass concentration of PM2.5. Months from August to October were the most significant months over the Antarctic. These findings imply that the signals of JJ–AAOI can be stored in Antarctic Sea ice from August to October before affecting the ABL which at the end also affects the pollutant distribution. Analysis of the relationship between dust surface mass concentration of PM2.5 and the large-scale circulation involved the empirical orthogonal function (EOF) of the decomposed winter dust surface mass concentration of PM2.5. The time series from the EOF1 analysis showed a wave train of four years of positive and negative (+, −, +) followed by a decadal negative value.

Keywords

  • Air pollution
  • atmospheric boundary layer
  • PM2.5
  • antarctic oscillation index

1. Introduction

Air pollution is one of the major global public health concerns. This has been reflected in the recently published report by the World Health Organization (WHO) that, only 10% of people live in cities, which conforms with the WHO air quality guideline. What is shocking is that air pollution causes death of one person in every nine people annually, and outdoor air pollution on its own is causing deaths of 3 million people every year [1]. It is also worth noting that, air pollutants in urban areas can be contributed by local sources and long-range transport of air masses [2, 3, 4], for instance, transport of dust mass from Gobi desert to East Asia [5, 6, 7] and from Sahara desert to Europe [8, 9]. These urban aerosols have been reported by a number of authors [10, 11] to have ecotoxicities that are potential for causing health problems [12, 13, 14].

So far, China is the fastest-growing country in the world with a pace which has never been seen before. This growth is partially contributed by heavy investments in manufacturing industries for different kinds of products. This has got an impact on air pollution as most of these industries to use coal as one of the major sources of power; the habitual use of coal for heating in the households by rural dwellers also contributes to the impacts [15, 16]. Besides, lifestyle changes caused by the change of economic status like the increase in the number of vehicles as it can be seen in most of the Chinese cities, to a greater extent worsen the air quality for the country. This has reinforced the Chinese government to make efforts in mitigating air pollution problems, which include the enactment of a stringent law (National Ambient Air Quality Standard (NAAQS)) in 2012 to curb the emission of air pollutants. The NAAQS stipulates the hourly, daily, and annual standards of NO2, SO2, CO, O3, PM10, and PM2.5; the PM10 and PM2.5 stand for particulate matter with the aerodynamic diameter of less than 10 microns and 2.5 microns, respectively. So far, a number of studies on boundary layer structure, which is the main determinant of air pollutants, are available online [3, 4, 17, 18, 19, 20, 21]. Apart from the pioneer studies [22, 23, 24, 25] on pollutants forecast by using large-scale circulations, little has been done on this area which is the key for planning and in managing pollutants. This chapter aims at highlighting the linkage between urban aerosol distribution and large-scale circulation, which are potential for forecasting pollutants.

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2. The linkage between boundary layer structure index (BLSI) and Antarctic oscillation index (AAOI)

In order to establish the relationship between the pollutants and the AAOI, Singular Value Decomposition (SVD) analysis was used to determine covariance of Geopotential height (GPH) and BLSI. The study by Bretherton et al. [26] advocates SVD as the powerful method of determining the spatial correlation of two fields. The lagging GPH from 1000 hPa to 10 hPa was used to depict the AAOI-like features and the leading BLSI of Wuhan city, China was calculated based on the method which was proposed by Zheng et al. [4]. Note that, the BLSI is capable of defining the condition of the lower atmosphere and change of ground air quality. This index (BLSI) considers both horizontal removal and vertical diffusion ability of the ABLS; it can effectively represent the condition of the lower atmosphere. Therefore, using it to characterize the pollutants’ distribution in lower atmosphere is appropriate. Similar to BLSI, the GPH characterizes coherent dynamic and thermodynamic components of the atmosphere from ground-level to troposphere [27]. The correlation analysis of GPH for the lagging months of June, July, and August (JJA–GPH) and the leading BLSI for December, January, and February (DJF–BLSI) was performed. Figure 1a shows the average boundary layer structure index (DJF–BLSI), and Figure 1b shows the correlation map of vertical structure of JJA–GPH; shaded areas show the significant correlation with BLSI at 95% confidence level. The analysis of the results shows that the coupled pattern of the lagging GPH–SVD and leading BLSI–SVD has the capacity to explain 51% of the total squared covariance of DJF–BLSI from the JJA–GPH like AAO pattern. The higher squared covariance which is explained by this leading SVD suggests that, this pattern is the primary phenomenon and can significantly influence the leading BLSI of Wuhan. This observation suggests that other modes are secondary phenomena. The positively correlated area which is significant at 95% confidence level for the GPH is situated South of 55°S throughout the troposphere and at the altitude of 700 hPa to 70 hPa at around 15°S to the equator (Figure 1b). Note that, the correlated area falls within the area used to define AAOI [28, 29]. This result, therefore, suggests that the AAOI-like pattern generated by the GPH has a significant influence on the pollutant distribution in Wuhan. Previous studies have also reported a similar lead–lag time scale of AAOI to influence dust mass frequency [30], and surface temperature [23] over North China. Moreover, the time series of the BLSI and the GPH has the correlation coefficient (r) of 0.49, significant at 95% confidence level (Figure 1c and d). The positive signals of the leading GPH during the months of June and July are observed to concede with the positive signals of the BLSI during the following months of December and January. Similarly, the negative phase of the leading GPH in August concedes with the negative phase of the BLSI in the following month of February. This lead–lag mechanism is consistent and assents the proposition that leading JJA–AAOI determines the condition of the BLSI in the following month (December, January, and February) which at the end determines the condition of the air quality.

Figure 1.

The leading mode of the singular value decomposition (SVD) of geopotential height (GPH) and boundary layer structure index (BLSI) showing (a) Average boundary layer structure index from December 2014 to February 2015 (DJFBLS) (b) The correlation map of the atmosphere showing the geopotential height (GPH) south of the equator from June to August 2014 and the BLSI of Wuhan. The shaded parts show the areas which are statistically significant at 95% confidence level (c) Time series of the BLSI anomaly (d) Time series of GPH anomaly.

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3. Antarctic oscillation index (AAOI) and pollutant distribution

Based on the established covariance of GPH–SVD and BLSI–SVD on the subsection above, it is evident that the AAOI has significant influence on the BLSI which at the end determines pollutants distribution. This subsection, therefore, correlated the average of June and July AAOI (JJ–AAOI) (i.e. boreal summer) with the average dust surface mass concentration of PM2.5 of November to February (boreal winter). This dust mass concentration of PM2.5 is the product of MERRA-2 atmospheric reanalysis which has been assimilated with ground and satellite observation. An assessment study on MERRA-2 surface dust mass concentration of PM2.5 by He et al. [31] reveals a significant correlation with surface measured PM2.5 over the Yangtze River Basin (YRB). Similar consistent observation between surface measured and MERRA-2 data (dust surface mass concentration of PM2.5) has also been reported in North China by Song et al. [32]. Therefore, MERRA-2 data are reliable for studying air pollution. It is also worth noting that, November is not a winter month but during this period of time the concentration of PM2.5 is higher, similar to what is experienced during the winter months (December, January, and February (DJF)). Therefore, November was included in the analysis as the winter month to capture its feedback mechanism of AAOI. A previous study by Fan and Wang [30] on the Antarctic oscillation (AAO) and dust weather frequency found that there was a significant correlation between AAO of DJF and surface air temperature in North China.

The average dust surface mass concentration of PM2.5 over the area (115° E–125° E and 30°N–40° N) which showed a significant correlation with the AAOI was determined and used to develop the time series of pollutants with the AAOI. The determined correlation coefficient on this area was 0.42, which is significant at a 95% confidence level. A closer look at the time series of AAOI (Figure 2), showed a consistent lead–lag effect except in the two scenarios (from 1988/1989 to 1993/1994 and 2013/2014 to 2017/2018) where the trends were not obvious. The observed trends which were not obvious are thought to be so because pollutants distribution over the area is determined by more than one system thus there is a possibility that other influential systems were stronger during this period of time than the effect of AAOI. For instance, a study by Chen and Wang [33] suggested the weakened northerly winds and the growth of inversion anomalies in the lower part of the troposphere and the weakened trough over East Asia to be the reason for haze occurrence. So, whenever one of the factors of this inter-dependable system changes, the whole system will behave differently from its normal behavior.

Figure 2.

Time series of average dust mass surface concentration of PM2.5 over the area (115°E–125° E and 30°N–40°N) during winter (November to February of the following year) and the average of June and July Antarctic oscillation index (JJ–AAOI) from the year 1980 to 2018. The lead–lag relationship is consistent except in the two scenarios (1988/1989 to 1993/1994 and 2013/2014 to 2017/2018).

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4. Forecast of dust mass concentration of PM2.5

Further, the ability of the June and July AAOI (JJ–AAOI) in describing the changes in winter dust mass concentration of PM2.5 and its potential for forecasting pollutants were tested. The linear regression method was used to develop the prediction equation for the dust surface mass concentration of PM2.5 over the region which showed a positive correlation with the AAOI. The dust surface mass concentration of PM2.5 of zonal (115° E–125° E, 30° N–40° N) region was de-trended and used to develop the anomaly time series of PM2.5 concentration. The data set of thirty years [30] from 1980 to 2009 was used to train the system and to formulate the forecasting equation while the data from 2010 to 2007 was used to run the prediction eq. Among the different models (linear, quadratic, cubic, exponential, and logarithmic) which were tested, the linear equation model was found to be the best model (Figure not shown). The developed linear regression equation was modified by adding a percentage error to the training data set and generating a new regression equation. This procedure was repeated several times until the percentage error was reduced, and a more convincing equation was generated. Note that, whenever the process is repeated, new coefficients were generated for the forecast Equation. A number of studies have also used the linear regression equation to forecast weather and climate parameters [34, 35, 36]. The best linear regression equation was;

y=9.1845x1.5146E1

The dependent variable (y) represents anomaly of the dust surface mass concentration of PM2.5, while the independent variable (x) represents the average of June and July AAOI (JJ–AAOI).

The predicted trend was almost the same with the measured values of the anomaly of dust surface mass concentration of PM2.5 (Figure 3), even though the magnitude of the values differs. The forecast equation predicted higher values in 3 cases than the anomaly of the actual values. The developed regression equation shows the potential of prediction as it can explain about 60% of the anomaly of the surface dust mass concentration over northeast China. Nevertheless, there was a difference between observed and predicted anomaly values (see Figure 3) because the dust surface mass concentration of PM2.5 does not entirely depend on one factor, thus, small discrepancies suggest the influence of other determining factors. Not only that but also the region (115° E–125° E, 30° N–40° N) covered by the average data is huge, so small changes in the dust mass concentration can result in a large discrepancy. Nevertheless, the correlation coefficient (r) between the predicted and the observed value was 0.77, which is significant at a 95% confidence limit (p-value equal to 0.024). This indicates that the ability of the average of June and July AAOI to predict the winter dust mass concentration of PM2.5 is substantial. Apart from other well-known pollutant sources and causes, the JJ–AAOI is of significant importance. Therefore, there is a need for a comprehensive study of its variation trend for achieving the purpose of air pollution management over mainland China.

Figure 3.

Prediction ability test of the linear regression equation for the anomaly of average dust surface mass concentration of PM2.5 for winter (November to February) using the average of June and July Antarctic oscillation index (JJ–AAOI) from the year 2010 to 2017.

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5. Antarctic oscillation index (AAOI) and ice concentration

For obtaining some more insights and the possible mechanisms that may be the causative agents, the AAOI, and the Antarctic Sea ice were studied. The correlation results of the AAOI and the Antarctic Sea ice concentration show that there is a significant correlation (at 90% confidence level) between them even though the area which shows a significant correlation decreases as the lead–lag time increases (Figure 4ag). This is because the Antarctic Sea ice distribution is significantly influenced by atmospheric pressure than other factors such as temperature and wind [37, 38, 39, 40]. It is worth noting that, the AAO is also defined based on the GPH anomalies. Nevertheless, the area which shows significant correlation changes with the time of the year (i.e. each month) shows different correlation results. Results from Figure 4a and b suggest that August and September are the most significant months because large areas over the Antarctica region show a significant correlation during these two months.

Figure 4.

Correlation map between the average of June and July Antarctic oscillation index (JJ–AAOI) and the Antarctic Sea ice over for the months (a) August (b) September (c) October (d) November (e) December (f) January (g) February for the period of 36 years (i.e. the year 1982–2017). The red (blue) color shows the area with a positive (negative) correlation with JJ–AAOI at a 90% confidence level.

Similarly, previous studies [41, 42] showed that the AAO signal normally tends to lead the climate anomalies by two to three months (one season). Likewise, a study by Carleton [39] revealed that the indices (Southern Oscillation Index (SOI), Trans-Polar Index (TPI)) over SH leads the Antarctic Sea ice for more than four months. The key areas identified in August and September were five [5]; the first area was between 30°E–50°E and 59.5°S–62°S, the second area was between 90°E–110°E and 57.5°S–61.5°S, the third area was between 110°E–170°E and 61°S–64°S, the fourth area was between 110°W–140°W and 65°S–68°S, and the fifth area was between 40°W–60°W and 59°S–65°S. Similar areas of Sea ice were determined in the study by Wu and Zhang [43] to have a strong influence on the atmosphere. The first, second, and fourth key areas showed a positive correlation with AAOI while the third and fifth areas showed a negative correlation. Interestingly, Figure 4c and d show that, during the austral spring season (October and November), the size of the areas which showed a significant correlation (positive and negative) has been reduced. The reduced areas indicate that the influence of stored signals is reduced with time. Similarly, Figure 4eg show the austral summer season (December, January, and February), in which the areas were further reduced and reached their minimum level at the end of February (Figure 4g). Concurrent results have been reported by Gupta and England [38] on their study of coupled ocean–atmosphere−ice response to variation in SAM (AAO) and by Hall and Visbeck [40] on their study on variability of SH Sea ice from AAO. The finding suggests that the anomalies of the AAOI in June and July can be stored at Antarctic Sea ice before it influences the ABLS which at the end determines the distribution and concentration of dust surface mass concentration of PM2.5 over East and North China. The signal of the AAOI can be imprinted and transmitted through the ice-sea-air system. Since the atmosphere on itself cannot store long memory due to the nature of atmospheric waves which most of the time are chaotic [42, 44, 45, 46], there is a need for the medium which can store this memory, such as the Sea ice. Therefore, the AAOI influences the boundary layer through the ice−sea−atmosphere interaction. Similar interaction of the ice−sea−atmosphere has been revealed by Yuan and Li [37] to be the most important interaction, which affects the atmospheric pressure and temperature. This lead–lag phenomenon is feasible because the observed atmospheric feedback mechanism is shorter than the atmospheric circulation of Rossby waves to travel from the Southern Hemisphere (SH) to the Northern Hemisphere (NH). Similar lead–lag time has been reported by Shen and Mickley [47] in the study of the effects of ENSO on summertime ozone pollution in the eastern United States. The subsection below used the key areas of Antarctic Sea ice to define a new ice index aiming at quantifying the influence of ice concentration on dust surface mass concentration.

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6. The ice index, AAOI, and pollutant distribution

In order to encounter the weight contribution of different areas within the same month, the regression analysis was performed. Figure 5 shows the weight contribution of five key areas, where the highest contribution was found to be originated at 30°E–50°E and 59.5°S–62°S with the weight of eight-folds, while the lowest contribution originated at 40°W–60°W and 59°S–65°S with the contribution of negative three folds.

Figure 5.

The average weight contribution of five key different regions for August to October for 36 years (i.e. the year 1982–2017). The red (blue) color shows the area with positive (negative) weight contribution of the key areas.

This weighting contribution was used to develop the ice index which was then correlated with AAOI; the correlation coefficient found was 0.6 and it was significant at a 99% confidence limit. The observed correlation is reasonable as AAOI has the tendency of regulating Sea ice through atmospheric, oceanic, and dynamic forcing over the Antarctica area [37, 38, 48]. Also, the difference in weight contribution suggests that the anomalies over the five correlated areas of Antarctica did not contribute equally to the observed trend of PM2.5 distribution. Moreover, the ice index developed showed a significant correlation at a 90% confidence limit with the dust surface mass concentration of PM2.5 around East and North China (Figure not shown). These observed results imply that, apart from other contributing factors, the Antarctic Sea ice plays a key role in determining the distribution of the pollutants over East and North China. At this juncture, one of the difficult questions that could arise is how does the dynamics over the Antarctic influence the pollutants on the other side of the hemisphere? One of the possible reasons that could be used to explain the occurrence of this mechanism is through the actions of wind. Furthermore, the correlation map of zonal and meridional winds with the ice index also showed a significant correlation over East and North China (Figure 6).

Figure 6.

The correlation map between the average of August to October Antarctic Sea ice index and the average zonal and meridional wind (average of November to February) at 850 hPa from 1981 to 2018. The marked areas passed the significance correlation test at a 90% confidence level.

Moreover, Figure 6 shows that the eastern and northern parts of China acted as the center of the cyclone, which favors the accumulation of pollutants from the high-pressure zone. A similar observation was reported by Liang and Wang [49] that, East Asia Jetstream (EAJ) is the dominant wind field in China. Therefore, this area acted as the convergence zone of pollutants from different areas. It was found that in this area, wind was originated from far areas such as the northern part of India. Likewise, as presented in Figure 6, this region is under the influence of southerly anomalies which resulted in the decline of clean and moist wind from the northern part. Moreover, the stronger southerly and weaker northerly anomaly has been reported to weaken East Asia winter monsoon [50]. This condition is thought to generate stable atmospheric conditions; a favorable condition for pollutants accumulation. This observation indicates that the higher August to October Sea ice index causes the southerly wind to be stronger while the northerly wind becomes weaker. A similar observation was reported by a number of authors [24, 33] that, higher August, September, and October AAO (ASO–AAO) cause weaker northerly winds over North China.

The correlation coefficient between the JJ–AAOI and the average zonal (80° E–130° E) zonal wind shows significant zonal dipole pattern, with the positive phase in mainland China. The selection of a small area is done purposely in order to capture the detailed information at a fine-scale from the global data. The averaging of these data is intended to minimize the effect which may be caused by the mesoscale phenomena. At the altitudes of 1000 hPa and 850 hPa, there are positively correlated areas over the Taklamakan desert; the size of the correlated area is decreasing with the altitude (Figure 7a and b). This observation suggests that the zonal wind at lower altitudes up to 500 hPa at the area around desert regions was decreasing with the altitude. That means, the higher JJ–AAOI corresponds to the higher low-level wind speeds which may result in the generation of dust and therefore increase the dust surface mass concentration of PM2.5. Different from what was observed at 1000 hPa, the East China Sea was observed to have a significant negative correlation at the altitude of 200 hPa (Figure 7d). Therefore, this shows that the zonal wind from East China Sea decreased, hence little moist and cleaner air was allowed to enter mainland China.

Figure 7.

Correlation coefficient between averaged zonal (80°E–130°E) zonal wind at (a) 1000 hPa (b) 850 hPa (c) 500 hPa (d) 200 hPa and average of June and July Antarctic oscillation index (JJAAOI). The marked areas passed the significance test at a 90% confidence level.

The lead–lag timescale of two months is feasible as revealed in the previous study by Fan and Wang [22] which also found that, dust-related circulations have a timescale of 30 to 60 days from SH to North China. Yuan and Li [37] reported a delayed response of two months from the Sea ice to large-scale atmospheric circulations. Not only these studies but also a study by Qin et al. [51] found a good correlation between April–May AAOI and summer (July–August) rainfall over North China. Similarly, the study by Yuan et al. [48] reveals that the positive phase of AAO during boreal spring can determine the late summer precipitation over North China. The lead–lag time between the positive phase of AAO and summer precipitation is about six months. Therefore, these previous studies further complement the feasibility of two months lead–lag mechanism between the pollutants and the anomaly over SH i.e. the anomalies of the Antarctic Sea ice and the JJ–AAOI. These results signify the need to further contemplate the potential of predicting the status of PM2.5 dust surface mass concentration at least two months in advance; for the purpose of air quality management. Similar suggestion of using AAO in predicting the following season was unveiled by a number of authors [41, 42, 46, 52].

Moreover, the latitude−altitude section for slopes of average zonal (80°E–130°E) meridional wind over mainland China and the average of September and October AAOI (SO–AAOI) (Figure 8a), shows a similar scenario to what has been portrayed in Figure 7. There is AAO like structure below the altitude of 850 hPa and positive anomalies at the altitude between 600 hPa and 70 hPa in SH at around 40°S and 60°S of the Equator (Figure 8a). Over the mainland China, there were positive anomalies below the altitude of 500 hPa with the center at around 850 hPa. At the equator, negative anomalies were observed below the altitude of 400 hPa. Similarly, the slope of zonal wind shows AAO like structure between 40°S and 60°S throughout the troposphere, with the positive anomalies area extending to the stratosphere (Figure 8b). Over the NH, there was a dipole like structure from 10° N to 80° N. As it has been the case for zonal and meridional winds, the slope of AAOI and GPH also show an AAO like structure (Figure 9) in the SH and the dipole like structure in the NH. The positive anomalies over mainland China were found between the pressure level of 700 hPa and 100 hPa. It is worth noting that, the dipole-like structure observed in NH was centered at around 200 hPa in almost all cases i.e. the height of the dominant EAJ. As it has been put forward by previous studies [48, 49], EAJ is important in determining the weather condition for mainland China.

Figure 8.

Latitude-altitude section for slopes of September and October Antarctic oscillation index (SO–AAOI) and mean zonal (80°E–130°E) of (a) meridional (v) wind (b) zonal (u) wind. The abscissa represents latitude while the ordinate represents pressure levels. The marked areas passed the significance test at a 90% confidence level.

Figure 9.

Latitude-altitude section for slopes of September and October Antarctic oscillation index (SO−AAOI) and mean zonal (80°E–130°E) geopotential height. The abscissa represents latitude while the ordinate represents pressure levels. The marked areas passed the significance test at a 90% confidence level.

The observed characteristics of the slope of AAOI with the GPH, zonal and meridional winds are clearly seen in Figure 6, which shows the correlation map indicating the most significant area being between 20°N and 50°N, and 100°E and 125°E. Therefore, this implies that the actions of winds and the influence of AAOI can potentially affect the distribution of pollutants over most parts of mainland China. Corroborated results have been reported by Fan and Wang [22, 30] study on dust in North China, Zheng et al. [42] study on the seasonal influence of AAOI on precipitation, and Wang and Fan [53] study on the linkage between southern hemisphere zonal wind and East Asian summer monsoon circulation. In general, these studies indicate that the possible mechanism of the linkage between Antarctic and NH is based mainly on meridional teleconnection.

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7. Dust mass concentration and large scale circulation

It is well established in the literature that, large-scale circulation in both, SH and NH affects the climate and weather patterns of China and Asia as well. So in order to get an insight into the possible mechanisms as to how the climatic factors influence the distribution of the pollutants over China, empirical orthogonal functions (EOFs) was used to decompose the variability of winter dust surface mass concentration of PM2.5 (November, December, January and February) from 1980 to 2018. As it has been pointed out in the previous subsection, November is also included in the analysis because it was found to be highly polluted during the winter months, therefore, its inclusion is necessary for capturing the broader picture of what is happening during the high pollutants periods. Figure 10a and b show the first EOF (EOF1) and second EOF (EOF2) loading of the dust mass surface concentration of PM2.5, respectively. The EOF1 explains 40% of the original loading of surface dust mass concentration of PM2.5 anomaly which shows a swath of a positive anomaly over the northwest and eastern part of China (Figure 10a). A previous study by Bian et al. [5] linked the dust pollution in eastern China with this high loading area identified by EOF1 as in this area, there is the largest desert in China (i.e. Taklamakan desert) and the Gobi desert. A study on the estimates of the ground concentration of PM2.5 based on satellite-derived aerosol optical depth by Ma et al. [54] also indicated the Tarim Basin (i.e. Taklamakan desert) and Gobi desert similar to what has been identified by EOF1 as the potential sources of PM2.5 in China. Elsewhere, Galindo et al. [8] found a high concentration of crustal element in PM10 samples in Italy during dust outbreaks in Sahara desert. Therefore, this substantiates that, these deserts can generate both coarse and fine particles causing high loading of PM2.5 dust mass concentration. A different scenario was observed on EOF2 which explains 29% of the total variance of dust mass concentration of PM2.5 because some parts of the central and northern areas showed a negative correlation (Figure 10b). That is, the area which was identified to explain much of the loading by EOF1 showed a negative correlation in EOF2 different from what was reported in the previous studies [5, 54]. This finding can be partially contributed by the small variance explained by EOF2 as compared to the one of EOF1. The maximum spatial loadings of EOF1 are found at 35°N–42°N and 75°E–110°E and the average spatial loading are found at around 22°N–40°N and 110°E–125°E. Time series of decomposed surface dust mass concentration of PM2.5 for the leading principal component (PC1) and second principal component (PC2) are as shown in Figure 11a and b, respectively. Before 1992, the time series of PC1 showed the four years wave train of negative and positive values (+, −, +) before it maintained the decadal negative value from 1997 to 2007. A different scenario was observed in the time series of PC2 as it showed a bi-decadal mode of negative values before the year 2000 and positive values after the year 2000 except in the year 2008 when it was negative. The value of PC2 post the year 2000 and the year 2009 have a time scale of eight and nine years of consecutive positive values, respectively. With due consideration to scant information from the variation trend of EOF2, this study did not consider EOF2 for further analysis. This is, therefore, suggested to provide the area of focus for other researchers to explore further on what is the possible association between EOF2 and the distribution of the pollutants.

Figure 10.

The loading of Empirical orthogonal function (EOF) of average dust surface mass concentration of PM2.5 during winter season (November to February) from 1980 to 2018 across China (a) EOF1 (b) EOF2.

Figure 11.

Normalized detrended time series of decomposed average dust surface mass concentration of PM2.5 during the winter season (November to February) from 1980 to 2018 (a) Leading principal component (PC1) (b) Second principal component (PC2).

Since, EOF1 explains much variance of dust mass concentration of PM2.5 during winter (Figure 10a), the correlation amongst climatic factors of boreal autumn (September and October) and PC1 were used to identify key areas with the influence on pollutants distribution in China. Figure 12 shows the correlation coefficient results between averaged zonal (80°E–130°E) zonal wind at different altitudes (1000 hPa, 850 hPa, 500 hPa, and 200 hPa) and PC1. The analysis of the results from the correlation map of PC1 and zonal wind showed significant zonal positive and negative tripole (+, −, +) patterns in the meridional direction. The center of negative correlation at 1000 hPa and 850 hPa is at East China Sea around 22°N and 125°E (Figure 12a and b). The negatively correlated area which was around East China Sea was observed to shift inland at 500 hPa and disappeared at 200 hPa (Figure 12c and d). The positive correlation in mainland China at 1000 hPa and 850 hPa was centered on the position of Taklamakan desert (40°N and 95°E) while for the upper level (500 hPa and 200 hPa) it was centered at 30°N and 95°E (Figure 12c and d). Observed significant zonal positive and negative dipole propagation suggests the influence of zonal wind in propagating the signals to mid-latitude.

Figure 12.

Correlation map of the leading principal component (PC1) and averaged zonal (80°E–130°E) zonal wind at (a) 1000 hPa (c) 850 hPa (e) 500 hPa (g) 200 hPa. The marked areas passed the significant test at 90% confidence level.

Figure 13a shows the composite difference of meridional circulation between the years of high and low-AAOI. The high and low years were selected after multiplying the standard deviation by 0.5 of standardized JJ–AAOI. The year 1979, 1984, 1985, 1989, 1993, 1998, 2004, 2010, 2015, and 2016 were selected as the years of high positive JJ–AAOI, while the years 1991, 1992, 1996, 1997, 2005, 2007, and 2009 were selected as the years of low negative JJ–AAOI. Similarly, Figure 13b shows the composite difference of meridional circulation between high and low PC1 years. The high and low years were also selected after multiplying the standard deviation by 0.5 of standardized PC1. The years with positive high (negative low) PC1 were 1983, 1985, 1991, 1992, 1995, 2008, 2009, 2011, and 2012 (1979, 1980, 1988, 1990, 1997, 1998, 2000, 2004, and 2005). The shadings which are seen on these figures (i.e. Figure 13a and b) denote the climatology average of June and July vertical velocity (i.e. omega). A careful look at these figures show the ascending motions are from the equator to around 40°N and descending motion at around 60°S and 20°N. The observed intensification of the westerlies at around 60°S and 75°S during the high AAOI years has been reported in previous studies [40, 52]. Since the global meridional circulations in both SH and NH are connected and share ascending air mass branches, therefore, the meridional circulation changes in SH will also affect the circulations in NH. The results from these figures show that the significant ascending and southerly anomalies exhibit around 25°N and 35°N during the positive JJ–AAOI years. That is to say, the higher PC1 is concurrent with the overlaying ascending southerly anomaly which is in one way or another endorsed by the positive phase of JJ–AAO. These observed scenarios during the positive phase of AAO are favorable for pollutant accumulation.

Figure 13.

Composite difference of September and October meridional circulation between high and low (a) June and July AAOI years (b) PC1. The shaded area represents the climatology vertical velocity (omega) with the units of 1% Pa/s while the black vector represents the composite difference which reaches 90% confidence level for student t-test.

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8. Conclusion

The linkage between large-scale circulation and the pollutants distribution was studied using the correlation map of AAOI and dust surface mass concentration of PM2.5 over mainland China. The area which was found to have a significant correlation was normalized and used to develop a time series of dust mass concentration. The correlation coefficient between time series of dust mass concentration of PM2.5 and AAOI was 0.42; significant at 95% confidence level. The lead–lag trend of the time series of pollutants and the JJ–AAOI was consistent except on two occasions (1988/89 to 1993/94 and 2013/14 to 2017/18). The inconsistency in these two occasions indicates that another prominent system was leading the AAO. On top of this, most of the high and low AAOI years did not fall within these two occasions which also support the existence of other influencing system signifying that pollutants distribution does not depend on only one factor. Moreover, the JJ–AAOI was found to have a good correlation with the Antarctic Sea ice concentration in the leading months over the key areas which were: 30°E–50°E and 59.5°S–62°S, 90°E–110°E and 57.5°S–61.5°S, 110°E–170°E and 61°S–64°S, 110°W–140°W and 65°S–68°S, and 40°W–60°W and 59°S–65°S. The correlation coefficient of ice index developed from the regression analysis of the key areas with AAOI was 0.6 and significant at 99% indicating that the signals of AAOI are imprinted on Antarctic Sea ice before affecting the ABL and dust mass concentration. It should be noted that mainland China acts as the center of the cyclone (convergence zone). The persistence of the positive phase of JAAO in NH was found to proceed up to December whereby the signals are transferred from higher latitudes to lower and mid-latitudes through tropospheric vortex and the circumpolar westerlies. The cross-equatorial signal transfer is evident at the pressure level of 850 hPa and 500 hPa.

Moreover, EOF1 was found to explain 40% of the total variability of dust mass concentration of PM2.5 over mainland China. This is indicated by a swath of positive anomaly over the northeast particularly over the Taklamakan and Gobi Desert as well as eastern part of the country. The maximum spatial loading of EOF1 centered at around 35°N–42°N and 75°E–110°E and 22°N–40°N and 110°E–125°E; these areas are potential sources for dust mass before they find their way to the atmosphere through wind. Contrary, EOF2 which explains 29% of the total variability showed a negative correlation with dust mass concentration of PM2.5 over potential sources identified by EOF1 which indicates that different mechanisms control dust mass concentration. EOF2 results could be partially contributed by its small variance. Furthermore, time series of decomposed dust mass concentration of PM2.5 for the PC1 revealed a four years wave train of positive and negative values (+, −, +) before the year 1992 and the decadal negative train after the year 1997; the trend is concurrent with the calculated high and low year of PC1. The time series of PC2 indicates the existence of a bi-decadal mode of negative values before the year 2000, and a positive value after, except in the year 2008 where there was a negative value. PC1 showed a significant zonally positive correlation with the zonal wind and negative tripole (+, −, +) pattern in the meridional direction. The positively correlated regions over mainland China were centered at Taklamakan desert (40°N and 95°E) as was the case for AAOI analysis. The zonal positive and negative pattern indicates that zonal wind influenced the propagation of signals to mid-latitude. Moreover, the composite difference of meridional wind among the years of high and low June AAOI and also the year of high and low PC1, showed that the significant ascending and southerly anomalies exhibit at around 25°N and 35°N during the years with high JAAOI. It is worth noting that, this identified area falls within mainland China. For further research, the AAO and key areas identified over Antarctic Sea ice can be used as the starting point on the efforts to develop a comprehensive forecasting model for pollutants distribution in the mainland China.

References

  1. 1. WHO. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Diseases. Geneva, Switzerland: WHO; 2016. Available from:http://apps.who.int/iris/bitstream/10665/250141/1/9789241511353-eng.pdf?ua=1
  2. 2. Mbululo Y, Qin J, Yuan Z. Boundary layer perspective assessment of air pollution status in Wuhan city from 2013 to 2017. Environmental Monitoring and Assessment. 2019;191(2):1-12
  3. 3. Mbululo Y, Qin J, Yuan Z. Evolution of atmospheric boundary layer structure and its relationship with air quality in Wuhan, China. Arabian Journal of Geosciences. 2017;10(22):1-12
  4. 4. Zheng X, Qin J, Liang S, Yuan Z, Mbululo Y. The development of boundary layer structure index (BLSI) and its relationship with ground air quality. Atmosphere (Basel). 2018;10(1):3
  5. 5. Bian H, Tie X, Cao J, Ying Z, Han S, Xue Y. Analysis of a severe dust storm event over China: Application of the WRF-dust model. Aerosol and Air Quality Research. 2011;11(4):419-428
  6. 6. Maki T, Hara K, Kobayashi F, Kurosaki Y, Kakikawa M, Matsuki A, et al. Vertical distribution of airborne bacterial communities in an Asian-dust downwind area, Noto Peninsula. Atmospheric Environment [Internet]. 2015;119:282-293. DOI: 10.1016/j.atmosenv.2015.08.052
  7. 7. Zhang X, Sharratt B, Liu L, Wang Z, Pan X, Lei J. East Asian dust storm in May 2017: Observations, modelling and its influence on Asia-Pacific region. Atmospheric Chemistry and Physics. 2018;18(11):8353-8371
  8. 8. Galindo N, Yubero E, Clemente Á, Nicolás JF, Varea M, Crespo J. PM events and changes in the chemical composition of urban aerosols: A case study in the western Mediterranean. Chemosphere. 2020;244
  9. 9. Barberán A, Henley J, Fierer N, Casamayor EO. Structure, inter-annual recurrence, and global-scale connectivity of airborne microbial communities. Science of the Total Environment. 2014;487(1):187-195. DOI: 10.1016/j.scitotenv.2014.04.030
  10. 10. Turóczi B, Hoffer A, Tóth Á, Kováts N, Ács A, Ferincz Á, et al. Comparative assessment of ecotoxicity of urban aerosol. Atmospheric Chemistry and Physics. 2012;12(16):7365-7370
  11. 11. Romano S, Perrone MR, Becagli S, Pietrogrande MC, Russo M, Caricato R, et al. Ecotoxicity, genotoxicity, and oxidative potential tests of atmospheric PM10 particles. Atmospheric Environment. 2020;221:117085. DOI: 10.1016/j.atmosenv.2019.117085
  12. 12. Pascal M, Corso M, Chanel O, Declercq C, Badaloni C, Cesaroni G, et al. Assessing the public health impacts of urban air pollution in 25 European cities: Results of the Aphekom project. Science of the Total Environment. 2013;449(2007105):390-400. DOI: 10.1016/j.scitotenv.2013.01.077
  13. 13. Li W, Ali E, El-Magd IA, Mourad MM, El-Askary H. Studying the impact on urban health over the greater delta region in Egypt due to aerosol variability using optical characteristics from satellite observations and ground-based AERONET measurements. Remote Sensing. 2019;11(17):1-24
  14. 14. Kanellopoulos PG, Verouti E, Chrysochou E, Koukoulakis K, Bakeas E. Primary and secondary organic aerosol in an urban/industrial site: Sources, health implications and the role of plastic enriched waste burning. Journal of Environmental Science (China). 2021;99:222-238. DOI: 10.1016/j.jes.2020.06.012
  15. 15. Li M, Zhang L. Haze in China: Current and future challenges. Environmental Pollution. 2014;189:85-86. DOI: 10.1016/j.envpol.2014.02.024
  16. 16. Lyu XP, Wang ZW, Cheng HR, Zhang F, Zhang G, Wang XM, et al. Chemical characteristics of submicron particulates (PM1.0) in Wuhan, Central China. Atmospheric Research. 2015;161:169-178
  17. 17. Mbululo Y, Qin J, Yuan Z, Nyihirani F, Zheng X. Boundary layer perspective assessment of air pollution status in Wuhan city from 2013 to 2017. Environmental Monitoring and Assessment. 2019;191(69):1-12. DOI: 10.1007/s10661-019-7206-9
  18. 18. Yuan Z, Qin J, Zheng X, Mbululo Y. The relationship between atmospheric circulation, boundary layer and near-surface turbulence in severe fog-haze pollution periods. The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP). 2020;1:200
  19. 19. Wu M, Wu D, Fan Q , Wang BM, Li HW, Fan SJ. Observational studies of the meteorological characteristics associated with poor air quality over the Pearl River Delta in China. Atmospheric Chemistry and Physics. 2013;13:10755-10766
  20. 20. Hu XM, Ma Z, Lin W, Zhang H, Hu J, Wang Y, et al. Impact of the Loess Plateau on the atmospheric boundary layer structure and air quality in the North China Plain: A case study. Science of the Total Environment. 2014;499:228-237. DOI: 10.1016/j.scitotenv.2014.08.053%255Cn
  21. 21. Qin J, Mbululo Y, Yang M, Yuan Z, Nyihirani F, Zheng X. Chemical composition and deposition fluxes of water-soluble inorganic ions on dry and wet deposition samples in Wuhan, China. International Journal of Environmental Research and Public Health. 2019;16(136):132. Available from:www.mdpi.com/journal/ijerph
  22. 22. Fan K, Wang H. Dust storms in North China in 2002: A case study of the low frequency oscillation. Advances in Atmospheric Sciences. 2007;24(1):15-23
  23. 23. Zheng F, Li J, Clark RT, Ding R, Li F, Wang L. Influence of the boreal spring southern annular mode on summer surface air temperature over Northeast China. Atmospheric Science Letters. 2015;16(2):155-161
  24. 24. Zhang Z, Gong D, Mao R, Qiao L, Kim SJ, Liu S. Possible influence of the Antarctic oscillation on haze pollution in North China. Journal of Geophysical Research – Atmospheres. 2019;124(3):1307-1321
  25. 25. Gao M, Sherman P, Song S, Yu Y, Wu Z, Mcelroy MB. Seasonal Prediction of Indian wintertime aerosol pollution using the ocean memory effect. Science Advances. 2019;5:eaav4157
  26. 26. Bretherton CS, Smith C, Wallace JM. An intercomparison of methods for finding coupled patterns in climate data. Journal of Climate. 1992;5:541-560
  27. 27. Li L, Zhang R, Wen M, Duan J, Qi Y. Effects of the atmospheric dynamic and thermodynamic fields on the Eastward propagation of Tibetan Plateau Vortices. Tellus, Series A: Dynamic Meteorology and Oceanography. 2019;71(1):1-12. DOI: 10.1080/16000870.2019.1647088
  28. 28. Gong D, Wang S. Definition of Antarctic oscillation index. Geophysical Research Letters. 1999;26(4):459-462
  29. 29. Thompson DWJ, Wallace JM. Annular modes in the extratropical circulation. Part I: Month-to-month variability. Journal of Climate. 2000;13(5):1018-1036. DOI: 10.1175/1520-0442%25282000%2529013%253C1018%253AAMITEC%253E2.0.CO%253B2
  30. 30. Fan K, Wang H. Antarctic oscillation and the dust weather frequency in North China. Geophysical Research Letters. 2004;31(10):1-5
  31. 31. He L, Lin A, Chen X, Zhou H, Zhou Z, He P. Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based verification, spatiotemporal distribution and meteorological dependence. Remote Sensing. 2019;11(4):460
  32. 32. Song Z, Fu D, Zhang X, Wu Y, Xia X, He J, et al. Diurnal and seasonal variability of PM2.5 and AOD in North China Plain: Comparison of MERRA-2 products and ground measurements. Atmospheric Environment. 2018;191:70-78. DOI: 10.1016/j.atmosenv.2018.08.012
  33. 33. Chen H, Wang H. Haze Days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012. Journal of Geophysical Research–Atmospheres. 2015;120:5895-5909
  34. 34. Prabakaran S, Naveen Kumar P, Sai Mani Tarun P. Rainfall prediction using modified linear regression. ARPN The Journal of Engineering and Applied Science. 2017;12(12):3715-3718
  35. 35. Hastenrath S. Prediction of Indian Monsoon Rainfall: Further exploration. Journal of Climate. 1988;1:298-304
  36. 36. Selvaraj RS, Aditya R. Statistical method of predicting the Northeast rainfall of Tamil Nadu. Universal Journal of Environmental Research and Technology. 2011;1(4):557-559
  37. 37. Yuan X, Li C. Climate modes in southern high latitudes and their impacts on Antarctic sea ice. Journal of Geophysical Research. 2008;113:1-13
  38. 38. Sen Gupta A, England MH. Coupled ocean—atmosphere—ice response to variations in the Southern Annular mode. Journal of Climate. 2006;19:4457-4486
  39. 39. Carleton AM. Antarctic sea-ice relationships with Indices of the Atmospheric circulation of the Southern Hemisphere. Climate Dynamics. 1989;3(4):207-220
  40. 40. Hall A, Visbeck M. Synchronous variability in the Southern Hemisphere atmosphere, sea ice, and ocean resulting from the Annular Mode. Journal of Climate. 2004;17(11):2249-2254
  41. 41. Fei Z, Jianping LI, Ting LIU. Some advances in studies of the climatic impacts of the Southern Hemisphere Annular mode. Journal of Meteorological Research. 2014;28:850-835
  42. 42. Zheng F, Li J, Wang L, Xie F, Li X. Cross-seasonal influence of the December—February Southern hemisphere annular mode on March-May meridional circulation and precipitation. Journal of Climate. 2015
  43. 43. Wu Q , Zhang X. Observed evidence of an impact of the Antarctic sea ice dipole on the Antarctic oscillation. Journal of Climate. 2011;24(16):4508-4518
  44. 44. Wang B, Wu R, Fu X. Pacific–East Asian teleconnection: how does ENSO affect East Asian climate?*. Journal of Climate. 2000;13:1517-1536
  45. 45. He S, Wang H. Impact of the November/December Arctic oscillation on the following January temperature in East Asia. Journal of Geophysical Research – Atmospheres. 2013;118(23):12981-12998
  46. 46. Wu Z, Li J, Wang B, Liu X. Can the Southern Hemisphere Annular mode affect China winter monsoon? Journal of Geophysical Research. 2009;114(June):1-11
  47. 47. Shen L, Mickley LJ. Effects of El Niño on summertime ozone air quality in the Eastern United States. Geophysical Research Letters. 2017;44(24):12,543-12,550
  48. 48. Yuan Z, Qin J, Li S, Huang S, Mbululo Y. Impact of Spring AAO on summertime precipitation in the North China Part: Observational analysis. Asia-Pacific. Journal of the Atmospheric Sciences. 2020;57(1):1-16. DOI: 10.1007/s13143-019-00157-2
  49. 49. Liang XZ, Wang WC. Associations between China monsoon rainfall and tropospheric jets. Quarterly Journal of the Royal Meteorological Society. 1998;124(552):2597-2623
  50. 50. Zhang G, Gao Y, Cai W, Leung LR, Wang S, Zhao B, et al. Seesaw haze pollution in North China modulated by the sub-seasonal variability of atmospheric circulation. Atmospheric Chemistry Physicseric Chemistry and Physics. 2019;15:565-576
  51. 51. Qin J, Wang P, Gong Y. Impacts of Antarctic oscillation on summer moisture transport and precipitation in Eastern China. Chinese Geographical Science. 2005;15(1):22-28
  52. 52. Liu T, Li J, Zheng F. Influence of the Boreal Autumn Southern Annular mode on winter precipitation over land in the Northern Hemisphere. Journal of Climate. 2015;28(22):8825-8839
  53. 53. Wang H, Fan K. Southern Hemisphere mean zonal wind in upper troposphere and East Asian summer monsoon circulation. Chinese Science Bulletin. 2006;51(12):1508-1514
  54. 54. Ma Z, Hu X, Huang L, Bi J, Liu Y. Estimating ground-level PM2.5 in China using satellite remote sensing. Environmental Science & Technology. 2014;48(13):7436-7444

Written By

Yassin Mbululo

Submitted: November 2nd, 2021 Reviewed: February 7th, 2022 Published: March 18th, 2022