Potential Effects Of Increased Urbanization In Athens Biology Essay

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Purpose The aim of this study is to investigate the potential effects of increased urbanization in the Athens city, Greece on the intrinsic features of the temporal fluctuations of the surface ozone concentration (SOZ).

Methods The detrended fluctuation analysis was applied to the mean monthly values of SOZ derived from ground-based observations collected at the centre of Athens basin during 1901-1940 and 1987-2007.

Results Despite the present-day SOZ doubling in respect to SOZ historic levels, its fluctuations exhibit long-range power-law persistence, with similar features in both time periods. This contributes to an improved understanding of our predictive powers and enables better environmental management and more efficient decision making processes.

Conclusions The extensive photochemistry enhancement observed in the Athens basin from the beginning of the 20th century until the beginning of the 21st century, seems not to have affected the long memory of SOZ correlations. The strength of that memory provides the limits of the air pollution predictability at various time scales.

Keywords: Air pollution, Historic observations, Detrended fluctuation analysis, Long-range correlations, Surface ozone, Urbanization

1 Introduction

For the last three decades, solar ultraviolet radiation reaching the troposphere has been increasing due to stratospheric ozone depletion (Cracknell and Varotsos 1994, 1995; Varotsos et al. 1994, 1995, 2001a; Varotsos 2002, 2004, 2005). This leads to an indirect amplification of the photochemical ozone production over most continental regions while, in parallel, the anthropogenic emissions of air pollutants (e.g. NOx) in the metropolitan areas are decreasing (Kondratyev and Varotsos 2001; Young 2005; Hocking et al. 2007). It is also important the role of the aerosol content to the solar ultraviolet radiation reaching the various atmospheric layers and the earth's surface and development of relevant modeling of regional or global scale (e.g. Kondratyev and Varotsos 1996). For example, measurements of the distributions of the aerosol characteristics and solar ultraviolet irradiance were conducted by using instrumentation flown on a Falcon aircraft over the entire Greek area from the sea up to the tropopause, showing an 4.3 ± 0.1% km-1 increase for altitudes ranging from the ground to 6.2 km (e.g. Katsambas et al. 1997; Alexandris et al. 1999).

Since the late 1980s there has been an upward trend of low SOZ values, while the peak values of the SOZ concentration have reduced. In Europe, the summer mean of daily maximum SOZ is in the order of 40-60 ppb over continental regions and lower (20-40 ppb) at the boundaries.

In the Athens basin (Greece) the air quality is often influenced by the see-breeze effect (Lalas et al. 1983; Asimakopoulos et al. 1992). Therefore, the chemical budget of the SOZ content is often determined by the marine alkali-halides on which the ozone uptake is strongly dependent from their composition (Ghosh and Varotsos 1999).

It is a truism that most of the atmospheric quantities obey non-linear laws which usually generate non-stationarities (Chen et al. 2002 and references therein). These non-stationarities often conceal the existing correlations in the examined time series and therefore new analytical techniques capable to eliminate nonstationarities in the data should be employed (Hu et al. 2001). The most recent methods used along these lines are wavelet techniques (e.g. Koscielny-Bunde et al. 1998) and Detrended Fluctuation Analysis (DFA) which was introduced by Peng et al. (1994) (e.g. Ausloos and Ivanova 2001; Weber and Talkner 2001; Chen et al. 2002; Collette and Ausloos 2004).

In the present study, we are handling the changes of the surface atmosphere by applying the above mentioned DFA method to the SOZ observations, collected at the National Observatory of Athens (NOA), during the period 1901-1940 and at the Patission monitoring station of the National Service of Air Pollution Monitoring (Map 1), during the period 1987-2007. We are focusing on the Athens area because this region has significant air pollution problems, due to high population density and considerable emissions of air pollutants, the intense sunshine, and the characteristic features of the topography (a basin surrounded by mountains).

Map 1 Athens area and locations of the observation stations (NOA, Patission).

2 Materials and methods

In the present study we have used the re-evaluated historic record of SOZ, which was measured at the NOA, during the period 1901-1940, using De James colometric papers. These papers were exposed to surface air during the daytime (8:00-20:00) and nighttime (20:00-8:00) and were shielded from the sun and rain. The measurements (correlated to a chromatic scale from 0 to 21) depend mainly on the ozone mixing ratio, relative humidity and exposure time. A full description of the re-evaluation method is presented by Cartalis and Varotsos (1994). We have also used SOZ measurements taken at 30 min intervals at the Patission monitoring station, located close to NOA (at about 3 km from NOA), during the period 1987-2007. The monitoring instruments were operated according to the Monitoring UV photometry technique, with detection limit 0.1 ppb.

To search efficiently for time scaling, we adopt a data analysis technique which is not debatable due to the non-stationarity of the data (Varotsos et al. 2005). Therefore, to study the temporal correlations of SOZ fluctuations, the Detrended Fluctuation Analysis (DFA) method is used.

This method of DFA, which stems from random walk theory, allows the detection of intrinsic self-similarity in non-stationary time series (Talkner and Weber 2000). Thus, the benefit of using this method is that it eliminates seasonal trends and non-stationarity effects. According to the DFA method, the time series is first integrated and then it is divided into non-overlapping N/τ segments of equal length, τ. In each segment, a least squares line is fitted to detrend the integrated time series by subtracting the locally fitted trend. The root-mean-square (rms) fluctuations Fd of this integrated and detrended time series is calculated over all time scales (segment sizes). In particular, the detrended fluctuation function F(τ) is calculated as (Kantelhardt et al. 2002):

, k=0, 1, 2, …, (1)

where z(t) is the polynomial of order l least-square fit to the τ data points contained into a segment.

For scaling dynamics, the segments' mean fluctuation F2(τ) is related to the scale of the segments' length by a power law:

(2)

and the power spectrum function scales with 1/f β, where β=2α -1 (Ausloos and Ivanova 2001).

The value of the exponent α implies the existence or not of long-range correlations. Furthermore, α is a precise measure of the maximum dimension of a multifractal process. When α ¹ 0.5 in a certain range of τ values then long-range correlations in that time interval exist, while α = 0.5 corresponds to the classical random walk (white noise). If 0 < α < 0.5, power-law anticorrelations are present (antipersistence). When 0.5 < α < 1.0, then persistent long-range power-law correlations prevail, while the case α = 1 corresponds to the so-called 1/f noise.

3 Results and discussion

It is well known that surface ozone is an important secondary pollutant in the boundary layer. Observational and modelling studies show that the elevated SOZ levels in the rural areas of industrialized countries, during summer, is mainly the product of long-range transport of SOZ precursors and multi-day photochemical production (Lalas et al. 1983; Asimakopoulos et al. 1992; Varotsos et al 2001b).

It should be taken into account that in the third decade of the 20th century, Athens was already a big town with a population of 700,000 habitants. During that period a rapid development of industry took place and many factories were installed west of the port of Piraeus. Therefore, the west wind (blowing in the direction from Piraeus to NOA) transported polluted air to the experimental site NOA. In particular, the emitted precursors at the industrial zone of Piraeus produced ozone photochemically during their journey from Piraeus to NOA.

Recently, Varotsos et al. (2001b) investigated the seasonal variation of the SOZ mixing ratio at Athens, Greece during the periods 1901-1940 and 1987-1998 and concluded that the nighttime SOZ mixing ratio remained approximately the same from the beginning until the end of the twentieth century, while the daytime SOZ mixing ratio has increased by approximately 1.8 times, a fact that may be explained by the enhancement of in situ photochemistry.

In the present study, the DFA method is applied to the deseasonalized and detrended mean monthly SOZ values (during daytime and nighttime, separately), for the periods 1901-1940 and 1987-2007, in order to search for the existence of long-range correlations. It has recently been recognized (Hu et al. 2001) that the existence of long-term trends in a time series may influence the results of the correlation analysis. Therefore, the effects of SOZ trends have to be distinguished from SOZ intrinsic fluctuations.

Fig. 1(a,c) depicts the deseasonalized and detrended SOZ mean monthly values at NOA and Patission station, during 1901-1940 and 1987-2007, respectively, for daytime (8:00-20:00), while Fig. 1(b,d) show the log-log plot of the function Fd for the previous data and the respective slope a.

Fig. 2(a,c) depict the deseasonalized and detrended SOZ mean monthly values at NOA and Patission station, during 1901-1940 and 1987-2007, respectively, for nighttime (20:00-8:00), while Fig. 2(b,d) show the log-log plot of the function Fd for the previous data and the respective slope a. Regarding Fig. 1(b,d) and 2(b,d), the DFA-exponents, for both daytime and nighttime, revealed persistent long-range power-law correlations during the periods 1901-1940 and 1987-2007 and for all time lags between 4 months - 5 years and 4 months - 10 years, respectively. Given that long range dependence and long memory are synonymous notions, this finding is equivalent to saying that the detrended SOZ anomalies exhibit long memory (i.e. that enables predictability), and are thus associated with fractal behaviour. In other words, the fluctuations of the detrended SOZ anomalies in small time intervals are closely related to the fluctuations in longer time intervals in a power-law fashion (when the time intervals vary from about 4 months to about 10 years). The larger is slope a the better is the predictability.

(a)

(b)

(c)

(d)

Fig. 1 (a) Deseasonalized and detrended SOZ mean monthly values (in μgr/m3), at NOA, during 1901-1940, for daytime (8:00-20:00), (b) the DFA-function in log-log plot for the above data and the respective best fit equation and correlation coefficient, y = 0.77x + 0.04 and R2 = 0.97, (c) deseasonalized and detrended SOZ mean monthly values, at Patission station, during 1987-2007, for daytime (8:00-20:00), (d) the DFA-function in log-log plot for the previous data and the respective best fit equation and correlation coefficient, y = 0.73x + 0.11 and R2 = 0.97

It is worthwhile noting that SOZ persistence at the beginning of the twentieth century seems to have similar features to the corresponding SOZ persistence of the period 1987-2007 (for both daytime and nighttime), leading to the result that the enhancement of in situ photochemistry in the Athens basin seems not to have affected the long-term memory of SOZ correlations from the beginning of the 20th century until the beginning of the 21st century (i.e. the predictability level is almost the same).

(a)

(b)

(c)

(d)

Fig. 2 (a) Deseasonalized and detrended SOZ mean monthly values (in μgr/m3), at NOA, during 1901-1940, for nighttime (20:00-8:00), (b) the DFA-function in log-log plot for the above data and the respective best fit equation and correlation coefficient, y = 0.76x + 0.08 and R2 = 0.97, (c) deseasonalized and detrended SOZ mean monthly values, at Patission station, during 1987-2007, for daytime (20:00-8:00), (d) the DFA-function in log-log plot for the previous data and the respective best fit equation and correlation coefficient, y = 0.75x + 0.01 and R2 = 0.96

In parallel, the long memory in the fluctuations of the detrended daytime SOZ anomalies, during both periods, may be considered similar to the corresponding long memory of the nighttime SOZ fluctuations. Given that the SOZ variability is closely associated with that of surface temperature the long-term memory in SOZ should be stemmed from the long-term memory in the surface temperature variability. In this context,

Kiraly et al. (2006) have showed that long-range temporal power-law correlations extending up to several years have been detected in surface temperature data from the Global Daily Climatology Network. Preliminary results from our analysis of the historical surface temperature in Athens revealed persistent long-range power-law correlations which resemble those of the wind speed and sun radiation. These results along with similar ones obtained from the analysis of the historical data from other sites will be presented in a forthcoming paper.

4 Conclusions

The present study focuses on the relative predictive power of the surface air pollution that can be expected at different time scales, and whether it vanishes altogether at certain ranges. To this end the DFA method is applied to the deseasonalized and detrended SOZ mean monthly values (during daytime and nighttime) for the periods 1901-1940 and 1987-2007. That application revealed persistent long-range power-law correlations (for all time lags between 4 months - 10 years) and exhibited long memory, associated with fractal structure that enables predictability. Moreover, SOZ persistence at the beginning of the twentieth century, showed similar features to the corresponding SOZ persistence at the beginning of the 21st century, a fact which means that the industrialisation and the enhancement of in situ photochemistry in the Athens basin did not affect the SOZ fractal behavior. Finally, very little difference was observed between daytime and nighttime SOZ fluctuations for both periods. The results obtained could contribute to the development of more reliable simulating models for the surface air-pollutants fluctuations and the time scale variations in their predictability (Varotsos et al. 2005; Broday 2010).

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