- Email: [email protected]
FFT analysis of atmospheric trace concentration of N2O continuously monitored by gas chromatography and cross-correlation to climate parameters
Microchemical Journal 71 (2002) 83–93
FFT analysis of atmospheric trace concentration of N2O continuously monitored by gas chromatography and crosscorrelation to climate parameters Yuichi Kamata*, Aritaka Matsunami, Kuniyuki Kitagawa, Norio Arai Research Center for Advanced Energy Conversion, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan Received 25 June 2001; received in revised form 21 September 2001; accepted 30 September 2001
Abstract To investigate the characteristics of N2 O concentration, we applied several types of time series analyses such as fast Fourier transform (FFT), auto-correlation, and cross-correlation, to 2.5-year time series data of trace N2O concentration continuously monitored by gas chromatography and meteorological data, measured in an urban area of Nagoya. It was found that there is a positive correlation between atmospheric N2 O concentration (ppbv) and, both steam pressure (hPa) and temperature (8C). In addition, negative and positive correlations in atmospheric pressure and in solar flux were also found, respectively. These findings suggest an enrichment of N2 O through environmental steam during the summer season, particularly in urban areas. On the other hand, the correlation to wind direction shows a variation with amplitude of 7 ppbv, from the north-west to the south-east, and a seasonal variation up to 12 ppbv, from winter to summer. These results support the hypothesis that atmospheric steam controls the N2O concentration in urban areas. In addition, the correlation with wind direction suggests the existence of an emission source in the direction of seaside areas. 䊚 2002 Elsevier Science B.V. All rights reserved. Keywords: N2O; Time series analysis; Urban; Correlation
1. Introduction Increasing atmospheric concentrations of longlived species, such as CFCs, N2O, CH4 extend globally ()5=103 km) and their dynamical time scale ()10 years) is longer than the chemical lifetimes w1x. These chemical species, as well as * Corresponding author. Present address: Department of Physics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8602, Japan. Tel.: q81-52-789-2919; fax: q81-52-7892920. E-mail address: [email protected] (Y. Kamata).
CO2, contribute to the phenomena of global warming and ozone depletion. To investigate the future prospects of the global environment, these chemical tracers are among important clues to global changes on a long time scale ()10 years; typical mixing time of long lived species, such as CH4, N2O and CFCs, which is equivalent to a spatial scale of )103 km). On the other hand, increases in the concentrations of these species are due to human activity, e.g. industrial wastes or combustion and artificial manure. On a short time scale (-10 years), local related phenomena include not only human activities but
0026-265X/02/$ - see front matter 䊚 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 6 - 2 6 5 X Ž 0 1 . 0 0 1 4 0 - 0
84
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
also climate changes, such as temperature, humidity, solar flux, wind speed and direction. These meteorological parameters may also control the atmospheric concentration of gases affecting global change. However, the origins of their dominant emission sources are still under qualitative consideration, particularly for trace level gases. Among these species, N2O has both a global warming and ozone destruction effect, and is emitted from excess artificial manure and combustion processes. N2O yields from combustion were supposed to occur mainly in urban areas and this directly reflects human activities w2x. The recent dramatic increase in atmospheric N2O concentration, though its concentration is 1000 times lower than that of CO2, has been clarified and the mechanisms of generation of N2O during combustion were also suggested w2–5x. The major contributor to the anthropogenic emission of N2O is thought to be soils including nitrogen fertilizers but with a large uncertainty (4 –60% of anthropogenic emission) w3,6x. Other major contributors include aquatic systems in rivers, estuaries and continental shelves, which account for approximately 35% of total N2O emissions w7x. Short time variability (-1 year) is found from local measurements by Derwent et al. (max. in February–May and min. in August–September) w8x and Tohjima et al. (decreasing in spring and summer) w9x. This inconsistency indicates a difference in local environment between the measurement sites. Zheng et al. w10x found explosive emissions from rice-based agro-ecosystems by in situ measurement of soil moisture and N2O emission on a time scale of several days. The results of crossroad and mobile tunable IR laser measurements by Jimenez et al. w11x show high atmospheric concentrations of N2O in motor vehicle exhaust gases (;12.6 ppm). These emission sources are among the causes of short time variability and local environmental effects. Thus the local N2O concentration in urban districts can be highly affected by these effects and consequently the time-change in atmospheric concentration has a complex structure.
In this study we applied time-series analysis to successively monitor data of atmospheric N2O concentration determined with a multi-dimensional gas chromatograph, in an urban district of Nagoya, Japan. Fast Fourier transform (FFT) and autocorrelation analysis were used to study the periodicity in the data series. Cross-correlation analysis was also applied to the series of N2O concentration (observed at the Research Center for Advanced Energy Conversion, Nagoya University) and climate parameters such as atmospheric pressure, temperature, steam pressure, wind velocity and solar flux. All climate parameters were obtained from the database of the Japan Meteorological Agency observed at a position 1 km north from the observation location of N2O concentration. 2. Materials and methods Nagoya, the fifth largest city of Japan, is located at the heart of the central district in Japan with a population of 2.2 million people, density of 6600 per km2, and area of 326 km2. The number of households is 840 000 and that of automobile owners amounts to 1.3 million (20% of freight use and 80% of passenger use) in Nagoya. Nagoya is located in the middle of the Nobi Plain including Ise Bay to the south, where power plants and bay side industrial district are concentrated. There is also a concentration of industrial activity in the south-east of the suburban area and the industrial area is spreading to the south-west along the bay. The observation site for successive measurement of atmospheric N2O concentration (Chikusa, Nagoya University), as shown in Fig. 1, is approximately 6 km from the downtown (Sakae) area of Nagoya to the east and located at (398049N and 1368589E) 34.7 m above sea level. Successive determinations of atmospheric N2O concentrations have been made with a multicolumn (multi-dimensional) gas chromatograph with an electron capture detector (ECD) at the Research Center for Advanced Energy Conversion, Nagoya University w12x. The average concentration was estimated to be ;0.35 ppmv with maximum amplitudes above 10% in the previous work using 1-year data in 1996 w13x.
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93 Fig. 1. The N2O measurement location of this work is shown by pointing arrows. Top and bottom in both maps is equivalent to north and south in the figure. This map is published by the Geographical Survey Institute, Japan.
85
86
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
Fig. 2. Time changes in atmospheric N2O concentration and climate parameters at Nagoya, Japan in 1995 – 1997. The abscissa indicates the measurement epochs for each data bins. The ordinate stands for (a) atmospheric N2O concentration in ppbv; (b) atmospheric pressure in hPa; (c) steam pressure in hPa; (d) temperature in degrees Celsius; (e) wind velocity in mys; and (f) solar flux of whole sky (2p steradian) in MJym2, respectively.
Fig. 2 shows the time-change curve of successively monitored atmospheric N2O concentration and those of other climate parameters in the urban area of Nagoya, Japan. These data were discretely
sampled every 1-h between May 1995 and December 1997, which covers 80% of 2.5 years except for the solar flux data. The solar flux data (MJy m2 per h) are averaged values over a period from
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
87
Fig. 3. Power spectrum density of 2.5-year data of N2O concentration and climate parameters at Nagoya, Japan. The abscissa indicates frequencies in unit of daysy1. The ordinate stands for power spectrum densities of (a) atmospheric N2O concentration; (b) atmospheric pressure; (c) steam pressure; (d) temperature; (e) wind velocity; and (f) solar flux, respectively.
88
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
Fig. 4. Auto-correlation coefficients of N2O concentration and climate parameters at Nagoya, Japan. The abscissa indicates delay times in units of days. The ordinate stands for autocorrelation coefficients of (a) atmospheric N2O concentration; (b) atmospheric pressure; (c) steam pressure; (d) temperature; (e) wind velocity; and (f) solar flux, respectively.
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
09.00 to 17.00 h of the measured days. All the climate data compared with the atmospheric N2O concentration in this work were obtained from the Japan Meteorological Agency. 3. Results and discussion 3.1. Power density spectra The best-known method for a frequency analysis of time series data is the fast Fourier transform (FFT). Fig. 3 shows the power density spectra for the 2.5-year time series data of the N2O concentration and climate parameters. The spectrum of the N2O concentration shows a clear peak equivalent to 1-day periodicity on the curve of simple 1y f dependency. On the other hand, the two periodic peaks are recognized for the climate parameters, at 0.5 days and 1.0 day except for the solar flux data. The spectrum of solar flux, related to cloud coverage, shows a periodic structure of approximately 0.5 months which is equivalent to the change in tide. In addition, the spectra of the wind velocity and atmospheric pressure show a flattened structure on a time scale longer than a week (equivalent to ‘white noise’). These characteristics indicate that these climate parameters are time-independent on the longer time scales. On the other hand, the spectra of the temperature and the steam pressure are relatively steep, which is similar to that of the N2O concentration. On a time scale shorter than a week, the steam pressure and the wind velocity show a similar inclination in these power spectra while those of temperature and pressure are steeper or flattened. It is noted from the properties of each data series that the characteristic power density spectrum of the N2O concentration is most similar to that of the steam pressure. To investigate the characteristics of time variation, we estimated power-law indexes of these spectra for periods shorter and longer than 7 days (Table 1). The spectra of wind velocity, atmospheric pressure and solar radiative flux reveal
89
Table 1 Power-law indexes of spectra for periods shorter and longer than 7 days Parameters
N2O concentration (ppbv) Temperature (8C) Pressure (hPa) Steam pressure (hPa) Wind velocity (mys) Solar flux (MJym3 per h)
Nagoya Index-S
Index-L
y0.79 y1.30 y1.49 y1.18 y0.94 y0.80
y0.48 y0.60 y0.23 y0.53 y0.10 y0.23
Index-S, power-law index (-7 days); Index-L, power-law index ()7 days).
flattened structures on a time scale longer than a week (equivalent to ‘white noise’). 3.2. Auto-correlation Auto-correlation is another expression of the Fourier transform, which is frequently used to search for periodicities and characteristic time scales w14,15x. The auto-correlation function (ACF) is defined as ACFŽt.s8XŽti.XŽtiqt. where X(ti) and t are the data value and delay time, respectively. If there is a certain periodicity, these structures appear in the correlation plot with an interval of the period. The decay time scale against the delay time t from tis0 is an indicator of relaxation time scale of the system. Fig. 4 shows the plots of auto-correlation coefficient (ACC) for the same data sets as those in Fig. 3, which are equivalent to ACF(t) normalized as ACF(ts0). The exponential decrease time scales are very similar for the temperature and steam pressure (Fig. 4). Their time scales are estimated to be longer than 100 days, while that of wind speed is estimated to be 8 h. The N2O concentration indicates an intermediate value of several days between those estimated from the plots of the temperature (or steam pressure) and wind velocity. Further quantitative estimation is difficult under
90
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
Fig. 5. Cross-correlation coefficients of N2 O concentration and climate parameters at Nagoya, Japan. The abscissa indicates delay times in unit of days. The ordinate stands for cross-correlation coefficients of (a) atmospheric pressure; (b) steam pressure; (c) temperature; (d) wind velocity; and (e) solar flux, against atmospheric N2O concentration, respectively.
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93 Table 2 Time delays of the peaks Parameters
Time delay (days)
Temperature (8C) Pressure (hPa) Steam pressure (hPa) Wind velocity (mys) Solar flux (MJym2 h)
y13 q7 y18 N.A. q32
such a simple assumption, because of complicated structures in the auto correlation plots. 3.3. Cross-correlation Cross-correlation was applied to investigate the degree of correlation between two different time series data, which shows a possible time delay between these parameters w14,15x. In this method, direct comparison of the selected time series data of different parameters is possible, which is not feasible by auto correlation analysis. The cross-correlation function (CCF) is defined as CCFŽt.s8XŽti.YŽtiqt. where X(ti), Y(tiqt) and t are data values of two different time series data and delay time, respectively. Fig. 5 shows the plot of cross-correlation coefficient (CCC), which is equivalent to CCF(t) normalized by CCF(ts0), of the N2O concentration vs. the climate parameters during June 1995 to December 1997 at Nagoya. The time delays of the peaks are listed in Table 2. The correlation with the wind velocity shows no clear sign while that with atmospheric pressure indicates the negative correlation and those with temperature, steam pressure and solar flux reveals the positive correlation on a time scale longer than a month. The negative correlation with the atmospheric pressure is consistent with that of the solar flux, which is a function of the cloud coverage in a local area. The positive correlation with the steam pressure is also in agreement with this correlation. These results suggest that N2O species captured by atmospheric water play an important role in
91
determining the observed atmospheric concentration. The positive correlation with temperature has still not been unraveled, taking into account the negative correlation between the temperature and the solubility of N2O in water. The negative correlation with the atmospheric pressure is consistent with that of solar flux. However, the positive delays of these parameters are still unclear. On the other hand, the positive correlation with positive delay against the steam pressure supports the hypothesis. The positive delay of steam pressure indicates that N2O species captured by atmospheric water enrich the observed atmospheric concentration. However, the positive correlation with temperature is not clearly understood because of negative correlation between temperature and solubility of N2O into water. The enrichment of N2O during the spring and summer seasons is estimated to be 12 ppbv wcorresponds to ;3.5% amplitude (peak-bottom) of the averaged concentrationx. The positive correlation with the steam pressure found in the crosscorrelation analysis (Fig. 5) is consistent with this tendency because of high humidity during the summer and spring seasons (April–September). 3.4. Wind direction and emission source The dependence on the wind direction is the key to obtaining spatial information of N2O concentration around the observation location. Then, we made the cross-correlation plots between the wind velocity and N2O concentration, using 16 sets of divided data with a minimum data bin of 4 days to different directions, as shown in Fig. 6. Fig. 6 clearly indicates that the observed concentration is increased with the wind flow from the south while it is decreased by wind from the north-west. The averaged atmospheric concentrations of N2O on the north-west and south directions are 345.4 and 352.8 ppbv, respectively, which corresponds to an amplitude of ;2.1% (peakbottom). This small value relative to the observed maximum amplitude of )10% is attributable to the smears in the data series.
92 Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93 Fig. 6. Cross-correlation plots of 16 directions from the observation location. The abscissas of each figure indicates delay times in units of days. The ordinates stand for cross-correlation coefficients between N2 O concentration and wind velocity using filtered data in each direction. The panels located at the center show the results with summed data (right: SSW – SSE; left: W – NNE).
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
93
Thus, the variation in the atmospheric concentration of N2O is mainly controlled by the wind flow direction (which is directly related to emission sources) and steam which concentrates chemical species in the atmosphere.
or the industrial area along Ise bay are among possible candidates. The unexplainable positive correlation with temperature suggests that a heat island effect plays an important role, particularly in urban areas.
4. Conclusions
Acknowledgements
We applied several time-series analyses to the data of atmospheric N2O concentration, successively determined with multi dimensional gas chromatography, and those of the climate parameters. The FFT spectra of the wind velocity and the atmospheric pressure show the time-independent characteristics on a time scale longer than a week, while the spectrum of N2O concentration indicates a simple 1y f dependency which is approximately similar to those of the steam pressure and the temperature. The observed complex and short-time variability, and the high concentration of approximately 350 ppbv, locally observed in Nagoya, is due to the continuous input from motor vehicles. To search for the characteristic time scales of the parameters, we applied the auto-correlation analysis to the data. However, there is no direct correlation between the auto-correlation profiles of N2O concentration and other climate parameters, which indicates that multiple parameters control the temporal behavior of the N2O concentration. Then, we applied the cross-correlation analysis to the same data series and found out the positive correlation with the temperature, the steam pressure and the solar flux on a time scale longer than a month. The observed positive correlation with the steam pressure is consistent with the solution of atmospheric N2O gases into the environmental steam. The result also indicates the enrichment of N2O concentration during the summer season, exemplified by the explosive increasing event in Fig. 2 (August–September 1996), and the wind directional dependence indicates the existence of emission sources in the southern region. The continental shelves which are likely enriched by urban wastes
The data analysis was fully supported by the Xray astronomy group, Department of Astrophysics, Nagoya University. The authors are thankful to the group members, as well as to the members of the software development team of NASAyGSFC (USA). We also appreciate the meteorological data from the Japan Meteorological Agency at Nagoya, Japan and Geographical Survey Institute, Japan. References w1x J.H. Seinfeld, S.N. Pandis, Atmospheric Chemistry and Physics, John Wiley & Sons, Inc, 1994. w2x J.C. Kramlich, W.P. Linak, Prog. Energy Combust. Sci. 20 (1994) 149. w3x M.A.K. Khalil, R.A. Rasmussen, J. Geophys. Res. 97 (1992) 14651. w4x N. Arai, J. Inst. Energy 67 (1994) 61. w5x N. Arai, A. Matsunami, K. Matsumoto, K. Kitagawa, N. Kobayashi, K. Asai, Anal. Commun. 34 (1997) 205. w6x J.H. Butler, J.W. Elkins, S.A. Montzka, T.M. Thompson, J. Geophys. Res. 94 (1989) 14865. w7x S.P. Seitzinger, C. Kroeze, R. Styles, Global Change Sci. 2 (2000) 267. w8x R.G. Derwent, P.G. Simmonds, S. Seuring, C. Dimmer, Atmos. Environ. 32 (1998) 145. w9x Y. Tohjima, H. Mukai, S. Maksyutov, et al., Global Sci. 2 (2000) 435. w10x K. Zheng, M. Wang, Y. Wang, et al., Global Sci. 2 (2000) 207. w11x J.L. Jimenez, J.B. McManus, J.H. Shorter, et al., Global Sci. 2 (2000) 397. w12x A. Matsunami, Y. Kamata, K. Kitagawa, T. Furuhata, N. Arai, International Journal of Global Energy Issues, Interscience Enterprises Limited, Switzerland (2000) 265. w13x Y. Kamata, A. Matsunami, T. Furuhata, K. Kitagawa, N. Arai, Energy Resour. 19 (1998) 365. w14x L. Stella, S.M. Kahn, J.E. Grindlay, Astrophys. J. 282 (1984) 713. w15x W. Priedhorsky, G. Garmire, R. Rothshild, E. Boldt, P. Serlemitsos, S. Holt, Astrophys. J. 233 (1979) 350.
FFT analysis of atmospheric trace concentration of N2O continuously monitored by gas chromatography and crosscorrelation to climate parameters Yuichi Kamata*, Aritaka Matsunami, Kuniyuki Kitagawa, Norio Arai Research Center for Advanced Energy Conversion, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan Received 25 June 2001; received in revised form 21 September 2001; accepted 30 September 2001
Abstract To investigate the characteristics of N2 O concentration, we applied several types of time series analyses such as fast Fourier transform (FFT), auto-correlation, and cross-correlation, to 2.5-year time series data of trace N2O concentration continuously monitored by gas chromatography and meteorological data, measured in an urban area of Nagoya. It was found that there is a positive correlation between atmospheric N2 O concentration (ppbv) and, both steam pressure (hPa) and temperature (8C). In addition, negative and positive correlations in atmospheric pressure and in solar flux were also found, respectively. These findings suggest an enrichment of N2 O through environmental steam during the summer season, particularly in urban areas. On the other hand, the correlation to wind direction shows a variation with amplitude of 7 ppbv, from the north-west to the south-east, and a seasonal variation up to 12 ppbv, from winter to summer. These results support the hypothesis that atmospheric steam controls the N2O concentration in urban areas. In addition, the correlation with wind direction suggests the existence of an emission source in the direction of seaside areas. 䊚 2002 Elsevier Science B.V. All rights reserved. Keywords: N2O; Time series analysis; Urban; Correlation
1. Introduction Increasing atmospheric concentrations of longlived species, such as CFCs, N2O, CH4 extend globally ()5=103 km) and their dynamical time scale ()10 years) is longer than the chemical lifetimes w1x. These chemical species, as well as * Corresponding author. Present address: Department of Physics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8602, Japan. Tel.: q81-52-789-2919; fax: q81-52-7892920. E-mail address: [email protected] (Y. Kamata).
CO2, contribute to the phenomena of global warming and ozone depletion. To investigate the future prospects of the global environment, these chemical tracers are among important clues to global changes on a long time scale ()10 years; typical mixing time of long lived species, such as CH4, N2O and CFCs, which is equivalent to a spatial scale of )103 km). On the other hand, increases in the concentrations of these species are due to human activity, e.g. industrial wastes or combustion and artificial manure. On a short time scale (-10 years), local related phenomena include not only human activities but
0026-265X/02/$ - see front matter 䊚 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 6 - 2 6 5 X Ž 0 1 . 0 0 1 4 0 - 0
84
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
also climate changes, such as temperature, humidity, solar flux, wind speed and direction. These meteorological parameters may also control the atmospheric concentration of gases affecting global change. However, the origins of their dominant emission sources are still under qualitative consideration, particularly for trace level gases. Among these species, N2O has both a global warming and ozone destruction effect, and is emitted from excess artificial manure and combustion processes. N2O yields from combustion were supposed to occur mainly in urban areas and this directly reflects human activities w2x. The recent dramatic increase in atmospheric N2O concentration, though its concentration is 1000 times lower than that of CO2, has been clarified and the mechanisms of generation of N2O during combustion were also suggested w2–5x. The major contributor to the anthropogenic emission of N2O is thought to be soils including nitrogen fertilizers but with a large uncertainty (4 –60% of anthropogenic emission) w3,6x. Other major contributors include aquatic systems in rivers, estuaries and continental shelves, which account for approximately 35% of total N2O emissions w7x. Short time variability (-1 year) is found from local measurements by Derwent et al. (max. in February–May and min. in August–September) w8x and Tohjima et al. (decreasing in spring and summer) w9x. This inconsistency indicates a difference in local environment between the measurement sites. Zheng et al. w10x found explosive emissions from rice-based agro-ecosystems by in situ measurement of soil moisture and N2O emission on a time scale of several days. The results of crossroad and mobile tunable IR laser measurements by Jimenez et al. w11x show high atmospheric concentrations of N2O in motor vehicle exhaust gases (;12.6 ppm). These emission sources are among the causes of short time variability and local environmental effects. Thus the local N2O concentration in urban districts can be highly affected by these effects and consequently the time-change in atmospheric concentration has a complex structure.
In this study we applied time-series analysis to successively monitor data of atmospheric N2O concentration determined with a multi-dimensional gas chromatograph, in an urban district of Nagoya, Japan. Fast Fourier transform (FFT) and autocorrelation analysis were used to study the periodicity in the data series. Cross-correlation analysis was also applied to the series of N2O concentration (observed at the Research Center for Advanced Energy Conversion, Nagoya University) and climate parameters such as atmospheric pressure, temperature, steam pressure, wind velocity and solar flux. All climate parameters were obtained from the database of the Japan Meteorological Agency observed at a position 1 km north from the observation location of N2O concentration. 2. Materials and methods Nagoya, the fifth largest city of Japan, is located at the heart of the central district in Japan with a population of 2.2 million people, density of 6600 per km2, and area of 326 km2. The number of households is 840 000 and that of automobile owners amounts to 1.3 million (20% of freight use and 80% of passenger use) in Nagoya. Nagoya is located in the middle of the Nobi Plain including Ise Bay to the south, where power plants and bay side industrial district are concentrated. There is also a concentration of industrial activity in the south-east of the suburban area and the industrial area is spreading to the south-west along the bay. The observation site for successive measurement of atmospheric N2O concentration (Chikusa, Nagoya University), as shown in Fig. 1, is approximately 6 km from the downtown (Sakae) area of Nagoya to the east and located at (398049N and 1368589E) 34.7 m above sea level. Successive determinations of atmospheric N2O concentrations have been made with a multicolumn (multi-dimensional) gas chromatograph with an electron capture detector (ECD) at the Research Center for Advanced Energy Conversion, Nagoya University w12x. The average concentration was estimated to be ;0.35 ppmv with maximum amplitudes above 10% in the previous work using 1-year data in 1996 w13x.
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93 Fig. 1. The N2O measurement location of this work is shown by pointing arrows. Top and bottom in both maps is equivalent to north and south in the figure. This map is published by the Geographical Survey Institute, Japan.
85
86
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
Fig. 2. Time changes in atmospheric N2O concentration and climate parameters at Nagoya, Japan in 1995 – 1997. The abscissa indicates the measurement epochs for each data bins. The ordinate stands for (a) atmospheric N2O concentration in ppbv; (b) atmospheric pressure in hPa; (c) steam pressure in hPa; (d) temperature in degrees Celsius; (e) wind velocity in mys; and (f) solar flux of whole sky (2p steradian) in MJym2, respectively.
Fig. 2 shows the time-change curve of successively monitored atmospheric N2O concentration and those of other climate parameters in the urban area of Nagoya, Japan. These data were discretely
sampled every 1-h between May 1995 and December 1997, which covers 80% of 2.5 years except for the solar flux data. The solar flux data (MJy m2 per h) are averaged values over a period from
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
87
Fig. 3. Power spectrum density of 2.5-year data of N2O concentration and climate parameters at Nagoya, Japan. The abscissa indicates frequencies in unit of daysy1. The ordinate stands for power spectrum densities of (a) atmospheric N2O concentration; (b) atmospheric pressure; (c) steam pressure; (d) temperature; (e) wind velocity; and (f) solar flux, respectively.
88
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
Fig. 4. Auto-correlation coefficients of N2O concentration and climate parameters at Nagoya, Japan. The abscissa indicates delay times in units of days. The ordinate stands for autocorrelation coefficients of (a) atmospheric N2O concentration; (b) atmospheric pressure; (c) steam pressure; (d) temperature; (e) wind velocity; and (f) solar flux, respectively.
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
09.00 to 17.00 h of the measured days. All the climate data compared with the atmospheric N2O concentration in this work were obtained from the Japan Meteorological Agency. 3. Results and discussion 3.1. Power density spectra The best-known method for a frequency analysis of time series data is the fast Fourier transform (FFT). Fig. 3 shows the power density spectra for the 2.5-year time series data of the N2O concentration and climate parameters. The spectrum of the N2O concentration shows a clear peak equivalent to 1-day periodicity on the curve of simple 1y f dependency. On the other hand, the two periodic peaks are recognized for the climate parameters, at 0.5 days and 1.0 day except for the solar flux data. The spectrum of solar flux, related to cloud coverage, shows a periodic structure of approximately 0.5 months which is equivalent to the change in tide. In addition, the spectra of the wind velocity and atmospheric pressure show a flattened structure on a time scale longer than a week (equivalent to ‘white noise’). These characteristics indicate that these climate parameters are time-independent on the longer time scales. On the other hand, the spectra of the temperature and the steam pressure are relatively steep, which is similar to that of the N2O concentration. On a time scale shorter than a week, the steam pressure and the wind velocity show a similar inclination in these power spectra while those of temperature and pressure are steeper or flattened. It is noted from the properties of each data series that the characteristic power density spectrum of the N2O concentration is most similar to that of the steam pressure. To investigate the characteristics of time variation, we estimated power-law indexes of these spectra for periods shorter and longer than 7 days (Table 1). The spectra of wind velocity, atmospheric pressure and solar radiative flux reveal
89
Table 1 Power-law indexes of spectra for periods shorter and longer than 7 days Parameters
N2O concentration (ppbv) Temperature (8C) Pressure (hPa) Steam pressure (hPa) Wind velocity (mys) Solar flux (MJym3 per h)
Nagoya Index-S
Index-L
y0.79 y1.30 y1.49 y1.18 y0.94 y0.80
y0.48 y0.60 y0.23 y0.53 y0.10 y0.23
Index-S, power-law index (-7 days); Index-L, power-law index ()7 days).
flattened structures on a time scale longer than a week (equivalent to ‘white noise’). 3.2. Auto-correlation Auto-correlation is another expression of the Fourier transform, which is frequently used to search for periodicities and characteristic time scales w14,15x. The auto-correlation function (ACF) is defined as ACFŽt.s8XŽti.XŽtiqt. where X(ti) and t are the data value and delay time, respectively. If there is a certain periodicity, these structures appear in the correlation plot with an interval of the period. The decay time scale against the delay time t from tis0 is an indicator of relaxation time scale of the system. Fig. 4 shows the plots of auto-correlation coefficient (ACC) for the same data sets as those in Fig. 3, which are equivalent to ACF(t) normalized as ACF(ts0). The exponential decrease time scales are very similar for the temperature and steam pressure (Fig. 4). Their time scales are estimated to be longer than 100 days, while that of wind speed is estimated to be 8 h. The N2O concentration indicates an intermediate value of several days between those estimated from the plots of the temperature (or steam pressure) and wind velocity. Further quantitative estimation is difficult under
90
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
Fig. 5. Cross-correlation coefficients of N2 O concentration and climate parameters at Nagoya, Japan. The abscissa indicates delay times in unit of days. The ordinate stands for cross-correlation coefficients of (a) atmospheric pressure; (b) steam pressure; (c) temperature; (d) wind velocity; and (e) solar flux, against atmospheric N2O concentration, respectively.
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93 Table 2 Time delays of the peaks Parameters
Time delay (days)
Temperature (8C) Pressure (hPa) Steam pressure (hPa) Wind velocity (mys) Solar flux (MJym2 h)
y13 q7 y18 N.A. q32
such a simple assumption, because of complicated structures in the auto correlation plots. 3.3. Cross-correlation Cross-correlation was applied to investigate the degree of correlation between two different time series data, which shows a possible time delay between these parameters w14,15x. In this method, direct comparison of the selected time series data of different parameters is possible, which is not feasible by auto correlation analysis. The cross-correlation function (CCF) is defined as CCFŽt.s8XŽti.YŽtiqt. where X(ti), Y(tiqt) and t are data values of two different time series data and delay time, respectively. Fig. 5 shows the plot of cross-correlation coefficient (CCC), which is equivalent to CCF(t) normalized by CCF(ts0), of the N2O concentration vs. the climate parameters during June 1995 to December 1997 at Nagoya. The time delays of the peaks are listed in Table 2. The correlation with the wind velocity shows no clear sign while that with atmospheric pressure indicates the negative correlation and those with temperature, steam pressure and solar flux reveals the positive correlation on a time scale longer than a month. The negative correlation with the atmospheric pressure is consistent with that of the solar flux, which is a function of the cloud coverage in a local area. The positive correlation with the steam pressure is also in agreement with this correlation. These results suggest that N2O species captured by atmospheric water play an important role in
91
determining the observed atmospheric concentration. The positive correlation with temperature has still not been unraveled, taking into account the negative correlation between the temperature and the solubility of N2O in water. The negative correlation with the atmospheric pressure is consistent with that of solar flux. However, the positive delays of these parameters are still unclear. On the other hand, the positive correlation with positive delay against the steam pressure supports the hypothesis. The positive delay of steam pressure indicates that N2O species captured by atmospheric water enrich the observed atmospheric concentration. However, the positive correlation with temperature is not clearly understood because of negative correlation between temperature and solubility of N2O into water. The enrichment of N2O during the spring and summer seasons is estimated to be 12 ppbv wcorresponds to ;3.5% amplitude (peak-bottom) of the averaged concentrationx. The positive correlation with the steam pressure found in the crosscorrelation analysis (Fig. 5) is consistent with this tendency because of high humidity during the summer and spring seasons (April–September). 3.4. Wind direction and emission source The dependence on the wind direction is the key to obtaining spatial information of N2O concentration around the observation location. Then, we made the cross-correlation plots between the wind velocity and N2O concentration, using 16 sets of divided data with a minimum data bin of 4 days to different directions, as shown in Fig. 6. Fig. 6 clearly indicates that the observed concentration is increased with the wind flow from the south while it is decreased by wind from the north-west. The averaged atmospheric concentrations of N2O on the north-west and south directions are 345.4 and 352.8 ppbv, respectively, which corresponds to an amplitude of ;2.1% (peakbottom). This small value relative to the observed maximum amplitude of )10% is attributable to the smears in the data series.
92 Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93 Fig. 6. Cross-correlation plots of 16 directions from the observation location. The abscissas of each figure indicates delay times in units of days. The ordinates stand for cross-correlation coefficients between N2 O concentration and wind velocity using filtered data in each direction. The panels located at the center show the results with summed data (right: SSW – SSE; left: W – NNE).
Y. Kamata et al. / Microchemical Journal 71 (2002) 83–93
93
Thus, the variation in the atmospheric concentration of N2O is mainly controlled by the wind flow direction (which is directly related to emission sources) and steam which concentrates chemical species in the atmosphere.
or the industrial area along Ise bay are among possible candidates. The unexplainable positive correlation with temperature suggests that a heat island effect plays an important role, particularly in urban areas.
4. Conclusions
Acknowledgements
We applied several time-series analyses to the data of atmospheric N2O concentration, successively determined with multi dimensional gas chromatography, and those of the climate parameters. The FFT spectra of the wind velocity and the atmospheric pressure show the time-independent characteristics on a time scale longer than a week, while the spectrum of N2O concentration indicates a simple 1y f dependency which is approximately similar to those of the steam pressure and the temperature. The observed complex and short-time variability, and the high concentration of approximately 350 ppbv, locally observed in Nagoya, is due to the continuous input from motor vehicles. To search for the characteristic time scales of the parameters, we applied the auto-correlation analysis to the data. However, there is no direct correlation between the auto-correlation profiles of N2O concentration and other climate parameters, which indicates that multiple parameters control the temporal behavior of the N2O concentration. Then, we applied the cross-correlation analysis to the same data series and found out the positive correlation with the temperature, the steam pressure and the solar flux on a time scale longer than a month. The observed positive correlation with the steam pressure is consistent with the solution of atmospheric N2O gases into the environmental steam. The result also indicates the enrichment of N2O concentration during the summer season, exemplified by the explosive increasing event in Fig. 2 (August–September 1996), and the wind directional dependence indicates the existence of emission sources in the southern region. The continental shelves which are likely enriched by urban wastes
The data analysis was fully supported by the Xray astronomy group, Department of Astrophysics, Nagoya University. The authors are thankful to the group members, as well as to the members of the software development team of NASAyGSFC (USA). We also appreciate the meteorological data from the Japan Meteorological Agency at Nagoya, Japan and Geographical Survey Institute, Japan. References w1x J.H. Seinfeld, S.N. Pandis, Atmospheric Chemistry and Physics, John Wiley & Sons, Inc, 1994. w2x J.C. Kramlich, W.P. Linak, Prog. Energy Combust. Sci. 20 (1994) 149. w3x M.A.K. Khalil, R.A. Rasmussen, J. Geophys. Res. 97 (1992) 14651. w4x N. Arai, J. Inst. Energy 67 (1994) 61. w5x N. Arai, A. Matsunami, K. Matsumoto, K. Kitagawa, N. Kobayashi, K. Asai, Anal. Commun. 34 (1997) 205. w6x J.H. Butler, J.W. Elkins, S.A. Montzka, T.M. Thompson, J. Geophys. Res. 94 (1989) 14865. w7x S.P. Seitzinger, C. Kroeze, R. Styles, Global Change Sci. 2 (2000) 267. w8x R.G. Derwent, P.G. Simmonds, S. Seuring, C. Dimmer, Atmos. Environ. 32 (1998) 145. w9x Y. Tohjima, H. Mukai, S. Maksyutov, et al., Global Sci. 2 (2000) 435. w10x K. Zheng, M. Wang, Y. Wang, et al., Global Sci. 2 (2000) 207. w11x J.L. Jimenez, J.B. McManus, J.H. Shorter, et al., Global Sci. 2 (2000) 397. w12x A. Matsunami, Y. Kamata, K. Kitagawa, T. Furuhata, N. Arai, International Journal of Global Energy Issues, Interscience Enterprises Limited, Switzerland (2000) 265. w13x Y. Kamata, A. Matsunami, T. Furuhata, K. Kitagawa, N. Arai, Energy Resour. 19 (1998) 365. w14x L. Stella, S.M. Kahn, J.E. Grindlay, Astrophys. J. 282 (1984) 713. w15x W. Priedhorsky, G. Garmire, R. Rothshild, E. Boldt, P. Serlemitsos, S. Holt, Astrophys. J. 233 (1979) 350.