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Air-chemistry turbulence: Power-law scaling and statistical regularity

机译:空气化学湍流:幂律定标和统计规律

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With the intent to gain further knowledge on the spectral structures and statistical regularities of surface atmospheric chemistry, the chemical gases (NO, NO_2, NO_x, CO, SO_2, and O_3) and aerosol (PM_(10)) measured at 74 air quality monitoring stations over the island of Taiwan are analyzed for the year of 2004 at hourly resolution. They represent a range of surface air quality with a mixed combination of geographic settings, and include urban/rural, coastal/inland, plain/hill, and industrial/agricultural locations. In addition to the well-known semi-diurnal and diurnal oscillations, weekly, and intermediate (20 ~ 30 days) peaks are also identified with the continuous wavelet transform (CWT). The spectra indicate power-law scaling regions for the frequencies higher than the diurnal and those lower than the diurnal with the average exponents of and 1, respectively. These dual-exponents are corroborated with those with the detrended fluctuation analysis in the corresponding time-lag regions. These exponents are mostly independent of the averages and standard deviations of time series measured at various geographic settings, i.e., the spatial inhomogeneities. In other words, they possess dominant universal structures. After spectral coefficients from the CWT decomposition are grouped according to the spectral bands, and inverted separately, the PDFs of the reconstructed time series for the high-frequency band demonstrate the interesting statistical regularity, 3 power-law scaling for the heavy tails, consistently. Such spectral peaks, dual-exponent structures, and power-law scaling in heavy tails are important structural information, but their relations to turbulence and mesoscale variability require further investigations. This could lead to a better understanding of the processes controlling air quality.
机译:为了进一步了解表面大气化学的光谱结构和统计规律,在74空气质量监测中测量了化学气体(NO,NO_2,NO_x,CO,SO_2和O_3)和气溶胶(PM_(10))以小时为单位分析2004年台湾岛上的台站。它们代表了各种地理环境,混合了地理环境,包括城市/农村,沿海/内陆,平原/丘陵和工业/农业位置。除了众所周知的半日和昼夜振荡外,还通过连续小波变换(CWT)识别出每周和中间(20〜30天)的峰值。频谱显示了幂律定标区域,这些区域的频率分别高于昼夜频率和低于昼夜频率,且平均指数分别为和和1。这些双指数与在相应的时滞区域中进行去趋势波动分析的双指数得到了证实。这些指数大多独立于在各种地理环境下测量的时间序列的平均值和标准偏差,即空间不均匀性。换句话说,它们拥有主导的普遍结构。在将CWT分解的频谱系数根据频谱带进行分组并分别反转之后,针对高频带的重建时间序列的PDF表现出有趣的统计规律,即针对重尾的3次幂律定标。这样的光谱峰,双指数结构和重尾巴中的幂律定标是重要的结构信息,但是它们与湍流和中尺度变异性的关系需要进一步研究。这可以使人们更好地了解控制空气质量的过程。

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