Milanchus Meghan L, Rao S Trivikrama, Zurbenko Igor G
a University of Albany , Albany , New York , USA.
J Air Waste Manag Assoc. 1998 Mar;48(3):201-215. doi: 10.1080/10473289.1998.10463673.
It is difficult to assess the effectiveness of regulatory programs in improving ozone air quality in the presence of meteorological fluctuations. In this paper, techniques are presented that improve upon previous methods for moderating the effects of meteorology on ozone concentrations. This approach entails the use of the relations between ozone and meteorological variables to construct meteorologically adjusted ozone time series. To this end, the effectiveness and usefulness of various methods for separating time series of ozone and meteorological data into long-term (climate- and policy-related), seasonal (solar-induced), and short-term (weather-related) components are examined. Correlations between baseline components (sum of long-term and seasonal variations) of ozone and meteorological variables are then investigated independently of correlations between short-term components (weather effects) of ozone and meteorological variables. This allows us to account for the effects of the dominant meteorological variables on each time scale embedded in time series of ozone data. Ozone time series that are devoid of seasonal and climatic variations as well as weather-related fluctuations can then be constructed to detect and track changes in ozone due to the emission control policies implemented. The results of this study reveal that the combination of solar radiation and specific humidity performs best in filtering the seasonal and climatic variations from the baseline component of the ozone data. The combination of temperature and dew point depression performs best in moderating the weather-related effects on the short-term component of ozone data. This method is able to explain about 65% of the variance in ozone data through meteorological variables at several locations examined here.
在存在气象波动的情况下，评估监管计划在改善臭氧空气质量方面的有效性是困难的。本文介绍了一些技术，这些技术改进了以往减轻气象对臭氧浓度影响的方法。这种方法需要利用臭氧与气象变量之间的关系来构建经气象调整的臭氧时间序列。为此，研究了将臭氧和气象数据的时间序列分离为长期（与气候和政策相关）、季节性（太阳诱发）和短期（与天气相关）成分的各种方法的有效性和实用性。然后独立于臭氧和气象变量的短期成分（天气影响）之间的相关性，研究臭氧的基线成分（长期和季节性变化之和）与气象变量之间的相关性。这使我们能够考虑主要气象变量对嵌入臭氧数据时间序列的每个时间尺度的影响。然后可以构建没有季节性和气候变化以及与天气相关波动的臭氧时间序列，以检测和跟踪由于实施排放控制政策而导致的臭氧变化。本研究结果表明，太阳辐射和比湿的组合在从臭氧数据的基线成分中滤除季节性和气候变化方面表现最佳。温度和露点降的组合在减轻天气对臭氧数据短期成分的影响方面表现最佳。通过这里研究的几个地点的气象变量，这种方法能够解释臭氧数据中约65%的方差。
