A review on methodology in O3-NOx-VOC sensitivity study

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

Applying model simulation and photochemical indicators to evaluate ozone sensitivity in southern Taiwan

Ozone sensitivity was investigated using CAMx simulations and photochemical indicator ratios at three sites (Pingtung City, Chao-Chou Town, and Kenting Town) in Pingtung County in southern Taiwan during 2003 and 2004. The CAMx simulations compared fairly well with the hourly concentrations of ozone. Simulation results also showed that Pingtung City was mainly a volatile organic compounds (VOC)-sensitive regime, while Chao-Chou Town was either a VOC-sensitive or a NOx-sensitive regime, depending on the seasons. Measurements of three photochemical indicators (H2O2, HNO3, and NOy) were conducted, and simulated three transition ranges of H2O2/HNO3 (0.5-0.8), O3/HNO3 (10.3-16.2) and O3/NOy (5.7-10.8) were adopted to assess the ozone sensitive regime at the three sites. The results indicated that the three transition ranges yield consistent results with CAMx simulations at most times at Pingtung City. However, both VOC-sensitive and NOx-sensitive regimes were important at the rural site Chao-Chou Town. Kenting Town, a touring site at the southern end of Taiwan, was predominated by a NOx-sensitive regime in four seasons.

REGIONAL PHOTOCHEMICAL AIR QUALITY MODELING:Model Formulations, History, and State of the Science

▪ Abstract  Regional air quality models have been used for scientific investigation of trace species dynamics for over two decades and are now beginning to take a central position in air quality management. In particular, they have been used for studying the transport and fate of atmospheric acids, photochemical oxidants (e.g. ozone), and more recently, aerosols. Such models are based on numerically solving the mass conservation equations for a chemically interacting system of species and are applied to horizontal domains of 1000s of kms. Primary applications include assessing the response of pollutant concentrations to emissions controls, quantifying the flux of pollutants across and out of a region, and understanding the impact of specific processes on pollutant concentrations.