Mathematical modeling of photochemical air pollution--3. Evaluation of the model

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.

A 3D Eulerian source-oriented model for an externally mixed aerosol

A 3D Eulerian source-oriented model for an externally mixed aerosol is developed and then used to compute the contribution that different emission sources make to regional fine particle concentrations in the South Coast Air Basin surrounding Los Angeles, CA, on September 25, 1996. The model simultaneously tracks fine particle concentrations (PM2.5), inhalable particle concentrations (PM10), ozone, and other gaseous pollutant concentrations yielding a tool that can be used to study the control of all of the regulated contaminants in the atmosphere within a single unified framework. Model predictions identify geographical areas that are influenced by PM2.5 associated with crustal material other than paved road dust, paved road dust, diesel engines, food cooking, noncatalyst equipped gasoline engines, catalyst-equipped gasoline engines, combustion of high sulfur-content fuel, other primary particle sources, sea salt, and marine background sulfate particles. The contribution that each of these source types makes to regional fine particle concentrations is distinct, reflecting the unique chemical composition, spatial distribution, and diurnal trends of primary emissions. The single largest contribution to regional PM2.5 in the South Coast Air Basin surrounding Los Angeles is associated with the accumulation of secondary ammonium nitrate on background marine sulfate particles. This pattern indicates that control of PM2.5 concentrations in Los Angeles must be accomplished through a program that includes both reductions in the emissions of gaseous precursors of secondary PM2.5 as well as control of primary particle emissions.

Methodology for designing air quality monitoring networks: II. Application to Las Vegas, Nevada, for carbon monoxide

An objective methodology presented in a companion paper (Liu et al., 1986) for determining the optimum number and disposition of ambient air quality stations in a monitoring network for carbon monoxide is applied to the Las Vegas, Nevada, area. The methodology utilizes an air quality simulation model to produce temporally-varying air quality patterns for each of a limited number of meteorological scenarios representative of the region of interest. These air quality patterns in turn serve as the data base in a two-step procedure for the identification and ranking of the most desirable monitoring locations (step 1) and the removal of redundancies in spatial coverage among the desired locations (step 2.)The performance of the air quality simulation model, a key element in the design methodology, was evaluated in the Las Vegas area in a special field measurement program. In the Las Vegas demonstration for carbon monoxide, 19 stations covering concentration maxima and 3 stations covering background concentrations in rural areas were identified and ranked. A 10-station network, for example, consisting of 7 stations for peak average concentrations and 3 stations for background concentrations, was selected for a desired minimum detection capability of 50% of concentration variations. Networks with fewer stations would be selected if smaller minimum detection capabilities of concentration variations are acceptable, and vice versa. Background stations could, of course, be deleted for networks with the sole purpose of discerning peak concentrations.