Joseph Chang

Acceptance criteria for urban dispersion model evaluation

The authors suggested acceptance criteria for rural dispersion models’ performance measures in this journal in 2004. The current paper suggests modified values of acceptance criteria for urban applications and tests them with tracer data from four urban field experiments. For the arc-maximum concentrations, the fractional bias should have a magnitude <0.67 (i.e., the relative mean bias is less than a factor of 2); the normalized mean-square error should be <6 (i.e., the random scatter is less than about 2.4 times the mean); and the fraction of predictions that are within a factor of two of the observations (FAC2) should be >0.3. For all data paired in space, for which a threshold concentration must always be defined, the normalized absolute difference should be <0.50, when the threshold is three times the instrument’s limit of quantification (LOQ). An overall criterion is then applied that the total set of acceptance criteria should be satisfied in at least half of the field experiments. These acceptance criteria are applied to evaluations of the US Department of Defense’s Joint Effects Model (JEM) with tracer data from US urban field experiments in Salt Lake City (U2000), Oklahoma City (JU2003), and Manhattan (MSG05 and MID05). JEM includes the SCIPUFF dispersion model with the urban canopy option and the urban dispersion model (UDM) option. In each set of evaluations, three or four likely options are tested for meteorological inputs (e.g., a local building top wind speed, the closest National Weather Service airport observations, or outputs from numerical weather prediction models). It is found that, due to large natural variability in the urban data, there is not a large difference between the performance measures for the two model options and the three or four meteorological input options. The more detailed UDM and the state-of-the-art numerical weather models do provide a slight improvement over the other options. The proposed urban dispersion model acceptance criteria are satisfied at over half of the field experiments.

Setting Acceptance Criteria for Air Quality Models

Is an air quality model acceptable for use (fit for purpose)? We now have several state-of-the-art statistical methodologies in place for calculating model performance measures and for determining the statistical significance of the results. But a more difficult question is whether, based on the calculated performance measures, the model is acceptable or not. This question is being addressed in several of the authors’ recent studies, and an example carried out for the Joint Effects Model (JEM) is the focus of the current paper. The rationale for selecting acceptance criteria for air quality models is described, and the results of applications of the method to four urban field experiments are presented. The values of the acceptance criteria are based on the authors’ experiences with a wide variety of air quality models and observations. For example, an acceptable FAC2 is>0.3 for urban scenarios. Furthermore, since a model is unlikely to fulfill all acceptance criteria at every field experiment site, we require that the model meet the individual criteria for at least 50% of the performance measures and field experiments and input options used in the study. It is shown here that the JEM model meets the 50% criterion.

Evaluation of the Hazard Prediction and Assessment Capability (HPAC) Model with the Oklahoma City Joint Urban 2003 (JU2003) Tracer Observations

Results are presented of an evaluation of the Hazard Prediction and Assessment Capability (HPAC) suite of models in an urban environment using data from the Joint Urban 2003 (JU2003) Field Experiment in Oklahoma City (OKC). JU2003 included 29 separate SF6 tracer continuous releases (of 30-minute duration) on ten days from a point source near ground level in or immediately upwind of the built-up downtown area. The ten Intensive Operating Periods (IOPs) consisted of six daytime periods and four nighttime periods. Tracer was sampled at over 100 locations at distances ranging from 0.1 to 4 km from the source.

Deposition Following Accidental Releases of Chlorine from Railcars

Chlorine releases to the atmosphere due to accidents involving railcars can be extremely hazardous to health, the environment, and man-made materials. Since the chlorine is released as a mixture of reactive gas and small (median diameter of 20–100 μm) aerosols, and the initial cloud has a very high concentration (>10,000 ppm), deposition to the surface can be important. The various mechanisms include dry deposition caused by chemical reactions between the gas and the surface (ground, vegetation, or materials), dry or wet deposition of small aerosols, and gravitational settling and impaction of larger aerosols. The state-of-the art in gas deposition modeling is based on the resistance analogy, which has been widely used in deposition modeling of a variety of air pollutants. The resistance formula is reviewed and it is shown that, even though chlorine is relatively reactive, its deposition may be inhibited by the increased aerodynamic resistance in the very stable cloud. A method is suggested for parameterizing the effect of the dense cloud on the aerodynamic resistance. Deposition measurement methods planned for the Jack Rabbit II (JR II) chlorine release field experiments are reviewed, where up to 10 tons of pressurized liquefied chlorine will be released in several field trials.