Our research on atmospheric chemistry problems aims at exploring the fundamental processes that govern the concentrations and distributions of trace gases and aerosols. Our research helps unravel of the complicated relations between the changing environment and human activities. We use statistical and numerical methods to analyze large datasets of tropospheric chemical measurements at surface sites, from balloons and aircraft, and by satellites. We have many colleagues in our school and Civil and Environmental Engineering working on various aspects of environmental issues.

Statistical analysis

We use data analysis to extract the underlying scientific principals from complex measurement data. Fairly simple statistical analysis can be powerful tools (e.g., Wang et al. [2000a]). Increasingly, however, the large amounts of measurements data demand more advanced statistical methods. One particular method we have used is the positive matrix factorization (PMF), which explores the covariance structure of the datasets. The technique has been used to examine the effects of intercontinental transport on tropospheric ozone (Wang et al. [2003b]); the sources of biogenic volatile organic compounds (VOCs); the sources of aerosols (PM2.5) in the Southeast (Liu et al. [2005a, b]). It is also used as a new way to evaluate model simulations of aerosols (Liu et al. [2005c]) and ozone enhancements by tropospheric photochemical production and transport from the stratosphere.

Bayesian inversion of trace gas emissions is another advanced statistical method we use in analyzing measurements and model simulations. By quantifying the difference between model simulations and observations, we derive the a posteriori emissions that best represent our current knowledge of the emissions processes and available atmospheric measurements. We have used the method to derive the global emissions of two environmentally important trace gases, isoprene (Shim et al. [2005]) and CH₃Cl (Yasuko et al. [2005]).

0-1 D modeling

Point (0-D) model is built with our current understanding of photochemical reactions. It captures the complexity of  photochemical interactions. The models are generally constrained by the observed concentrations of some (long-lived) species. The model predictions of the other (generally shorter-lived) species can be compared to the observations. Disagreement between simulated and observed concentrations therefore indicates the problems in our current understanding of photochemistry and points to the need for laboratory studies. We have applied a point model to examine the photochemistry at northern mid and high latitudes (Wang et al. [2003a]).

The 1-D model captures the effects of vertical transport in addition to photochemical reactions in the 0-D model. Over the tropical Pacific, convective transport is a critical process that defines the characteristics of the photochemical environment (Wang et al. [2000b; 2001]). When analyzing the measurements over the South Pole, we find that the diffusion transport in the boundary layer often dictates the effects of snow emissions and deposition and hence the photochemistry near the surface.

Regional and global 3-D modeling

The 3-D models attempt to simulate the interaction between photochemistry and dynamic transport. The processes include emissions, transport, photochemistry, and deposition. We have developed or applied several regional or global chemical transport models.

We have developed a regional 3-D chemical transport model. Data assimilation of the meteorological fields is conducted using the NCAR/Penn State MM5 model. For high-latitude simulations, the polar version of MM5 is used. We applied the modeling system to examine the halogen-driven oxidation of ozone and hydrocarbons in the Arctic spring (Zeng et al. [2003, 2005]); the signals of lightning NO production and convective transport of CO over North America and the western North Atlantic in satellite (GOME and MOPITT) measurements (Choi et al. [2005]); the late-spring increase of trans-Pacific pollution transport in the upper troposphere (Wang et al. [2005]); the seasonal transition of photochemistry during spring over North America; the effects of snow NO_x emissions on photochemistry over Antarctica.

Using this regional 3-D modeling system, we have provided operational (daily) 48-hour forecasts of O₃ and its precursors over the U.S since summer, 2003. In addition to this modeling system, we use the EPA Models-3/CMAQ system to investigate the interactions of biosphere, forest fires, air quality, and climate over the Southeast.

Previously we have applied a global 3-D chemical transport model to examine the origin of tropospheric ozone and global ozone and OH changes due to human activities since preindustrial tims (Wang et al. [1998a, b, c]; Wang and Jacob [1998]). We currently use the GEOS-CHEM model for global simulations. The assimilated meteorological fields are provided by the Goddard Earth Observing System (GEOS) of the NASA Global Modeling Assimilation Office. The model has been used to investigate the distributions of global CH₃Cl (Yasuko et al. [2004]); Bayesian inversion of global isoprene and CH₃Cl sources (Shim et al. [2005]; Yasuko et al. [2005]); the tracer (ethane and propane) correlation in the troposphere and its usage in evaluating model transport (Wang and Zeng [2004]); ozone enhancements at northern mid latitudes.