Modeling the Dynamics of Air Pollutants: Trans-Boundary Impacts in the Mexicali-Imperial Valley Border Region

Abstract

Air pollution continues to be an increasing problem in the largest metropolitan areas and regional industrial and commercial corridors in the world. This is also the case in Mexico. Current air quality trends in Mexico indicate that major urban centers continue to exceed the Mexican Ambient Air Quality Standards (MAAQS) for ozone (O3) and particulate matter with less than 10 microns of aerodynamic diameter (PM10), while other cities are starting to show warning signs of future air quality problems (Zuk et al., 2007). PM2.5 monitors are just starting to be deployed around the country, thus no extensive historical data is available on this pollutant. Some of the urban centers of concern share a common airshed with twin cities across the international border with the United States of America (USA), bringing additional complexity to the study of air pollution dynamics in the region. In this sense, transboundary air pollution across USA and Mexico has become a rising problem due to increased commercial and industrial activities in the border region. Trans-boundary air pollution has been studied at different levels in different areas of the border region (Mukerjee, 2001). Two main areas can be identified as the ones that have drawn most of the attention. The first one is the Lower California Area: Tijuana/San Diego, Mexicali/CalexicoImperial Valley (Figure 1). Here, most of the attention has been on primary PM (e.g., Osornio-Vargas et al., 1991; Chow et al., 2000; Sheya et al., 2000; Kelly et al., 2010), with some studies addressing secondary pollutants (e.g., Zielinska et al., 2001). The second area is the airshed formed by Ciudad Juarez-El Paso-Sunland Park. Perhaps, this area is the one that has received most of the attention regarding trans-boundary air pollution and in a more comprehensive fashion (Currey et al., 2005). Two of the key steps to improving air quality in a region are identifying, quantitatively, the emissions from sources that affect the area, and assessing how those emissions evolve in the atmosphere to impact pollutant concentrations. Both are difficult, and both can be subject to uncertainties. Air quality modeling is key to both steps because it provides a means to do