Modeling Air Quality in Urban Areas: A Cell-Based Statistical Approach

By Guldmann, Jean-Michel; Kim, Hag-Yeol | Geographical Analysis, April 2001 | Go to article overview

Modeling Air Quality in Urban Areas: A Cell-Based Statistical Approach


Guldmann, Jean-Michel, Kim, Hag-Yeol, Geographical Analysis


Statistical regression models are presented that explain the observed variations, across urban areas, in the concentrations of two major pollutants, ozone and carbon monoxide. Model specification and estimation are based on an explicit and new spatial framework derived from the theoretical concept of well-mixed cells, whereby the basic Fickian system of diffusion equations is integrated over the regional space partitioned into a grid of large cells. The concentration in each cell results from the balance of pollutant flows into and out of this cell and of pollutant emissions and removal within that cell, and is expressed as the sum of two concentration contributions: (1) the local effect, dependent upon pollution-related factors around the measuring station, and (2) the regional effect, dependent upon pollutant flows originating outside the local area. A large database is developed, making extensive use of GIS technology, to spatially relate such data as pollution measurements, meteorological factors, land-us e characteristics, census socioeconomic data, and major highway network characteristics. The results confirm the appropriateness of the well-mixed cell framework, are in line with general knowledge regarding the determinants of ozone and carbon monoxide concentrations, and clarify the role of transportation, residential fuel use, economic activities, natural environments, and meteorological factors such as temperature and solar radiation. About 50 percent of the variations in concentrations are explained by these models. Several areas of further research are outlined.

The enactments of the U.S. Clean Air Acts (1963, 1970) and subsequent Amendments (1977, 1990) have led, over 1970-1999, to a 31 percent decrease in aggregate pollution emissions, including decreases of 31 percent for carbon monoxide (CO), 37 percent for sulfur dioxide ([SO.sub.2]), 71 percent for particulate matter ([PM.sub.10]), 98 percent for lead (Pb), and 42 percent for volatile organic compounds (VOGs), and an increase of 17 percent for nitrogen oxides ([NO.sub.x]). More striking, these improvements have occurred while the population grew by 33 percent, the vehicle miles traveled (VMT) by 140 percent, and the gross domestic product (GDP) by 147 percent. However, this aggregate assessment masks the fact that, in 1999, 150 million tons of air pollution were released into the air, and approximately 62 million people lived in counties with monitored concentrations above the primary standards (those designed to protect public health) for one or more of the six principal pollutants, particularly ozone in the Northeast, California, some rural areas, and some national parks. Also, despite a 60-80 percent decline in per-car emissions since the 1960s, total emissions from mobile sources have not decreased proportionately, because more frequent and longer trips are generated by more people and cars (EPA 2000a, 2000b).

Air quality management and planning are therefore still necessary, and even more so in the future due to rapid urban development, vehicular traffic congestion, and growing energy consumption. Quantitative models relating urban and public policy decisions to the resulting air quality are thus still needed. However, a review of the literature points to an extremely heterogeneous body of modeling methodologies and empirical studies. Most of them are site specific and capture only a specific aspect of the pollution problem (for example, role of economic output, or traffic, or meteorology), and few distinguish between local, regional, and national effects. Building on the well-mixed cell theoretical approach to pollution diffusion analysis, this paper develops a completely new spatial framework for the statistical estimation, through regression analysis, of more comprehensive air quality models, that relate the concentrations of air pollutants in an urban area to (1) local urban factors, including meteorological, socioeconomic, land-use, and transportation characteristics, and (2) background pollution flows originating out of the urban area and contributed by regional and national sources. …

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