Air Quality and Characteristic Community Parameters

McMullen, Thomas B.; Fensterstock, Jack C.; Faoro, Robert B.; Smith, R.D.

doi:10.1080/00022470.1968.10469170

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Gifford and Hanna estimate the following model: C = kQ/u, with C = pollution concentration, Q = source emission per unit area, u = average wind speed, and k = parameter (function of city size). McMullen et al (1968) use correlation and regression analyses over a sample of sixty-six metropolitan statistical areas (MSA) to relate sixteen air quality measures (particulates, VOCs, sulfates, SOz, etc. ) to thirteen community variables, showing that lead concentration is primarily a function of gasoline sales, SOz concentration of SO2 emissions, and sulfate concentration of ambient SOz.…”

Section: Aggregate Modelsmentioning

confidence: 99%

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

Guldmann¹,

Kim

2001

Geographical Analysis

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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 specijkatwn 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 difusion 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 i s expressed as the sum of two concentration contributions: (1) the local efect, dependent upon pollution-related factors around the measuring station, and (2) the regional g e d , dependent upon pollutant flows originating outside the local area. A large database is developed, making extensive use of CIS technology, to spatially relate such data as pollution measurements, meteorological factors, landuse characteristics, census socioeconomic data, and major highway network characteristics. The results con$rm 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. a 31 percent decrease in aggregate pollution emissions, including decreases of 31 percent for carbon monoxide (CO), 37 percent for sulfur dioxide (SO,), 71 percent for particulate matter (PM,,), 98 percent Financial support from the Urban Affairs and Urban Assistance Program, Ohio Board of Regents, is gratefully acknowled ed. Many thanks are due to Phil Viton, Steve Gordon, and three anonymous referees of this journal for hekful comments and suggestions on earlier drafts. Jean-Michel Guldmann is a professor in the Jean-Michel Guldmann and Hag-Ye01 Kim / 157for lead (Pb), and 42 percent for volatile organic compounds (VOCs), and an increase of 17 percent for nitrogen oxides (NO,). 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 an...

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Section: Aggregate Modelsmentioning

confidence: 99%