Pollutants emission statistics

Human-made sources cover a wide spectrum of chemical and physical activities and are the major contributors to urban air pollution. Air pollutants in the United States pour out from over 10 million vehicles, the refuse of approximately 300 million people, the generation of billions of kilowatts of electricity, and the production of innumerable products demanded by everyday living. Hundreds of millions of tons of air pollutants are generated annually in the United States alone. The six pollutants identified in the Clean Air Act are shown in Table 22-14. Annual emission statistics for these six pollutants are considered major indicators of the U.S. air quality. During the 1970 to 2003 period, the total emissions of the six pollutants declined by 51 percent, while at the same time the gross domestic product increased By 176 percent, the population by 39 percent, and energy consumption by 45 percent. Total emissions in the United States are summarized by source category for the year 1998 in Table 22-15. 

TABLE 22-14 Six Principal Pollutants Annual Emission Statistics/ Millions of Tons per Year... 

There are some limited statistics that may be needed for measuring and reporting pollution emissions. These... 

Empirical—statistical models ate based on estabUshing a relationship between historically observed air quaUty and the corresponding emissions. The linear rollback model is simple to use and requites few data, and for these reasons has been widely appHed (3,4). Linear rollback models assume that the highest measured pollutant concentration is proportional to the basinwide emission rate, plus the background value that is,... 

One of the most significant Indices of air pollution potential Is provided by statistics on motor vehicle use. On a global scale the number of vehicles continues to rise although the rate of growth has slowed In North America and Europe as a whole, where vehicle densities are the highest In the world. Since 1979, the most rapid growth has occurred In Asia and South America where vehicle ownership has more than doubled (11). In the developing countries, as well as many others, these vehicles are not equipped with emission control devices. 

Rational air pollution control strategies require the establishment of reliable relationships between air quality and emission (Chapter 5). Diffusion models for inert (nonreacting) agents have long been used in air pollution control and in the study of air pollution effects. Major advances have been made in incorporating the complex chemical reaction schemes of photochemical smog in diffusion models for air basins. In addition to these deterministic models, statistical relationships that are based on aerometric data and that relate oxidant concentrations to emission measurements have been determined. 

Kousa et al. [20] classified exposure models as statistical, mathematical and mathematical-stochastic models. Statistical models are based on the historical data and capture the past statistical trend of pollutants [21]. The mathematical modelling, also called deterministic modelling, involves application of emission inventories, combined with air quality and population activity modelling. The stochastic approach attempts to include a treatment of the inherent uncertainties of the model [22],... 

Problems of environmental pollution by particulate emissions as a part of the complex complicated palette of environmental analytical problems will be covered in the centre of this section. Airborne particulates and their composition are affected by manifold influences. The main influences shall be ascertained. The power and the limits of multivariate statistics when applied to environmental problems under the compromising conditions of routine monitoring shall also be demonstrated. 

The large variance of the elemental depositions, also demonstrated by the very uncertain temporal courses of the elemental deposition rate (Fig. 7-2), strongly limits visual inspection of the obtained data, the interpretation can be subjective only. Otherwise practically all simple correlation coefficients are significant. Both facts show that it seems to be useful to apply advanced statistical methods to attempt recognition of possible existing data structures which may enable the characterization of pollutant loading and the possible identification of emission sources. 

Frequentist methods are fundamentally predicated upon statistical inference based on the Central Limit Theorem. For example, suppose that one wishes to estimate the mean emission factor for a specific pollutant emitted from a specific source category under specific conditions. Because of the cost of collecting measurements, it is not practical to measure each and every such emission source, which would result in a census of the actual population distribution of emissions. With limited resources, one instead would prefer to randomly select a representative sample of such sources. Suppose 10 sources were selected. The mean emission rate is calculated based upon these 10 sources, and a probability distribution model could be fit to the random sample of data. If this process is repeated many times, with a different set of 10 random samples each time, the results will vary. The variation in results for estimates of a given statistic, such as the mean, based upon random sampling is quantified using a sampling distribution. From sampling distributions, confidence intervals are obtained. Thus, the commonly used 95% confidence interval for the mean is a frequentist inference... 

The lack of a correlation was also appreciated in a follow-up study in which the data presented in the Dutch report20 (particulate concentration, water content, carbon monoxide concentration, furnace temperature, etc.) were compared with the PCDD/F emissions using statistical regression techniques.21 The aim was to determine which operating and emission parameters were strongly related to PCDD/F emissions. The data set was divided into two groups on the basis of the type of air pollution control device installed ... 

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