Averaging time, pollutant concentration

A very useful format in which to display air quality data for analysis is that of Fig. 4-8, which has as its abscissa averaging time expressed in two different time units and, as its ordinate, concentration of the pollutant at the receptor. This type of chart is called an arrowhead chart and includes enough information to characterize fully the variability of concentration at the receptor.  [c.53]

In general, air quality data are classified as a function of time, location, and magnitude. Several statistical parameters may be used to characterize a group of air pollution concentrations, including the arithmetic mean, the median, and the geometric mean. These parameters may be determined over averaging times of up to 1 year. In addition to these three parameters, a measure of the variability of a data set, such as the standard deviation  [c.226]

Workers in the field of water resources are accustomed to thinking in terms of watersheds and watershed management. It was these people who introduced the term airshed to describe the geographic area requiring unified management for achieving air pollution control. The term airshed was not well received because its physical connotation is wrong. It was followed by the term air basin, which was closer to the mark but still had the wrong physical connotation, since unified air pollution control management is needed in flat land devoid of valleys and basins. The term that finally evolved was air quality control region, meaning the geographic area including the sources significant to production of air pollution in an urbanized area and the receptors significantly affected thereby. If long averaging time isopleths (i.e., lines of equal pollution concentration) of a pollutant such as suspended particulate matter are drawn on the map of an area, there will be an isopleth that is at essentially the same concentration as the background concentration. The area within this isopleth meets the description of an air quality control region.  [c.424]

Direct-reading meters estimate air concentrations through one of several detection principles. These may report specific chemicals (e.g., CO2 by infrared light), chemical groups (e.g., certain volatile organics by photoionization potential), or broad pollutant categories (e.g., all respirable particles by scattered light). Detection limits and averaging time developed for industrial use may or may not be appropriate for lAQ.  [c.239]

Particulate Matter. In the air pollution field, the terms particulate matter, particulates, particles, and aerosols (qv) are used interchangeably and all refer to finely divided sohds and hquids dispersed in the air. The original EPA primary standards were for total suspended particulates, TSP, the weight of any particulate matter collected on the filter of a high volume air sampler. On the average, these samplers collect particles that ate less than about 30—40 )Tm in diameter, but collection efficiencies vary according to both wind direction and speed. In 1987, the term PM q, particulate matter having an aerodynamic diameter of 10 )Tm or less, was introduced. The 10-)Tm diameter was chosen because 50% of the 10-)Tm particles deposit in the respiratory tract below the larynx during oral breathing and the fraction deposited decreases as particle diameter increases above 10 )Tm. Because the NAAQS standard (see Table 3) was only enacted in 1987, currentiy available PM q data ate insufficient to determine trends. However, from 1979 to 1988 TSP emissions declined 22% and ambient concentrations decreased 20% (4).  [c.373]

Steel companies typically pay a disposal fee of 150 to 200 per ton of dust. With an average zinc concentration of 19%, much of the EAF dust is sent offsite for zinc recovery. Most of the EAF dust recovery options are only economically viable for dust with a zinc content of at least 15 to 20%. Facilities that manufacture specialty steels such as stainless steel with a lower zinc content still have opportunities to recover chromium and nickel from the EAF dust. In-process recycling of EAF dust involves pelletizing and then reusing the pellets in the furnace, however, recycling of EAF dust on-site has not proven to be technically or economically competitive for all mill operations. Improvements in technologies have made off-site recovery a cost-effective alternative to thermal treatment or secure landfill disposal. Table 7 provides some examples of pollution prevention practices aimed at reducing air emissions and capturing energy credits. Remember that this is a very energy intensive manufacturing process, so any efforts aimed at reducing energy requirements are of particular interest to plant managers.  [c.127]

Coliform bacteria, typified by Escherichia coli and fecal streptococci (enterococci), reside in the intestinal tract of man. These are excreted in large numbers in the feces of humans and other warm-blooded animals. Typical concentrations average about 50,000,000 coliforms per gram. Untreated domestic wastewater generally contains more than 3,000,000 coliforms per 100 ml. Pathogenic bacteria and viruses causing enteric diseases originate from the same source (that is, fecal discharges of diseased persons). Consequently, water contaminated by fecal pollution is identified as being potentially dangerous by the presence of coliform bacteria.  [c.460]

Turbulent Transport and Diffusion. There are two pollutant transport terms in equation 5 an advection term, in which pollutants are carried along with the time-averaged mean wind flow and a dispersion term representing transport resulting from local turbulence. The averaging time that deterrnines the mean winds is related to the spatial scale of the system being modeled. Minutes may be appropriate for urban-scale simulations, multihour averages for the regional scale, and daily to weekly averages for determining long-term concentrations of nonreactive pollutants.  [c.381]

The averaging time of the rapid-response record [Fig. 4-1 (a)] is an inherent characteristic of the instrument and the data acquisition system. It can become almost an instantaneous record of concentration at the receptor. However, in most cases this is not desirable, because such an instantaneous record cannot be put to any practical air pollution control use. What such a record reveals is something of the turbulent structure of the atmosphere, and thus it has some utility in meteorological research. In communications  [c.42]

To understand the meaning of the information given, let us concentrate on the data for 1-hr averaging time. In the course of a year there will be 8760 such values, one for each hour. If all 8760 are arrayed in decreasing value, there will be one maximum value and one minimum value. (For some pollutants the minimum value is indefinite if it is below the minimum detectable value of the analytical method or instrument employed.) In this array, the value 2628 from the maximum will be the value for which 30 /r of all values are greater and 70% are lower. Similarly, the value 876 from the maximum will be the one for which 10% of all values are greater and 9( are lower. The 1% value will be between the 87th and 88th values from the maximum, and the 0.1% value will lie between the 8th and 9th values in the array.  [c.53]

The principal framework of empirical equations which form a basis for estimahng concentrations from point sources is commonly referred to as the Gaussian plume model. Employing a three-dimensional axis system of downwind, crosswind, and vertical with the origin at the ground, it assumes that concentrations from a continuously emitting plume are proportional to the emission rate, that these concentrations are diluted by the wind at the point of emission at a rate inversely proportional to the wind speed, and that the time- averaged (about 1 h) pollutant concentrations crosswind and vertically near the source are well described by Gaussian or normal (bell-shaped) distributions. The standard deviations of plume concentration in these two directions are empirically related to the levels of turbulence in the atmosphere and increase with distance from the source.  [c.296]

Elevated ground-level ozone exposures affect agricultural crops and trees, especially slow growing crops and long-lived trees. Ozone damages the leaves and needles of sensitive plants, causing visible alterations such as defoliation and change of leaf color. In North America, tropospheric ozone is believed responsible for about 90% of the damage to plants. Agricultural crops show reduced plant growth and decreased yield. According to the U.S. Office of Technology Assessment (OTA), a 120 figlrc seasonal average of seven-hour mean ground-level ozone concentrations is likely to lead to reductions in crop yields in the range of 16 to 35% for cotton, 0.9 to 51 % for wheat, 5.3 to 24% for soybeans, and 0.3 to 5.1 % for corn. In addition to physiological damage, ground-level ozone may cause reduced resistance to fungi, bacteria, viruses, and insects, reducing growth and inhibiting yield and reproduction. These impacts on sensitive species may result in declines in agricultural crop quality and the reduction of biodiversity in natural ecosystems. The impact of the exposure of plants to ground-level ozone depends not only on the duration and concentration of exposure but also on its frequency, the interval between exposures, the time of day and the season, site-specific conditions, and the developmental stage of plants. Additionally, ground-level ozone is part of a complex relationship among several air pollutants and other factors such as climatic and meteorological conditions and nutrient balances. For example, the presence of sulfur dioxide may increase the sensitivity of certain plants to leaf injury by ground-level ozone. Also the presence of ground-level ozone may increase the growth-suppressing effects of nitrogen dioxide.  [c.31]

See pages that mention the term Averaging time, pollutant concentration : [c.317]    [c.360]    [c.456]   
Fundamentals of air pollution (1994) -- [ c.41 , c.42 ]