AIR QUALITY MONITORING, ASSESSMENT AND MANAGEMENT by Nicolás A. Mazzeo (ed.)
By Nicolás A. Mazzeo (ed.)
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Additional resources for AIR QUALITY MONITORING, ASSESSMENT AND MANAGEMENT
Example text
The concentration distribution of selected air pollutants over the study area is shown in Fig. 4. If we compare the pollutants distribution (Fig. 4) with the Saudi Arabian national air quality standard (Table 2), it is clear that some regions within the study are experiencing high level of air pollution threats. For this study, the social and cost objective data of each grid were also modeled as fuzzy variables according to the fuzzy scale mentioned in Table 1. Pollutant SO2 Inhalable Particulates (fpm) Nitrogen Oxides Defined as Nitrogen Dioxide (NO2) Carbon Monoxide (CO) Measurement period Limit 30 day period, one hour average 730 µg/m3 12 month period, 24 hour average 365 µg/m3 12month period, annual average 80(µg/m3) 12-month period, the 24-hour maximum 340(µg/m3) 12-month period, the annual average 80(µg/m3) 30 day period, the one-hour average 660(µg/m3) 12-month period, the annual average 100(µg/m3) 30-day period, the one-hour average 40 (mg/m3) 30-day period, the 8-hour average 10(mg/m3) Table 2.
With the same sensor array, Blixt and Borch (Blixt & Borch, 1999) showed that the degree of spoilage of vacuum-packed beef could be determined quantitatively. They also developed mathematical model, describing the relationships between the degree of spoilage, as determined by a sensory panel, and the sensor signal magnitudes of the electronic nose. 1) Y = b0 + b1 × S1 + b2 × S2 + ... 1) where Y is the degree of spoilage, bx is the unweighted regression coefficient obtained from partial least-squares regression (PLS), and Sx is the standardized sensor signal magnitude of the electronic nose.
5 is also fuzzy value. For decision purpose the comparisons of fuzzy data is not straightforward. To obtained crisp value of SC, centroidal method (Yager, 1980) is used. The centroid index of the fuzzy number represents the crisp score of an alternative Ai. If the fuzzy SC for a grid Ai is SC Ai (α1, β1, γ1), then the crisp score of that location can be computed as follows: 30 Air Quality Monitoring, Assessment and Management SC x ( Ai ) = (β1 − α 1 )(α 1 + 2 / 3(β1 − α 1 )) + (λ 1 − β1 )(β1 + 1 / 3(λ 1 − β1 )) (β1 − α 1 ) + (λ 1 − β1 ) (6) where, SCx(Ai) is the crisp score of grid Ai and α1, β1, and γ1 are the lowest, most likely and maximum values of SC Ai .