利用环境空气质量数据进行决策.ppt

Xie’xie 1- * If the linear relationship between two variables is significant, a least square linear regression line, or line of “best fit” may be drawn to represent the data. This relationship can then be used to determine the value of an unknown variable. For example, if the ambient air concentration is unknown, but linearly related to the response of an ambient air monitor, we can estimate the ambient air concentration based on an observed response from the air monitor. Algebraically, a straight line has the following form: y = mx + b, where y = dependent variable plotted on the ordinate, x = explanatory variable plotted on the abscissa, b = the point at which the line intercepts the y-axis at x = 0, and m n= slope, which shows how much a change of 1 unit of x affects y. In this slide, we see that there is a linear relationship between FRM 2.5 sampler vs. continuous PM 2.5. Using the graph, if we record a value from the continuous PM 2.5 (x-axis). We then can read up to the line and across to obtain the FRM 2.5 value. For 20 ug/m3 on the x-axis for continuous PM 2.5, we read up and across to see that the FRM 2.5 would give a value of 21 ug/m3. 1- * Pie charts are an effective means of presenting environmental data in an easy fashion. Pie charts have the ability to transmit a lot of data without someone spending a lot of time trying to figure out the illustration. This slide shows that in 2002, the majority of SO2 emissions (86 percent) in the U.S. originated from fuel combustion, while only 9 percent derived from industrial processes. For the decision maker, this information is important when considering controls of SO2 emissions. The data are presented neatly and quickly for the decision maker to use in future decisions. 1- * Trend charts are an excellent way to show variations in pollutants over time. The reviewer can quickly look at the chart and determine the characteristics of each pollutant over the period of time. Pollutant concentrations can quickly