Development of a NOx Chemistry Module for EDMS/AEDT to Predict NO2 Concentrations (2017) / Chapter Skim
Currently Skimming:
Pages 29-55
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 29... ...
29 Comparison of Dispersion Without Chemistry - The total NOx results from CALPUFF and SCICHEM are based solely on the effects of atmospheric dispersion (no chemistry)
|
From page 30... ...
30 hour durations, etc.) are also assessed.
|
From page 31... ...
31 Method Summary Single independent variable regression similar to ARM2 but based on airport‐specific data. Multivariable regression based on airport‐specific data that empirically accounts for both NO oxidation and NO2 photolysis through inclusion of atmospheric parameters: T, O3, θ, and S. Assumes the air masses are in a photostationary state and analytically determines the NO2/NOX ratio. User Requirements/Data Needs User must choose the diurnal category to use the appropriate regression equation. User determines hourly NOX concentration using AERMOD. User determines hourly NOX concentration using AERMOD, and specifies T, O3, θ, and S from available meteorological and other datasets. User supplies T, O3, and θ from available meteorological and other datasets. Based upon detailed regression assessments, the sets of regressed equations in Table 9 were selected for further evaluations involving time-paired comparisons of modeled and measured NO2/NOx values. Table 9: Selected Equations for Comparisons of Modeled and Measured NO2/NOx Values Method 1: Simple Regression Equation 1: Unconstrained regressed equation NO2/NOx=1‐0.8*
|
From page 32... ...
The asses values.
|
From page 33... ...
Plots of N Various m following behavior o O2/NOX vers 2nd Ord ore complex plots show f the tail end us NOX using er Polynomi Night equations w several crite of the curve these equati al ere also eva ria that wer s. 33 ons are prese Log luated, starti e used to re nted below: ng with a m ject equation Power Day ix of differen forms, mo t formations stly based o .
|
From page 34... ...
Y = a+ (Increase Y = a*
|
From page 35... ...
Y = a (Head ch Most of th illustrate t summariz The statis including 1/(b*
|
From page 36... ...
As shown in negativ y= y= Plots of th , the equatio e NO2/NOX r (d*
|
From page 37... ...
The equat such as 1 early. Oth y y y y y y These equ and illust equations Hour ions appear t 2-15 will nee er suitable eq =a+b*
|
From page 38... ...
For simpl plots of th y=a*
|
From page 39... ...
Most of th y= In contras equation m y= Although provide an of coeffic NO2/NOx intervals. While som largely fi constraint point (0,1 was appro to that poi y= y= e hourly rang 1‐.9*
|
From page 40... ...
The latter see if mor followed b Equation NO2/NOx NO2/NOx NO2/NOx NO2/NOx NO2/NOx NO2/NOx equation is a e accurate fit y the corresp =1‐0.8*
|
From page 41... ...
41 The "Meas" day data points refers to the measured (monitored) data while the "Constrained" equation refers to the weighting that was implemented to force the equation to approximately approach the 0,1 point.
|
From page 42... ...
Each term illustrate t (e.g., a* x1 he regression x3Pow y x4Pow y x5Pow y , b*
|
From page 44... ...
The avera that from results do linear cha this NO2/N others. It dependen variable a the predic ge Adjusted the correspo not appear t racteristic tai OX vs.
|
From page 45... ...
While the effects of of Tempe the predic of the oth other vari term repre formulatio below: se plots prov the other var rature, Zenith ted points to er variables. ables)
|
From page 47... ...
Some of t Method 1 on the firs try to iden modified: Y In contras Y These equ the data, presented Although correspon Similarly, have nega equations Ra2 value on an und formulatio plots are p hese equatio evaluations) t term or wit tify suitable = a+b*
|
From page 48... ...
The focus As indica other term Based on recommen Y However, cap of 0.1 horizontal appear to (e.g., NO2 in regressing ted, most of s showed sm the Ra2 va ded: = a*
|
From page 49... ...
6.3 Meth An analyt compariso work (i.e. Equation irradiation regression included m Unlike M 1450, etc.
|
From page 50... ...
The Ra2 v Also, the contribute Because t prediction zero NO2/ the same r by the fol Me alues (about Prob(t) value to the predic he predicted z s were invest NOX predicti egressed resu lowing plots.
|
From page 51... ...
An attemp not conve the ADL converge. and the nu For comp below: t was made rge to a solu dataset (57 re The failures mber of data leteness, the Coefficie Adjusted to regress the tion (i.e., the cords out of are due to th points result full ADL dat nt of Multipl coefficient o NO2 vs NOx data for LA regressions f 1,469)
|
From page 52... ...
52 The relatively small difference in Ra2 values between the full dataset and the ADL dataset may suggest the ADL dataset may contribute significantly to the overall fit. However, the number of data points (the YUL dataset has over 6,000 data points)
|
From page 53... ...
M3Eq9 M3Eq10 M3Eq11 M3Eq12 M3Eq13 M3Eq14 The additi those that Notwithst methods 1 solution a F1 = a*
|
From page 54... ...
The high contribute Equations but these M3Eq6 w Solar Irra formation Since Equ to predict from Meth 6.4 Prefe Based upo in Section Method fo near airpo Prob(t) value significantly M3Eq13 an equations bo ith a Ra2 valu diation (S)
|
From page 55... ...
55 Method 1 ‐ Equation 1 NO2/NOx=1‐0.8*
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.