Air quality management chapter 5 receptor modeling for air quality management
This presentation is the property of its rightful owner.
Sponsored Links
1 / 27

Air Quality Management Chapter 5 Receptor Modeling for Air Quality Management PowerPoint PPT Presentation


  • 124 Views
  • Uploaded on
  • Presentation posted in: General

Air Quality Management Chapter 5 Receptor Modeling for Air Quality Management. Ref: R.E. Hester and RM. Harrison, “Air Quality Management”, The Royal Society of Chemistry, Thomas Graham House, 1997. by Ping-hung Chen. Contents. 5.4 Secondary Arosol Mass 5.5 Apportionment of VOC.

Download Presentation

Air Quality Management Chapter 5 Receptor Modeling for Air Quality Management

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Air quality management chapter 5 receptor modeling for air quality management

Air Quality Management Chapter 5 Receptor Modeling for Air Quality Management

Ref: R.E. Hester and RM. Harrison, “Air Quality Management”, The Royal Society of Chemistry, Thomas Graham House, 1997

by Ping-hung Chen


Contents

Contents

5.4 Secondary Arosol Mass

5.5 Apportionment of VOC


5 4 secondary arosol mass

5.4 Secondary Arosol Mass

Secondary Aerosol Mass

The usual results of CMB analysis

list ‘sulfate’ as a source or possibly describe it as ‘regional sulfate’

Similar results are typically obtained through factor analysis

to develop effective control strategies

necessary to attribute the secondary particle mass to the original gaseous precursor sources


5 4 secondary arosol mass1

5.4 Secondary Arosol Mass

Spatial Analysis

Examining the variation of a number of measured species in samples at a single site

input data are the values of a single variable

measured at a variety of sites at multiple times

Thus, the analysis is seeking spatial and temporal variations of the measured variable

Area of Influence analysis

Malm et al. used EOF in addition to a trajectory based method


5 4 secondary arosol mass2

5.4 Secondary Arosol Mass

SO2 sources

Henry et al. used a modified EOF analysis to look for SO2 sources over the southwestern US

Data are from 3 day long samples from the National Park Service sampling network

Area of high positive values are likely source areas

Negative values represent regions that serve as sulfur sinks


5 4 secondary arosol mass3

5.4 Secondary Arosol Mass


5 4 secondary arosol mass4

5.4 Secondary Arosol Mass

Methods Incorporating Back Trajectories

Potential Source Contribution Function (PSCF)

Residence Time Analysis

Air parcel has spent a given time within that grid cell

Annular area/single grid cell area

= pi[(Dij+L)^2 – (Dij-L)^2]/4L^2


5 4 secondary arosol mass5

5.4 Secondary Arosol Mass


5 4 secondary arosol mass6

5.4 Secondary Arosol Mass

Residence Time Probability


5 4 secondary arosol mass7

5.4 Secondary Arosol Mass

Areas of Influence Analysis

Identifying those extreme samples at each measureement site

Region was divided into 1o latitude by 1o longitude

The extreme valued residence times will be highest around that site

To eliminate this central tendency, deviding the residence time value by an equal probabilty residence time surface

A new function assuming an air parcel can arrive at the receptor from any direction with equal probability

This new function is the extreme source contribution function (ESCF)


5 4 secondary arosol mass8

5.4 Secondary Arosol Mass

In AIA

Identified source cells

Calculated average ESCF for each receptor

Plot cell values to locate the source

Plot EOF results on the same map for comparison

Figure 4 shows the AIA results


5 4 secondary arosol mass9

5.4 Secondary Arosol Mass

AIA results


5 4 secondary arosol mass10

5.4 Secondary Arosol Mass

EOF result


5 4 secondary arosol mass11

5.4 Secondary Arosol Mass

Quantitative Bias Trajectory Analysis

Lamb’s equation 3

A(x,t) = f(T, x,t)

T(x,t)=f(Q, R,D,^) ---- equation 4

Q: probability of air parcel at x’ at t for receptor x ---- eq. 7

R: probability of material not lost by dry deposition

D: proportional to Kd dry deposition --- eq. 5

^: proportional to Kw wet deposition --- eq. 6


5 4 secondary arosol mass12

5.4 Secondary Arosol Mass

Potential Source Contribution Function (PSCF)

Both chemical and meteorological data for each filter sample are needed

Eq. 12 P[Aij]=nij/N

Eq. 13 P[Bij]=mij/N

Eq. 14 Pij=P[Bij]/P[Aij]=mij/nij

Pij is the conditional probability

Sufficient number of endpoints should provide accurate estimates of the source locations


5 4 secondary arosol mass13

5.4 Secondary Arosol Mass

PSCF map for SO2, Claremont, CA


5 4 secondary arosol mass14

5.4 Secondary Arosol Mass

PSCF map for SO42-, Claremont, CA


5 4 secondary arosol mass15

5.4 Secondary Arosol Mass

Emissions estimates for the SoCAB


5 4 secondary arosol mass16

5.4 Secondary Arosol Mass

PSCF-based Source Apportionment Model

PSCF is using mean value for the recptor cells

No source apportionment

Apportionment method

Eq. 16

Rijx=PijxEijx + [My/Mx]PijyEijx


5 4 secondary arosol mass17

5.4 Secondary Arosol Mass

PSCF-weighted emissions estimates for SO2 to SO42-


5 4 secondary arosol mass18

5.4 Secondary Arosol Mass

PSCF-weighted emissions estimates for NOx to NOy


5 5 apportionment of voc

5.5 Apportionment of VOC

Problems of identifying the emission source

A large number of small sources and there can be dfficulties in obtaining representative source samples

The methods depend only on the measured ambient data would be useful if they can identify source locations and apportion the VOCs to theose sources

Wet and dry deposition and dispersion information is important


5 5 apportionment of voc1

5.5 Apportionment of VOC

Analysis begins with

Eq. 17

Average C = [(sum of Cxt)/(sum of t)]

Eq. 18

Cil = cl(Xil/average Xl)

Simulation

Iteration of the field

Change is less than 0.5%


5 5 apportionment of voc2

5.5 Apportionment of VOC

Results

The areas of high concentration are focused around the highway network particularly to the east of the downtown ring road

Point source emissions

Several specific areas to the southeast of the downtown area have high contributions to the measured concentrations


5 5 apportionment of voc3

5.5 Apportionment of VOC

Residence time weighted concentration for ethene


5 5 apportionment of voc4

5.5 Apportionment of VOC

Residence time weighted concentration for 2-methylpentane


Air quality management chapter 5 receptor modeling for air quality management

提問討論


  • Login