# Presentation: 522978

## The Twin Cities VOC/PM2.5 Personal Exposure Study

- <date/time>

**The Twin Cities VOC/PM****2.5**** Personal Exposure Study**

**Funding Sources: EPA STAR Grants**
**GR825241-01-0**** and ****R827928-010**
**Gregory C. Pratt, Don Bock, Chun Yi Wu**
**Minnesota Pollution Control Agency, St. Paul**
**Ken Sexton*, John Adgate, Gurumurthy Ramachandran**
**University of Minnesota, Minneapolis**
**Thomas Stock, Maria Morandi**
**University of Texas, Houston**
***now at University of Texas, Brownsville**

## Personal

_**Personal**_
**PM****2.5**
**OVM**

_**Indoor**_
**PM****2.5**
**OVM**

_**Outdoor**_
**PM****2.5**
**OVM**

_**Neighborhood**_
**PM****2.5 ****(FRM)**
**OVM**
**VOC Canister**

_**Central Site**_
**PM****2.5 ****(FRM)**
**VOC Canister**

_**Modeling**_
**VOCs**

**N=70 subjects**
**6-12/person**

**N=3**

**N=2**

**PM****2.5****: 116 24-hour periods**
**VOCs: 58 48-hour periods**

**Notes:**
Study design
Personal samples = gold standard
Each of the other measures is physically further removed from the personal samples.  How good are each of the other measures?

There were 70 participants in 3 neighborhoods.  VOCs were measured for each participant on about six occasions (48 hours each).

There were 116 total days of monitoring consisting of 58 48-hour periods.  For VOCs each 48-hour period was considered one sample.  Each of the 58 48-hour periods was modeled for each participant. 

## Personal PM2.5 Monitor

**Personal PM****2.5 ****Monitor**

## PM2.5 Concentrations at Neighborhood Sites

**PM****2.5**** Concentrations at Neighborhood Sites**

**Notes:**
Modeled concentrations for each study participant over all the days of monitoring.  In general, the modeled benzene concentrations were lowest in Battle Creek, slightly higher in East St. Paul, and highest in Phillips.  There was also more variability in the modeled concentrations among the homes in Phillips.  There were 5 monitors, the first two were in East St. Paul, the third in Battle Creek, and the last two were in Phillips and nearby downtown Minneapolis.

## Slide 5

**Notes:**
Modeled concentrations for each study participant over all the days of monitoring.  In general, the modeled benzene concentrations were lowest in Battle Creek, slightly higher in East St. Paul, and highest in Phillips.  There was also more variability in the modeled concentrations among the homes in Phillips.  There were 5 monitors, the first two were in East St. Paul, the third in Battle Creek, and the last two were in Phillips and nearby downtown Minneapolis.

## Slide 6

**Notes:**
Modeled concentrations for each study participant over all the days of monitoring.  In general, the modeled benzene concentrations were lowest in Battle Creek, slightly higher in East St. Paul, and highest in Phillips.  There was also more variability in the modeled concentrations among the homes in Phillips.  There were 5 monitors, the first two were in East St. Paul, the third in Battle Creek, and the last two were in Phillips and nearby downtown Minneapolis.

## Slide 7

**Notes:**
Modeled concentrations for each study participant over all the days of monitoring.  In general, the modeled benzene concentrations were lowest in Battle Creek, slightly higher in East St. Paul, and highest in Phillips.  There was also more variability in the modeled concentrations among the homes in Phillips.  There were 5 monitors, the first two were in East St. Paul, the third in Battle Creek, and the last two were in Phillips and nearby downtown Minneapolis.

## Longitudinal PM2.5 Correlations 

**Longitudinal PM****2.5**** Correlations **

## Real-Time PM2.5 Measurements

**Real-Time PM****2.5**** Measurements**

**Notes:**
Modeled concentrations for each study participant over all the days of monitoring.  In general, the modeled benzene concentrations were lowest in Battle Creek, slightly higher in East St. Paul, and highest in Phillips.  There was also more variability in the modeled concentrations among the homes in Phillips.  There were 5 monitors, the first two were in East St. Paul, the third in Battle Creek, and the last two were in Phillips and nearby downtown Minneapolis.

## PIO PM2.5 Results

**O  similar across communities**
**P > I > O for most subjects**
**O not correlated with P, and weakly correlated with I (r = 0.27)**
**I moderate predictor of  P (r = 0.51)**
**Longitudinal correlation low**
**Outdoor central monitoring sites underestimate PM****2.5**** exposures**

## 3M Personal Organic Vapor Monitor (OVM)

## Outdoor

**Outdoor**

**Indoor**

**Personal**

**Chloroform**

**D-Limonene**

**Benzene**

**Toluene**

## 10-2                10-1                 1                   101                 102 

**Personal Exposure**

**Residential Indoor Concentration**

## Benzene

**Benzene**

**1:1**

## Slide 15

| VOC | Cancer bench-mark | Personal |  | Indoor |  | Outdoor |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | Median | 90% | Median | 90% | Median | 90% |
| benzene | 1.3 | 3.2 | 18.3 | 1.9 | 15.3 | 1.3 | 3.3 |
| carbon tetrachloride | 0.7 | 0.6 | 0.9 | 0.5 | 0.9 | 0.6 | 0.9 |
| chloroform | 0.4 | 1.0 | 3.9 | 0.9 | 3.4 | 0.1 | 0.2 |
| p-dichloro-benzene | 0.9 | 0.4 | 5.1 | 0.2 | 1.5 | 0.1 | 0.2 |
| methylene chloride | 20 | 1.4 | 12.1 | 1.1 | 11.5 | 0.4 | 0.8 |
| Trichloro-ethylene | 5 | 0.2 | 1.4 | 0.2 | 0.8 | 0.1 | 0.3 |

## VOC Results

**P > I > O**
**P and I within person variability ****≈ 1 or****der of magnitude**
**P and I between person variability ****≈**** 2 orders of magnitude or more**
**O not correlated with P and weakly correlated with I **
**I moderate predictor of  P **
**Outdoor central monitoring sites underestimate VOC exposures**

## Why is P not correlated with O and only weakly with I?

**70% of time indoors at home**
**Microenvironments of high concentration (commuting, gas station, dry cleaner, etc.)**

## Air Dispersion Modeling of VOCs

- <date/time>

_**Air Dispersion Modeling of VOCs**_

**Model = ISCST3 version 01001 (EPA regulatory model)**
**Met data = 1999 MSP airport**
**Modeled times = 58 48-hour periods corresponding to measurement periods**
**Receptors = community monitoring sites (OVMs and canisters) and outside participant homes (OVMs)**

**Notes:**
Regulatory default model options

## Sources

- <date/time>

_**Sources**_

**Point Sources**** - ****large stationary sources inventoried individually (424 in metro)**
**Mobile Sources**** - ****cars, trucks, planes, trains, boats, construction equipment, farm equipment, off-road vehicles, lawn and garden equipment, etc. (apportioned to census tracts)**
**Area Sources**** - ****smaller stationary sources inventoried collectively (22 categories apportioned to census tracts)**

## Benzene Emissions

**Benzene Emissions**

**Notes:**
Benzene emissions are predominantly from mobile sources.  The pattern of emissions clearly follows the major roads.  This is true for several of the pollutants that were dominated by mobile source emissions.

## Unmatched

**Unmatched**

**Space**

**Time**

**Space&Time**

**BCK**

**PHI**

**ESP**

**OVM**

**CAN**

**Measured Benzene**

**Modeled Benzene**

**Model estimate represents the ensemble average of all possible realizations**

## Slide 22

**Notes:**
Modeled concentrations for each study participant over all the days of monitoring.  In general, the modeled benzene concentrations were lowest in Battle Creek, slightly higher in East St. Paul, and highest in Phillips.  There was also more variability in the modeled concentrations among the homes in Phillips.  There were 5 monitors, the first two were in East St. Paul, the third in Battle Creek, and the last two were in Phillips and nearby downtown Minneapolis.

## Benzene Emissions

**Benzene Emissions**

**Notes:**
Benzene emissions are predominantly from mobile sources.  The pattern of emissions clearly follows the major roads.  This is true for several of the pollutants that were dominated by mobile source emissions.

## Conclusions

**Generally for measured PM****2.5**** & VOCs:**
**Personal > Indoor > Outdoor**
**High cross-sectional and longitudinal variability**
**Outdoor not a good predictor of personal —indoor better, but not great  >> implies microenvironments are important**

## Conclusions

**ISCST model predictions (matched in time and space) average within factor of 2 of measured outdoor for most VOCs (better unmatched)**
**Modeled (as with monitored) O concentration not good predictor of P**
**OVMs compared well with canisters for most VOCs in this study – poorly for some VOCs**

## Extra Slides

## Point Sources

**Emissions of 82 pollutants using RAPIDS**
**Company review of emission estimates**
**Source locations by GIS address-matching + GPS**
**Stack parameters averaged over all sources at a facility from (by priority):**
**DELTA (state permitting system) **
**Default OTAG values by SCC code **
**Average OTAG values**

**Notes:**
Emissions extraction from 1999 Rapids for state (CYW)
Emissions summed by pollutant for each facility		
UTMs taken from				
	1. Address-matched UTMs with interactive rematch (237 metro area facilities)	2. Additional batch address-matching (660 facilities statewide)		3. Additional interactive match (3 facilities)
	4. Lat Lons converted to NAD83 UTMs using SECTIC	
Stack parameters taken from			
	1. Delta (permitting system)values averaged over all sources at a facility
	2. Default OTAG values by SCC code averaged over all sources at a facility 	for metro area facilities without Delta values (237 metro facilities)		3.  Average OTAG values for all other facilities (e.g., most out-state facilities)
Total number of facilities = 958 (State) and 424 (Metro)		
Emissions are in pounds per year
RAPIDS is an emission inventory database system developed for the Great Lakes Commission and used by a number of Great Lakes states.
GIS stands for Geographic Information System
GPS stands for Global Positioning System
OTAG is the Ozone Transport and Assessment Group, a regional planning organization

## Mobile Sources - On-Road and Non-Road

**Miles of each road category in each census tract calculated using GIS**
**MnDOT traffic count data obtained (counts by county and road category)**
**Used GIS to calculate VMT in census tract**
**Emission Factors (per VMT) from RAPIDS (based on Mobile 5 model)**
**Emissions assigned to census tract and modeled as an area source**

**Notes:**
Mobile sources include on-road sources like cars, trucks, and buses as well as non-road sources like farm equipment, construction equipment, planes, trains, boats, lawnmowers, garden equipment, etc.

RAPIDS is an emission inventory database system developed for the Great Lakes Commission and used by a number of Great Lakes states.
MnDOT is the Minnesota Department of Transportation
GIS stands for Geographic Information System
VMT = Vehicle Miles Traveled
The Mobile 5 model is the EPA regulatory model used to estimate emissions from motor vehicles

## Mobile Sources - Rail and Air

**RAPIDS rail emission were apportioned to census tracts based on the length of rail line in the tract**
**Airport-related emissions from each airport in RAPIDS were apportioned to the census tract containing the airport**

**Notes:**
RAPIDS is an emission inventory database system developed for the Great Lakes Commission and used by a number of Great Lakes states.

## Area Source Categories - 1

**Notes:**
Area sources are stationary sources that are too small to be included in the emission inventory system as point sources.  

Area source emissions were inventoried using the RAPDS system.  For several pollutants the emissions estimates were countywide sums.  The county values were attributed to census tracts by the method indicated in the table.  In other cases (e.g.,landfills)  emissions were apportioned directly to the census tract in which they occurred.

## Area Source Categories - 2

**Notes:**
Area source emissions were inventoried using the RAPDS system.  For several pollutants the emissions estimates were countywide sums.  The county values were attributed to census tracts by the method indicated in the table.  In other cases (e.g.,landfills)  emissions were apportioned directly to the census tract in which they occurred.

## Slide 32

**Notes:**
Benzene emissions are predominantly from mobile sources.  The pattern of emissions clearly follows the major roads.  This is true for several of the pollutants that were dominated by mobile source emissions.

## Regressions between modeled and monitored concentrations

**Notes:**
This table summarizes the relationships between modeled and measured concentrations for 12 pollutants.  Log transformed values were used since the data is approximately log-normally distributed.  The relationships are divided into the categories of comparisons with values measured using canisters, outdoor OVMs, indoor OVMs, and personal OVMs.  The R2adj.  values are shown as well as colors to designate the strength of the relationship.  The pattern that emerges is that the model can predict (with statistical significance) the concentrations of several pollutants in outdoor air (whether measured by canister or OVM).  This is shown by the yellow (p<=0.001) and green (p<=0.05) shaded areas in the table.  In contrast, the model predictions of concentrations in the ambient air at participants homes is not as well related to the indoor concentrations or the personal concentrations of these 12 pollutants.

These results suggest that 
	1.  The model can be used to estimate typical outdoor concentrations of these VOCs, but may miss the high and low concentrations on a given day.
	2.  Some of the pollutants (1,3-butadiene, carbon tetrachloride, chloroform, methylene chloride, naphthalene, styrene, tetrachloroethylene, and trichloroethylene) are not well-estimated by the model.  It is likely that the emission inventory is the source of the error because the model preforms reasonably well for other pollutants.

## Slide 34

**Notes:**
Carbon tetrachloride is not listed in the table because virtually all comes from background, i.e., historical emissions that have persisted in the atmosphere.

## Slide 35

**Notes:**
Carbon tetrachloride is not listed in the table because virtually all comes from background, i.e., historical emissions that have persisted in the atmosphere.

## VOCs

**Phillips Neighborhood Monitoring Site**

**PM****10**

**PM****2.5**

**VOCs**

## Canister 

**Canister **
**VOC Monitor**

**Notes:**
Closer view of VOC cannister monitor (from a different site not in the study)

## Slide 38

**Notes:**
Carbon tetrachloride is not listed in the table because virtually all comes from background, i.e., historical emissions that have persisted in the atmosphere.

## Slide 39

| Model-Monitor Comparisons |  | Monitor Site Canisters |  |  |
| --- | --- | --- | --- | --- |
|  |  | BCK
(n=54) | ESP
(n=55) | PHI
(n=51) |
| Benzene | Mon. mean
mean diff.
RMSE
Fx-Bias | 0.9
0.3
0.5
-0.3 | 1.9
-0.6
1.1
0.4 | 1.5
1.4
1.8
-0.6 |
| Ethylbenzene | Mon. mean
mean diff.
RMSE
Fx-Bias | 0.4
-0.0
0.2
0.1 | 1.1
-0.6
0.9
0.9 | 0.8
0.2
0.5
-0.3 |
| Styrene | Mon. mean
mean diff.
RMSE
Fx-Bias | 0.3
-0.2
0.2
0.6 | 0.4
-0.2
0.3
0.7 | 0.4
0.1
0.3
-0.2 |
| Toluene | Mon. mean
mean diff.
RMSE
Fx-Bias | 2.0
1.7
2.1
-0.6 | 8.4
-2.3
7.1
0.3 | 3.9
4.0
5.0
-0.7 |
| Trichloroethylene | Mon. mean
mean diff.
RMSE
Fx-Bias | 0.3
-0.1
0.3
0.7 | 0.4
-0.3
0.4
0.9 | 0.6
-0.4
0.5
1.0 |
| Xylenes | Mon. mean
mean diff.
RMSE
Fx-Bias | 1.8
0.8
1.2
-0.4 | 5.1
-1.9
3.3
0.5 | 3.7
2.4
3.3
-0.5 |

**Notes:**
This table summarizes the relationships between modeled and measured concentrations for 12 pollutants.  Log transformed values were used since the data is approximately log-normally distributed.  The relationships are divided into the categories of comparisons with values measured using canisters, outdoor OVMs, indoor OVMs, and personal OVMs.  The R2adj.  values are shown as well as colors to designate the strength of the relationship.  The pattern that emerges is that the model can predict (with statistical significance) the concentrations of several pollutants in outdoor air (whether measured by canister or OVM).  This is shown by the yellow (p<=0.001) and green (p<=0.05) shaded areas in the table.  In contrast, the model predictions of concentrations in the ambient air at participants homes is not as well related to the indoor concentrations or the personal concentrations of these 12 pollutants.

These results suggest that 
	1.  The model can be used to estimate typical outdoor concentrations of these VOCs, but may miss the high and low concentrations on a given day.
	2.  Some of the pollutants (1,3-butadiene, carbon tetrachloride, chloroform, methylene chloride, naphthalene, styrene, tetrachloroethylene, and trichloroethylene) are not well-estimated by the model.  It is likely that the emission inventory is the source of the error because the model preforms reasonably well for other pollutants.

## Benzene

**Benzene**

**Ethylbenzene**

**Xylenes**

**BCK**

**ESP**

**PHI**

**Measurement**

**Model**

- 1:1

## Residential Indoor Concentration

**Residential Indoor Concentration**

## Personal Exposure

**Personal Exposure**

## Summary/Conclusions:

**Generally for measured VOCs/PM2.5:**
**P > I > O**
**Relatively high P-O/P-I longitudinal correlation coefficients mean that in healthy adults the variability in VOC exposures can be reasonably predicted within individuals over time.**
**This was not true for PM****2.5****, probably because of low outdoor variability and activity patterns of the working adult population**
**Risk assessments based on outdoor VOC measures appear to seriously underestimate lifetime cancer risks from these compounds**

## VOCs Measured

| VOCs Measured with OVM Badges (and FRM) |  |
| --- | --- |
| Benzene | a-Pinene |
| Carbon tetrachloride | b-Pinene  |
| Chloroform | Styrene |
| p‐Dichlorobenzene | Tetrachloroethylene (PERC) |
| Ethylbenzene | Toluene |
| d-Limonene | Trichloroethylene  |
| Methylene Chloride | m,p-Xylene |
|  | o-Xylene |
|  |  |

**Notes:**
Note 3 categories of compounds: 
Not everything can be accurately measured with the badges (though those compounds in the 2nd column can be measured using the FRM cannister method)
3rd column compounds either global background (CCl4 or are low outdoors but are relatively common in personal exposure monitoring studies.)
-PERC=Tetrachloroethylene
-TCE=Trichloroethyelne

THIS TALK WILL FOCUS ON BENZENE AS A MODEL COMPOUND BECAUSE IT IS UBIQUITOUS AND ITS RISK NUMBERS:

EPA IRIS RISK NUMBERS
Air Concentrations at Specified Risk Levels: 
Risk LevelConcentrationE-4 (1 in 10,000)13.0 to 45.0 μg/m3E-5 (1 in 100,000)1.3 to 4.5 μg/m3E-6 (1 in 1,000,000)0.13 to 0.45 μg/m3

## PM2.5 Measurements

**Central sites:  FRM**
**Personal and Indoor at home:  MSP impactors, pumps, time dairies**
**Flow rates O>I>P**
**Detection Limits:  P>I>O**
**Pretty good (but not perfect) temporal match**

**Notes:**
Regulatory default model options

48 hour periods represent the mean of means:  48 1-hour averages for chemicals

## Number of People/Samples(Non-Smoking Adults)

**VOCs:  71 Subjects**
- 2-18 samples per subject
- 58 48-hr sampling periods
- P = 288
- I  = 292
- O = 132

**PM2.5:  29 Subjects**
- 7-15 samples per subject
- 112 24-hr sampling periods
- P= 332
- I = 294
- O= 270

## Primary VOC Sources Indoors(source:  Wallace 1991*)

| Pollutant | Sources |
| --- | --- |
| Chloroform | Chlorinated water, especially when heated as in showering, dishwashing, etc. |
| p-Dichlorobenzene | Mothballs, toilet block deodorizers, other consumer products (check labels), chemical manufacturing industry |
| -  and -Pinene | Cleaning products, room fresheners |
| d-Limonene | Cleaning products, room fresheners |

- 	*Chapter 11 in:  Indoor Air Pollution: A Health Perspective.  Eds. Samet, J.M. and Spengler, J.D.  The Johns Hopkins University Press, Baltimore, MD, p.253-27.

## Slide 48

**Notes:**
Figure 3, HAPS paper 2. Plots of (a) Indoor Residential Concentrations and (b) Outdoor Community Concentrations versus Personal Exposures to Benzene, Methylene Chloride, and Toluene for Participants in the Study.

## VOC Results:  PIO

- Consistent P>I>O observed for 13 of 15 chemicals
- Exceptions:  Carbon Tetrachloride, Chloroform
- I does better than O
- Underestimation is greater at the upper end of the exposure distribution
- Central sites under estimate actual exposures for urban residents even when measured in their own community

## Longitudinal VOC Results

- How well do O levels predict I and P within people over time?
- Mixed model approach:
- Adjust for season and community effects
- Address issue of within person and within monitoring period autocorrelation

**Notes:**
P-O:  Benzene, Ethylbenzene, Styrene > 0.5 median xylenes median ~0.4
PI:  >0.8 are benzene chloroform, methylene chloride, alpha and beta pinene, xylenes

## Longitudinal VOC Results

- Benzene:
- P-O median r=0.59 (range -0.85-0.99)
- P-I  median  r=0.86 (range -0.26-0.99)
- p-Dichlorobenzene
- P-O median r=0.00  (range -0.72-0.98)
- P-I  median  r=0.57   (range -0.54-0.99)

## People pass through microenvironments of high concentration (commuting, gas station, dry cleaner, etc.)

- Higher income homes may
-  use more consumer products
-  have air conditioning
-  have attached garage
-  have tighter construction
-  spend more time commuting