### Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut

Connecticut Department of Environmental Protection Bureau of Air Management
February 7, 2011Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 1

Table of Contents

Acknowledge ment s .............................................................................................................. ...... 4

Executive Summary ............................................................................................................. ...... 5

Introduc tion .................................................................................................................. ............ 7

Background Information .............................................................................................................. 7

Project Design ................................................................................................................ ........... 8

Measurement Methods ...........................................................................................................

.... 10

AethalometerTM Data Processing .................................................................................................

. 12

Modeling Methods ..............................................................................................................

....... 13

Summary of Result s ............................................................................................................

...... 14

• Modeling Result s Summary ................................................................................................. 14

• Overall by Si te Summa ry .................................................................................................... 16

• Monthly Averages of WSPM Contribution ............................................................................... 17

• PM 2.5 daily averages > 15 μ g/m 3 ; PM 2.5 hourly averages > 30 μ g/m 3

......................................... 18

• Wood Smoke Events .......................................................................................................... 20

• Delta-C vs. PAH

................................................................................................................. 28

• Modeled Source Contributions

.............................................................................................. 30

Conclusions

.............................................................................................................................. 32Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 2

Acronyms and Abbreviations

AQI – Air Quality Index AQS – Air Quality System CAA – Clean Air Act CFR – Code of Federal Regulations CO – carbon monoxide CTDEP – Connecticut Department of Environmental Protection CV – coefficient of variance DAS – data acquisition system DQA – data quality assessment DQO – data quality objective EPA – Environmental Protection Agency FEM – Federal Equivalent Method FRM – Federal Reference Method GC – gas chromatography GC/MS – gas chromatography/mass spectrometry GIS – geographical information systems GPS – global positioning system HAP – hazardous air pollutant HPLC – high performance liquid chromatography ICP/MS – inductively coupled plasma/mass spectrometry IMPROVE – Interagency Monitoring of Protected Visual Environments IO – Inorganic IT – information technology
LAN – local area network LMP – limited maintenance plan MQO – measurement quality objectives MSA – metropolitan statistical area NAAQS – National Ambient Air Quality Standards NIST – National Institute of Standards and Technology NOx – nitrogen oxides NOy – reactive oxides of nitrogen NPAP – National Performance Audit Program OAQPS – Office of Air Quality Planning and Standards OWB – Outdoor Wood Boilers PAMS – Photochemical Assessment Monitoring Stations P&A – precision and accuracy
PM 2.5 – fine particulate matter (2.5 microns)
PM 10 – respirable particulate matter (10 microns)
PM 10-2.5 – coarse particulate matter (PM 10 – PM 2.5 ) QA – quality assurance QA/QC – quality assurance/quality control QAPP – quality assurance project plan QMP – quality management plan RH – relative humidity RPD – relative percent difference SIP – State Implementation Plan SLAMS – state and local monitoring stations SO 2 – sulfur dioxide SOP – standard operating procedure STN – Speciation Trends Network SQL- Structured Query Language (Database Language) TSP – total suspended particulateEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 3

VOC – volatile organic compound WSPM – wood smoke particulate matterEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 4

This project was conducted by the Connecticut De partment of Environmental Protection (CTDEP) and funded by the United States Environmental Protec tion Agency (US EPA) through a 2005 Local-Scale Air Toxics Ambient Monitoring Grant Soli citation (RFA NO: OAR-EMAD-05-16). This is the final report submitted to EPA to fulfill the final grant obligation. Below is t he project title, category an d applicant information by which the proposal for this projec t was submitted for consideration.

Project Title: Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut

Category: Source Identification and Characterization (Bin 1)

Applicant Information: Connecticut Department of Environmental Protection, Bureau of Air Management

Project Manager/Contact: Peter Babich, Sc.D., Supervising Environmental Analyst CTDEP, Bureau of Air Management
79 Elm Street Hartford, CT 06106-5127 Phone: (860) 424-3027 Fax: (860) 424-4063 pete.babich@ct.gov



### Acknowledgements

CTDEP gratefully acknowledges the efforts of both G eorge Allen and Lisa Rector of NESCAUM on the OWB portion of this project, as well as, Mr. Allen’s input an d assistance with the study design for the wood smoke ambient air monitoring efforts. CTDEP also acknowledges Dr. Jay Turner of Washington University and Mr. Allen for their timely efforts in developing the Aethal ometerTM Data Masher to allow for processing of the black carbon/wood smoke data while taking into account the newly discovered s pot-saturation effects. Finally, CTDEP acknowledges Tom McGrat h with the State of Massachusetts for facilitating the accessibility of the Springfield, MA ambient air data in order to include in the analysis and results of this project.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 5



### Executive Summary

The CTDEP conducted an ambient air monitoring study from September 2006 through April 2008 that characterized the contribution of particulate matter fr om wood burning sources. Monitoring was conducted at seven sites and modeling was applied to the data coll ected to apportion the sources and quantify wood smoke particulate matter (WSPM) concentrations.

This study confirmed that the 2-channel AethalometerTM (Magee Sci entific) does provide a real-time wood smoke indicator. Using modeling, a scaling factor wa s derived to quantify wood smoke concentrations. This scaling factor was somewhat variable from site to site; however, a reasonable approximation of WSPM

concentrations could be determined.

The impetus for this work was to compare emission inv entory estimates of WSPM in Connecticut to actual ambient air monitoring measurements. The 2002 Mi d-Atlantic Regional Air Management Association (MARAMA) emission estimates for WSPM was that 38% of Connecticut PM 2.5 emissions are from wood burning sources. Results from this study indicate that on an annual average, the WSPM contribution to total

PM 2.5 measurements range from 1.7 to 17.3% for the six Co nnecticut sites evaluated in this study (10.8% for Springfield, MA). Even though these are estimates for only six distinct sites in Connecticut, these sites are representative of Connecticut as a whole with rura l, urban, valley and non-valley sites represented.

As one would expect, the contribution of WSPM is strongly linked to the season, with highest contributions in the colder, winter months and much lower contributi ons during the warmer, summer months. For all sites, maximum monthly average WSPM contributions in the winter ranged from 10.8 to 41.3%, while maximum monthly summertime averages ranged from near zero to 6.1% (See Figure ES-1).

Figure ES

­ 1: Average Monthly Percent Contribution of WSPM to Total PM 2.5Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 6

In addition to approximating average annual WSPM contri butions in Connecticut, another objective of this project was to determine WSPM contributions when total PM 2.5 observations are elevated. This is significant given the new daily PM 2.5 standard of 35 μ g/m 3 and with the probability of the establishment of a sub-daily

PM 2.5 standard in the future. Understandi ng the sources that contribute to elevated PM levels is key for effectively developing and eval uating control strategies.

WSPM contributions were evaluated for daily PM 2.5 concentrations above 15 μ g/m 3 . This level corresponds to the “Moderate” breakpoint for the Air Quality Index . When the 24-hr average temperature was less than 45 degrees Fahrenheit (oF) and the daily PM 2.5 concentrations were greater than 15 μ g/m 3 , the average WSPM contribution was 23.9% (r ange of 8.9 to 38.0%) of the average total PM 2.5 concentrations. When the 24-hr average temperature was greater than 45oF and the daily PM 2.5 concentrations were greater than 15 μ g/m 3

, the average WSPM contribution was 4.2% (ran ge of 0.9 to 5.5%) of the average total PM 2.5 concentrations.

A similar evaluation was conducted for WSPM contributions when hourly PM 2.5 concentrations were greater

than 30 μ g/m 3 . When the hourly average temperature was less than 32oF and the hourly PM 2.5 concentrations were greater than 30 μ g/m 3 , the average WSPM contribution was 36.3% (maximum of 56.8%) of the average total PM 2.5 concentrations. When the hourly averag e temperature was less than 45oF and the
hourly PM 2.5 concentrations were greater than 30 μ g/m 3 , the average WSPM contribution dropped to 22.8% (maximum of 45.0%) of the average total PM 2.5 concentrations. When the hourly average temperature was greater than 45oF and the hourly PM 2.5 concentrations were greater than 30 μ g/m 3 , the average WSPM contribution was 1.7% (range of 0.6 to 3.5%) of the average total PM 2.5 concentrations.

Although the objective of this study was to quantify the contribution of Connecticut WSPM emissions primarily during the wintertime, wood-burning season, it also provided valuable information on the effects of summertime forest fire transport events. Analysis in this report shows that these forest fire events can significantly contribute to both PM 2.5 and ozone exceedance days. These WSPM methodologies could also prove useful in documenting future exceptional events.

Modeling conducted through this project not only prov ided a highly time resolved profile for wood burning sources, but also resulted in valuable profile and contri bution information for other sources such as fresh and aged mobile sources, home heating oil and secondary aer osols with hourly time resolution. Understanding

the contributions of these sources and the diurnal an d seasonal patterns can greatly enhance the ability to develop control strategies to address these sources, as well as, determine the effectiveness of both current and future mitigation efforts. Therefore, future analyses of the other sources could provide useful information on where appropriate mitigation efforts could be applied in the most effective manner.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 7



### Introduction

The Connecticut Department of Environmental Protection (CTDEP), Bureau of Air Management, was awarded a US EPA grant to assess wood smok e contributions to fine particle matter (PM 2.5 ) in Connecticut, and also to conduct monitoring and testing to characte rize the emissions for an emerging source known as outdoor wood furnaces (OWFs), outdoor wood boilers (OWBs), or hydronic heaters.

The first part of this project utilized ambi ent measurements to evaluate the portion of PM 2.5 concentrations that can be attributed to wood burning. The seco nd part of this project characterized the PM 2.5 emissions associated with OWBs, an emerging, uncontrolled and unregulated source that has been largely uncharacterized up to this point.

Although this was one grant award, the findings are presen ted in two separate reports; this report addresses the findings relative to the ambient air monitoring po rtion of the study and NESCAUM has prepared the final report for the OWB source charac terization portion of the study. 1

The main objective of the wood smoke monitoring port ion of this study was to better characterize the

contribution of wood smoke to ambient PM 2.5 concentrations in Connecticut. This was accomplished by selecting monitoring sites that are re presentative of areas that are impac ted by wood smoke sources. The ambient wood smoke monitoring effort: 1) ch aracterized the impact of wood burning on PM 2.5 concentrations; 2) assessed the contribution of wood smoke to PM 2.5 during wintertime inversion events; 3) assessed emission inventory estimates; 4) evaluated modeling result s with monitoring data; 5) provide information that could prove useful in developing future control and reduction strategies.; and 6) built upon new techniques that quantify PM 2.5 concentrations from wood smoke on a real-t ime basis. This study will provide valuable

information as the CTDEP looks to ident ify risk reduction strategies and develop PM 2.5 and Regional Haze State Implementation Plans (SIPs).



### Background Information

The severity of potential health effects and magnitud e of populations affected by wood smoke pollutants have led health scientists to conclude that exposure to it should be minimal. Wood smoke is comprised of numerous constituents including PM 2.5 , carbon monoxide (CO), volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons ( PAHs). Some VOCs and PAHs are respiratory irritants and also have carcinogenic and mutagenic properties, while carbon monoxide exposure has been associated with adverse respiratory and cardiac effects 2

, 3

, 4

, 5 . Exposure to PM 2.5 may play a large role in observed health effects

1 Contribution of Wood Smoke to Particle Matter Levels in Connecticut

– Source Characterization of Outdoor Wood

Furnaces. Prepared by NESCAUM in conjunction with this project. September 9, 2008. http://www.nescaum.org/documents/source

‐ characterization

‐ of

‐ outdoor

‐ wood

‐ furnaces/ctdep

‐ owb

‐ test

‐ report.pdf

2 http://www.epa.gov/burnwise/pdfs/wo odsmoke_health_effects_jan07.pd

3 Simpson

, Christopher D. and Luke P. Naeher

. 2010.Biological Monitoring of Wood Smoke. Inhalation Toxicology. Vol

22 (2).Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 8

related to wood smoke. Fine particles are of health concern because of their association with serious cardiopulmonary health outcomes and their impact on a large number of susceptible population subgroups, including young children, asthmatics, persons with resp iratory or heart disease, diabetics, and the elderly. 6

There is increasing concern regardi ng the impact of wood smoke from residential wood combustion (RWC) sources on air quality. According to the 2002 Mid-Atl antic Regional Air Management Association (MARAMA) emissions inventory, wood smoke contributes 38% of the PM 2.5 emissions in Connecticut. Woodstoves are
now available that are EPA-certified; however, the majority of the wood stoves in operation are older units with no pollution control devices. As fuel prices have risen, so have the sales of woodstoves and OWBs 7

.

A pilot study conducted in Rutland, VT in 2003 looked at evaluating an approach that quantified, in near realtime, the contribution of wood smoke to PM 2.5 concentrations (WSPM). 8 The pilot project succeeded in apportioning the PM 2.5 into several source categories. The Rutlan d study also identified ways in which this approach could be improved upon in future studies; the ke y concept of using the delta C signal as a marker

for real-time woodsmoke emissions is integrated in this proposal.



### Project Design

The CTDEP conducted wood smoke monitoring that wa s comprised of one newly established core site and six satellite sites. Monitoring was conducted from Septembe r 2006 through April 200 8. A full range of parameters were operated at the core site while the six satellite sites coll ected appropriate data to assess WSPM using information obtained and applied from the core site. See Figure 1 for a map of the monitoring sites used in this study.

Core site: The core site was in the Thomaston area, loca ted in western Connecticut. Thomaston is located in the Naugatuck Valley and is prone to wintertime inve rsions, which trap pollutants close to the ground. The emphasis was placed on collecting highly sensitive, highly time-resolved data to best characterize and apportion sources. A comprehensive selection of pollut ants were measured at this site to best assess the

PM 2.5 contributions from wood smoke. Continuous PM 2.5 , along with volatile and non-volatile fractions of

PM 2.5 , were measured in addition to continuous speciation methods to obtain highly time-resolved organic carbon (OC), elemental carbon (EC), sulfate (SO 4 ), and the optical absorption of PM at two wavelengths

4 Noonan

, Curtis W. and John R. Balmes

. 2010. Biomass Smoke Exposures. Inhalation Toxicology. Vol 22(2)

5 Lewtas, Joellen. 2007. Air pollution combustion emissions: Characterization of causative agents and mechanisms

associated with cancer, reproductive, and cardiovascular effects

. Mutation Research. Vol 636 (1

‐ 3)

6 http://www.epa.gov/burnwise/pdfs/wo odsmoke_health_effects_jan07.pdf

7 NESCAUM. 2006. Assessment of Outdoor Wood

‐ Fired Boilers.

8 Allen, George A., Babich, Peter C. and Poirot, Richard L. Paper No. 16. “ Evaluation of a New Approach for Real Time

Assessment of Wood Smoke PM.” Air & Waste Management Association Visibility Specialty Conference on Regional

and Global Perspectives on Haze: Causes, Consequences and Controversies. Asheville, NC, October 25

‐ 29, 2004.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 9

(370 and 880 nm). Trace-gas analyzers were used to obtain carbon monoxide (CO), sulfur dioxide (SO 2 ), and reactive oxides of nitrogen (NO y ). Highly time-resolved measurements of trace metals and PAHs were collected, as well. This information was used to quan tify WSPM and to relate the measurements to other wood smoke markers (i.e., potassium) and other pollutants (i.e., PAHs).

Satellite sites: A total of five satellite sites in Connecticut and one satellite site in Massachusetts were included in the wood smoke monitoring network. Three of the sites were located in Danbury, East Hartford

and Mansfield. These sites were selected based on the fa ct that they are located in valleys that are prone towintertime inversions and are located in western, central and eastern Connecticut, respectively. A twowavelength AethalometerTM were deployed to each site to provide semi-quantitative, real-time WSPM

estimates. These WSPM measurements will be bette r quantified using analysis performed on data obtained

from the core site. The other two Connecticut sites were located on Mohawk Mountain in Cornwall and at

Criscuolo Park in New Haven. Both Mohawk Mountain and Criscuolo Park are established sites that are recently approved NCORE sites with a comprehensive selection of parameters currently being measured. The Cornwall Mohawk Mountain site is a rural background site located well above inversion boundary layers and is in the Mid-Atlantic/Northeast Visibility Union (MANE-VU) Rural Aerosol In tensive Network (RAIN).

The New Haven Criscuolo Park site is a coastal urban site which is prone to inversion conditions, but is presumably more heavily impacted by mobile sources re lative to wood smoke sources. The final satellite site selected was the Springfield, MA site based on its proximity to Connecticut and that a comprehensive suite of measurements were already being obtained at this established site that could be utilized for WSPM

estimates.

Figure 1: Wood Smoke Monitoring NetworkEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 10



### Measurement Methods

Monitoring that was established to assess wood smoke contributions to PM 2.5 focused on obtaining highresolution measurements to better evaluate temporal and spatial distributions. Following is a list of the methodologies that were deployed and the pollutants meas ured in order to meet the study objectives. Table 1 lists the sites along with what instrumentation is currently deployed and what was added at each site for this study.

Table 1: Monitoring Methodologies for Core and Satellite Sites



### Core site Satellite sites

Thomaston Cornwall Danbury East

Hartford New

Haven Mansfield Springfield,

MA
Specific Wood Smoke Indicator Measurements

Two

‐ wavelength

AethalometerTM X X X X X X X

PM 2.5 

### Mass Measurements

FDMS TEOM X X X

MetOne BAM X X X X

PM 2.5 

### FRM 1/3 1/3 1/3 1/1 1/1 1/3

PM 2.5 

### Speciation Measurements

Sunset OCEC X X

Continuous SO 4 

### X X

3

‐ slot DRUM X

IMPROVE X

STN X

Trace

‐ Gas Measurements

Trace CO X *

Trace SO 2 

### X X

Trace NO y 

### Criteria Gas (non

‐ Trace) Measurements

CO X X X

SO 2 

### X X X

NO
x 

### X X X

Ozone X X X X

Particle

‐ bound PAHs

EcoChem PAH X
Meteorological Parameters

Climatronics X X X X X X X

Data Acquisition

Data Acquisition DRDAS DRDAS DRDAS DRDAS DRDAS DRDAS ESCEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 11

WSPM: A two-wavelength AethalometerTM (Magee Scientif ic Models AE21 and AE22) was operated at all

seven sites. The AethalometerTM was used to measure the optical absorption of PM at 880 nm (BC) and 370 nm (UV-C). The AethalometersTM at all sites were op erated at 2 LPM with a PM-1 (BGI/Magee Scientific SCC 0.732) size selective inlet. Field studies have sh own that BC measurements ar e strongly correlated with thermal elemental carbon (EC) measurements. 9,10,11 The difference between the UV-C and BC channels (Delta-C) has been shown to be a specific, semi-quantitative indicator of wood-smoke related PM. The Rutland study showed that the Delt a-C measurement was specific to WSPM even in presence of PM

associated with mobile sources. The AethalometerTM was configured to capture fi ve-minute measurements.

Continuous PM 2.5 : An FDMS-B TEOM (Thermo Series 8500) was operated at the core site to obtain hourly

PM 2.5 concentrations, as well as volatile and non-volatile fractions of PM 2.5 , which is valuable in terms of source apportionment. An FDMS-B TEOM was also operated at the New Haven Criscuolo Park and East Hartford McAuliffe Park sites. MetOne BAMs were operated at the Cornwall, Danbury, New Haven and Springfield, MA sites to obtain continuous PM 2.5 measurements.

Continuous Speciation: A continuous OC/EC analyzer (Sunset Laboratory Model 3) was deployed to the

core site to collect two-hour OC and EC measurements. A continuous SO 4 analyzer (Thermo Environmental Model 5020 SPA) was deployed at the core site to ob tain 15-minute sulfate av erages. Both of these analyzers were also operated at the Cornwall site.

Gaseous Pollutants: Trace-gas analyzers were deployed at the core site for CO (Thermo Environmental Model 48C-TLE) and SO 2 (Thermo Environmental Model 43C-TLE). T he utilization of trace instrumentation is important in order to increase the sensitivity, wh ich allows increased confidence in assessing source contributions. NO, NO 2 and NO
x were also obtained at the core site. Gas analyzers were also operated at other satellite sites to allow addition source apportionment modeling to be conducted to compare with results

from the core site in Thomaston.

Trace Metals: A slotted 3-Drum Impactor (DELTA Group wa s deployed at the core site to obtain highresolution trace metal and optical attenuation measurements for three size ranges. Among the target metals to be measured are high priority HAPs that include ar senic, beryllium, cadmium, chromium, manganese and

nickel.

PAHs: A continuous PAH analyzer (EcoC hem PAS 2000) was deploy ed at the core site to obtain real-time particle-bound PAH measurements.

9 Hansen, A.D.A., Rosen, H., Novakov, T. 1984

. The AethalometerTM ‐ an instrument for the real

‐ time measurement of optical absorption by aerosol particles. Sci. Total Environ. (36) 191.

10 Allen, George A., Lawrence, Joy and Koutrakis, Petros. 1999

. Field validation of a semi

‐ continuous method for

aerosol black carbon (Aethalometer) and temporal patterns of summertime hourly black carbon measurements in
southwestern Pennsylvania. Atmos. Environ. (33) 817

‐ 823.

11 Babich, Peter C., Davey, Mark E., Allen, George A. and Koutrakis, Petros. 2000

. Method comparisons for particulate
nitrate, elemental carbon, and PM 2.5 mass in seven U.S. cities. J. Air & Waste Management Assoc. 50 (8).Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 12

Meteorological Parameters: A meteorological system (Climatronics) was operated at the core site to obtain wind speed, wind direction, ambient temperature, barometric pressure and dew point. Similar meteorological systems were also operated at the six satellite sites.

Data Management and Analysis: Ambient monitoring data related to this project was managed in a SQL Server database located on a server at CTDEP Headquar ters in Hartford, CT. Data was polled, stored, validated and then generated into Air Quality System (AQS) files for reporting to the US EPA. The monitoring data was analyzed to characterize spat ial resolution, concentration gradients and source signatures of air pollutants specific to wood smoke so urces. The analysis performed on the data obtained in this study built upon results from the pilot study cond ucted in Rutland, VT. Unmix modeling was applied to apportion measured PM 2.5 from the core site located in Thomaston into several source categories. The resulting factor to convert the difference of the tw o channels from the AethalometerTM into WSPM was then applied to the data obtained at the satellite sites. Running the Unmix model on the data obtained at several of the satellite sites was possible fo r the sites that had a co mprehensive selection of parameters currently in operation. This was used to compare the results from the core site; however, the core site provided the most robust analysis, given the time-resolved speciation data and the high sensitivity of the trace gas measurements. Comparisons of these results against mo re traditional measures of wood smoke indicators (i.e., PAHs) were also made.



### AethalometerTM Data Processing

The Magee Scientific AethalometerTM is the widely us ed method to measure atmospheric BC on a near-real time basis. The AethalometerTM measures optical att enuation on a quartz fiber tape. It has been shown that BC measurements typically decrease with increased spot loading – a saturation effect that is a function of spot loading. 12,13 The AethalometerTM DataMasher is a post-processing software package used to validate 1-

min and 5-min AethalometerTM data and create valid one-hou r averages. Earlier versions of the DataMasher did not compensate for the filter saturation effect. This study funded the development of an enhanced version of the DataMasher that applies a correction to the raw AethalometerTM data to compensate for the filter

saturation effects. All AethalometerTM data in this study was post-processed with the newly developed

DataMasher with saturation e ffect correction capabilities.

Although results from the original AethalometerTM Data Masher vs. the newer DataMasher with saturation effect correction were reasonably comparable as demonstrated in Figure 2, there is an enhanced response

in both the BC and UVC channels and t here’s essentially a zeroing-out of the Delta-C channel. In the earlier
version of the DataMasher, where the saturation matrix effects were not compensated for, Delta-C routinely fell below zero in the warmer weather months with the presence of little or no WSPM.

12 Virkkula, Aki, et al

. 2007

. A Simple Procedure for Correcting Loading Effects of AethalometerTM Data. J. of Air &

Waste Management Assoc. (57) 1214

‐ 1222.

13 Turner, Jay R., Hansen, Anthony D. and Allen, George A. Paper No. 37. “Methodologies to Compensate for Optical

Saturation and Scattering in AethalometerTM Black Carbon Measurements.” Symposium on Air Quality Measurement
Methods and Technology, San Francisco, CA, April 30

– May 2, 2007.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 13

Figure 2: BC and Delta

­ C hourly averages post

­ processed with DataMasher with and without filter

saturation effect correction.



### Modeling Methods

Source apportionment was determi ned with Unmix modeling, an EPA approved software. Unmix takes a given set of pollutants and the corresponding concent rations and determines the amount of sources, the source compositions and the source contributions to the given sample set. 14,15,16 . For detailed procedures on data formatting, estimating source profiles, etc., for the Unmix model ing software, see the EPA Unmix 6.0

Fundamentals and User Guide. 9

For the purposes of this study, Unmix modeling was co nducted for four of the monitoring sites; Thomaston, East Hartford and New Haven sites in Connecticut, as well as, the Springfield site in Massachusetts. WSPM monitoring was also conducted at Danbury, Cornwall and Mansfield; however the modeling was not run due

14 Henry, R.C. 1997. History and Fundamentals of Multivariate Air Quality Receptor Models. Chemometrics Intelligent
Laboratory Systems (37) 37

‐ 42.

15 Environmental Protection Agency. R&D. 2007. EPA UNMIX 6.0 Fundamentals and User Guide. EPA/600/R-07/089

16 Christensen, William F., Schauer



### , James J. and Lingwall, Jeff W. 2006. Iterated Confirmatory Factor Analysis for

Pollution Source Apportionment. Environmetrics (17) 663–681.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 14

to a lack of gaseous and/or particulate data. For the pu rpose of assessing the contribution of WSPM to total

PM 2.5 concentrations, it was concluded that the four data sets modeled would be sufficient.

The Springfield, MA data set did not include volatile c oncentrations, thus only four sources were considered. The three Connecticut data sets included gaseous concentrations (NO, NOx, NO 2 and SO2), particulate concentrations (volatile and non-volatile) and the th ree Aethalometer Channels (BC, UVC and Delta-C), allowing for the potential of fi ve sources (only if present).

Several modeling runs were conducted on the Thomaston dataset to assess the entire study period, as well as, individual quarters to determine any seasonality to the modeling results. Single modeling runs were conducted for the East Hartford, New Haven and Springfield sites that covered data obtained for the duration of the study period.

The Unmix source composition results showed that defin ing pollutants were evident in each resulting source,

hence chemical markers were evident. A combination of the Unmix source composition (chemical markers), time series of source strength, and source strength dependency on wind direction was used for determining the source type. Then using the following chemical markers; Delta-C as the chemical marker for wood smoke, SO 2 as the marker for oil combustion, Volatile PM as the marker for non-local industrial sources, NO

as the marker for fresh combustion and NO 2 as the marker for aged combustion; factors could be developed for each source type to determine a particular sources PM 2.5 portion.



### Summary of Results

This section summarizes the results obtained in this study. First, the modeling results and scaling factor determinations are addressed. Then an overall summary by site along with monthly averages are presented. The analysis then focuses on more resolute sampling periods, estimating the contribution of WSPM when elevated 24-hour and hourly PM 2.5 levels were observed, along with binning that information into different temperature categories. Finally, both winter and su mmer wood smoke driven events are looked at to determine the contribution of WSPM during these events and the impact on both the ozone and PM 2.5 measurements.



### • Modeling Results Summary

Unmix Modeling was conducted for the Thomaston, Ea st Hartford, New Haven and Springfield, MA sites. Table 2 shows the scaling factors determined for all the modeling runs. The scaling factor is defined as the number that Delta-C must be multiplied by to calculat e WSPM concentrations. As shown in Table 2, the scaling factors were reasonably consistent for most modeling runs,(with the ex ception of the New Haven individual quarter runs). The lowest scaling factor, a 2.7 estimated at Thomaston for 1 st quarter 2008, is suspect based on inconsistent volatile PM measur ements from the FDMS 8500 unit which affected the modeling results. This quarter was not included in the determination of the uni versal, study-wide scaling factor that was used to scale the Delta-C WS signal to WSPM concentrations at all sites. The scaling factor for Thomaston for the five-quarter (4 th quarter 2006 through 4 th quarter 2007) model run, which included the volatile channel, was 5.0. The scaling factors fo r the individual one-quarter modeling runs of 2006-2008Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 15

winter periods were 8.2, 8.4, 6.9 and 6. It was deter mined that not including the volatile PM channel was appropriate for running the Unmix model. In addition it was also determined that the New Haven model runs were not representative scaling factors for wood smoke. The large variety of PM 2.5 sources and the minimal contribution of wood smoke indicator (Delta C) created an artificially high wood smoke scaling factor. Thus a more reliable site to base a wood smoke scaling factor would be that which wood smoke is a larger

contributor to the whole PM 2.5 , such as Thomaston. Therefore the un iversal, study-wide scaling factor was determined to be 7.8, was estimated by taking the average of the scaling factors from the three valid winter one-quarter modeling runs at Thomaston (1 st quarter of 2008 was excluded as it was determined unreliable due to suspect 8500 FDMS volatile PM data). For consis tency across the study period and for all analysis conducted relative to this project, all Delta-C observations were scaled up to WSPM concentrations using the universal, study-wide scaling factor of 7.8.

Table 2: Scaling Factors Determined from Unmix Modeling; Delta

­ C*Scaling Factor = WSPM ( μ

g/m 3 )

Site

Time Period Scaling Factor With

Volatiles Scaling Factor

Without Volatiles

Thomaston Universal, Study-wide Scaling Factor based on average of Thomaston individual quarterly model
runs from: Qtr 4, 2006 through Qtr 4,

2007 6.30 7.80

Qtr 4, 2006 through Qtr 4,

2007 5.0 N/A

Qtr 4, 2006 6.0 8.2
Qtr 1, 2007 7.8 8.4
Qtr 2, 2007 5.1 N/ A
Qtr 3, 2007 6.4 N/ A
Qtr 4, 2007 6.2 6.9
Qtr 1, 2008 2.7 6.1

East Hartford Qtr 4, 2006 through Qtr 1,

2008 6.4 N/A

New Haven Qtr 4, 2006 through Qtr 1,

2008 4.8 N/A

Qtr 4 2006 N/ A 24.0 17

Qtr 1 2007 N/ A 22.7 17

Qtr 4 2007 N/ A 6.9

Qtr 1 2008 N/ A 10.5 17

Springfield, MA Qtr 4, 2006 through Qtr 1,

2008 N/A 8.8

17 T he New Haven model runs were not representative sca ling factors for wood smoke. The large variety of

PM 2.5 sources and the minimal contribution of wood smoke indicator (Delta C) created an artificially high wood smoke scaling factor.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 16



### • Overall by Site Summary

The overall annual average of t he contribution of WSPM to PM 2.5 was determined by taking the average of the weighted monthly averages dependi ng on the availability of valid averages for a given month. For instance, if January 2007 and January 2008 monthly aver ages were available for a given site, then the average of these two months were taken to represen t January. If only August 2007 was available for a given site, then that average alone was taken to represent August. Once the 12 monthly averages established, then an annual average was taken for each site. These results are shown in Table 3. Data utilized to determine annual averages was primarily from the Sept ember 2006 through April 2008 time period; however, when monthly averages at the East Hartford McAuliffe Park site were not available due to inadequate data capture, data obtained from January 2006 through August 2006 was substituted.

Annual PM 2.5 concentrations were determined by transforming continuous PM 2.5 data to be “FRM-like” based on regressions performed on PM 2.5 FRM and continuous PM 2.5 measurements obtained at each site for the study period. This allowed PM 2.5 FRM-like values to be established for each hour of each day for the entire study period to give a complete assessment of PM 2.5 measurements and fill in the holes created from one-inthree day PM 2.5 FRM sampling.

The annual average contribution of WSPM ranged from 1.7% of the total PM 2.5 measured at the New Haven site up to 17.3% at Thomaston. The average annual WSPM contribution at the Cornwall Mohawk Mountain site, elevation 1,656 ft., was 5.0 % of the total PM 2.5 concentrations observed. The remaining three Connecticut sites, Danbury, East Hartford and Mans field, were at 11.6%, 12.3% and 11.9%, respectively. The average annual WSPM contribution at the Spri ngfield, MA site was 10.8% of the total PM 2.5 measurements

Table 3: Annual Averages for PM 2.5

, WSPM and Contribution of WSPM to Total PM 2.5



### Site Overall Annual Average

PM 2.5 

### ( μ g/m 3 

### ) WSPM
( μ g/m 3 

### ) Contribution of WSPM to

Total PM 2.5



### Cornwall
(Mohawk Mountain) 8.0 0.32 5.0 %

Danbury 11.7 1.10 11.6%

East Hartford 10.7 1.06 12.3%

Mansfield 10.0* 0.96 11.9%

New Haven 11.6 0.16 1.7 %

Thomaston 9.7 1.36 17.3%

Springfield, MA 10.6 0.92 10.8%

*No PM 2.5 sampling at Mansfield; substituted everyday PM 2.5 values from East Hartford McAuliffe Park to

estimate WSPM contributions at Mansfield site for the period of September 2006 through March 2008.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 17



### • Monthly Averages of WSPM Contribution

Monthly average WSPM contributions to PM 2.5 were determined by averaging all valid hourly FRM-like PM 2.5 and WSPM values. Figure 3 is a time-series graph of t he monthly WSPM contributions for each site from January 2006 through April 2008. As expected, the WSPM contribution is strongly correlated with the time of year. The colder, winter months WSPM contributions are much greater than what is observed during the warmer, summer months. For all sites, maximum monthl y average WSPM contributions in the winter ranged from 10.8 to 41.3%, while maximum monthly summertim e averages ranged from zero to 6.1%. Figure 4 presents the same weighted monthly averages that we re used to determine the annual average contribution
to WSPM to total PM 2.5 percentages in Table 3. Also on the Figure 4 time series graph, are the average monthly temperatures by site in degrees Fahrenhei t (oF) on the secondary y- axis. Again, the strong correlation to season/monthly temperature is apparent in Figure 4.

Figure 3: Monthly average WSPM contribution to total PM 2.5 concentrationsEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 18

Figure 4: Monthly average WSPM contributions and temperatures by site



### • PM 2.5 

### daily averages > 15 μ g/m 3 

### ; PM 2.5 

### hourly averages > 30 μ g/m 3

Fine particulate data was binned to look at WSPM contributions when PM 2.5 concentrations were elevated. In addition to separating by PM 2.5 concentrations, average ambient te mperature measurements were also
utilized to assess overall WSPM contributions. Tabl e 4 shows the breakdown by site when the 24-hr PM 2.5 concentration was greater than 15 μ g/m 3 and separated for when the daily average ambient temperature was less than and greater than 45oF. For the five site s in Table 4, the average WSPM contribution to PM 2.5 was 23.9% when the 24-hr PM 2.5 concentration was greater than 15 μ g/m 3 and the 24-hr average ambient temperature measurement was less than 45oF. Conversely, when the 24-hr PM 2.5 concentration was greater

than 15 μ g/m 3 and the 24-hr average ambient temperature me asurement was greater than 45oF, the average
WSPM contribution to PM 2.5 was only 4.2%. Figure 5 also illustrates the differences for the two temperature categories at the five sites.

Table 4 also shows the breakdown by site when the hourly PM 2.5 concentration was greater than 30 μ g/m 3
and separated for when the daily average ambient te mperature was less than 32oF, less than 45oF and greater than 45oF. For the five sites in T able 4, the average WSPM contribution to PM 2.5 was 30.9% when the hourly PM 2.5 concentration was greater than 30 μ g/m 3 and the hourly average ambient temperature measurement was less than 32oF. When the hourly am bient temperature was less than 45oF and the hourlyEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 19

PM 2.5 concentration was greater than 30 μ g/m 3 , the WSPM contribution dropped to 22.8%. When the hourly

PM 2.5 concentration was greater than 30 μ g/m 3 and the hourly average ambient temperature was greater than 45oF, the average WSPM contribution to PM 2.5 was only 1.7%. Figure 6 also illustrates the differences for these three temperature cate gories at the five sites.

Table 4: WSPM Contribution to Total PM 2.5 by Temperature Category for elevated hourly and 24

­ hour PM 2.5 concentrations

Site 24-hr PM 2.5 FRM > 15 μ g/m 3

Contribution of WSPM to Total PM 2.5 1-hr PM 2.5 FRM-like > 30 μ g/m 3

Contribution of WSPM to Total PM 2.5
< 45o F > 45o F < 32o F < 45o F > 45o F

Cornwall (Mohawk Mountain) 13.5 % 2.4 % 0.9 % 0.5 % 0.6 %

Danbury 30.0 % 10.0 % 56.8 % 45.1 % 2.6 %

New Haven 8.9 % 0.9 % 25.4 % 15.5 % 0.9 %

Springfield, MA 28.8 % 2.2 % 50.0 % 34.9 % 0.9 %

Thomaston 38.0 % 5.5 % 48.4 % 17.8 % 3.5 %

Figure 5: WSPM Contribution to Total PM 2.5 by Temperature Category for elevated 24

­ hr PM 2.5 concentrationsEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 20

Figure 6: WSPM Contribution to Total PM 2.5 by Temperature Category for elevated hourly PM 2.5 concentrations



### • Wood Smoke Events

A typical winter season will have a greater WSPM cont ribution in periods of temperatures at or below 45 o F as noted in Figures 5 and 6. In addition to displaying a temperature correlation, the magnitude and period of peak contribution is dependent on atmospheric stability . As one would expect with a local source, stable inversion conditions tend to enhance and elongate pe riods of high WSPM. The degree of overall contribution varies as weather systems move into and out of the area. During these periods of atmospheric stability, especially areas that are prone to ai r stagnation, such as valley locations, local wood burning sources can significantly contribute to ambient PM 2.5 concentrations.

An example of these wintertime inversion events is sh own in Figure 7. Although there are several inversiontype events illustrated in Figure 7, the event on Dece mber 6, 2006 was most pronounced with hourly PM 2.5 concentrations approaching 50 μ g/m 3 and WSPM concentrations well over 10 μ g/m 3 . PAH levels also following the same trend as the PM 2.5 and WSPM concentrations. PM 2.5 , WSPM and PAH concentrations built up through the evening of December 5 th and the early morning hours of December 6 th as winds speeds were less than 1 m/s and ambient temperatures steadi ly dropped. Approximately mid-morning of December

6 th , as ambient temperatures rose and wind speed in creased, the inversion lifted and all air pollutantEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 21

concentrations decreased significantly with PAH and WSPM levels approaching zero and PM 2.5 concentrations approaching 5 μ g/m 3 . Figure 8 focuses in on the December 5 th to December 6 th wood smoke

event.

The diurnal pattern of WSPM influences is common in the winter. Figure 9 illustrates the higher concentration of WSPM during the early morning an d evening hours and significantly lowe r WSPM levels from mid-morning to late afternoon. This is partly due to inversion- like meteorological conditions common during the colder, winter months, but also corresponds to when wood burning actually occurs, with very little wood-burning conducted during the work-day hours and then increa sed burning during the traditional at-home evening hours. As atmospheric stability increases throughout the night, WSPM levels build up until the inversion lifts – usually mid-morning. Figure 9 is a composite of all hourly WSPM concentration averages, spread and 95 th
percentiles at the Thomaston site from two winter periods (December 1, 2006 through March 31, 2007 and December 1, 2007 through March 31, 2008).

Figure 7: Classic Winter Inversion Event

– Thomaston, CT; Nov. 29, 2006 through Dec. 11, 2006Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 22

Figure 8: Classic Winter Inversion Event

– Thomaston, CT; Dec. 4, 2006 through Dec. 6, 2006Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 23

Figure 9: WSPM Average Hourly Concentrations at Thomaston, CT; Dec. 1, 2006 through Mar. 31, 2007;

Dec. 1, 2007 through Mar. 31, 2008.

In addition to the evaluation of WSPM contributions to typical wintertime inversion events, analysis conducted in this study was also used to identify possi ble forest fire transport events. On May 25, 2007 the

entire state observed elevated ozone and PM 2.5 levels (See Figure 10). In fa ct, this event was seen on a regional scale with high ozone and PM 2.5 throughout New England (See Figure 11). Together this suggested a large scale transport event which wa s further validated by Figure 12, which displays the back trajectories and corresponding satellite images of the days preceding t he pollutant spike. Wildfire smoke plumes can be seen emerging from the areas indicated as the air mass’ origination by the NOAA Hysplit model.

Although it seems apparent that the source of t hese pollutant spikes is wood smoke, the WSPM concentrations, although elevated during this period, did not necessarily correlate with the times that other pollutants were elevate (See Figure 13). This in co mbination with the large spikes in PAHs, ozone and PM 2.5 , PAHs and secondary pollutants, can be attributed to the magnitude of the source, weather patterns and reaction time allotted for the initial plume. Recent findings have noted that primary wood smoke pollutants (those that would be detected on the AethalometerTM UV channel) are oxidized through ozonolysis and ultraviolet reactions when in the atmosphere for an extended period of time. 18,19,20 The oxidation of these primary organic pollutants result in secondary organic pollutants which are no longer detectable in the UV spectrum.

18 Jimenez, J.L., et al

. 2009. Evolution of Organic Aerosols in the Atmosphere. Science (326) 1525

‐ 1529.

19 Ward, Tony and Lange, Todd. 2010. The Impact of Wood Smoke on Ambient PM 2.5 in the Northern Rocky Mountain
Valley Communities. Environmental Pollution. (158) 723

‐ 729.

20 Zhang, Xuan, Chen, Zhongming, Wang. Hongli, He, Shuzhong and Daoming, Haug. 2009. An Important Pathway for
Ozonolysis of Alpha

‐ Pinene and Beta

‐ Pinene in Aqueous Phase and its Atmospheric Implications. Atmospheric

Environment

. (43) 4465

‐ 4471.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 24

So, although the Delta-C measurement pr ovides a consistent indicator of wood smoke for local wood burning sources, it is much less reliable for wood smoke t hat has undergone significant transport, particularly when exposed to UV radiation.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 25

Figure10: Hourly Ozone and PM 2.5 Concentrations; May 2007Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 26

Figure 11: 24

­ hour Average PM 2.5 Concentrations; May 25, 2007Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 27

Figure 12: NOAA Hysplit Model and Satellite Images of Wildfire Event; May 25, 2007Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 28

Figure 13: PM 2.5

, WSPM, PAH and Ozone Hourly Concentrations; Thomaston, CT

– May 2007 Wildfire Event



### • Delta-C vs. PAH

Traditionally, PAH measurements have been used as an i ndicator of the presence of wood smoke. In this study, although PAH concentrations did follow WSPM co ncentrations when WSPM levels were elevated, there were many occasions when PAH levels were el evated and Delta-C was not. This could indicate the presence of transport wood smoke; when wood smok e is transported over long distances the primary organic compounds have been oxidized and thereby not det ectable by the Aethalom eterTM UV-C channel, as

described in the section above.

Also note Figure 7; this figure also displays instan ces where PAH and WSPM (calcu lated from Delta-C) track with each other and instances where they do not. During December 4, 2006 through December 6, 2006

PM 2.5 , WSPM and PAHs are all elevated during inversion conditions and then all drop off significantly once the inversion lifts and atmospheric mixing occurs. Several days earlier, November 29-30, 2006, PM 2.5 and
PAH measurements are elevated while WSPM is near ze ro. Given the fact that ambient temperatures are

near 70 o F, it is probable that the elevated PM 2.5 and PAH concentrations are not from local wood burning

sources. Mobile sources are known to drive ambi ent PAH concentrations, a New England based study
estimated mobile sources to account for ~76-84% of the PAH concentrations observed and wood smoke toEvaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 29

account for 13-16% of the PAH concentrations. 21 Figure 14 is a scatterplot of all hourly WSPM (as derived from the Delta-C) and PAH measurements collected at the Thomaston site during the study period, note the poor correlation. Figure 15 displays the BC as a func tion of PAH concentrations during the summer month of August 2007. August 2007 is a period in which one would not expect wood smoke, thus the primary driver of PAH would likely be mobile sources. BC being another known mobile source 22 would then be expected to correlate with the PAH concentrations, figure 15 displays this correlation which is stronger than figure 14, as would be expected.

Figure 14: WSPM and PAH Hourly Concentrations; Thomaston, CT

– October 2006 through April 2008

21 Golomb D., Barry Fisher, P. Varanusupakul, M. Koleda and T. Rooney. 2001

. “Atmospheric Deposition of Polycyclic

Aromatic Hydrocarbons Near New England Coastal Waters.” Atmospheric Environment

. (35) 36

. 6245

‐ 6258

22 NREL, 2004

. “Mobile Source Black Carbon Emissions.” Black Carbon and Climate Change Technical Workshop San
Diego, CA

.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 30

Figure 15: BC and PAH Hourly Concentrations; Thomaston, CT –August 2007



### • Modeled Source Contributions

In addition to utilizing Unmix modeling results to deter mine Delta-C scaling factor s and in turn approximate
WSPM contributions to total PM 2.5 , the modeling also provides highly time resolved information on fresh and aged motor vehicle, oil burning and sec ondary aerosol sources. Figure 16 illu strates the source contributions to hourly PM 2.5 concentrations for the Thomaston and New Ha ven sites for the period of January 1, 2007 through March 31, 2007. This shows the average source contributions to total PM 2.5 from each source for

each hour and demonstrates how the value of this highly time resolved data set and modeling results. Patterns that emerge include the morning rush-hour peak for mobile sources, the steady contribution from oil-burning sources and decrease in the wood burni ng sources during the tradi tional work-day hours.Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 31

Figure 16: Source Contributions; Thomaston & New Haven, CT; January 1, 2007 to March 31, 2007Evaluation of Wood Smoke Contribution to Pa rticle Matter in Connecticut 32



### Conclusions

This project has built on previous work that ident ifies the Delta-C measurem ent from a 2-wavelength AethalometerTM to be a specific indicator of wood smok e. Through modeling, a scaling factor was determined to convert the wood smoke indicator signal into WSPM concentrations. Although there was some variability between scaling factors derived from different sites or different seasons at the same site, they were consistent enough to apply a study-wide scaling factor of 7.8 to achieve a reasonable approximation of
WSPM concentrations to determine WSPM contributions to total PM 2.5 measurements.

With WSPM concentrations determined for each site at an hourly time resolution, analysis was conducted to evaluate the impact of WSPM on an annual and monthly basis, as well as, when daily and hourly PM 2.5 concentrations were elevated and categorized in va rious temperature bins. Although the annual average
WSPM contributions to PM 2.5 (1.7 to 17.3% for all sites) did not approach the 38% estimate of PM 2.5 emissions in Connecticut from wood burning source s from 2002 MARAMA emission estimates, WSPM was shown to contribute significantly to total PM 2.5 during the colder, winter months, particularly when the meteorological conditions were conducive to atmospheric stability and wintertime inversions. The maximum monthly average of WSPM contributions to PM 2.5 levels was 41.3% with maximum daily and hourly contributions approaching 74. 3% and 100%, respectively.

Potential future efforts related to th is work could include fully evaluating the modeling results for all sources. The source apportionment modeling not only provided valuable information related to wood smoke source profiles, but the modeling also quant ified contributions from other sources such as fresh and aged mobile sources, home heating oil and secondary aerosols with hourly time resolution. Understanding the
contributions of these sources and the diurnal and seasonal patterns can greatly enhance the ability to develop control strategies to address these sources, as well as, determine the effectiveness of both current and future mitigation efforts.

Another area that warrants additional investigation is determining an adequate marker or indicator to quantify, or at least qualify, wood smoke that is tran sported regionally. Due to photochemical reactions, it does appear that the optical characte ristics of the wood smoke are altered enough to render absorption
measurements at 370 nm to be ineffective.