Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 

Connecticut Department of Environmental Protection 

Bureau of Air Management 

February 7, 2011
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 1

Table of Contents 

Acknowledgements .................................................................................................................... 4

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

Introduction .............................................................................................................................. 7

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

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

Measurement Methods ............................................................................................................... 10

AethalometerTM Data Processing .................................................................................................. 12

Modeling Methods ..................................................................................................................... 13

Summary of Results .................................................................................................................. 14

• Modeling Results Summary ................................................................................................. 14

• Overall by Site Summary .................................................................................................... 16

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

• PM2.5 daily averages > 15 μg/m3; PM2.5 hourly averages > 30 μg/m3 ......................................... 18

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

• Delta-C vs. PAH ................................................................................................................. 28

• Modeled Source Contributions .............................................................................................. 30

Conclusions .............................................................................................................................. 32
Evaluation of Wood Smoke Contribution to Particle 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 

PM2.5 – fine particulate matter (2.5 microns) 

PM10 – respirable particulate matter (10 microns) 

PM10-2.5 – coarse particulate matter (PM10 – PM2.5) 

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 

SO2 – sulfur dioxide 

SOP – standard operating procedure 

STN – Speciation Trends Network 

SQL- Structured Query Language (Database Language) 

TSP – total suspended particulate 
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 3

VOC – volatile organic compound 

WSPM – wood smoke particulate matter 
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 4

This project was conducted by the Connecticut Department of Environmental Protection (CTDEP) and 

funded by the United States Environmental Protection Agency (US EPA) through a 2005 Local-Scale Air 

Toxics Ambient Monitoring Grant Solicitation (RFA NO: OAR-EMAD-05-16). This is the final report submitted 

to EPA to fulfill the final grant obligation. Below is the project title, category and applicant information by 

which the proposal for this project 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 George Allen and Lisa Rector of NESCAUM on the OWB 

portion of this project, as well as, Mr. Allen’s input and 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 AethalometerTM Data Masher to allow for processing of the 

black carbon/wood smoke data while taking into account the newly discovered spot-saturation effects. 

Finally, CTDEP acknowledges Tom McGrath 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 Particle 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 from wood burning sources. Monitoring was conducted at 

seven sites and modeling was applied to the data collected to apportion the sources and quantify wood 

smoke particulate matter (WSPM) concentrations. 

This study confirmed that the 2-channel AethalometerTM (Magee Scientific) does provide a real-time wood 

smoke indicator. Using modeling, a scaling factor was 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 inventory estimates of WSPM in Connecticut to actual 

ambient air monitoring measurements. The 2002 Mid-Atlantic Regional Air Management Association 

(MARAMA) emission estimates for WSPM was that 38% of Connecticut PM2.5 emissions are from wood 

burning sources. Results from this study indicate that on an annual average, the WSPM contribution to total 

PM2.5 measurements range from 1.7 to 17.3% for the six Connecticut 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 rural, 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 contributions 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 ES­1: Average Monthly Percent Contribution of WSPM to Total PM2.5
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 6

In addition to approximating average annual WSPM contributions in Connecticut, another objective of this 

project was to determine WSPM contributions when total PM2.5 observations are elevated. This is significant 

given the new daily PM2.5 standard of 35 μg/m3

 and with the probability of the establishment of a sub-daily 

PM2.5 standard in the future. Understanding the sources that contribute to elevated PM levels is key for 

effectively developing and evaluating control strategies. 

WSPM contributions were evaluated for daily PM2.5 concentrations above 15 μg/m3

. 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 PM2.5 concentrations were greater than 15 μg/m3

, the average WSPM 

contribution was 23.9% (range of 8.9 to 38.0%) of the average total PM2.5 concentrations. When the 24-hr 

average temperature was greater than 45oF and the daily PM2.5 concentrations were greater than 15 μg/m3

, 

the average WSPM contribution was 4.2% (range of 0.9 to 5.5%) of the average total PM2.5 concentrations. 

A similar evaluation was conducted for WSPM contributions when hourly PM2.5 concentrations were greater 

than 30 μg/m3

. When the hourly average temperature was less than 32oF and the hourly PM2.5

concentrations were greater than 30 μg/m3

, the average WSPM contribution was 36.3% (maximum of 56.8%) 

of the average total PM2.5 concentrations. When the hourly average temperature was less than 45oF and the 

hourly PM2.5 concentrations were greater than 30 μg/m3

, the average WSPM contribution dropped to 22.8% 

(maximum of 45.0%) of the average total PM2.5 concentrations. When the hourly average temperature was 

greater than 45oF and the hourly PM2.5 concentrations were greater than 30 μg/m3

, the average WSPM 

contribution was 1.7% (range of 0.6 to 3.5%) of the average total PM2.5 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 PM2.5 and ozone exceedance days. These WSPM methodologies could also 

prove useful in documenting future exceptional events. 

Modeling conducted through this project not only provided a highly time resolved profile for wood burning 

sources, but also resulted in valuable profile and contribution information for 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. 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 Particle 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 smoke contributions to fine particle matter (PM2.5) in Connecticut, 

and also to conduct monitoring and testing to characterize 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 ambient measurements to evaluate the portion of PM2.5 concentrations 

that can be attributed to wood burning. The second part of this project characterized the PM2.5 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 presented in two separate reports; this report addresses 

the findings relative to the ambient air monitoring portion of the study and NESCAUM has prepared the final 

report for the OWB source characterization portion of the study.1

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

contribution of wood smoke to ambient PM2.5 concentrations in Connecticut. This was accomplished by 

selecting monitoring sites that are representative of areas that are impacted by wood smoke sources. The 

ambient wood smoke monitoring effort: 1) characterized the impact of wood burning on PM2.5 concentrations; 

2) assessed the contribution of wood smoke to PM2.5 during wintertime inversion events; 3) assessed 

emission inventory estimates; 4) evaluated modeling results 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 PM2.5 concentrations from wood smoke on a real-time basis. This study will provide valuable 

information as the CTDEP looks to identify risk reduction strategies and develop PM2.5 and Regional Haze 

State Implementation Plans (SIPs). 

Background Information 

The severity of potential health effects and magnitude 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 PM2.5, 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 effects2

,

3

,

4

,

5

. Exposure to PM2.5 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/woodsmoke_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 Particle 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 respiratory or heart disease, diabetics, and the elderly.6

There is increasing concern regarding the impact of wood smoke from residential wood combustion (RWC) 

sources on air quality. According to the 2002 Mid-Atlantic Regional Air Management Association (MARAMA) 

emissions inventory, wood smoke contributes 38% of the PM2.5 emissions in Connecticut. Woodstoves are 

now available that are EPA-certified; however, the majority of the woodstoves in operation are older units 

with no pollution control devices. As fuel prices have risen, so have the sales of woodstoves and OWBs7. 

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 PM2.5 concentrations (WSPM).8

 The pilot project succeeded in 

apportioning the PM2.5 into several source categories. The Rutland study also identified ways in which this 

approach could be improved upon in future studies; the key 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 was comprised of one newly established core site and 

six satellite sites. Monitoring was conducted from September 2006 through April 2008. A full range of 

parameters were operated at the core site while the six satellite sites collected 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, located in western Connecticut. Thomaston is located 

in the Naugatuck Valley and is prone to wintertime inversions, 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 pollutants were measured at this site to best assess the 

PM2.5 contributions from wood smoke. Continuous PM2.5, along with volatile and non-volatile fractions of 

PM2.5, were measured in addition to continuous speciation methods to obtain highly time-resolved organic 

carbon (OC), elemental carbon (EC), sulfate (SO4), 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/woodsmoke_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 Particle Matter in Connecticut 9

(370 and 880 nm). Trace-gas analyzers were used to obtain carbon monoxide (CO), sulfur dioxide (SO2), 

and reactive oxides of nitrogen (NOy). Highly time-resolved measurements of trace metals and PAHs were 

collected, as well. This information was used to quantify 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 fact that they are located in valleys that are prone to 

wintertime 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 better 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 Intensive 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 relative 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 Network
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 10

Measurement Methods 

Monitoring that was established to assess wood smoke contributions to PM2.5 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 measured 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

PM2.5 Mass Measurements

FDMS TEOM X    X X    

MetOne BAM    X X    X    X

PM2.5 FRM 1/3 1/3 1/3 1/1 1/1    1/3

PM2.5 Speciation Measurements

Sunset OCEC X X          

Continuous SO4 X X          

3‐slot DRUM X            

IMPROVE    X          

STN         X    

Trace‐Gas Measurements

Trace CO X *          

Trace SO2 X X          

Trace NOy             

Criteria Gas (non‐Trace) Measurements

CO       X X    X

SO2     X    X    X

NOx 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 ESC
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 11

WSPM: A two-wavelength AethalometerTM (Magee Scientific 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 operated at 2 LPM with a PM-1 (BGI/Magee Scientific 

SCC 0.732) size selective inlet. Field studies have shown that BC measurements are 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 Delta-C measurement was specific to WSPM even in presence of PM 

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

Continuous PM2.5: An FDMS-B TEOM (Thermo Series 8500) was operated at the core site to obtain hourly 

PM2.5 concentrations, as well as volatile and non-volatile fractions of PM2.5, 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 PM2.5 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 SO4 analyzer (Thermo Environmental 

Model 5020 SPA) was deployed at the core site to obtain 15-minute sulfate averages. 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 SO2 (Thermo Environmental Model 43C-TLE). The utilization of trace instrumentation is 

important in order to increase the sensitivity, which allows increased confidence in assessing source 

contributions. NO, NO2 and NOx 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 was 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 arsenic, beryllium, cadmium, chromium, manganese and 

nickel. 

PAHs: A continuous PAH analyzer (EcoChem PAS 2000) was deployed 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 PM2.5 mass in seven U.S. cities.  J. Air & Waste Management Assoc. 50 (8).
Evaluation of Wood Smoke Contribution to Particle 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 Headquarters 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 spatial resolution, concentration gradients and source 

signatures of air pollutants specific to wood smoke sources. The analysis performed on the data obtained in 

this study built upon results from the pilot study conducted in Rutland, VT. Unmix modeling was applied to 

apportion measured PM2.5 from the core site located in Thomaston into several source categories. The 

resulting factor to convert the difference of the two 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 for the sites that had a comprehensive 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 more traditional measures of wood smoke indicators 

(i.e., PAHs) were also made. 

AethalometerTM Data Processing 

The Magee Scientific AethalometerTM is the widely used method to measure atmospheric BC on a near-real 

time basis. The AethalometerTM measures optical attenuation 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-hour 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 effect correction capabilities. 

Although results from the original AethalometerTM DataMasher 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 there’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. 

                                                           

12Virkkula, Aki, et al.  2007.  A Simple Procedure for Correcting Loading Effects of AethalometerTM Data.  J.  of Air &

Waste Management Assoc. (57) 1214‐1222.

13Turner, 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 Particle Matter in Connecticut 13

Figure 2: BC and Delta­C hourly averages post­processed with DataMasher with and without filter

saturation effect correction.

Modeling Methods 

Source apportionment was determined with Unmix modeling, an EPA approved software. Unmix takes a 

given set of pollutants and the corresponding concentrations 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 modeling software, see the EPA Unmix 6.0 

Fundamentals and User Guide.9

 

For the purposes of this study, Unmix modeling was conducted 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 Particle Matter in Connecticut 14

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

PM2.5 concentrations, it was concluded that the four data sets modeled would be sufficient. 

The Springfield, MA data set did not include volatile concentrations, thus only four sources were considered. 

The three Connecticut data sets included gaseous concentrations (NO, NOx, NO2 and SO2), particulate 

concentrations (volatile and non-volatile) and the three Aethalometer Channels (BC, UVC and Delta-C), 

allowing for the potential of five 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 defining 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, SO2 as the marker for oil combustion, Volatile PM as the marker for non-local industrial sources, NO 

as the marker for fresh combustion and NO2 as the marker for aged combustion; factors could be developed 

for each source type to determine a particular sources PM2.5 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 PM2.5 levels were observed, along with binning that information into different 

temperature categories. Finally, both winter and summer wood smoke driven events are looked at to 

determine the contribution of WSPM during these events and the impact on both the ozone and PM2.5

measurements. 

• Modeling Results Summary 

Unmix Modeling was conducted for the Thomaston, East 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 calculate WSPM concentrations. As shown in Table 2, the 

scaling factors were reasonably consistent for most modeling runs,(with the exception of the New Haven 

individual quarter runs). The lowest scaling factor, a 2.7 estimated at Thomaston for 1st quarter 2008, is 

suspect based on inconsistent volatile PM measurements from the FDMS 8500 unit which affected the 

modeling results. This quarter was not included in the determination of the universal, 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 (4th quarter 2006 through 4th quarter 2007) model run, which included the 

volatile channel, was 5.0. The scaling factors for the individual one-quarter modeling runs of 2006-2008 
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 15

winter periods were 8.2, 8.4, 6.9 and 6. It was determined 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 PM2.5 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 PM2.5, such as Thomaston. Therefore the universal, 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 (1st quarter of 2008 was excluded as it was determined unreliable 

due to suspect 8500 FDMS volatile PM data). For consistency 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; Delta­C*Scaling Factor = WSPM (μg/m3)

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 24.017

Qtr 1 2007 N/A 22.717

Qtr 4 2007 N/A 6.9

Qtr 1 2008 N/A 10.517

Springfield, MA Qtr 4, 2006 through Qtr 1, 

2008 N/A 8.8 

                                                           

17 The New Haven model runs were not representative scaling factors for wood smoke. The large variety of 

PM2.5 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 Particle Matter in Connecticut 16

• Overall by Site Summary 

The overall annual average of the contribution of WSPM to PM2.5 was determined by taking the average of 

the weighted monthly averages depending on the availability of valid averages for a given month. For 

instance, if January 2007 and January 2008 monthly averages were available for a given site, then the 

average of these two months were taken to represent 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 September 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 PM2.5 concentrations were determined by transforming continuous PM2.5 data to be “FRM-like” based 

on regressions performed on PM2.5 FRM and continuous PM2.5 measurements obtained at each site for the 

study period. This allowed PM2.5 FRM-like values to be established for each hour of each day for the entire 

study period to give a complete assessment of PM2.5 measurements and fill in the holes created from one-inthree day PM2.5 FRM sampling. 

The annual average contribution of WSPM ranged from 1.7% of the total PM2.5 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 PM2.5 concentrations observed. The remaining three 

Connecticut sites, Danbury, East Hartford and Mansfield, were at 11.6%, 12.3% and 11.9%, respectively. 

The average annual WSPM contribution at the Springfield, MA site was 10.8% of the total PM2.5

measurements 

Table 3: Annual Averages for PM2.5, WSPM and Contribution of WSPM to Total PM2.5

Site

Overall Annual Average

PM2.5  

(μg/m3)

WSPM

(μg/m3)

Contribution of WSPM to

Total PM2.5

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 PM2.5 sampling at Mansfield; substituted everyday PM2.5 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 Particle Matter in Connecticut 17

• Monthly Averages of WSPM Contribution 

Monthly average WSPM contributions to PM2.5 were determined by averaging all valid hourly FRM-like PM2.5

and WSPM values. Figure 3 is a time-series graph of the 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 monthly average WSPM contributions in the winter ranged 

from 10.8 to 41.3%, while maximum monthly summertime averages ranged from zero to 6.1%. Figure 4 

presents the same weighted monthly averages that were used to determine the annual average contribution 

to WSPM to total PM2.5 percentages in Table 3. Also on the Figure 4 time series graph, are the average 

monthly temperatures by site in degrees Fahrenheit (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 PM2.5 concentrations

  
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 18

Figure 4: Monthly average WSPM contributions and temperatures by site

• PM2.5 daily averages > 15 μg/m3; PM2.5 hourly averages > 30 μg/m3

Fine particulate data was binned to look at WSPM contributions when PM2.5 concentrations were elevated. In 

addition to separating by PM2.5 concentrations, average ambient temperature measurements were also 

utilized to assess overall WSPM contributions. Table 4 shows the breakdown by site when the 24-hr PM2.5

concentration was greater than 15 μg/m3

 and separated for when the daily average ambient temperature 

was less than and greater than 45oF. For the five sites in Table 4, the average WSPM contribution to PM2.5

was 23.9% when the 24-hr PM2.5 concentration was greater than 15 μg/m3

 and the 24-hr average ambient 

temperature measurement was less than 45oF. Conversely, when the 24-hr PM2.5 concentration was greater 

than 15 μg/m3

 and the 24-hr average ambient temperature measurement was greater than 45oF, the average 

WSPM contribution to PM2.5 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 PM2.5 concentration was greater than 30 μg/m3

and separated for when the daily average ambient temperature was less than 32oF, less than 45oF and 

greater than 45oF. For the five sites in Table 4, the average WSPM contribution to PM2.5 was 30.9% when 

the hourly PM2.5 concentration was greater than 30 μg/m3

 and the hourly average ambient temperature 

measurement was less than 32oF. When the hourly ambient temperature was less than 45oF and the hourly 
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 19

PM2.5 concentration was greater than 30 μg/m3, the WSPM contribution dropped to 22.8%. When the hourly 

PM2.5 concentration was greater than 30 μg/m3

 and the hourly average ambient temperature was greater 

than 45oF, the average WSPM contribution to PM2.5 was only 1.7%. Figure 6 also illustrates the differences 

for these three temperature categories at the five sites. 

Table 4: WSPM Contribution to Total PM2.5 by Temperature Category for elevated hourly and 24­hour PM2.5

concentrations

Site 

24-hr PM2.5 FRM > 15 μg/m3

Contribution of WSPM to Total PM2.5

1-hr PM2.5 FRM-like > 30 μg/m3

Contribution of WSPM to Total PM2.5

< 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 PM2.5 by Temperature Category for elevated 24­hr PM2.5

concentrations
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 20

Figure 6: WSPM Contribution to Total PM2.5 by Temperature Category for elevated hourly PM2.5

concentrations

• Wood Smoke Events 

A typical winter season will have a greater WSPM contribution in periods of temperatures at or below 45o

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 periods 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 air stagnation, such as valley locations, local wood burning sources can 

significantly contribute to ambient PM2.5 concentrations. 

An example of these wintertime inversion events is shown in Figure 7. Although there are several inversiontype events illustrated in Figure 7, the event on December 6, 2006 was most pronounced with hourly PM2.5

concentrations approaching 50 μg/m3

 and WSPM concentrations well over 10 μg/m3. PAH levels also 

following the same trend as the PM2.5 and WSPM concentrations. PM2.5, WSPM and PAH concentrations 

built up through the evening of December 5th and the early morning hours of December 6th as winds speeds 

were less than 1 m/s and ambient temperatures steadily dropped. Approximately mid-morning of December 

6th, as ambient temperatures rose and wind speed increased, the inversion lifted and all air pollutant 
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 21

concentrations decreased significantly with PAH and WSPM levels approaching zero and PM2.5

concentrations approaching 5 μg/m3

. Figure 8 focuses in on the December 5th to December 6th 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 and evening hours and significantly lower 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 increased 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 95th

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, 2006
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 22

Figure 8: Classic Winter Inversion Event – Thomaston, CT; Dec. 4, 2006 through Dec. 6, 2006

   
Evaluation of Wood Smoke Contribution to Particle 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 possible forest fire transport events. On May 25, 2007 the 

entire state observed elevated ozone and PM2.5 levels (See Figure 10). In fact, this event was seen on a 

regional scale with high ozone and PM2.5 throughout New England (See Figure 11). Together this suggested 

a large scale transport event which was further validated by Figure 12, which displays the back trajectories 

and corresponding satellite images of the days preceding the 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 these 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 combination with the large spikes in PAHs, ozone and PM2.5, 

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 PM2.5 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 Particle Matter in Connecticut 24

So, although the Delta-C measurement provides a consistent indicator of wood smoke for local wood burning 

sources, it is much less reliable for wood smoke that has undergone significant transport, particularly when 

exposed to UV radiation. 
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 25

Figure10: Hourly Ozone and PM2.5 Concentrations; May 2007
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 26

Figure 11: 24­hour Average PM2.5 Concentrations; May 25, 2007  

   
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 27

Figure 12: NOAA Hysplit Model and Satellite Images of Wildfire Event; May 25, 2007

   
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 28

Figure 13: PM2.5, WSPM, PAH and Ozone Hourly Concentrations; Thomaston, CT – May 2007 Wildfire Event

• Delta-C vs. PAH 

Traditionally, PAH measurements have been used as an indicator of the presence of wood smoke. In this 

study, although PAH concentrations did follow WSPM concentrations when WSPM levels were elevated, 

there were many occasions when PAH levels were elevated and Delta-C was not. This could indicate the 

presence of transport wood smoke; when wood smoke is transported over long distances the primary 

organic compounds have been oxidized and thereby not detectable by the AethalometerTM UV-C channel, as 

described in the section above. 

Also note Figure 7; this figure also displays instances where PAH and WSPM (calculated from Delta-C) track 

with each other and instances where they do not. During December 4, 2006 through December 6, 2006 

PM2.5, 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, PM2.5 and 

PAH measurements are elevated while WSPM is near zero. Given the fact that ambient temperatures are 

near 70oF, it is probable that the elevated PM2.5 and PAH concentrations are not from local wood burning 

sources. Mobile sources are known to drive ambient PAH concentrations, a New England based study 

estimated mobile sources to account for ~76-84% of the PAH concentrations observed and wood smoke to 
Evaluation of Wood Smoke Contribution to Particle 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 function 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 source22 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 Particle 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 determine Delta-C scaling factors and in turn approximate 

WSPM contributions to total PM2.5, the modeling also provides highly time resolved information on fresh and 

aged motor vehicle, oil burning and secondary aerosol sources. Figure 16 illustrates the source contributions 

to hourly PM2.5 concentrations for the Thomaston and New Haven sites for the period of January 1, 2007 

through March 31, 2007. This shows the average source contributions to total PM2.5 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 burning sources during the traditional work-day hours. 

   
Evaluation of Wood Smoke Contribution to Particle Matter in Connecticut 31

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

Conclusions 

This project has built on previous work that identifies the Delta-C measurement from a 2-wavelength 

AethalometerTM to be a specific indicator of wood smoke. 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 PM2.5 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 PM2.5

concentrations were elevated and categorized in various temperature bins. Although the annual average 

WSPM contributions to PM2.5 (1.7 to 17.3% for all sites) did not approach the 38% estimate of PM2.5

emissions in Connecticut from wood burning sources from 2002 MARAMA emission estimates, WSPM was 

shown to contribute significantly to total PM2.5 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 PM2.5 levels was 41.3% with maximum daily and hourly 

contributions approaching 74.3% and 100%, respectively. 

Potential future efforts related to this 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 quantified 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 transported regionally. Due to photochemical reactions, it 

does appear that the optical characteristics of the wood smoke are altered enough to render absorption 

measurements at 370 nm to be ineffective.