SPATIAL AND SEASONAL VARIABILITY OF FINE ...

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Providence Engineering and. Environmental Group LLC. 1200 Walnut Hill Lane, Suite 1000. Irving, Texas 75038. (972) 550-9326. Project Number 455-006 ...

JULY 2014

SPATIAL AND SEASONAL VARIABILITY OF FINE PARTICLES IN URBAN NEIGHBORHOODS OF THE SAN JOAQUIN VALLEY MEASUREMENTS DURING THE WINTER AND SUMMER OF 2013

Final Report

Prepared By: Providence Engineering and Environmental Group LLC 1200 Walnut Hill Lane, Suite 1000 Irving, Texas 75038 (972) 550-9326 Project Number 455-006

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

TABLE OF CONTENTS EXECUTIVE SUMMARY ...................................................................................... 2 1.1 Conduct of the Study ................................................................................. 2 1.2 Results and Discussion.............................................................................. 2 1.3 Conclusions ............................................................................................... 5 INTRODUCTION .................................................................................................. 7 2.1 Background................................................................................................ 7 2.2 Overview of the Study ................................................................................ 8 2.3 Contents of This Report ............................................................................. 9 STUDY DESIGN AND METHODS ..................................................................... 11 3.1 PM Sampling Instrumentation .................................................................. 11 3.2 Pilot Study................................................................................................ 13 3.3 Site Selection ........................................................................................... 13 3.4 Sampling Campaigns ............................................................................... 14 3.5 Computer Controlled Scanning Electron Microscopy ............................... 18 3.6 Data Analysis ........................................................................................... 18 3.7 Application of ART 2A Model ................................................................... 19 3.8 Source Apportionment Using Positive Matrix Factorization (PMF) .......... 20 RESULTS AND DISCUSSION ........................................................................... 22 4.1 Meteorological Conditions........................................................................ 22 4.2 Particulate Matter Total Mass Concentration ........................................... 23 4.3 Particle Class Membership and Particle Size Distribution........................ 28 4.4 Replicate Sample Analysis ...................................................................... 47 4.5 Comparison with Federal Reference Monitor (FRM) Measurements ....... 47 4.6 Coefficient of Divergence (COD) ............................................................. 50 4.7 Pearson Correlation Coefficient (COR) .................................................... 51 4.8 Significant Particle Class Membership Types .......................................... 52 4.9 Partitioning of Semi-Volatile Organic Compounds ................................... 55 4.10 Source Profile Characterization and Inter-Site Variability Analysis .......... 56 4.11 Positive Matrix Factorization .................................................................... 57 SUMMARY AND CONCLUSIONS ..................................................................... 66 REFERENCES ................................................................................................... 69 SUPPLEMENTAL TABLES & FIGURES ............................................................ 72

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LIST OF TABLES Table 1 - Sites and Sampling Periods for the Winter and Summer Campaigns ............ 17 Table 2 - Summary of Sampling .................................................................................... 18 Table 3 – PM2.5 Mass Concentrations, Winter Samples ................................................ 25 Table 4 – PM2.5 Mass Concentrations, Summer Samples ............................................. 26 Table 5 – PM10 Mass Concentrations, Winter Samples ................................................ 27 Table 6 – PM10 Mass Concentrations, Summer Samples ............................................. 28 Table 7 – PM2.5 Class Groupings, Winter Samples ....................................................... 31 Table 8 - PM2.5 Likely Source Types, Winter Samples .................................................. 32 Table 9 - PM2.5 Class Groupings, Summer Samples..................................................... 35 Table 10 - PM2.5 Likely Source Types, Summer Samples ............................................. 35 Table 11 – PM10 Class Groupings, Winter Samples...................................................... 38 Table 12 - PM10 Likely Source Types, Winter Samples ................................................. 40 Table 13 – PM10 Class Groupings, Summer Samples .................................................. 44 Table 14 - PM10 Likely Source Types, Summer Samples.............................................. 45 Table 15 – Summary of COD and COR Values in PM10 and PM2.5 Samples ................ 53 Table 16 – Significant Particle Class Memberships, Winter PM2.5 Samples.................. 54 Table 17 – Significant Particle Class Memberships, Summer PM2.5 Samples .............. 55 Table A-1 - COD and COR for PM2.5 Samples, Winter Campaign ................................ 73 Table A-2 - COD and COR for PM2.5 Samples, Summer Campaign ............................. 74 Table A-3 - COD and COR for PM10 Samples, Winter Campaign ................................. 75 Table A-4 - COD and COR for PM10 Samples, Summer Campaign .............................. 76

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LIST OF FIGURES Figure ES-1 – Summary of Identified Sources, Winter PM2.5 .......................................... 3 Figure ES-2 – Summary of Identified Sources, Summer PM2.5 ....................................... 3 Figure ES-3 - Winter PM2.5 Concentrations, Sources Affecting Each Neighborhood ...... 4 Figure ES-4 - Summer PM2.5 Concentrations, Sources Affecting Each Neighborhood ... 5 Figure 1 – UNC Passive Sampler Shelter Mounted on a Streetlight Pole ..................... 12 Figure 2 – UNC Passive Sampler Shelter, Showing Sampler ....................................... 12 Figure 3 – Locations of PM Sampling Sites................................................................... 16 Figure 4 – Wind Speed and Direction in Fresno, CA, Winter Campaign ....................... 23 Figure 5 – Wind Speed and Direction in Fresno, CA, Summer Campaign .................... 24 Figure 6 – PM2.5 Particle Size Distribution, Winter Campaign ....................................... 33 Figure 7 – PM2.5 Particle Size Distribution, Summer Campaign .................................... 36 Figure 8 – PM10 Particle Size Distribution, Winter Campaign ........................................ 41 Figure 9 – PM10 Particle Size Distribution, Summer Campaign..................................... 46 Figure 10 - Comparison of Replicate Winter PM10 Samples.......................................... 49 Figure 11 - Comparison of Replicate Winter PM2.5 Samples ......................................... 50 Figure 12 – SVOC in Particulate and Vapor Phase Samples ........................................ 56 Figure 13 - Correlation between Measured and PMF-Predicted PM2.5 Concentrations. 58 Figure 14 - PM2.5 Source Profiles Resolved by PMF, Winter Campaign ........................ 60 Figure 15 - Total Mass Concentrations and Source Contributions in Each Neighborhood, Winter Campaign .......................................................................................................... 61 Figure 16 - PM2.5 Source Profiles Resolved by PMF, Summer Campaign ..................... 64 Figure 17 – Total Mass Concentrations and Source Contributions in Each Neighborhood, Summer Campaign ....................................................................................................... 65 Figure A-1 - Winter PM2.5 in Each Particle Class for Each Site Group .......................... 77 Figure A-2 - Summer PM2.5 in Each Particle Class for Each Site Group ....................... 79 Figure A-3 - Total Mass Concentrations and Source Contributions in Each Neighborhood, Winter Campaign .......................................................................................................... 81 Figure A-4 - Total Mass Concentrations and Source Contributions in Each Neighborhood, Summer Campaign ....................................................................................................... 82

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT PREFACE This report was prepared by Providence Engineering and Environmental Group, LLC (Providence). This project was funded by the San Joaquin Valley Air Pollution Control District (SJVAPCD). The Principal Investigator was Dr. Suresh Raja and the Project Manager was Scott Nester. This project was technically supported by multiple individuals from three organizations including Srikar Middala, David Morrow, and Dr. Neelesh Sule of Providence; Gary Casuccio, Traci Lersch and Roger R. West of RJ Lee Group, Incorporated; and Punith Nallathamby and Rickie Salas of ENERCON Services, Incorporated. The project team wishes to thank Michael Carrera, Nathan Trevino, and the Air Monitoring staff of SJVAPCD for their assistance in deploying monitors for this project. The project team also wishes to thank the following people and organizations for allowing their facilities to be used as air pollution monitoring sites: Scott Krauter of City of Fresno, Peter Martin of City of Bakersfield, City of Clovis, County of Kings, County of Madera, Rosa Maldonado of Kettleman City Community Services District, and Fernando Amador and Joe Guerrero of the California Air Resources Board. Finally, the project team especially wishes to thank Dr. David Lighthall of SJVAPCD for his support and guidance on this project.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT ABSTRACT Particulate Matter (PM) exposure has drawn considerable attention in recent years due to its link to human mortality and morbidity. Spatial heterogeneity of PM chemical constituents is an important factor in studies of both short- and long-term effects. This is because there is potential for exposure misclassification in time-series epidemiological studies when regressing health outcomes against source contributions estimated at a single central monitoring site. The primary goal of the present study is to better understand the spatial variability of fine-particle exposure, in terms of particle size and composition, in a range of urban and populated rural areas. An important secondary goal is to advance the understanding of emission source categories that contribute to particulate matter air pollution in the San Joaquin Valley. Winter and summer campaigns were conducted to assess the seasonal and spatial variability of fine particulate matter using passive samplers. Passive samplers were installed at 25 sites in the Fresno-Clovis area, six sites in Bakersfield, two sites each in Kettleman City and Fairmead, and one site each in Corcoran, Turlock and Madera. After collection, the PM samples were analyzed for chemical components, and particle size and morphology using a computer controlled scanning-electron microscope. Elemental composition of individual particles was then classified using an Adaptive Resonance Theory neural networks algorithm (Carpenter, 1991). Based on particle class memberships, inter-site and intra-urban variability in PM exposure was analyzed in this work. In addition to this, speciation data was used to assess differences in source profiles between each neighborhood. Source profiles were developed using Positive Matrix Factorization. A total of eight source categories were resolved for the summer samples. A total of nine source categories were resolved for the samples collected during winter. The sources identified in this work include residential wood burning, mineral dust, crustal materials, carbonaceous soot particles, vehicle brake wear, manufacturing, cooking, and re-suspended road dust.

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1.0 EXECUTIVE SUMMARY

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT EXECUTIVE SUMMARY Spatial heterogeneity of particulate matter (PM) chemical constituents is an important factor in studies of both short- and long-term health effects of air pollution. The primary goal of this study is to better understand the spatial and seasonal variability of fine particle exposure, in terms of particle size and composition, in a range of urban and populated rural areas in the San Joaquin Valley (SJV). An important secondary goal is to advance the understanding of emission source categories that contribute to particulate matter air pollution in the SJV. 1.1

Conduct of the Study

The critical objective in this study was to generate a data set that represents PM exposure - in terms of chemical components and size distribution - in a range of neighborhoods in a single SJV urban area, along with data that represents PM exposure in other SJV cities and smaller communities. Achieving this objective required the use of a relatively large number of PM samplers arrayed in a dense network in Fresno, and also deployed in other SJV communities for comparison purposes. To provide PM chemical, size, and mass data in a cost-effective manner, the project employed University of North Carolina (UNC) passive samplers. PM samples obtained by these passive samplers are analyzed in a laboratory using scanning electron microscope techniques. After a small pilot study, sampling occurred during two seasons in the main study. The first sampling campaign was conducted during Winter 2013 and the second campaign was conducted during Summer 2013. Passive PM samplers were installed at 25 sites in Fresno/Clovis (24 sites during the Winter Campaign), six sites in Bakersfield, two each in Fairmead and Kettleman City, and one each in Turlock, Madera, and Corcoran. After each sampling campaign, the PM samples were transported to a laboratory, where each particle in each of the samples was analyzed for size, shape and elemental composition using a computer-controlled scanning electron microscope. Particle class memberships of single particle data were obtained by using the Adaptive Resonance Theory 2A (ART 2A) algorithm. Source profiles and their contributions for each of the sites studied in this work were developed using Positive Matrix Factorization (PMF). 1.2

Results and Discussion

Clustering of fine particles yielded 23 different fine particle classes for samples collected in the winter and 13 fine particle class memberships for the samples collected during the summer. Winter PM2.5 samples contained more clusters of carbonaceous particles than the samples collected during the summer. Particle class memberships obtained from the ART 2A processing were further processed using PMF in order to obtain source profiles and source contributions at the different neighborhood monitoring locations. The source profile obtained using the hybrid “ART 2A+PMF” modeling approach showed that not only the mass values of PM2.5 were widely heterogeneous in the sites/neighborhoods studied in this work, but the exposure to particle types are characterized by a wide variety of

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT sources in each of the neighborhoods. The sources, in general, included residential wood burning, landscaping activities, mineral dust, crustal materials, carbonaceous soot particles, vehicle brake wear, industrial metals, cooking, and re-suspended road dust. About 18% of the total PM2.5 mass in the winter was estimated to be derived from cooking and residential wood combustion activities. Figures ES-1 and ES-2 provide a summary of the PM2.5 sources identified using PMF during the summer and winter campaigns. Engine oil burning, mineral dust, and cooking/wood combustion particles were identified as the major sources in the winter samples. Summer samples were predominant in mechanical abrasion particles such as mineral dust, crustal, and re-suspended road dust. However, carbonaceous soot was also dominant in the summer samples. Vehicle brake wear remained somewhat uniform in both the summer and winter samples. Industrial metals was more predominant in the summer samples than in the winter samples. Landscaping activity was not apparent in the winter samples. However, landscaping activity accounted for 4% of the PM2.5 mass in the summer samples. Figure ES-1 – Summary of Identified Sources, Winter PM2.5

Figure ES-2 – Summary of Identified Sources, Summer PM2.5

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As shown in Figures ES-3 and ES-4, strong homogeneity in particle mass was present in most neighborhoods from sources such as mineral dust and crustal materials during both seasons, and sources such as landscaping activity in the summer. However, strong spatial variability was evident in anthropogenic sources such as industrial metals, vehicle brake wear, construction/gypsum and cooking/wood combustion particles. As further described in this report, particles from vehicle brake wear were dominant in neighborhoods such as Clovis High School (HS), Bakersfield and Kettleman City, particularly in the smaller PM1 size range. Neighborhoods such as Fresno HS, Calwa and Sunnyside HS had higher contributions from vehicle brake wear in the coarser PM2.5-1 size range. In the winter samples, vehicle brake wear particles were dominant in neighborhoods such as Edison HS, Central HS, Fresno HS, Clovis HS, Figarden Loop and Kettleman City. Figure ES-3 - WInter PM2.5 Concentrations, Sources Affecting Each Neighborhood

Cooking/Wood Comb.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Figure ES-4 - Summer PM2.5 Concentrations, Sources Affecting Each Neighborhood

Carbonaceous Soot

This result indicates that contribution to PM2.5 mass from anthropogenic sources such as vehicle brake wear is likely present year round in neighborhoods such as Clovis HS, Fresno HS, and Kettleman City. Cooking and wood combustion particles present in winter samples had higher contribution to the total PM2.5 mass at Fresno HS, Figarden Loop and McLane HS. 1.3

Conclusions

The results from the passive sampling campaigns, CCSEM analysis, and “ART 2A + PMF” source profile and contribution modeling provided a wealth of data indicating heterogeneity of fine PM both in potential sources and their measured concentrations. Carbonaceous species dominated in the winter samples, while summer samples were profound in elemental species that may have both anthropogenic and natural origins.

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2.0

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INTRODUCTION

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT INTRODUCTION Accurate characterization of the relative influences of various sources that contribute to PM mass is a prerequisite for studies on health effects and for the development of effective policies to mitigate the health effects of urban air pollution. Several studies have highlighted the spatial variability of particulate matter exposure in the ambient atmosphere, including a recent study by Bell et al., (2011). Spatial heterogeneity of PM chemical constituents is an important factor in studies of both short- and long-term air pollution health effects. The primary goal of this study is to better understand the spatial variability of fine particle exposure, in terms of particle size and composition, in a range of urban and populated rural areas. An important secondary goal is to advance the understanding of emission source categories that contribute to particulate matter air pollution in the San Joaquin Valley. This study was designed to improve on the coarse spatial resolution of previous studies by using a relatively large number of samplers arrayed densely in a single metropolitan area, and providing speciated, integrated PM samples. 2.1

Background

Although airborne PM is typically composed of a variety of chemical elements and molecules including metals and organic compounds, the National Ambient Air Quality Standards (NAAQS) for PM2.5 and PM10 do not specifically address chemical composition. Over the last three decades, however, US EPA researchers and independent health scientists have generated a substantial body of evidence indicating that certain chemical species within PM, as well as the smaller size ranges of PM, are primary drivers of respiratory, cardiovascular, and immunological health risk. A parallel body of experimental and epidemiological research has demonstrated that proximity to PM emission sources is a significant factor in the magnitude of PM health risk. In urban areas, sources of PM are numerous and pervasive, and include vehicles, streets and highways, residential wood combustion, charbroiling, landscaping activities, and construction earthmoving activities. Urban neighborhoods that are in close proximity to each other may have significantly different exposure to PM levels and PM chemical constituents because of differing sources within the neighborhoods. These geographic differences in PM exposure represent neighborhood-scale differences in human health risk. PM mitigation strategies that are primarily designed to attain NAAQS can be further tailored to reduce health risk by achieving reductions in specific chemical and size classes.

In the San Joaquin Valley (SJV), PM mass concentration levels in large metropolitan areas and smaller rural communities are well documented, as are differences in PM levels between the northern, central, and southern portions of the SJV, and seasonal differences. Chemical compositions of PM in several Valley urban areas have also been documented via the California Air Resources Board air monitoring speciation network and recent regional studies. Chemical and size differences of PM within individual SJV urban areas and between urban areas and smaller rural communities are much less documented. These topics are important for the development of a strategy that seeks to reduce SJV air quality health risk, and these topics are the subjects of this study.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT 2.2

Overview of the Study

Aware that San Joaquin Valley Air Pollution Control District (SJVAPCD) was seeking additional research for their PM2.5 health risk reduction strategy, in mid-2012 Providence Engineering and Environmental Group LLC (Providence) proposed a study to measure and analyze the chemical components and particle size distribution of PM in a range of neighborhoods in the Fresno urban area and in several other communities in the SJV. The results of the study are intended to provide more detailed information to SJVAPCD for the ongoing design of health-focused attainment strategies and future health effects research. The main elements of the study are presented briefly below, and are described in more detail in Section 3.0. Instrument selection: The key to this study was to employ a relatively large number of PM samplers to generate a geographically dense array of chemical- and sizedifferentiated data. To achieve this objective in a cost-effective manner, we chose the University of North Carolina (UNC) passive samplers that have been used successfully by Providence researchers as well as by US EPA and its contractors. PM samples obtained by these passive samplers are analyzed in a laboratory using scanning electron microscope (SEM) techniques. Pilot Study: In the Pilot Study, samplers were installed at 11 sites in the Fresno area to assess the ability of UNC passive samplers to reasonably characterize intra-urban PM distinctions, and gain insight on the spacing of sampling sites for the main sampling campaigns. After collection, the particulate samples were analyzed for chemical components and particle sizes, and speciated into particle classes. Site Selection: Based on the results of the Pilot Study and the desire for inter-regional PM data, Providence and SJVAPCD staff designated sampling sites in 14 neighborhoods in Fresno/Clovis, four neighborhoods in Bakersfield, and the communities of Turlock, Fairmead, Madera, Kettleman City, and Corcoran. PM Sampling: For the main field campaigns in winter and summer 2013, the project team deployed a total of 41 samplers in the communities listed above. Each sampling period was approximately four weeks. Particle identification: After collection, the particles collected in the UNC passive samplers were analyzed for their chemical components and particle sizes using computer controlled scanning electron microscopy (CCSEM). In this process, each particle is characterized by its combination of chemical elements, shape, size, and mass. Data Analysis: Sample speciation was then conducted by classifying the particles into significant class memberships using the ART 2A neural networks algorithm. After determining the particle class memberships, the clusters derived from the ART 2A classification were subjected to Positive Matrix Factorization (PMF) as part of a source apportionment analysis. The hybrid “ART 2A + PMF” approach made it possible to

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT identify the source profiles and spatial variation of each of the sources affecting each site and neighborhood. 2.3

Contents of This Report

In Section 3.0 of this report, we describe the design and methods of the study to determine the spatial variability of PM composition and PM sources. In Section 4.0, we document the intra-urban and inter-community variability in particle class type exposures measured and analyzed during both the summer and winter seasons, and we present the source profiles and their contributions. In Section 5.0, we present conclusions gleaned from these analyses.

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3.0

STUDY DESIGN AND METHODS

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT STUDY DESIGN AND METHODS The design and methods used for this study follow directly from the objectives for spatially dense PM data for the Fresno urban area and for inter-regional PM data. The data analysis methods followed the choice of sampling instruments and the desired format of the results, especially the source apportionment objective. The following fundamental design elements are summarized from the October 2012 proposal to the SJVAPCD Governing Board.  

    

High spatial resolution of monitoring in a single urban area to elucidate differences in PM composition and sources; Fresno urban area to be studied as it will be the field research site for a major federally-funded children’s’ health study that will also be collecting PM2.5 chemical speciation data via more traditional techniques (an extension of the FACES project); Smaller communities in the north and south SJV will be sampled; Seasonal characterization of particle exposure: monitors will be operated in winter and summer; Characterization of particles: Computer-controlled scanning electron microscopy will be used to measure the size and total mass of all particles collected in the PM0.2 to PM2.5 micron size range; Chemical speciation of particles: Elemental composition of each particle in this range will be determined by X-ray diffraction; Source apportionment: The mix of elements found in each particle and the total mass of each seasonal sample will be used to estimate the mix of sources that predominate in each neighborhood or community.

Sections 3.1 through 3.8 provide more detail on the study design and methods. 3.1

PM Sampling Instrumentation

The key to this field research study was to employ a relatively large number of PM samplers to generate a geographically dense array of chemical and size-differentiated PM data. To achieve this objective in a cost-effective manner, the project team selected the University of North Carolina (UNC) passive samplers originally developed by Wagner and Leith, (2001a, 2001b and 2001c), to collect PM samples. The UNC passive samplers have been used successfully by Providence researchers as well as by US EPA and its contractors. The samplers are small and lightweight, can be mounted easily in the field, and do not need external electrical power or batteries. Although the UNC passive samplers are relatively inexpensive and easy to deploy, the collected samples must be analyzed in a laboratory using the scanning electron microscope (SEM) techniques described below. The UNC passive sampler consists of a shelter, a standard SEM stub, a collection substrate, and a protective mesh cap. Figure 1 and Figure 2 are photographs of a passive sampler shelter mounted on a streetlight pole.

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Figure 1 – UNC Passive Sampler Shelter Mounted on a Streetlight Pole

Figure 2 - UNC Passive Sampler Shelter, Showing Sampler

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT In this study, most of the samplers were mounted on streetlight poles or other similar structures at heights of approximately 3.6 meters above the ground. The shelter for the sampler is about 8-inches in diameter, while the sampler itself (visible only in Figure 2) is about 1-inch in diameter. Some shelters held two samplers to provide replicate samples for quality assurance purposes. During sampling, particles are transported by gravity, diffusion, and inertia through the 150 µm-diameter holes in the mesh cap, and deposit on a substrate mounted on the stub. The stub is oriented such that the substrate is horizontal. After sampling for about four weeks, the shelters were transported to the laboratory, the mesh cap is removed, the stub is placed in a scanning electron microscope (Aspex Personal SEM), and the particles are counted, sized, and composition-assayed using computer controlled scanning electron microscopy (CCSEM) analytical procedures. For this study, RJ Lee Group provided the passive samplers and the CCSEM services. Although passive samplers can provide measurements of particle exposures from local sources on a community level scale, it is important to understand that they do not capture secondary particles from long range transport. Also, acknowledging that a significant portion of the wintertime PM in the SJV is volatile and would not be retained by the passive samplers, the project team also deployed a Tisch Model TE-1000 PM2.5 high-volume sampler (Hi-Vol) that collected both particulate matter and semi-volatile PM during the winter campaign. The filters from the Hi-Vol were analyzed by the laboratory of the Center for Air Resources Engineering & Science at Clarkson University. 3.2

Pilot Study

A Pilot Study was conducted in the Fresno metropolitan area to assess the ability of UNC passive samplers to reasonably characterize intra-urban PM distinctions, and gain insight on the appropriate spacing of sampling sites during the main sampling campaigns. In the Pilot Study, samplers were installed at 11 sites in four neighborhoods in the Fresno-Clovis area for four weeks in November and December 2012. After collection, PM2.5 samples were analyzed for chemical components and particle sizes using CCSEM, described below. Sample speciation was then conducted by classifying the particles using the Adaptive Resonance Theory 2A (ART 2A) neural networks algorithm. Results from the Pilot Study demonstrated the ability of the passive samplers and the associated analytical techniques to reflect different particle class memberships as well as inter-site and intra-urban variability in particle composition and exposure. The Pilot Study also indicated that, as much as possible in the main sampling campaigns, each residential neighborhood should be represented by the average of two sites: one site on the edge of the neighborhood near a heavily traveled street, and one in the interior of the neighborhood. 3.3

Site Selection

For the purpose of intra-urban comparisons of PM exposure, the project team sought to capture a wide range of conditions in a single urban area, which meant obtaining samples from as many Fresno-Clovis neighborhoods as possible. For regional comparison

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT purposes, the team sought to sample in several smaller communities in the SJV, as well as in Bakersfield where some of the SJV’s highest PM readings are recorded. Given these objectives and a definite budget for samplers, we selected 14 neighborhoods in Fresno/Clovis, four neighborhoods in Bakersfield, and the communities of Turlock, Fairmead, Madera, Kettleman City, and Corcoran. Figure 3 provides a graphic view of the sampling sites, while the geographic coordinates are shown in Table 1. Certain sampling locations were identified by SJVAPCD staff in consideration of demographic characteristics and proximity to significant sources of PM. In selecting the sampling sites for Fresno, the project team sought to maintain a relatively consistent spacing among neighborhoods. The team found that the spacing of public high schools coincided well with the neighborhoods to be sampled. Therefore in this report, most Fresno neighborhoods are labeled according to the closest high school, although no samplers were located on school grounds. For the Bakersfield sites, Bakersfield A is an older neighborhood in the east-central part of town, and Bakersfield B is a newer, northwestern neighborhood. To facilitate the site acquisition process and provide the potential for collocated data, eight of the samplers were deployed at air monitoring stations operated by SJVAPCD and the California Air Resources Board. For the other sampling sites, the project team obtained permission from all municipalities for use of their streetlight and utility poles. The Tisch Hi-Vol sampler was collocated with a passive sampler at the ARB Garland Monitoring Station in Fresno during the Winter Campaign. 3.4

Sampling Campaigns

The sampling campaign periods were planned to represent typical winter and summer weather in the San Joaquin Valley. The Winter Campaign was conducted from January 16, 2013 to February 18, 2013. The Summer Campaign was conducted from July 6, 2013 to August 12, 2013. The sampling start- and stop-dates are shown in Table 1. In both of the main campaigns, 41 passive sampler shelters were installed; four of the shelters contained a replicate sampler for quality control and quality assurance assessments. Additionally, in each campaign, three sites (i.e., streetlight poles) were randomly selected and equipped with an additional shelter/sampler, to provide a total of seven replicate samples for each campaign. Table 1 shows the full complement of primary and replicate samplers deployed. It is noted that a sampler was installed in Downtown Modesto for the Winter Campaign, but the sampler was compromised; for the Summer Campaign, that sampler was installed in Downtown Fresno. In Table 1, the shaded cells indicate the samples that were lost or compromised; there were four in the Winter Campaign and one in the Summer Campaign. Table 2 shows the net number of samples obtained and the number of neighborhoods sampled in each campaign.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Figure 3 - Locations of PM Sampling Sites

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 1 - Sites and Sampling Periods for the Winter and Summer Campaigns Winter Campaign Sample ID

Installation Date/Time

Removal Date/Time

Sample ID

Installation Date/Time

Removal Date/Time

3864

7/6/13 10:00

8/9/13 16:10

Sampling Time (days) 34.3

3651 3652 3662 3660 3661 3666

1/21/13 14:45 1/21/13 14:45 1/21/13 15:05 1/21/13 13:10 1/21/13 13:40 1/17/13 13:00

2/19/13 3:10 2/19/13 3:10 2/19/13 3:40 2/19/13 1:20 2/19/13 1:50 2/14/13 13:13

3872 3873 3876 3853 3854

7/6/13 10:25 7/6/13 11:15 7/6/13 11:30 7/11/13 8:30 7/11/13 8:30

8/9/13 16:26 8/9/13 16:59 8/9/13 17:09 8/8/13 12:14 8/8/13 12:14

34.3 34.2 34.2 28.2 28.2

-118.9997

3665

1/17/13 12:30

2/14/13 12:30

28.0

3859

7/11/13 9:15

8/8/13 11:28

28.1

36.81106 36.80855 36.71318

-119.8153 -119.8103 -119.7573

36.70743

-119.7498

36.77461

-119.8711

3616 3617 3604 3655 3656 3624

1/17/13 15:30 1/17/13 15:55 1/17/13 12:20 1/18/13 9:40 1/18/13 9:40 1/18/13 14:20

2/14/13 21:20 2/14/13 10:15 2/14/13 12:55 2/14/13 12:40 2/14/13 12:40 2/15/13 7:30

28.2 27.8 28.0 27.1 27.1 27.7

3879 3866 3889 3893

7/7/13 12:10 7/7/13 12:20 7/8/13 11:10 7/8/13 11:25

8/2/13 12:42 8/2/13 12:52 8/5/13 11:43 8/5/13 11:53

26.0 26.0 28.0 28.0

1/18/13 14:00 1/16/13 13:45 1/16/13 14:25 1/17/13 8:45

2/15/13 7:15 2/13/13 12:25 2/13/13 12:40 2/14/13 13:40

27.7 27.9 27.9 28.2

1/16/13 16:50 1/16/13 16:40 1/17/13 15:45 1/17/13 12:50 1/17/13 13:25 1/17/13 13:25 1/18/13 15:55 1/18/13 16:20 1/17/13 16:30 1/17/13 16:50

2/13/13 11:30 2/13/13 11:50 2/14/13 10:08 2/14/13 13:10 2/14/13 13:30 2/14/13 13:30 2/18/13 17:10 2/18/13 17:30 2/14/13 11:15 missing

27.8 27.8 27.8 28.0 28.0 28.0 31.1 31.1 27.8

7/7/13 10:45 7/7/13 10:45 7/7/13 11:00 7/7/13 19:10 7/7/13 19:25 7/11/13 10:00 7/11/13 10:00 7/8/13 19:45 7/8/13 19:30 7/11/13 10:15 7/8/13 10:15 7/8/13 10:35 7/8/13 10:35 7/7/13 9:30 7/7/13 9:45 7/7/13 11:20 7/7/13 11:40 7/15/13 9:10

8/2/13 11:04 8/2/13 11:04 8/2/13 11:17 missing 8/5/13 10:24 8/8/13 13:20 8/8/13 13:20 8/5/13 9:34 8/5/13 9:45 8/8/13 9:20 8/2/13 9:30 8/2/13 9:16 8/2/13 9:16 8/2/13 10:15 8/2/13 10:26 8/2/13 11:38 8/2/13 11:46 8/12/13 9:15

26.0 26.0 26.0

3605 3606 3670 3603 3601 3602 3664 3663 3618 3619

3857 3858 3865 3867 3870 3851 3852 3883 3882 3862 3880 3884 3885 3878 3875 3868 3869 3860

36.77685 36.82315 36.82674

-119.874 -119.6838 -119.6877

Villa @ Bullard

36.81929

-119.7164

3623 3613 3612 3671

Everglade @ Lester Fuller @ Loyola Patterson @ Hale California @ Lee

36.86477 36.86138 36.1022 36.72136

-119.779 -119.7755 -119.5656 -119.8009

Merced @ Strother

36.72664

-119.8079

Fairmead @ Sinclair Yates @ Elm Bullard @ Cecilia Stuart @ Lodi Tulare @ H Street

36.82674 37.07765 36.82298 36.82435 36.73245

-120.1951 -120.1942 -119.8748 -119.8779 -119.7931

1st St @ Garland Ave

36.7853

-119.7731

3625

1/22/13 12:00

2/20/13 9:00

28.9

3863

7/11/13 8:00

8/8/13 7:45

28.0

Floradora @ Del Mar

36.7601

-119.7957

3620

1/17/13 17:30

2/15/13 6:40

28.6

3888

7/8/13 9:50

8/2/13 13:30

25.2

Neighborhood

Sampling Site Location

Latitude

Longitude

Bakersfield A (East)

Niles @ Palm

35.37729

-118.9708

Williams @ Flower Bayshore @ Waler Noriega @ Wallawalla

35.38274 35.4095 35.40546

-118.9757 -119.1256 -119.122

California @ Stockdale

35.35662

-119.0626

Watts Drive @ So Union

35.33167

Arthur @ Scott Shaw @ Harrison 10th north of Burns Barton @ Hoxie

Bakersfield B (West) Bakersfield Calif. Ave Station Bakersfield Muni Station Bullard HS Calwa Central HS East Campus Clovis HS Clovis Station Clovis West HS Corcoran Station Edison HS Fairmead Figarden Loop Fresno Downtown Fresno Garland Station Fresno HS

Summer Campaign Sampling Time (days) 28.5 28.5 28.5 28.5 28.5 28.0

Blythe South of Princeton Cecila south of Cornell Bullard @Renn Stanford @ Escalon

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28.6 28.1 28.1 27.6 27.6 28.0 25.0 25.0 25.0 26.0 26.0 26.0 26.0 28.0

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Winter Campaign Neighborhood

Kettleman City Madera City Station McLane HS Modesto Station Roosevelt HS

Sunnyside HS Turlock Station

Sample ID

Installation Date/Time

Removal Date/Time

3892 3874 3877

7/8/13 10:00 7/6/13 14:20 7/6/13 14:40

8/2/13 13:40 8/6/13 13:45 8/6/13 14:00

Sampling Time (days) 25.2 31.0 31.0

3855 3856 3895 3890 3891

7/11/13 9:00 7/11/13 9:00 7/8/13 19:15 7/8/13 19:00 7/8/13 19:00

8/8/13 11:48 8/8/13 11:48 8/5/13 9:02 8/5/13 9:10 8/5/13 9:10

28.1 28.1 27.6 27.6 27.6

28.1 28.1 28.1 28.1 28.0 27.9

3881

7/8/13 11:35

8/5/13 11:25

28.0

3886 3887 3894 3871

7/8/13 11:50 7/8/13 11:50 7/8/13 18:20 7/7/13 20:10

8/5/13 11:31 8/5/13 11:31 8/5/13 10:50 8/5/13 11:09

28.0 28.0 27.7 28.6

27.9

3861

7/11/13 13:35

8/8/13 10:31

27.9

Sample ID

Installation Date/Time

Removal Date/Time

3621 3622 3657 3658 3669

1/17/13 17:50 1/18/13 11:25 1/18/13 11:55 1/18/13 11:55 1/17/13 10:35

2/15/13 6:55 2/14/13 17:15 2/14/13 17:35 2/14/13 17:35 2/14/13 9:50

1/16/13 16:05 1/16/13 16:15 1/16/13 16:15 1/18/13 14:00 1/16/13 12:45 1/16/13 12:45 1/16/13 13:15 1/16/13 13:15 1/16/13 15:05 1/16/13 15:30

2/13/13 13:35 2/13/13 13:15 2/13/13 13:15 missing 2/13/13 14:45 2/13/13 14:45 2/13/13 15:00 2/13/13 15:00 2/13/13 14:30 2/13/13 14:10

27.9 27.9 27.9

-119.699 -119.6961

3609 3607 3608 3667 3653 3654 3614 3615 3611 3610

-120.8359

3668

1/17/13 11:00

2/14/13 9:00

Sampling Site Location

Latitude

Longitude

Olive @ Delphia Community Svcs. Dist.

36.75765 36.00788

-119.7939 -119.9666

Fire Station parking lot

36.0075

-119.9601

Rd 28 @ Ave 14

36.95326

-120.0342

Ashlan @ Angus

36.79399

-119.7754

Holland @ Angus

36.79781

-119.7768

14th @ H Kings Canyon @ Recreation Woodrow @ Huntington Clovis @ Church Laurite @ Phillip S Minaret @ Cottonwood

37.64223

-120.9942

36.73583

-119.7382

36.74021

-119.7427

36.71478 36.71281 37.48822

Table 2 - Summary of Sampling Winter Campaign Samples deployed 45 Samples lost or compromised 4 Replicate samples obtained 5 Primary samples obtained 36 Neighborhoods sampled 21

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Summer Campaign Sampling Time (days) 28.6 27.2 27.2 27.2 28.0

17

Summer Campaign 45 1 7 37 22

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

3.5

Computer Controlled Scanning Electron Microscopy

PM samples collected on the sampler substrates were analyzed using CCSEM (Mamane et al., 2000). The CCSEM data is based upon automated feature analysis performed using the Tescan MIRA 3 Field Emission Scanning Electron Microscope. CCSEM analysis gathers information on size, shape, and elemental composition on a particle-byparticle basis. Briefly, CCSEM scans the collection substrate of the SEM stub for individual particles and provides a fluoresced X-ray spectrum and an image of each particle. The analysis is performed by rastering the electron beam over the sample while monitoring the resultant backscattered signal. At each point, the image intensity is compared to a preset threshold level. Once a coordinate is reached where the signal is above the threshold level, the electron beam is driven across the particle in a preset pattern to determine the size of the particle. Upon measurement of the particle size, the elemental composition of the particle is determined through collection of characteristic X-rays using energy dispersive spectroscopy (EDS) techniques. Individual particles characterized during the CCSEM analysis are grouped into particle classes based on their elemental composition. The mass of an individual particle is calculated by multiplying the assigned density of the particle by its volume. Each particle is assigned a density based on a common oxide in proportion to the elements present, as determined by the EDS analysis. The data acquired in this manner contains information for a large number of particles that are tabulated in order to create a raw dataset for further analysis in this work. The raw data file contains information on a particle-by-particle basis, with each row containing information on particle size, morphology, and elemental composition. The elements measured in this study include carbon (C), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), barium (Ba), and lead (Pb). 3.6

Data Analysis

CCSEM measurements provide a representation of the particle composition, but can rarely be used to provide accurate elemental concentrations on a particle-by-particle basis. Therefore, in order to use the semi-quantitative data to obtain new quantitative variables, particles were classified into homogeneous groups. Particle classes or groups represent the types of particles present in the air, and the mass of particles in a given class is a quantitative measure of particle composition. Since there are peaks in the spectrum that arise from statistical fluctuations in the X-ray spectrum arising from the short acquisition time (5 sec/particle) and influence of the background substrate, the data was subjected to noise reduction and composition data was normalized to unit length. Detailed steps for pretreatment of single particle data obtained from CCSEM measurements have been given by Kim et al., (1987), and most

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT recently reported by Lagudu et al., (2010) and Kumar et al., (2012). Any X-ray count for a given element less than twice the square root of total X-ray count for an individual particle was set to zero. After noise reduction, the X-ray spectrum for each particle data was normalized to unit length. The volume of each particle was estimated from the maximum diameter (dmax) and perpendicular diameter (dperp) (Hopke and Song, 1997). The mass of each particle was then estimated from the volume of a spheroid of revolution and an estimated density based on the elemental composition. The concentration of each particle has been estimated with the deposition velocity model discussed by Ott et al., (2008) and Wagner et al., (2012) using a surface roughness length of 0.5 to determine the friction velocity. The diffusivity of the particle was estimated using the Stokes-Einstein equation. The total concentration of fine particles at a single site was estimated by summing the concentration contribution of all the particles at that site. 3.7

Application of ART 2A Model

The chemical information derived from CCSEM serves as a basis for classification of particles into classes with similar composition. The normalized unit length data were analyzed with the ART 2A algorithm (Carpenter et al., 1991). The ART 2A algorithm is written in C++ with the GNU Scientific Library package, and compiled using a GNU C++ Compiler. In the present work, particle classification using ART 2A was performed for different vigilance factors, and a vigilance factor of 0.5 was found to yield a good classification of homogeneous classes in terms of the chemical elements contained in each group. Final results were obtained using a vigilance factor of 0.5 and a learning rate of 0.5 for the winter samples, and a vigilance factor of 0.5 and a learning rate of 0.7 for the summer samples. In each of the runs, the ART 2A algorithm was run using a standard Linux system. The number of “fine” particles with aerodynamic diameter less than 2.5 µm (PM2.5) was 18,377 in the Winter Campaign and 11,707 in the Summer Campaign. The number of “coarse” particles with aerodynamic diameter between 2.5 µm and 10 µm (PM10-2.5) was 18,475 in the Winter Campaign and 27,984 in the Summer Campaign. The number of particles less than 10 µm (PM10) was 36,852 in the Winter Campaign and 39,691 in the Summer Campaign. Particle classification was conducted in three separate runs, one for each size fraction. Particle groups/clusters representing more than 1% of the total number of particles were considered for further analysis. All post-processing and numerical analysis of data was conducted using SCILAB numerical computation software, version 5.4.1. The post processing computations involved several automated steps to analyze the single particle data efficiently. Each cluster obtained from the ART 2A analysis had elemental information, aerodynamic diameter, and mass of each particle. The masses were summed to yield the total mass in each particle class membership obtained using ART 2A. The elemental average of each of the elements in each cluster was compared to the elemental average of all the particles for that season (referred to as the Global Average). If a particular element was found to have a higher average value than the Global Average of an element, it was considered to be a defining element in the class. In this way all the classes of the clusters

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT were identified and highlighted with the defining element for each particle class membership. 3.8

Source Apportionment Using Positive Matrix Factorization (PMF)

The clusters obtained from the ART 2A analysis were analyzed using the US EPA PMF model, version number 3.0.2.2. Data for PMF analysis includes two matrices: the “X” matrix with the average concentration of particles at each site and in each particle class membership; and the “S” matrix with the errors associated with the measurements in the “X” matrix. Each particle class membership with a particle count greater than 1% of the total global particle count (for each season) was further processed before use in the PMF modeling. Particle masses in each class and at each site were summed up in the following size ranges: 0.2-0.5 µm; 0.5-1.0 µm; 1.0-1.5 µm; 1.5-2.0 µm; and 2.0-2.5 µm. This operation yielded five size bins for each particle class membership in each site, and, as a result, the summation helped increase the sample size for the PMF analysis. The matrix obtained from this data processing will be referenced as the “X” matrix from here on. The uncertainties (in the “S” matrix) were calculated as 5% of the measured concentration plus one third of the least significant value present in the “X” matrix for each particle class membership. All values in the “X” and “S” matrices were ensured to contain non-zero values. Zero or empty cells in the “X” matrix were replaced with one third of the least significant value present in each particle class membership of the concentration matrix “X”. This consequently helps populate the “S” matrix using the formulas discussed above. After preprocessing the input files as discussed above, the input values were further screened in the PMF program using the following signal-to-noise ratio (S/N) criteria: particle classes in the “X” matrix with S/N ratios less than 2 were not further considered for PMF modeling. In the matrices analyzed in this work, all particles memberships had good S/N ratio (greater than 2), and, therefore, none of the class memberships were excluded from the analysis. In addition to this, correlation plots between each particle class memberships were also checked to see if there were any obvious relationships between them. In this analysis, no direct relationship was evident between each particle class membership.

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

4.0

RESULTS AND DISCUSSION

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT RESULTS AND DISCUSSION 4.1

Meteorological Conditions

Figure 4 and Figure 5 provide the wind rose plots with wind speed and wind direction in Fresno during the sampling periods. The average wind speed was 2.6 mph (1.15 m/s) during the Winter Campaign. Figure 4 shows predominant transport of air masses from the northwest directions. Calms prevailed for about 54% of the sampling duration during the Winter Campaign. According to NOAA (www.cnrfc.noaa.gov/arc_search.php), during January and February 2013 Fresno and Bakersfield received 1.47” and 1.43” of precipitation, respectively. The normal precipitation levels for this two-month period are 4.22” for Fresno and 2.40” for Bakersfield. Figure 5 shows that the average wind speed was 7.2 mph (3.2 m/s) during the Summer Campaign. Calms prevailed in Fresno for about 13% of the time. A trace of precipitation was recorded in Fresno and Bakersfield during July and August 2013, which is typical of the dry San Joaquin Valley summers.

NORTH

10% 8% 6% 4% 2% WEST

EAST

WIND SPEED (m/s) >= 11.1 8.8 - 11.1

SOUTH

5.7 - 8.8 3.6 - 5.7 2.1 - 3.6 0.5 - 2.1 Calms: 53.92%

Figure 4 – Wind Speed and Direction in Fresno, CA, Winter Campaign

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

NORTH

40% 32% 24% 16% 8% EAST

WEST

WIND SPEED (m/s) >= 11.1 8.8 - 11.1

SOUTH

5.7 - 8.8 3.6 - 5.7 2.1 - 3.6 0.5 - 2.1 Calms: 12.64%

Figure 5 – Wind Speed and Direction in Fresno, CA, Summer Campaign

4.2

Particulate Matter Total Mass Concentration

Tables 3 through 6 show the total mass concentrations for each sample taken in the field measurement campaigns. The concentrations reported are average PM2.5 mass concentrations measured over the four-week period of each campaign. The tables are sorted with the highest concentrations at the top of the table. Table 3 and Table 4 provide the PM2.5 mass concentrations measured in the winter and summer campaigns, respectively. Table 5 and Table 6 show the PM10 mass concentrations measured in the winter and summer campaigns, respectively.

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Table 3 – PM2.5 Mass Concentrations, Winter Samples Neighborhoods Bakersfield A Calwa Corcoran Stn Bakersfield Muni Stn. Clovis HS Fairmead Bakersfield A Sunnyside HS McLane HS Fresno HS Fresno HS Madera City Stn Bullard HS Central HS East Campus Turlock Stn Bakersfield Calif Ave Stn Clovis West HS Sunnyside HS Fairmead Kettleman City Kettleman City Clovis Station Edison HS Roosevelt HS Bakersfield A Bullard HS Clovis HS Bakersfield B Roosevelt HS McLane HS Figarden Loop Bakersfield B Roosevelt HS Clovis West HS Kettleman City Central HS East Campus Roosevelt HS Calwa Fresno Garland Station Calwa Edison HS Edison HS Figarden Loop McLane HS Modesto Stn

Location Williams @ Flower 10th north of Burns Patterson @ Hale Watts Drive @ So Union Stanford @ Escalon Yates @ Elm Niles @ Palm Laurite @ Phillip Ashlan @ Angus Olive @ Delphia Floradora @ Del Mar Rd 28 @ Ave 14 Shaw @ Harrison Blythe south of Princeton S Minaret @ Cottonwood California @ Stockdale Everglade @ Lester Clovis @ Church Fairmead @ Sinclair Community Services Dist Fire Station parking lot Villa @ Bullard Merced @ Strother Kings Canyon @ Recreation Niles @ Palm Arthur @ Scott Bullard @Renn Noriega @ Wallawalla Kings Canyon @ Recreation Holland @ Angus Bullard @ Cecilia Bayshore @ Waler Woodrow @ Huntington Fuller @ Loyola Fire Station parking lot Cecila south of Cornell Woodrow @ Huntington Barton @ Hoxie Garland @ First Barton @ Hoxie California @ Lee Merced @ Strother Stuart @ Lodi Holland @ Angus 14th @ H

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Sample ID S#3662 S#3604 S#3670 S#3665 S#3612 S#3663 S#3652 S#3610 S#3609 S#3621 S#3620 S#3669 S#3617 S#3624 S#3668 S#3666 S#3605 S#3611 S#3664 S#3622 S#3658 S#3671 S#3601 S#3653 S#3651 S#3616 S#3613 S#3661 S#3654 S#3608 S#3618 S#3660 S#3614 S#3606 S#3657 S#3623 S#3615 S#3656 S#3625 S#3655 S#3603 S#3602 S#3619 S#3607 S#3667

PM2.5 Concentration (µg/m3) 9.24 5.91 4.46 4.3 3.27 2.94 2.81 2.8 2.76 2.67 2.58 2.48 2.4 2.34 2.24 2.18 2.18 2.08 1.93 1.87 1.86 1.85 1.69 1.61 1.57 1.55 1.51 1.47 1.47 1.43 1.41 1.39 1.39 1.24 1.21 1.19 1.10 1.05 1.00 0.96 0.88 No Data No Data No Data No Data

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 4 – PM2.5 Mass Concentrations, Summer Samples Neighborhoods Location Sample ID PM2.5 Concentration (µg/m3) Turlock Stn S Minaret @ Cottonwood S#3861 15.68§ Edison HS California @ Lee S#3880 5.71 Sunnyside HS Clovis @ Church S#3894 4.00 Bakersfield A Niles @ Palm S#3864 3.74 Fairmead Fairmead @ Sinclair S#3878 3.54 Fresno HS Olive @ Delphia S#3892 3.41 Madera City Stn Rd 28 @ Ave 14 S#3855 3.04 Corcoran Stn Patterson @ Hale S#3862 2.97 Bakersfield Muni Stn Watts Drive @ So Union S#3859 2.81 Bakersfield Calif Ave Stn California @ Stockdale S#3853 2.73 Clovis Station Villa @ Bullard S#3852 2.68 Bakersfield B Noriega @ Wallawalla S#3876 2.60 Kettleman City Fire Station parking lot S#3877 2.59 Calwa 10th north of Burns S#3889 2.56 Fresno HS Floradora @ Del Mar S#3888 2.53 Bakersfield Calif Ave Stn California @ Stockdale S#3854 2.49 Bakersfield A Williams @ Flower S#3872 2.44 Clovis Station Villa @ Bullard S#3851 2.42 Roosevelt HS Kings Canyon @ Recreation S#3881 2.41 Edison HS Merced @ Strother S#3884 2.25 Calwa Barton @ Hoxie S#3893 2.24 Kettleman City Community Services Dist S#3874 2.18 Roosevelt HS Woodrow @ Huntington S#3886 2.18 Central HS East Campus Cecila south of Cornell S#3865 2.16 McLane HS Ashlan @ Angus S#3895 2.11 Bullard HS Shaw @ Harrison S#3866 2.07 Central HS East Campus Blythe south of Princeton S#3858 2.04 Central HS East Campus Blythe south of Princeton S#3857 2.03 Sunnyside HS Laurite @ Phillip S#3871 1.90 Madera City Stn Rd 28 @ Ave 14 S#3856 1.88 Clovis West HS Everglade @ Lester S#3883 1.82 Edison HS Merced @ Strother S#3885 1.77 Bakersfield B Bayshore @ Waler S#3873 1.74 Roosevelt HS Woodrow @ Huntington S#3887 1.73 McLane HS Holland @ Angus S#3890 1.72 Clovis HS Stanford @ Escalon S#3870 1.68 Bullard HS Arthur @ Scott S#3879 1.63 McLane HS Holland @ Angus S#3891 1.58 Fairmead Yates @ Elm S#3875 1.56 Fresno Garland Station Garland @ First S#3863 1.51 Fresno Downtown Tulare @ H Street S#3860 1.49 Figarden Loop Stuart @ Lodi S#3869 1.41 Figarden Loop Bullard @ Cecilia S#3868 1.31 Clovis West HS Fuller @ Loyola S#3882 1.30 Clovis HS Bullard @Renn S#3867 No Data § - The Turlock sample had large flaky particles in the low magnification (as seen in the CCSEM instrument) and, therefore, this sample was likely contaminated.

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PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 5 – PM10 Mass Concentrations, Winter Samples Neighborhoods Bakersfield A Sunnyside HS Calwa Bakersfield A Corcoran Stn Bakersfield Calif Ave Stn Bakersfield A Roosevelt HS Fresno HS Fresno HS Bullard HS Sunnyside HS Kettleman City McLane neighborhood Kettleman City Bakersfield Muni Stn. Roosevelt HS Madera City Stn Bakersfield B Bakersfield B Calwa Fairmead Calwa Edison HS Edison HS Central HS East Campus McLane neighborhood Fairmead Turlock Stn Roosevelt HS Clovis Station Roosevelt HS Clovis West HS Clovis HS Clovis HS Kettleman City Figarden Loop Bullard HS Fresno Garland Station Central HS East Campus Clovis West HS Edison HS Figarden Loop McLane neighborhood Modesto Stn

Location Williams @ Flower Laurite @ Phillip 10th north of Burns Niles @ Palm Patterson @ Hale California @ Stockdale Niles @ Palm Kings Canyon @ Recreation Olive @ Delphia Floradora @ Del Mar Shaw @ Harrison Clovis @ Church Fire Station parking lot Holland @ Angus Fire Station parking lot Watts Drive @ So Union Kings Canyon @ Recreation Rd 28 @ Ave 14 Noriega @ Wallawalla Bayshore @ Waler Barton @ Hoxie Fairmead @ Sinclair Barton @ Hoxie California @ Lee Merced @ Strother Blythe south of Princeton Ashlan @ Angus Yates @ Elm S Minaret @ Cottonwood Woodrow @ Huntington Villa @ Bullard Woodrow @ Huntington Everglade @ Lester Stanford @ Escalon Bullard @Renn Community Services Dist Bullard @ Cecilia Arthur @ Scott Garland @ First Cecila south of Cornell Fuller @ Loyola Merced @ Strother Stuart @ Lodi Holland @ Angus 14th @ H

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Sample ID S#3662 S#3610 S#3604 S#3652 S#3670 S#3666 S#3651 S#3653 S#3621 S#3620 S#3617 S#3611 S#3658 S#3608 S#3657 S#3665 S#3654 S#3669 S#3661 S#3660 S#3655 S#3664 S#3656 S#3603 S#3601 S#3624 S#3609 S#3663 S#3668 S#3615 S#3671 S#3614 S#3605 S#3612 S#3613 S#3622 S#3618 S#3616 S#3625 S#3623 S#3606 S#3602 S#3619 S#3607 S#3667

PM10 Concentration (µg/m3) 34.21 27.7 27.48 24.77 20.98 20.86 19.16 18.95 18.16 18.08 17.82 17.1 15.99 15.39 15.06 14.83 14.72 14.54 14.35 14.3 14.08 13.15 12.95 12.91 12.89 12.8 12.75 12.61 12.37 12.21 11.96 11.94 11.15 10.99 10.89 10.31 10.25 10.09 8.65 7.97 7.85 0 0 0 No Data

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 6 – PM10 Mass Concentrations, Summer Samples Neighborhoods Turlock Stn Bakersfield Calif Ave Stn Fairmead Clovis Station Bakersfield A Calwa Bakersfield A Calwa Bakersfield Calif Ave Stn Edison HS Sunnyside HS Bakersfield B Edison HS Fresno HS Central HS East Campus Corcoran Stn Bakersfield B Central HS East Campus Central HS East Campus Kettleman City Kettleman City Clovis Station Figarden Loop Roosevelt HS Sunnyside HS Edison HS Fresno HS Fresno Downtown Clovis HS Clovis West HS Madera City Stn Roosevelt HS McLane HS Bullard HS Bullard HS McLane HS Clovis West HS Roosevelt HS Bakersfield Muni Stn. Figarden Loop Fairmead Madera City Stn McLane HS Fresno Garland Station Clovis HS

Location S Minaret @ Cottonwood California @ Stockdale Fairmead @ Sinclair Villa @ Bullard Niles @ Palm 10th north of Burns Williams @ Flower Barton @ Hoxie California @ Stockdale Merced @ Strother Clovis @ Church Bayshore @ Waler California @ Lee Olive @ Delphia Cecila south of Cornell Patterson @ Hale Noriega @ Wallawalla Blythe south of Princeton Blythe south of Princeton Community Services Dist Fire Station parking lot Villa @ Bullard Stuart @ Lodi Woodrow @ Huntington Laurite @ Phillip Merced @ Strother Floradora @ Del Mar Tulare @ H Street Stanford @ Escalon Everglade @ Lester Rd 28 @ Ave 14 Kings Canyon @ Recreation Holland @ Angus Shaw @ Harrison Arthur @ Scott Ashlan @ Angus Fuller @ Loyola Woodrow @ Huntington Watts Drive @ So Union Bullard @ Cecilia Yates @ Elm Rd 28 @ Ave 14 Holland @ Angus Garland @ First Bullard @Renn

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Sample ID S#3861 S#3854 S#3878 S#3851 S#3864 S#3889 S#3872 S#3893 S#3853 S#3885 S#3894 S#3873 S#3880 S#3892 S#3865 S#3862 S#3876 S#3858 S#3857 S#3874 S#3877 S#3852 S#3869 S#3886 S#3871 S#3884 S#3888 S#3860 S#3870 S#3883 S#3856 S#3881 S#3890 S#3866 S#3879 S#3895 S#3882 S#3887 S#3859 S#3868 S#3875 S#3855 S#3891 S#3863 S#3867

PM10 Concentration (µg/m3) 36.45 36.39 35.72 33.81 33.34 33.33 33.28 32.22 32.05 31.64 31.27 29.79 29.48 28.76 28.54 28.06 27.93 27.21 27.14 26.87 25.72 24.63 23.94 23.58 23.16 22.92 22.39 22.02 21.72 21.36 21.24 20.66 19.96 19.65 19.6 19.26 19.19 19.02 18.76 18.76 18.49 17.73 17.4 16.5 0

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT 4.3

Particle Class Membership and Particle Size Distribution

Particle class memberships were determined based on the predominant elements present in each particle cluster/group. The total mass of elements in each particle class was calculated by summing the masses in each particle group. In order to identify the type of particles associated with each of the particle class memberships identified by ART 2A, the elemental average of each of the elements in a cluster was compared to the elemental average of all particles (Global Average). An element was considered to be a defining element of a class if it had higher average value than the Global Average. As a result, the mass of particles in a given particle class is a quantitative measure of particle composition. Tables 7, 9, 11, and 13 list each particle class separately. Tables 8, 10, 12, and 14 show the particle classes grouped by “Likely Source Type;” in these tables the average diameter (Dp) of the combined classes is the weighted average based on the mass of each class. Further grouping of PM2.5 particles is performed in the Positive Matrix Factorization process, as described in Section 4.11. 4.3.1 Fine Particles (PM2.5) Winter Campaign In order determine the fine particle groupings, normalized unit vectors defining each element was used for processing. Clustering of fine particles yielded 23 different fine particle classes. Table 7 presents the particle class memberships in each particle cluster/group along with likely particle source types for the winter samples. Table 8 groups the classes by Likely Source Type. Samples from the Winter Campaign had a variety of particles that suggest sources including vehicle emissions, tire and brake wear, and metallic traffic emissions. Most of the particle mass appears to have been derived from vehicle-related traffic emissions, sulfur bearing aerosol particles and combustion of biomass (i.e., wood) and vehicle engine oil/fuel. Carbonaceous road dust was identified by the presence of high concentrations of carbon along with elements associated with the road dust. Clusters with carbon and/or phosphorus have been suggested to represent biologically derived particles (Newman et al., 1995). However, abrasion of asphalt roads or tire wear deposited on road surfaces can also be sources of carbon, in addition to tailpipe emissions and deposition of primary biological materials. Mineral dust particles are primarily oxides of metals such as iron, aluminum and titanium, while crustal materials are predominantly carbonates of calcium and other soil derived materials. Nearly 28% of the total PM2.5 mass was likely emitted from vehicle and traffic related sources. Since, potassium (K) has been identified as marker for biomass combustion particle classes numbered 11 and 21 were likely emitted from biomass combustion. In the samples studied in the Winter Campaign, about 11% of the total PM2.5 mass is estimated to be derived from biomass combustion, likely due to residential wood burning.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Figure 6 provides the particle size distribution (PSD) in the 23 particle classes identified in the fine particles size range. Most of the particle groups that were likely derived from combustion sources were skewed towards lower particle size ranges. On the other hand, mineral dust and soil and crustal particles were somewhat uniformly distributed in all size bins less than 2.5 µm.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 7 – PM2.5 Class Groupings, Winter Samples No.

Likely Source Type

1

Soil/Crustal

2

Aged Sea-Salt

C

3

Biogenic

C

4

Soil/Crustal

Na

Mg

Al

Si

5

Soil/Crustal with Metals

Na

Mg

Al

Si

6

Tire and Brake Wear

Mg

Al

7

Industrial/Metals

8

Industrial/Metals

9

Metals/Crustal

10

Ca & K - Bearing Sulfur and Chloride Aerosol Biomass Combustion

11 12

Elements – PM2.5

Na Na

Al

Si

K

Mg

Cl P

C

Na

Al

Si

Al

Si

P

13

C

14

Crustal

15

Mineral Dust

16

Metals/Crustal

17

Soil/Crustal

18

Metals/Crustal

19

Vehicle Engine Emissions

20 21

Construction Materials/ Gypsum Biomass Combustion

Na

22

Metallic Traffic Emissions

Na

Mg

23

Metallic Traffic Emissions

Na

Mg

C

S Na

Mg

Al

Na

Mg

Al

Mg Na

Si

Mg

Al

Cr

Mn

Fe

Ni

Cu

Fe

Ni

Cu

Ni

Cu

Mn Mn

K

Ca

Cr

Cl

K

Ca

Cr

Mn

Cr

Mn

Cl

Ti

Si

K Ca

Ti

S

Ca

P

K Ti

S P P

S

Ni

Mn

Ca

Cr Ti

30

Fe

362

723.93

0.78

13.72

637

792.64

0.82

8.85

578

2628.88

1.03

14.42

713

Zn

2967.83

1.07

16.16

788

Cu

2998.75

1.10

17.19

690

Cu

3007.51

1.01

19.85

875

3103.68

1.02

26.49

992

3199.37

0.98

23.14

958

Cu

3284.51

1.08

18.43

787

Cu

3390.05

1.09

19.47

850

3412.37

0.98

20.83

1000

3474.21

1.07

19.67

945

3589.64

1.05

21.26

1002

3720.88

1.17

18.51

720

3760.43

1.06

27.14

1068

3823.75

0.99

24.63

1053

4001.10

1.13

21.14

884

4135.37

1.13

21.57

859

4461.66

1.12

23.01

919

Cu

Zn

Zn

Zn Ni

5.51

787

Mn Mn

0.64

761

Fe Cr

207.58

17.21

Ni

Ti

149

17.98

Ca K

2.10

1.08

Ni

Cr

0.57

1.02

Mn Cr

Cl S

Al

Ti

Fe

Particle Count

2862.38

Ni Mn

Total Conc. (ng/m3)

2824.99

Zn

Cr

S

P

Si

Ti

Average DP (µm)

Zn

Fe

Ti

P P

C

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Cr

Ti

Fe

Cl

Cl

Si

Al Na

Ti

Ca

Cu

Mn

Fe Si

Al

Cr

Ca

Cl S

Ni Ni

K

P

Aged Sulfur-Bearing Carbon Particles Metallic Traffic Emissions

Fe

Ti

P C

Ti Ca

P Mg

Fe

K

Cl

Na

Ti

Total Mass (pg) 55.94

Cu

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 8 - PM2.5 Likely Source Types, Winter Samples Likely Source Type Metallic Traffic Emissions Metals/Crustal Biomass Combustion Industrial/Metals Soil/Crustal Construction Materials/ Gypsum Vehicle Engine Emissions Mineral Dust Crustal Aged Sulfur-Bearing Carbon Particles Ca & K - Bearing Sulfur and Chloride Aerosol Tire and Brake Wear Soil/Crustal with Metals Biogenic Aged Sea-Salt

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Class Grouping No. 13, 22, 23 9, 16, 18 11, 21 7, 8 1, 4, 17 20 19 15 14 12 10 6 5 3 2

Total Mass (pg) 11,882 10,194 7,105 5,830 4,438 3,824 3,760 3,412 3,390 3,199 3,008 2,825 2,629 724 208

31

Average DP (µm) 1.11 1.12 1.08 1.07 1.00 0.99 1.06 0.98 1.09 0.98 1.01 1.02 1.03 0.78 0.64

Particle Count Total Conc (ng/m3) 2,565 63.01 2,355 55.37 1,876 47.63 1,575 33.37 1,729 32.21 1,053 24.63 1,068 27.14 1,000 20.83 850 19.47 958 23.14 875 19.85 761 17.98 713 14.42 637 13.72 362 5.51

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 6 – PM2.5 Particle Size Distribution, Winter Campaign

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Summer Campaign Clustering of fine particles yielded 13 different fine particle classes for the summer samples. Table 9 presents the particle class memberships in each particle cluster/group along with likely particle source types. Table 10 groups the classes by Likely Source Type. Particle class memberships for the summer samples were deficient of carbonaceous species in comparison to the winter samples. However, one particle class membership was exclusively rich in carbon, and another class membership was rich in carbon and other elements including phosphorus, chromium, manganese and nickel. Both of these class memberships were present predominantly in particles less than 1 µm in size, suggesting combustion sources for these particles clusters, as shown in Figure 7. Other class memberships with likely sources include industrial sources rich in elements such as magnesium, aluminum, chromium, iron, copper and zinc. Several particle class memberships have elemental signatures of re-suspended road dust, crustal materials and mineral dust. Most of the particle class memberships were skewed with particle diameters greater than 1 µm, except for particles with likely sources such as combustion particles and industrial activity as shown in Figure 7. The PMF modeling discussed in Section 4.11 provides additional details on the possible source profiles.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 9 - PM2.5 Class Groupings, Summer Samples No.

Likely Source Type

1

Crustal Materials

Na

Mg

2

Landscaping Activity

Na

Mg

3

Re-Suspended Dust

Na

Mg

4

Combustion Sources

5

Re-Suspended Dust

6

Industrial Metals

8

Industrial

9

Crustal

10

Mineral/Crustal

11

Combustion Sources

12

Re-Suspended Dust

13

Road Dust

Total Mass (pg)

Average Dp (µm)

Particle Count

Zn

2749.14

1.29

626

Zn

1712.40

1.28

473

Zn

2656.21

1.14

695

Ni

1978.35

0.87

1345

Ni

3230.17

1.29

749

2593.92

1.03

753

3103.75

1.01

1223

2986.98

1.23

744

3197.12

1.07

752

4095.86

0.70

3046

2004.44

1.14

499

1802.45

0.94

801

Elements - PM2.5 Al Si

C

P

S

Cl

K

Ca

S

Cl

K

Ca

Fe Cr

S

Cr

P Mg

Al

Si

K

Al Mg Mg

Ca

S

Ti

Al

Cr

Mn

Cr

Mn

Fe

Cr

Mn

Fe

Ca

Al

Si

Al

Si

Cl S

K K

Fe

Ni

Cu

Cu

Zn

Mn Ti

Ca

Cu Cr

Mn

Fe

C Na

Mg

Al

Si

Mg

Al

Si

P

Cl S

Cl

K

Ti

Mn

Fe

Cu

Ca

Table 10 - PM2.5 Likely Source Types, Summer Samples Likely Source Type Re-Suspended Dust Combustion Sources Crustal Materials Mineral/Crustal Industrial Industrial Metals Road Dust Landscaping Activity

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Class Groupings 3, 5, 12 4, 11 1, 9 10 8 6 13 2

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Total Mass (pg) 7,891 6,074 5,737 3,197 3,104 2,594 1,802 1,712

Average DP (µm) 1.20 0.76 1.26 1.07 1.01 1.03 0.94 1.28

Particle Count 1,943 4,391 1,370 752 1,223 753 801 473

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 7 – PM2.5 Particle Size Distribution, Summer Campaign

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT 4.3.2 Total Particulate Matter (PM10) Winter Campaign Clustering of PM10 particles using the ART 2A algorithm yielded 86 different particle classes. However, as indicated earlier, only particle classes with total particle count greater than 1% of the total number of PM10 particles were considered for further analysis. Table 11 presents the defined particle classes based on the predominant elements present in each of the cluster files along with likely sources of each particle class type. Table 12 groups the particle classes by Likely Source Type. Similar to the PM2.5 samples, PM10 had a variety of source types. Clear distinction exists between particles derived from soil crustal materials and mineral dust, and particles derived from fossil fuel, biomass combustion and metallic species from metal works, and engine and brake wear particles. For example, particle class 43, with sodium, aluminum, silicon, potassium, calcium, and manganese as the defining elements, had an average particle diameter of 4.72, while particle class 28 with predominant elements carbon, magnesium, sulfur, manganese, and zinc, and particle class 21 with carbon, phosphorous, chromium, and zinc had average diameters of 3.39 and 2.63, respectively. In a recent study, Moreno et al., (2013) reported that metallic emissions (zinc, copper, chromium, and iron) and re-suspended mineral dust (calcium, aluminum, and silicon) were closely associated with traffic flow. In the present study, carbon particles were present with other crustal mineral dust elements as predominant elements in several particles classes. Carbon particles detected in the present samples, are likely due to abrasion of asphalt roads. Tire wear deposited on road surfaces can also be significant source of carbon, in addition to tailpipe emissions. The major sources of winter PM10 were identified as soil/road dust, characterized by the presence of silicon, aluminum, calcium, potassium, iron, carbon, magnesium, and sulfur. Particles classes containing soil and mineral dust markers had much coarser particle size distributions (shown in Figure 8) than particle classes that had biomass and fossil fuel combustion markers. Carbon with copper has been suggested as a marker for brake wear (Schauer et al., 2006). The PSD from brake wear (particle class number 8) indicates that most particles were present in the fine fraction, i.e., less than 2.5 m.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 11 – PM10 Class Groupings, Winter Samples No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Likely Source Type Vehicular Emissions Biogenic Vehicular Road Dust Vehicular Emissions Brake wear Metal/Road Dust Engine Oil Combustion Brake wear Carbonaceous Metals Industrial Metals Biomass/Cooking Brake Wear Industrial Metals Crustal Materials Sulfur bearing Vehicle Emissions Brake Wear Sulfur bearing Vehicle Emissions Biomass Combustion Metallic Emissions Vehicular Road Dust Engine Oil Combustion Crustal/Metals Aged Carbon/Sea Salt Aged Particles Silica Dust

Elements – PM10 C C C C C C

Mg

Na

Al

Cr K

Mg

Cl Al S

Ti

Cr Cr

Cl

Mn Mn

Cl

Ti Ti

Mg Si Mg

Al

P

S S

K

Ca

Cl Cl

Ti Ti

Si

Al

Zn

Ca P

S

Cr Cr

Mn Mn Fe

Cr Cr

Ti

Na

S

Mn

Cl

Al

Si

Na

Al

Si

C

Ca Cr

P

Na Na

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Al

Si Si

Ni Ni

Cu Cu

Mn

Fe Fe

Zn

Zn Ni

Zn Cu

Cr

Si

Zn Fe

P

Cu Cu

Ti

P Al

Ni Ni Ni

Fe K

Na

Cu

Mn Mn

Si

Mg

Cu

Ni S

Na Na

Ni

Fe Fe

Mg

C

C

Ca Ca

Cl P

C C

C

Fe

Ca

S S

Na

C

S S

Mg

C C C C C C C

P P

S S

Cl

Ca Ca

Cr Cu Ti

37

Cu

Total Mass (pg) 26,840 34,198

Average DP (µm) 1.84 1.57

51,646

2.55

36,781 52,569 47,699

2.28 2.22 2.03

44,450

1.33

51,408

1.63

61,268

2.37

56,932 74,942 74,529 80,833 125,484

2.52 3.19 2.53 2.75 3.58

105,831

2.80

147,524

2.94

106,588

3.88

111,565

3.17

102,038

2.88

153,601

3.56

132,867

2.63

196,432

4.36

126,022

3.45

172,531 171,666

3.66 4.98

Particle Count 507 703 566 372 598 494 659 753 685 391 426 534 459 621 787 1122 471 515 585 760 1006 706 548 781 515

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT No. 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Likely Source Type Sulfur Bearing Carbonaceous Dust Crustal Engine Oil Combustion Crustal Soil Dust/Crustal Soil Dust Biomass Combustion Soil Dust Metallic Emissions/ Road Dust Mineral Dust Mineral Dust Mineral Dust Soil Dust Soil Dust Mineral Dust Metallic Emissions Mineral Dust Soil/Crustal

Elements – PM10

C

Mg

Si Al

C

P P

Mg Na

Mg Mg Mg Na Na Mg Mg Mg Na

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S S

Cl

S Al Al Al

Si Si Si

Al

Si Si

Al Al Al Al Al Al Al Al Al

Si Si Si Si Si Si Si Si Si

P

Ca

Mn

Ca Ca

Mn Mn

Cr

Fe Zn

K Cu S S

K

Ca Ca

Ti Ti

Cr

Fe

Ca S

K K K

Ti

Cu Cu

Mn

Fe Fe

Zn

Cu Ca Fe

K K K K

Fe Fe Ca

38

Mn

Ni

Total Mass (pg)

Average DP (µm)

198,130

3.36

130,399

2.92

207,220

3.39

176,562 186,430 181,859

4.39 5.93 5.50

209,704

4.31

131,023

3.83

220,335

4.54

205,749 308,587 278,301 339,282 334,901 294,031 298,570 254,803 295,401

4.23 4.67 4.76 6.20 4.46 3.88 4.87 5.07 4.72

Particle Count 948 586 1042 491 380 443 626 377 487 443 905 674 639 945 1037 531 483 699

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Table 12 - PM10 Likely Source Types, Winter Samples Likely Source Type Soil/Crustal Mineral Dust Crustal Materials Metallic Emissions Engine Oil Combustion Brake Wear Biomass Combustion Metallic Emissions/Road Dust Sulfur bearing Vehicle Emissions Vehicular Road Dust Sulfur Bearing -Carbonaceous Dust Crustal/Metals Aged Particles Silica Dust Industrial Metals Aged Carbon/Sea Salt Biomass/Cooking Vehicular Emissions Carbonaceous Metals Metal/Road Dust Biogenic

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Class Grouping No. 30, 31, 33, 38, 39, 43 35, 36, 37, 40, 42 14, 27, 29 19, 41 7, 21, 28 5, 8, 12, 16 18, 32 34 15, 17 3, 20 26 22 24 25 10, 13 23 11 1, 4 9 6 2

39

Total Mass (pg) 1,468,896 1,341,471 432,445 400,608 384,537 326,030 321,269 220,335 212,419 205,247 198,130 196,432 172,531 171,666 137,765 126,022 74,942 63,621 61,268 47,699 34,198

Average DP (µm) 5.76 4.52 3.71 4.36 2.89 2.52 3.91 4.54 3.34 3.31 3.36 4.36 3.66 4.98 2.65 3.45 3.19 2.09 2.37 2.03 1.57

Particle Count 3,483 3,542 2,383 1,116 2,707 3,007 1,141 487 1,258 1,326 948 706 781 515 850 548 426 879 685 494 703

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 8 – PM10 Particle Size Distribution, Winter Campaign

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 8, continued

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Summer Campaign Clustering of PM10 particles in the summer samples using the ART 2A algorithm yielded 61 different particle class memberships. Table 13 presents the defined particle classes based on the predominant elements present in each of the cluster files along with the likely sources of each particle class type in the summer samples. Table 14 groups the particle classes by Likely Source Type. Figure 12 provides the particle size distribution of PM10 samples collected in the summer. Several particle class memberships had average diameters that ranged between 3 µm and 5 µm, suggesting mechanically generated particles. However, three particle class memberships had average particle diameters less than 3 µm. These particle classes were rich in carbon, iron, sulfur, chromium, manganese and copper. Several of these particle classes were likely generated by combustion sources (both fossil fuel and biomass), tire and brake wear and industrial operations.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table 13 – PM10 Class Groupings, Summer Samples No. Likely Source Type 1 Carbonaceous Soot 2 Biomass/Cooking 3 Crustal 4 Mineral Dust 5 Mineral Dust 6 Mineral Dust 7 Soil Dust 8 Soil Dust 9 Diesel Exhaust 10 Mineral Dust 11 Soil Dust 12 Mineral Dust 13 Mineral Dust 14 Re-Suspended Road Dust 15 Mineral Dust 16 Soil Dust 17 Re-Suspended Road Dust 18 Mineral Dust 19 Mineral Dust 20 Re-Suspended Road Dust 21 Tire/Brake Wear 22 Re-Suspended Road Dust 23 Mineral Dust 24 Re-Suspended Road Dust 25 Re-Suspended Road Dust 26 Re-Suspended Road Dust 27 Mineral Dust 28 Mineral Dust 29 Tire/Brake Wear 30 Road Dust 31 Re-Suspended Road Dust 33 Soil Dust

Elements – PM10 C C Na Na Na Na C Na Na

Na Na Na Na

Mg Al Al Mg Al Mg Al Mg Al Al Mg Al Mg Al Mg Al Mg Al Mg Al Mg Al Mg Al Mg Al Al Al

Si Si Si Si Si Si Si Si Si Si Si Si Si

S S

C C

455-006 PM Variability Final Report rev 3

Na Mg Al

Cr Ti Ca Ca

S

P

S

Ti Ca Ca Ti Ca Ca Ti

Si Si Si P P P P P P

S S S S

K Cl K K

S S S

Cl Cl Cl

K K K

Fe Mn Fe Mn Fe Fe Fe Mn Fe

Ca

K

P

Si Si Si

K K K K K

C Na Mg Na Al Na Mg Al Mg Al Na Mg Mg Na Mg

Fe

Cl

P

P Si Si Si

K K K K K K K

Fe Mn Fe Mn Fe Mn Mn Fe Mn Fe Mn Fe Fe

Zn

Zn

Cu Ni Cu

Ca Ca

Zn Mn

Ti Ca Ti Ca Ca Ca

K

Fe

Zn Cu Cu Zn

Fe Mn Cr Mn Fe Ni Cu Mn Zn

Ca

43

Total Mass Average Dp Particle Count (pg) (µm) 295,341 1.82 6437 236,157 3.09 2090 687,072 5.61 1688 467,695 4.17 1568 751,682 6.2 1558 528,999 5.52 1316 579,346 5.79 1253 371,874 4.81 1189 95,973 2.54 1168 163,820 4.02 1025 327,444 5.38 1005 598,627 6.46 963 253,890 4.82 950 242,689 4.74 919 314,126 5.36 875 314,094 4.48 860 428,590 5.41 823 321,547 5.24 814 374,704 5.52 814 97,139 3.93 668 79,965 3.15 665 165,798 5.19 600 200,076 5.61 586 234,204 4.54 583 490,528 7.56 575 168,913 4.38 532 325,606 6.14 506 309,333 6.42 484 43,913 2.85 445 127,491 5.75 434 157,864 5.49 430 174,264 5.77 422

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Table 14 - PM10 Likely Source Types, Summer Samples Likely Source Type Mineral Dust Re-Suspended Road Dust Soil Dust Crustal Carbonaceous Soot Biomass/Cooking Road Dust Tire/Brake Wear Diesel Exhaust

455-006 PM Variability Final Report rev 3

Class Grouping No. 4, 5, 6, 10, 12, 13, 15, 18, 19, 23, 27, 28 14, 17, 20, 22, 24, 25, 26, 31 7, 8, 11, 16, 33 3 1 2 30 21, 29 9

44

Total Mass (pg) 4,610,105 1,985,725 1,767,022 687,072 295,341 236,157 127,491 123,878 95,973

Average DP (µm) 5.60 5.58 5.27 5.61 1.82 3.09 5.75 3.04 2.54

Particle Count 11,459 5,130 4,729 1,688 6,437 2,090 434 1,110 1,168

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 9 – PM10 Particle Size Distribution, Summer Campaign

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 9, continued

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT 4.4

Replicate Sample Analysis

The quality of the fine particle samples collected using passive samplers was assessed using replicate samples. Correlation plots of replicate sample mass concentrations indicate that there was generally a good agreement between a pair of replicate samples collected at each of the four sites in the Winter Campaign. Figure 10 shows the correlation plot between the regular and the replicate PM10 samples. The r2 value between the replicate sample and the regular sample ranged between 0.90 and 0.96. Figure 11 shows the correlation plot between the regular and the replicate PM2.5 samples. The r2 value between the replicate sample and the regular sample ranged between 0.56 and 0.96. The correlation between replicate and regular samples collected in the summer had similar agreement. In Figures 10 and 11, the points represent 0.1 μm size-bin comparisons, the black solid line is the regression line, and the black dashed lines are the 95% confidence intervals. 4.5

Comparison with Federal Reference Monitor (FRM) Measurements

Concentrations measured in this work were compared with the concentrations measured using an FRM sampler. Although the PM10 concentrations measured using the FRM sampler closely matched the masses measured in this work, the PM2.5 mass was lower. To determine the air flow rate for the passive samplers, the friction velocity (u*) was calculated using an effective surface roughness length (z0) of 0.5 m for obstacles in an urban area, and the monthly averaged wind speed was obtained from NOAA. The average friction velocity was 2.81 m/s during the Summer Campaign and 0.96 m/s during the Winter Campaign, while the ideal wind speed for passive samplers is 0.4 m/s or less. However, by using a the flat plate shelter with the UNC passive sampler as in this work, the effect of wind on the measured concentrations is expected to be negligible (Ott and Peters, 2008). Based on measurements from this work, nearly 50% of the total wintertime PM2.5 mass is made up of semivolatile species. A significant portion of the semivolatile particle mass is likely lost during the sampling, but contributions from other sources identified here are expected to have lesser losses from the sampler. Therefore, the discrepancy in the measured concentrations is likely attributed to the loss of secondary particles from the passive sampler.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 10 – Comparison of Replicate Winter PM10 Samples

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure 11 – Comparison of Replicate Winter PM2.5 Samples

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT 4.6

Coefficient of Divergence (COD)

Since the sites selected in the Winter Campaign were not equally spaced in a grid, interpolation schemes such as spline and kriging calculations using ArcGIS version 10.1 software did not yield reasonable spatial profile plots. It is possible to obtain reasonable spatial profiles using a smaller sub-set of sites that are geographically located in close proximity. For example, interpolation can be conducted using all sites located inside the Fresno area. Nevertheless, a quantitative measure of spatial heterogeneity can be examined by calculating the coefficient of divergence (COD) and/or Pearson correlation coefficient (COR) using the particle class mass concentrations or particle class mass at all the sampling sites studied in this work. The COD between two sampling sites j and k is defined as shown in the equation below (Wongphatarakul et al., 1998):

COD jk 

1 p  ( xij  xik )    p i 1  ( xij  xik ) 

2

In the above equation, xij is the source contribution per sampling interval i, estimated at site j, and p is the total number of particle classes. The data points in the calculation of COD between sampling sites j and k were total numbers of particles in each cluster (i.e. p=34). Table A-1 (in Section 7) shows the average COD between each site and the remaining sites in the Winter Campaign in the PM2.5 samples. The COD averages ranged between 0.29 and 0.75, suggesting strong heterogeneity in the PM2.5 samples, although the degree of heterogeneity was somewhat less than the heterogeneity in the Winter PM10 samples. Table A-2 shows the average COD values for the PM2.5 samples collected during the Summer Campaign. A handful of sites (Central HS East Campus - Cecilia South of Cornell, Figarden Loop - Stuart @ Lodi, Fresno HS - Olive @ Delphia, Edison HS Merced @ Strother, Roosevelt HS - Woodrow @ Huntington, Sunnyside HS - Clovis @ Church, California Ave Station, Clovis Station) had COD values that were greater than 0.4. These sites are shown in bold font to indicate their higher heterogeneity. All other sites that exhibited lower heterogeneity are shown in normal font in this table. Table A-3 shows the average COD between each site and the other sites for winter PM10. The average values ranged between 0.51 and 0.80 in these samples. According to the US EPA (Wilson et al., 2005), COD values greater than 0.2 suggests heterogeneity of concentrations, while values greater than 0.4 suggest strong heterogeneity. Table A-4 shows the calculated COD values for PM10 samples collected from the sites during the Summer Campaign. The COD values were in general less than 0.4 in these samples, indicating lesser spatial heterogeneity than in the winter samples. We recall that discussions on the particle class memberships and the associated particle size

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT distributions suggested that several Summer PM10 particle classes were likely generated mechanically, while several classes in the winter samples were likely due to sources other than mechanical processes. Table 15 below summarizes the grand averages of the COD values in the PM10 and PM2.5 particle size groups. The ordered pairs of minimum and maximum of the averaged COD in PM10 and PM2.5 samples were (0.55, 0.80) and (0.34, 0.71), respectively, in the winter samples. The values of average COD indicate a very high spatial variability across the sampling sites in the Winter Campaign, and PM2.5 was slightly less heterogeneous than PM10. In the summer samples, the ordered pairs of minimum and maximum for PM10 and PM2.5 were (0.18, 0.51) and (0.21, 0.60), respectively. Based on the COD values, the spatial heterogeneity of the summer samples was slightly lower than that of the winter samples. 4.7

Pearson Correlation Coefficient (COR)

The Pearson correlation coefficient, COR, between two sampling sites j and k is defined as shown in the equation below. 1 p  ( xij  x j ) ( xij  xk ) p i 1 COR jk  1 p 1 p 2 ( xij  x j ) ( xik  xk ) 2   p i 1 p i 1 In the above equation, xij is the source contribution per sampling interval i, estimated at site j, and p is the total number of particle classes. Similarly, xik is the source contribution per sampling interval i, estimated at site k, and p is the total number of particle classes. x j and xk are the average mass in each particle class at sites j and k, respectively. The variables in the COR equation are same as defined in the COD equation. The over-bar denotes the mean of a variable over the number of sampling intervals. The Pearson correlation coefficient provides information on how well the particle class membership contributions at different sites co-vary. For example, if the estimated particle concentrations at two sites are similar, the COR approaches one. When the estimated concentrations diverge, the COR value approaches zero. Values for COR are shown in Tables A-1 through A-4. Table 15 summarizes the grand averages of the COR. The grand average COR values of the winter samples were 0.45 for PM10 and 0.41 for PM2.5. The grand average COR values of the summer samples were 0.82 for PM10 and 0.44 for PM2.5. COR values further reaffirm that the PM samples were spatially not very well correlated during the winter sampling period in comparison the summer sampling period. In addition to this, we note that although the PM10 particles were highly correlated during the summer, the PM2.5 particles were poorly correlated. Results from this work, therefore, suggest that PM2.5 samples measured in the San Joaquin Valley during this work exhibit strong heterogeneity during both winter and summer.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT In terms of seasonal variation, we note that winter samples are more heterogeneous than the summer samples. The heterogeneity is more pronounced in the PM 10 size fraction than in the PM2.5 size fraction. Table 15 – Summary of COD and COR Values in PM10 and PM2.5 Samples Grand Average of All Sites

Particle Size Group

Season

PM10 PM2.5

4.8

Minimum COD

Maximum COD

Average COD

Minimum COR

Maximum COR

Average COR

Winter

0.51

0.80

0.61

0.13

0.74

0.45

Summer

0.18

0.51

0.27

0.46

0.92

0.82

Winter

0.29

0.75

0.42

0.09

0.74

0.41

Summer

0.21

0.60

0.36

0.07

0.81

0.44

Significant Particle Class Membership Types

Table 16 and Table 17 show the first, second and third significant particle class memberships identified in the Winter Campaign and Summer Campaign PM2.5 samples. As shown in Table 16 for winter samples, the first significant particle class memberships are identified mostly as metallic traffic emissions mixed with soil/crustal materials. In the samples from Calwa, the first significant class membership contains biomass combustion markers. For the second significant particle class membership, the Clovis Neighborhood sample showed markers of gypsum and construction materials. Other neighborhoods including Bullard HS and Fresno HS had significant influences of tire and brake wear markers. Neighborhoods such as the Clovis West Neighborhood and Central HS East Campus had markers of vehicle engine emissions. Markers of metallic traffic emissions was the third significant particle class membership from the Clovis neighborhood sites. As shown in Table 17 for summer samples, landscaping and crustal materials were predominant in most sites. However, sites such as neighborhoods in Clovis, Edison and Sunnyside were abundant in carbon particles. Summer samples were loaded with carbon particles as the second significant particle class membership. The third significant class membership in summer samples were some form of landscaping-generated PM materials and carbon-rich particles. In general, the summer particles are likely derived from mechanical abrasion processes

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Table 16 – Significant Particle Class Memberships, Winter PM2.5 Samples 1st Likely Source Bakersfield Neighborhoods Bullard HS Calwa Central HS East Campus Clovis neighborhood Clovis West Neighborhood Edison HS Fairmead-Madera County Figarden Loop Fresno Garland Station Fresno HS Kettleman City-Kings County McLane Neighborhood Roosevelt HS Sunnyside Neighborhood

Metallic Traffic Emissions Metallic Traffic Emissions Biomass Combustion Metallic Traffic Emissions Biomass Combustion Biomass Combustion Metallic Traffic Emissions Metallic Traffic Emissions Metallic Traffic Emissions Metals/Crustal Metallic Traffic Emissions Soil/Crustal with Metals Metallic Traffic Emissions Metallic Traffic Emissions Metals/Crustal

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2nd Loading (pg) 145 146 140 102 198 139 139 143 164 194 215 121 156 117 131

Likely Source Crustal Metallic Traffic Emissions Metallic Traffic Emissions Vehicle Engine Emissions Construction Materials/Gypsum Vehicle Engine Emissions Metallic Traffic Emissions Construction Materials/Gypsum Crustal Metallic Traffic Emissions Metallic Traffic Emissions Mineral Dust Crustal Construction Materials/Gypsum Construction Materials/Gypsum

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3rd Loading (pg) 114 144 135 76 186 101 136 115 150 148 134 116 129 114 119

Likely Source Metals/Crustal Construction Materials/Gypsum Metallic Traffic Emissions Crustal Metallic Traffic Emissions Crustal Mineral Dust Vehicle Engine Emissions Metallic Traffic Emissions Mineral Dust Crustal Biomass Combustion Biomass Combustion Soil/Crustal with Metals Tire and Brake Wear

Loading (pg) 114 140 122 73 170 97 116 112 141 122 133 115 121 107 115

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Table 17 – Significant Particle Class Memberships, Summer PM2.5 Samples 1st Likely Source Bakersfield Neighborhoods Bullard HS Calwa Clovis neighborhood Clovis West Neighborhood Edison HS Fairmead-Madera County Figarden Loop Fresno HS Garland Monitoring Station Kettleman City-Kings County McLane Neighborhood Roosevelt HS Sunnyside Neighborhood

Combustion Sources Re-suspended Dust Crustal Re-Suspended Dust Mineral/Crustal Re-suspended Dust Industrial Combustion Sources Re-suspended Dust Combustion Sources Combustion Sources Mineral/Crustal Re-suspended Dust Re-suspended Dust

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2nd Loading (pg) 108 102 79 81 93 93 86 112 119 151 128 150 102 173

Likely Source Crustal Materials Industrial Metals Mineral/Crustal Mineral/Crustal Crustal Crustal Combustion Sources Re-Suspended Dust Combustion Sources Crustal Crustal Materials Combustion Sources Combustion Sources Combustion Sources

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3rd Loading (pg) 91 84 78 78 81 80 85 63 113 122 100 107 89 168

Likely Source Crustal Combustion Sources Re-suspended Dust Landscaping Activity Combustion Sources Combustion Sources Industrial Metals Industrial Industrial Industrial Metals Re-suspended Dust Crustal Re-Suspended Dust Re-Suspended Dust

Loading (pg) 90 81 76 75 68 77 79 51 98 109 78 98 82 133

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Partitioning of Semi-Volatile Organic Compounds

In order to measure the phase partitioning of organic compounds including levoglucosan and SVOC, a high volume PM2.5 sampler (Tisch Model TE-1000) equipped with 90 mm quartz filters and Polyurethane Foam (PUF) plugs was operated during the Winter Campaign. Filter and PUF samples were taken daily over the four-week period, but to control analytical costs, daily samples were combined and analyzed to represent the six multi-day periods shown in Figure 12. Figure 12 shows the partitioning of alkanes, polycyclic aromatic hydrocarbons (PAH), organic acids and levoglucosan. About 74% of the alkanes, 63% of the PAH, 45% of the hopanes and cholestanes, 56% of the organic acids, and 10% of the levoglucosan were in the vapor phase. The volume-weighted mass concentrations of SVOC were 178.5 nanograms per cubic meter (ng/m3) in the vapor phase (captured in the PUF plug samples), and 183.6 ng/m3 in the solid phase (captured on the filter samples). The average mass fraction of SVOC measured in the PUF was 49% in the winter high-volume air samples. This indicates that there was substantial loss of SVOC species from the passive samplers.

Figure 12 –SVOC in Particulate and Vapor Phase Samples

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4.10

Source Profile Characterization and Inter-Site Variability Analysis

The wintertime particle classes shown in Table 7 and Table 11 were further resolved on a site-by-site basis and plotted to study the inter-site variability and intra-urban variability. Since there are multiple sites in each neighborhood, inspection of inter-site variability is more intuitive if the particle mass membership is averaged for the sites within each site group. In this analysis, site groups generally coincide with the neighborhoods shown in Table 1. The exceptions are that the “Bakersfield Neighborhoods” site group includes all four Bakersfield A and B samples, and the “SJVAPCD Stations” site group is comprised of all samples from regulatory monitoring stations except for the Fresno Garland Station. Figure A-1 (in Section 7.0) shows the average winter PM2.5 mass in each site group and in each of the particle classes identified by the ART 2A algorithm. Figure A-2 shows analogous groupings for the PM10 samples. The error bars in these plots are one standard deviation, and indicate the variability in the mass loadings across the multiple sites within each site group. Comparison of PM10 and PM2.5 class memberships indicate that PM2.5 has very few classes that suggest road dust and crustal materials. Most of the class memberships appear to be derived from combustion of fossil fuel and biomass, and from brake and tire wear. Based on the error bars, both PM2.5 and PM10 show spatial variability within each site group across several particle class memberships. For PM2.5, the highest mass loading was present in the Clovis neighborhood, and lowest mass loading was present in the Central HS East Campus neighborhood. The Bakersfield Neighborhoods site group had the highest PM10 loading, while the Clovis West neighborhood had the lowest PM10 loading. Based on the particle class memberships and the particle size distribution plots for PM10, the Bakersfield neighborhoods had higher impacts from soil/crustal and mineral dust particles. The particle size distribution results, Figures 6 through 9, also suggest that the particle classes that had the highest mass loadings also had higher particles counts in the coarser fraction of PM10. Among the 16 site groups, the Clovis HS neighborhood had the highest mass loading of carbon, phosphorous, sulfur, chlorine, manganese, and zinc particle groups. These elemental markers suggest the presence of engine and brake wear particles, and higher impacts from mobile sources, particularly in the Clovis neighborhood, followed by the Figarden Loop and Bullard HS neighborhoods. From this analysis, we also conclude that in samples collected from a number of sites, there are too many particle clusters for straightforward characterization. Although, the elemental speciation of individual particle clusters are valuable, identification of source profiles in grouped factors provides better insight into the sources of particles and PM exposure. Therefore, PMF was selected to provide more insight about potential sources rather than analyzing individual particle class memberships. The PMF analysis is discussed below.

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Positive Matrix Factorization

The fundamental principle of source/receptor relationships is that mass conservation can be assumed and a mass balance analysis can be used to identify and apportion sources of airborne particulate matter in the atmosphere. This methodology has generally been referred to within the air pollution research community as receptor modeling (Hopke, 1991; Paatero and Tapper, 1994). The PMF model conducts the factor analysis explicitly, as a least-squares solution, and the most important goal of PMF is obtain physically realistic solutions. The goal of the present PMF analysis is to find the true dimensionality of the sources (that is the numbers of sources) and the relationship of the particle class memberships, deduced from the classification conducted using ART 2A, to the actual ambient particulate matter sources. Figure 13 shows the correlation between the measured and PMFpredicted PM2.5 concentration for both the summer and winter samples: the PMF model results fit the data well, with a correlation coefficient of 0.988 for winter samples and 0.97 for summer. This indicates that the source categories resolved by PMF - nine for winter and eight for summer - sufficiently account for the measured PM2.5.

Figure 13 – Correlation between Measured and PMF-Predicted PM2.5 Concentrations 4.11.1 Winter Campaign The PMF source profiles for the winter campaign are shown in Figure 14; a total of nine factors (source categories) were resolved. The first factor was identified as mineral dust, followed by crustal as the second source, where the crustal factor was somewhat rich in calcium particles. Combustion of engine oil typically leads to the release of zinc and nickel. Therefore, presence of nickel, zinc and carbon in the particle clusters in this factor suggests likely influence of emissions of engine oil burning from the tailpipe. The fourth factor had nominal contributions from all clusters suggesting it could be re-suspended road dust. The fifth factor was identified as metals followed by brake wear as the sixth factor. The seventh factor was identified as another source of mineral dust. The presence of calcium and sulfur in the eighth factor suggests an influence of gypsum from

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT construction activities. The ninth factor was identified as possible sources of cooking and wood burning because of the presence of typical markers such as potassium, sodium and sulfur. The PMF analysis was actually performed for PM1 (smaller than 1 µm) and PM2.5-1 (between 1 and 2.5 µm) to be able to clearly delineate the combustion particles which typically have a PSD mode less than 1 µm. These size fractions were estimated based on single-particle size data from the CCSEM analysis. In this report, total PM2.5 mass concentration and the percentage contribution of each factor in each neighborhood are shown in Figure 15, while the PM2.5-1 and PM1 size fractions are shown separately in Figure A-3 in Section 7.0. The average PM1 was mostly around 1 µg/m3, except in neighborhoods such as Madera City, Corcoran, Bakersfield A and Bakersfield Muni Station. PM2.5-1 was about 1 µg/m3 in most neighborhoods. However, Fairmead, Fresno HS, Sunnyside HS, Calwa and Bakersfield B had concentrations greater than 1 µg/m3. One of the major contributors to the PM1 is re-suspended road dust (also referred to as respirable road dust). However, other source contributions were equally important. For example, engine oil burning was predominant on busy streets in the Roosevelt, Central HS East Campus, Clovis West and Sunnyside neighborhoods. The mineral dust contribution was greater in PM2.5-1, especially at the Bakersfield Muni Station, Corcoran Station, and Clovis Station. The contribution of construction/gypsum and cooking/biomass seemed to vary highly between each neighborhood. Cooking and biomass particles were highest in the McLane, Central HS East, Fresno HS, and Figarden Loop neighborhoods in the winter samples. In general, PM2.5 followed the sources identified for PM2.5-1 because greater PM mass was present in this larger fraction of PM2.5. However, sources such as engine oil burning and re-suspended road dust had higher mass contributions in the PM1 size group than in the PM2.5-1 size group. This result emphasizes the variability in exposure with respect to particle size from sources.

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Cooking/Wood Combustion

Figure 14 – PM2.5 Source Profiles Resolved by PMF, Winter Campaign

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Cooking/Wood Comb.

Figure 15 – Total Mass Concentrations and Source Contributions in Each Neighborhood, Winter Campaign * PM concentration on the right axis is based on actual mass fractions collected on the passive samplers.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT 4.11.2 Summer Campaign The source profiles obtained from PMF for the Summer Campaign are shown in Figure 16. Figure 17 gives the percentage contribution of each source in the various sampled neighborhoods along with the measured PM2.5 concentration. Figure A-4 shows the concentrations and factors for the PM2.5-1 and PM1 size fractions. In the summer, PM mass was dominated by the PM2.5-1 size group. However, PMF results indicated higher mass contributions in the smaller particle fraction for sources such as carbonaceous soot and landscaping activity. The total mass was lower in the PM 1 size group than in the PM2.5-1 size group. Moreover, this trend was more pronounced in the summer samples than in the winter samples. Similar to the winter samples, samples collected during the summer had a higher concentration of PM2.5-1 in comparison to the PM1 size fraction. The PM concentrations at the Turlock Station were significantly higher when compared to other neighborhoods, and the sample is likely contaminated. It is noted that concentration generated by any source category in a neighborhood can be calculated as the product of the total mass concentration and percent contribution of the source. The first factor, comprised of major loadings of C-P-Cr-Mn-Ni and Na-Mg-S-Cl-Ka-Ca-CrZn particle class memberships, is identified here as landscaping activity. Elements other than carbon and chromium are main constituents of fertilizers. Vegetative detritus from can contribute towards the carbon, motor oil and diesel from landscaping equipment can contribute towards zinc and sulfur, and the other metals reflect the engine wear of landscaping equipment. The second factor with its high loading from carbon and a few metals was identified as carbonaceous soot, which is generated from the incomplete combustion of carbon-containing fuels. Carbonaceous soot was present widely in all the neighborhoods. The third factor was identified as diesel exhaust; major contributions of carbon and sulfur would be released during combustion. Contributions from diesel exhaust are almost evenly distributed throughout all the neighborhoods, with a slight PM1 increase at the Turlock Station. Diesel exhaust was more important in neighborhoods such as Bakersfield, Calwa, Clovis, and Fairmead in PM1, and in Figarden Loop, Corcoran and Bakersfield in PM2.5-1. Based on the source profiles in Figure 16, diesel exhaust particles may be mixed together with other road dust particles. The fourth factor contained particle clusters of mainly metals mixed with minerals, and was therefore was classified as mineral dust. Mineral dust was also present somewhat uniformly in all the neighborhoods with an average percent contribution of 4%. The fifth factor, comprised of titanium, copper, and iron which could possibly be generated from vehicle brake wear, were predominant in Clovis, Bakersfield and Kettleman City in the PM1 size range. Brake wear of vehicles is suggested to amount to about 20% of total traffic emissions (Gasser et al., 2009). The presence of most minerals along with silicon and calcium led to the possibility that the sixth factor was re-suspended road dust, which is road dust that becomes airborne due to vehicle movement, or landscaping, e.g., leaf blowers. Re-suspended road dust was important in Clovis, Bakersfield, Turlock and

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Madera. The seventh factor consisted of clusters with silicon, calcium, sodium, chlorine, manganese, and potassium, suggesting that this factor is crustal in origin. The industrial metals sources (the eighth factor) were identified by major contributions from a cluster which consisted of aluminum, titanium, chromium, manganese, iron, copper, and zinc, and minor contributions from carbon; these metals are used in manufacturing processes as alloys or by themselves, and arc welding processes are accompanied by mild carbon emissions. This source is identified as a small source affecting several of the neighborhoods in the sampling domain but higher in Clovis, Madera, Kettleman City and the McLane neighborhood in Fresno.

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Industrial Metals

Figure 16 – PM2.5 Source Profiles Resolved by PMF, Summer Campaign

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Carbonaceous Soot

Figure 17 – Total Mass Concentrations and Source Contributions in Each Neighborhood, Summer Campaign * PM concentration on the right axis is based on actual mass fractions collected on the passive samplers.

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5.0

SUMMARY AND CONCLUSIONS

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT SUMMARY AND CONCLUSIONS UNC passive samplers were used to explore the spatial variability of particulate matter concentrations and composition in Fresno and other communities in the San Joaquin Valley during the winter and summer of 2013. This work demonstrates that passive sampling coupled with CCSEM, ART 2A, and PMF analysis is a powerful tool to assess the spatial and temporal variability of ambient coarse particulate matter in an urban/rural area. Considerable heterogeneity in both composition and concentration were observed between adjacent sites as indicated by composition profiles in each site group and the two seasons, based on the calculated coefficient of divergence values. In general, combustion particles including engine oil and biomass combustion, landscaping activity, and brake wear and tire wear were the major sources of the fine particles during both seasons. Mineral dust and crustal materials were major sources in the PM10 particle size group. Particle class memberships obtained from ART 2A were determined based on the predominant elements present in each of the particle cluster/group. The total mass of elements in each particle class was calculated by summing the masses in each particle group. In order to identify the type of particles associated with each of the particle class files identified by ART 2A, the average mass of each of the elements in a cluster was compared to the global elemental average of all particles. An element was considered to be a defining element of a class if it has higher average value than the global elemental average. As a result, the mass of particles in a given particle class is a quantitative measure of particle composition. Clustering of fine particles yielded 23 different fine particle classes for samples in the winter campaign and 13 fine particle class memberships for the samples collected during summer. Particles from the winter contained more particle clusters associated with carbonaceous species than PM2.5 samples collected during the summer. The classification of single particle data using ART 2A alone provided information with too many particle class memberships. Therefore, the particle class memberships needed to undergo additional steps using factor analysis to deduce the true dimensionality of the sources. To this end, particle classes obtained from the ART 2A processing were further refined using PMF in order to obtain source profiles and contributions. The source profiles obtained from the PMF analysis showed that not only is the mass of PM2.5 widely heterogeneous, the exposure to particle types are also characterized by a wide variety of sources in each of the neighborhoods. The sources, in general, during the summer include: landscaping and other activities/equipment that entrain mineral dust, crustal materials, and carbonaceous soot; vehicular exhaust emissions; and manufacturing activities. Source contributions in winter PM samples included biomass combustion and cooking, vehicular emissions and re-suspended road dust. It is important to note that passive samplers do not capture secondary particles from long range transport, however, they provide a measure of particle exposures from local sources on a community level scale.

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In conclusion, the study provided a wealth of data that can be very useful to policy makers and air quality professionals, as the results show the contributions of a variety of source types on a neighborhood by neighborhood basis. As seen in this work, exposure of a specific source type is not always uniform. Sources such as mineral dust, and landscaping activities that are performed somewhat consistently throughout an urban area remain fairly homogeneous, while industrial sources and vehicular emissions are not uniform. The variability of PM source profiles and contributions at a neighborhood level accounts for the heterogeneity of exposure and, therefore, can be employed to assess and reduce the health risks associated with specific types of exposure.

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6.0

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT REFERENCES Bell, M.L., Ebisu, K. and Peng, R.D. Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research. J Expos Sci Environ Epidemiol 21, 372-384. Carpenter, G.A., Grosberg, S., Rosen, D. B. ART 2-A: An Adaptive Resonance Algorithm Category Learning and Recognition. Neural Networks 1991. 4: p. pp 493-5. Gasser, M.,, Riediker, M., Mueller, L., Perrenoud, A., Blank, F., Gehr, P., and RothenRutishauser, B. Toxic effects of brake wear particles on epithelial lung cells in vitro. Particle and Fibre Toxicology, 2009, 6: 30. Hopke, P.K., Song, X.H. Classification of single particles by neural networks based on the computer-controlled scanning electron microscopy data. Analytica Chimica Acta, 1997. 348: 375-388. Hopke, P.K., ed. (1991) Receptor Modeling for Air Quality Management, Elsevier Science, Amsterdam. Kim. D., Hopke, P.K., Massart. D.L., Kaufman. L., Cassucio. G. S. Multivariate Analysis of CCSEM Auto Emission Data. The Science of the Total Environment, 1987. 59: p. 141-155. Lagudu, U. R. K., S. Raja, P. K. Hopke, D. C. Chalupa, M. J. Utell, G. Casuccio, T. L. Lersch and R. R. West. Heterogeneity of Coarse Particles in an Urban Area. Environmental Science & Technology, 2011. 45(8): 3288-3296. Mamane, Y., Willis, R., Conner, T. Evaluation of Computer-Controlled Scanning Electron Microscopy Applied to an Ambient Urban Aerosol Sample. Aerosol Science and Technology, 2000. 34: p. 97-107. Moreno, T., Karanasiou, A., Amato, F., Lucarelli, F., Nava, S., Calzolai, G., Chiari, M., Coz, E., Artíñano, B., Lumbreras, J., Borge, R., Boldo, E., Linares, C., Alastuey, A., Querol, X., Gibbons, W. Daily and hourly sourcing of metallic and mineral dust in urban air contaminated by traffic and coal-burning emissions. Atmospheric Environment, Volume 68, April 2013, Pages 33-44. Newman, E. I. Phosphorous inputs to terrestrial ecosystems. J. Ecol. 1995, 83, 713–726. Ott, D.K., Peters, T.M., A Shelter to protect a Passive Sampler for Coarse Particulate Matter, PM 10-2.5. Aerosol Science and Technology, 2008. 42: p. 299-309. Paatero, P. and U. Tapper (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values, Environmetrics 5:111-126. Schauer, J. J., Lough, G. C., Shafer, M. M., Christensen, W. F., Arndt, M. F., DeMinter, J. T., Park, J. S. Characterization of Metals Emitted from Motor Vehicles; HEI Report 133; Health Effects Institute, 2006. Wagner, J., Leith, D., Passive Aerosol Sampler. I: Principle of Operation. Aerosol Science and Technology, 2001a. 34 (2): p. 186-192.

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Wagner, J., Leith, D., Passive Aerosol Sampler. II: Wind Tunnel Experiments. Aerosol Science and Technology 2001b. 34 (2): p. 193-201. Wagner, J., Leith, D., Field Tests of a Passive Aerosol Sampler. Journal of Aerosol Science, 2001c. 32: p. 33-48. Wagner, J., Naik-Patel, K., Wall, S., Harnly, M., Measurement of ambient particulate matter concentrations and particle types near agricultural burns using electron microscopy and passive samplers, Atmospheric Environment, Volume 54, July 2012, Pages 260-271.

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7.0

SUPPLEMENTAL TABLES & FIGURES

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SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT SUPPLEMENTAL TABLES & FIGURES Table A-1 - COD and COR for PM2.5 Samples, Winter Campaign Neighborhood

Site

Bakersfield A Bakersfield A Bakersfield A Bakersfield B Bakersfield B Bakersfield Calif Ave Stn Bakersfield Muni Stn. Bullard HS Bullard HS Calwa Calwa Calwa Central HS East Campus Central HS East Campus Clovis HS Clovis HS Clovis Station Clovis West HS Clovis West HS Corcoran Stn Edison HS Edison HS Edison HS Fairmead Fairmead Figarden Loop Figarden Loop Fresno Garland Station Fresno HS Fresno HS Kettleman City Kettleman City Kettleman City Madera City Stn McLane neighborhood McLane neighborhood McLane neighborhood Modesto Stn Roosevelt HS Roosevelt HS Roosevelt HS Roosevelt HS Sunnyside HS Sunnyside HS Turlock Stn

Niles @ Palm Niles @ Palm Williams @ Flower Bayshore @ Waler Noriega @ Wallawalla California @ Stockdale Watts Drive @ So Union Arthur @ Scott Shaw @ Harrison 10th north of Burns Barton @ Hoxie Barton @ Hoxie Blythe south of Princeton Cecila south of Cornell Bullard @Renn Stanford @ Escalon Villa @ Bullard Everglade @ Lester Fuller @ Loyola Patterson @ Hale California @ Lee Merced @ Strother Merced @ Strother Fairmead @ Sinclair Yates @ Elm Bullard @ Cecilia Stuart @ Lodi Floradora @ Del Mar Olive @ Delphia Community Services Dist Fire Station parking lot Fire Station parking lot Rd 28 @ Ave 14 Ashlan @ Angus Holland @ Angus Holland @ Angus 14th @ H Kings Canyon @ Recreation Kings Canyon @ Recreation Woodrow @ Huntington Woodrow @ Huntington Clovis @ Church Laurite @ Phillip S Minaret @ Cottonwood

455-006 PM Variability Final Report rev 3

Sample ID 3651 3652 3662 3660 3661 3666 3665 3616 3617 3604 3655 3656 3624 3623 3613 3612 3671 3605 3606 3670 3603 3601 3602 3664 3663 3618 3619 3625 3620 3621 3622 3657 3658 3669 3609 3607 3608 3667 3653 3654 3614 3615 3611 3610 3668

72

Min COD 0.35 0.26 0.24 0.27 0.37 0.28 0.56 0.25 0.3 0.28 0.32 0.25 0.26 0.37 0.22 0.22 0.24 0.28 0.3 0.34 0.28 0.25 Nan 0.28 0.26 0.25 Nan 0.29 0.22 0.15 0.15 0.33 0.27 0.25 0.22 Nan 0.29 Nan 0.31 0.32 0.22 0.6 0.31 0.3 0.23

Max COD 0.69 0.75 0.8 0.71 0.71 0.7 0.82 0.76 0.77 0.79 0.66 0.75 0.7 0.69 0.81 0.82 0.78 0.76 0.67 0.66 0.7 0.79 Nan 0.69 0.78 0.76 Nan 0.76 0.79 0.79 0.8 0.66 0.78 0.73 0.8 Nan 0.71 Nan 0.67 0.7 0.82 0.82 0.72 0.74 0.74

Avg COD 0.48 0.36 0.47 0.39 0.47 0.38 0.71 0.4 0.41 0.45 0.47 0.36 0.38 0.45 0.39 0.4 0.41 0.38 0.43 0.46 0.39 0.39 Nan 0.4 0.38 0.38 Nan 0.41 0.36 0.39 0.39 0.42 0.41 0.37 0.38 Nan 0.41 Nan 0.43 0.46 0.41 0.73 0.41 0.41 0.35

Min COR 0.06 0.15 0.07 0 0 0.17 0.05 0.08 0 0.21 0.02 0.23 0.05 0.14 0.21 0.12 0.08 0.22 0.06 0.13 0.12 0.02 Nan 0.04 0.06 0.13 Nan 0.07 0.1 0.05 0.09 0.04 0.11 0.1 0.09 Nan 0.02 Nan 0.02 0.06 0.1 0.02 0.14 0.14 0.18

Max COR 0.64 0.69 0.83 0.76 0.7 0.81 0.6 0.76 0.79 0.85 0.59 0.67 0.68 0.82 0.77 0.68 0.82 0.81 0.65 0.77 0.75 0.82 Nan 0.71 0.69 0.71 Nan 0.71 0.74 0.82 0.82 0.73 0.7 0.79 0.81 Nan 0.81 Nan 0.66 0.7 0.7 0.71 0.76 0.7 0.85

Avg COR 0.32 0.42 0.37 0.39 0.25 0.46 0.33 0.41 0.37 0.43 0.3 0.45 0.42 0.42 0.52 0.48 0.4 0.45 0.38 0.37 0.47 0.45 Nan 0.4 0.4 0.42 Nan 0.38 0.45 0.41 0.38 0.42 0.35 0.4 0.49 Nan 0.4 Nan 0.36 0.46 0.44 0.33 0.43 0.42 0.5

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table A-2 - COD and COR for PM2.5 Samples, Summer Campaign Neighborhood

Site

Bakersfield A Bakersfield A Bakersfield B Bakersfield B Bakersfield Calif Ave Stn Bakersfield Calif Ave Stn Bakersfield Muni Stn. Bullard HS Bullard HS Calwa Calwa Central HS East Campus Central HS East Campus Central HS East Campus Clovis HS Clovis HS Clovis Station Clovis Station Clovis West HS Clovis West HS Corcoran Stn Edison HS Edison HS Edison HS Fairmead Fairmead Figarden Loop Figarden Loop Fresno Downtown Fresno Garland Station Fresno HS Fresno HS Kettleman City Kettleman City Madera City Stn Madera City Stn McLane HS McLane HS McLane HS Roosevelt HS Roosevelt HS Roosevelt HS Sunnyside HS Sunnyside HS Turlock Stn

Niles @ Palm Williams @ Flower Bayshore @ Waler Noriega @ Wallawalla California @ Stockdale California @ Stockdale Watts Drive @ So Union Arthur @ Scott Shaw @ Harrison 10th north of Burns Barton @ Hoxie Blythe south of Princeton Blythe south of Princeton Cecila south of Cornell Bullard @Renn Stanford @ Escalon Villa @ Bullard Villa @ Bullard Everglade @ Lester Fuller @ Loyola Patterson @ Hale California @ Lee Merced @ Strother Merced @ Strother Fairmead @ Sinclair Yates @ Elm Bullard @ Cecilia Stuart @ Lodi Tulare @ H Street Garland @ First Floradora @ Del Mar Olive @ Delphia Community Services Dist Fire Station parking lot Rd 28 @ Ave 14 Rd 28 @ Ave 14 Ashlan @ Angus Holland @ Angus Holland @ Angus Kings Canyon @ Recreation Woodrow @ Huntington Woodrow @ Huntington Clovis @ Church Laurite @ Phillip S Minaret @ Cottonwood

455-006 PM Variability Final Report rev 3

Sample ID 3864 3872 3873 3876 3853 3854 3859 3879 3866 3889 3893 3857 3858 3865 3867 3870 3851 3852 3883 3882 3862 3880 3884 3885 3878 3875 3868 3869 3860 3863 3888 3892 3874 3877 3855 3856 3895 3890 3891 3881 3886 3887 3894 3871 3861

73

Min COD 0.27 0.17 0.2 0.3 0.38 0.17 0.23 0.19 0.18 0.17 0.2 0.21 0.18 0.28 Nan 0.19 0.4 0.18 0.16 0.24 0.21 0.23 0.22 0.17 0.22 0.17 0.2 0.2 0.19 0.15 0.2 0.34 0.19 0.14 0.22 0.16 0.15 0.2 0.18 0.23 0.17 0.2 0.22 0.25 0.14

Max COD 0.64 0.56 0.58 0.61 0.66 0.53 0.64 0.55 0.62 0.52 0.59 0.55 0.56 0.57 Nan 0.59 0.73 0.58 0.57 0.53 0.57 0.69 0.57 0.66 0.57 0.57 0.56 0.56 0.59 0.61 0.67 0.58 0.59 0.61 0.55 0.57 0.63 0.64 0.63 0.65 0.66 0.64 0.73 0.56 0.64

Avg COD 0.38 0.31 0.32 0.4 0.5 0.32 0.37 0.33 0.32 0.32 0.34 0.32 0.34 0.4 Nan 0.3 0.58 0.33 0.32 0.35 0.37 0.38 0.32 0.46 0.31 0.29 0.35 0.4 0.32 0.33 0.37 0.43 0.31 0.31 0.35 0.34 0.33 0.35 0.35 0.35 0.48 0.36 0.45 0.39 0.33

Min COR 0.06 0.18 0.07 0.02 0 0.22 0.01 0.08 0.16 0.02 0.07 0.07 0.24 0.02 Nan 0.14 0.02 0.05 0 0 0.04 0.1 0.15 0.06 0.08 0.17 0.04 0 0.07 0.07 0.05 0.13 0.09 0.13 0 0 0.14 0.06 0.13 0.01 0.02 0.07 0.08 0.01 0.09

Max COR 0.65 0.9 0.82 0.84 0.57 0.9 0.78 0.64 0.85 0.88 0.78 0.95 0.87 0.81 Nan 0.68 0.75 0.83 0.9 0.71 0.84 0.78 0.64 0.82 0.85 0.88 0.92 0.92 0.81 0.8 0.85 0.8 0.84 0.86 0.79 0.9 0.81 0.75 0.77 0.72 0.82 0.89 0.95 0.68 0.8

Avg COR 0.37 0.52 0.46 0.31 0.28 0.54 0.43 0.37 0.47 0.53 0.45 0.46 0.57 0.36 Nan 0.46 0.35 0.45 0.46 0.34 0.44 0.46 0.37 0.39 0.43 0.53 0.47 0.44 0.41 0.51 0.5 0.41 0.4 0.53 0.4 0.37 0.52 0.44 0.51 0.33 0.36 0.4 0.53 0.34 0.51

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table A-3 - COD and COR for PM10 Samples, Winter Campaign Neighborhood Bakersfield A Bakersfield A Bakersfield A Bakersfield B Bakersfield B Bakersfield Calif Ave Stn Bakersfield Muni Stn. Bullard HS Bullard HS Calwa Calwa Calwa Central HS East Campus Central HS East Campus Clovis HS Clovis HS Clovis Station Clovis West HS Clovis West HS Corcoran Stn Edison HS Edison HS Edison HS Fairmead Fairmead Figarden Loop Figarden Loop Fresno Garland Station Fresno HS Fresno HS Kettleman City Kettleman City Kettleman City Madera City Stn McLane neighborhood McLane neighborhood McLane neighborhood Modesto Stn Roosevelt HS Roosevelt HS Roosevelt HS Roosevelt HS Sunnyside HS Sunnyside HS Turlock Stn

Site Niles @ Palm Niles @ Palm Williams @ Flower Bayshore @ Waler Noriega @ Wallawalla California @ Stockdale Watts Drive @ So Union Arthur @ Scott Shaw @ Harrison 10th north of Burns Barton @ Hoxie Barton @ Hoxie Blythe south of Princeton Cecila south of Cornell Bullard @Renn Stanford @ Escalon Villa @ Bullard Everglade @ Lester Fuller @ Loyola Patterson @ Hale California @ Lee Merced @ Strother Merced @ Strother Fairmead @ Sinclair Yates @ Elm Bullard @ Cecilia Stuart @ Lodi Floradora @ Del Mar Olive @ Delphia Community Services Dist Fire Station parking lot Fire Station parking lot Rd 28 @ Ave 14 Ashlan @ Angus Holland @ Angus Holland @ Angus 14th @ H Kings Canyon @ Recreation Kings Canyon @ Recreation Woodrow @ Huntington Woodrow @ Huntington Clovis @ Church Laurite @ Phillip S Minaret @ Cottonwood

455-006 PM Variability Final Report rev 3

Sample ID 3651 3652 3662 3660 3661 3666 3665 3616 3617 3604 3655 3656 3624 3623 3613 3612 3671 3605 3606 3670 3603 3601 3602 3664 3663 3618 3619 3625 3620 3621 3622 3657 3658 3669 3609 3607 3608 3667 3653 3654 3614 3615 3611 3610 3668

74

Min COD 0.47 0.5 0.55 0.46 0.63 0.46 0.7 0.56 0.44 0.52 0.47 0.48 0.56 0.52 0.49 0.44 0.52 0.56 0.55 0.65 0.51 0.44 Nan 0.53 0.48 0.49 Nan 0.48 0.44 0.47 0.51 0.5 0.53 0.53 0.44 Nan 0.55 Nan 0.47 0.48 0.44 0.63 0.51 0.47 0.54

Max COD 0.83 0.84 0.77 0.85 0.82 0.82 0.85 0.81 0.82 0.76 0.8 0.85 0.75 0.76 0.83 0.77 0.8 0.76 0.75 0.79 0.76 0.82 Nan 0.78 0.79 0.79 Nan 0.81 0.81 0.81 0.82 0.82 0.81 0.78 0.81 Nan 0.78 Nan 0.82 0.84 0.81 0.79 0.8 0.8 0.78

Avg COD 0.6 0.58 0.62 0.59 0.69 0.57 0.8 0.63 0.57 0.61 0.59 0.58 0.62 0.61 0.59 0.56 0.59 0.63 0.63 0.72 0.62 0.57 Nan 0.6 0.6 0.57 Nan 0.59 0.55 0.58 0.6 0.6 0.64 0.62 0.55 Nan 0.62 Nan 0.61 0.59 0.55 0.69 0.61 0.58 0.6

Min COR 0.2 0.26 0.04 0.21 0.03 0.19 0.07 0.1 0.03 0.03 0.19 0.17 0.08 0.15 0.03 0.18 0.22 0.07 0.16 0.19 0.06 0.22 Nan 0.1 0.14 0.19 Nan 0.06 0.29 0.11 0.08 0.3 0.12 0.05 0.18 Nan 0.15 Nan 0.04 0.1 0.17 0.05 0.05 0.04 0.13

Max COR 0.81 0.79 0.51 0.78 0.47 0.79 0.67 0.7 0.6 0.74 0.78 0.85 0.72 0.81 0.54 0.68 0.8 0.85 0.72 0.78 0.79 0.8 Nan 0.74 0.68 0.81 Nan 0.8 0.75 0.77 0.77 0.78 0.77 0.68 0.69 Nan 0.54 Nan 0.85 0.84 0.78 0.61 0.8 0.85 0.83

Avg COR 0.55 0.56 0.22 0.5 0.22 0.55 0.39 0.42 0.33 0.37 0.5 0.49 0.45 0.56 0.22 0.47 0.53 0.47 0.44 0.42 0.47 0.53 Nan 0.39 0.43 0.51 Nan 0.44 0.53 0.44 0.44 0.58 0.43 0.43 0.5 Nan 0.38 Nan 0.44 0.51 0.51 0.4 0.44 0.38 0.48

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT Table A-4 - COD and COR for PM10 Samples, Summer Campaign Neighborhood

Site

Bakersfield A Bakersfield A Bakersfield B Bakersfield B Bakersfield Calif Ave Stn Bakersfield Calif Ave Stn Bakersfield Muni Stn. Bullard HS Bullard HS Calwa Calwa Central HS East Campus Central HS East Campus Central HS East Campus Clovis HS Clovis HS Clovis Station Clovis Station Clovis West HS Clovis West HS Corcoran Stn Edison HS Edison HS Edison HS Fairmead Fairmead Figarden Loop Figarden Loop Fresno Downtown Fresno Garland Station Fresno HS Fresno HS Kettleman City Kettleman City Madera City Stn Madera City Stn McLane HS McLane HS McLane HS Roosevelt HS Roosevelt HS Roosevelt HS Sunnyside HS Sunnyside HS Turlock Stn

Niles @ Palm Williams @ Flower Bayshore @ Waler Noriega @ Wallawalla California @ Stockdale California @ Stockdale Watts Drive @ So Union Arthur @ Scott Shaw @ Harrison 10th north of Burns Barton @ Hoxie Blythe south of Princeton Blythe south of Princeton Cecila south of Cornell Bullard @Renn Stanford @ Escalon Villa @ Bullard Villa @ Bullard Everglade @ Lester Fuller @ Loyola Patterson @ Hale California @ Lee Merced @ Strother Merced @ Strother Fairmead @ Sinclair Yates @ Elm Bullard @ Cecilia Stuart @ Lodi Tulare @ H Street Garland @ First Floradora @ Del Mar Olive @ Delphia Community Services Dist Fire Station parking lot Rd 28 @ Ave 14 Rd 28 @ Ave 14 Ashlan @ Angus Holland @ Angus Holland @ Angus Kings Canyon @ Recreation Woodrow @ Huntington Woodrow @ Huntington Clovis @ Church Laurite @ Phillip S Minaret @ Cottonwood

455-006 PM Variability Final Report rev 3

Sample ID 3864 3872 3873 3876 3853 3854 3859 3879 3866 3889 3893 3857 3858 3865 3867 3870 3851 3852 3883 3882 3862 3880 3884 3885 3878 3875 3868 3869 3860 3863 3888 3892 3874 3877 3855 3856 3895 3890 3891 3881 3886 3887 3894 3871 3861

75

Min COD 0.23 0.16 0.13 0.22 0.28 0.14 0.19 0.16 0.19 0.17 0.12 0.12 0.16 0.15 Nan 0.14 0.25 0.14 0.16 0.18 0.13 0.17 0.19 0.2 0.29 0.13 0.13 0.15 0.15 0.15 0.17 0.26 0.15 0.22 0.21 0.15 0.15 0.12 0.14 0.12 0.24 0.14 0.23 0.16 0.4

Max COD 0.46 0.55 0.54 0.5 0.43 0.54 0.56 0.52 0.5 0.5 0.55 0.55 0.48 0.51 Nan 0.55 0.42 0.54 0.5 0.54 0.54 0.43 0.46 0.49 0.46 0.55 0.53 0.51 0.56 0.54 0.46 0.45 0.55 0.51 0.44 0.54 0.52 0.56 0.53 0.54 0.42 0.53 0.46 0.53 0.56

Avg COD 0.29 0.25 0.23 0.29 0.38 0.24 0.26 0.24 0.25 0.24 0.23 0.23 0.25 0.24 Nan 0.23 0.34 0.23 0.23 0.27 0.24 0.29 0.26 0.26 0.33 0.25 0.23 0.24 0.25 0.24 0.27 0.34 0.24 0.32 0.28 0.24 0.23 0.23 0.23 0.22 0.31 0.24 0.3 0.25 0.51

Min COR 0.36 0.36 0.47 0.35 0.54 0.43 0.39 0.49 0.44 0.52 0.43 0.49 0.52 0.47 Nan 0.47 0.45 0.46 0.46 0.48 0.51 0.5 0.52 0.37 0.47 0.47 0.47 0.45 0.45 0.47 0.48 0.42 0.48 0.33 0.46 0.39 0.5 0.49 0.47 0.53 0.43 0.46 0.53 0.51 0.33

Max COR 0.95 0.93 0.94 0.91 0.9 0.94 0.94 0.95 0.87 0.92 0.94 0.95 0.95 0.93 Nan 0.93 0.91 0.94 0.95 0.9 0.94 0.92 0.94 0.91 0.8 0.95 0.94 0.95 0.95 0.93 0.9 0.89 0.93 0.91 0.94 0.95 0.94 0.94 0.94 0.95 0.89 0.95 0.88 0.92 0.54

Avg COR 0.83 0.84 0.87 0.68 0.82 0.85 0.83 0.86 0.78 0.82 0.85 0.86 0.85 0.81 Nan 0.85 0.81 0.87 0.87 0.82 0.87 0.83 0.85 0.79 0.67 0.86 0.86 0.85 0.86 0.84 0.82 0.79 0.84 0.65 0.84 0.86 0.85 0.87 0.85 0.88 0.81 0.86 0.77 0.84 0.46

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure A-1 – Winter PM2.5 in Each Particle Class for Each Site Group

455-006 PM Variability Final Report rev 3

76

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure A-1, continued

455-006 PM Variability Final Report rev 3

77

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure A-2 – Winter PM10 in Each Particle Class for Each Site Group

455-006 PM Variability Final Report rev 3

78

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

Figure A-2, continued

455-006 PM Variability Final Report rev 3

79

PROVIDENCE

SAN JOAQUIN VALLEY AIR POLLUTION CONTROL DISTRICT

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