ANALYSIS OF AMBIENT FINE PARTICULATE MATTER, PM 2.5, IN PITTSBURGH USING TIME-SERIES TECHNIQUES AND METEOROLOGY

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1 ANALYSIS OF AMBIENT FINE PARTICULATE MATTER, PM 2.5, IN PITTSBURGH USING TIME-SERIES TECHNIQUES AND METEOROLOGY A thesis presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirement for degree Master of Science Galina Zubkova June 2003

2 This thesis entitled ANALYSIS OF AMBIENT FINE PARTICULATE MATTER, PM 2.5, IN PITTSBURGH USING TIME-SERIES TECHNIQUES AND METEOROLOGY BY GALINA ZUBKOVA has been approved for the Environmental Studies Program and the College of Arts and Sciences by Ronald Isaac Assistant Professor of Geography Leslie A. Flemming Dean, College of Arts and Sciences

3 ZUBKOVA, GALINA. M.S. June Environmental Studies Analysis of Ambient Fine Particulate Matter, PM 2.5, in Pittsburgh Using Time-Series Techniques and Meteorology (101 pp.) Director of Thesis: Ronald Isaac Abstract This thesis presents the results of the fine particulate matter concentration analysis in two sites in Pittsburgh for one year period. The sites represent a downtown commercial area (CMU) and a suburb residential area (NETL). The thesis focuses on variations of PM 2.5 through time and tries to create a model to predict its concentrations. Time-series and meteorology methods were used to investigate association of PM 2.5 with independent variables including fine particulate matter preceding values. The results help to determine the best predictors depositing into the model. The analysis showed seasonality in PM 2.5 distribution and possible regional allocation of its concentrations. Diurnal variations showed two patterns. The first presents peaks in nights and early mornings which occurred during relatively low 24 hour PM 2.5 levels. The highest 1 hour concentrations in afternoons were observed during the days with high 24 hour average concentrations. Analysis of PM 2.5 relationship with meteorological variables showed the highest correlation with temperatures and wind characteristics. The other variables impact PM 2.5 variations, but the relationship is probably non-linear. Investigating the influence of atmospheric stability on PM 2.5, Lifted Index was used as an independent variable. The

4 contribution of Lifted Index is significant, but its relationship with PM 2.5 appeared to be negative. However, the most significant contributor to the model predicting current PM 2.5 level is one hour lagged PM 2.5 values. The models containing meteorological and copollutants variables were run to predict fine particulate matter concentrations, but the highest prediction was shown by those which contained the lagged variable. Approved: Ronald Isaac Assistant Professor of Geography

5 5 Acknowledgements I would like to express my deep gratitude to those who contributed in the completion of this thesis. First of all, I want to acknowledge the Muskie Fellowship Program of Bureau of Educational and Cultural Affairs, US Department of State, which gave me an opportunity to do research and study at Ohio University. My thanks to Dr. Ronald Isaac whose advises significantly enriched this work. My sincere gratitude to Dr. Kevin Crist for his encouragement and help. I also wish to thank Dr. Christopher Boone for his valuable support. To all of you who were there for me while I was writing the thesis willing to help, to all of you, I will be always grateful.

6 6 Table of Contents Page Abstract...3 Acknowledgements...5 List of Tables...8 List of Figures...10 Equations...13 Abbreviations...14 Chapter 1 Introduction PM 2.5 Origin PM 2.5 Regulation Health Hazards of PM Research Questions...17 Chapter 2 Review of Existing Literature on PM Temporal Variability of Ambient PM 2.5 Concentrations The Influence of Meteorological variables on Ambient PM 2.5 Concentrations Characterization of Ambient PM 2.5 in the Ohio River Valley Region Model Describing the Mechanisms and Factors Determining the Lifetime of Fine Particulate Matter...25 Chapter 3 Research Methodology Study Design...28

7 Sampling Locations Measurement Methods Data Analysis Time Series Analysis Methods...31 Chapter 4 Results Variations in Ambient PM 2.5 at the CMU site PM 2.5 Association with Meteorological Conditions at the CMU site PM 2.5 Association with Co-Pollutants Diurnal PM 2.5 Variations Ambient PM 2.5 at the NETL site Impact of Atmospheric Stability Modeling and Predicting PM 2.5 Concentrations General Hourly Model Meteorological Hourly Model Stability Daily Model Time Hourly Model Regional Hourly Model Two Sites Hourly Model Chemistry Hourly Model...87 Chapter 5 Conclusions The Worst Case Scenario Future Research...95 References...96

8 8 List of Tables Table Page 4.1. Descriptive statistics of 1 hr PM 2.5 concentrations data at CMU Site for the Period from 07/01/2001 to 06/30/ Crosstabulation of 1 hour PM 2.5 concentration and wind speed categories at CMU site for the period from 07/01/2001 to 06/30/ Crosstabulation of PM 2.5 concentration and wind direction categories at CMU site for the period from 07/01/2001 to 06/30/ Descriptive statistics of 1hr average values of co-pollutants at CMU site from 07/01/2001 to 06/30/ Day with low and high concentrations of PM 2.5 at CMU site Descriptive Statistics of 1 hour average PM 2.5 concentrations at NETL site from 01/01/2001 to 12/31/ Stability classes Stability indices General model summary and F nested models test Meteorological model summary and nested models F test Stability model summary and nested model F test Model involving 1, 2 and 3 hour lags to predict current PM 2.5 concentrations Time model summary and nested models F test Regional model summary and nested models F test...83

9 Summary of the two sites model using 1 hour PM 2.5 concentrations at CMU and NETL sites for a half year period from 07/01/2001 to 12/31/ Chemistry model summary...88

10 10 List of Figures Figure Page 2.1. Model describing the mechanisms and factors determining lifetime of fine particles Location of the sampling sites Histogram of 1 hour PM 2.5 concentration data at the CMU site for the period from 07/01/2001 to 06/30/ The plot of 24 hour PM 2.5 concentration data at the CMU site for the period from 07/01/2001 to 06/30/ hour PM 2.5 concentrations and temperature at the CMU site for the period from 07/01/2001 to 06/30/ Scatter plot of 24 hour PM 2.5 concentrations versus temperature values Transformed 24 hour PM 2.5 concentrations and atmospheric pressure values at the CMU site for the period from 07/01/2001 to 06/30/ Scatter plot of 24 hour average values of PM 2.5 concentrations and relative humidity at the CMU site for the period from 07/01/2001 to 06/30/ hour PM 2.5 concentration and precipitation values at the CMU site for July and August hour PM 2.5 concentration and precipitation values at the CMU site in September

11 hour PM 2.5 and O 3 concentrations at the CMU site for the period from 07/01/2001 to 06/30/ Scatter plot of 24 hour PM 2.5 and O 3 concentrations at the CMU site for the period from 07/01/2001 to 06/30/ Scatter plot matrix of the independent variables and the dependent variable for 24 hour average data at the CMU site High PM 2.5 level day plot of 1 hr PM 2.5 and O 3 concentration values on 06/25/2002 with 24 average of µg/m 3 and max 1 hr PM 2.5 concentration of 75.41µg/m 3 at Low PM 2.5 level day plot of 1 hr PM 2.5 and O 3 concentration values on 09/14/2001 with 24 average concentration of 4.29 µg/m 3 and 1hr maximum of 7.33 µg/m 3 at 6.00 a.m Low PM 2.5 level day plot of 1 hr PM 2.5 and O 3 concentration values on 02/24/2002 with 24 hour average of PM 2.5 of µg/m 3 and high 1 hr maximum PM 2.5 concentration of µg/m3 at 5.00 a.m Low PM 2.5 level day plot of 1 hr PM 2.5 and O 3 concentration values on 04/17/2002 with 24 average of 32.6 µg/m 3 and high 1 hr max PM 2.5 concentration of µg/m 3 at 6.00 a.m High PM 2.5 level day plot of 1hr PM2.5 and O 3 concentration values on 08/01/2001 with 24 average of µg/m 3 and maximum 1 hour PM 2.5 concentration of µg/m 3 at Histogram of 1 hour PM 2.5 data at the NETL site for the period from 01/01/2001 to 12/31/

12 hour average PM 2.5 concentrations at the NETL site for the period from 01/01/2001 to 12/31/ hour PM 2.5 and temperature data distribution at the NETL site from 01/01/2001 to 12/31/ Transformed 24 hour PM 2.5 concentrations and atmospheric pressure values at NETL site for the period from 01/01/2001 to 12/31/ General model. Histogram of studentized residuals General model. Studentized residuals vs. predicted values General model. Predicted vs. observed 1 hour PM 2.5 data Meteorological model. Predicted vs. observed 1 hour PM 2.5 data Lifted Index in the morning and evening during a study period from 07/01/2001 to 06/31/2001 for the Pittsburgh meteorological station Stability model. Predicted vs. measured 24 hour PM 2.5 data Partial autocorrelation of 1 hour PM 2.5 series Regional model. Predicted vs. observed 1 hour PM 2.5 data Two sites model. Predicted vs. measured 1 hour PM 2.5 data Chemistry model. Predicted vs. observed 1 hour PM 2.5 data...90

13 13 Equations Equation Page 4.1. Temperature PM 2.5 relationship quadratic equation Temperature PM 2.5 relationship quadratic equation for the 1 hour data at the CMU site Ozone PM 2.5 relationship quadratic equation Ozone - PM 2.5 relationship quadratic equation for the 1 hour data at the CMU site General hourly model regression equation Time hourly model regression equation...81

14 14 Abbreviations CMU - Carnegie Mellon University d ae - aerodynamic diameter km - kilometer LI - Lifted Index mm - millimeter NAAQS - National Ambient Air Quality Standards NETL - National Energy Technology Laboratory NO 2 - nitrogen dioxide NO - nitrogen monoxide NO x - nitrogen oxides O 3 - ozone PAQS - Pittsburgh Air Quality Study PM - particulate matter PM particulate matter with size 2.5 micrometer or less PM 10 - particulate matter with size 10 micrometer or less SOA - secondary organic aerosol SO 2 - sulfur dioxide TEOMs - Tapered Element Oscillating Microbalances UPORVP - Upper Ohio River Valley Project U.S. EPA - the United States Environmental Protection Agency µm - micrometer

15 15 Chapter 1 Introduction 1.1. PM 2.5 Origin Particulate matter is liquid and solid particles in atmosphere. They are directly emitted from diverse natural processes and human activities such as forest fires, agricultural practices, construction works and fuel combustion or formed in the air by gases which are also products of fuel combustion (EPA). They can contain heavy metals, acids, biological and other organic and inorganic material (EPA). Particulate matter size ranges from a few nanometers to tens of micrometers varying according to its mass median aerodynamic diameter (d ae ) defined as the diameter of a sphere of unit density (particle density is equal to 1g/cm 3 ) that has the same terminal velocity in still air as the particle in question (Wark et. al. 14). Particulate matter with a size equal to or less than 2.5 micrometer (µm) is referred as PM 2.5 or fine particulate matter (EPA) PM 2.5 Regulation Particulate matter was initially defined by the Clean Air Act of 1970 as one of the criteria pollutants, and National Ambient Air Quality Standards (NAAQS) for mass of particulate matter were established under Title I of this act (Wark et. al. 69). The primary annual and 24-hour average standards were 75 and 260 micrograms per cubic meter respectively (The National Research Council 3). In 1987, the mass particulate matter standard was revised to include only particles with a size less than or equal to 10 micrometers, and it was referred as PM 10 standard (Wark et.al. 189). In late 1996, the EPA proposed and in 1997 updated NAAQS adding the PM 2.5 standard. The annual standard level is 15 µg/m 3 as an arithmetic mean of the annual average for 3 consecutive

16 16 years, and daily standard is 65 µg/m 3 calculated as average concentration over 3 consecutive years of 98th percentiles of daily value of PM 2.5 (Federal Register) Health Hazards of PM 2.5 The reasons for this strong concern were results of two studies, the Harvard Six Cities study and the American Cancer Society study, which reported increases in mortality associated with long-term levels of fine particles. According to the Harvard Study, an increase in the two day mean of PM 2.5 of 10.0 µg/m 3 leads to increase in total daily mortality of 1.5% (HEI). Particulate matter is considered by the U.S. Environmental Protection Agency (EPA) as [a] very serious hazard to the respiratory organs directly by penetrating into the lungs and depositing there or by toxic effect (EPA). Seniors, children and people with respiratory and cardiovascular diseases are the most vulnerable groups to particulate matter pollution (EPA). Respiratory diseases such as asthma develop as a result of breathing particles (EPA). Children are unprotected because their immune and respiratory systems are still developing (EPA). Forty percent of all asthma cases are children who make up only 25 percent of the population (EPA). Effects of particulate matter also include visibility reduction, increase of precipitation possibility, reduction of solar radiation, and it enhances chemical reactions in the atmosphere (Wark et. al. 189). However, there are still gaps in understanding this phenomenon whose size distribution, composition, and morphology can vary significantly in space and time (PAQS). There is a large necessity to characterize temporal and spatial variability of fine particles, understand the processes that control their formation and removal, and

17 17 overcome a large uncertainty in the negative effects of ambient PM on human health (PAQS). Because of growing concerns on its effects on human health, numerous researches have followed the Harvard and American Cancer Society studies. They investigate the PM 2.5 concentrations, its origins, chemical characterizations and spatial and temporal variations. However, the geographic area of the Ohio River Valley which includes southeastern Ohio, western Pennsylvania and northwestern West Virginia has not been very much covered (OURVP). The Ohio River Valley has dense coal-based power production utilities, heavy industry (e.g., coke and steel making), light industry and transportation sources emitting directly particulate matter or gases that originate it. The Upper Ohio River Valley Project (UORVP), Pittsburgh Air Quality Study (PAQS) and National Energy Technology Laboratory (NETL) In-House R&D Ambient Air Quality Research Project are currently being conducted Research Questions The data for this study were obtained from the PAQS and NETL projects. These projects were initiated by United States Department of Energy under Air Quality Research program to decrease the scientific uncertainty about the formation and distribution of fine particulate matter (NETL). To support this goal continuous fine particulate matter, sulfur dioxide, ozone, nitrogen oxides, carbon monoxide and weather related data are being collected in urban and rural sites of Pittsburgh region within these projects. NETL s sampling site and PAQS Supersite (CMU) were used by this thesis as sites to characterize PM 2.5 in Pittsburgh region. Temporal variation of PM 2.5, the dependence of PM 2.5 on its own lagged concentrations as well as association with

18 18 meteorological conditions and co-pollutants were investigated to find the best predictors of fine particulate matter. The results obtained from these analyses were deposited into a model for the prediction of future values of PM 2.5. Thus, ambient fine particulate matter concentrations in Pittsburgh were analyzed to answer the following questions. 1) What are PM 2.5 variations over the time in Pittsburgh? How and why does PM 2.5 change through the time? 2) What are the regional variations of PM 2.5? 3) What is the impact of meteorological conditions including atmospheric stability on PM 2.5 concentrations over time? 4) Do co-pollutants concentrations influence PM 2.5 levels in the atmosphere over time? 5) Can fine particulate concentrations be predicted with a general model including previous time series of the PM 2.5 concentrations, meteorological variables and co-pollutants? The results can be useful for characterization of the ambient PM 2.5 in the study area and for development of the regulation in the region. This analysis might suggest recommendations in predicting a level of PM 2.5 and issuing warnings for groups of people sensitive to elevated levels of fine particulate matter such as seniors, children and respiratory and cardiovascular patients. Additionally, this analysis may contribute to a better understanding of PM 2.5 phenomenon s effects on human health including studies of airborne pollution cancer risk.

19 19 Chapter 2 Review of Existing Literature on PM 2.5 The lifetime of any pollutant from its origination to removal is the critical component of the forecasting. Transportation, dispersion or concentration of originated pollutant depends on many factors including its physics, sources, composition, topography and meteorology of the region (Wark et. al. 109). The airborne cycle ends when a pollutant deposit on surfaces, is washed out from the atmosphere or escape into the space (Wark et. al. 109). The lifetime of PM 2.5 varies from a few seconds to weeks (Wark et. al. 12). The particles with size less than 0.1 µm are the subject to Brownian motions which are caused by collision with individual molecules and increase with a decrease of particulate size (Wark et. al. 12). Particles with size more than 0.1 µm but less than 2 µm have very small setting velocities in still air and can be rainout and washout (Wark et. al. 12). These particles can be transported over thousands of kilometers (Wark et. al. 12). It can explain that increases in PM 2.5 concentrations in urban areas followed by increases in rural areas with a discernable lag time (Chow et. al. 16, Buzorius et. al. 564). However, urban areas have higher concentrations than rural areas, and chemical composition in non-urban areas also differs (Chow et. al. 17). Chow, with colleagues, comparing urban and rural sites in Central California made a conclusion that the fine particle concentrations are conditioned by regional scale interaction of source emissions, chemical transformation, vertical mixing, horizontal transport, and deposition (Chow et. al. 17).

20 Temporal Variability of Ambient PM 2.5 Concentrations The differences in concentrations depending on a time of the day and sources were found as researchers analyzed diurnal particulate concentrations. An increase of primary pollutant concentrations is observed nights and early morning, and it can be explained by residential wood combustion and heavy traffic (Chow et. al. 19, VanCuren 15). Since the mixing layer is deep during day hours primary particulate matter level is not high while concentrations of secondary particulate matter increase at noon (Chow et. al. 23, VanCuren 8). Sunlight creates conditions for photochemical reactions. Also, elevated concentrations of both primary and secondary particulates can be observed at rural sites in Central California during daytime but with a discernable lag time because they mix aloft in urban areas and then are transported aloft to non-urban areas (Chow et. al. 16). Weber found similar two peaks during a day in Atlanta. Rapid increases in concentrations in early mornings are associated with local mobile sources and characterized by elementary (soot) and organic carbon while coal-burning power plants surrounding Atlanta as sources of SO 2 were responsible for peaks in late afternoon (Weber, et. al. 90). High variations of fine particulate during the day produce short term exposures affecting human heath. High 1 hr maximums of PM 2.5 observed during low 24- hour averaged concentrations highly correlate. (Weber, et. al. 90, Chuersuwan et. al. 1785) The seasonal variations of PM 2.5 on the east coast are presented by high concentrations especially of secondary PM 2.5 lasting from late spring through summer to early fall and low PM 2.5 from late fall through winter to early spring (Chuersuwan et. al. 1787, McKendry 1100, Vukovich et. al. 581). Summer is characterized by elevated

21 21 concentrations in southern California also although the highest PM concentrations were observed in November (Kim et. al. 2058) The Influence of Meteorological variables on Ambient PM 2.5 Concentrations Meteorological conditions and their influence on particulate concentrations and its variations are emphasized by many researchers. Synoptic-scale conditions result day-today changes in PM 2.5 concentrations (Chow et.al. 2072, Hien et. al. 3483). The periods of stagnation characterized by high pressure, low winds, clear sky, inversions and presence of a dense fog are responsible for the highest concentrations (Kim et. al. 2058, Whiteaker et. al. 2350, Marcazzan et. al. 76). For example, the highest aerosol concentrations in Pennsylvania were observed during the synoptic type with high pressure predominating to the east (Zelenka 872). The period of haze is also referred to high PM 2.5 concentrations (Whiteaker et. al. 2350). The periods of clearing and rain are related to low concentrations of PM 2.5 (Whiteaker et. al. 2352) although some authors concluded that rainfall and relative humidity impact daily variation of larger particles only (Hien et. al. 3484, VanCuren 14). Since the relationship between rain and fine particulate concentrations is very uncertain, the impact of rain and humidity on PM 2.5 levels has to be investigated. Among meteorological parameters the wind characteristics and temperature are considered as the most important factors determining PM 2.5 (Hien et. al. 3484, Zelenka 872). The vertical distribution of temperature and wind characteristics allows identify inversions and their impact on a level of PM 2.5 (Hien et. al. 3484). Nocturnal radiation

22 22 inversions resulted by radiational cooling during nights with dry air, clear skies and light winds represent a stable surface air layer extended upward less than 500 m (Wark et. al. 124). These inversions contribute to short-term pollution problems because they occur within the layer of atmosphere where sources emit the pollutant (Wark et. al. 124). Additionally, conditions associated with radiation inversion, clear skies and low wind speeds do not allow cleaning by precipitation or flashing by lateral movement (Wark et. al. 124). Therefore, radiation inversions may result in an elevated level of PM 2.5 at nights and early mornings while mixing upper level air transported from regional sources down to the surface lead to elevated concentrations in the afternoons (Weber et. al. 90). Another type of inversions which are responsible for prolonged PM 2.5 episodes are subsidence temperature inversions (Wark et. al. 123). These inversions formed by a downward flow of air in a high-pressure air mass occur some distance above surface and emission sources for several days (Hien et. al. 3478). Therefore, inversions limit the ability of atmosphere to disperse the pollutant by decreasing the convective mixing layer, the vertical extent to which [convective] mixing takes place (Wark. et. al. 130). The mixing height depends on topography of the place, season, and the time of a day (Wark. et. al. 130). Solar radiation during the day increases the temperature and, therefore, convective mixing. By the same reason it is at a maximum in the summer (Wark. et. al. 131). Assimilative capacity of industrial zones where power plants are the major PM 2.5 sources and ventilation coefficients are the highest in the periods with high-speed wind conditions which are favorable for dispersion, and these periods have the lowest pollution potential (Manju et. al. 3463, Marcazzan, et. al. 77). Diurnal variations showed the best

23 23 capacity to assimilate pollutants at noon and the lowest capacity during nights and mornings (Manju et. al. 3463). Therefore, low wind speeds are associated with elevated PM 2.5 concentration from the local sources while high wind speeds assist to dispersion of the pollutant and, at the same time, transportation from the regional sources. 2.3 Characterization of Ambient PM 2.5 in the Ohio River Valley Region Fine particulate concentrations in the region are influenced by regional effects rather than local ones (UORVP, PAQS). The regional sources upwind of this area were responsible for the concentrations of particulates transported from the west (PAQS). For example, the Upper Ohio River Project s results have similar trends in PM 2.5 levels for urban and rural sites in the region (UORVP). However, average particulate concentrations in Lawrenceville (urban site) were higher than in Holbrook (rural). Average 24 hour particulate levels exceeding 25 µg/m 3 (episodes) were occurring over several consecutive days (UORVP). Temporal analyses showed seasonal variations with peaks in summer time and low levels during the winter. Organic carbon and sulfate prevail in the summer, and nitrate was found among these compounds in winter (PAQS). These compounds have minimum variation during the day and are associated with regional transport and meteorology (PAQS). Domination of local source emissions of elemental carbon is confirmed by its diurnal variation (PAQS). Local wood and fugitive sources combustion are major sources of organic carbon in winter while in summer time industries (coke production) and diesel combustion are dominant sources of elemental carbon (Cabada et. al. 740) During summer, the aerosol consists of more water and more acid compounds

24 24 while it is dryer and more neutral in winter. Since contribution of secondary organic aerosol (SOA) to total PM 2.5 concentrations in winter is very small, while it consists of more than 50 % of SOA in summer (PAQS). Meteorology associated with high levels of PM 2.5 is presented by warm stagnation, high pressure and low wind speeds. Southwest wind direction is dominant during high PM 2.5 concentrations (UORVP). Two major patterns of wind conditions were described in the technical report on the UORV project during PM 2.5 episodes in late spring and summer months. The first pattern is presented by a wind shift followed by calm winds that was connected by authors to local sources (UORVP). The second pattern demonstrates a possibility of particulate transport across the region by strong southerly winds (from the southeast through the southwest) (UORVP). Analyzing wind rose and high particulate concentrations, researchers confirmed that northerly winds are associated with calm conditions, but southwesterly through southeasterly directions show wind speed within 6 mph (UORVP). Back trajectory analysis proved that the main direction of the air parcel during the episodes is from the southwest (UORVP). The cluster analysis confirmed this conclusion indicating that a southwest cluster associated with passing the Ohio River Valley occurred more frequently than other clusters (UORVP). Additionally a cold front passage is responsible for transportation of pollutants (Anderson et. al. 267). Thus, the level of PM 2.5 pollution is very much affected by sources of the Ohio River Valley (UORVP). Moreover, fine particulate concentrations on the east coast and, in particular, in New Jersey are associated with transport of the pollutants from the Ohio River Valley region since the highest PM 2.5 concentrations were observed during west and southwest winds (Chuersuwan et. al. 1783). However,

25 25 additional analysis of synoptic surface winds, local emissions and photochemical interactions has to be done to confirm the trends found (OURVP) Model Describing the Mechanisms and Factors Determining the Lifetime of Fine Particulate Matter To summarize the results of studies conducted to better understand the phenomenon of fine particulate matter a model of particles lifetime was adopted from Kenneth Wark and his colleagues book Air Pollution. Its Origin and Control. (Figure 2.1). The model illustrates the mechanisms that originate fine particles, influence their presence in atmosphere and cause their removal from the atmosphere. Also, it shows the factors that promote PM 2.5 accumulation or dispersion. Fine particles are formed by condensation, sublimation or chemical reactions (Wark et. al. 10). Some of them are emitted directly from sources and called as primary pollutants. Secondary pollutants are born in atmosphere by chemical reactions between pollutants and chemical species that are already in atmosphere. As we can see on the Figure 2-1 the emissions appear in the atmosphere as vapor which condensates to primary particles or chemically interacts with other gases forming secondary pollutant that condensates to droplets after or without nucleation process. Both primary and secondary particles pass process of coagulation. After coagulation they are rainout or washout from the atmosphere. However, there are factors that are added to the model since they are present in this system and influence this cycle. They are current and lagged meteorological factors, other gasses concentrations in atmosphere, and, also, past values of fine particulate

26 26 Sources combusting fuel Hot vapor Chemical conversion of gases to low volatility vapors Meteorology Condensation O 3 PM 2.5 Low volatility vapor Solar Radiation T 0 C Light Clouds Wind Pressure Humidity Rain Inversions Primary particles Chain aggregates SO 2 NO x Homogeneous nucleation Condensation growth of nuclei Coagulation Droplets Coagulation Coagulation Rainout and washout Fine particles Figure 2.1: Model describing the mechanisms and factors determining the lifetime of fine particles (Wark et. al. 12)

27 27 matter. The origination of primary and secondary pollutants is affected by humidity and solar radiation that determines temperature and light amount which are very important in chemical reactions. Clouds participate in the process by decreasing the amount of incoming solar radiation. Concentrations of the particulate matter itself as well as copollutants already existing in the atmosphere serve as surrogate for chemistry (Wark et. al. 12). Meteorological factors such as atmospheric pressure, temperature and wind speed and direction assist accumulation or dispersion of the pollutant. Inversion layers promote the high concentrations by limiting the layer of convective mixing. High wind speeds and precipitations assist removing the pollutant from the atmosphere. Although topography and sources present in the region can make corrections to the model the mechanism determining PM 2.5 lifecycle will be similar. Therefore, a model aiming to predict concentrations of PM 2.5 in the future has to consider all the processes and factors described above.

28 28 Chapter 3 Research Methodology 3.1 Study Design Sampling Locations Fine particulate matter data for this thesis were obtained from two sites in Pittsburgh (Figure 3.1). Figure 3.1: Location of the sampling sites (National Atlas). The sites are represented by a star The Monitoring Station (latitude and longitude ) of NETL s Office of Science and Technology is located on an open hill in a suburban area (South Park) of Pittsburgh. Sampling height above the ground is 3.2 meters while ground

29 29 elevation is meters above sea level. Since major roads and freeways are not close to the site transportations emissions do not contribute highly to fine particulate concentrations (Anderson et. al. 262). There are two small NETL combustion researches facilities close (200 m) to the sampling site and a coal-fired steam plant serving NETL approximately 600 meters from the site. However, they do not contribute to PM 2.5 concentration since they are downwind of sampling site (Anderson et. al. 262). There are also two coal-fired power plants (410 and 450 MW) about 10 km to the southeast which are also downwind of the site. However, major coal-fired power production facilities, chemical plants, iron, steel and coke processing facilities located in the Ohio River Valley are to the northwest, west and southwest of the site. Since prevailed wind directions in the region are northwest, west and southwest, these sources are major contributors to particulate concentrations (Anderson et. al. 262). Another site whose data was used for analysis is the Central Supersite (latitude and longitude ) of Pittsburgh Air Quality Study located adjacent to the Carnegie Mellon University (CMU) campus in Schenley Park close to downtown Pittsburgh. The size of the park is more than a square kilometer (PAQS). The closest ( several hundred meters ) transportation emissions source is Forbes Avenue with considerable traffic. There are no other major sources close to sampling site (PAQS). Upwind direction prevails on the site. Thus, regional sources are main contributors to PM 2.5 level. Sampling height is 6.7 meters, and ground elevation is meters above sea level.

30 Measurement Methods Continuous fine particulate mass has been automatically measured by Tapered Element Oscillating Microbalances (TEOM) series 1400a equipped with 2.5-µm inlets (URG EH). TEOM operated using a 3 L/min sample airstream flow rate at 50 C filter and sample airstream conditioning temperature (DEST). This temperature reducing humidity effect results some loss of semi-volatile material (DEST). Particulate mass is determined by a precision electronic counter which detects the frequency of the tapered element changes [(in a 2 second period) in conforming to] the mass changes in the filters under the control of an electronic circuit (Liu 32). The TEOMs run 24 hours seven days a week. This is considered as an advantage of this method compared with filter-based samplers (PAQS). 3.2 Data Analysis 30 minute readings of the TEOMs software represent continuous fine particulate matter mass measurements on NETL site for the period from January 2001 to December Ten minute samplings were obtained also by TEOMs on the CMU site from July 2001 to October One and 24 hour average PM 2.5 concentration values were used for analysis. Descriptive statistics were found to describe the fine particulate data at both site CMU and NETL. Average monthly, seasonal and annual data were compared with national standards. Time series techniques were used to identify seasonal effects and other cyclic changes such as diurnal variations and day to day variations in the data. Relationship between PM 2.5 observations at different times was investigated by an autocorrelation

31 31 procedure. Correlation analysis of PM 2.5 and meteorological variables and co-pollutants helped to identify the significant explanatory variables and the significant time lags of these independent variables in predicting particulate concentrations. The findings contributed as inputs to the forecasting model for prediction of particulate concentrations. This model represents linear combination of the past values of dependent variable and current and lagged values of independent variables. Additionally, the other six models were run to find if chemistry, meteorology and time components can separately explain PM 2.5 variations. Software whose time-series tools were used to analyze data and predict PM 2.5 level is Statistical Package for Social Science (SPSS) version Time Series Analysis Methods Time Series Analysis is a method for describing, explaining and predicting the variations of a variable that changes over time (Brockwell et. al. 1). The description of the data means identifying any seasonal effects, cyclical components or trends (Chatfield 5). The relationship between observations inside the variable can be explained by autocorrelation, the correlation of a variable with itself (Kachigan 155). In other words, correlation of the lagged values (preceding values) determines dependencies existing among the successive values (Kachigan 155). It provides with better understanding of the variable variation from day to day, how and why it changes. Also, the existence of correlations between successive values of a variable help to predict future values (Kachigan 155).

32 32 Another analysis technique, cross-correlation assists in finding the association between two or more variables over time and detect which independent variables can serve as the best explanatory variables of dependent variable (Chatfield 6). This method is used to identify specific time lags of the independent variables used in forecasting model. Thus, it helps to find good predictors of the variable whose future values are forecasted. Finally, time series techniques help to develop a model forecasting a value in response (output) time series as a linear combination of its own past values and current and past values of input time series (Rizzo et. al. 594). For example, time series analysis was used to define the relationship between co-pollutants, O 3, NO x, CO, SO 2, and particulate matter with size less than 10 µm in urban areas in the US (Rizzo et. al. 593). A time series transfer-function model helped to find significant autocorrelation within the variables and cross-correlation between all gasses and PM 10 series (Rizzo et. al. 604). Relationship of same day and lagged values of ozone concentration with PM 10 explained projected reduction in PM concentrations with decreasing ozone level (Rizzo et. al. 605). Although the authors analyzed and predicted PM 10 they made a conclusion that ozone and other gasses would be even better predictors for PM 2.5 as photochemically depended pollutant (Rizzo et. al. 604). Also, the meteorological time series such as radiation inversions and surface wind speeds were used as predictors of ground-level smoke concentrations in the time-series model (Milionis 2811). Cyclical and long-term components in particulate pollution concentrations can be found by time series models. For example, long term fine particulate trends in the DC area were identified by time-series techniques (Vukovich

33 33 573). They were affected by climate variables, such as temperature and dew point, and trends of co-pollutants (SO 2 ) (Vukovich 573). A time series model examining the relationship between particulate concentrations and traffic activity at the Caldecott Tunnel in Orinda, CA, indicated current particulate level dependence on the levels determined by previous three sampling periods and, also, on past traffic loads (Holmen 601). Multilagged regression, time-series analysis technique was used to explain the relationship between particulate concentrations and traffic intensity (Holmen 607). Therefore, time-series analysis techniques as tool which meet the goals of this research were used to explain variation of fine particulate matter concentrations over time in Pittsburgh and predict their future values.

34 34 Chapter 4 Results 4.1 Variations in Ambient PM 2.5 at the CMU Site The one and 24 hour average PM 2.5 concentration data collected during a one year period from July 1, 2001 to June 30, 2002 at the CMU site were considered in the analysis. Additionally, meteorological and co-pollutant concentration data were analyzed. They included 1 and 24 hour values of wind speed and direction, temperature, precipitation, humidity, pressure and radiation, ozone (O 3 ), sulfur dioxide (SO 2 ), oxides of nitrogen (NO x ) and carbon monoxide (CO). The database had missing data which were replaced using linear interpolation technique. Table 4.1 presents the descriptive statistics for 1 hour PM 2.5 and meteorological data at the CMU site. The mean value of 1 hour PM 2.5 concentrations at the CMU site was 17.2 µg/m 3 larger than the annual standard (15 µg/m 3 ). The maximum 1 hour average value was equal to µg/m 3 on June, 2002 at while the minimum value is 0.29 µg/m 3 on 12/17/01 at A concentration of 7.58 µg/m 3 was the most frequently occurring value in the data. The distribution of particulate concentrations was presented by a histogram (Figure 4.1). Although the distribution had positive skewness, and the tail was toward large values, 75 % of observation values were equal or less than 21.9 µg/m 3 value. So small values are more likely than large values, but there are some large values which are far removed from the rest and which have an impact on the data. The distribution of the sample values was approximately normal since more than 68% of values fall within one standard deviation from the mean value. Therefore, we can make an assumption that this sample can represent PM 2.5 population for our analysis.

35 35 Table 4.1: Descriptive statistics of 1 hour PM 2.5 concentration data at the CMU site for the period from 07/01/2001 to 06/30/2002. PM 2.5, µg/m 3 Wind speed, Wind Direction, Precipitation, Temperature, Humidity, Pressure, Statistics m/s degrees mm 0 C % mmhg Radiation N Valid Missing 458 (5.3%) 38 (.43%) 721 (8.2%) 38 (.43%) 37 (.42%) 38 (.43%) 38 (.43%) 38 (.43%) Mean Median Mode Std. Deviation Skewness Kurtosis Minimum Maximum Percentiles

36 PM2.5 Figure 4.1: Histogram of 1 hour PM 2.5 concentration data at the CMU site for the period from 07/01/2001 to 06/30/2002 Figure 4.2 was made using 24 hour average concentrations and shows the seasonality of PM 2.5 concentrations during the year. We can see the high concentrations in the summer months and lows in the winter. The mean value for the summer was 22.7 µg/m 3 while the average concentration for the winter was 11.5 µg/m 3. Although low PM 2.5 levels were observed during winter months, the minimum 24 hour average concentration occurred in the fall. The lowest 24 hr average concentration (4.29 µg/m 3 ) was on September 14, 2001, when the mean PM 2.5 value in fall was 14.6 µg/m 3. The highest 24 hour value (56.63 µg/m 3 ) was observed on June 25, It does not exceed the daily standard which is equal to 65 µg/m 3. July and August were

37 37 characterized by a large number of peaks with values exceeding 40 µg/m 3, and the low concentrations of PM 2.5 for these months are relatively high PM2.5, µg/m /04/ /15/ /27/ /08/ /20/ /01/ /30/ /11/ /23/ /27/ /08/ /20/ /01/ /12/ /24/ /05/ /14/ /26/ /07/ /19/2001 Date Figure 4.2: The plot of 24 hour PM 2.5 concentration data at the CMU site for the period from 07/01/2001 to 06/30/ PM 2.5 Association with Meteorological Conditions at the CMU site To describe the relationship between PM 2.5 and wind characteristics, which are considered one of the best explanatory variables, PM 2.5 concentrations, wind speed, and direction were categorized. Tables 4.2 and 4.3 show 1 hour PM 2.5 concentrations changing according to wind speed and direction. In most cases (82.7%) high concentrations (>50 µg/m 3 ) occurred during calm (<2 m/s) conditions. More than 50

38 38 percent of moderate (10-30 µg/m 3 ) and high moderate (30-50 µg/m 3 ) concentration values also occurred during calm conditions. Low particulate concentration values occurred during different wind conditions. Forty-two percent of the cases are associated with a wind speed less than 2 m/s. Twenty- three percent and 27.4% were observed during low (2-3 m/s) and moderate (3-5 m/s) speeds. Only 3.3% of all cases occurred during high moderate and high wind speeds. Therefore, high speeds probably do not play an important role by not resulting in a low PM 2.5 level. Table 4.2: Crosstabulation of 1 hour PM 2.5 concentration and wind speed categories at the CMU site for the period from 07/01/2001 to 06/30/2002. PM 2.5 Wind Speed >50 Total µg/m3 µg/m3 µg/m3 µg/m3 <2 m/s % 61.7% 76.6% 82.7% 57.5% 2-3 m/s % 20.2% 19.6% 16.3% 21.1% 2-5 m/s % 16.1% 3.7% 1.0% 18.0% 5-6 m/s % 1.6%.1%.0% 2.5% >5 m/s %.4%.0%.0%.8% Total % 100.0% 100.0% 100.0% 100.0%

39 39 Table 4.3: Crosstabulation of PM 2.5 concentration and wind direction categories at the CMU site for the period from 07/01/2001 to 06/30/2002 PM 2.5 Wind Direction >50 Total µg/m3 µg/m3 µg/m3 µg/m3 NE (0 45 degrees) % 15.7% 21.8% 24.2% 14.7% NEE (45 90 degrees) % 4.9% 5.3% 5.3% 5.0% SEE ( degrees) % 11.9% 19.0% 25.3% 10.7% SE ( degrees) % 19.4% 24.7% 18.4% 16.8% SW ( degrees) % 12.6% 12.2% 14.7% 13.1% SWW ( degrees) % 12.8% 7.9% 9.5% 14.1% NWW ( degrees) % 13.5% 5.8% 1.1% 15.0% NW ( degrees) % 9.3% 3.3% 1.6% 10.6% Total % 100.0% 100.0% 100.0% 100.0% The dominant wind direction during high PM 2.5 concentrations (42.1 %) was southeastern ( degrees). Elevated particulate concentrations during northeastern direction (0-90 degrees) accounted for 29.5%. About 24% of high concentrations occurred during southwestern winds and only 2.7% were associated with northwest direction. This partly confirms findings of research made in the Ohio River Valley. Southern directions were dominant during high particulate levels, but western winds were associated more with low concentrations than with high ones. Twenty four hour average values of temperature and PM 2.5 were plotted against the time to better understand their relationship (Figure 4.3). We can see from Figure 4.3

40 40 that the PM 2.5 curve in most cases repeats the temperature curve, not only showing the same seasonality effects but also following, in most cases, peaks and falls of temperature curve. Figure 4.4 is a scatter plot of 24 hour values of PM 2.5 and temperature. It shows a curvilinear relationship in the form of a positive parabola meaning that pollutant values start increasing as temperature exceeds a threshold value. Moreover, this increase occurs on very high rate. A positive sign of the parabola is indicated by β 2 coefficient of the second order temperature variable in the quadratic regression equation made to investigate this relationship which is Y = β 0 + β 1 X 1 + β 2 X ε (Equation 4.1) where Y is dependent variable, X 1 is independent variable (temperature) and ε is random error. As result, the equation that minimizes SSE (sum of square error) for the data is Y = X X (Equation 4.2)

41 /04/ /15/ /27/ /08/ /20/ /01/ /30/ /11/ /23/ /27/ /08/ /20/ /01/ /12/ /24/ /05/ /14/ /26/ /07/ /19/2001 PM2.5 Temperature Date Figure 4.3: 24 hour PM 2.5 concentrations and temperature at the CMU site for the period from 07/01/2001 to 06/30/ PM2.5, µg/m Temperature, degrees Celsius Figure 4.4: Scatter plot of 24 hour PM 2.5 concentrations versus temperature values.

42 42 The association between atmospheric pressure and PM 2.5 is presented by Figure 4.5. It shows transformed 24 hour values (a difference between consecutive observations) of these two variables against time. We were able to appreciate that there was a difference in variation of these two variables according to season. PM 2.5 concentrations variation was larger in summer than in the winter while pressure showed higher variations during cold months than during the summer /04/ /15/ /27/ /08/ /20/ /30/ /11/ /23/ /27/ /08/ /20/ /01/ /12/ /24/ /05/ /14/ /26/ /07/ /19/2001 PM2.5 Pressure Date Figure 4.5: Transformed 24 hour PM 2.5 concentrations and atmospheric pressure values at the CMU site for the period from 07/01/2001 to 06/30/2002 A scatter plot of 24 hour average PM 2.5 and humidity values does not show a linear relationship (Figure 4.6). The points are distributed randomly and do not present

43 43 any pattern. High concentrations of fine particulate matter were associated with humidity values close to the mean value (63.8%). The curve summarizes the relationship between variables by looking at cases that have similar value for X (humidity) and figuring out where a reasonable average Y (PM 2.5 ) value for them might be. We can see that PM 2.5 does not change very much as humidity increases PM2.5, µg/m Relative Humidity, % Figure 4.6: Scatter plot of 24 hour average values of PM 2.5 concentrations and relative humidity at the CMU site for the period from 07/01/2001 to 06/30/2002 To investigate the relationship between PM 2.5 and precipitation, the plot of 1hour average values of both variables for July and August (high PM 2.5 concentration months)