Continuous instruments in Cincinnati, Ohio. A Thesis Sent to the. Division of Research and Advanced Studies of the University of Cincinnati

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2 The evaluation of PM 2.5 measurements by Federal Reference Method (FRM) and Continuous instruments in Cincinnati, Ohio A Thesis Sent to the Division of Research and Advanced Studies of the University of Cincinnati In partial fulfillment of the requirement for the degree of Master of Science In the School of Energy, Environment, Biological and Medical Engineering of the College of Engineering and Applied Sciences November 211 by Carlos Roberto Pacas Herrera Bachelor of Agronomic Engineering, Universidad Autónoma Agraria Antonio Narro, México 22 Committee Mingming Lu, Ph. D. (Chair) Tim Keener, Ph. D. Anna Kelly, HCDOES. Xianlie Wan, Ph. D. i

3 The evaluation of PM 2.5 measurements by Federal Reference Method (FRM) and Continuous instruments in Cincinnati, Ohio ABSTRACT The filter based methods have been the gold standard of PM measurement in spite of its cost and labor intensiveness. Meanwhile many continuous instruments have been employed side by side at many US sites, and the results from the continuous instruments have been used in forecasting air quality index and applications other than compliance. This goal of this study is to evaluate the data agreement of fine particulate matter (PM 2.5 ) concentrations from filter based Federal Reference Method (FRM) and continuous instruments, such as tapered element oscillating microbalance (TEOM), and β-gaugenephelometers (BAM). The study was performed on five sites (Lebanon, Middletown, Batavia, Sycamore, and Taft) managed by the Hamilton County Department of Environmental Service (HCDOES), Ohio. According to EPA, PM 2.5 is considered as Class III. Class III included all methods related with PM 2.5 or PM samples from the atmosphere that are base in 24-hour filter sample. The monitoring period ranges from January 24 to December 21. The concentrations from continuous monitoring were averaged in 24 hours in order to compare with FRM. Also each data is three day data. To analyze the data a linear regression was performed between continuous and FRM monthly data and seasonal data. Thus, the intercept and the slope of the linear regression were put into an EPA template to evaluate the equivalence of the candidate method vs. the standard FRM. The same approaches were performed with all the instruments of HCDOES. Some continuous instrument exhibited higher correlation with the FRM than others. However, Seasonal changes such as winter and summer resulted in larger statistical differences, especially in March and October. Analyses also were performance such as multi linear and non-linear, where the temperature was part of the analyses. ii

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5 Acknowledgements This work would not be possible without the help of my committee who help me in each step of this research. Firstly I would like to thank my advisor, Dr. Mingming Lu who was involved since my acceptance to work with her and provide me guidance in the research. I would also like thank the other members of my committee, Dr. Tim Keener, Dr. Xianlie Wan, and Anna Kelley. Each of them contributed ideas, time, and information to this project and my sincere appreciation is extended to each of them. I would like to thank the Hamilton County Department of Environmental Services for their support in helping in the collecting data and allowing us to use it for this study. Much of the data obtained in this project would not have been possible without their help. My gratitude is also extended to Dr. Marepalli Rao. I would also like to thank the Fulbright Program for funding my personal project that helped me to start and finish this new step in my professional development. Thanks for all their support. I would like to thank many of my peers and groupmembers who s help was indispensable in the completion of this project. My deepest gratitude is extended towards Patcha Huntra, Qingshi Tu, Jiangchuan Hu, Chaichana Chaiwatpongsakorn for their help. Also, I would like to thank my family; my mother Elsa, my sister Ana Elsy, my brother in law Julio, my niece Stephanie whose love and support I couldn t do without. Finally, to greatest thankful to my friends Veronica who help me to reach this personal project, my second family in Cincinnati Gabriel, Maria, Carmen, Ivonne, Angelica, Miguel, Pablo, Alberto and, Miguel and Nazanin that made me feel as part of their life in this two years and all my friends that still and forever will be part of my life.

6 Table of Contents 1. OBJECTIVE INTRODUCTION AND LITERATURE REVIEW Instrument description Continuous Method (CM) Federal Reference Method Equipment (FRM) Quality Assessment of FRM Differences in concentration measurements AREA OF STUDY Lebanon Sycamore Taft Batavia Middletown METODOLOGY Data External data Linear-regression Differences in percentage Acceptance limit polygon Multiple-regression Non-linear Regression Process RESULTS Lebanon site Year Year Year Sycamore site Year Year Year Year Year

7 5.2.6 Year Batavia site Year Year Year Year Year Middletown site Year Year Year Year Year Year Taft site Year Year Year Year Year Year Sycamore daily data with all instruments TEOM BAM SHARP Summary of results Multiple linear regressions Non-linear regression results for Taft and Sycamore CONCLUSION FUTURE WORK REFERENCES Appendix A Maps of the sites Appendix B Acceptance limits Polygon Appendix C Multiplelinear Regression

8 Appendix D Nonlinear Regression

9 List of Figures Figure 1: TEOM Used by HCDOES Figure 2: THERMO SHARP used by HCDOES... 6 Figure 3 Data sheet of SHARP... 6 Figure 4 Data sheet BAM... 8 Figure 5 (a) and (b) WINS Impactor of BGI. (c) BGI (internal) Figure 6 Summary of activities in the labs... 1 Figure 7 Lab activities related to filter weight Figure 8 Linear regressions for SFS and TEOM Figure 9 Comparisons 1-hour average FDMS vs. TEOM (a), FDMS vs. RAMS (b) FDMS vs. 5 C TEOM Figure 1 Statistical analysis of measurement of the study made in Lindon and Rubidoux. Source: Glover, Figure 11. (a) shows the correlation by a linear regression of the TEOM-FDMS vs. FRM meassured at Queens. (b) shows the correlation of BAM vs. FRM messured at Addison Figure 12 Results by linear regression laboratory test vs. field test Figure 13 Description of PM 1 and PM 2.5 samplers Figure 14 a) Linear regression and b) analyses of linear regression and R Figure 15 Linear regression of HiVol vs. TEOM and BAM Figure 16 Monitoring information related to the study... 2 Figure 17 parameters estimated calculated with SAS software Figure 18 summary of the study Figure 19 Parameter used in the study Figure 2 Non-linear regression model Figure 21 Lebanon site Figure 22 Sycamore site Figure 23: Taft site Figure 24 Map of the sites Figure 25: Graph of a linear regression Figure 26: Seasonal differences Figure 27: Acceptance limits graph Figure 28: Times series graph. Concentration ( and BAM) Figure 29: Scatter plot of vs. BAM for Lebanon in Figure 3 Acceptance limits for Lebanon site Figure 31 Differences between FRM vs. CM... 4 Figure 32 (a) Analysis of summer season and (b) the acceptance limits Figure 33 (a) analysis of winter season and (b) the acceptance limits Figure 34 Time-series graph. Lebanon site Figure 35: Scatter plot for Lebanon site Figure 36: Scatter plot with outliners removed Figure 37: Acceptance limits graphs. (a) All data vs. (b) outliners removed Figure 38: Differences (%) between FRM vs. CM Figure 39 summer scatter plot. (a) All data plot vs. (b) removed outliners Figure 4: Acceptance limits for (a)full data vs. (b) acceptance limit graph with outlier removed Figure 41: Scatter plot for winter. Lebanon Figure 42: Acceptance limit for winter. Lebanon Figure 43: Time-series graph. Lebanon site

10 Figure 44 Scatter plot for the year 29. Lebanon site Figure 45 Acceptance limit graph. Lebanon site Figure 46: Differences (%) FRM vs. CM in Lebanon site Figure 47: summer scatter plot. Lebanon site Figure 48: Acceptance limit graph. Lebanon site summer Figure 49: Time-series Sycamore site 24. Concentration vs. BAM (µg/m 3 ) Figure 5 Scatter plot Sycamore site. vs. TEOM Figure 51: Acceptance limit graph. Winter Sycamore Figure 52: Scatter plot (outliers removed) and acceptance limits graph. Winter, Sycamore site Figure 53: Time-series graph. vs. Bam Lebanon site 25 (µg/m 3 ) Figure 54: scatter plot of vs BAM. Sycamore site Figure 55: Acceptance limits. Sycamore site Figure 56: Differences (%) vs. BAM. Sycamore Figure 57: Scatter plot of Sycamore site Figure 58: Acceptance limits for summer. Sycamore site Figure 59: Scatter plot with the acceptance limit graph (outliers removed) Figure 6: Scatter plot for winter. Sycamore site Figure 61: Acceptance limits for winter 25. Sycamore site Figure 62: Time-series concentration vs. BAM (µg/m 3 ). Sycamore site Figure 63: Scatter plot vs. BAM. Sycamore site Figure 64: Acceptance limit. Sycamore site Figure 65: Differences (%) of vs. BAM. Sycamore Figure 66 Summer analysis. Scatter plot vs BAM and Acceptance limit graph Figure 67: Winter analysis. Scatter plot vs. BAM and Acceptance limit graph Figure 68: Time-series graph vs. BAM(µg/m 3 ). Sycamore Figure 69: a) Scatter plot vs. BAM. b) Acceptance limits Figure 7: a) Outliers removed. b) Acceptance limit graph. c) Summer scatter plot. d) Summer scatter plot outliers removed Figure 71: (a) scatter plot for winter and (b) acceptance limits graph Figure 72: a) Times-series vs. BAM (µg/m 3 ) b) Scatter plot vs. BAM c)acceptance limits graph Figure 73: Scatter plot(a) and acceptance limits graph(b) Figure 74: a) Scatter plot raw data for summer. b) Scatter plot outliers removed. c) Acceptance limits raw data. d) Acceptance limits outliers removed Figure 75: Time series graph vs. SHARP (µg/m 3 ) Figure 76: a) Scatter plot of raw data vs. SHARP. b) Scatter plot with outliers removed. c) Acceptance limits graph for raw data. d) Acceptance limits graph with outliers removed Figure 77 Time-series graph. vs. TEOM Figure 78: Differences (%) vs. TEOM. Batavia Figure 79: Scatter plot entire year vs TEOM (a). Seasonal scatter plot for summer (b). Seasonal scatter plot for winter (c) Figure 8: (a)time-series vs TEOM 26(µg/m3). (b) Differences (%) between and TEOM Figure 81: Scatter plot entire year (a). Scatter plot summer (b). Scatter plot winter (c) Figure 82: (a) Time-series vs. TEOM and (b) Differences (%) FRM vs. CM Figure 83: Scatter plot for entire year (a). Scatter plot outliers removed (b) Figure 84: (a) Summer scatter plot all data and (b) summer scatter plot outliers removed

11 Figure 85 Winter scatter plot all data vs. TEOM (a) and scatter plot with outliers removed (b) Figure 86: Time-series plot vs. TEOM Figure 87 Scatter plot vs. TEOM all data Figure 88 Seasonal analyses for summer (a), (b) winter all data and (c) winter outliers removed Figure 89: Time-series graph vs.teom (µg/m 3 )... 9 Figure 9 Scatter plot vs. TEOM Figure 91 summer scatter plot vs. TEOM Figure 92: Time-series plot vs BAM (µg/m 3 ) Figure 93: Log difference FRM-Cm vs. time Figure 94: Scatter plot entire year (a) and scatter plot for summer (b) Figure 95: Time-series plot vs. BAM (µg/m 3 ) Figure 96: Scatter plot entire year (a) and scatter plot outliers removed (b) Figure 97: (a) Seasonal summer plot all data and outliers removed. (b) Seasonal winter plot all data and outliers removed Figure 98 Time-series graph vs. BAM (µg/m 3 ) Figure 99 Scatter plot entire year Figure 1: Seasonal plots. Summer (a) and winter (b) Figure 11: Time-series plot (µg/m 3 )... 1 Figure 12 Scatter plot entire year Figure 13 Natural log differences -BAM vs. time Figure 14 Scatter plot summer (a) and winter (b) Figure 15 Time-series vs. BAM (µg/m 3 ) Figure 16 Scatter plot vs. BAM Figure 17 Summer scatter plot (a) all data and outliers removed and (b) winter scatter plot all data and outliers removed Figure 18 Time-series plot vs BAM (µg/m 3 ) Figure 19: Scatter plot vs. BAM. Middletown Figure 11: Summer plot vs. BAM Figure 111 Time-series graph vs TEOM (µg/m 3 ). Taft Figure 112 Scatter plot vs. TEOM (a) all data (b) outliers removed Figure 113 Scatter plot for (a) summer all data and outliers removed and (b) winter Figure 114 Time-series plot vs. TEOMNT (µg/m 3 ) Figure 115 Scatter plot vs. TEOMNT Figure 116 Seasonal plots for summer (a) and winter (b) Figure 117 Time-series plot (µg/m 3 ). Taft Figure 118: Scatter plot TEOM vs.. Taft Figure 119: Seasonal plots for summer (a) and (b)winter all data and outliers removed Figure 12 Time-series plot (µg/m 3 ). vs TEOMNT Figure 121: Scatter plot vs. TEOMNT Figure 122 Scatter plot for summer Figure 123 Time-series plot TEOM vs. (µg/m 3 ) Figure 124 Scatter plot vs. TEOM Figure 125 Seasonal plots for summer (a) and winter (b) Figure 126 Time-series plot vs. TEOM (µg/m 3 ) Figure 127 Scatter plot vs. TEOM

12 Figure 128 Summer plot (a) and winter plot (b) Figure 129 Time-series plot vs. TEOM (µg/m 3 ) Figure 13 Scatter plot vs. TEOM. (a) all data (b) outliers removed Figure 131 Summer scatter plot vs. TEOM Figure 132 Time-series plot BGI vs. TEOM Figure 133 Scatter plot BGI vs. TEOM Figure 134: Summer scatter plot BGI vs. TEOM Figure 135 Time-series plot vs. TEOM (µg/m 3 ) Figure 136 (a)scatter plot vs. TEOM. (b) Scatter plot vs. TEOM outliers removed Figure 137: Seasonal plots for summer (a) all data and outlier removed and (b) winter Figure 138 Time series-plot (µg/m 3 ) Figure 139 Scatter plot vs. BAM (a) all data (b) outliers removed Figure 14 Seasonal scatter plot for summer (a) all data and outliers removed. (b) winter Figure 141 Time-plot series vs. SHARP (µg/m 3 ). Sycamore Figure 142 Scatter plot vs. SHARP Figure 143 Scatter plot for summer (a) all data and outliers removed (b) winter

13 Index of Tables Table 1 Sites of HCDOES with the type of monitoring Table 3 Example of HCDOES recorded data Table 4 Downloaded data from the Airport Table 5: Average temperature for summer and winter from the airport Table 6: Average temperature for winter from Sycamore site Table 7 Acceptance limits of the polygon Table 8 Descriptive statistic of Lebanon site Table 9 T-test for two samples Table 1 (a) Descriptive statistic and (b) t-test. Lebanon Table 11 Descriptive statistic and t-test Table 12: Statistical analysis. Descriptive statistical and t-test for two samples Table 13 Descriptive statistic and t-test Table 14: Descriptive statistic and t-test vs. BAM. Sycamore Table 15: Descriptive statistic and t-test. Batavia Table 16: Descriptive statistic and t-test. Batavia Table 17 Descriptive statistic and t-test. Batavia Table 18: Descriptive statistic and t-test analysis. Batavia Table 19: Descriptive statistic. Batavia Table 2: statistical analyses. Middletown Table 21 Statistical analyses. Middletown Table 22 Statistical analyses. Middletown Table 23 Statistical analyses. Middletown Table 24 Statistical analyses. Middletown Table 25 Statistical analyses. Middletown Table 26 Statistical analyses. Taft Table 27 statistical analyses and TEOMNT. Taft Table 28 statistical analysis. Taft Table 29 statistical analyses. Taft Table 3 statistical analyses. TEOM Table 31 Statistical analyses. Taft Table 32 statistical analyses. Taft Table 33 statistical analyses. Taft Table 34 statistical analyses for TEOM. Sycamore Table 35 Statistical analyses for BAM. Sycamore Table 36 Statistical analyses for SHARP. Sycamore Table 37 Slope and intercept of each instrument Table 38 Comparison of R-square of linear regression vs. multiple linear regressions Table 39 R-square of non-linear, linear and multi linear regression

14 1. OBJECTIVE Measurement of PM 2.5 has become an important requirement for the protection of human health and the environment. There are two types of methods for instruments used in collecting PM 2.5 concentrations. The first one is also called the gold standard as is used for compliance purposes, which works with filter based Federal Reference Method (FRM), and the second one is the continuous method (CM). The FRM methods are costly and labor intensive, as it requires staff to place, collect, and weigh the filters constantly (one in three days or one in six days). Even though the gold standard with FRM methods are certified by EPA, various continuous device have been used in air quality forecasting, such the air quality index, and have replaced the filter measurements in other countries, such as China and Japan. Continuous instruments have advantages such as offering faster reading, and less labor. However, the agreement between the CM data has not been consistent, especially at different locations. The important issue of measuring PM 2.5, according to Hamilton County Department of Environmental Service [1], is the agreement between the FRM and the CM and that should be within the limit of 1%. In practical consideration, the close agreement of these two types of instruments is essential especially when a region is at the borderline of excedance. Therefore, the overall goal of this research is to examine the agreement of various FRM and CM instruments the HCDOES manages, and better understand the contributing factors, such as the location/source types, the meteorology and the instrument types, etc. All the monitoring five sites used are managed by HCDOES. Multiple years of PM 2.5, data were obtained, from various FRM and CM instruments. First linear regression has been used between the FRM and CM. The US EPA suggested that the comparison of the two measuring methods shall be made by using a linear regression analysis, and the linear regression formula is required 1

15 to fall into the acceptance limits boundary [2]. For the sites that fall out of the acceptance polygon, temperature variations is also integrated into the assessment in the forms of multilinear and non-linear regression. The temperature variation was also considered separately for summer time and winter time. The goal of the multiple linear regressions is to help improve the fit of the correlation (as result depicted by R-square). Concentrations of FRM and CM will be analyzed including the temperature of the monitoring area. However, the analyses will be divided into yearly and seasonal (summer and winter). Should the analyses indicate a low R-square or the model is outside of the acceptance limits, the decision was made to apply a non-linear regression analysis. Nevertheless, the acceptance limits graph cannot be used in the multiple and non-linear method because a slope and intercept is needed. According to HCDOES, Were the concentrations greater the majority of the time in one method vs. the other. However, in preliminary results higher concentration were found in both methods. During seasonal changes the equipment behavior, showed different tendency. Higher concentration values during summer were collected for CM. Setup of the equipment might be the responsible of the difference showed. Some Instruments have been set up by default at 5 C [3]. Five year of data will be presented in this document (24-29) 2

16 2. INTRODUCTION AND LITERATURE REVIEW Particulate matter (PM) is a mixture between particles and gas. Measures of particle matter are important due the size of particles that can be inhalable. The sizes of inhalable particles are from PM 1 micrometers to PM 2.5 micrometers in diameter. Also there is a category, known as fine particles, with diameters of PM 2.5 micrometers or less [4]. In The United States, particulate matter smaller than 1 micrometers are regulated. Smaller particles can reach the lungs and cause serious diseases. The principle health problems related with particulate matter are: irritation of the airways, difficulty breathing, decreased lung function, asthma, irregular heartbeat and lung disease in general [5]. Determining composition, concentration, and size distribution of particles will define the location and retention time of particles in the respiratory tract [6]. Different instruments have been used for PM measurement. Equivalence of gravimetric methods such as Oscillating Microbalance (TEOM) and Beta Attenuation (BAM) have been used as a tool to compare with the Federal Reference Method (FRM), the gold standard of PM measurement despite its cost and labor intensiveness.. The equivalence methods used instruments such as the Filter Dynamics Measurement System (TEOM), the Sharp Monitor and Continuous Monitoring equipment. 2.1 Instrument description Hamilton County used different equipment to PM measurement. The devices used are: A Synchronized Hybrid Ambient Real-time Particulate Monitor (SHARP), a Tapered Element Oscillating Microbalance- Filter Dynamics Measurement System (TEOM-FDMS), Beta Attenuation Monitor (BAM) and the - BGI (FRM). 3

17 2.1.1 Continuous Method (CM) Tapered Element Oscillating Microbalance (TEOM) The TEOM has been used as CM since 1998 in US. The device operates with a flow rate of 3 L/min and 5 C [7]. TEOM takes an amount of PM for nonvolatile and volatile PM fraction. The samples are collected in a sampler collection filter and the equipment studies the trend of its materials. This device also, provides a representative PM mass concentration in a time between one-hour and 24-hour average. The concentration is shows in units of µg/m 3 of mass concentration of PM 1, PM 2.5, and PM 1. This equipment manages PM mass concentration average. The sampling process it s trough out the exchangeable filter. The FDMS removes aerosols at a temperature of 4 C. The filters used for this equipment are standard filter cassette of 47 mm in diameter. Also, a purge filter is used in the sample method. The purge flow air and the sample are passed throughout the filter to generate a direct measurement of the sample [8]. Figure 1 shows the filter of the FDMS 85 and the device. According to the company the device does not require any calibration due to the quality of the material that they used to build it. Nevertheless, the flow is always regulated by the flow controllers that adjust the requirements of the inlet with the ambient temperature and pressure of the TEOM. The instrument is standardizing at 5 C to keep the temperature always exceeding the dew point. However, losses in PM concentration can be caused by the volatilization of compounds. Less concentration reading can be found. On the other hand, the TEOM also can be upgraded to a TEOM-FDMS. The FDMS (Filter Dynamic Measurement System) is a part of the instrument that can be plug to TEOM. The FDMS is a sampling of ambient air that changes the path of the airstream every six minutes. The TEOM-FDMS measure of the core and volatiles and semi volatile of the mass collected. [9]. 4

18 Figure 1: TEOM Used by HCDOES [1] SHARP A model THERMO 53 belonging to HCDOES was used to measure the PM 2.5 concentrations. A synchronized Hybrid Ambient Real-time Particulate Monitor (SHARP) measures the particulate mass in a real time measurement. A high sensitivity light scattering photometer is used to the mass concentration calculation. Furthermore, uses an intelligent moisture reduction (IMR) to keep humidity low at the air that reaches the filter. The principle of the operation of a SHARP instrument is a light scattering and beta attenuation. The light is scattered by and 88 nm illumination source. According to the manual of the CM data 53 [11], is real-time equipment to measure concentrations of PM 1, PM 2.5 and PM 1. in a real time. A beta attenuation mass sensor is used into the SHARP, which has incorporated a high sensitivity light scattering photometer that sends a signal as a continuous time average. Also, incorporate a system that helps to maintain the relative humidity of the filter tape that passes through the filter. The intelligent moisture reduction (IMR) helps to reduce the internal temperature. The results, losing semi-volatiles are almost negligible. The SHARP works with the principle of light scattering (nephelometry) also for concentrations of aerosol works with a beta attenuation monitor. 5

19 Figure 2: THERMO SHARP used by HCDOES. Figure 3 Data sheet of SHARP [1] BETA ATTENUATION MONITOR (BAM 12) The principle of the BAM is the measure and the record of PM concentrations. The units used are in milligrams of micrograms per cubic meter. An emission of high-energy electrons is collected by a clean 6

20 filter tape. The beta rays is detected and counted to set up a zero reading. A reduction of the beta ray signal is detected (unclean spot) and it s used to determine the mass of concentration of PM on the filter tape. The BAM is U.S. EPA equivalence for PM 1 and PM 2.5 just with the model [11]. This device is cataloged as FEM (Federal Equivalent Method). Furthermore, BAM is having a classification as class III according with EPA [2] 7

21 Figure 4 Data sheet BAM [11] Federal Reference Method Equipment (FRM) and BGI The Reference Ambient Air Sampler () and the BGI were used for this research as FRM [12]. Both devices fulfill the requirements of the National Ambient Air Quality Standard (NAAQS). The two devices use the same inlet. An air flow of Lpm is the intake of both. Basically, there are two differences between them. The first one is that the can hold >7 filters at the same time. On the other hand, the BGI holds just one. The second difference is the impactor to remove the PM 1. The plate use oil to separate bigger particles. On the other hand, BGI uses a WINS (Well Impactor Ninety-Six) system to separate the bigger particles and collect the small one. However, WINS can be used in both [13]. Also, BGI has less moving parts compared with. This helps to the maintenance of the equipment. In addition, the structure of the and BGI helps to reduce possible evaporation in the sampling according. Indirectly, the methodology of and BGI has a labor for the pre and post filter weight. Both devices use the same inlet. 8

22 a) b) Figure 5 (a) and (b) WINS Impactor of BGI. (c) BGI (internal). c) 2.3 Quality Assessment of FRM The quality assessment (QA) for FRM is made to assure the data collected by the and the BGI. There are two parts of QA: pre-sampling and post-sampling. Steps must be followed in the lab to warranty the QA. The follow activities are performances by HCDOES and were taken from the Method Compendium [14]. Figure 6 shows the steps marked by EPA for labs in the region 1 to 1. Furthermore, inside the lab is presented the activities that must be followed. First, the new filters should be checked to see any damage can have by the transportation because this can affect the concentration reading. If the filter will not be used immediately, the filter should be storage in cold temp. 9

23 Figure 6 Summary of activities in the labs [14] 1

24 a) b) b) c) Figure 7 Lab activities related to filter weight The room or the lab where the filters will be weighted must be with air conditioning with a mean of 2 to 23 C. Furthermore, the relative humidity must be at 3 to 4%. In the Figure 7, shows a post-sampling stage, the filters are kept into a filter case (a) before and after weight. The electric charge is removed from the filter (b). Filters are weighted in a microbalance (c) and, the data are transferred into software which calculates the concentration in a difference of weight between the new filter and the used filter (d). 11

25 2.4 Differences in concentration measurements. Vega 21 [15], made a comparison between the federal equivalent method (FEM) and the tapered element oscillating microbalance (TEOM). Five sites in Mexico City were used for the study during February and March, The sampling time was 24h and collected a total of 58 PM 1 samples. The equipment used was a TEOM as a FEM. The temperature of the TEOM was settled 3 C or 5 C to reduce the thermal expansion. For FRM (Federal Reference Method) a PM 1 sequential filter sampler (SFS) was used. Basically, all sites are residential, industrial or a mixed used land. For the analysis, a simple linear regression was used (Y= mx + b). Results are presented in Figure 8. Figure 8 Linear regressions for SFS and TEOM. A comparison of the results of this study vs. the actual study showed all sites were outside the acceptance limits. Values for slope and intercept closest to 1 and indicate if the CM data is in or out the acceptance. Grover 25 [16] performed a study in Lindon, Utah, and Rubidoux, California during February July 23. For this study a FDMS, TEOM, differential TEOM monitor, Real-Time Total Ambient Mass Sampler (RAMS), PC-BOSS and a Continuous Nitrate Monitor were used. In Lindon one-hour average was measured in a period of two weeks. The equipment used in this period of time was the RAMS and the 12

26 FDMS. In Rubidoux site, the FDMS and the differential TEOM were used. In a time series graph the study explain the agreement among the instruments. Figure 9 Comparisons 1-hour average FDMS vs. TEOM (a), FDMS vs. RAMS (b) FDMS vs. 5 C TEOM. Figure 9 shows a comparison among instruments in a time series graph. Figure 9A shows 474 data points of FDMS vs. Differential TEOM. A total of 38 concentration peaks were found. According to the author 21ug/m3 was the higher difference between the equipment. However, no explanation was found related to difference. Figure 9B shows RAMS vs. FDMS. Differences were found in comparison between Figure 9A and Figure 9B. Differences were associated to fires close to the measurement area. Figure 9C is basically related to speciation in mass concentration (no related with the research). To evaluate the trend of the equipment a linear regression was apply. 13

27 Figure 1 Statistical analysis of measurement of the study made in Lindon and Rubidoux. Source: Glover, 25 The results found in the study, showed that Lindon and Rubidoux data were inside the acceptance limits; compared with the acceptance limits graph. However, differences in the measurements were found. Schwab et al [17] compared PM2.5 measurements from FRM and continues methods. The FRM sampler is a R&P Partisol-plus (225) sequential air sampler, the continuous instrument included a BAM (Met One 12) and a TEOM (FEMS). The two sampling sides are Queens, which is urban and close to traffic sources (within 1km to the highway) and Addison, which is a rural site located in a state park. 14

28 Figure 11. (a) shows the correlation by a linear regression of the TEOM-FDMS vs. FRM meassured at Queens. (b) shows the correlation of BAM vs. FRM messured at Addison The linear regression of the TEOM FDMS with the FRM is expressed as y= 1.25 x +.63, with a R square of.95, and that of the BAM and the FRM is expressed as y=1.28 x , with a R square of.88.a high correlation of.93 in R square is obtained for the BAM and TEOM FDMS. Both of the linear regression equations are outside of the compliance polygon. The study indicated that FRM reading is less than both of the two continuous instruments, and the main difference is the loss of semi-volatile nitrates and organic carbon (OC) from FRM samplers. Zhu et al [18] compared different PM2.5 instrument in the state of New Jersey. Gravimetric instruments, used as reference, were evaluated vs. CM in laboratory test and in the field. For CM TEOM (TEOM 14), TEOM filter dynamics measurement system (FDMS) and BAM were evaluated. Correlation among instruments where found. The nephelometers had a good correlation (R 2 =.97) and also the TEOM showed a decent correlation (R 2 =.85). On the other hand, BAM s showed poor correlation (R 2 =.6). During laboratory test, in summertime, the correlation of the 24h measurement gravimetric method vs. 15

29 CM was high. The temp recorder during the study was in the range of -1.7 and 1.2 C with a mean of 2.1 C and RH was recorder in the range of 23 to 98%. During warmer weather the temp was recorder in the range of 14.3 and 24.4 C and the RH was recorder in 34% - 96%. Figure 12 Results by linear regression laboratory test vs. field test. Source: AWMA The results of this study conclude that CM can replace FRM. Also, the TEOM-FDMS showed higher accuracy compared with TEOM and BAM to minimize the loss of mass concentration during warm weather. Chow 26 et al [19] Conducted different concentration measurements with different sampling devices. An amount of 2 samplers were directed including FRM units, minivols, sequential filter samplers, dichotomous samplers, Micro-orifice Uniform Deposit Impactor (MOUDIs), BAMs, TEOMS, and nephelometers were monitored in California s San Joaquin Valley. 16

30 Figure 13 Description of PM 1 and PM 2.5 samplers. Source: Chow, 26. The linear regression of each instrument is presented in Figure 14a where the slope, intercept, and correlation (R 2 ) is presented. Comparison with the results of the acceptance limit graphs showed values in slope closest to 1 and intercept closest to are inside the limits. Values of X are represented for FRM and Y is representing for CM. Figure 14b shows the data inside the acceptance limit polygon. 17

31 a) b) Sampler (Y) Sampler (X) Slope Intercept R 2 RP2K_2 AN AN4 AN AN4 SFS Figure 14 a) Linear regression and b) analyses of linear regression and R 2. Furthermore, the results of the study showed a high correlation. Values from.99 to.93 were found in Fresno, CA. site. Also, a comparison every six days was made with FRM was made. An average of 44.4 µg/m 3 of variability of PM 2.5 during the winter (Dec 99 Feb 2) was recorded. An annual averages 15 µgm-3 is permitted by U.S. National Ambient Air Quality Standards (NAAQS). For the three months showed, the annual averages are distant for the limits of AQI. Hauck [2] made a comparison of gravimetric PM, TEOM and BAM. Some statistical differences were found. Figure 15 is showed the concentrations for winter and summer. Throughout the months of July to August, the concentration of the TEOM was higher that the HiVol and BAM. However, in cold weather (January-February) the HiVol showed a higher concentration and TEOM had a trend opposite to the one showed for warm weather. The BAM also was higher that the TEOM in the mass concentration. The 18

32 temperature in the summer had averages of 2 C to 25 C. The temperature during the summer the temperature had an average of 15 C or less. Similar trends were found in different species such as ammonia, SO 4, OC and, NO 3. In a linear regression, a comparison of the instruments was made for the summer and winter: TEOM vs. HiVol and BAM vs. HIVol. The trend of the concentration of the CM vs. the FRM suffered seasonal changes in the area of study. Figure 15 Linear regression of HiVol vs. TEOM and BAM Source: Hauck, 24 Lewtas 21 et al [21] found 17% of a higher concentration in comparison of TEOM vs. FRM. However, the same study mentioned, in an F-test, statistical differences were not significant in the same period of time. During summer, no correlations were found in the difference between TEOM and FRM. The temp 19

33 and the RH were recorded between and 22-98%. Temperatures higher than 2 C the differences in mass concentrations increased when the ambient temp increased (R 2 =.32). TEOM reported higher concentrations compared with the FRM during the warm weather. Losses of VOC S by volatilization could be explained for high temperatures. Semi-volatile carbon species (SVOC) might be volatilized by particles collected on filters [21]. The relative humidity (HR) is another important fact in the variation of the concentration between BAM vs. gravimetric method. Differences in mass concentration were reported in conditioning of 2 C and 5% of concentration. The hygroscopic of the material used for the filter with BAM and humidity of the particles can represent differences in the results [22]. Rizzo 23 [23] worked in sites of Region 5 with data of EPA and AQS. Figure 16 shows the monitoring information related with the study. Figure 16 Monitoring information related to the study Scatters plot of FRM vs. TEOM was made in the study to show the performance of the collecting of PM 2.5 concentration. Values of R 2 were found by linear regression. However, a non-linear regression was made 2

34 and daily temperature was included into the regression. Figure 17 shows the parameters estimated calculated with SAS software. All parameter have a relation with the daily average temperature. Figure 17 parameters estimated calculated with SAS software Where: β= Intercept, β1= Coefficient of the temp, β2=coefficient of temps greater than the knot, β3= Coefficient of the CM, β4= Coefficient of the interaction of TEOM and the temp less than the knot, β5= Coefficient of the interaction of TEOM and the temp less than the knot, and Knot= Is the temp where the linear relationship of FRM and CM changes [2] 21

35 The methodology for this study is explained farther in this document. Where is explained what models were used in the non-linear regression. Kashuba 28 [24] presented a study for 4 sites of region 5: Mayville, WI, Grand Rapids, MI, Chicago, IL, and East St. Louis, IL. The use of the land was diverse basically rural and urban. The uses of the land were agriculture, urban, and industrial use. The equipment used in the study was FRM, TEOM, Nephelometer, and β-gauge. Figure 18 summary of the study Furthermore, this study also applied linear regression to compare the instrument. The linear model used for the author is as follows Results of the study are presented in Figure 18. However, linear and non-linear regressions are presented as well in the results. 22

36 Figure 19 Parameter used in the study The nonlinear model is constructed using nonlinear regression in computer software to fit FRM, Continuous, and meteorological variables. The following relationship is presented in Figure 2. Figure 2 Non-linear regression model (Kashuba, 28) 23

37 Where: CONT is the independent continuous method value, MET is the independent meteorological value: temperature (or humidity), bˆ is the model-estimated intercept, bˆ1 is the model-estimated coefficient for meteorological values less than the knot, bˆ 2 is the model-estimated coefficient for the meteorological values greater than the knot, b ˆ 3 is the model-estimated coefficient for the CM, bˆ4 is the model-estimated coefficient of the interaction between CM and meteorological measurements for meteorological values less than the knot, bˆ 5 is the model-estimated coefficient of the interaction between the CM and meteorological measurements for meteorological values greater than the knot, knot is the model-estimated meteorological value at which the linear relationship between the FRM and continuous measurement changes [24]. The results concluded the equipment was affected by the ambient humidity and temperature. Furthermore FRM register higher concentration compared with CM. Furthermore, the improved nonlinear regression model showed better improvement with respect the R 2. 24

38 3. AREA OF STUDY According to the HCDOES air monitoring plan (25) there are 19 monitoring sites for various PM measurements, out of which 9 sites have PM 2.5 FRM, 5 PM 2.5 continuous methods, 5 PM 1, and 2 have PM 2.5 speciation. In this project the five CM sites were analyzed: Lebanon, Taft, Middletown, Batavia, and Sycamore. Details of these sites are listed in Table 1 Lebanon This is a State and Local Air Monitoring System (SLAMS) site and the description given by Hamilton County Department of Environmental service is a residential land use. The City center and the prevalent wind direction goes SW to NE. It is mainly residential (suburban) with some industries. Figure 21 Lebanon site. Source: HCDOES, 21 Sycamore The description of the site is land use surrounding this site is classified as commercial and typed as suburban. The measures made in this site are O3 and PM2.5. This site has been used for AQI forecasting in Cincinnati OH. It is in close proximity, 256 meters, to the near edge of I-275 (284 meters from the center of I-275), an interstate highway loop around the metro Cincinnati area and has major local roads 25

39 such as the Reed Hartman highway, 568 meters away from the sampling site (58 meters from the center of the highway). There are a mix of residential and industrial facilities, such as Ethicon Endo Surgery, P&G, Toyota, and smaller companies. Figure 22 Sycamore site. Source: HCDOES, 21 Taft This is a city site just of exit 3 of I-71. It s close to the University of Cincinnati and several major hospitals. It is also a residential neighborhood. The Pollutants monitored at this site include PM 2.5, PM 1, and ozone. Similar to Sycamore, there are a mix of traffic, stationary (e.g. power plants) and area PM sources. Figure 23: Taft site. Source: Hamilton County,

40 Batavia This site is designated as SLAMS site located east of the City of Cincinnati. The Land use is considered residential and location type suburban. It can be considered a different air mass than that of downtown Cincinnati and the interstate corridors. Middletown This site is A SLAMS site; land use is commercial with area source influence. The data used in this study was collected by the Monitoring & Analysis staff of HCDOES. Five years of data was analyzed and compared two different methods: Federal Reference Method (FRM) and Continuous method. Eighteen hours of hourly monitoring data is needed for a day to be considered valid and to be analyzed in this study.. N Figure 24 Map of the sites. Hamilton Source: Hamilton County 27

41 Table 1 Sites of HCDOES with the type of monitoring source: HCDOES, 29 Name site AQS site ID MSA Address Monitoring Data Continuous Method used Lebanon Cincinnati- 43 Southeast PM 2.5 FRM, BAM Middletown Street, Lebanon PM 2.5 Continuous, O 3. Sycamore Cincinnati Grooms PM 2.5 FRM, April 7 BAM also Middletown Road, Cincinnati PM 2.5 May 8 for one year Continuous, O 3. (28-29) SHARP and TEOM Taft Cincinnati- 25 Wm. PM 2.5 FRM, TEOM- Middletown Howard Taft, PM 2.5 FDMS and Cincinnati Continuous, O 3, TEOMNT PM 1, NO3 Batavia Cincinnati- Middletown 24 Clermont Center Drive, Batavia O3, PM 2.5 Population Exposure (Cincinnati, OH TEOM- FDMS KY) ; Spatial Scale: O 3 Urban, PM 2.5 Middletown Hamilton- Middletown Hook Field Airport, Middletown O 3, SO2 PM BAM 28

42 4. METODOLOGY Data from Taft site (HCDOES), Lebanon, Middletown, Batavia, and Sycamore were used to be plotted from 24 to 29. TEOM (FDMS), TEOMNT, BAM, and SHARP were used to do a comparison with the FRM equipment. All the data were provided by the HCDOES following protocols to control the Quality Assurance (QA) of the filters. A linear regression was used in all sites with the Continuous equipment. Also, an external source of input like temperature was used. 4.1 Data All the data are in µg/m 3. All the FRM data were collected every three days. Measurement from FRM and Continuous Method were collected the same days. Each site has a specific schedule. Thus, days will be different among sites. However, differences in time are just one or maximum two days. Table 2 shows an example of how HCDOES collect and keep the data. Table 2 Example of HCDOES recorded data. Batavia Taft Date TM / f-t % Diff BGI TM/ f-t % Diff 1-Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Feb Table 2 shows the measurement date, FRM () measure, Continuous measure, and the percent of difference among them. Some values must be removed due to the malfunction with the equipment or abnormal readings. Furthermore, some outliers were found. However, the plot was made including all the data. A second plot, in some cases, was made to show the difference with outliers and without 29

43 outliers. First the missing data, represented in codes, are removed. Basic statistics are made to compare differences or statistics differences between the two methods. Plotting data such as time series, linear regression and seasonal linear regression is made to do the comparison. The basic statistic that was made was the linear regression. Values of R 2 =.7 represents a correlation R=.84 [26]. 4.2 External data According to EPA [26], external data such as precipitation, humidity, wind direction, and temperature can be used to improve the correlation. For this document, the external data used were the ambient temperature. The ambient temperature used in this document for Taft, Lebanon, and Batavia sites was downloaded from the data base of Lunken Airport. For Sycamore site the data used were for equipment installed along with the monitoring equipment. Table 3 shows how the format of temperature recorded is downloaded from the airport [27]. Table 3 Downloaded data from the Airport. STN : Station number WBAM: Weather bureau air force NAVY YEARMODA: Year, month and day Temp: Temp ( F) DEWP: Dew point The ambient data were used for multiple regressions. The objective of the regression was to improve the R-square calculated in the linear regression. All the downloaded data were in Fahrenheit. A 3

44 BAM conversion was needed to change the data into centigrade degrees. The improved model using the ambient data will be explained later in this document. Some temperature data were removed to fix it for every three days similar to the concentration data. 4.3 Linear-regression A linear regression can be applied to show the agreement between FRM and CM to be reported as AQI. The objective of a linear regression is to find a correlation between point X and point Y. The goal is find the linearity (R 2 ) of data [28, 29]. Values of R-square are from to 1. When values with a high linearity are found in a plot, the R 2 is close to 1. Values with a low linearity are close to to.7. For this document statistical software was used to find the values. The best fitted value of R-square (R 2 ) is given using the software. To correlate both methods a simple linear regression was made for all the devices and all the years of the study. Different studies have been using a simple linear regression equation to show the comparison between the FRM and CM. According with EPA [26] the minimum R- square to be considered into the limits should be R 2.7. Furthermore, into the linear regression graph a linear tendency was plotted. With the linear tendency a formula of intercept ± slope was found using software (MS Excel). Thus, the formula was used to be plotted into an acceptance limits plot y =.9621x R² =.8148 Sycamore Figure 25: Graph of a linear regression. The linear regression was applied for annual data. However, a seasonal linear regression was made to compare the trend of the equipment when ambient temperature is included into the CM data. 31

45 % The linear regression formula is as follows. Where: FRM= /BGI; = intercept; and = slope [26] 4.4 Differences in percentage Differences (%) were plotted in concentration in a time-series. To plot the differences the following formula was used. ( ) ( ) ( ) Where CM= Continuous Method FRM= Federal Reference Method In Figure 26 shows an example of a differences graph. Negative percentage shows FRM collected higher concentration than CM. 6. Differences Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul Figure 26: Seasonal differences. 32

46 According to some studies [19, 23], seasonal plots were made during summer and winter. For this study two databases were reviewed to collect info. One was the airport data base and the second one was the Sycamore site. For summer averages temperatures were taken yearly and the warmest three or four months were taken. Furthermore, for winter coldest months were taken and plotted similar with the yearly data. In Table 4 and Table 5 are shown the tables for the temperature from the airport and Sycamore. Table 4: Average temperature for summer and winter from the airport. SUMMER WINTER AVG. TEMPERATURE(F) FOR SUMMER AND WINTER FOR AIRPORT Jun Jul Aug Sep Nov Dec Jan(next year) Feb(next year) Table 5: Average temperature for winter from Sycamore site. Summer Winter AVG. TEMPERATURE (F) FOR SUMMER AND WINTER FOR SYCAMORE Jun Jul Aug Sep Dec Jan Feb

47 Intercept 4.5 Acceptance limit polygon The acceptance limit polygon has the goal to show how different is one method from the other one. It s a graph proposed by EPA which is delimited by limits of tolerance. A scatter plot was made with the data of the FRM vs. CM. Also a linear regression formula was found. The Intercept and slope of the linear regression formula was plotted into the acceptance limit graph. The slope is represented by the X-axis and the intercept is represented by the Y-axis. In Figure 27 shows a model of acceptance limit graph [2] Slope Figure 27: Acceptance limits graph.. When the linear regression is plot into the acceptance limits graph, it easily can be seen if the data is inside or outside the graph. Values in the linear regression closest to one for the intercept and slope can be interpreted as the values will be in the limit acceptance graph. The limits of the acceptance limits graph were taken from the 4 CFR The limits are presented in Table 6. Table 6 Acceptance limits of the polygon Applicable Slope Intercept

48 4.6 Multiple-regression In order to increase the R-square, a multiple linear regression was performed for the data sets where results of the simple linear regression equation were out of the acceptance polygon. For a multiple linear regression, the ambient temperature was included to interact with the CM. The multiple regressions are not shown in acceptance polygon The model used was the following. Y = 1X1 2 X 2 Where: = Regression constant X1=FRM data X2= Temp data = Error term All the information given by statistical software regarding to a multiple linear regression will be presented in the Appendix C. 4.7 Non-linear Regression Process The nonlinear model was used principally in the Taft and Sycamore site. The CM data used was the one proposed by Rizzo [2, 23]. Table 38 shows the results of R-square of these sites. However, no acceptance limits graph can be applied in this model. The calculation was done by the SAS software. 35

49 The process that SAS uses to calculate the nonlinear regression is as follows: 1) Start with six estimated values where the linear regression formula is used (the true values of the nonlinear regression), the rest is estimates and no precise. 2) The process uses a generated curve defined by the initial values, where the sums of the squares are used. 3) The variables are adjusted to make the curve closer to the data position. 4) SAS will start the integrations in a loop until the sum of the sum of squares does not chance. 5) The program will present the numbers of integrations and the new values. To find the R-square, the CM data sum of the square is dividing by the corrected total sum of square. The CM data of nonlinear regression used by SAS is the Gauss-Newton. However, some times, the software cannot find a converge (fit).the R-square value always will remain the same. The CM data vs. the gold standard data presented in this work is related with linear regression equations. Two cases are presented meteorological value (knot) above the data. Second, the knot was below the data. However, the initial values are guesses. Final values can be different if the knot change. Difference with the usual linear regression is that the nonlinear regression model cannot be solved analytically. The full integration made by SAS will be presented in the Appendix D. 36

50 5. RESULTS In this document five sites were analyzed. Each site will be showed separately to facilitate the analysis of each instrument, during the years that were analyzed. 5.1 Lebanon site Three day measurements for 3 years were collected in Lebanon site. The results presented are from 27 to 29. In the Figure 28 a times series of Lebanon site is presented Year 27 Figure 28: Times series graph. Concentration ( and BAM). The first comparison made after a time series graph was a descriptive statistic to find differences between the two methods this is shown in Table 7. 37

51 Table 7 Descriptive statistic of Lebanon site. BAM Mean Mean Tipic error Tipic error Median 12 Median Moda 1.3 Moda 11.9 Standard Deviation Standard Deviation Variance Variance Asimetric coenficient Asimetric coenficient Range 39.5 Range 37.2 Min 1.3 Min 5.2 Max 4.8 Max 42.4 Sum Sum Count 12 Count 12 Confidence level(95.%) Confidence level(95.%) The main statistic comparison made included the mean, the median, the standard deviation, and the variance. Very close values were found. Mean and standard deviation did not show statistic difference. The confidence level used was 95%. Also a t-test for two samples was applied to see any statistical differences. Table 8 shows the analysis. Table 8 T-test for two samples. BAM Mean Varience n Correlation mean difference Grades of freedom 11 Statistic t P(T<=t) one tail.7645 t critic value(one tail) P(T<=t) two tails.1529 t critic value (two tails) The t-test shows statistical difference because the p-value for two tails showed a value of.1529 which is smaller than.5. Also the analysis showed a high correlation of

52 BAM According to EPA a scatter plot for the two methods should be done. For the Lebanon site in 27 data from vs. BAM were plotted in a scatter graph. Furthermore, a tendency line (linear regression) and a 45 lines (R 2 =1) were plotted. Figure 29 shows the scatter plot for Lebanon site in LEBANON 27 y =.9261x R² = Figure 29: Scatter plot of vs. BAM for Lebanon in 27. The higher amounts of measures were reported between values of 7-18ug/m 3. The scatter plot showed, visually, a regular tendency across the 45 line. This means that the two methods have a high correlation. However, some outliners in the graph show 3 days of higher concentration with respect to BAM. The linear regression was of R 2 =.86. The outliners helped to reduce the linearity. Nevertheless, values of the linear regression formula closes to 1 and less than make the linear regressions, plotted from the CM vs. gold standard data, are inside the limits of the acceptance polygon. Nonetheless, a plot of the acceptance limits was made to corroborate if the linear regression is within the limits this is shown in Figure 3. 39

53 % Intercept Slope Figure 3 Acceptance limits for Lebanon site 27. When the linear regression formula was plotted into the graph, the results showed that the CM data was inside the limits. No more analysis was made. For the all year data the CM data was inside the limits. However, seasonal analyses were made to do a comparison for the rest of the years. A graph of the differences (%) of both methods vs. time was plotted (Figure 31). The results showed that during the winter time, February, there was an abnormal trend. In fact the concentration of the instruments was uniform in the measurement for the complete year. Differences Jan 3-Feb 3-Mar 3-Apr 3-May 3-Jun 3-Jul 3-Aug 3-Sep 3-Oct 3-Nov Figure 31 Differences between FRM vs. CM 4

54 BAM For summer moths (June-August) concentration of vs. BAM was plotted into a scatter plot (Figure 32). a) b) Summer(Jun-Aug) y =.9166x R² = Figure 32 (a) Analysis of summer season and (b) the acceptance limits. The seasonal scatter plot showed a linear tendency. The linear regression line is close to the 45 line. No outliners were found in the graph. There are two spots where the concentration was measured. The first spot are in the range of 1 to 15 µg/m 3. The second concentration spot was in the range of 25 to 28 µg/m 3. The R-square was.9626 which indicate the linearity and correlation of both methods for summer. Also the linear regression formula was in the range of and 2. The linear regression was inside the acceptance limits. For winter months (Dec-Feb) also the scatter plot was made with the acceptance limits (Figure 33). 41

55 BAM a) b) Winter(Dec-Feb) y =.9329x R² = Figure 33 (a) analysis of winter season and (b) the acceptance limits. The seasonal scatter plot for winter had a linear tendency. However was scatter in the range of concentrations between µg/m 3. The linear regression formula shows values closes to 1 and less of 2 which indicate that the CM data can be inside the acceptance limits. Furthermore, the linear regression was plotted into the acceptance limits graph. The dot inside the rectangle shows that the CM data fits inside the acceptance limits. 42

56 5.1.2 Year 28 A times series plot was graph to see the tendency of the two methods. This will help to check graphically the trend of the methods. Figure 34 Time-series graph. Lebanon site 28. During April measures of was lower compared with the BAM. However, in October had measures higher than BAM. During winter (Dec 8 to Feb 9) time measured a high concentration compared to. To analyze the differences in concentration analytical statistics and t-test was made. 43

57 Table 9 (a) Descriptive statistic and (b) t-test. Lebanon 28. a) b) BAM Mean Variance Observations Correlation Mean Difference df 116 t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail BAM Mean Mean Standard Standard Error Error Median 1.4 Median 11.6 Mode 8.9 Mode 11.4 Standard Standard Deviation Deviation Sample Variance Sample Variance Kurtosis Kurtosis Skewness Skewness Range 25.2 Range 27.6 Minimum 2.5 Minimum 3.2 Maximum 27.7 Maximum 3.8 Sum Sum Count 117 Count 117 Confidence Confidence Level Level (95.%) (95.%) A comparison was made to find statistics differences. Mean and standard deviation did not show any statistical differences. However the correlation for the complete year was.856. Nevertheless, the p- value for two tails was really low which means statistical differences. Furthermore, an annually scatter plot was made to show the linear regression (Figure 35). 44

58 BAM Lebanon y =.8922x R² = Figure 35: Scatter plot for Lebanon site. 28. The regression line showed in the plot reported a low R 2 =.7. Nevertheless, values of R2.7 are into the limits for a comparison between the gold standard and Continuous method. The plot had a scatters plot in difference ranges. In comparison with the last year more outliners were found. Also the linear model was higher than the expected R 2 =1. Outliners were removed (6) to improve the CM data and the linearity. The result is shown in Figure 36. Figure 36: Scatter plot with outliners removed. 45

59 A maximum of six outlines were removed. The results showed R-square improvement. In addition, the linear regression formula was improved and the improvement was showed when a comparison of the acceptance limits of both graphs was made. a) b) Figure 37: Acceptance limits graphs. (a) All data vs. (b) outliners removed. Figure 37 shows two graphs. The first one shows the plot of the linear regression outside the acceptance graph. On the other hand, the second one shows the linear regression, where the outliners where removed. The linear regression was inside the graph. A difference between two methods was made to verify the trend between correlations vs. time. Figure 38 shows the plot. 46

60 BAM % 25. Differences FRM vs. CM Jan Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec -1. Figure 38: Differences (%) between FRM vs. CM. Differences in the transition time were found. Variation in April and higher differences between October and December was found. The rest of the season was stable. Seasonal analyses were made to find the differences in summer and winter. a) b) SUMMER(Jun-Aug) y = 1.167x R² = Figure 39 summer scatter plot. (a) All data plot vs. (b) removed outliners. Summer scatter plot in the Figure 39 was made to shows the linear regression for moths since June to August. Some outliers were found in a whole year data. To avoid remove a lot of data just one outliers 47

61 was removed. However, when the outliers was removed the R-squared decrease. Improvements in the linear regression equation were found. Also the acceptance limits graphs of the Figure 4 showed that with all data the CM data was close to the limits and with an outliers removed the CM data fit into the limits. a) b) Figure 4: Acceptance limits for (a) full data vs. (b) acceptance limit graph with outlier removed. For winter, the data was plotted into a scatter plot and a comparison with an acceptance limits graph was made. In the Figure 41 shows the trend of the data during winter. 48

62 BAM WINTER(Dec-Feb) y = x R² = Figure 41: Scatter plot for winter. Lebanon 28. The tendency for winter was linear trend. The concentration was located in the range of 6 to 14 µg/m 3. However, higher concentration was found. Nevertheless, no outliers were found so no data was removed. A high R-square (.94) was found which indicate the linearity of the CM data. Additionally, even though a good correlation of the CM data, the acceptance limits graph showed the linear regression plot was outside the limits. In Figure 42 shows the acceptance limits graph Figure 42: Acceptance limit for winter. Lebanon

63 5.1.3 Year 29 As the previous years a time-series plot was made to compare the trend of both devices with respect of time. The Figure 43 shows that trend. Figure 43: Time-series graph. Lebanon site 29. The comparison of these two instruments is similar. However, showed less concentration capture compare with BAM. The concentration tendency had the same trend with less concentration. Descriptive statistic was made to see differences. Table 1 Descriptive statistic and t-test. BAM t test for two samples Mean Mean Tipic error Tipic error Median 1.45 Median 11.6 Moda 8.1 Moda 11.2 Standard Deviation Standard Deviation Variance Variance Asimetric coenficient Asimetric coenficient Range 22.2 Range 24.8 Min 3.4 Min 5.1 Max 25.6 Max 29.9 Sum Sum Count 112 Count 112 Confidence level(95.%) Confidence level(95.%) BAM Mean Varience n Correlation mean difference Grades of freedom 111 Statistic t P(T<=t) one tail E-12 t critic value(one tail) P(T<=t) two tails 1.968E-11 t critic value (two tails)

64 BAM The descriptive statistic (Table 1) did not show clear differences. Even though there were a difference in the mean and the variance, the t-test value with two tails shows statistical differences. Also a high correlation (.9433) between them was found. The scatter plot will show more differences in the CM data. Furthermore, high correlation of high R-square does not mean that the linear regression will be inside the acceptance limits. However, in this case for 29 the CM data fit into the limits. The result is shown in Figure 44 and Figure 45. Lebanon y =.9947x R² = Figure 44 Scatter plot for the year 29. Lebanon site. In the range of 5 and 12ug/m 3 the concentration was similar with the CM data. The tendency line shows linearity between the gold standard and the comparison between two methods. However, after 15ug/m 3 higher concentration take by the BAM compare with the made the CM data decrease with respect to the gold standard. Nevertheless, high R-square (.89) and values of linear regression equation shows good values closes to 1 and 2. To show the performance of the CM data in Figure 45 shows the acceptance limit polygon. 51

65 % Figure 45 Acceptance limit graph. Lebanon site 29 Differences in the natural log were plotted to see the trend of the equipment during the time. This is shown in Figure Differences FRM vs. CM Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec Figure 46: Differences (%) FRM vs. CM in Lebanon site 29. The differences that were plotted into the graph had shown some variation during the year. However, during summer the variations are smaller. Summer plot (Figure 47) was made to review the CM data trend during that dates. 52

66 BAM Summer(Jun-Aug) y = 1.339x R² = Figure 47: summer scatter plot. Lebanon site 29. The concentration trend during summer showed a linear tendency with respect the gold standard model. The R-square (.984) had an improvement with respect to the whole year. The values of the linear regression equation show that the CM data will inside the limits. However, some outliers close to the CM data were found but the CM data was not affecting the trend into the limits. This is represented in Figure Figure 48: Acceptance limit graph. Lebanon site summer

67 5.2 Sycamore site Year 24 Sycamore site started monitoring PM 2.5 since 24. The data that will be presented in this document starts since August 24. Five years of data will be presented for this site. The results will be showed year by year. Figure 49: Time-series Sycamore site 24. Concentration vs. BAM (µg/m 3 ) BAM showed, since August of 24, higher concentration measure compare with. Nevertheless, for winter season, January and February of 25 was plotted to show the trend of the BAM compared with the. Differences were analyzed to see if statistical differences are significant. To help to evaluate the differences of measurement and statistics scatter plot and descriptive analysis were analyzed for the winter time. Also, an acceptance limit graph was plotted to show the performance of the CM data. Raw data graph and outliers were removed to improve the R-square in the CM data. In Figure 5, Figure 51, and Figure 52 is presented the results for this year. 54

68 Teom Winter(Dec-Feb) y = x R² = Figure 5 Scatter plot Sycamore site. vs. TEOM. The raw graph showed a good linear regression. The R-square (.9615) indicated a good agreement. However the 45 showed the CM data was slight higher than expected. The slope of the CM data compared with the line did not help to fit it into the acceptance limit graph. This is shown in Figure Figure 51: Acceptance limit graph. Winter Sycamore 24 Outliers were removed. A maximum of two samples were removed to improve the CM data. Even though, a small improved (R 2 =.9622) was found the plot of the linear regression formula was out of the limits. This is presented in Figure 52 55

69 . a) b) Figure 52: Scatter plot (outliers removed) and acceptance limits graph. Winter, Sycamore site Year 25 For the year 25 a whole year data was analyzed. The trend of the comparison of two devices is presented in a time-series graph of vs. TEOM. This is presented in Figure 53. Figure 53: Time-series graph. vs. Bam Lebanon site 25 (µg/m 3 ). 56

70 The trend of the with respect to concentration collecting, as the previous year, was lower compared with the BAM. To analyze the trend, a scatter plot and statistical analysis were made to compare the two methods. No statistical differences were found with the analysis. However, seasonal analyses were performed to show the trend of the CM data vs. date. Table 11: Statistical analysis. Descriptive statistical and t-test for two samples. BAM t test for two samples Mean Mean Tipic error Tipic error Median Median Moda 21.7 Moda 11.4 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 42.9 Range 51.5 Min 4.5 Min 6.1 Max 47.4 Max 57.6 Sum Sum Count 118 Count 118 Confidence l Confidence l BAM Mean Varience n Correlation mean difference Grades of freedom 117 Statistic t P(T<=t) one tail 2.93E-27 t critic value(one tail) P(T<=t) two tails E-27 t critic value (two tails) The statistical analyses made for the year 25 showed some differences in the mean of each device. Values for the median, standard deviation and variance also showed statistical differences. However, a t-test was made to check if the differences were significant and the results showed statistical differences with a p-value for two tails of E-27. Also, a really high correlation was found (r=.96). The trend was analyzed with the scatter plot of Figure 54 and also the acceptance limits graph of the linear regression plot was made. 57

71 Intercept BAM SYCAMORE y = x R² = Figure 54: scatter plot of vs. BAM. Sycamore site 25. Figure 54 showed a good agreement. A high R-square (.9234) was found. However, the 45 line showed that the CM data was higher. The mayor concentration of PM 2.5 was found between 5 and 2 µg/m 3. Higher concentration also was found with BAM with respect to. No outliers were removed. The linear equation was plotted in the acceptance limit graph and is shown in Figure Slope Figure 55: Acceptance limits. Sycamore site

72 BAM % The differences of the comparison of the two devices were plotted. Differences in percentage also was plotted to show the regression of the concentration vs. time. Figure 56 supports the fact to analyzing seasonal regressions. Differences FRM vs. BAM Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec -4. Figure 56: Differences (%) vs. BAM. Sycamore Summer(Jun-Aug) y = x R² = Figure 57: Scatter plot of Sycamore site

73 Intercept Intercept Figure 57 presented a scatter plot for summer showed concentration rage between 6 and 28 µg/m 3. However, the trend of the CM data had the same pattern of the whole year model but higher than the 45 C line. The plot of the linear regression equation showed the CM data was out of the acceptance. This is shown in Figure Slope Figure 58: Acceptance limits for summer. Sycamore site. Outliers were removed. Improvement in the R-square (.9543) was found. A maximum of one outlier was removed from the original data. The result is shown in Figure Slope Figure 59: Scatter plot with the acceptance limit graph (outliers removed). 6

74 Intercept BAM The same procedure was made for winter. Scatter plot and acceptance limits graph were made and outliers were removed. Figure 6 shows the scatter plot for winter Winter(Dec-Feb) y =.9329x R² = Figure 6: Scatter plot for winter. Sycamore site 25. The scatter plot showed the CM data was higher than the 45 line. The amount of concentration was located in the range of 1 to 13ug/m 3. The R 2 =.8493 indicated a high correlation. However, the R- square value can be improving it but the CM data still outside. In Figure 61 shows the acceptance limits graph Slope Figure 61: Acceptance limits for winter 25. Sycamore site. 61

75 5.2.3 Year 26. As the previous year, Sycamore site will be analyzed year by year. A time series was made to do a comparison between two different instruments. The graph will show the trend of each instrument. As the previous year BAM was higher than the gold standard. The only time where the trend of the was similar with the BAM was in the transition of warm weather to cold weather BAM Jan 2-Feb 2-Mar 2-Apr 2-May 2-Jun 2-Jul 2-Aug 2-Sep 2-Oct 2-Nov 2-Dec Figure 62: Time-series concentration vs. BAM (µg/m 3 ). Sycamore site 26. Descriptive statistic was made to check differences between the two methods. Furthermore, a t-test was running to compare and analyzed the differences. 62

76 BAM Table 12 Descriptive statistic and t-test. t test for two samples BAM Mean Varience n Correlation mean difference Grades of freedom 114 Statistic t P(T<=t) one tail E-32 t critic value(one tail) P(T<=t) two tails E-32 t critic value (two tails) BAM Mean Mean Tipic error Tipic error Median 1.7 Median 14.2 Moda 1.1 Moda 12.2 Standard Deviation Standard Deviation Variance Variance Curtosis Curtosis Asimetric coenficient Asimetric coenficient Range 3.8 Range 39 Min 4.2 Min 3.1 Max 35 Max 42.1 Sum 1511 Sum Count 115 Count 115 Confidence level(95.%) Confidence level(95.%) The descriptive statistic was made to analyze any differences between two methods. Even though, difference can be identified. To find if any differences are statistical dissimilar a t-test was run. The p- value (3.2327E-32) showed that statistical differences were found. Furthermore a scatter plot is presented in Figure SYCAMORE 26 y = 1.826x R² = Figure 63: Scatter plot vs. BAM. Sycamore site

77 The scatter plot showed a high R-square (.9117) which indicate a good correlation between the two methods. However, if a comparison is made with the 45 line there are differences. The CM data was higher than the gold standard. The amount of concentration was in the range of 5. to 12 ug/m 3. However, higher concentration was found. Also the graph showed a uniform tendency. Also the linear regression was plotted into an acceptance graph. No outliers were removed. Figure 64 shows the acceptance Figure 64: Acceptance limit. Sycamore site 26. Also, to analyze the variation with respect the time, a graph with the log natural differences was presented to check the trend. Figure 65 shows the tendency. During summer showed higher values in the measure of PM 2.5. Negative values in the percentage differences, means that collected higher concentration values. 64

78 BAM % Difference FRM vs. CM Jan 2-Feb 2-Mar 2-Apr 2-May 2-Jun 2-Jul 2-Aug 2-Sep 2-Oct 2-Nov 2-Dec -4. Figure 65: Differences (%) of vs. BAM. Sycamore Summer(Jun-Aug) y = 1.147x R² =

79 Figure 66 Summer analysis. Scatter plot vs. BAM and Acceptance limit graph. The summer analyses were made to increase the correlation between the two instruments. Two scatter plots were made (Figure 66). One of the plots includes the whole data. The second one, outliers were removed (3). The CM data for summer was similar compared with the graph for the whole year. The CM data was higher compare with the 45 line. The original R-square (.8966) was improved (R 2 =.9125) when outliers were removed. However, in both graphs the acceptance limits graph showed the CM data was out of the limits. 66

80 BAM Winter(Dec-Feb) y =.852x R² = Figure 67: Winter analysis. Scatter plot vs. BAM and Acceptance limit graph. The winter analyses (Figure 67) corroborated the summer analysis. The CM data was higher compared with the 45 line. The differences compared with the whole year and the summer analysis is the linear regression. The winter analysis showed a really low R-square (.5869). The trend of the concentration showed outliers. However, outliers were removed (3) and the R-square was improved (.6996) but the values were low according what AQI document said as minimum of R-square (.7). Even though, when the linear regression formula was plotted the acceptance limit graph showed that both graphics were out of the limits. 67

81 5.2.4 Year 27 Figure 68: Time-series graph vs. BAM(µg/m 3 ). Sycamore 27. The time-series of Sycamore 27 compared with previous years showed higher concentration measure for BAM. Higher concentration differences were found during summer. However, during cold weather differences were found. Differences were analyzed by descriptive statistic to see how divergent the data were. Table 13: Descriptive statistic and t-test vs. BAM. Sycamore 27. BAM Mean Mean Standard Error Standard Error Median 12.6 Median 16.4 Mode 9.6 Mode 22.8 Standard Deviation Standard Deviation Sample Variance Sample Variance Kurtosis Kurtosis Skewness Skewness Range 36.7 Range 42.9 Minimum 4.1 Minimum 5.8 Maximum 4.8 Maximum 48.7 Sum Sum Count 15 Count 15 Confidence Level(95.%) Confidence Level(95.%) BAM Mean Variance Observations Correlation Mean Difference df 14 t Stat P(T<=t) one-tail E-16 t Critical one-tail P(T<=t) two-tail E-16 t Critical two-tail

82 BAM Descriptive statistic showed that differences were found. Mean, media and standard deviation showed differences. However, a correlation of.8975 was found. Which indicate a high correlation but not enough according with the requirements of AQI. The p-value for two tails showed significant difference. Scatter plots and acceptance limit graph were plotted to show the trend of the linear regression model. Figure 69 shows the trends. a) y = x R² =.857 Sycamore b) Figure 69: a) Scatter plot vs. BAM. b) Acceptance limits. The scatter plot of 27 showed a higher trend compared with the CM data. The biggest concentration measured was observed 8 to 14ug/m 3. Also, some outliers were found. Small R-square was found (.857) for those outliers. To improve the CM data, outliers were removed (5) to increase the R-square. 69

83 BAM Figure 7 shows the improved model. However, seasonal graphs also were plotted to show the trend of the same model with respect of time. a) b) c) d) Summer(Jun-Sep) y = 1.882x R² = Figure 7: a) Outliers removed. b) Acceptance limit graph. c) Summer scatter plot. d) Summer scatter plot outliers removed. The improved model (outliers removed) the R-square value was.92. A maximum of five outliers were removed. However, when the linear regression was plotted into the acceptance limit graph showed the CM data out of the limits. Also, summer graph was made to show the trend during summer. The R-square with all data during summer was However, some outliers were found and removed 7

84 BAM (3) to improve the CM data. The result of R-square value with the outliers removed increased in Nevertheless, the linear regression formula show the CM data was outside the limits. Figure 71 shows the winter graph. a) b) Winter(Dec-Feb) 4 3 y =.9175x R² = Figure 71: (a) scatter plot for winter and (b) acceptance limits graph The winter plot showed a linear trend of the CM data. The value of R-square (.912) showed the trend. However, the CM data was out of the limits. 71

85 BAM Year 28 a) b) Sycamore y =.9621x R² = c) Figure 72: a) Times-series vs. BAM (µg/m 3 ) b) Scatter plot vs. BAM c) Acceptance limits graph. 72

86 Figure 72 shows a time series graph for BAM in 28 showed higher concentration measure compared with (a). The only time when was higher was during March and September. The R-square (.8148) showed linearity for the CM data (b). However, the linear regression formula showed the CM data is out of the limits(c). Nevertheless, some outliers were found into the graph. The outliers were removed to improve the CM data. Figure 73 shows the improvement. a) b) Figure 73: Scatter plot (a) and acceptance limits graph (b). A slightly improve was found in R-square (.8367). However, the linear regression formula showed the CM data was out of the limits (b). Also, seasonal analyses were made to show the trend of the CM data with respect of time. 73

87 a) b) 4 SUMMER(Jun-Aug) 3 y = x R² = BAM c) d) Figure 74: a) Scatter plot raw data for summer. b) Scatter plot outliers removed. c) Acceptance limits raw data. d) Acceptance limits outliers removed. Seasonal analyses were made for summer. Scatter plot with raw data for summer (a) was made to show the trend of the CM data during summer. However, outliers were found. Slightly improve of R-square was found when the outliers were removed (2) (b). Furthermore, both linear regression formulas showed that the CM data was out for the whole data(c) and with the outliers removed (d). No winter analysis was made due for 29 the BAM was replace for a SHARP. 74

88 5.2.6 Year Sharp Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec Figure 75: Time series graph vs. SHARP (µg/m 3 ) Since 29 an equipment replacement was made by HCDOES. Differences were found in the trend in the time-series compared with the previous years. For 29 measured greater concentration compared with SHARP in cold weather. On the other hand, SHAPR showed greater concentration measure for the rest of the year. For a time series graph the trend for 29 showed better correlation of the gold standard vs. CM. Scatter plot will be showed to analyze in the Figure 76 shows the analyses. 75

89 SHARP SHARP a) b) Sycamore 29 y =.925x R² = y =.9873x R² =.8482 Sycamore c) d) Figure 76: a) Scatter plot of raw data vs. SHARP. b) Scatter plot with outliers removed. c) Acceptance limits graph for raw data. d) Acceptance limits graph with outliers removed. The performance of the SHARP compared with the and the BAM was better than previous years. The R-square (.8288) shows correlation for the CM data (a). However, the linear regression formula shows a high slope which means is out of the limits(c). However, outliers were found in the CM data which means that R-square can be improved. Outliers were removed (5) (b). A slightly improved in the R-square was found (.8482) and the linear regression formula was plotted into the acceptance limits graph (d). 76

90 5.3 Batavia site Year 25 Figure 77 Time-series graph. vs. TEOM The trends of the two devices were similar during warm weather. However, was slightly higher in concentration readings in cold weather. A statistical analysis was made to show differences between them. Table 14 shows the statistical analysis. Table 14: Descriptive statistic and t-test. Batavia 25. t test for two samples TM / f-t Mean Varience n Correlation mean difference Grades of freedom 11 Statistic t P(T<=t) one tail E-5 t critic value(one tail) P(T<=t) two tails E-5 t critic value (two tails) TM / f-t Mean Mean Tipic error Tipic error Median 13.8 Median 13.1 Moda 11.7 Moda 13.5 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 36.3 Range 4.2 Min 4.5 Min 2.3 Max 4.8 Max 42.5 Sum Sum Count 111 Count 111 Confidence l Confidence l The mean and the p-value for two-tails showed statistical differences. Figure 77 shows the trend of the entire year and also the seasonal comparisons. Nevertheless, a high correlation (.98) showed almost a 77

91 % perfect trend compared with the gold standard. The differences of concentration vs. time did not showed pikes that can change the trend of the devices. In Figure 78 is presented the graph of the differences vs. time. Differences Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec Figure 78: Differences (%) vs. TEOM. Batavia

92 TEOM TEOM TEOM a) BATAVIA 25 y = 1.537x R² = b) Summer(Jun-Aug) Winter(Dec-Feb) y = 1.998x R² = y =.9815x R² = Figure 79: Scatter plot entire year vs. TEOM (a). Seasonal scatter plot for summer (b). Seasonal scatter plot for winter (c). The comparison between two methods showed a high correlation. Correlation values between.94 and.98 were found during 25 for this site. The best correlation was found for summer where the correlation was.98. On the other hand, the lowest correlation found was during winter. This was an expected result due during the time-series graph differences during cold weather were found. The linear regression formula showed that the CM data was inside the acceptance limits graph. This will be showed in the Appendix B 79

93 % Year 26 a) b) Differences Jan 2-Feb 2-Mar 2-Apr 2-May 2-Jun 2-Jul 2-Aug 2-Sep 2-Oct 2-Nov 2-Dec Figure 8: (a) Time-series vs. TEOM 26(µg/m3). (b) Differences (%) between and TEOM. 8

94 TEOM TEOM TEOM A comparison of 25 and 26, showed a different trend of the TEOM. During the entire year TEOM showed higher concentration readings. Previous year the trend was similar but in cold weather. Scatter plot was made to show the tendency for this year. Figure 81 shows the scatter plots for entire year and for seasonal. a) BATAVIA 26 y = 1.16x R² = b) c) Summer(Jun-Aug) y = x R² = Winter(Dec-Feb) y =.9342x R² = Figure 81: Scatter plot entire year (a). Scatter plot summer (b). Scatter plot winter (c). The R-square in the three scatter plot was very high. Values of R-square from.9 to.98 were found. The concentration measured showed a linear tendency if it s compared with the 45 line. An R 2 =

95 (a) was found for the entire year which means a good agreement between the two methods. The higher correlation of R-square (.9818) was found for warm weather (b). On the other hand, this two high R- square does not mean that the linear regression formula is into the acceptance limits. For cold weather(c) the smaller R-square (.938) linear regression equation showed that was inside the acceptance limits. Also Table 15 shows that there are statistic differences. The p-value in the t-test showed differences between them. Table 15: Descriptive statistic and t-test. Batavia 26 TM / f-t t test for two samples Mean Mean Tipic error Tipic error Median 1.4 Median 1.4 Moda 9.5 Moda 9.5 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 3.8 Range 37.4 Min 3.4 Min 1.3 Max 34.2 Max 38.7 Sum Sum Count 115 Count 115 Confidence l Confidence l TM / f-t Mean Varience n Correlation mean difference Grades of freedom 114 Statistic t P(T<=t) one tail E-5 t critic value(one tail) P(T<=t) two tails t critic value (two tail

96 % Year 27 a) b) Differences FRM vs. CM Jan 3-Feb 3-Mar 3-Apr 3-May 3-Jun 3-Jul 3-Aug 3-Sep 3-Oct 3-Nov 3-Dec -1. Figure 82: (a) Time-series vs. TEOM and (b) Differences (%) FRM vs. CM The trend of the vs. TEOM compared with the previous years showed differences. Previous years showed TEOM was higher during the entire year. On the other hand, this year the trend in concentration measured was similar between the two methods. For warm weather showed higher concentration collecting while TEOM showed a higher concentration in cold weather (a). The differences graph (b) shows that during transition season (Sep-Nov) greater differences were found. Descriptive statistic was made to found differences. However, differences were found according with the t-test in Table 16. Also a high correlation was found (.972) which means a good agreement between the two methods. 83

97 TEOM Table 16 Descriptive statistic and t-test. Batavia 27 TEOM Mean Mean Tipic error Tipic error Median 12.5 Median 12.5 Moda 8.6 Moda 8.5 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 36.9 Range 45.1 Min 3.9 Min 1.6 Max 4.8 Max 46.7 Sum Sum Count 114 Count 114 Confidence l Confidence l t test for two samples TEOM Mean Varience n Correlation mean difference Grades of freedom 113 Statistic t P(T<=t) one tail t critic value(one tail) P(T<=t) two tails t critic value (two tails) a) BATAVIA y = x R² = b) Figure 83: Scatter plot for entire year (a). Scatter plot outliers removed (b) 84

98 TEOM Figure 83 shows the scatter plot of the entire year (a). This graph presented a good R-square (.9414). However, the linear regression formula shows that the CM data is outside of the acceptance limits. Outliers were found and removed (4). The new plot (b) showed a smaller R-square (.9339). On the other hand, the linear regression formula found in this new graph showed that the CM data is inside of the acceptance limits. a) Summer(Jun-Aug) 5 4 y = 1.146x R² = Figure 84: (a) Summer scatter plot all data and (b) summer scatter plot outliers removed. Furthermore, a summer analysis was made to show the trend in warm weather and is showed in the Figure 84. A high R-square (.9571) was found for the graph with all data (a). Even though the high R- square the linear regression formula showed the CM data was outside the acceptance limits. A second graph was plotted (b). Outliers were removed (3) to improve the CM data. A new R-square was found (.9624) and also the linear regression equation showed that the CM data is inside the acceptance limits. Furthermore, a winter analysis was made to show the trend in cold weather. This is presented in Figure

99 TEOM a) Winter(Dec-Feb) 3 2 y = 1.916x R² = b) Winter (Dec-Feb) Figure 85 Winter scatter plot all data vs. TEOM (a) and scatter plot with outliers removed (b). A scatter graph was plotted with all the data (a) for winter. Although a high R-square was found (.9619) the linear regression formula showed the CM data was outside the acceptance limits. Outliers were removed (1) to improve the CM data (b). A slightly improve was found in the R-square (.9635) and the linear regression formula showed the CM data is inside the acceptance limits. 86

100 5.3.4 Year 28 a) Figure 86: Time-series plot vs. TEOM Table 17: Descriptive statistic and t-test analysis. Batavia 28. TEOM Mean Mean 1.82 Standard Error Standard Error Median 1.6 Median 9.4 Mode 12.2 Mode 12 Standard Deviation Standard Deviation Sample Variance Sample Variance Kurtosis Kurtosis Skewness Skewness Range 23.3 Range 28.1 Minimum 2 Minimum 1.9 Maximum 25.3 Maximum 3 Sum Sum Count 115 Count 115 Confidence Level(95.%) Confidence Level(95.%) TEOM Mean Variance Observations Correlation Mean Difference df 114 t Stat P(T<=t) one-tail E-8 t Critical one-tail P(T<=t) two-tail E-7 t Critical two-tail The TEOM trend for this year was similar to the previous year. TEOM collected more PM 2.5 concentration during cold weather whereas showed higher concentration collection during warmer weather (Figure 86). The statistical analyses showed differences between methods. Furthermore, the analysis showed a high correlation (.9587) and the p-values for two- tails showed a 87

101 TEOM low number which means statistical differences (Table 17). A scatter plot was made to show the trend of vs. TEOM. The CM data showed a high R-square (.9189). The linear regression formula showed the CM data is close to the acceptance limits. This is shown in the Figure 87. Also shows the maximum concentration collected is between 6 to 15µg/m 3. Batavia y = 1.944x R² = Figure 87 Scatter plot vs. TEOM all data. As the previous years, a seasonal analysis was made for summer and for winter. Although, the entire year model was fitted into the acceptance limits, seasonal plots were made to see the trend of the instruments during seasons. Summer and winter showed trend complete different. While summer is shown in Figure 88 (a) the CM data found showed was below the gold standard. High R-square was found (.93) but the CM data was far away from the standard. Also, is presented the plot of winter. This season will be explained later. 88

102 TEOM TEOM a) Summer y = 1.28x R² = c) b) Winter(Dec-Feb) y =.854x R² = Figure 88 Seasonal analyses for summer (a), (b) winter all data and (c) winter outliers removed. Winter had a high R-square (.9269) and the CM data evaluated was close to the gold standard. However, linear regression model (b) showed that the CM data was out of the acceptance limits. Outliers were removed (2) to improve the CM data. However, the R-square with outliers removed showed R-square was reduced from.9269 to.9121 but the CM data was inside the acceptance limits. 89

103 5.3.5 Year 29 A time-series graph and descriptive statistic was made for this year as the previous years. This is presented in Figure 89. The trend was similar and not visual changes were found. Also a statistic analysis was made to found differences. However, statistical differences were found (Table 18). Figure 89: Time-series graph vs..teom (µg/m 3 ) TM / f-t Mean Mean Tipic error Tipic error Median 1.5 Median 9.75 Moda 1 Moda 8.8 Standard Deviatio Standard Deviatio Variance Variance Curtosis Curtosis Asimetric coenfic Asimetric coenfici Range 2.6 Range 23.9 Min 2.9 Min 1.8 Max 23.5 Max 25.7 Sum Sum Count 12 Count 12 Confidence level( Confidence level( Table 18: Descriptive statistic. Batavia 29 TM / f-t Mean Varience n Correlation mean difference Grades of freedom 119 Statistic t P(T<=t) one tail E-5 t critic value(one ta P(T<=t) two tails E-5 t critic value (two t A scatter plot also was made to show the tendency of both instruments. This is shown in Figure 9. 9

104 TEOM TEOM Batavia y = 1.514x R² = Figure 9 Scatter plot vs. TEOM. The scatter plot showed the CM data was lower compared with the gold standard. The bigger concentration collected was into the range of 5 and 15un/m 3. The R-square was high (.833). However, previous year was higher but the linear regression formula shows the CM data is inside the acceptance limits. Furthermore, a summer analysis was made to corroborate if the trend of the CM data is still inside the acceptance limits. This is shown in Figure 91. Summer 2 1 y = 1.133x R² = Figure 91 summer scatter plot vs. TEOM 91

105 5.4 Middletown site Year 24 Following the order of the previous years, a time-series graph was made to compare the trend of the two methods vs. time. This is shown in Figure 92. However, no just visual can be compared so see differences also statistics analyses were made to corroborate such differences (Table 19). a) BAM Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec Figure 92: Time-series plot vs. BAM (µg/m 3 ) Table 19: statistical analyses. Middletown 24 BAM t test for two samples Mean Mean Tipic error Tipic error Median 11.7 Median 13.5 Moda 6.2 Moda 6.6 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 33.3 Range 32.9 Min 3 Min 3 Max 36.3 Max 35.9 Sum Sum Count 118 Count 118 Confidence l Confidence l BAM Mean Varience n Correlation mean difference Grades of freedom 117 Statistic t P(T<=t) one tail E-14 t critic value(one tail) P(T<=t) two tails 1.892E-13 t critic value (two tails)

106 BAM % No differences were found for this year. The low value of p-value showed differences. However, seasonal analyses were made to corroborate the trend vs. time. In Figure 93 shows the trend of the differences (%) between both methods vs. time. A smooth trend was showed. However, during summer and cold weather some changes were noticed. Also an entire year and a summer plot were made in Figure 94 to compare the trend. 15. Differences FRM vs. CM Jan 1-Feb 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec Figure 93: Log difference FRM-Cm vs. time y = 1.47x R² =.9272 MIDDLETOWN b) 93

107 BAM y = 1.583x R² =.949 Summer(Jul-Sep) Figure 94: Scatter plot entire year (a) and scatter plot for summer (b) A high R-square was found in the CM data for this year (a). For the entire year an R-square=.9272 showed a good linear trend. Furthermore, the linear regression formula showed the CM data was inside the acceptance limits. The bigger concentration range was between 5. and 15 µg/m 3. Also for summer the scatter plot was made (b) to compare the trend. A higher R-square was found (.949) and also the formula linear regression formula showed the CM data was inside the acceptance limits Year 25 To explain the trend for 25, a time-series and statistical analyses were made. This is shown in Figure 95 and Table 2 Figure 95: Time-series plot vs. BAM (µg/m 3 ) 94

108 BAM Table 2 Statistical analyses. Middletown 25 BAM t-test: Paired Two Sample for Means Mean Mean Standard Error Standard Error Median 14.5 Median Mode 15 Mode 11.4 Standard Deviation Standard Deviation Sample Variance Sample Variance Kurtosis Kurtosis Skewness Skewness Range 43 Range 58 Minimum 4.1 Minimum 2.6 Maximum 47.1 Maximum 6.6 Sum Sum Count 122 Count 122 Confidence Level(95.%) Confidence Level(95.%) BAM Mean Variance Observations Pearson Corr Hypothesized df 121 t Stat P(T<=t) one-t t Critical one P(T<=t) two-t t Critical two BAM during cold weather had a trend in collecting PM 2.5 higher than. On the other hand, trend showed that the concentration was higher during warm weather (a). No statistical differences were found. Two-tails value showed no differences because a high value was found ( ). Also, a high correlation was found (.9669) which means good agreement between both methods. This agreement is presented in Figure 96 where the entire year was plotted in a scatter plot. a) b) Middletown y = x R² = Figure 96: Scatter plot entire year (a) and scatter plot outliers removed (b). Middletown site showed a high linearity (a). Also a high R-square was found (.935) which means a high correlation between both methods. However, the linear regression formula showed indicated that the 95

109 BAM BAM CM data was outside of the acceptance limits. Outliers were found (5). To improve the CM data those outliers were removed. A maximum of 6 data point were removed to increase the R-square. The result is presented in the second graph (b). A slightly improve in the R-square was found (.9339). However, the linear regression formula showed the CM data was inside the acceptance limits. Also Figure 97 shows the seasonal plots. a) Summer(Jun-Aug) y = x R² = b) Winter(Dec-Feb) y = 1.176x R² = Figure 97: (a) Seasonal summer plot all data and outliers removed. (b) Seasonal winter plot all data and outliers removed. The data plotted for summer showed differences. A comparison between the time-series plot with the summer plot is made (a) the result shows the CM data is outside the acceptance limits. When was 96

110 higher than BAM the CM data was outside the limits. Outliers were removed (2). Even though, both graphics for summer showed a high R-square (.931 and.9545) the linear regression formula the CM data was outside the acceptance limits. On the other hand, a winter plot was made (b). However, the trend for winter was better than summer. A high R-square was found (.9645). However, the linear regression formula showed that the CM data is outside of the acceptance limits. Outliers (2) were removed to improve the CM data. The result showed a slightly improve of the R-square (.9691). The linear regression formula showed the CM data is inside the acceptance limits Year 26. a) Figure 98 Time-series graph vs. BAM (µg/m 3 ) 97

111 BAM Table 21 Statistical analyses. Middletown 26 BAM Mean Mean Tipic error Tipic error Median 11.1 Median 11.5 Moda 5.4 Moda 6.3 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 33.1 Range 38.2 Min 2.9 Min 2.8 Max 36 Max 41 Sum Sum Count 99 Count 99 Confidence l Confidence l t test for two samples BAM Mean Varience n Correlation mean difference Grades of freedom 98 Statistic t P(T<=t) one tail t critic value(one tai P(T<=t) two tails t critic value (two ta The time-series graph (Figure 98) for this year was different compared with the previous year. Last year for warm weather was higher than BAM in the concentration collecting. On the other hand, for this year BAM was greater than almost all year bur few days in April. Furthermore, statistical analyses (Table 21) were the mean and the median was close in both methods. Also a high correlation was found (.9647) which indicate good agreement between them. Nevertheless, the p-value for two tails indicates no differences. Also in Figure 99 shows the scatter plot for the entire year Middletown 26 y = 1.27x R² = Figure 99 Scatter plot entire year. 98

112 BAM BAM A high R-square (.9399) and also the linear regression formula are showing the CM data is outside the acceptance limits. No variation was found and no outliers were removed since the agreement in the linearity of the CM data. Also seasonal plot were made. This is shown in Figure 1. a) Summer(Jun-Aug) y = x R² = b) Winter(Dec-Feb) y = 1.211x R² = Figure 1: Seasonal plots. Summer (a) and winter (b). Furthermore, the winter linear regression formula (b) showed that the CM data is inside the acceptance limit. Even though R-square for winter is low (.9148) the CM data linearity is good for cold weather. 99

113 5.4.4 Year 27 a) Figure 11: Time-series plot (µg/m 3 ) Table 22 Statistical analyses. Middletown 27 BAM t test for two samples Mean Mean BAM Tipic error Tipic error Mean Median 13.4 Median 14.5 Varience Moda 13.4 Moda 9.1 n Standard Deviation Standard Deviation Variance Variance Correlation Curtosis Curtosis mean difference Asimetric coenficient Asimetric coenficient Grades of freedom 17 Range 33 Range 38.4 Statistic t Min 4.6 Min 3.5 P(T<=t) one tail Max 37.6 Max 41.9 t critic value(one tail) Sum Sum Count 18 Count 18 P(T<=t) two tails Confidence level(95.%) Confidence level(95.%) t critic value (two tails) In the times-series plot (Figure 11) for this year, BAM showed higher measure of PM 2.5. In April, October, and December was different for few days. A high correlation was found (.97) also the statistical analyses (Table 22) showed no differences between the methods. The result of the good agreement is shown in the Figure 1 where a scatter plot for an entire year was made. 1

114 % BAM MIDDLETOWN y = 1.677x R² = Figure 12 Scatter plot entire year. The scatter plot showed in the Figure 1 showed a good linearity. A high R-square was found (.957) and also the tendency line was close to the gold standard. The amount of concentration of PM 2.5 for this year is in the range of 5. and 15 ug/m 3. However, higher concentration was found. Furthermore, the linear regression formula showed the CM data was inside the acceptance limits. Also the Figure 13 shows the differences (%) for this year. 4. Differences FRM vs. CM Jan Feb 3-Mar 3-Apr 3-May 3-Jun 3-Jul 3-Aug 3-Sep 3-Oct 3-Nov 3-Dec Figure 13 Natural log differences -BAM vs. time. To compare if this differences affected the trend in summer and winter scatter plot were plotted. This is shown in the Figure 14(a) and (b). 11

115 BAM BAM a) b) Summer(Jun-Aug) y = 1.81x R² = Winter(Dec-Feb) y =.9971x R² = Figure 14 Scatter plot summer (a) and winter (b). Both graph showed a high R-square,.969 for summer and.934 for winter. Also the linear regression formula showed that both models are inside the acceptance limits. No data was removed. Both plots were plotted with the whole data. 12

116 5.4.5 Year 28 a) Figure 15 Time-series vs. BAM (µg/m 3 ) Table 23 Statistical analyses. Middletown 28. BAM t-test: Paired Two Sample for Means Mean Mean BAM Standard Error Standard Error Mean Median 11.8 Median 12.2 Variance Mode 8.2 Mode 14.8 Observations Standard Deviation Standard Deviation Sample Variance Sample Variance Pearson Correlation Kurtosis Kurtosis Hypothesized Mean Skewness Skewness df 114 Range 35 Range 34 t Stat Minimum 3.4 Minimum 2.2 P(T<=t) one-tail Maximum 38.4 Maximum 36.2 t Critical one-tail Sum Sum Count 115 Count 115 P(T<=t) two-tail Confidence Level(95.% Confidence Level( t Critical two-tail As the previous year the time series-plot (Figure 15) show that BAM had a higher concentration collecting compared with the. Few days of March, May and December was high. Furthermore, statistical analyses showed no differences. Mean, standard deviation and the range were almost the same. Also the p-value, Showed in Table 23, for two tails had a value of.2683 which indicate that there are not statistical differences. Also to study the trend of both methods a scatter plot was made. This is shown in Figure

117 BAM y = 1.435x +.11 R² =.8337 Middeltown Figure 16 Scatter plot vs. BAM. The linear model almost fitted with the gold standard. The concentration collected showed dispersion, which can explain the low R-square (.8337). However, the linear regression formula showed that the CM data is inside the acceptance limits. Nevertheless, when seasonal graphs were plotted, data was removed to fit the data into the acceptance limits. However, is considered that the CM data responded well for warm and for cold weather. Still the summer graph needs to be analyzed due when data were removed (6) the R-square found was really low. The R-square decrease from.73 to.66 but the acceptance limits showed that the CM data is inside. This case enforces the fact that high R-square does not necessary will be inside of the acceptance limits. HCDOES will determinate if a low R-square and inside the acceptance limits can be considered as a good analysis due that according with AQI, R-square should be.7. In Figure 17 shows the summer plot (a) and the winter plot (b). 14

118 BAM BAM a) SUMMER(Jun-Aug) y = x R² = b) WINTER(Dec-Feb) y = x R² = Figure 17 Summer scatter plot (a) all data and outliers removed and (b) winter scatter plot all data and outliers removed Winter (b) showed no problems compared with summer. The R-square found was (.8932). However, outliers were found and removed (2). The R-square was improved to.973 and the linear regression formula showed the CM data was inside the acceptance limit. 15

119 5.4.6 Year 29. BAM Figure 18 Time-series plot vs. BAM (µg/m 3 ) Table 24 Statistical analyses. Middletown 29 BAM Mean Mean Standard Error Standard Error Median 11 Median 11.8 Mode 14 Mode 15 Standard Deviation Standard Deviation Sample Variance Sample Variance Kurtosis Kurtosis Skewness Skewness Range 27.6 Range 29.7 Minimum 3.9 Minimum 3.3 Maximum 31.5 Maximum 33 Sum Sum Count 17 Count 17 Confidence Level(95.%) Confidence Level( t-test: Paired Two Sample for Means BAM Mean Variance Observations Pearson Correlatio Hypothesized Mea df 16 t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail The time series-plot as the previous year showed that BAM collected more concentration of PM 2.5 than. However, few days of May, August, November, and December were greater. Also statistical analyses were made (Table 24). Mean, standard deviation, median, and mode were very similar. More important, the p-value for two-tail showed no statistic differences. Even though, no statistical differences were found a scatter plot was made so corroborate the analyses. 16

120 BAM BAM Middletown y =.9769x R² = Figure 19: Scatter plot vs. BAM. Middletown 29 The scatter plot for this year (Figure 19) showed a tendency similar to previous year. The range of concentration was between 5. and 15.. However, higher concentration was collected. Even though, the R-square was not so high (.8675) as previous years the linear regression formula showed that the CM data is inside the acceptance limits. Also the trend line of the CM data was similar to the gold standard. Also, seasonal plots were made to study the trend of the CM data. Summer y = 1.41x R² = Figure 11: Summer plot vs. BAM 17

121 Few data was plotted into the summer scatter plot due instrument issues. However, the linear regression formula showed that the CM data is inside the acceptance limits. 5.5 Taft Year 24 Taft site has two different devices. One in the TEOM and the second one is the TEOM with no tephelometer. This site two will be presented in two graphs. A time-series plot and statistical analyses were made and are shown in Figure 111. Figure 111 Time-series graph vs. TEOM (µg/m 3 ). Taft 24 Table 25 Statistical analyses. Taft 24 TEOMfdms Mean Mean t test for two samples Tipic error Tipic error TEOMfdms Median 13.8 Median 17 Mean Moda 14.5 Moda 19.5 Standard Deviation Standard Deviation Varience Variance Variance n Curtosis Curtosis Correlation Asimetric coenficien Asimetric coenficie mean difference Range 37.7 Range 42.6 Grades of freedom 114 Min 3.9 Min 3.3 Statistic t Max 41.6 Max 45.9 P(T<=t) one tail 2.517E-2 Sum Sum t critic value(one tail) Count 115 Count 115 P(T<=t) two tails 5.35E-2 Confidence level( Confidence level( t critic value (two tails)

122 TEOM TEOM In a time-series plot during cold weather had a lower concentration collecting. However, during the warmer weather and TEOM had similar trend. More important are the statistical analyses (Table 25) because the p-value for two-tails showed a low value (5.35E-2) but with a good correlation (.94). This means that there are statistic differences. These differences can be observed in the Figure 112. a) b) y = 1.947x R² =.8919 TAFT TAFT 24 y = 1.93x R² = Figure 112 Scatter plot vs. TEOM (a) all data (b) outliers removed. The scatter plot of vs. TEOM showed that the CM data was low if it compared with the gold standard. In the Figure 112(a) is showed the raw data for the TEOM. A high R-square was found (.8919). However, the linear regression formula showed that the CM data was outside the acceptance limits. Some outliers were found and removed (5). A new graph was made (b) and a higher R-square was found (.9272). However, even with outliers removed the CM data was out of the acceptance limits. Also, seasonal scatter plot were made to compare the trend of the CM data for summer and winter. The Figure 113 shows the scatters plot. 19

123 TEOM TEOM a) Summer(Jul-Sep) y = 1.784x R² = b) Winter(Dec-Feb) y = 1.323x R² = Figure 113 Scatter plot for (a) summer all data and outliers removed and (b) winter. For summer, the tendency of the CM data was higher than the gold standard. However, a high R-square was found (.9656) but the CM data was outside of the acceptance limits. One outlier was removed to improve the CM data. A new and higher R-square was found (.9818) and the linear regression formula showed the CM data is in the limits of acceptance. On the other hand, winter plot was different. The trend line of the CM data was almost the same of the gold standard. Also a high R-square was found (.9557) and the linear regression formula showed that the CM data was inside the acceptance limits graph. 11

124 Also for the same Taft site, a TEOMNT (TEOM with no tephelometer) was collecting PM 2.5 concentration. The trend of this equipment is different with respect to the TEOM. This is shown in the time-series plot (Figure 114) and the statistical analyses in Table 26. Figure 114 Time-series plot vs. TEOMNT (µg/m 3 ) Table 26 statistical analyses and TEOMNT. Taft 24. **TEOM t test for two samples Mean Mean Tipic error Tipic error **TEOM Median 13.8 Median 12.8 Mean Moda 14.5 Moda 7.4 Varience Standard Deviation Standard Deviation n Variance Variance Correlation Curtosis Curtosis mean difference Asimetric coenficient Asimetric coenficien Range 37.7 Range 41 Grades of freedom 112 Min 3.9 Min 3.7 Statistic t Max 41.6 Max 44.7 P(T<=t) one tail Sum Sum t critic value(one tail) Count 113 Count 113 P(T<=t) two tails Confidence level( Confidence level( t critic value (two tails) In the time-series plot the trend of this equipment was different than the TEOM. In a comparison TEOMNT showed in the same time that for cold weather was lower in sampling than TEOM. On the other hand, this equipment showed than for the same period of time was the opposite. Most 111

125 TEOMNT important the bigger differences were in the statistical analyses (Table 26). Mean, median, and standard deviation was similar. Nevertheless, a high correlation (.9281) and the p-value showed (.24) no statistical differences. Also this is presented in a scatter plot in the Figure TAFT y =.9238x R² = Figure 115 Scatter plot vs. TEOMNT. Even though, the R-square was low than previous year, the trend line showed the CM data had almost a same trend that the gold standard. Also the linear regression formula showed that the CM data is inside the acceptance limits. Nevertheless, seasonal graphs were made to compare the trend of the CM data with respect of time. This shows in the Figure

126 TEOMNT TEOMNT a) b) Summer(Jul-Sep) y = 1.278x R² = Winter(Dec-Feb) y = 1.373x R² = Figure 116 Seasonal plots for summer (a) and winter (b). The two seasonal plots showed a high R-square. Summer showed the highest R-square (.9749) and also the trend of the CM data showed that was similar to the gold standard. Furthermore, the linear regression model showed that the CM data was inside the acceptance limits. Nevertheless, the winter graph showed similar tendency. However, the R-square was lower than summer (.9293) but the CM data is inside the acceptance limits. 113

127 5.5.2 Year 25 As the previous year two analyses will be presented: One analysis for TEOM and the second one for the TEOMNT. A time-series graph was made to show the trend of the instruments with respect of time. Also statistical analyses were made to identify differences. In the Figure 117 shows the time-series plot and statistical analyses (Table 27 ). Figure 117 Time-series plot (µg/m 3 ). Taft 25. Table 27 statistical analysis. Taft 25. TEOM t test for two samples Mean Mean Tipic error Tipic error TEOM Median 16.2 Median 19.7 Mean Moda 17.9 Moda 9 Varience SD SD n Variance Variance Correlation Curtosis Curtosis mean difference Asimetric coenfic Asimetric coenficien Grades of freedom 114 Range 47.2 Range 53 Min 4.9 Min 5.6 Statistic t Max 52.1 Max 58.6 P(T<=t) one tail E-36 Sum Sum t critic value(one tail) Count 115 Count 115 P(T<=t) two tails 2.483E-36 Confidence level( Confidence level( t critic value (two tails)

128 TEOM TEOM showed in the time-series plot that collected more concentration than. However, had similar trend with less concentration with respect to TEOM. Differences were found in the statistical analyses. The p-value found was really low (2.48E-36) which indicates that statistical differences were found between the two methods. To show the differences a scatter plot was made and is presented in Figure Taft y = 1.875x R² = Figure 118: Scatter plot TEOM vs.. Taft 25 The scatter plot showed a good agreement between the methods. A high R-square was found (.9722). However, the trend line was higher than the gold standard. The CM data was outside the acceptance limits. 115

129 TEOM TEOM a) y = 1.893x R² =.9913 Summer(Jun-Aug) b) Winter(Dec-Feb) y = 1.12x R² = Figure 119: Seasonal plots for summer (a) and (b) winter all data and outliers removed. Summer plot (a) showed one on the highest R-square (.9913). However, when the trend of the CM data is compared with the gold standard trend, is higher. More important mention the CM data was outside of the acceptance limits. Nevertheless, a winter (b) plot was made with a high R-square (.964). However, some outliers were found. These outliers were removed (2) to improve the CM data. The new R-square (.9323) showed a new linear regression equation. But the CM data was outside of the acceptance limits. 116

130 Also for the same site other equipment was evaluated (TEOMNT) the same procedure was made. In the figure 118 shows the time-series plot of the equipment and the statistical analyses for the instrument. As the previous year the TEOMNT had better agreement than the TEOM. Scatters plot were also made for the entire year and seasonal graph as well. a) TEOMNT Figure 12 Time-series plot (µg/m 3 ). vs. TEOMNT Table 28 statistical analyses. Taft 25 **TEOM Mean Mean t test for two samples Tipic error Tipic error **TEOM Median 16.2 Median 14.4 Mean Moda 17.9 Moda 9.8 Standard Dev Standard Dev Varience Variance Variance n Curtosis Curtosis Correlation Asimetric coe Asimetric coe mean difference Range 47.2 Range 48.8 Grades of freedom 114 Min 4.9 Min 5.7 Statistic t Max 52.1 Max 54.5 P(T<=t) one tail.819 Sum Sum t critic value(one tail) Count 115 Count 115 P(T<=t) two tails.1638 Confidence l Confidence l t critic value (two tails)

131 TEOMNT Differences were found for this year. For 24 TEOMNT was always higher than. This year had more concentration collection than TEOMNT (Figure 12). This can be corroborating in the analyses. The p-value (Table 28) was lower than the alpha value. This indicates that statistical differences were found. However, the analyses found a really high correlation (.96) which helped to find that the CM data had a good agreement. Figure 121 shows this agreement throughout scatter plots y = 1.114x R² =.9271 TAFT Figure 121: Scatter plot vs. TEOMNT. The trend line of the CM data was similar to the gold standard. However, outliers were found. On the other hand, the R-square found was high (.9271). The linear regression formula showed that the CM data is inside the acceptance limits. No data was removed. However, removing the outliers the R-square can be improved. As the previous year also seasonal plots were made to study the trend of the CM data with respect of time. This is shown in Figure

132 TEOMNT y = 1.538x R² =.9715 Summer(Jun-Aug) Figure 122 Scatter plot for summer. The scatter plot showed a linear tendency. The CM data was similar with the gold standard. Furthermore, a high R-square was found (.9715). Nevertheless, the linear regression formula showed that the CM data is inside the acceptance limits Year 26 For this year just one instrument will be presented (TEOM). As the previous years a time-series plot and statistical analyses were made to shown the tendency of the CM data. This is presented in the Figure 123 a) Figure 123 Time-series plot TEOM vs. (µg/m 3 ) 119

133 TEOM Table 29 statistical analyses. TEOM 26. TM/ f-t Mean Mean Tipic error Tipic error Median 11.6 Median 15.5 Moda 8.5 Moda 1.8 Standard Dev Standard Dev Variance Variance Curtosis Curtosis Asimetric coe Asimetric coe Range 31.2 Range 39.1 Min 3.3 Min 2.3 Max 34.5 Max 41.4 Sum Sum Count 119 Count 119 Confidence l Confidence l t test for two samples TM/ f-t Mean Varience n Correlation mean differe Grades of fre 118 Statistic t P(T<=t) one t E-28 t critic value P(T<=t) two t 7.255E-28 t critic value The scatter plot (Figure 123) showed that TEOM had a higher concentration compared with TEOM. Absolute differences will be finding in this model. The statistical analyses (Table 29) made showed the differences in the CM data. Even though, a high correlation was found (.9557) the low p-value (7.25E- 28) showed statistical differences between methods. Such differences will be presented in Figure 124 with a scatter plot and a linear regression y = x R² =.9135 TAFT Figure 124 Scatter plot vs. TEOM 12

134 TEOM TEOM The trend line of the CM data was higher than the gold standard. However, a good R-square was found. On the other hand, the linear regression model showed than the CM data was outside the acceptance limits. However, for this site seasonal plots responded better than the entire year. Summer and winter scatter plot (Figure 125) were plotted to study the tendency. a) b) Summer(Jun-Aug) 1. y = 1.172x R² = Winter(Dec-Feb) y = 1.262x R² = Figure 125 Seasonal plots for summer (a) and winter (b) The seasonal plot for summer (a) showed a high R-square (.954) however, a high R-square means that the CM data will be into the acceptance limits. On the other hand, for winter a really low R-square was found (.4456). However, the CM data was inside the acceptance limits. If outliers for winter are removed the CM data will be outside the limits. This site will be analyzed with a multiple linear regression to increase the R-square. 121

135 5.5.4 Year 27 Figure 126 Time-series plot vs. TEOM (µg/m 3 ) Table 3 Statistical analyses. Taft 27 TEOM t test for two samples Mean Mean TEOM Tipic error Tipic error Mean Median 12.7 Median 19.7 Moda 7.6 Moda 11.9 Varience Standard Dev Standard Dev n Variance Variance Correlation Curtosis Curtosis mean difference Asimetric coe Asimetric coe Grades of freedom 1 Range 37.9 Range 44.2 Statistic t Min 4 Min 5.1 P(T<=t) one tail 3.925E-33 Max 41.9 Max 49.3 Sum Sum t critic value(one tail) Count 11 Count 11 P(T<=t) two tails E-33 Confidence l Confidence l t critic value (two tails) Figure 126 shows the time-series plot of the trend of the and TEOM. However, showed a lower concentration compared with TEOM during almost all year. Just a few days of February was higher than TEOM. Those differences were presented in the statistical analyses (Table 3) where the p- value for two-tails showed a really low number (6.18E-33). Also the mean, the media and the standard deviation showed the differences. Furthermore, scatters plots were made to study the trend of the instruments. 122

136 TEOM TEOM TEOM TAFT y = x R² = Figure 127 Scatter plot vs. TEOM The scatter plot of the Figure 127 showed the CM data higher than the gold standard trend. However, the R-square (.8837) found was in agreement with the AQI. The linear regression model in the graphic showed that the CM data was outside of the acceptance limit. Nevertheless, seasonal plots were made to compare the trend of the CM data s versus time. This is shown in the Figure 128. a) b) Summer(Jun-Aug) y = 1.741x R² = Winter(Dec-Feb) y = x R² = Figure 128 summer plot (a) and winter plot (b) 123

137 The seasonal scatters plot for vs. TEOM showed a good R-square. Summer showed and R-square =.9788 and winter showed an R-square=.891. Even though a good R-square in both scatter plots, the CM data trend according with the linear regression formula is out of the acceptance limits Year 28 Figure 129 Time-series plot vs. TEOM (µg/m 3 ) Table 31 statistical analyses. Taft 28 TM/ f-t t-test: Paired Two Sample for Means TM/ f-t Mean Mean Standard Error Standard Error Mean Median Median Variance Mode 14.1 Mode 12 Observations Standard Deviation Standard Deviation Pearson Correlation Sample Variance Sample Variance Kurtosis Kurtosis Hypothesized Mean Skewness Skewness df 111 Range 28.2 Range 35 t Stat Minimum 3.3 Minimum 1.9 P(T<=t) one-tail E-1 Maximum 31.5 Maximum 36.9 t Critical one-tail Sum Sum Count 112 Count 112 P(T<=t) two-tail E-9 Confidence Level(95.%) Confidence Level(95.%) t Critical two-tail

138 TEOM The trend in the time-series plot showed in the Figure 129 was similar with the previous year. TEOM collected higher concentration of PM 2.5 compared with. Also those differences were found in the statistical analyses (Table 31). The most important factor was the p-value that was really low. This means that statistical differences were found. Such differences also were presented in the scatter plot. This is presented in the Figure 13. a) (b) Taft 28 y = x R² = Figure 13 Scatter plot vs. TEOM. (a) all data (b) outliers removed. Figure 13 two scatter plots are presented. The first one represents a plot with all the raw data. The second one represents the same data with outliers removed (6). The first graph had a low R-square (.783) and showed a scatter data compared with the gold standard. In fact, the CM data was higher than the gold standard. Almost always, when differences in the slope of the CM data vs. the gold standard are found the same evaluated model is out of the acceptance limits. Even though, differences between the CM data and the gold standard were found. Seasonal plot was made to study the CM data trend during summer. This is shown in Figure

139 TEOM Summer(Jun-Aug) y = 1.575x R² = Figure 131 summer scatter plot vs. TEOM For the summer scatter plot a high R-square was found (.9771). Nevertheless, the most important topic here is the CM data trend. Previous year, even seasonal plots the CM data was higher or lower compared with the gold standard. The CM data for summer of this year was inside the acceptance limits Year 29 For the year 29, the was changed to a BGI. However, due the BGI is a filter base similar and certificate for EPA the results were similar when was used. To follow the methodology of the previous year a time-series plot, statistical analyses, scatter plot for the entire year, and seasonal plot (summer) were made. Figure 132 Time-series plot BGI vs. TEOM 126

140 TEOM Table 32 statistical analyses. Taft 29. BGI TM/ f-t Mean 12.8 Mean Tipic error Tipic error Median 11.1 Median 18 Moda 11.1 Moda 19.2 Standard Deviation Standard Deviation Variance Variance Curtosis Curtosis Asimetric coenficient Asimetric coenficient Range 24.5 Range 29.3 Min 4 Min 6.8 Max 28.5 Max 36.1 Sum Sum Count 114 Count 114 Confidence level(95.%) Confidence level(95.%) BGI TM/ f-t Mean Varience n Correlation mean difference Grades of freedom 113 Statistic t P(T<=t) one tail E-44 t critic value(one tail) P(T<=t) two tails E-44 t critic value (two tails) The time series-plot indicates the same tendency with respect of the previous year. BGI was lower in concentration collecting with respect to TEOM. During the warmer weather the differences in concentration collecting were reduced. Also during winter the differences were reduced. However, in transition weather (cold to warm) the differences were bigger. Furthermore, to quantify the differences statistical analyses were made (Table 32 ). The most important fact was the low number of p-value which indicated statistical differences between the methods. Also a scatter plot was made to show the differences. This is presented in the Figure y = 1.62x R² =.858 Taft BGI Figure 133 Scatter plot BGI vs. TEOM. The scatter plot for this year showed the CM data was transversal to the gold standard trend. For low concentration the CM data evaluated showed that was high compared with the gold standard. On the 127

141 TEOM other hand, for higher concentration the CM data was low compared with the gold standard. Such trend differences showed a linear regression equation out of the acceptance limits even though the R-square found was high (.858). Also a summer scatter plot (Figure 134) was made to see the trend during warm weather y = 1.631x R² =.8138 Figure 134: Summer scatter plot BGI vs. TEOM. Summer BGI The summer scatter plot showed the same tendency of the scatter plot for the entire year. The R-square found was relative high (.8138). However, the linear regression formula showed that the CM data is outside of the acceptance limits. 5.6 Sycamore daily data with all instruments TEOM For sycamore between May 27 and April 28 daily data was recorded by different equipment. Although, these years were previous analyzed the data was recorded every three days. In this analysis the data presented is daily data. To analyze the present site scatter plot for one year and seasonal scatter plot were made for each instrument. As the rest of the year Figure 135 shows the time-series plot and data analyses for TEOM. 128

142 Figure 135 Time-series plot vs. TEOM (µg/m 3 ) Table 33 statistical analyses for TEOM. Sycamore. TEOM Mean Mean Tipic error Tipic error Median Median 15 Moda 7.5 Moda 1.9 Standard Deviation Standard Deviation Variance Variance Curtosis Curtosis Asimetric coenficient Asimetric coenficien Range 35.9 Range 48 Min 3.3 Min Max 39.2 Max 48 Sum Sum 5414 Count 322 Count 322 Confidence level( Confidence level( TEOM Mean Varience n Correlation mean difference Grades of freedom 321 Statistic t P(T<=t) one tail E-18 t critic value(one tail) P(T<=t) two tails E-18 t critic value (two tails The times-series plot show that vs. TEOM showed similar trend vs. time. However, during warm weather and transition weather (warm to cold) always TEOM showed a higher concentration collecting compared with. Such differences were reported in the statistical analyses (Table 33). The differences were reported in the p-value for two tails, which showed a low value (4.8465E-18). Most important is that the correlation (.8771) was low compared with the requirements of AQI. Figure 136 shows the scatter plot for TEOM. a) b) 129

143 TEOM Sycamore y = 1.95x R² = Figure 136 (a) Scatter plot vs. TEOM. (b) Scatter plot vs. TEOM outliers removed. The scatter plot of vs. TEOM showed that the raw data graph had a low R-square (.7693) and also the trend of the CM data was higher than the gold standard. Some data was removed (1) to increase the R-square. The result showed a slightly improve in the R-square. In the first case the linear regression formula showed that the CM data was close but outside of the acceptance model. On the other hand, the second linear regression formula showed that the CM data was inside the acceptance limits. Furthermore, seasonal plot were made to show the trend of the CM data. a) 13

144 TEOM TEOM y =.9269x R² =.5695 SUMMER (Jun-Aug) b) WINTER SYCAMORE (dec-feb) y = 1.278x R² = Figure 137: Seasonal plots for summer (a) all data and outlier removed and (b) winter. Seasonal plot for summer showed that plotting the entire data when outliers were present. The R- square for the entire graphic was low (.5695) and the linear regression formula show that the CM data was out of the acceptance limits. Outliers were removed (2) and the R-square had an improvement (.866). However, the linear regression formula is still outside of the acceptance limits. On the other hand, the trend for winter was completely different. The R-square found was high (.8748) and the linear regression formula showed that the CM data is inside the acceptance limits polygon. No data was removed. 131

145 5.6.2 BAM Figure 138 Time series-plot (µg/m 3 ) Table 34 Statistical analyses for BAM. Sycamore. BAM Mean Varience n Correlation mean difference Grades of freedom 342 Statistic t P(T<=t) one tail 1.548E-58 t critic value(one tail) P(T<=t) two tails 3.816E-58 t critic value (two tails) BAM Mean Mean Tipic error Tipic error Median 13 Median 16 Moda 11.5 Moda 18.8 Standard Deviation Standard Deviation Variance Variance Curtosis Curtosis Asimetric coenficient Asimetric coenficient Range 37.5 Range 44.2 Min 3.3 Min 4.7 Max 4.8 Max 48.9 Sum Sum Count 343 Count 343 Confidence level(95.% Confidence level(95.%) The times-series plot (Figure 138) showed that the devices had similar trend during transition weather (warm-cold) and higher differences between them were seen in cold weather. Differences can be confirmed with the p-value (3.81E-58) which showed a low number. However, the correlation (.932) showed is high. a) b) 132

146 BAM BAM Sycamore y = 1.128x R² = Figure 139 Scatter plot vs. BAM (a) all data (b) outliers removed. The scatter plot showed a high R-square (.8654). However, the trend of this model was higher than the gold standard. That is the reason why the linear regression formula showed that the CM data is outside of the acceptance limits. Some outliers were removed (9) to improve the CM data, but the improvement was a slightly R-square (.8795) improved. The CM data is still outside the acceptance limits. Nevertheless, seasonal plots were made to see the tendency of the CM data. a) SUMMER(Jun-Sep) y = x R² = b) 133

147 BAM y =.97x R² =.8432 Winter(Dec-Feb) Figure 14 Seasonal scatter plot for summer (a) all data and outliers removed. (b) Winter. The seasonal plots (Figure 14) for summer and winter had different trend. For summer the R-square found with the entire data was high (.8871). However, the linear regression showed that the CM data was out of the limits. Outliers were removed (2) to try to improve the CM data in R-square and in the linear regression. However, the R-square improve (.9185) but the linear regression formula was out of the limits. On the other hand, the winter plot showed a trend close to the gold standard. Even though, the R-square was smaller than the summer plot, the linear regression formula indicated that the CM data is inside the acceptance limits SHARP Figure 141 Time-plot series vs. SHARP (µg/m 3 ). Sycamore 134

148 SHARP Table 35 Statistical analyses for SHARP. Sycamore. Sharp Mean Mean Tipic error Tipic error Median 12.2 Median 13.9 Moda 11.5 Moda 12.5 Standard Deviation Standard Deviatio Variance Variance Curtosis Curtosis Asimetric coenficie Asimetric coenfici Range 37.5 Range 47 Min 3.3 Min 2.6 Max 4.8 Max 49.6 Sum Sum Count 33 Count 33 Confidence level( Confidence level( Sharp Mean Varience n Correlation mean difference Grades of freedom 32 Statistic t P(T<=t) one tail 9.595E-15 t critic value(one tail) P(T<=t) two tails E-14 t critic value (two tails) The trend of vs. SHARP was better than the other devices. The time-series plot showed no high differences. However, in the statistical analyses the p-value for two-tails showed a low number (1.91E- 14) which means statistical differences even though the correlation was high (.9). Nevertheless, scatter plots were made to see the differences. Sycamore y =.9962x R² = Figure 142 Scatter plot vs. SHARP 135

149 SHARP SHARP The trend of the CM data showed in the Figure 142 was a little higher compared with the gold standard. However, compared with the other devices, the linear regression formula showed that the CM data was inside the acceptance limits. Although, outliers were found in the plot, high R-square value (.8161) fitted model, no data were removed. However, seasonal plots were made to see the trend vs. date. a) Summer(Jun-Sep) y =.966x R² = b) y =.9577x R² =.916 Winter(Dec-Feb) Figure 143 Scatter plot for summer (a) all data and outliers removed (b) winter. Figure 143 shows a trend similar to the scatter plot for the entire year. The CM data fits into the limits during summer. However, the R-square (.61) is too low with respect to the AQI requirements. Outliers 136

150 were removed to improve the R-square. A new R-square was found (.7649) and yet the CM data is inside the limits. On the other hand, winter plot had better agreement. The R-square (.916) found showed that come along with a linear regression formula that was inside the limits. No outliers were removed. Table 36 is a summary that presents the slope and intercept of each linear regression model. Values in the slope closest to 1 and values of intercept closest to can warrant that the CM data will be inside the acceptance limits. Table 36 Slope and intercept of each instrument Slope Intercept Slope Intercept Slope Intercept Slope Intercept Slope Intercept Slope Intercept TEOM Taft no outliers Summer no outliers Winter no outliers TEOMNT Taft Summer Winter BAM Middle no outliers Summer no outliers Winter no outliers BAM Sycam no outliers Summer no outliers Winter no outliers TEOM Batav no outliers Summer no outliers Winter no outliers BAM Lebanon Summer no outliers ` Winter SHARP Syca no outliers Summer no outliers

151 Table 36 indicates that the data from TEOM at Taft, and BAM at Sycamore, TEOM at Sycamore are constantly outside the compliance polygon. The other sites, such as Middletown, Lebanon and Batavia, the CM (TEOM and BAM) data agrees better with the FRM and are mostly within the compliance polygon. It might be that the Taft and Sycamore sites are more subject to urban sources, and more subjected to the pollutants carried by the predominant wind 5.7 Summary of results Multiple linear regressions. A multiple linear regressions were made to increase the CM data (R-square). However, in this area will show the results of the analyses. Appendix C will be showed the complete analyses. Table 37 shows the differences in R-square the results of the multiple linear regressions vs. linear regression. Table 37 Comparison of R-square of linear regression vs. multiple linear regressions LINEAR Multiple linear R LINEAR Multiple linear R LINEAR Multiple linear R LINEAR Multiple linear R LINEAR Multiple linear R LINEAR Multiple linear R TEOMNT TAFT TEOM TAFT TEOM TAFT TEOM TAFT TEOM TAFT TEOM TAFT BAM MIDDL TEOMN TAFT TEOM BATA TEOM BATA TEOM BATA TEOM BATA TEOM TAFT TEOM BATA BAM MIDD BAM MIDD BAM MIDD BAM MIDD BAM SYCA BAM SYCA BAM SYCA Bam SYCA BAM LEBAN BAM LEBAN BAM LEBAN Non-linear regression results for Taft and Sycamore The results of non-linear regression are presented as R-square in Table 38. Furthermore, linear and multi linear regression are presented to make a comparison of the three methods. Even though, a slightly improvement was found in the R-square the results cannot be used yet, for a publication with the AQI. The standards for R-square of AQI mention validation for simple and multi linear regression not for nonlinear regression. [28].The complete integrations made using SAS software are presented in Appendix 138

152 D. In addition, the analyses of multiple linear regression and non-linear regression included all data, no outliers were removed. Table 38 R-square of non-linear, linear and multi linear regression. NON-LINEAR REGRESSION RESULTS TAFT Sycamore Year Non-linear Linear TEOM Multi Non-linear Linear BAM Multi CONCLUSION The agreement of FRM and CM PM2.5 measurements was evaluated at the following five sites managed by the HCDOES Taft, Sycamore, Lebanon, Middletown and Batavia. The FRM is a filter based method. The various CM technologies include beta attenuation, TEOM (regular and filter dynamic) and beta attenuation with a nephelometer. Approximately six years of data from 24 to 29 were analyzed at most sites. Linear regression between the CM and FRM data were first performed for each year. Also for summer and winter. The regression results, such as the slope and intercept, were plotted into the acceptance polygon EPA has suggested [26]. The data in Lebanon, Batavia, and Middletown (entire year and winter) mostly fall within the acceptance polygon. This is an indication that the FRM and CM measurements at these sites agree well and it would be feasible to replace FRM 139

153 with CM. The regression for Taft and Sycamore sites, however, are out of the acceptance polygon for most of the times, although the R-square values sometimes are high (>.95). Both of the sites are at urban locations, which are subject to complex sources including traffic and industry. Through the investigation, it is also learned that there is no correlation between R- squared and acceptance limit. Some years a high R-square (>.95) showed that the model was outside the acceptance limits. On the other hand, a low R-square (<.7) showed, in one year, better agreement compared with the acceptance limits graph. Removal of outliers (less than 5 out of 135) were performed at some sites for some years, which can slightly improve the R- square values and result in the CM data to be within the acceptance polygon. In addition, temperature data were also obtained from the actual site (such as Sycamore) or from the NOAA site (at the Lunken Airport), and were incorporated into multilinear and nonlinear regressions (Taft and Sycamore). Both regressions can result in R-square improvements and the improvement is greater for nonlinear regression. This indicates that temperature is a contributing factor to the data agreement between FRM and CM. Instrument wise, the BAM and Sharp instruments showed higher correlation than the TEOM. It s important to mention that CM has reliability in other countries like Mexico [15], Zimbabwe [31], Mediterranean area [32], and Nepal [33]. 14

154 7. FUTURE WORK As future works, Sycamore and Taft sites needs more analyses due the lack of agreement between the two methods. Daily data with the Sycamore site showed better agreement with equipment such as BAM and SHARP. For these two sites, it would be desirable to further study the speciation data in order to understand which species might contribute to data variation. One minor issue arose with the t-test at some sites (Sycamore and Taft), that it failed to indicate that the FRM and CM data are statistically different. A paired t-test should be performed. Also for sites as Lebanon, Batavia, and Middletown an economical study can be made to show the benefits that HCDOES can save when just one method is used. 141

155 8. REFERENCES [1] HCDOES. Hamilton County Environmental Services, (26). Pm2.5 FRM vs. CM: one site many numbers [2] EPA. (21) CFR Chapter 1, Retrieved from title4-vol5/pdf/cfr-21-title4-vol5-sec53-35.pdf visited date 1/1/1 [3] Cobourn, W.G. (21). An enhanced pm2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmospheric Environment, 44, [4] USEPA, (29, October 15). Particulate matter. Retrieved from [5] USEPA, (28). Particulate matter. Retrieved 3/13/1 from [6] ACGIH (21). Air Sampling Instrument for Evaluation of Atmospheric Contaminants, 9th Ed. Cincinnati, OH: ACGIH, Inc. [7] Meyer, M, Patashnick, H, & Rupprecht, E. (2). Development of a sample equilibration system for the TEOM continuous PM monitor. Air & Waste manage. Assoc., 5, [8] Thermo, (21). Filter dynamics measurement system, model 85. Retrieved 3/13/1 from [9] faq. Retrieved 3/1/211. [1] Lowermanhattan. (25, September). Teom series 14a ambient particulate monitor. Retrieved from 142

156 [1] Thermo, (21). Sharp monitor, model 53. Retrieved from [11] BAM (Owner Manual) [12] (Owner Manual) [13] BGI (Owner Manual) [14] EPA, Environmental Protection Agency, Office of Air Quality Planning and Standards. (1998).Quality assurance guidance document pm2.5 mass weighing laboratory standard operating procedures for the performance evaluation program [15] Vega, E., E. Reyes., A. Wellens., G.Sanchez., J.C. Chow., J.G. Watson (23). Comparison of continuous and filter based mass measurements in Mexico City. Atmospheric environment, 37, [16] Glover, B., Kleinman, M., Eatough, M., Eatough, D., Hopke, P., Long, R., Wilson, W., Meyer, M., Ambs, J. (25). Measurement of total PM 2.5 mass (nonvolatile plus semivolatile) with the filter dynamic measurement system tapered element oscillating microbalance monitor. Journal of Geophysical Research, 11, [17] Schwab, J., Felton, H., Rattigan, V., & Demerjian, K. (26). New York state urban and rural measurements of continuous pm 2. 5 mass by fdms, teom, and bam. Journal of the Air & Waste Management Association, 56,

157 [18] Zhu, K., Zhang, J., & Lioy, P. (26). Evaluation and comparison of continuous fine particulate matter monitors for measurements of ambient aerosols. Journal of the Air & Waste Management Association, 57, [19] Chow, J., Watson, J., Lowenthal, D., Chen, L., Tropp, R., Park, K., Magliano, K. (26). Pm2.5 and pm1 mass measurements in California s San Joaquin valley. Aerosol Science and Technology, 4, [2] Hauck, H. (24). On the equivalence of gravimetric pm data with TEOM and beta-attenuation measurements. Journal of Aerosol Science, 35, [21] Lewtas, J., Pang, Y., Booth, D., Reimer, S., Eatough, D., Gundel, L. (21). Comparison of sampling methods for semi-volatile organic carbon associated with pm2.5. Aerosol Science and Technology, 34, [22] Takahashi, K. (28). Examination of discrepancies between beta-attenuation and gravimetric methods for the monitoring of particulate matter. Atmospheric environment, 42, [23] Rizzo, M., Scheff, P.,Kaldy,W., (23). Adjusting tapered element oscillating microbalance data for comparison with federal reference method PM 2.5 measurements in region 5. Air & Waste manage. Assoc., 53( ), [24] Kashuba, R., & Scheff, P. (28). Nonlinear regression adjustments of multiple continuous monitoring methods produce effective characterization of short-term fine particulate matter. Air & Waste manage. Assoc., 58, [25] HCDOES (29). Division of air pollution control air monitoring plan. Retrieved from visited: Feb

158 [26] EPA,. Environmental Protection Agency, (22). Data quality objectives (DQOs) for relating federal reference method (FRM) and Continuous pm2.5 measurement to report an air quality index (AQI)(EPA- 454/B-2-2). North Carolina [27] NOAA,. (211). NNDC climate data online. Retrieved from [28] Bortnick, S., Coutant, B., Eberly, S., (22). Using continuous PM2.5 monitoring data to report an air quality index. Air & Waste manage. Assoc., 52, [29] Hill, J., Ryszkiewicz, E., Turner, J., (26). Field performance evaluation of the thermo electron model 53 sharp for measurement of ambient pm mass concentration. 99th Annual Meeting of the Air & Waste Management Association, New Orleans, LA, [3] Lee, J.., Hopke, P., Holsen, T., Polissar, A., Lee, D., Edgerton, E., Ondov, J., Allen, E., (21) Measurements of fine particle mass concentrations using continuous and integrated monitors in eastern us cities. Aerosol Science and Technology, 39, [31] Kuvarega, A.T., Taru, P., (28). Ambiental dust speciation and metal content variation in TSP, PM 1 and PM in urban atmospheric air of Harare (Zimbabwe).Environ Monit Assess, 144, [32] Pateraki, St., Asimakopoulos, D.N., Maggos, TH., Vasilakos, Ch., (21). Particulate matter levels in a Suburban Mediterranean area: analysis of a 53-month long experimental campaign. Journal of Hazardous Materials, 182, [33] Aryal, R., Lee, B., Karki, R., Gurung, A., Baral, B., Byeon, S. (29). Dynamics of PM 2.5 concentrations in Kathmandu valley, Nepal. Journal of Hazardous Materials, 168,

159 146

160 Appendix A Maps of the sites Lebanon site Sycamore site 147

161 Taft site Batavia site 148

162 Middletown site 149