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SpectraSort: A data analysis program for real-time aerosol analysis by aerosol time-of-flight mass spectrometry
Chemometrics and Intelligent Laboratory Systems 37 Ž1997. 197–203
SpectraSort: A data analysis program for real-time aerosol analysis by aerosol time-of-flight mass spectrometry David P. Fergenson ) , Don-Yuan Liu, Philip J. Silva, Kimberly A. Prather UniÕersity of California, RiÕerside, CA 92521, USA
Abstract A computer program, SpectraSort, has been written to facilitate the analysis of ambient aerosol data acquired in our laboratory using aerosol time-of-flight mass spectrometry ŽATOFMS.. ATOFMS is a unique aerosol analysis technique developed to obtain the size and chemical composition of individual aerosol particles. Conventional aerosol analysis methods can only provide the average chemical composition of many particles for a given size range, or the size of individual particles, but not their chemical composition. Knowledge of both the size and composition of individual aerosol particles ultimately will help evaluate particle toxicity and reactivity, as well as assist in the identification of particle emission sources. These three pieces of information are vital in any rigorous attempt to regulate particulate pollution in the atmosphere. At present, in ATOFMS data analysis, each individual particle mass spectrum must be calibrated manually, and any compositional information tabulated for subsequent correlation with the size of the corresponding particle. SpectraSort greatly facilitates the processing of particle size and composition information by maximizing the efficiency of manual classification; but, a fully automated solution is necessary if ATOFMS is to evolve into a routine real-time aerosol analysis tool. Keywords: Aerosols; SpectraSort; Mass spectrometry; ATOFMS
1. Introduction Recently developed in our laboratory, aerosol time-of-flight mass spectrometry ŽATOFMS . is a technique that can be used to determine the size and chemical composition of individual particles in real time. In order to obtain this information, ATOFMS combines two well-established techniques, aerodynamic particle sizing w1x and reflectron time-of-flight mass spectrometry w2x. Previous papers have described the operating principles of ATOFMS w3x. Recent applications of ATOFMS have demonstrated its
)
Corresponding author.
effectiveness for characterizing atmospheric aerosol and environmental tobacco smoke particles w4,5x. Conventional aerosol analysis techniques yield information either on the bulk chemical composition of all particles within a given size range, or the size distribution of individual particles with no chemical composition information. Until now, no in-situ data has been available on how chemical composition of indiÕidual ambient aerosols varies as a function of size because no analytical technique has existed that could precisely determine both pieces of information simultaneously. Among the features that make ATOFMS so effective at aerosol analysis is its ability for rapid aerosol particle analysis Žup to 600 particles in one minute..
0169-7439r97r$17.00 Copyright q 1997 Published by Elsevier Science B.V. All rights reserved. PII S 0 1 6 9 - 7 4 3 9 Ž 9 7 . 0 0 0 0 6 - 3
Fig. 1. The SpectraSort data entry window and the calibrated mass spectrum of a single particle.
198 D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
When analyzing ambient aerosol particles for one twenty-four hour period at an average acquisition rate of 20 particles per minute Ža typical rate at normal ambient particle concentrations., the mass spectra of nearly twenty-nine thousand particles are acquired, linked with size data, and stored. Ultimately, the spectra must be organized into some sort of chemical classification scheme. Presented here is an overview of the first generation of data analysis software that is currently being used to analyze the tremendous amount of data that the ATOFMS instrument produces. At present, the method of analysis is slow, labor-intensive, and subject to operator bias, and, as a result, computer-automated alternatives are being investigated.
199
2. Experimental SpectraSort was developed in Microsoft Visual Basic 3.0, professional edition, on a Pentium 90 IBM-PC compatible computer running Microsoft Windows 3.11. Visual Basic was selected over packaged data analysis programs for the customizability of its user interface and its ease and rapidity of software development. Using Tofware ŽIlys Software, Pittsburgh, Pennsylvania., the mass spectrum of each individual particle is calibrated, identifying the massrcharge ratios of all detected peaks. The operator identifies which of up to sixty user-defined Boolean characteristics typify a mass spectrum, and enters them in SpectraSort.
Fig. 2. A search window in SpectraSort, showing a search for all particles containing inorganic components.
200
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
Fig. 1 shows the data entry window, along with a particle time-of-flight mass spectrum calibrated using TOFWARE. This particular mass spectrum shows mainly inorganic salt peaks, but organic molecules such as nicotine and polycyclic aromatic hydrocarbons have been detected in particles measured by ATOFMS. The mass-to-charge ratios of all detected ions from the particle are indicated in the TOFWARE window. Typically, the ions are singly charged and, thus, the mass-to-charge ratios represent the mass Žmolecular or atomic. of the individual ions. The boxes to the left of the particle attributes in the SpectraSort windows are checked to correspond to the
chemical characteristics of one specific particle mass spectrum. As shown in this figure, the particle characteristics can include the presence of individual species, whether it contains inorganic or organic components, or more general information about the particle, such as if it appears to be of marine or terrestrial origin. Once the particle’s chemical composition data has been tabulated from the particle mass spectrum, the process is repeated for all subsequent particles. After the discrete chemical characteristics of all of the particles have been assigned, a search is performed to find the sizes of all particles with charac-
Fig. 3. Size histograms for three different chemical class searches displayed simultaneously with the size histogram of all particles within the time window.
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
teristics that match a particular chemical profile. The user can decide whether or not to test for each of the sixty characteristics, and whether each characteristic being tested for should be present or absent. Fig. 2 shows the window for a search configured to find all inorganic particles that specifically contain sodium, magnesium potassium, and calcium. It should be noted that some of the user-defined particle characteristics in Fig. 2 are used to convey a general impression that the operator had of a particle, such as whether its composition was predominantly inorganic, or whether it contained a significant amount of nitrate species. An X in a box to the left of the word ‘Test?’ instructs the computer to check for this specific attribute in all particles sampled during a particular time interval. Specifically, if the user wants to
201
test for a specific attribute, an X is placed in the box next to the label ‘TrF’ to instruct the computer to select only those particles with that attribute, while the absence of an X instructs the computer to select only those particles without that attribute. Once the search terms have been entered, SpectraSort automatically plots a histogram of the sizes of all particles that have tested true in that search. The size histograms of up to six searches can be displayed simultaneously for comparison as shown in Fig. 3. A slide bar at the bottom of the histogram window, as shown in Fig. 3, allows the user to select the specific time window of aerosol collection to be plotted out of the entire sampling period. By using the slide bar at the bottom of the histogram window, the user can scroll backwards and forwards in time to observe size and
Fig. 4. A three dimensional view of the particle size histograms corresponding to the three searches.
202
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
composition changes of the relative populations of different particles that occur throughout the sampling period. The numbers at the bottom of the histograms are directly proportional to the aerodynamic sizes of the particles. Using this method for displaying size histograms, discrete compositional classes can be isolated, and correlations of particle size versus composition can be determined. Also, the chemical species that are associated with individual particles or a particular source can be established, as can the relative frequency of particles from that source. Searches for different particle types can be added or subtracted so a search for any Boolean combination of discrete characteristics is possible. After several searches have been performed, the user can plot a three dimensional view of the data from these histograms for comparison of size versus chemical composition ŽFig. 4., or plot a pie chart showing the relative abundance of the particles in the different chemical classes. The y-axis in the 3D plot represents the frequency of particles in a given size bin. The x-scale Ži.e., particle size. can be plotted logarithmically to allow for the comparison of histograms of particles with sizes ranging over several orders of magnitude. The size histogram data can be exported as an ASCII file for manipulation using various spreadsheet programs.
3. Results and discussion Generally, in ATOFMS, particle classification requires that discrete pieces of chemical composition information be extracted from the mass spectra. Then, some grouping scheme pertaining to that information, quantitative or qualitative, must be used to differentiate the particle classes from one another. The discrete pieces of information that form the basis for particle segregation may take the form of the presence of a single mass-to-charge ratio corresponding to a particular species or the coexistence of several peaks. It is possible for the data to be quantitative as well; for example, the height or area of peaks can be tabulated. In certain cases, quantitative information can be used to determine a specific piece of Boolean information, as for the case of peaks that must appear in certain ratios to one another to signify the presence of a particular species Ži.e., relative isotopic
abundance.. Other times, the intensity of a peak can be used to represent the quantity of a particular species in the particle. Once extracted from the particle mass spectra, these pieces of information must be tabulated in some way to find what types of particles were present at the time of sampling. What constitutes a given type of particle and how many types exist must be determined as part of the classification scheme. Once the particles are classified according to composition, a size histogram of all particles in one composition class can be plotted and the size versus composition correlation established. This correlation is the first step in the determination of an aerosol particle’s origin and of the overall particle toxicity in a given sampling interval. Currently, one extracts discrete chemical information from each particle and decides what pieces of information determine whether or not a particle belongs in a particular group. The drawbacks to this system are twofold: Ž1. the decision of which discrete characteristics should constitute a particle type is subject to personal bias, and Ž2. the process of manually assigning discrete characteristics to a particle is extremely slow. Currently, it takes approximately one-hundred hours to calibrate and characterize the mass spectra of particles acquired in one day. By automating the process in some way, it may be possible to determine non-readily-discernible particle classes and analyze data in real time at a rate approaching that of collection. Additionally, SpectraSort is only practical for tracking eight to ten particle classes while there may be hundreds in existence for any given time interval. As particle types change over time and new ones appear, a data analysis technique with the ability to update particle classes continuously is necessary. SpectraSort automates the mechanical aspects of the particulate data analysis process, but it is not an efficient method. A researcher’s input is required to determine which discrete aspects of a particle are present or absent and no data currently exists on the exact link between the size and chemical composition of aerosol particles. Therefore, computer-automated methods for determining particle classes and assigning particles to those classes are currently being investigated. A copy of the Visual Basic 4.0 16bit source code is available from the authors upon request.
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
Acknowledgements The authors would like to thank Dimitra Stratis for her advice concerning the user interface aspects of the program.
References w1x B. Dahneke, Nat. Phys. Sci. 244 Ž1973. 54–55. w2x B.A. Mamyrin, V.I. Karataev, D.V. Shmikk and V.A. Zagulin, Sov. Phys. JETP 37 Ž1973. 45–48.
203
w3x K.A. Prather, T. Nordmeyer and K. Salt, Anal. Chem. 66 Ž1994. 1403–1407. w4x B.D. Morrical and K.A. Prather, Environ. Sci. Technol., submitted for publication. w5x C.A. Noble and K.A. Prather, Environ. Sci. Technol. 30 Ž1996. 2667–2680.
SpectraSort: A data analysis program for real-time aerosol analysis by aerosol time-of-flight mass spectrometry David P. Fergenson ) , Don-Yuan Liu, Philip J. Silva, Kimberly A. Prather UniÕersity of California, RiÕerside, CA 92521, USA
Abstract A computer program, SpectraSort, has been written to facilitate the analysis of ambient aerosol data acquired in our laboratory using aerosol time-of-flight mass spectrometry ŽATOFMS.. ATOFMS is a unique aerosol analysis technique developed to obtain the size and chemical composition of individual aerosol particles. Conventional aerosol analysis methods can only provide the average chemical composition of many particles for a given size range, or the size of individual particles, but not their chemical composition. Knowledge of both the size and composition of individual aerosol particles ultimately will help evaluate particle toxicity and reactivity, as well as assist in the identification of particle emission sources. These three pieces of information are vital in any rigorous attempt to regulate particulate pollution in the atmosphere. At present, in ATOFMS data analysis, each individual particle mass spectrum must be calibrated manually, and any compositional information tabulated for subsequent correlation with the size of the corresponding particle. SpectraSort greatly facilitates the processing of particle size and composition information by maximizing the efficiency of manual classification; but, a fully automated solution is necessary if ATOFMS is to evolve into a routine real-time aerosol analysis tool. Keywords: Aerosols; SpectraSort; Mass spectrometry; ATOFMS
1. Introduction Recently developed in our laboratory, aerosol time-of-flight mass spectrometry ŽATOFMS . is a technique that can be used to determine the size and chemical composition of individual particles in real time. In order to obtain this information, ATOFMS combines two well-established techniques, aerodynamic particle sizing w1x and reflectron time-of-flight mass spectrometry w2x. Previous papers have described the operating principles of ATOFMS w3x. Recent applications of ATOFMS have demonstrated its
)
Corresponding author.
effectiveness for characterizing atmospheric aerosol and environmental tobacco smoke particles w4,5x. Conventional aerosol analysis techniques yield information either on the bulk chemical composition of all particles within a given size range, or the size distribution of individual particles with no chemical composition information. Until now, no in-situ data has been available on how chemical composition of indiÕidual ambient aerosols varies as a function of size because no analytical technique has existed that could precisely determine both pieces of information simultaneously. Among the features that make ATOFMS so effective at aerosol analysis is its ability for rapid aerosol particle analysis Žup to 600 particles in one minute..
0169-7439r97r$17.00 Copyright q 1997 Published by Elsevier Science B.V. All rights reserved. PII S 0 1 6 9 - 7 4 3 9 Ž 9 7 . 0 0 0 0 6 - 3
Fig. 1. The SpectraSort data entry window and the calibrated mass spectrum of a single particle.
198 D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
When analyzing ambient aerosol particles for one twenty-four hour period at an average acquisition rate of 20 particles per minute Ža typical rate at normal ambient particle concentrations., the mass spectra of nearly twenty-nine thousand particles are acquired, linked with size data, and stored. Ultimately, the spectra must be organized into some sort of chemical classification scheme. Presented here is an overview of the first generation of data analysis software that is currently being used to analyze the tremendous amount of data that the ATOFMS instrument produces. At present, the method of analysis is slow, labor-intensive, and subject to operator bias, and, as a result, computer-automated alternatives are being investigated.
199
2. Experimental SpectraSort was developed in Microsoft Visual Basic 3.0, professional edition, on a Pentium 90 IBM-PC compatible computer running Microsoft Windows 3.11. Visual Basic was selected over packaged data analysis programs for the customizability of its user interface and its ease and rapidity of software development. Using Tofware ŽIlys Software, Pittsburgh, Pennsylvania., the mass spectrum of each individual particle is calibrated, identifying the massrcharge ratios of all detected peaks. The operator identifies which of up to sixty user-defined Boolean characteristics typify a mass spectrum, and enters them in SpectraSort.
Fig. 2. A search window in SpectraSort, showing a search for all particles containing inorganic components.
200
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
Fig. 1 shows the data entry window, along with a particle time-of-flight mass spectrum calibrated using TOFWARE. This particular mass spectrum shows mainly inorganic salt peaks, but organic molecules such as nicotine and polycyclic aromatic hydrocarbons have been detected in particles measured by ATOFMS. The mass-to-charge ratios of all detected ions from the particle are indicated in the TOFWARE window. Typically, the ions are singly charged and, thus, the mass-to-charge ratios represent the mass Žmolecular or atomic. of the individual ions. The boxes to the left of the particle attributes in the SpectraSort windows are checked to correspond to the
chemical characteristics of one specific particle mass spectrum. As shown in this figure, the particle characteristics can include the presence of individual species, whether it contains inorganic or organic components, or more general information about the particle, such as if it appears to be of marine or terrestrial origin. Once the particle’s chemical composition data has been tabulated from the particle mass spectrum, the process is repeated for all subsequent particles. After the discrete chemical characteristics of all of the particles have been assigned, a search is performed to find the sizes of all particles with charac-
Fig. 3. Size histograms for three different chemical class searches displayed simultaneously with the size histogram of all particles within the time window.
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
teristics that match a particular chemical profile. The user can decide whether or not to test for each of the sixty characteristics, and whether each characteristic being tested for should be present or absent. Fig. 2 shows the window for a search configured to find all inorganic particles that specifically contain sodium, magnesium potassium, and calcium. It should be noted that some of the user-defined particle characteristics in Fig. 2 are used to convey a general impression that the operator had of a particle, such as whether its composition was predominantly inorganic, or whether it contained a significant amount of nitrate species. An X in a box to the left of the word ‘Test?’ instructs the computer to check for this specific attribute in all particles sampled during a particular time interval. Specifically, if the user wants to
201
test for a specific attribute, an X is placed in the box next to the label ‘TrF’ to instruct the computer to select only those particles with that attribute, while the absence of an X instructs the computer to select only those particles without that attribute. Once the search terms have been entered, SpectraSort automatically plots a histogram of the sizes of all particles that have tested true in that search. The size histograms of up to six searches can be displayed simultaneously for comparison as shown in Fig. 3. A slide bar at the bottom of the histogram window, as shown in Fig. 3, allows the user to select the specific time window of aerosol collection to be plotted out of the entire sampling period. By using the slide bar at the bottom of the histogram window, the user can scroll backwards and forwards in time to observe size and
Fig. 4. A three dimensional view of the particle size histograms corresponding to the three searches.
202
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
composition changes of the relative populations of different particles that occur throughout the sampling period. The numbers at the bottom of the histograms are directly proportional to the aerodynamic sizes of the particles. Using this method for displaying size histograms, discrete compositional classes can be isolated, and correlations of particle size versus composition can be determined. Also, the chemical species that are associated with individual particles or a particular source can be established, as can the relative frequency of particles from that source. Searches for different particle types can be added or subtracted so a search for any Boolean combination of discrete characteristics is possible. After several searches have been performed, the user can plot a three dimensional view of the data from these histograms for comparison of size versus chemical composition ŽFig. 4., or plot a pie chart showing the relative abundance of the particles in the different chemical classes. The y-axis in the 3D plot represents the frequency of particles in a given size bin. The x-scale Ži.e., particle size. can be plotted logarithmically to allow for the comparison of histograms of particles with sizes ranging over several orders of magnitude. The size histogram data can be exported as an ASCII file for manipulation using various spreadsheet programs.
3. Results and discussion Generally, in ATOFMS, particle classification requires that discrete pieces of chemical composition information be extracted from the mass spectra. Then, some grouping scheme pertaining to that information, quantitative or qualitative, must be used to differentiate the particle classes from one another. The discrete pieces of information that form the basis for particle segregation may take the form of the presence of a single mass-to-charge ratio corresponding to a particular species or the coexistence of several peaks. It is possible for the data to be quantitative as well; for example, the height or area of peaks can be tabulated. In certain cases, quantitative information can be used to determine a specific piece of Boolean information, as for the case of peaks that must appear in certain ratios to one another to signify the presence of a particular species Ži.e., relative isotopic
abundance.. Other times, the intensity of a peak can be used to represent the quantity of a particular species in the particle. Once extracted from the particle mass spectra, these pieces of information must be tabulated in some way to find what types of particles were present at the time of sampling. What constitutes a given type of particle and how many types exist must be determined as part of the classification scheme. Once the particles are classified according to composition, a size histogram of all particles in one composition class can be plotted and the size versus composition correlation established. This correlation is the first step in the determination of an aerosol particle’s origin and of the overall particle toxicity in a given sampling interval. Currently, one extracts discrete chemical information from each particle and decides what pieces of information determine whether or not a particle belongs in a particular group. The drawbacks to this system are twofold: Ž1. the decision of which discrete characteristics should constitute a particle type is subject to personal bias, and Ž2. the process of manually assigning discrete characteristics to a particle is extremely slow. Currently, it takes approximately one-hundred hours to calibrate and characterize the mass spectra of particles acquired in one day. By automating the process in some way, it may be possible to determine non-readily-discernible particle classes and analyze data in real time at a rate approaching that of collection. Additionally, SpectraSort is only practical for tracking eight to ten particle classes while there may be hundreds in existence for any given time interval. As particle types change over time and new ones appear, a data analysis technique with the ability to update particle classes continuously is necessary. SpectraSort automates the mechanical aspects of the particulate data analysis process, but it is not an efficient method. A researcher’s input is required to determine which discrete aspects of a particle are present or absent and no data currently exists on the exact link between the size and chemical composition of aerosol particles. Therefore, computer-automated methods for determining particle classes and assigning particles to those classes are currently being investigated. A copy of the Visual Basic 4.0 16bit source code is available from the authors upon request.
D.P. Fergenson et al.r Chemometrics and Intelligent Laboratory Systems 37 (1997) 197–203
Acknowledgements The authors would like to thank Dimitra Stratis for her advice concerning the user interface aspects of the program.
References w1x B. Dahneke, Nat. Phys. Sci. 244 Ž1973. 54–55. w2x B.A. Mamyrin, V.I. Karataev, D.V. Shmikk and V.A. Zagulin, Sov. Phys. JETP 37 Ž1973. 45–48.
203
w3x K.A. Prather, T. Nordmeyer and K. Salt, Anal. Chem. 66 Ž1994. 1403–1407. w4x B.D. Morrical and K.A. Prather, Environ. Sci. Technol., submitted for publication. w5x C.A. Noble and K.A. Prather, Environ. Sci. Technol. 30 Ž1996. 2667–2680.